An online nitrogen production mobile phone APP remote monitoring system

Abstract

The application provides an online nitrogen production mobile phone APP remote monitoring system, which comprises an environment sensing module, an air self-adaptive processing module, a separation nitrogen production module and a prediction nitrogen production equipment life module. The environment sensing module is used for collecting environment parameters of a nitrogen production environment. The air self-adaptive processing module is used for dynamically adjusting pretreatment parameters of air drying treatment according to the environment parameters, so as to form standardized input air. The separation nitrogen production module is used for processing the standardized input air by adopting an adsorption separation process, and outputs nitrogen gas meeting requirements. The prediction nitrogen production equipment life module is used for constructing a linear decay model of production capacity changing with cumulative running time based on normalized flow data corrected by an environment thermal effect, and measuring the remaining service life of a carbon adsorbent when the current performance of the carbon adsorbent decreases to a predicted invalidation time by extrapolating the decay trend of the linear decay model. Through the system, adaptive protection and energy efficiency optimization of a process source are realized, and through data-driven life prediction, active preventive maintenance of equipment is realized.

Description

Technical Field

[0001] This invention proposes an online nitrogen generator remote monitoring system via a mobile app, which relates to the field of remote information monitoring technology. Background Technology

[0002] Pressure Swing Adsorption (PSA) nitrogen generators utilize the selective adsorption properties of carbon molecular sieves to separate nitrogen from compressed air, and are widely used in industrial production. However, existing nitrogen generator systems suffer from the following shortcomings in operation and maintenance: Air pretreatment lacks adaptability, affecting adsorbent lifespan. Existing pretreatment equipment, such as refrigerated dryers, typically operates in a constant mode (e.g., fixed-frequency compression, timed drainage), unable to dynamically adjust according to seasonal or diurnal variations in ambient temperature and humidity. In high-temperature and high-humidity environments, insufficient pretreatment can lead to moisture entering the adsorption tower and causing carbon molecular sieve pulverization, while in low-temperature and dry environments, excessive operation results in unnecessary energy waste. There is a lack of accurate lifespan prediction mechanisms, leading to passive maintenance. Traditional equipment maintenance relies mainly on manual periodic inspections or post-failure repairs. Although some high-end equipment has flow and purity monitoring functions, the thermodynamic impact of ambient temperature on the adsorption performance of carbon molecular sieves is not considered. Because adsorption efficiency fluctuates with temperature, directly using raw flow data for trend analysis introduces significant noise, making it impossible to accurately fit the true decay curve of the carbon molecular sieve, hindering accurate remaining lifespan prediction and remote early warning. Summary of the Invention

[0003] This invention provides an online nitrogen generator remote monitoring system via a mobile app to solve the aforementioned problems: This invention proposes an online nitrogen generator remote monitoring system via a mobile app, the system comprising: The environmental sensing module is used to collect environmental parameters of the nitrogen production environment; An air adaptive processing module is used to dynamically adjust the pre-processing parameters for drying the air according to the environmental parameters, so as to form standardized input air. The nitrogen generation module uses an adsorption separation process to treat the standardized input air, forming a stratification of nitrogen and other gases, and outputting nitrogen that meets the requirements. The nitrogen production equipment life prediction module constructs a linear decay model of capacity change with cumulative operating time based on normalized flow data corrected for environmental thermal effects. By extrapolating the decay trend of the linear decay model, it calculates the remaining service life of the carbon adsorbent before its current performance drops to the expected failure time.

[0004] Furthermore, the environment perception module includes: The sensor detection unit includes a temperature sensor, an atmospheric pressure sensor, and a humidity sensor. The temperature sensor is used to collect the temperature of the nitrogen production environment, the atmospheric pressure sensor is used to collect the ambient atmospheric pressure, and the humidity sensor is used to collect the humidity of the nitrogen production environment, thereby obtaining analog signals of environmental parameters. The signal conditioning unit is used to perform low-pass filtering, signal amplification, and anti-interference processing on the analog signals collected by various sensors to obtain digital signals of environmental parameters. The data transmission unit is used to transmit the digital signals of the environmental parameters to the air adaptive processing module in real time via the I2C bus interface.

