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

Abstract

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

Description

Technical Field

[0001] The present invention relates to the technical field of solar heating monitoring, and in particular to a solar heating antifreeze monitoring system and method based on sensor measurement. Background Art

[0002] In solar heating systems, pipe freezing in low-temperature environments has always been a key challenge affecting system reliability. Conventional systems rely on fixed-point sensor deployments, which can easily create "monitoring blind spots" in areas not directly covered, such as pipe corners, valve wells, and other complex structures. This results in a lack of timely warnings when freezing events occur. Sensors installed in complex structures are also prone to contact deviations due to the physical structure, resulting in reduced data accuracy. The typical solution is to reroute the pipeline, but this involves a large and time-consuming engineering effort. Existing sensor monitoring also lacks traceability analysis of abnormal monitoring data. When sensors experience measurement deviations due to installation angle, aging, or environmental interference, it is difficult to quickly locate the root cause of the problem. Traditional anti-freeze strategies are often based on single-sensor threshold judgments, without integrating correlation analysis with adjacent sensors, making it impossible to provide early warnings at the incipient stage of freezing risks. Summary of the Invention

[0003] The object of the present invention is to provide a solar heating antifreeze 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 antifreeze monitoring method based on sensor measurement, the method comprising the following steps: Step S100: Collecting sensor deployment locations for solar heating and antifreeze protection, including omnidirectional and angular deployments; based on the deployment locations and historical abnormal monitoring events, determining suspected monitoring blind spots of the solar heating device to be monitored, and sensors to be calibrated in the suspected monitoring blind spots; Step S200: Acquire adjacent sensors of the same type as the sensor to be calibrated in the suspected monitoring blind spot and arranged in all directions as target analysis sensors. The target analysis sensors are sensors located in the forward and backward directions of the transmission pipeline of the sensor to be calibrated based on the direction of solar pipeline heating. The best control sensor among the target analysis sensors is obtained for sensor monitoring data analysis. Step S300: extracting monitoring data of the best control sensor and the sensor to be calibrated within the safety monitoring period, and verifying the correlation between the determination of the best control sensor and the abnormal monitoring event; Step S400: Based on the correlation result, output the abnormal response model of the sensor to be calibrated.

[0005] Furthermore, determining the suspected monitoring blind area of ​​the solar heating device to be monitored includes the following process: Omnidirectional layout means that the sensor device is parallel to the solar pipe without any angle; deflection layout means that the sensor device is not parallel to the solar pipe but has an angle; Abnormal monitoring events refer to events that record the freezing position and freezing time of solar heating pipes and the monitoring data of various sensors during freezing; Marking frozen locations where no sensors are installed in abnormal monitoring events as suspected monitoring blind spots, and marking frozen locations where sensors are installed at an angle and the sensor monitoring data fails to trigger a freezing warning as suspected monitoring blind spots; Find the sensor of the same type within the preset deployment range with the shortest distance to the center point of the suspected monitoring blind spot as the sensor to be calibrated; if there is no sensor to be calibrated, respond to the deployment requirement.

[0006] Furthermore, step S200 includes the following contents: Step S210: Divide the target analysis sensor into a forward sensor and a backward sensor, and extract the sensor monitoring value P of the forward sensor and the backward sensor at the time of responding to the abnormal monitoring event respectively. (1、2) , where P1 represents the monitoring value obtained by the forward sensor and P2 represents the monitoring value obtained by the backward sensor; calculate the monitoring deviation rate W between the target analysis sensor and the sensor to be corrected at the abnormal response time 异常 (1、2) , W (1、2) =|P (1、2) -P0| / L (1、2) , where P0 represents the monitoring value recorded by the sensor to be calibrated at the time of abnormal response, L (1、2) Indicates the path transmission lengths between the forward sensor and the backward sensor and the sensor to be calibrated based on the heating pipe; Step S220: Select the monitoring time corresponding to the preset period before the abnormal monitoring event response time as the target monitoring time, obtain the sensor monitoring value recorded at the target monitoring time, and 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: Calculate the response fluctuation value R of the target analysis sensor and the sensor to be calibrated based on the monitoring deviation rate at the target monitoring time and the monitoring deviation rate at the abnormal response time, 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 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 for the sensor to be calibrated.

