Solar heat supply anti-freezing monitoring system and method based on sensor measurement

Abstract

The application discloses a solar heating anti-freezing monitoring system and method based on sensor measurement, relates to the technical field of solar heating monitoring, and comprises a layout position acquisition module, a suspicious monitoring blind area determination module, an optimal contrast sensor analysis module, a correlation verification module and an abnormal response model analysis module; the layout position acquisition module is used for acquiring the layout position of a sensor built for solar heating anti-freezing; the suspicious monitoring blind area determination module is used for determining the suspicious monitoring blind area of a solar heating device to be monitored and the sensor to be corrected in the suspicious monitoring blind area; the optimal contrast sensor analysis module is used for acquiring the optimal contrast sensor in the target analysis sensor through sensor monitoring data analysis; the correlation verification module is used for verifying the correlation between the determination of the optimal contrast sensor and an abnormal monitoring event; and the abnormal response model analysis module is used for outputting the abnormal response model of the sensor to be corrected.

Description

Technical Field

[0001] This invention relates to the field of solar heating monitoring technology, specifically to a solar heating anti-freezing monitoring system and method based on sensor measurements. Background Technology

[0002] In solar heating systems, pipe freezing in low-temperature environments has always been a key challenge affecting system reliability. Current technologies often rely on fixed-point sensor deployments, which can create "monitoring blind spots" in areas not directly covered, such as pipe bends and valve wells, leading to delayed warnings when freezing events occur. Furthermore, sensors placed in complex structures are prone to contact deviations due to physical structures, resulting in reduced data accuracy. Common solutions involve rerouting the pipeline, but this involves significant engineering work and is time-consuming. Additionally, existing sensor monitoring lacks source analysis for abnormal monitoring data; when sensors deviate due to installation angle, aging, or environmental interference, it's difficult to quickly pinpoint the root cause. Moreover, traditional anti-freezing strategies often rely on single sensor threshold judgments without considering the correlation analysis of adjacent sensors, failing to provide early warnings at the initial stage of freezing risk. Summary of the Invention

[0003] The purpose of this invention is to provide a solar heating anti-freezing monitoring system and method based on sensor measurement to solve the problems raised in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a solar heating anti-freezing monitoring method based on sensor measurement, the method comprising the following steps:

[0005] Step S100: Collect the sensor deployment locations for solar heating antifreeze installation, including omnidirectional and off-angle deployments; based on the deployment locations and historical abnormal monitoring events, identify the suspected monitoring blind spots of the solar heating device to be monitored, as well as the sensors in the suspected monitoring blind spots to be calibrated.

[0006] Step S200: Identify adjacent sensors of the same type as the sensor to be calibrated in the suspected monitoring blind zone and which are deployed in all directions as target analysis sensors. Target analysis sensors refer to sensors located in front of and behind the transmission pipeline of the sensor to be calibrated based on the heating direction of the solar pipeline; and acquire sensor monitoring data to analyze the best reference sensor among the target analysis sensors.

[0007] Step S300: Extract monitoring data from the best control sensor and the sensor to be calibrated within the safety monitoring period, and verify the correlation between the determination of the best control sensor and the abnormal monitoring events;

[0008] Step S400: Based on the correlation results, output the abnormal response model of the sensor to be calibrated.

[0009] Furthermore, identifying potential monitoring blind spots for the solar heating system to be monitored includes the following processes:

[0010] Full-range deployment means that the sensor device and the solar pipe are parallel and in close contact without any angle; off-angle deployment means that the sensor device and the solar pipe are not parallel and in close contact with each other and there is an angle.

[0011] Anomaly monitoring events refer to events that record the location, time, and monitoring data of various sensors during the freezing of solar heating pipes.

[0012] The frozen locations without sensors in the abnormal monitoring events are marked as suspected monitoring blind spots, as are the frozen locations with sensors installed at an off-center angles and whose sensor monitoring data fails to trigger a freeze warning.

[0013] The sensor with the shortest distance between the same type of sensor and the center point of the suspected monitoring blind zone within the preset deployment range is identified as the sensor to be calibrated; if no sensor to be calibrated is found, the deployment request is responded to.