[0005] Furthermore, the air adaptive processing module includes: Construct an environmental feature vector unit, and construct a nitrogen production environmental feature vector based on the environmental parameters; The output control parameter vector unit inputs the nitrogen production environment feature vector into the pre-trained cold and dry load optimization model, and the cold and dry load optimization model outputs the control parameter vector. The drive control unit adjusts the nitrogen generator's operating state according to the control parameter vector to form standardized input air.

[0006] Furthermore, the control parameter vector includes: the target operating frequency of the refrigerant compressor of the refrigerated dryer, the opening interval time of the electronic drain valve, and the single opening duration of the electronic drain valve.

[0007] Furthermore, the output control parameter vector unit includes: Construct a full-condition test matrix subunit to build a set of discrete operating condition points covering the operating domain of the nitrogen generator in the environmental simulation chamber according to preset temperature gradient, humidity gradient and air flow gradient. The optimal frequency tag subunit is used to obtain the optimal operating parameter tag subunit. For each discrete operating point, the electronic drain valve of the refrigerated dryer is placed in a fixed drain mode, and the operating frequency of the refrigerated dryer compressor is gradually reduced. At the same time, the pressure dew point of the dried air is monitored in real time. When the pressure dew point reaches the preset critical threshold (i.e. the limit value that meets the drying requirements of the standardized input air), the compressor operating frequency at this time is recorded as the optimal frequency tag for that discrete operating point. The marking start interval time subunit is used to control the electronic drain valve of the refrigerated dryer to remain closed based on the current optimal frequency tag when the compressor operating frequency is fixed as the optimal frequency tag. The liquid level sensor monitors the liquid level change in the condensate collection chamber of the refrigerated dryer in real time, records the time required for the liquid level to rise from the waterless reset state to the preset warning upper limit state, and marks it as the start interval time under the discrete operating condition point. The single opening duration subunit is used to trigger the opening of the electronic drain valve when the liquid level reaches the preset warning upper limit state, record the duration required for the liquid level to drop from the warning upper limit state to the waterless reset state, and mark it as the single opening duration of the electronic drain valve. The optimal drainage timing tag subunit is used to determine the opening interval and single opening duration as the optimal drainage timing tag.

[0008] Furthermore, the output control parameter vector unit further includes: Construct model sub-units, using the environmental parameters as input vectors and the control parameters as output vectors, to build a supervised learning regression model; The training model subunit uses multiple discrete operating point data and their corresponding optimal frequency labels and optimal drainage time series labels to form a training dataset to train the regression model, so as to establish a nonlinear mapping relationship from environmental operating parameters to optimal control parameters and obtain a pre-trained cold and dry load optimization model.

[0009] Furthermore, the nitrogen separation module includes: The adsorption separation unit controls the opening of the air inlet valve to allow the standardized input air to enter the adsorption tower, where oxygen molecules, carbon dioxide, and moisture are preferentially adsorbed by the micropores of the carbon molecular sieve, and nitrogen is enriched at the top of the adsorption tower. The reverse desorption and regeneration unit is used to perform a pressure reduction action after the carbon molecular sieve is saturated with adsorption, forcing the oxygen molecules and impurity gases captured by the carbon molecular sieve to desorb from the micropores. The quality control buffer output unit is used to temporarily store and stabilize the output nitrogen flow, while simultaneously detecting the nitrogen purity and flow rate data of the nitrogen flow in real time. When the detection data meets the command requirements, the nitrogen is delivered to the gas user and the production data is uploaded to the mobile APP. When the detection data fails to meet the standards, a backflow is triggered.