[0007] When the response fluctuation values corresponding to both sensors are less than the threshold, it means that neither the forward nor the backward sensor has a 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 (i.e., it may be at a safe or transitional moment to abnormality). Therefore, in this case, 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 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.

[0008] 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.

[0009] Further, step S300 includes the following: 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 ; 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 has no relevance to the abnormal monitoring event; no relevance 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 the absolute value of the difference between R - R 安 is greater than the difference threshold, output that the determination of the best reference sensor has relevance to the abnormal monitoring event.

[0010] Furthermore, step S400 includes the following steps: When the correlation result is irrelevant or the best control sensor is not 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 of the sensor corresponding to the type of freezing. 标 , based on all abnormal monitoring events corresponding to the same solar heating and antifreeze system, calculate the monitoring difference d of each abnormal event, d=pp 标 , and construct the monitoring difference interval A, A=[dmin,dmax]; dmin represents the minimum monitoring difference, dmax represents the maximum monitoring difference; calculate the abnormal response model Q1 of the sensor to be corrected, Q1: p 实 -p 标 ∈[dmin,dmax]; where P 实 Indicates the real-time sensor monitoring value; when p is satisfied 实 -p 标 ∈[dmin,dmax] triggers an abnormal response, indicating that there is a freezing risk in the suspicious monitoring blind area; When the correlation result is correlation and R>R 安 When the abnormal response model Q2 of the sensor to be corrected is constructed, Q2: R 待实 ≥R; where R 待实 Indicates the real-time response fluctuation value of the sensor to be calibrated corresponding to the best reference sensor within a unit time period; When the correlation result is related and R <R 安 When the abnormal response model Q3 of the sensor to be corrected is constructed, Q3: R 待实 ≤R; And when the judgment relationship in the model is met, an early warning response is given that there is a freezing risk in the suspicious monitoring blind area within the preset time period.

[0011] The present application uses a solar heating antifreeze system to construct an early warning response structure with different models when the suspicious monitoring blind area is in different data association situations. This can not only provide timely early warning when freezing may occur, but also quickly capture monitoring blind areas that are not caused by reasonable sensor layout; it can also perform freezing response monitoring of blind areas based on the data logic level without changing the layout of the heating pipeline sensors; the present application can also achieve early warning response when there is an optimal control sensor in the monitoring blind area, so as to discover the possible freezing risk in the monitoring blind area earlier, reduce the possibility of freezing, and facilitate better preparation of solutions for the occurrence of freezing in advance.

[0012] A solar heating antifreeze monitoring system based on sensor measurement, which includes a deployment location acquisition module, a suspicious monitoring blind area determination module, an optimal control sensor analysis module, a correlation verification module, and an abnormal response model analysis module; The layout location acquisition module is used to collect the layout locations of sensors built for solar heating and antifreeze; The suspicious monitoring blind area determination module is used to determine the suspicious monitoring blind area of ​​the solar heating device to be monitored and the sensors to be calibrated in the suspicious monitoring blind area based on the deployment location and abnormal monitoring events in historical records; The best control sensor analysis module is used to obtain the best control sensor among the sensor monitoring data analysis target analysis sensors; 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.

[0013] 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; The sensor monitoring value extraction unit is used to extract the sensor monitoring values ​​of the forward sensor and the backward sensor at the time of responding to the abnormal monitoring event; The monitoring deviation rate calculation unit is used to calculate the monitoring deviation rate of the target analysis sensor and the sensor to be corrected at the abnormal response time, and the monitoring deviation rate of the target analysis sensor and the sensor to be corrected 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 control sensor based on the relationship between the fluctuation value and the fluctuation value threshold.