[0014] Furthermore, step S200 includes the following:

[0015] Step S210: Divide the target analysis sensors into forward sensors and backward sensors, and extract the sensor monitoring values ​​P of the forward and backward sensors at the moment of responding to the abnormal monitoring event. (1、2) Where P1 represents the monitoring value acquired by the forward sensor and P2 represents the monitoring value acquired by the backward sensor; calculate the monitoring deviation rate W between the target analysis sensor and the sensor to be calibrated at the time of abnormal response. 异常 (1、2) W (1、2) =|P (1、2) -P0| / L (1、2) Where P0 represents the monitored value recorded by the sensor to be calibrated at the moment of abnormal response, and L... (1、2) This indicates the path transmission length between the forward and backward sensors and the sensor to be calibrated, based on the heating pipeline.

[0016] Step S220: Select the monitoring time corresponding to the preset time period before the response time of the abnormal monitoring event as the target monitoring time, and obtain the sensor monitoring values ​​recorded at the target monitoring time. Calculate the monitoring deviation rate W between the target analysis sensor and the sensor to be calibrated at the target monitoring time. 目标 (1、2) ;

[0017] Step S230: Based on the monitoring deviation rate at the target monitoring time and the monitoring deviation rate at the abnormal response time, calculate the response fluctuation value R of the target analysis sensor and the sensor to be calibrated, R = |W 异常(1、2) -W 目标 (1、2) | / T, where T represents the duration corresponding to the preset time period; set the response fluctuation value threshold R0. If all target analysis sensors satisfy R < R0, output that there is no best reference sensor; if there are target analysis sensors that satisfy R ≥ R0, select the target analysis sensor corresponding to the maximum response fluctuation value R as the best reference sensor for the sensor to be calibrated.

[0018] When the response fluctuation values corresponding to two sensors are both less than the threshold, it means that whether it is the forward or backward sensor, there is no fluctuation change in the monitoring difference at different positions at the moment of the abnormal event and a certain moment in the previous time period (that is, it may be at a safe or transitional moment to abnormality). Therefore, from this situation, it can be inferred that the monitoring error generated by the sensor to be calibrated has no direct associated impact with the adjacent monitoring sensors; if there is an obvious fluctuation in the monitoring values of the target analysis sensor and the sensor to be calibrated as the response moment of the abnormal event approaches, it means that the abnormal changes in the suspected monitoring blind area can be associated and sensed from the adjacent monitoring sensors.

[0019] Selecting the target analysis sensor with a larger response fluctuation value as the best reference sensor indicates that its data has a greater impact on the sensor to be calibrated, and the data is more intuitive.

[0020] Further, step S300 includes the following:

[0021] Step S310: The safety monitoring period refers to the monitoring period that is greater than the preset time period duration and within which all sensor data recorded by the solar heating system corresponding to the best reference sensor and the sensor to be calibrated do not have a freeze warning. Divide the safety monitoring period into several unit monitoring periods in equal proportion, extract the monitoring data in each unit monitoring period, and calculate the response fluctuation value R of the best reference sensor and the sensor to be calibrated in the unit monitoring period 单 ; For the response fluctuation value R in each unit monitoring period 单 Take the average value as the response fluctuation value R finally corresponding to the safety monitoring period 安 ;

[0022] Step S320: If the absolute value of the difference between R - R 安 is less than or equal to the difference threshold, output that the determination of the best reference sensor and the relevance of the abnormal monitoring event are not relevant; not relevant means that regardless of whether the monitoring period is abnormal, the response fluctuation values of the best reference sensor and the sensor to be calibrated are similar. Therefore, it is deduced that the generation of this response fluctuation value is not caused by the abnormality in the suspected monitoring blind area, but by the objective loss of the physical structure caused by the angular layout of the sensors in the suspected monitoring blind area; if R - R 安If the absolute value of the difference is greater than the difference threshold, then the determination of the best control sensor and the correlation between the abnormal monitoring event are considered to be related.