[0010] Furthermore, the nitrogen generator life prediction module includes: The full-load reference unit is used to record the initial maximum gas production flow rate at a set nitrogen purity after the equipment is first replaced with carbon adsorbent, and to mark it as the full-load reference capacity of the nitrogen generator. The data is stored in the database unit, and the current real-time nitrogen purity and real-time instantaneous flow rate are collected synchronously. When the real-time nitrogen purity is greater than or equal to the set nitrogen production purity, the current real-time instantaneous flow rate is recorded as an effective production capacity data point, and stored in the historical database in combination with the cumulative running time. The performance degradation rate unit is used to periodically retrieve multiple valid capacity data points from the historical database, use a linear regression algorithm to fit a degradation curve of capacity over time, and calculate the downward slope of the curve to obtain the performance degradation rate. The time axis extrapolation unit is used to set a minimum flow threshold and extrapolate the degradation curve to the future time axis based on the performance degradation rate. The remaining lifetime unit is used to determine the expected failure time when the decay curve intersects with the minimum flow threshold, and the difference between the current time and the expected failure time is taken as the remaining lifetime of the carbon adsorbent.

[0011] Furthermore, the nitrogen generator life prediction module also includes: The unit for storing temperature-generated gas efficiency correction coefficient table is used to preset the standard operating temperature and store the temperature-generated gas efficiency correction coefficient table. The temperature-generated gas efficiency correction coefficient table defines the theoretical attenuation or gain ratio of the generated gas flow rate when the inlet temperature deviates from the standard operating temperature by 1°C. The environmental correction coefficient acquisition unit simultaneously acquires the equipment's intake air temperature while collecting real-time instantaneous flow rate, and obtains the environmental correction coefficient corresponding to the current intake air temperature according to the correction coefficient table. The normalized flow unit is used to convert the collected real-time instantaneous flow into normalized flow under standard operating conditions.

[0012] In the step of storing effective production capacity data points into the historical database, the normalized flow rate is used to replace the original real-time instantaneous flow rate for storage; in the subsequent step of using a linear regression algorithm to fit the decay curve, the calculation is based on the normalized flow rate data to eliminate the interference of environmental temperature fluctuations on lifetime prediction.

[0013] Furthermore, the nitrogen generator life prediction module also includes: The push unit pushes the remaining lifespan data to the user's mobile app for display.

[0014] The beneficial effects of this invention are: source protection and energy saving. Through environmentally adaptive pretreatment, it not only eliminates adsorbent pulverization caused by incomplete water removal, but also avoids energy waste caused by overtreatment; accurate operation and maintenance prediction solves the industry problem of large fluctuations and difficulty in predicting production capacity data caused by environmental temperature differences, and realizes proactive preventive maintenance based on the actual performance degradation trend, which greatly improves the accuracy of remote monitoring. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of an online nitrogen generator remote monitoring system via a mobile app, as described in this invention. Figure 2 This is a schematic diagram of the air adaptive processing module described in this invention; Figure 3 This is a schematic diagram of the output control parameter vector unit described in this invention. Detailed Implementation

[0016] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0017] Numerous specific details are set forth in the following description to provide a thorough understanding of the invention. The described embodiments are only a part of, and not all, of the embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0019] According to one embodiment of the present invention, the system includes: The environmental sensing module is used to collect environmental parameters of the nitrogen production environment; An air adaptive processing module is used to dynamically adjust the pre-processing parameters for drying the air according to the environmental parameters, so as to form standardized input air. The nitrogen generation module uses an adsorption separation process to treat the standardized input air, forming a stratification of nitrogen and other gases, and outputting nitrogen that meets the requirements. The nitrogen generator life prediction module constructs a linear decay model of carbon adsorbent capacity as a function of cumulative operating time based on normalized flow data corrected for environmental thermal effects. By extrapolating the decay trend of the linear decay model, the remaining service life of the carbon adsorbent is calculated before its current performance deteriorates to the expected failure time.

[0020] The working principle and effects of the above technical solution are as follows: The system first senses the ambient temperature and humidity in real time, and dynamically adjusts the load and drainage strategy of the refrigerated dryer accordingly to ensure that standardized air of constant quality is input to the nitrogen generation module under different operating conditions. During the nitrogen generation process, the system uses a temperature correction algorithm to eliminate interference from environmental thermal effects, converts the real-time production capacity into a normalized flow rate, and constructs a linear degradation model based on this. The remaining service life of the carbon adsorbent is accurately calculated by extrapolating the trend line. Source protection and energy saving are achieved through environmental adaptive pretreatment, which not only prevents adsorbent pulverization caused by incomplete water removal, but also avoids energy waste caused by over-treatment. Precise operation and maintenance prediction solves the industry problem of large fluctuations and difficulty in predicting production capacity data caused by environmental temperature differences, and realizes proactive preventive maintenance based on the actual performance degradation trend, which greatly improves the accuracy of remote monitoring.

