Intelligent Monitoring and Early Warning System for Condensate Water in HVAC Systems Based on the Internet of Things

Abstract

The present invention relates to the technical field of leakage detection, specifically an intelligent monitoring and early warning system for condensate water in a heating, ventilation, and air conditioning (HVAC) system based on the Internet of Things. The system includes a spectrum sensing scheduling module, a data transmission optimization module, a vibration and pressure monitoring module, a gas composition analysis module, and a leakage early warning module. In the present invention, by dynamically scheduling the wireless spectrum bandwidth and optimizing the data transmission timing, it ensures the priority transmission of key monitoring data, improves timeliness and accuracy, reduces data loss caused by interference and signal instability. Combining the analysis of vibration and pressure sensors, it can identify leaks in real time and confirm the leak location and trend through gas composition monitoring, providing a comprehensive risk analysis, enhancing sensitivity and accuracy. The temperature and humidity sensing enhances the assessment of environmental changes, further improving the monitoring and early warning functions, reducing manual intervention, improving the device's autonomous monitoring and alarm capabilities, and optimizing resource utilization and early warning response speed.

Description

Technical Field

[0001] The present invention relates to the technical field of leak detection, and particularly to an intelligent monitoring and early warning system for condensate water in a heating, ventilation, and air conditioning (HVAC) system based on the Internet of Things. Background Art

[0002] The technical field of leak detection includes technical means for monitoring, identifying, and early warning of leaks in pipelines, containers, equipment, and related fluid transportation systems. The core content of this technical field includes leak monitoring sensors, data collection and analysis, remote monitoring, and early warning systems, etc. Leak detection relies on means such as pressure change detection, flow balance analysis, acoustic signal analysis, optical imaging detection, and gas sensing technology to evaluate the integrity of the target system in real time or regularly. The leak detection technology is widely applied in multiple industries such as water supply networks, oil and gas transportation pipelines, chemical equipment, and HVAC systems, and realizes the rapid identification and early warning of leak points through various physical and chemical detection methods and intelligent analysis means.

[0003] Among them, the intelligent monitoring and early warning system for condensate water in an HVAC system based on the Internet of Things refers to a system that uses sensor networks, wireless communication, and data analysis technology to monitor the condensate water discharge situation in an HVAC system and provide abnormal early warning. For problems such as condensate water leakage and abnormal discharge, it covers technical matters such as flow monitoring, conductivity detection, liquid level measurement, and temperature and humidity sensing. Specifically, flow sensors and conductivity sensors are arranged at key positions in the condensate water drainage pipeline to detect water flow changes and water quality characteristics, and a liquid level sensor is used to monitor the accumulated water height. At the same time, the temperature and humidity sensor is combined to sense the environmental state change. The data is uploaded to a remote server through wireless communication, and real-time comparison and analysis are carried out using the set monitoring rules, and whether to trigger an early warning signal is judged according to the threshold value.

[0004] Leak detection in the prior art relies on sensors and analysis systems to collect and compare data in real time. However, in actual operation, the transmission frequency, timing control, and data accuracy are restricted by various factors, resulting in the inability to capture early leak signals in a timely and accurate manner. For example, there is frequency band interference during signal transmission. Especially when multiple monitoring points in a pipeline system work simultaneously, data loss or transmission delay is likely to occur, affecting the response ability of the overall system. The traditional system has insufficient joint analysis of multi-dimensional signals such as pipeline pressure and vibration. It can only monitor a single indicator, lacking a comprehensive assessment of leak risks. The abnormal manifestations of condensate leakage are the coexistence of multiple physical phenomena. The prior art's processing of multi-dimensional signals is relatively simple, resulting in the inability to comprehensively capture potential risks in complex environments. The limitation of gas component analysis is another shortcoming of the prior art, making it difficult to provide sufficiently accurate leak location determination and trend prediction, thus delaying leak warning and response. The prior art is not sensitive enough to dynamic changes, missing the best alarm opportunity, bringing unnecessary difficulties and risks to maintenance and emergency handling. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art, and a condensate intelligent monitoring and warning system for a heating, ventilation, and air conditioning (HVAC) system based on the Internet of Things is proposed.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: The condensate intelligent monitoring and warning system for a heating, ventilation, and air conditioning (HVAC) system based on the Internet of Things includes:

[0007] The spectrum sensing and scheduling module extracts the spectrum density of the condensate monitoring node based on the signal strength value, signal-to-noise ratio value, and interference index value of the HVAC pipeline sensor, screens the available frequency bands of the HVAC data gateway, and obtains the spectrum scheduling and allocation value;

[0008] The data transmission optimization module extracts the HVAC data gateway data based on the spectrum scheduling and allocation value, analyzes the transmission priority of the condensate monitoring data, and adjusts the transmission timing of the HVAC data gateway to obtain the condensate data transmission parameter set;

[0009] The vibration and pressure monitoring module calls the condensate data transmission parameter set, extracts the vibration signal of the pipeline vibration sensor and the pressure signal of the condensate pressure sensor, and identifies the vibration offset and pressure fluctuation index to obtain the condensate pipe pressure state parameter;

[0010] The gas component analysis module extracts the gas concentration value, humidity value, and temperature value of the condensate detection based on the condensate pipe pressure state parameter, analyzes the abnormal situation of the gas content in the condensate, and obtains the gas monitoring parameter set;

[0011] Based on the gas monitoring parameter set, the leakage warning module predicts and analyzes the leakage location and leakage trend, identifies the risk of condensate water leakage and issues a warning, so as to obtain the real-time warning result of condensate water leakage.