[0023] Furthermore, step S400 includes the following steps:

[0024] When the correlation result is uncorrelated or no best control sensor is output, obtain the monitoring value p recorded by the sensor to be calibrated at the time of the abnormal monitoring event response and the standard value p that meets the corresponding type of sensor for freezing. 标 Based on all abnormal monitoring events corresponding to the same solar heating and antifreeze system, calculate the monitoring difference d for each abnormal event, where d = pp 标 And construct the monitoring difference interval A, A=[dmin,dmax]; dmin represents the minimum monitoring difference, and dmax represents the maximum monitoring difference; calculate the abnormal response model Q1 of the sensor to be calibrated, Q1: p 实 -p 标 ∈[dmin,dmax]; where P 实 This represents the real-time sensor monitoring value; when p is satisfied... 实 -p 标 ∈[dmin,dmax] triggers an abnormal response, indicating a risk of freezing in a suspected monitoring blind zone;

[0025] When the correlation result is relevant and R>R 安 Then, construct the abnormal response model Q2 of the sensor to be calibrated, Q2:R 待实 ≥R; where R 待实 This represents the real-time response fluctuation value of the sensor to be calibrated compared to the best control sensor within a unit of time period;

[0026] When the correlation result is relevant and R <R 安 Then, construct the abnormal response model Q3 of the sensor to be calibrated, where Q3: R 待实 ≤R;

[0027] When the judgment relationship in the model is satisfied, an early warning response will be issued indicating that there is a risk of freezing in the suspected monitoring blind area within a preset time period.

[0028] This application utilizes a solar heating antifreeze system to construct different early warning response structures based on varying data associations within suspected monitoring blind areas. This not only provides timely warnings when freezing is likely to occur and quickly identifies monitoring blind areas caused by inadequate sensor deployment, but also enables data logic-based freezing response monitoring of blind areas without altering the sensor layout in the heating pipeline. Furthermore, this application allows for early warning responses when an optimal reference sensor exists in the monitoring blind area, enabling earlier detection of potential freezing risks, reducing the likelihood of freezing, and facilitating better contingency plans to address freezing in advance.

[0029] The solar heating antifreeze monitoring system based on sensor measurement includes a deployment location acquisition module, a suspected monitoring blind zone determination module, an optimal control sensor analysis module, a correlation verification module, and an abnormal response model analysis module.

[0030] The deployment location acquisition module is used to collect the deployment locations of sensors installed for solar heating and frost protection.

[0031] The suspected monitoring blind zone determination module is used to determine the suspected monitoring blind zones of the solar heating device to be monitored, as well as the sensors in the suspected monitoring blind zones, based on the deployment location and abnormal monitoring events in historical records.

[0032] The optimal control sensor analysis module is used to acquire sensor monitoring data, analyze the target sensor, and identify the optimal control sensor among the sensors.

[0033] The correlation verification module is used to verify the correlation between the determination of the best control sensor and the abnormal monitoring events;

[0034] The abnormal response model analysis module is used to output the abnormal response model of the sensor to be calibrated.

[0035] Furthermore, the optimal control sensor analysis module includes a sensor monitoring value extraction unit, a monitoring deviation rate calculation unit, and a response fluctuation value calculation unit;

[0036] The sensor monitoring value extraction unit is used to extract the sensor monitoring values ​​of the forward sensor and the backward sensor at the moment of responding to an abnormal monitoring event;

[0037] The monitoring deviation rate calculation unit is used to calculate the monitoring deviation rate between the target analysis sensor and the sensor to be calibrated at the abnormal response time, as well as the monitoring deviation rate between the target analysis sensor and the sensor to be calibrated at the target monitoring time.

[0038] The response fluctuation value calculation unit is used to calculate the response fluctuation values ​​of the target analysis sensor and the sensor to be calibrated, set the response fluctuation value threshold, and output the best reference sensor based on the relationship between the fluctuation value and the fluctuation value threshold.

[0039] Furthermore, the anomaly response model analysis module includes a correlation result differentiation unit and an anomaly response model construction unit;

[0040] The correlation result differentiation unit is used to distinguish between two types of correlation results: those that are unrelated or do not output the best control sensor, and those that are related.