[0021] In one embodiment of the present invention, the environment sensing module includes: The sensor detection unit includes a temperature sensor, an atmospheric pressure sensor, and a humidity sensor. The temperature sensor is used to collect the temperature of the nitrogen production environment, the atmospheric pressure sensor is used to collect the ambient atmospheric pressure, and the humidity sensor is used to collect the humidity of the nitrogen production environment, thereby obtaining analog signals of environmental parameters. The signal conditioning unit is used to perform low-pass filtering, signal amplification, and anti-interference processing on the analog signals collected by various sensors to obtain digital signals of environmental parameters. The data transmission unit is used to transmit the digital signals of the environmental parameters to the air adaptive processing module in real time via the I2C bus interface.

[0022] The working principle and effects of the above technical solution are as follows: An integrated sensor array collects real-time analog signals of ambient temperature, humidity, and air pressure. These signals are then processed by a signal conditioning unit through low-pass filtering and amplification to remove noise and convert them into digital signals. Finally, the high signal-to-noise ratio environmental data is transmitted to the processing module in real-time via an I2C bus. To ensure sampling accuracy, hardware-level filtering and anti-interference processing effectively eliminate the influence of electromagnetic interference on weak analog signals, ensuring the authenticity of the environmental data. Communication is efficient and stable, employing a standard I2C digital interface to achieve high-speed, low-latency transmission of multi-dimensional environmental parameters (temperature / humidity / pressure), providing a reliable data foundation for the system's adaptive adjustment.

[0023] In one embodiment of the present invention, the air adaptive processing module includes: Construct an environmental feature vector unit, and construct a nitrogen production environmental feature vector based on the environmental parameters; The output control parameter vector unit inputs the nitrogen production environment feature vector into the pre-trained cold and dry load optimization model, and the cold and dry load optimization model outputs the control parameter vector. The drive control unit adjusts the nitrogen generator's operating state according to the control parameter vector to form standardized input air.

[0024] The working principle and effects of the above technical solution are as follows: The system vectorizes real-time environmental data and inputs it into a pre-trained cooling-drying load optimization model for inference. Through the mapping relationship established by the model, the optimal control strategy under the current operating conditions is calculated. Based on this, the equipment dynamically adjusts its operating status and outputs dry air that meets standards. Intelligent on-demand control, using model inference to replace traditional fixed or simple feedback control, can accurately match complex and changing environmental conditions, achieving on-demand allocation of cooling capacity and drainage; ensuring consistent air supply, regardless of fluctuations in the external environment, it ensures that the air quality input to the nitrogen generation module remains at a standardized level, effectively preventing a decrease in nitrogen generation efficiency or damage to the adsorbent due to air supply fluctuations.

[0025] In one embodiment of the present invention, the control parameter vector includes: the target operating frequency of the refrigerant compressor of the refrigerated dryer, the opening interval time of the electronic drain valve, and the single opening duration of the electronic drain valve.