[0014] Furthermore, the abnormal response model analysis module includes a correlation result distinguishing unit and an abnormal response model building unit; The correlation result distinguishing unit is used to distinguish between two types: the correlation result is irrelevant or the best control sensor is not output, and the correlation result is relevant; The abnormal response model construction unit is used to construct an abnormal response model based on different correlation results, extract the sensor monitoring value obtained in real time, and judge whether there is a freezing risk in the suspicious blind spot based on the abnormal response model.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This application automatically identifies suspicious monitoring blind spots by analyzing the correlation between historical abnormal events and sensor deployment locations, such as frozen locations where no sensors are installed or locations where sensors are deployed at an angle but miss reports, and locates sensors that need to be corrected to reduce the missed detection rate; 2. This application selects adjacent sensors of the same type as the sensor to be calibrated and arranged in all directions as the "best control sensors" and calibrates the monitoring deviation through response fluctuation value analysis to avoid misjudgment of antifreeze strategies due to measurement errors.

[0016] 3. This application does not require large-scale modification of sensor wiring, and blind spot monitoring can be covered only through data logic optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a structural schematic diagram of the solar heating antifreeze monitoring method based on sensor measurement of the present invention. DETAILED DESCRIPTION

[0018] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0019] Example: Figure 1 As shown, the present invention provides a solar heating antifreeze monitoring method based on sensor measurement, the method comprising the following steps: Step S100: Collecting sensor deployment locations for solar heating and antifreeze protection, including omnidirectional and angular deployments; based on the deployment locations and historical abnormal monitoring events, determining suspected monitoring blind spots of the solar heating device to be monitored, and sensors to be calibrated in the suspected monitoring blind spots; Step S200: Acquire adjacent sensors of the same type as the sensor to be calibrated in the suspected monitoring blind spot and arranged in all directions as target analysis sensors. The target analysis sensors are sensors located in the forward and backward directions of the transmission pipeline of the sensor to be calibrated based on the direction of solar pipeline heating. The best control sensor among the target analysis sensors is obtained for sensor monitoring data analysis. Step S300: extracting monitoring data of the best control sensor and the sensor to be calibrated within the safety monitoring period, and verifying the correlation between the determination of the best control sensor and the abnormal monitoring event; Step S400: Based on the correlation result, output the abnormal response model of the sensor to be calibrated.

[0020] Determining the suspected blind spots of the solar heating installation to be monitored involves the following process: Omnidirectional layout means that the sensor device is parallel to the solar pipe without any angle; deflection layout means that the sensor device is not parallel to the solar pipe but has an angle; Abnormal monitoring events refer to events that record the freezing position and freezing time of solar heating pipes and the monitoring data of various sensors during freezing; Marking frozen locations where no sensors are installed in abnormal monitoring events as suspected monitoring blind spots, and marking frozen locations where sensors are installed at an angle and the sensor monitoring data fails to trigger a freezing warning as suspected monitoring blind spots; If there are sensors deployed in all directions but the sensor monitoring data fails to trigger the freeze warning, the response is a sensor failure abnormality; Find the sensor of the same type within the preset deployment range with the shortest distance to the center point of the suspected monitoring blind spot as the sensor to be calibrated; if there is no sensor to be calibrated, respond to the deployment requirement.

[0021] In this application, the same suspected blind spot can contain multiple sensors to be calibrated. When there are multiple sensors, each represents a different 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. Abnormal monitoring events are generated for each solar heating system. An abnormal monitoring event monitors all pipelines in an independent solar heating system. However, in this application, the identification of suspected blind spots may involve detecting freezing due to abnormal responses from sensor equipment in other pipelines.

[0022] Step S200 includes the following contents: Step S210: Divide the target analysis sensor into a forward sensor and a backward sensor, and extract the sensor monitoring value P of the forward sensor and the backward sensor at the time of responding to the abnormal monitoring event respectively. (1、2) , where P1 represents the monitoring value obtained by the forward sensor and P2 represents the monitoring value obtained by the backward sensor; calculate the monitoring deviation rate W between the target analysis sensor and the sensor to be corrected at the abnormal response time 异常 (1、2) , W (1、2) =|P (1、2) -P0| / L (1、2) , where P0 represents the monitoring value recorded by the sensor to be calibrated at the time of abnormal response, L (1、2) Indicates the path transmission lengths between the forward sensor and the backward sensor and the sensor to be calibrated based on the heating pipe; Step S220: Select the monitoring time corresponding to the preset period before the abnormal monitoring event response time as the target monitoring time, obtain the sensor monitoring value recorded at the target monitoring time, and 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: Calculate the response fluctuation value R of the target analysis sensor and the sensor to be calibrated based on the monitoring deviation rate at the target monitoring time and the monitoring deviation rate at the abnormal response time, R=|W 异常 (1、2) -W 目标 (1、2)| / T, where T represents the duration corresponding to the preset time period; for calculating the response fluctuation value, the monitoring deviation rates corresponding to the forward sensor and the backward sensor need to be extracted separately, and then the corresponding response fluctuation values are obtained respectively; 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 corrected.