[0012] As a further solution of the present invention, the spectrum scheduling allocation value includes frequency band selection, signal strength threshold, signal-to-noise ratio parameter, interference index, the condensate water data transmission parameter set includes transmission priority, data flow, timing adjustment parameter, throughput index, the condensate pipe pressure state parameter includes vibration amplitude, pressure fluctuation amplitude, leakage level, the gas monitoring parameter set includes gas concentration, ambient humidity, temperature parameter, leakage gas composition, and the real-time warning result of condensate water leakage includes leakage location, leakage trend, risk level.

[0013] As a further solution of the present invention, the spectrum sensing scheduling module includes;

[0014] The signal parameter calculation sub-module calculates the signal interference correction index based on the signal strength value, signal-to-noise ratio value, and interference index value of the HVAC pipeline sensor, using the formula:

[0015] ;

[0016] Normalize the HVAC pipeline sensor data to obtain the normalized signal parameter value;

[0017] Wherein, represents the signal interference correction index, represents the signal strength value, represents the signal-to-noise ratio value, represents the interference index value, represents the interference threshold;

[0018] The spectrum density extraction sub-module calculates the spectrum density of the condensate water monitoring node based on the normalized signal parameter value, identifies the distribution of the spectrum density through the integral operation of the signal power, and obtains the spectrum density value of the condensate water monitoring node;

[0019] The frequency band screening and scheduling sub-module calls the spectrum density value of the condensate water monitoring node, screens the available frequency bands of the HVAC data gateway, analyzes the adaptability of the spectrum occupancy rate and interference distribution, and generates the spectrum scheduling allocation value.

[0020] As a further solution of the present invention, the data transmission optimization module includes;

[0021] The data extraction sub-module extracts data from the HVAC data gateway based on the spectrum scheduling allocation value, screens the condensate water monitoring related data, analyzes the data type and transmission rate, and establishes the condensate water monitoring data set;

[0022] The transmission priority calculation sub-module calls the condensate monitoring data set, analyzes the criticality and latency sensitivity of the data, sets a data latency threshold, and uses the formula:

[0023] ;

[0024] Calculate the transmission priority value of the condensate data;

[0025] Wherein, represents the transmission priority value of the condensate data, represents the data refresh rate, represents the latency sensitivity of the current data, represents the latency sensitivity threshold, represents the queuing time of the data in the queue, represents the packet length;

[0026] The transmission timing adjustment sub-module calls the transmission priority value of the condensate data, adjusts the transmission timing of the HVAC data gateway, optimizes the data queue order according to the priority, and generates a condensate data transmission parameter set.

[0027] As a further solution of the present invention, the vibration pressure monitoring module includes;

[0028] The signal acquisition sub-module calls the condensate data transmission parameter set, extracts the vibration signal of the pipeline vibration sensor and the pressure signal of the condensate pressure sensor, analyzes the vibration amplitude, vibration frequency and pressure change rate, and obtains a vibration pressure signal set;

[0029] The vibration and pressure calculation sub-module is based on the vibration pressure signal set, sets a vibration offset reference, and uses the formula:

[0030] ;

[0031] Calculate the vibration offset value, combine it with the pipeline condensate pressure signal, analyze the fluctuation situation, compare the pressure change rate with the pressure stability threshold, and obtain the pressure fluctuation index;

[0032] Wherein, represents the vibration offset value, represents the amplitude of the vibration signal, represents the measured vibration signal frequency, represents the vibration reference frequency, represents the time duration value of the vibration signal, represents the th noise interference amount of the interference data point, is the total number of interference data points;

[0033] The condenser pressure state recognition sub-module calls the vibration offset value and the pressure fluctuation index, analyzes the pipeline vibration characteristics and the pressure distribution pattern, judges the condenser pressure state, matches the abnormal vibration and the abnormal pressure fluctuation relationship, and obtains the condenser pressure state parameters.

[0034] As a further solution of the present invention, the gas composition analysis module includes;

[0035] The gas data acquisition sub-module extracts the gas concentration value, humidity value and temperature value in the condenser based on the condenser pressure state parameters, analyzes the gas type and the concentration change trend, and obtains the gas characteristic parameter set;

[0036] The gas abnormality calculation sub-module sets a normal leakage threshold based on the gas characteristic parameter set, combines the change characteristics of the differential gas, analyzes the gas leakage mode, judges the correlation between the gas concentration and the humidity and temperature, and identifies the abnormal fluctuation trend. Using the formula:

[0037] ;

[0038] Performs operations to obtain the condenser gas abnormality index;

[0039] Wherein, represents the condenser gas abnormality index, represents the gas concentration value, represents the humidity in the monitoring area, represents the reference humidity, represents the current ambient temperature, represents the gas diffusion rate;

[0040] The condenser gas monitoring sub-module calls the condenser gas abnormality index, analyzes the gas concentration abnormality in the condensate water abnormal area, screens the gas abnormal fluctuation value, matches the gas type, and generates the gas monitoring parameter set.