[0026] In one embodiment of the present invention, the output control parameter vector unit includes: Construct a full-condition test matrix subunit to build a set of discrete operating condition points covering the operating domain of the nitrogen generator in the environmental simulation chamber according to preset temperature gradient, humidity gradient and air flow gradient. The optimal frequency tag subunit is used to obtain the optimal operating parameter tag subunit. For each discrete operating point, the electronic drain valve of the refrigerated dryer is placed in a fixed drain mode, and the operating frequency of the refrigerated dryer compressor is gradually reduced. At the same time, the pressure dew point of the dried air is monitored in real time. When the pressure dew point reaches the preset critical threshold (i.e. the limit value that meets the drying requirements of the standardized input air), the compressor operating frequency at this time is recorded as the optimal frequency tag for that discrete operating point. The marking start interval time subunit is used to control the electronic drain valve of the refrigerated dryer to remain closed based on the current optimal frequency tag when the compressor operating frequency is fixed as the optimal frequency tag. The liquid level sensor monitors the liquid level change in the condensate collection chamber of the refrigerated dryer in real time, records the time required for the liquid level to rise from the waterless reset state to the preset warning upper limit state, and marks it as the start interval time under the discrete operating condition point. The single opening duration subunit is used to trigger the opening of the electronic drain valve when the liquid level reaches the preset warning upper limit state, record the duration required for the liquid level to drop from the warning upper limit state to the waterless reset state, and mark it as the single opening duration of the electronic drain valve. The optimal drainage timing tag subunit is used to determine the opening interval and single opening duration as the optimal drainage timing tag.

[0027] The working principle and effects of the above technical solution are as follows: By constructing a full-condition test matrix in an environmental simulation chamber, the model training labels are obtained using the limit approximation method. First, under the premise of ensuring that the air dew point meets the standard, the compressor frequency is gradually reduced to find the lowest energy consumption point (optimal frequency). Second, at this frequency, the accumulation and discharge rate of condensate are monitored in real time by a liquid level sensor, and the precise filling and discharging time (optimal drainage sequence) is measured, thereby establishing a control parameter dataset that includes optimal energy consumption and minimum gas loss. Extreme energy-saving operation is achieved by calibrating the critical frequency to meet drying requirements, ensuring that the system never outputs excess cooling capacity during operation, thus maximizing energy efficiency. Compression gas loss is eliminated by abandoning the traditional estimated timed drainage method. The valve opening and closing time is accurately calibrated using measured liquid level data, achieving drainage when the water is full and closing when the water is empty, effectively avoiding compressed air leakage (gas loss) caused by prolonged valve opening.

[0028] In one embodiment of the present invention, the output control parameter vector unit further includes: Construct model sub-units, using the environmental parameters as input vectors and the control parameters as output vectors, to build a supervised learning regression model; The training model subunit uses multiple discrete operating point data and their corresponding optimal frequency labels and optimal drainage time series labels to form a training dataset to train the regression model, so as to establish a nonlinear mapping relationship from environmental operating parameters to optimal control parameters and obtain a pre-trained cold and dry load optimization model.

[0029] The working principle and effects of the above technical solution are as follows: A supervised regression model is constructed using environmental parameters as input and optimal control parameters as output. The model is trained using discrete optimal test data under all operating conditions to fit the nonlinear mapping relationship between environmental conditions and the optimal operating state of the equipment, thereby generating a pre-trained model that can be used for real-time inference. Full-domain continuous coverage, through model fitting, fills the gaps between discrete test points, achieving accurate prediction of any continuous environmental conditions and solving the problem of control blind spots in traditional table lookup methods. High-precision nonlinear fitting effectively captures the complex nonlinear characteristics between environmental temperature and humidity and cold / dry loads, ensuring the accuracy and robustness of the control strategy throughout the entire operating domain.

[0030] In one embodiment of the present invention, the nitrogen separation module includes: The adsorption separation unit controls the opening of the air inlet valve to allow the standardized input air to enter the adsorption tower, where oxygen molecules, carbon dioxide, and moisture are preferentially adsorbed by the micropores of the carbon molecular sieve, and nitrogen is enriched at the top of the adsorption tower. The reverse desorption and regeneration unit is used to perform a pressure reduction action after the carbon molecular sieve is saturated with adsorption, forcing the oxygen molecules and impurity gases captured by the carbon molecular sieve to desorb from the micropores. The quality control buffer output unit is used to temporarily store and stabilize the output nitrogen flow, while simultaneously detecting the nitrogen purity and flow rate data of the nitrogen flow in real time. When the detection data meets the command requirements, the nitrogen is delivered to the gas user and the production data is uploaded to the mobile APP. When the detection data fails to meet the standards, a backflow is triggered.