[0041] The anomaly response model construction unit is used to build anomaly response models based on different correlation results, extract real-time sensor monitoring values, and determine whether there is a risk of freezing in suspicious blind areas based on the anomaly response model.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] 1. This application automatically identifies suspicious monitoring blind spots, such as frozen locations where no sensors are installed or missed detection locations where sensors are installed at off-center angles, by analyzing the correlation between historical abnormal events and sensor deployment locations, and locates the sensors that need to be calibrated, thereby reducing the false alarm rate.

[0044] 2. This application selects adjacent sensors of the same type as the sensor to be calibrated and deployed in all directions as the "best reference sensor" and analyzes the calibration monitoring deviation through response fluctuation value analysis; to avoid misjudgment of antifreeze strategy due to measurement error.

[0045] 3. This application does not require large-scale modification of sensor wiring; blind spot monitoring can be covered simply by optimizing data logic. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the solar heating antifreeze monitoring method based on sensor measurement according to the present invention. Detailed Implementation

[0047] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Example: Figure 1 As shown, this invention provides a solar heating anti-freezing monitoring method based on sensor measurements, the method comprising the following steps:

[0049] Step S100: Collect the sensor deployment locations for solar heating antifreeze installation, including omnidirectional and off-angle deployments; based on the deployment locations and historical abnormal monitoring events, identify the suspected monitoring blind spots of the solar heating device to be monitored, as well as the sensors in the suspected monitoring blind spots to be calibrated.

[0050] Step S200: Identify adjacent sensors of the same type as the sensor to be calibrated in the suspected monitoring blind zone and which are deployed in all directions as target analysis sensors. Target analysis sensors refer to sensors located in front of and behind the transmission pipeline of the sensor to be calibrated based on the heating direction of the solar pipeline; and acquire sensor monitoring data to analyze the best reference sensor among the target analysis sensors.

[0051] Step S300: Extract monitoring data from the best control sensor and the sensor to be calibrated within the safety monitoring period, and verify the correlation between the determination of the best control sensor and the abnormal monitoring events;

[0052] Step S400: Based on the correlation results, output the abnormal response model of the sensor to be calibrated.

[0053] Identifying potential monitoring blind spots for solar heating systems involves the following processes:

[0054] Full-range deployment means that the sensor device and the solar pipe are parallel and in close contact without any angle; off-angle deployment means that the sensor device and the solar pipe are not parallel and in close contact with each other and there is an angle.

[0055] Anomaly monitoring events refer to events that record the location, time, and monitoring data of various sensors during the freezing of solar heating pipes.

[0056] The frozen locations without sensors in the abnormal monitoring events are marked as suspected monitoring blind spots, as are the frozen locations with sensors installed at an off-center angles and whose sensor monitoring data fails to trigger a freeze warning.

[0057] If there are sensors that are deployed in all directions but the sensor monitoring data fails to trigger a freeze warning, then the sensor malfunction is indicated.

[0058] The sensor with the shortest distance between the same type of sensor and the center point of the suspected monitoring blind zone within the preset deployment range is identified as the sensor to be calibrated; if no sensor to be calibrated is found, the deployment request is responded to.

[0059] In this application, there can be multiple sensors to be calibrated for the same suspected monitoring blind zone. When there are multiple sensors, each represents a type of sensor. For example, if there are two sensors to be calibrated, one is a temperature sensor and the other is a pressure sensor. Anomaly monitoring events are generated for each solar heating system. An anomaly monitoring event monitors all pipes of an independent solar heating system. However, the identification of suspected monitoring blind zones in this application may be due to sensor devices on other pipes responding abnormally and conducting pipe inspections to discover freezes.