[0041] As a further solution of the present invention, the leakage warning module includes;

[0042] The leakage trend analysis sub-module analyzes the change trend of the leakage gas concentration and the leakage diffusion rate based on the gas monitoring parameter set, sets the leakage trend reference value, and obtains the leakage trend parameter set;

[0043] The leakage risk identification sub-module sets a risk identification threshold based on the leakage trend parameter set. Using the formula:

[0044] ;

[0045] Combines the gas data in the differential area, analyzes the condensate water leakage risk level, and performs operations to obtain the leakage risk index;

[0046] Among them, represents the leakage risk index, represents the current gas concentration, represents the leakage reference concentration, represents the gas diffusion rate, represents the diffusion time interval, represents the current environmental humidity, represents the reference humidity in the leakage area, represents the number of steps for diffusion calculation;

[0047] The intelligent early warning sub-module calls the leakage risk index, analyzes the condensation water leakage risk level, screens the risk over-limit value, matches the leakage trend characteristics, and generates a real-time early warning result for condensation water leakage.

[0048] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0049] In the present invention, by dynamically scheduling the spectrum bandwidth of wireless transmission, the transmission timing of condensation water data is optimized, ensuring the priority transmission of key monitoring data, improving the timeliness and accuracy of information processing, better adapting to the signal quality differences between different monitoring points in the pipeline, avoiding data loss or transmission delay caused by interference or signal instability, combining the signal analysis of vibration and pressure sensors, effectively capturing the subtle changes in pipeline vibration and condensation water pressure fluctuations, being able to identify potential leakage problems in real time, and further confirming the leakage location and trend through the monitoring of gas components, providing a comprehensive leakage risk analysis, improving the sensitivity and accuracy of detection, being able to quickly respond to abnormal situations in practical applications, giving early warnings and avoiding disastrous consequences, the dynamic perception of temperature and humidity changes enables the system to accurately evaluate the impact of environmental conditions on leakage risk, thus further improving the overall monitoring and early warning functions, not only reducing manual intervention, but also significantly improving the autonomous monitoring and alarm capabilities of the equipment, optimizing the use efficiency of resources and the early warning response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is the system flow chart of the present invention;

[0051] Figure 2 is the acquisition flow chart of the spectrum sensing scheduling module in the present invention;

[0052] Figure 3 is the acquisition flow chart of the data transmission optimization module in the present invention;

[0053] Figure 4 is the acquisition flow chart of the vibration and pressure monitoring module in the present invention;

[0054] Figure 5 is the acquisition flow chart of the gas component analysis module in the present invention;

[0055] Figure 6 This is the acquisition flow chart of the leakage warning module in the present invention. Detailed implementation manners

[0056] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0057] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more unless otherwise specifically defined.

[0058] Please refer to Figure 1 , the intelligent monitoring and warning system for condensate water of the HVAC system based on the Internet of Things includes:

[0059] The spectrum sensing and scheduling module extracts the spectrum density of the condensate water monitoring node based on the signal strength value, signal-to-noise ratio value, and interference index value of the HVAC pipeline sensor, screens the available frequency bands of the HVAC data gateway, and obtains the spectrum scheduling and allocation value;

[0060] The data transmission optimization module extracts the HVAC data gateway data based on the spectrum scheduling and allocation value, analyzes the transmission priority of the condensate water monitoring data, and adjusts the transmission timing of the HVAC data gateway to obtain the condensate water data transmission parameter set;

[0061] The vibration and pressure monitoring module calls the condensate water data transmission parameter set, extracts the vibration signal of the pipeline vibration sensor and the pressure signal of the condensate water pressure sensor, and identifies the vibration offset and pressure fluctuation index to obtain the condensate pipe pressure state parameter;

[0062] The gas composition analysis module extracts the gas concentration value, humidity value, and temperature value of the condensate water detection based on the condensate pipe pressure state parameter, analyzes the abnormal situation of the gas content in the condensate water, and obtains the gas monitoring parameter set;

[0063] The leakage warning module predicts and analyzes the leakage location and leakage trend based on the gas monitoring parameter set, identifies the condensate water leakage risk and gives a warning to obtain the real-time warning result of the condensate water leakage.

[0064] The spectrum scheduling allocation values include frequency band selection, signal strength threshold, signal-to-noise ratio parameter, interference index, the condensate water data transmission parameter set includes transmission priority, data flow, timing adjustment parameter, throughput index, the condensate pipe pressure state parameter includes vibration amplitude, pressure fluctuation amplitude, leakage level, the gas monitoring parameter set includes gas concentration, ambient humidity, temperature parameter, leaked gas component, and the real-time early warning result of condensate water leakage includes leakage location, leakage trend, risk level.