[0023] When the response fluctuation values corresponding to both sensors are 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 an abnormal event at the moment of the abnormal event and a certain moment in the previous time period (i.e., 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 corrected has no direct associated influence 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 corrected 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.

[0024] Selecting the target analysis sensor with a larger response fluctuation value as the best reference sensor indicates that it has a greater influence on the data of the sensor to be corrected, and the data is more intuitive.

[0025] Step S300 includes the following: 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 corrected 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 respectively, and calculate the response fluctuation value R of the best reference sensor and the sensor to be corrected in the unit monitoring period. 单 ; When analyzing each unit monitoring period, the selected time nodes are the start time and the end time of each unit monitoring period, and the response fluctuation value R within each unit monitoring period 单 Take the average value as the response fluctuation value R finally corresponding to the safety monitoring period. 安 ; Step S320: If R - R 安If the absolute value of the difference between the two is less than or equal to the difference threshold, the correlation between the determination of the best control sensor and the abnormal monitoring event is irrelevant; irrelevant means that regardless of whether the monitoring cycle is abnormal or not, the response fluctuation values ​​of the best control sensor and the sensor to be corrected are similar, so it is deduced that the generation of this response fluctuation value is not caused by the abnormality of the suspicious monitoring blind area, but the objective loss of the physical structure caused by the setting of the sensor at an angle in the suspicious monitoring blind area; if RR 安 If the absolute value of the difference is greater than the difference threshold, the determination of the best control sensor is outputted and the correlation with the abnormal monitoring event is correlated.

[0026] Step S400 includes the following steps: When the correlation result is irrelevant or the best control sensor is not 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 of the sensor corresponding to the type of freezing. 标 , based on all abnormal monitoring events corresponding to the same solar heating and antifreeze system, calculate the monitoring difference d of each abnormal event, d=pp 标 , and construct the monitoring difference interval A, A=[dmin,dmax]; dmin represents the minimum monitoring difference, dmax represents the maximum monitoring difference; calculate the abnormal response model Q1 of the sensor to be corrected, Q1: p 实 -p 标 ∈[dmin,dmax]; where P 实 Indicates the real-time sensor monitoring value; when p is satisfied 实 -p 标 ∈[dmin,dmax] triggers an abnormal response, indicating that there is a freezing risk in the suspicious monitoring blind area; When the correlation result is correlation and R>R 安 When the abnormal response model Q2 of the sensor to be corrected is constructed, Q2: R 待实 ≥R; where R 待实 Indicates the real-time response fluctuation value of the sensor to be calibrated corresponding to the best reference sensor within a unit time period; When the correlation result is related and R <R 安 When the abnormal response model Q3 of the sensor to be corrected is constructed, Q3: R 待实 ≤R; When the model's judgment relationship is met, the system will issue an early warning response indicating a potential freeze risk in the suspected monitoring blind spot within a preset time period. The early warning response levels for Q2 and Q3 differ from those for Q1. A Q1 warning in the model indicates a potential freeze, while Q2 and Q3 warnings indicate a potential risk within a preset future time period.

[0027] The present application uses a solar heating antifreeze system to construct an early warning response structure with different models when the suspicious monitoring blind area is in different data association situations. This can not only provide timely early warning when freezing may occur, but also quickly capture monitoring blind areas that are not caused by reasonable sensor layout; it can also perform freezing response monitoring of blind areas based on the data logic level without changing the layout of the heating pipeline sensors; the present application can also achieve early warning response when there is an optimal control sensor in the monitoring blind area, so as to discover the possible freezing risk in the monitoring blind area earlier, reduce the possibility of freezing, and facilitate better preparation of solutions for the occurrence of freezing in advance.