[0060] Step S200 includes the following:

[0061] Step S210: Divide the target analysis sensors into forward sensors and backward sensors, and extract the sensor monitoring values ​​P of the forward and backward sensors at the moment of responding to the abnormal monitoring event. (1、2) Where P1 represents the monitoring value acquired by the forward sensor and P2 represents the monitoring value acquired by the backward sensor; calculate the monitoring deviation rate W between the target analysis sensor and the sensor to be calibrated at the time of abnormal response. 异常 (1、2) W (1、2) =|P (1、2) -P0| / L (1、2) Where P0 represents the monitored value recorded by the sensor to be calibrated at the moment of abnormal response, and L... (1、2)It is shown that the forward sensor and the backward sensor are respectively based on the path transmission length of the heating pipeline with the sensor to be calibrated;

[0062] Step S220: Select the monitoring moment corresponding to the previous preset period before the abnormal monitoring event response moment as the target monitoring moment, obtain the sensor monitoring value recorded at the target monitoring moment, and calculate the monitoring deviation rate W of the target analysis sensor and the sensor to be calibrated at the target monitoring moment 目标 (1、2) ;

[0063] Step S230: Based on the monitoring deviation rate at the target monitoring moment and the monitoring deviation rate at the abnormal response moment, calculate the response fluctuation value R of the target analysis sensor and the sensor to be calibrated, R = |W 异常 (1、2) -W 目标 (1、2) | / T, where T represents the duration corresponding to the preset period; for the calculation of the response fluctuation value, the monitoring deviation rate corresponding to the forward sensor and the monitoring deviation rate corresponding to the backward sensor need to be extracted respectively to obtain the corresponding response fluctuation values; set the response fluctuation value threshold R0. If all target analysis sensors satisfy R < R0, output that there is no best reference sensor; if there is a target analysis sensor that satisfies R ≥ R0, select the target analysis sensor corresponding to the maximum response fluctuation value R as the best reference sensor of the sensor to be calibrated.

[0064] When the response fluctuation values corresponding to the two sensors are both less than the threshold, it means that neither the forward nor the backward sensor has a fluctuating change in the monitoring difference at different positions due to the occurrence of the abnormal event at the abnormal event occurrence moment and a certain moment corresponding to the previous period (i.e., it may be at a safe or abnormal transition moment). Therefore, from this situation, it can be inferred that the monitoring error generated by the sensor to be calibrated has no direct associated impact on the adjacent monitoring sensors; if there is an obvious fluctuation in the monitoring values of the target analysis sensor and the sensor to be calibrated as the abnormal event response moment approaches, it means that the adjacent monitoring sensors can be associated to affect the perception of abnormal changes in the suspicious monitoring blind area.

[0065] Select the target analysis sensor with a larger response fluctuation value as the best reference sensor, indicating that it has a greater impact on the data of the sensor to be calibrated and the data is more intuitive.

[0066] Step S300 includes the following:

[0067] Step S310: The safety monitoring period refers to the monitoring period that is longer than the preset time period and includes the longest monitoring period during which all sensor data recorded by the solar heating system corresponding to the best control sensor and the sensor to be calibrated are free from freezing warnings. The safety monitoring period is divided into several unit monitoring periods in an equal proportion. The monitoring data in each unit monitoring period is extracted, and the response fluctuation value R of the best control sensor and the sensor to be calibrated in the unit monitoring period is calculated. 单 During the analysis of each unit monitoring cycle, the selected time points are the start and end times of each unit monitoring cycle, and the response fluctuation value R within each unit monitoring cycle is recorded. 单 The average value is taken as the final response fluctuation value R corresponding to the safety monitoring cycle. 安 ;

[0068] Step S320: If RR 安 If the absolute value of the difference is less than or equal to the difference threshold, then the correlation between the determination of the best control sensor and the abnormal monitoring event is uncorrelated; uncorrelated means that regardless of whether the monitoring period is abnormal, the response fluctuation values ​​of the best control sensor and the sensor to be calibrated are similar. Therefore, it can be deduced that the generation of this response fluctuation value is not caused by the abnormality of the suspected monitoring blind zone, but by the objective loss of the physical structure caused by the off-angle placement of the sensor in the suspected monitoring blind zone; if RR 安 If the absolute value of the difference is greater than the difference threshold, then the determination of the best control sensor and the correlation between the abnormal monitoring event are considered to be related.