[0065] Please refer to Figure 2 , the spectrum sensing scheduling module includes;

[0066] The signal parameter calculation sub-module calculates the signal interference correction index based on the signal strength value, signal-to-noise ratio value, and interference index value of the HVAC pipeline sensor, using the formula:

[0067] ;

[0068] Normalize the HVAC pipeline sensor data to obtain the normalized signal parameter value;

[0069] Among them, represents the signal interference correction index, represents the signal strength value, represents the signal-to-noise ratio value, represents the interference index value, represents the interference threshold;

[0070] First, the signal strength value ( ) can be directly measured by the sensor. For example, in the HVAC pipeline, the signal strength detected by the sensor is 0.8 watts. The signal-to-noise ratio ( ) represents the ratio of the signal power to the noise power and can be obtained by measuring the signal and noise powers and calculating their ratio. Assuming the measured signal power is 0.8 watts and the noise power is 0.2 watts, the signal-to-noise ratio is 4 (i.e., ). The interference index value ( ) reflects the degree of interference in the environment and can be obtained by analyzing the interference components in the signal spectrum. Assuming the interference index is 0.3 and the set interference threshold ( ) is 0.2, which represents the maximum acceptable interference level of the system, calculate the signal interference correction index ( ), using the formula:

[0071] ;

[0072] Substitute the above values to get:

[0073] ;

[0074] The sensor data is normalized using this correction index. Normalization is to adjust the data to a specific range, from 0 to 1, to eliminate the influence between different dimensions. For example, divide the measured signal intensity of 0.8 watts by the maximum signal intensity of 1 watt to get a normalized signal intensity of 0.8. Combine with the signal interference correction index to adjust the normalized signal. Assume the normalized signal intensity is 0.8 and the signal interference correction index is 0.38, then the normalized signal parameter value is the product of the two, that is, 0.8×0.38 = 0.304, and the normalized signal parameter value is obtained.

[0075] Based on the normalized signal parameter value, the spectral density extraction sub-module calculates the spectral density of the condensate water monitoring node, identifies the distribution of the spectral density through the integral operation of the signal power, and obtains the spectral density value of the condensate water monitoring node;

[0076] The spectral density distribution is obtained by using the signal power integral operation. The Fourier transform is performed on the normalized signal parameter value to obtain its spectral representation. The square operation is performed on the spectrum to obtain the power spectral density. The integral is performed on the power spectral density within the frequency range of interest to obtain the spectral density value. Assume the power at a specific frequency after Fourier transform is 0.5 watts, then the spectral density is 0.5 watt·hertz, and the spectral density value of the condensate water monitoring node is obtained.

[0077] The frequency band screening and scheduling sub-module calls the spectral density value of the condensate water monitoring node, screens the available frequency bands of the HVAC data gateway, analyzes the adaptability of the spectrum occupancy rate and the interference distribution, and generates a spectrum scheduling allocation value;

[0078] Calculate the adaptability of the spectrum occupancy rate and the interference distribution. Set a spectrum occupancy rate threshold, for example, 70%, which means that when the occupancy rate of a certain frequency band is lower than 70%, this frequency band is considered available. Analyze the interference distribution of each frequency band and calculate its adaptability with the acceptable interference level of the system. Assume the occupancy rate of a certain frequency band is 60% and the interference level is lower than the set interference threshold, then this frequency band is considered suitable for use, select the optimal frequency band, and obtain the spectrum scheduling allocation value.

[0079] Please refer to Figure 3 , the data transmission optimization module includes;

[0080] Based on the spectrum scheduling allocation value, the data extraction sub-module extracts data from the HVAC data gateway, screens the condensate water monitoring associated data, analyzes the data type and transmission rate, and establishes a condensate water monitoring data set;

[0081] First, analyze the data types stored in the data gateway, and extract the data records related to condensate monitoring. The HVAC data gateway stores multiple types of data, including ambient temperature, humidity, air velocity, and condensate flow data. During the data extraction process, it is necessary to identify the data frame structure and determine the storage location and format of the condensate data. For example, the data frame format stored in a certain gateway includes a data identifier, timestamp, numerical data, error correction information, etc. When extracting, use the data identifier to filter relevant data. Assume that the data identifier 0010 represents condensate monitoring data, then filter the data frames with the identifier 0010 and read the numerical data therein. Further analyze the data transmission rate. Assume that the data frame is updated once per second, then the data transmission rate is 1Hz. If the data update frequency is increased to 10Hz, it means that 10 data are received per second. The extracted data is converted in format to make it suitable for subsequent analysis, and a condensate monitoring data set is constructed. This data set contains information such as transmission time, data category, and data value.