[0031] The working principle and effects of the above technical solution are as follows: Pressure Swing Adsorption (PSA) technology is used, utilizing carbon molecular sieves to preferentially adsorb oxygen and impurities under high pressure to separate nitrogen, and then regenerating and recycling the molecular sieves through depressurization and desorption. At the output end, the nitrogen flow is buffered, stabilized, and subjected to closed-loop quality control. Based on real-time purity / flow data, intelligent judgment is made to execute external delivery, data upload, or return of non-conforming products. Through closed-loop control of detection-judgment-return, a strict quality control line is constructed, completely preventing non-conforming nitrogen from flowing into the user's production line due to equipment fluctuations. The buffer design eliminates the inherent pressure pulsations of the PSA process, ensuring a stable gas supply. Simultaneously, remote data monitoring enables visualization and traceability management of the production process.

[0032] In one embodiment of the present invention, the nitrogen generator life prediction module includes: The full-load reference unit is used to record the initial maximum gas production flow rate at a set nitrogen purity after the equipment is first replaced with carbon adsorbent, and to mark it as the full-load reference capacity of the nitrogen generator. The data is stored in the database unit, and the current real-time nitrogen purity and real-time instantaneous flow rate are collected synchronously. When the real-time nitrogen purity is greater than or equal to the set nitrogen production purity, the current real-time instantaneous flow rate is recorded as an effective production capacity data point, and stored in the historical database in combination with the cumulative running time. The performance degradation rate unit is used to periodically retrieve multiple valid capacity data points from the historical database, use a linear regression algorithm to fit a degradation curve of capacity over time, and calculate the downward slope of the curve to obtain the performance degradation rate. The time axis extrapolation unit is used to set a minimum flow threshold and extrapolate the degradation curve to the future time axis based on the performance degradation rate. The remaining lifetime unit is used to determine the expected failure time when the decay curve intersects with the minimum flow threshold, and the difference between the current time and the expected failure time is taken as the remaining lifetime of the carbon adsorbent.

[0033] The working principle and effects of the above technical solution are as follows: Based on the initial full-load flow rate, a historical dataset is continuously constructed by collecting effective production capacity data. A linear regression algorithm is used to fit the decline curve of production capacity over operating time. By extrapolating this curve to the future time axis, the intersection point with the minimum flow rate threshold is located, and the remaining lifespan of the carbon adsorbent is derived in real time by calculating the difference. Data-driven predictive maintenance abandons the crude fixed-cycle replacement model and dynamically predicts based on actual performance degradation trends. This avoids waste caused by premature adsorbent failure and prevents quality risks associated with exceeding the service life. It also eliminates sudden downtime, providing users with a visualized lifespan countdown, allowing them to schedule maintenance plans in advance based on the remaining lifespan and avoid unplanned production stoppages caused by sudden performance degradation of core components.

[0034] In one embodiment of the present invention, the nitrogen generator life prediction module further includes: The unit for storing temperature-generated gas efficiency correction coefficient table is used to preset the standard operating temperature and store the temperature-generated gas efficiency correction coefficient table. The temperature-generated gas efficiency correction coefficient table defines the theoretical attenuation or gain ratio of the generated gas flow rate when the inlet temperature deviates from the standard operating temperature by 1°C. The environmental correction coefficient acquisition unit simultaneously acquires the equipment's intake air temperature while collecting real-time instantaneous flow rate, and obtains the environmental correction coefficient corresponding to the current intake air temperature according to the correction coefficient table. The normalized flow unit is used to convert the collected real-time instantaneous flow into normalized flow under standard operating conditions.

[0035] In the step of storing effective production capacity data points into the historical database, the normalized flow rate is used to replace the original real-time instantaneous flow rate for storage; in the subsequent step of using a linear regression algorithm to fit the decay curve, the calculation is based on the normalized flow rate data to eliminate the interference of environmental temperature fluctuations on lifetime prediction.

[0036] In one embodiment of the present invention, the nitrogen generator life prediction module further includes: The push unit pushes the remaining lifespan data to the user's mobile app for display.