[0069] Step S400 includes the following steps:

[0070] When the correlation result is uncorrelated or no best control sensor is output, obtain the monitoring value p recorded by the sensor to be calibrated at the time of the abnormal monitoring event response and the standard value p that meets the freezing requirement for the corresponding type of sensor. 标 Based on all abnormal monitoring events corresponding to the same solar heating and antifreeze system, calculate the monitoring difference d for each abnormal event, where d = pp 标 And construct the monitoring difference interval A, A=[dmin,dmax]; dmin represents the minimum monitoring difference, and dmax represents the maximum monitoring difference; calculate the abnormal response model Q1 of the sensor to be calibrated, Q1: p 实 -p 标 ∈[dmin,dmax]; where P 实 This represents the real-time sensor monitoring value; when p is satisfied... 实 -p 标 ∈[dmin,dmax] triggers an abnormal response, indicating a risk of freezing in a suspected monitoring blind zone;

[0071] When the correlation result is relevant and R>R 安Then, construct the abnormal response model Q2 of the sensor to be calibrated, Q2:R 待实 ≥R; where R 待实 This represents the real-time response fluctuation value of the sensor to be calibrated compared to the best control sensor within a unit of time period;

[0072] When the correlation result is relevant and R <R 安 Then, construct the abnormal response model Q3 of the sensor to be calibrated, where Q3: R 待实 ≤R;

[0073] When the judgment relationship in the model is satisfied, the warning response indicates that the suspected monitoring blind area has a risk of freezing within a preset time period. The warning response levels of Q2 and Q3 are different from those of Q1. The warning of Q1 indicates that it may have already frozen, while the warnings of Q2 and Q3 indicate that there may be a risk within the preset time period in the future.

[0074] This application utilizes a solar heating antifreeze system to construct different early warning response structures based on varying data associations within suspected monitoring blind areas. This not only provides timely warnings when freezing is likely to occur and quickly identifies monitoring blind areas caused by inadequate sensor deployment, but also enables data logic-based freezing response monitoring of blind areas without altering the sensor layout in the heating pipeline. Furthermore, this application allows for early warning responses when an optimal reference sensor exists in the monitoring blind area, enabling earlier detection of potential freezing risks, reducing the likelihood of freezing, and facilitating better contingency plans to address freezing in advance.

[0075] The solar heating antifreeze monitoring system based on sensor measurement includes a deployment location acquisition module, a suspected monitoring blind zone determination module, an optimal control sensor analysis module, a correlation verification module, and an abnormal response model analysis module.

[0076] The deployment location acquisition module is used to collect the deployment locations of sensors installed for solar heating and frost protection.

[0077] The suspected monitoring blind zone determination module is used to determine the suspected monitoring blind zones of the solar heating device to be monitored, as well as the sensors in the suspected monitoring blind zones, based on the deployment location and abnormal monitoring events in historical records.

[0078] The optimal control sensor analysis module is used to acquire sensor monitoring data, analyze the target sensor, and identify the optimal control sensor among the sensors.

[0079] The correlation verification module is used to verify the correlation between the determination of the best control sensor and the abnormal monitoring events;

[0080] The abnormal response model analysis module is used to output the abnormal response model of the sensor to be calibrated.

[0081] The optimal control sensor analysis module includes a sensor monitoring value extraction unit, a monitoring deviation rate calculation unit, and a response fluctuation value calculation unit.

[0082] The sensor monitoring value extraction unit is used to extract the sensor monitoring values ​​of the forward sensor and the backward sensor at the moment of responding to an abnormal monitoring event;

[0083] The monitoring deviation rate calculation unit is used to calculate the monitoring deviation rate between the target analysis sensor and the sensor to be calibrated at the abnormal response time, as well as the monitoring deviation rate between the target analysis sensor and the sensor to be calibrated at the target monitoring time.

[0084] The response fluctuation value calculation unit is used to calculate the response fluctuation values ​​of the target analysis sensor and the sensor to be calibrated, set the response fluctuation value threshold, and output the best reference sensor based on the relationship between the fluctuation value and the fluctuation value threshold.

[0085] The anomaly response model analysis module includes a correlation result differentiation unit and an anomaly response model construction unit;

[0086] The correlation result differentiation unit is used to distinguish between two types of correlation results: those that are unrelated or do not output the best control sensor, and those that are related.

[0087] The anomaly response model construction unit is used to build anomaly response models based on different correlation results, extract real-time sensor monitoring values, and determine whether there is a risk of freezing in suspicious blind areas based on the anomaly response model.