[0082] The transmission priority calculation sub-module calls the condensate monitoring data set, analyzes the criticality and delay sensitivity of the data, sets the data delay threshold, and uses the formula:

[0083] ;

[0084] Calculate the transmission priority value of the condensate data;

[0085] Among them, represents the transmission priority value of the condensate data, represents the data refresh rate, represents the delay sensitivity of the current data, represents the delay sensitivity threshold, represents the queuing time of the data in the queue, represents the packet length;

[0086] First, the importance of the data can be evaluated by calculating the change rate of the data. Assume that the change range of the condensate flow data in the past five minutes is greater than the set change threshold, then the data is marked as high-priority data. For example, set the change threshold to 5%. If the measured condensate flow changes from 2.0L / min to 2.2L / min within five minutes, then the change rate is , exceeding the threshold, so the priority is higher. Calculate the delay sensitivity of the data. Assume that the standard transmission delay threshold of the condensate data is 2 seconds. If the transmission delay of a certain data packet exceeds this value, its priority needs to be adjusted. Use the formula:

[0087] ;

[0088] Among them, represents the data refresh rate, assumed to be 5Hz, Represents the latency sensitivity of the current data. If the current data transmission is delayed by 3 seconds, then , Represents the latency sensitivity threshold, set to 2 seconds, Represents the queuing time of the data in the queue, set to 0.5 seconds, Represents the packet length, set to 256 bytes, and substitute into the calculation:

[0089] ;

[0090] Obtain the transmission priority value of the condensate water data. The higher the data priority value, the faster the data needs to be transmitted.

[0091] The transmission timing adjustment sub-module calls the transmission priority value of the condensate water data, adjusts the transmission timing of the HVAC data gateway, optimizes the data queue order according to the priority, and generates a set of condensate water data transmission parameters;

[0092] First, sort the data according to the priority, set the transmission time interval. Assume that high-priority data needs to be transmitted within 1 second, while low-priority data can be delayed until 5 seconds later. Calculate the expected transmission timing of different data packets. Assume that there are currently three data packets, where the priority of packet A is 0.08, the priority of packet B is 0.05, and the priority of packet C is 0.02. Then packet A is sent first, packet B second, and packet C last. Execute data transmission according to the adjusted transmission queue, optimize the scheduling order of the data stream, and generate a set of condensate water data transmission parameters.

[0093] Please refer to Figure 4 , the vibration pressure monitoring module includes;

[0094] The signal acquisition sub-module calls the set of condensate water data transmission parameters, extracts the vibration signal of the pipeline vibration sensor and the pressure signal of the condensate water pressure sensor, analyzes the vibration amplitude, vibration frequency and pressure change rate, and obtains a set of vibration pressure signals;

[0095] First, vibration sensors are installed at key positions of the pipeline, such as support points, bending parts, or valve connections. They are mainly used to detect the vibration state of the pipeline. The main parameters for vibration signal acquisition include vibration amplitude, vibration frequency, and instantaneous acceleration. Suppose the amplitude detected by the vibration sensor is 0.05g and the acceleration is 0.3m / s2. Then this data needs to be analyzed by time series to analyze the fluctuation pattern of pipeline vibration. The condensate pressure sensor is installed at the low point of the pipeline or where the pressure changes suddenly. It is mainly used to measure the pressure of condensate flow. The parameters for pressure signal acquisition include instantaneous pressure value, pressure change rate, and pressure duration. Suppose the pressure measured by the condensate pressure sensor is 1.2MPa and the pressure change rate is 0.05MPa / s. Then it is necessary to further calculate its fluctuation in a short period of time, analyze the time synchronization of vibration signals and pressure signals, ensure data alignment, organize the data format, and normalize the parameters of different units for subsequent calculations to obtain the vibration-pressure signal set.

[0096] Based on the vibration-pressure signal set, the vibration and pressure calculation sub-module sets a vibration offset reference and uses the formula:

[0097] ;

[0098] Calculate the vibration offset value, combine it with the pipeline condensate pressure signal, analyze the fluctuation situation, compare the pressure change rate with the pressure stability threshold, and obtain the pressure fluctuation index;

[0099] Among them, represents the vibration offset value, represents the amplitude of the vibration signal, represents the measured vibration signal frequency, represents the vibration reference frequency, represents the time duration value of the vibration signal, represents the th noise interference amount of the interference data point, is the total number of interference data points;

[0100] First, the calculation of the vibration offset value needs to consider the deviation degree of the vibration signal. Set a vibration offset reference value. For example, the reference frequency of a certain pipeline during normal operation is 50Hz. If the currently measured vibration frequency is 55Hz, then the vibration signal offset amount needs to be calculated to measure the degree of vibration change. Use the formula:

[0101] ;

[0102] Among them, assume the amplitude of the vibration signal is 0.05g, the measured vibration signal frequency is 55Hz, and the vibration reference frequency is 50Hz, the time duration value of the vibration signal is 2 seconds, the noise interference amount of the vibration data Set according to different sampling points, assume , each is 0.002, 0.003, 0.001 respectively, then calculate:

[0103] ;

[0104] ;

[0105] ;

[0106] Obtain the vibration offset value, combine with the condensate water pressure signal in the pipeline, analyze its fluctuation situation. Assume the pressure stability threshold is set to 0.1MPa. If the pressure fluctuation amplitude is greater than this value, the pressure fluctuation index will rise. For example, if the measured condensate water pressure changes from 1.2MPa to 1.4MPa within 10 seconds, then the pressure change rate is calculated as MPa / s, which is less than the threshold. Therefore, the fluctuation index remains within the normal range. Combine the vibration offset value and the pressure fluctuation index to judge its change trend for subsequent state recognition and calculate the pressure fluctuation index.