[0028] A solar heating antifreeze monitoring system based on sensor measurement, which includes a deployment location acquisition module, a suspicious monitoring blind area determination module, an optimal control sensor analysis module, a correlation verification module, and an abnormal response model analysis module; The layout location acquisition module is used to collect the layout locations of sensors built for solar heating and antifreeze; The suspicious monitoring blind area determination module is used to determine the suspicious monitoring blind area of ​​the solar heating device to be monitored and the sensors to be calibrated in the suspicious monitoring blind area based on the deployment location and abnormal monitoring events in historical records; The best control sensor analysis module is used to obtain the best control sensor among the sensor monitoring data analysis target analysis sensors; 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.

[0029] 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 time of responding to the abnormal monitoring event; The monitoring deviation rate calculation unit is used to calculate the monitoring deviation rate of the target analysis sensor and the sensor to be corrected at the abnormal response time, and the monitoring deviation rate of the target analysis sensor and the sensor to be corrected 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 control sensor based on the relationship between the fluctuation value and the fluctuation value threshold.

[0030] The abnormal response model analysis module includes a correlation result distinguishing unit and an abnormal response model building unit; The correlation result distinguishing unit is used to distinguish between two types: the correlation result is irrelevant or the best control sensor is not output, and the correlation result is relevant; The abnormal response model construction unit is used to construct an abnormal response model based on different correlation results, extract the sensor monitoring value obtained in real time, and judge whether there is a freezing risk in the suspicious blind spot based on the abnormal response model.

[0031] 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 aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A solar heating antifreeze monitoring method based on sensor measurement, characterized by: 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 corrected in the suspicious monitoring blind areas. Step S200: Obtain the adjacent sensors with the same type as the sensors to be corrected 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 corrected. And obtain the best comparison sensors in the target analysis sensors by analyzing the sensor monitoring data. Step S300: Extract the monitoring data of the best comparison sensors and the sensors to be corrected during the safety monitoring period, and verify the relevance between the determination of the best comparison sensors and the abnormity monitoring events. Step S400: Based on the relevance result, output the abnormity response model of the sensors to be corrected.

2. The solar heating antifreeze monitoring method based on sensor measurement according to claim 1 is 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 in contact with the solar pipeline without an included angle. The angular layout means that the sensor device is not parallel and in contact with the solar pipeline and has an included angle. The abnormity monitoring event refers to the event that records the freezing positions, freezing times of the solar heating pipelines and the monitoring data of various sensors at the freezing time. 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 whose sensor monitoring data fails to trigger the freezing warning as suspicious monitoring blind areas. Search for the sensor with the shortest distance from the same type of sensors to the center point of the suspicious monitoring blind area within the preset layout range as the sensor to be corrected. If there is no sensor to be corrected, respond to the layout requirement.

3. The solar heating antifreeze monitoring method based on sensor measurement according to claim 1, characterized in that: Step S200 includes the following content: Step S210: Divide the target analysis sensor into a forward sensor and a backward sensor, and extract the sensor monitoring value P of the forward sensor and the backward sensor at the time of responding to the abnormal monitoring event respectively. (1、2) , where P1 represents the monitoring value obtained by the forward sensor and P2 represents the monitoring value obtained by the backward sensor; calculate the monitoring deviation rate W between the target analysis sensor and the sensor to be corrected at the abnormal response time 异常 (1、2) , W (1、2) =|P (1、2) -P0| / L (1、2) , where P0 represents the monitoring value recorded by the sensor to be calibrated at the time of abnormal response, L (1、2) Indicates the path transmission lengths between the forward sensor and the backward sensor and the sensor to be calibrated based on the heating pipe; Step S220: Select the monitoring time corresponding to the preset period before the abnormal monitoring event response time as the target monitoring time, obtain the sensor monitoring value recorded at the target monitoring time, and 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: Calculate the response fluctuation value R of the target analysis sensor and the sensor to be calibrated based on the monitoring deviation rate at the target monitoring time and the monitoring deviation rate at the abnormal response time, 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 satisfy R < R0, output that there is no best comparison 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 comparison sensor of the sensors to be corrected.