[0037] The working principle and effects of the above technical solution are as follows: A temperature correction mechanism is introduced, and the efficiency correction coefficient is queried based on the real-time intake air temperature to convert the instantaneous flow rate under fluctuating operating conditions into a normalized flow rate under standard operating conditions. This cleaned data is used for historical storage and regression modeling to eliminate environmental interference, and the final calculated remaining lifespan is pushed to the user's mobile app in real time. By eliminating environmental noise and accurately tracing the source, the normalization process removes the hard interference of seasonal or diurnal temperature differences on gas production, ensuring that the decay curve only reflects the physical loss of the carbon adsorbent itself, significantly improving the confidence and robustness of the lifespan prediction model. Operation and maintenance are made accessible at your fingertips; mobile push notifications break the spatial limitations of equipment operation and maintenance, allowing users to monitor the status of core consumables anytime, anywhere without on-site inspections, achieving remote and intelligent equipment management.

[0038] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A remote monitoring system for online nitrogen generation via mobile app, characterized in that, The system includes: The environmental sensing module is used to collect environmental parameters of the nitrogen production environment; An air adaptive processing module is used to dynamically adjust the pre-processing parameters for drying the air according to the environmental parameters, so as to form standardized input air. The nitrogen generation module uses an adsorption separation process to treat the standardized input air, forming a stratification of nitrogen and other gases, and outputting nitrogen that meets the requirements. The nitrogen production equipment life prediction module constructs a linear decay model of capacity change with cumulative operating time based on normalized flow data corrected for environmental thermal effects. By extrapolating the decay trend of the linear decay model, it calculates the remaining service life of the carbon adsorbent before its current performance drops to the expected failure time.

2. The online nitrogen generator remote monitoring system via mobile app according to claim 1, characterized in that, The environment sensing module includes: The sensor detection unit includes a temperature sensor, an atmospheric pressure sensor, and a humidity sensor. The temperature sensor is used to collect the temperature of the nitrogen production environment, the atmospheric pressure sensor is used to collect the ambient atmospheric pressure, and the humidity sensor is used to collect the humidity of the nitrogen production environment, thereby obtaining analog signals of environmental parameters. The signal conditioning unit is used to perform low-pass filtering, signal amplification, and anti-interference processing on the analog signals collected by various sensors to obtain digital signals of environmental parameters. The data transmission unit is used to transmit the digital signals of the environmental parameters to the air adaptive processing module in real time via the I2C bus interface.

3. The online nitrogen generator remote monitoring system via mobile app according to claim 1, characterized in that, The air adaptive processing module includes: Construct an environmental feature vector unit, and construct a nitrogen production environmental feature vector based on the environmental parameters; The output control parameter vector unit inputs the nitrogen production environment feature vector into the pre-trained cold and dry load optimization model, and the cold and dry load optimization model outputs the control parameter vector. The drive control unit adjusts the nitrogen generator's operating state according to the control parameter vector to form standardized input air.

4. The online nitrogen generator remote monitoring system via mobile app according to claim 3, characterized in that, The control parameter vector includes: the target operating frequency of the refrigerant compressor of the refrigerated dryer, the opening interval of the electronic drain valve, and the duration of a single opening of the electronic drain valve.

5. The online nitrogen generator remote monitoring system via mobile app according to claim 3, characterized in that, The output control parameter vector unit includes: Construct a full-condition test matrix subunit to build a set of discrete operating condition points covering the operating domain of the nitrogen generator in the environmental simulation chamber according to preset temperature gradient, humidity gradient and air flow gradient. The optimal frequency tag subunit is used to obtain the optimal operating parameter tag subunit. For each discrete operating point, the electronic drain valve of the refrigerated dryer is placed in a fixed drain mode, and the operating frequency of the refrigerated dryer compressor is gradually reduced. At the same time, the pressure dew point of the dried air is monitored in real time. When the pressure dew point reaches the preset critical threshold, the compressor operating frequency at this time is recorded as the optimal frequency tag for that discrete operating point. The marking start interval time subunit is used to control the electronic drain valve of the refrigerated dryer to remain closed based on the current optimal frequency tag when the compressor operating frequency is fixed as the optimal frequency tag. The liquid level sensor monitors the liquid level change in the condensate collection chamber of the refrigerated dryer in real time, records the time required for the liquid level to rise from the waterless reset state to the preset warning upper limit state, and marks it as the start interval time under the discrete operating condition point. The single opening duration subunit is used to trigger the opening of the electronic drain valve when the liquid level reaches the preset warning upper limit state, record the duration required for the liquid level to drop from the warning upper limit state to the waterless reset state, and mark it as the single opening duration of the electronic drain valve. The optimal drainage timing tag subunit is used to determine the opening interval and single opening duration as the optimal drainage timing tag.