[0107] The condensate pipe pressure state recognition sub-module calls the vibration offset value and the pressure fluctuation index, analyzes the pipeline vibration characteristics and pressure distribution pattern, judges the condensate pipe pressure state, matches the relationship between abnormal vibration and abnormal pressure fluctuation, and obtains the condensate pipe pressure state parameters;

[0108] First, compare the pipeline historical vibration data, screen out the abnormal vibration values, and set the vibration abnormality threshold. For example, if the vibration offset value exceeds 0.1g, it is considered that there is an abnormal situation. Assume the calculated vibration offset value is 0.0806g, which does not exceed the threshold, so the current vibration state belongs to the normal range. At the same time, compare the pressure fluctuation index, screen out the abnormal pressure fluctuation values. Assume the pressure fluctuation index threshold is set to 0.03MPa / s. If the calculated pressure change rate is 0.02MPa / s, it does not exceed the set range. Match the relationship between abnormal vibration and abnormal pressure fluctuation. If abnormal vibration and abnormal pressure fluctuation occur simultaneously, it is judged that the risk of condensate water leakage increases, and the condensate pipe pressure state parameters are obtained.

[0109] Please refer to Figure 5 , the gas composition analysis module includes;

[0110] The gas data acquisition sub-module extracts the gas concentration value, humidity value and temperature value in the condensate pipe based on the condensate pipe pressure state parameters, analyzes the gas type and concentration change trend, and obtains the gas characteristic parameter set;

[0111] First, gas samples are collected from the detection area. Multiple sensors are installed near the condensate leakage point, including gas concentration sensors, temperature and humidity sensors, etc. The sensors are distributed at different heights and positions to ensure the integrity and accuracy of the data. Assume that 5 sensors are arranged in a certain area, 3 of which measure gas concentration and 2 measure temperature and humidity. The preliminary data obtained from the measurement includes CO 2 、H 2 O and O 2 concentration values, ambient temperature, and relative humidity. When analyzing the data, it is necessary to perform denoising processing to remove abnormal data points. For example, if the CO 2 concentration suddenly increases to 5000 ppm at a certain time point, while the values before and after are around 400 ppm, then this data point is determined to be noise and excluded. Further analyze the change trend of the data and calculate the concentration increase of different gas components. Assume that the concentration of a certain gas increases from 200 ppm to 500 ppm within 10 minutes, then the increase is . Calculate the influence of temperature and humidity. Assume that the temperature rises from 25 °C to 30 °C and the humidity increases from 40% to 50%, then it indicates that there are large fluctuations in temperature and humidity in the gas leakage area. Finally, integrate all the data to construct a gas characteristic parameter set.

[0112] Based on the gas characteristic parameter set, the gas anomaly calculation sub-module sets a normal leakage threshold, combines the change characteristics of different gases, analyzes the gas leakage mode, judges the correlation between gas concentration and humidity, temperature, and identifies abnormal fluctuation trends. The formula is used:

[0113] ;

[0114] Calculate the gas anomaly index of the condensate pipe through operation;

[0115] Among them, represents the gas anomaly index of the condensate pipe, represents the gas concentration value, represents the humidity of the monitoring area, represents the reference humidity, represents the current ambient temperature, represents the gas diffusion rate;

[0116] First, calculate the change rate of the gas concentration value relative to the environment, and set the background concentration reference value. Assume that the normal concentration of O 2 is 20.9%. If the monitored value drops to 19.5%, then the concentration change rate is calculated as , for humidity, calculate the deviation between the predicted humidity in the leakage area and the ambient reference humidity. Assuming the humidity in the monitored area is 65% and the reference humidity is 50%, the deviation is 15%. The influence of the ambient temperature is reflected by calculating the temperature increment. Set the standard ambient temperature as 25°C. If the current temperature is 30°C, the temperature increment is 5°C. Considering the above parameters, use the formula:

[0117] ;

[0118] where, set , , , , , substitute into the calculation:

[0119] ;

[0120] ;

[0121] Operate to obtain the abnormal index of the condenser gas. Combine the change characteristics of different gases, analyze the gas leakage mode, judge the correlation between gas concentration and humidity and temperature, and identify the abnormal fluctuation trend.

[0122] The condenser gas monitoring sub-module calls the abnormal index of the condenser gas, analyzes the abnormal gas concentration in the abnormal condensate water area, screens the abnormal gas fluctuation values, matches the gas types, and generates a gas monitoring parameter set;

[0123] First, screen the abnormal gas leakage fluctuation values. Set the abnormal gas leakage threshold. Assume the threshold is set to 25. If the calculated abnormal index of the condenser gas is 27.7, it is considered that the gas is abnormal. Match the gas types and analyze the characteristic patterns of different gases during predicted leakage. For example, a sudden increase in the concentration of CO 2 and a decrease in the concentration of O 2 are related to pipeline leakage, while a sharp increase in humidity is related to the evaporation of condensate water. Combine the data trends of each sensor to identify the predictability of gas leakage and generate a gas monitoring parameter set.