4. The solar heating antifreeze monitoring method based on sensor measurement according to claim 1 is characterized in that: Step S300 includes the following: Step S310: The safety monitoring period is a monitoring period that is longer than a preset time period and contains the maximum duration of all sensor data recorded by the solar heating system corresponding to the best control sensor and the sensor to be calibrated, and there is no freezing warning; the safety monitoring period is divided into a number of unit monitoring periods in equal proportion, and the monitoring data in each unit monitoring period is extracted respectively, 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 ; The response fluctuation value R of each unit during the monitoring period 单 Take the average value as the final response fluctuation value R corresponding to the safety monitoring period 安 ; Step S320: If RR 安 If the absolute value of the difference between RR and RR is less than or equal to the difference threshold, the determination of the best control sensor is irrelevant to the abnormal monitoring event. 安 If the absolute value of the difference is greater than the difference threshold, the determination of the best control sensor is outputted and the correlation with the abnormal monitoring event is correlated.

5. The solar heating antifreeze monitoring method based on sensor measurement according to claim 1, characterized in that: Step S400 includes the following steps: When the correlation result is irrelevant or the best control sensor is not 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 of the sensor corresponding to the type of freezing. 标 , based on all abnormal monitoring events corresponding to the same solar heating and antifreeze system, calculate the monitoring difference d of each abnormal event, d=pp 标 , and construct the monitoring difference interval A, A=[dmin,dmax]; dmin represents the minimum monitoring difference, dmax represents the maximum monitoring difference; calculate the abnormal response model Q1 of the sensor to be corrected, Q1: p 实 -p 标 ∈[dmin,dmax]; where P 实 Indicates the real-time sensor monitoring value; when p is satisfied 实 -p 标 ∈[dmin,dmax] triggers an abnormal response, indicating that there is a freezing risk in the suspicious monitoring blind area; When the correlation result is correlation and R>R 安 When the abnormal response model Q2 of the sensor to be corrected is constructed, Q2: R 待实 ≥R; where R 待实 Indicates the real-time response fluctuation value of the sensor to be calibrated corresponding to the best reference sensor within a unit time period; When the correlation result is related and R <R 安 When the abnormal response model Q3 of the sensor to be corrected is constructed, 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.

6. A solar heating antifreeze monitoring system based on sensor measurement, applying the solar heating antifreeze monitoring method based on sensor measurement according to any one of claims 1 to 5, 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. The layout position collection module is used to collect the sensor layout positions built for solar heating anti-freezing. The suspicious monitoring blind area determination module is used to determine the suspicious monitoring blind areas of the solar heating device to be monitored and the sensors to be corrected in the suspicious monitoring blind areas based on the layout positions and the abnormity monitoring events recorded historically. The best comparison sensor analysis module is used to obtain the best comparison sensors in the target analysis sensors by analyzing the sensor monitoring data. 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.

7. The solar heating antifreeze monitoring system based on sensor measurement according to claim 6, 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 time of responding to the abnormal monitoring event; The monitoring deviation rate calculation unit is used to calculate the monitoring deviation rate of the target analysis sensor and the sensor to be corrected at the abnormal response time, and the monitoring deviation rate of the target analysis sensor and the sensor to be corrected 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 control sensor based on the relationship between the fluctuation value and the fluctuation value threshold.

8. The solar heating antifreeze monitoring system based on sensor measurement according to claim 7, characterized in that: The abnormal response model analysis module includes a correlation result distinguishing unit and an abnormal response model building unit; The correlation result distinguishing unit is used to distinguish between two types: the correlation result is irrelevant or the best control sensor is not output, and the correlation result is relevant; The abnormal response model construction unit is used to construct an abnormal response model based on different correlation results, extract the sensor monitoring value obtained in real time, and judge whether there is a freezing risk in the suspicious blind spot based on the abnormal response model.

Images