6. The online nitrogen generator remote monitoring system via mobile app according to claim 5, characterized in that, The output control parameter vector unit further includes: Construct model sub-units, using the environmental parameters as input vectors and the control parameters as output vectors, to build a supervised learning regression model; The training model subunit uses multiple discrete operating point data and their corresponding optimal frequency labels and optimal drainage time series labels to form a training dataset to train the regression model, so as to establish a nonlinear mapping relationship from environmental operating parameters to optimal control parameters and obtain a pre-trained cold and dry load optimization model.

7. The online nitrogen generator remote monitoring system via mobile app as described in claim 1, characterized in that, The nitrogen separation module includes: The adsorption separation unit controls the opening of the air inlet valve to allow the standardized input air to enter the adsorption tower, where oxygen molecules, carbon dioxide, and moisture are preferentially adsorbed by the micropores of the carbon molecular sieve, and nitrogen is enriched at the top of the adsorption tower. The reverse desorption and regeneration unit is used to perform a pressure reduction action after the carbon molecular sieve is saturated with adsorption, forcing the oxygen molecules and impurity gases captured by the carbon molecular sieve to desorb from the micropores. The quality control buffer output unit is used to temporarily store and stabilize the output nitrogen flow, while simultaneously detecting the nitrogen purity and flow rate data of the nitrogen flow in real time. When the detection data meets the command requirements, the nitrogen is delivered to the gas user and the production data is uploaded to the mobile APP. When the detection data fails to meet the standards, a backflow is triggered.

8. The online nitrogen generator remote monitoring system via mobile app according to claim 1, characterized in that, The nitrogen generator life prediction module includes: The full-load reference unit is used to record the initial maximum gas production flow rate at a set nitrogen purity after the equipment is first replaced with carbon adsorbent, and to mark it as the full-load reference capacity of the nitrogen generator. The data is stored in the database unit, and the current real-time nitrogen purity and real-time instantaneous flow rate are collected synchronously. When the real-time nitrogen purity is greater than or equal to the set nitrogen production purity, the current real-time instantaneous flow rate is recorded as an effective production capacity data point, and stored in the historical database in combination with the cumulative running time. The performance degradation rate unit is used to periodically retrieve multiple valid capacity data points from the historical database, use a linear regression algorithm to fit a degradation curve of capacity over time, and calculate the downward slope of the curve to obtain the performance degradation rate. The time axis extrapolation unit is used to set a minimum flow threshold and extrapolate the degradation curve to the future time axis based on the performance degradation rate. The remaining lifetime unit is used to determine the expected failure time when the decay curve intersects with the minimum flow threshold, and the difference between the current time and the expected failure time is taken as the remaining lifetime of the carbon adsorbent.

9. The online nitrogen generator remote monitoring system via mobile app according to claim 8, characterized in that, The nitrogen generator life prediction module also includes: The unit for storing temperature-generated gas efficiency correction coefficient table is used to preset the standard operating temperature and store the temperature-generated gas efficiency correction coefficient table. The environmental correction coefficient acquisition unit simultaneously acquires the equipment's intake air temperature while collecting real-time instantaneous flow rate, and obtains the environmental correction coefficient corresponding to the current intake air temperature according to the correction coefficient table. The normalized flow unit is used to convert the collected real-time instantaneous flow into normalized flow under standard operating conditions.

10. The online nitrogen generator remote monitoring system via mobile app according to claim 8, characterized in that, The nitrogen generator life prediction module also includes: The push unit pushes the remaining lifespan data to the user's mobile app for display.

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