[0124] Please refer to Figure 6 , the leakage warning module includes;

[0125] The leakage trend analysis sub-module analyzes the change trend of the leakage gas concentration based on the gas monitoring parameter set, analyzes the leakage diffusion rate, sets the leakage trend reference value, and obtains the leakage trend parameter set;

[0126] First, obtain the gas data measured by different sensors, including CO 2 , H 2 O, O 2Equal component concentration values, as well as temperature and humidity data in the leakage area. The data collection period is set to 10 minutes to analyze the changes of each gas at different time points. Assume that the CO 2 concentration increases from 400 ppm to 1000 ppm within 10 minutes, and the calculated change rate is , for the humidity data, the reference humidity is set to 50%. If the humidity detection value in the leakage area is 65%, then the humidity deviation is , calculate the gas diffusion rate. Set the initial gas concentration diffusion radius to 0.5 m, and it diffuses to 1.5 m after 5 minutes. The calculated rate is , generate a leakage trend parameter set based on all parameters.

[0127] Based on the leakage trend parameter set, the leakage risk identification sub-module sets a risk identification threshold and uses the formula:

[0128] ;

[0129] Combined with the gas data in the differential area, analyze the condensate water leakage risk level and calculate the leakage risk index;

[0130] Among them, represents the leakage risk index, represents the current gas concentration, represents the leakage reference concentration, represents the gas diffusion rate, represents the diffusion time interval, represents the current environmental humidity, represents the leakage area reference humidity, represents the number of diffusion calculation steps;

[0131] First, compare the gas concentration changes at different time points and calculate the leakage rate. Assume that the gas concentration in the leakage area rises from 300 ppm to 1200 ppm within 10 minutes, and the reference gas change rate is set to 100 ppm / min. The calculated leakage rate is , if the calculated rate is lower than the reference rate, it is considered that the leakage trend is slow. If it exceeds the reference rate, it is determined as a high-risk leakage. Calculate the humidity change rate. Set the standard environmental humidity change to 5% / min. If the humidity in the leakage area rises from 40% to 60% within 5 minutes, the change rate is , use the formula:

[0132] ;

[0133] Set the current gas concentration , the leakage reference concentration , the gas diffusion rate , the diffusion time , the environmental humidity , reference humidity , calculate:

[0134] ;

[0135] ;

[0136] Obtain the leakage risk index through operations, which is used to identify the risk level of the leakage area and judge the severity of the leakage trend.

[0137] The intelligent early warning sub-module calls the leakage risk index, analyzes the condensate leakage risk level, screens the risk over-limit values, matches the leakage trend characteristics, and generates real-time early warning results for condensate leakage;

[0138] First, set the threshold of the leakage risk level. Assume that the high-risk threshold is 900. If the calculated leakage risk index is 931, it is determined as a high-risk area. Compare the leakage trend parameters and calculate the fluctuation range of the leakage trend. Assume that the fluctuation range of the leakage concentration changes by more than 50% within 30 minutes, then it is determined as a sudden leakage. If the fluctuation range is less than 10%, it is determined as a slow penetration. Combine the characteristics of different leakage modes, match the leakage area type, and generate real-time early warning results for condensate leakage.

[0139] The above is only the preferred embodiment of the present invention, and it does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. The intelligent monitoring and early warning system for condensed water in HVAC system based on the Internet of Things is characterized by: The system comprises: The spectrum sensing scheduling module extracts the spectrum density of the condensate monitoring node based on the signal strength value, signal-to-noise ratio value, and interference index value of the HVAC pipeline sensor, screens the available frequency bands of the HVAC data gateway, and obtains the spectrum scheduling allocation value; The data transmission optimization module extracts HVAC data gateway data based on the spectrum scheduling allocation value, analyzes the condensate monitoring data transmission priority, adjusts the transmission timing of the HVAC data gateway, and obtains a condensate data transmission parameter set; The vibration pressure monitoring module calls the condensate data transmission parameter set, extracts the vibration signal of the pipeline vibration sensor and the pressure signal of the condensate pressure sensor, identifies the vibration offset and the pressure fluctuation index, and obtains the condensate pipe pressure state parameter; The gas composition analysis module extracts the gas concentration value, humidity value, and temperature value of the condensed water detection based on the condenser pressure state parameter, analyzes the abnormality of the gas content in the condensed water, and obtains a gas monitoring parameter set; The leakage warning module predicts and analyzes the leakage location and leakage trend based on the gas monitoring parameter set, identifies the risk of condensate leakage and issues a warning, and obtains a real-time warning result of condensate leakage.

2. The intelligent monitoring and early warning system for condensed water in HVAC system based on the Internet of Things according to claim 1 is characterized in that: The spectrum scheduling allocation value includes frequency band selection, signal strength threshold, signal-to-noise ratio parameter, and interference index; the condensate data transmission parameter set includes transmission priority, data flow, timing adjustment parameters, and throughput index; the condenser pressure state parameters include vibration amplitude, pressure fluctuation amplitude, and leakage level; the gas monitoring parameter set includes gas concentration, ambient humidity, temperature parameters, and leakage gas composition; the condensate leakage real-time warning result includes leakage location, leakage trend, and risk level.