[0088] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A solar heating antifreeze monitoring method based on sensor measurement, characterized in that: The method includes the following steps: Step S100: Collect the sensor layout positions built for solar heating anti-freezing. The layout positions include all-round layout and angular layout. Based on the layout positions and the abnormity monitoring events recorded historically, determine the suspicious monitoring blind areas of the solar heating device to be monitored and the sensors to be calibrated in the suspicious monitoring blind areas. Step S200: Obtain adjacent sensors with the same type as the sensors to be calibrated in the suspicious monitoring blind areas and with all-round layout as the target analysis sensors. The target analysis sensors refer to the sensors based on the solar pipeline heating direction and located in the forward and backward directions of the transmission pipeline of the sensors to be calibrated. And obtain the best comparison sensor in the target analysis sensors by analyzing the sensor monitoring data. Step S300: Extract the monitoring data of the best comparison sensor and the sensors to be calibrated during the safety monitoring period, and verify the relevance between the determination of the best comparison sensor and the abnormity monitoring events. The step S300 includes the following: Step S310: The safety monitoring period refers to a monitoring period longer than a preset time period, during which all sensor data recorded by the solar heating system corresponding to the best control sensor and the sensor to be calibrated are within the maximum duration of any freeze warning; the safety monitoring period is divided into several unit monitoring periods proportionally, and the monitoring data within each unit monitoring period is extracted to calculate the response fluctuation value R of the best control sensor and the sensor to be calibrated in the unit monitoring period. 单 The response fluctuation value R within each unit monitoring period 单 The average value is taken as the final response fluctuation value R corresponding to the safety monitoring cycle. 安 ; Step S320: If RR 安 If the absolute value of the difference is less than or equal to the difference threshold, then the determination of the best control sensor and the correlation with the abnormal monitoring event are unrelated; if RR 安 If the absolute value of the difference is greater than the difference threshold, then the correlation between the determination of the best control sensor and the abnormal monitoring event is considered to be positive. Step S400: Output the abnormity response model of the sensors to be calibrated based on the relevance result. The step S400 includes the following steps: When the correlation result is uncorrelated or no best control sensor is output, obtain the monitoring value p recorded by the sensor to be calibrated at the time of the abnormal monitoring event response and the standard value p that meets the freezing requirement for the corresponding type of sensor. 标 Based on all abnormal monitoring events corresponding to the same solar heating and antifreeze system, calculate the monitoring difference d for each abnormal event, where d = pp 标 And construct the monitoring difference interval A, A=[dmin,dmax]; dmin represents the minimum monitoring difference, and dmax represents the maximum monitoring difference; calculate the abnormal response model Q1 of the sensor to be calibrated, Q1: p 实 -p 标 ∈[dmin,dmax]; where P 实 This represents the real-time sensor monitoring value; when p is satisfied... 实 -p 标 ∈[dmin,dmax] triggers an abnormal response, indicating a risk of freezing in a suspected monitoring blind zone; When the correlation result is relevant and R>R 安 Then, construct the abnormal response model Q2 of the sensor to be calibrated, Q2:R 待实 ≥R; where R 待实 This represents the real-time response fluctuation value of the sensor to be calibrated compared to the best control sensor within a unit of time period; When the correlation result is relevant and R <R 安 Then, construct the abnormal response model Q3 of the sensor to be calibrated, where Q3: R 待实 ≤R; And when the judgment relationship in the model is satisfied, give an early warning that there is a freezing risk in the suspicious monitoring blind area within the preset time period.

2. The solar heating antifreeze monitoring method based on sensor measurement according to claim 1, characterized in that: The determination of the suspicious monitoring blind areas of the solar heating device to be monitored includes the following process: The all-round layout means that the sensor device is parallel and fitted to the solar pipeline without an included angle. The angular layout means that the sensor device is not parallel and fitted to the solar pipeline and there is an included angle. The abnormity monitoring event refers to the event that records the freezing position, freezing time of the solar heating pipeline and the monitoring data of various sensors at the time of freezing. Mark the freezing positions without sensors in the abnormity monitoring events as suspicious monitoring blind areas, and mark the freezing positions with sensors with angular layout and the monitoring data of the sensors not triggering the freezing warning as suspicious monitoring blind areas. Search for the sensor with the shortest distance from the center point of the suspicious monitoring blind area among the sensors of the same type within the preset layout range as the sensor to be calibrated. If there is no sensor to be calibrated, respond to the layout requirement.