3. The intelligent monitoring and early warning system for condensed water in HVAC system based on the Internet of Things according to claim 1 is characterized in that: The spectrum sensing scheduling module includes: The signal parameter calculation submodule calculates the signal interference correction index based on the signal strength value, signal-to-noise ratio value, and interference index value of the HVAC pipe sensor using the formula: ； Normalize the HVAC pipe sensor data to obtain normalized signal parameter values; in, represents the signal interference correction index, Represents the signal strength value, represents the signal-to-noise ratio value, represents the interference index value, represents the interference threshold; The spectrum density extraction submodule calculates the spectrum density of the condensate monitoring node based on the normalized signal parameter value, identifies the distribution of the spectrum density through the integral operation of the signal power, and obtains the spectrum density value of the condensate monitoring node; The frequency band screening and scheduling submodule calls the spectrum density value of the condensate monitoring node, screens the available frequency bands of the HVAC data gateway, analyzes the spectrum occupancy rate and interference distribution adaptability, and generates a spectrum scheduling allocation value.

4. The intelligent monitoring and early warning system for condensed water in HVAC system based on the Internet of Things according to claim 2 is characterized in that: The data transmission optimization module includes: The data extraction submodule extracts data from the HVAC data gateway based on the spectrum scheduling allocation value, filters the condensate monitoring related data, analyzes the data type and transmission rate, and establishes a condensate monitoring data set; The transmission priority calculation submodule calls the condensate monitoring data set, analyzes the criticality and delay sensitivity of the data, sets the data delay threshold, and uses the formula: ； Calculate the condensate water data transmission priority value; in, Represents the condensate data transmission priority value, Represents the data refresh rate, Represents the delay sensitivity of the current data, represents the delay sensitivity threshold, Represents the queuing time of data in the queue, Represents the length of the data packet; The transmission timing adjustment submodule calls the condensed water data transmission priority value, adjusts the transmission timing of the HVAC data gateway, optimizes the data queue order according to the priority, and generates a condensed water data transmission parameter set.

5. The intelligent monitoring and early warning system for condensed water in HVAC system based on the Internet of Things according to claim 3 is characterized in that: The vibration pressure monitoring module comprises: The signal acquisition submodule calls the condensate data transmission parameter set, extracts the vibration signal of the pipeline vibration sensor and the pressure signal of the condensate pressure sensor, analyzes the vibration amplitude, vibration frequency and pressure change rate, and obtains the vibration pressure signal set; The vibration and pressure calculation submodule sets the vibration offset reference based on the vibration pressure signal set, using the formula: ； The vibration offset value is calculated and combined with the pipeline condensate pressure signal to analyze the fluctuation situation, and the pressure change rate is compared with the pressure stability threshold to obtain the pressure fluctuation index; in, Represents the vibration offset value, represents the amplitude of the vibration signal, represents the measured vibration signal frequency, represents the vibration reference frequency, Represents the time duration value of the vibration signal, Representative The noise interference amount of the interfering data points, is the total number of interference data points; The condenser pressure state identification submodule calls the vibration offset value and the pressure fluctuation index, analyzes the pipeline vibration characteristics and the pressure distribution pattern, determines the condenser pressure state, matches the abnormal vibration and abnormal pressure fluctuation relationship, and obtains the condenser pressure state parameters.

6. The intelligent monitoring and early warning system for condensed water in HVAC system based on the Internet of Things according to claim 4 is characterized in that: The gas composition analysis module comprises: The gas data acquisition submodule extracts the gas concentration value, humidity value and temperature value in the condenser based on the condenser pressure state parameter, analyzes the gas type and concentration change trend, and obtains the gas characteristic parameter set; The gas anomaly calculation submodule sets the normal leakage threshold based on the gas characteristic parameter set, analyzes the gas leakage mode in combination with the change characteristics of the differentiated gas, determines the correlation between gas concentration and humidity and temperature, and identifies the abnormal fluctuation trend, using the formula: ； Calculate and obtain the abnormal index of condenser gas; in, Represents the abnormal index of condenser gas. Represents the gas concentration value, Represents the humidity of the monitoring area. Represents the base humidity, Represents the current ambient temperature. represents the gas diffusion rate; The condenser gas monitoring submodule calls the condenser gas anomaly index, analyzes the abnormal gas concentration in the condensate water abnormal area, screens the gas abnormal fluctuation value, matches the gas type, and generates a gas monitoring parameter set.

7. The intelligent monitoring and early warning system for condensed water in HVAC system based on the Internet of Things according to claim 5 is characterized in that: The leakage warning module comprises: The leakage trend analysis submodule analyzes the leakage gas concentration change trend, analyzes the leakage diffusion rate, sets the leakage trend reference value, and obtains the leakage trend parameter set based on the gas monitoring parameter set; The leakage risk identification submodule sets the risk identification threshold based on the leakage trend parameter set, using the formula: ； Combined with the gas data of differentiated areas, the risk level of condensate leakage is analyzed and the leakage risk index is calculated; in, represents the leakage risk index, Represents the current gas concentration, represents the leakage reference concentration, represents the gas diffusion rate, represents the diffusion time interval, Represents the current ambient humidity. Represents the baseline humidity in the leak area, Represents the number of diffusion calculation steps; The intelligent early warning submodule calls the leakage risk index, analyzes the condensate leakage risk level, screens the risk exceeding limit value, matches the leakage trend characteristics, and generates a real-time early warning result for condensate leakage.

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