3. The solar heating antifreeze monitoring method based on sensor measurement according to claim 1, characterized in that: The step S200 includes the following content: Step S210: Divide the target analysis sensors into forward sensors and backward sensors, and extract the sensor monitoring values ​​P of the forward and backward sensors at the moment of responding to the abnormal monitoring event. (1、2) Where P1 represents the monitoring value acquired by the forward sensor and P2 represents the monitoring value acquired by the backward sensor; calculate the monitoring deviation rate W between the target analysis sensor and the sensor to be calibrated at the time of abnormal response. 异常 (1、2) W (1、2) =|P (1、2) -P0| / L (1、2) Where P0 represents the monitored value recorded by the sensor to be calibrated at the moment of abnormal response, and L... (1、2) This indicates the path transmission length between the forward and backward sensors and the sensor to be calibrated, based on the heating pipeline. Step S220: Select the monitoring time corresponding to the preset time period before the response time of the abnormal monitoring event as the target monitoring time, and obtain the sensor monitoring values ​​recorded at the target monitoring time. Calculate the monitoring deviation rate W between the target analysis sensor and the sensor to be calibrated at the target monitoring time. 目标 (1、2) ; Step S230: Based on the monitoring deviation rate at the target monitoring time and the monitoring deviation rate at the abnormal response time, calculate the response fluctuation value R of the target analysis sensor and the sensor to be calibrated, R = |W 异常 (1、2) -W 目标 (1、2) | / T, where T represents the duration of the preset time period; Set the response fluctuation value threshold R0. If all the target analysis sensors meet R < R0, output that there is no best comparison sensor. If there are target analysis sensors that meet R ≥ R0, select the target analysis sensor corresponding to the maximum response fluctuation value R as the best comparison sensor of the sensors to be calibrated.

4. A solar heating anti-freezing monitoring system based on sensor measurement, using the solar heating anti-freezing monitoring method based on sensor measurement as described in any one of claims 1-3, characterized in that: The system includes a layout position collection module, a suspicious monitoring blind area determination module, a best comparison sensor analysis module, a relevance verification module and an abnormity response model analysis module. [[ID= ​ ​ The correlation verification module is used to verify the correlation between the determination of the best control sensor and the abnormal monitoring event; The abnormal response model analysis module is used to output the abnormal response model of the sensor to be calibrated.

5. The solar heating anti-freezing monitoring system based on sensor measurement according to claim 4, characterized in that: The optimal control sensor analysis module includes a sensor monitoring value extraction unit, a monitoring deviation rate calculation unit, and a response fluctuation value calculation unit. The sensor monitoring value extraction unit is used to extract the sensor monitoring values ​​of the forward sensor and the backward sensor at the moment of responding to the abnormal monitoring event; The monitoring deviation rate calculation unit is used to calculate the monitoring deviation rate between the target analysis sensor and the sensor to be calibrated at the abnormal response time, as well as the monitoring deviation rate between the target analysis sensor and the sensor to be calibrated at the target monitoring time. The response fluctuation value calculation unit is used to calculate the response fluctuation values ​​of the target analysis sensor and the sensor to be calibrated, set the response fluctuation value threshold, and output the best reference sensor based on the relationship between the fluctuation value and the fluctuation value threshold.

6. The solar heating anti-freezing monitoring system based on sensor measurement according to claim 5, characterized in that: The anomaly response model analysis module includes a correlation result differentiation unit and an anomaly response model construction unit; The correlation result differentiation unit is used to differentiate between two types of correlation results: those that are unrelated or do not output the best control sensor, and those that are related. The abnormal response model construction unit is used to construct an abnormal response model based on different correlation results, extract real-time sensor monitoring values, and determine whether there is a risk of freezing in the suspicious blind area based on the abnormal response model.

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