Autonomous intelligent wake-up gas sensing system and working method thereof

Abstract

The invention discloses an autonomous intelligent wake-up gas sensing system and a working method thereof. The autonomous intelligent wake-up gas sensing system comprises a temperature sensor, an expansion sensor, a shell, a gas pump, an electromagnetic valve, a power supply, a gas sensor and a controller, wherein a power supply, a gas sensor and a controller are arranged in the shell; an air pump, an electromagnetic valve, a temperature sensor and an expansion sensor are arranged outside the shell; one end of the pipeline extends into the shell, and the other end of the pipeline is connected with the air pump and the air outlet end of the electromagnetic valve; the power supply ends of the air pump and the electromagnetic valve are connected with a power supply through lines; the control ends of the air pump, the electromagnetic valve, the temperature sensor, the expansion sensor and the gas sensor are connected with the controller through lines; and the power supply end of the controller is connected with a power supply through a circuit. According to the invention, effective protection of the sensor in a harsh environment is realized, and long-term stability of the sensor is guaranteed.

Description

Technical Field

[0001] The present invention relates to an autonomous intelligent awakening gas sensing system and a working method thereof, belonging to the technical field of gas detection. Background Art

[0002] Existing sensors are susceptible to long-term effects from factors such as ambient temperature and humidity, leading to zero drift and, in turn, affecting measurement accuracy. Furthermore, traditional sensors typically require continuous operation, resulting in excessive energy consumption. This is particularly true in battery-powered applications, where continuous sensor operation significantly shortens the device's lifespan. In harsh environments, such as those with high temperatures, high humidity, or corrosive gases, sensors are susceptible to damage, making long-term stability difficult to guarantee.

[0003] In addition, battery packs are at risk of self-explosion or deflagration, especially during long-term use, due to overcharging, overheating or internal short circuits, which may lead to serious safety accidents. The reliability and lifespan of existing sensors in detecting battery status still pose great challenges. The safety of battery packs not only involves the characteristics of the battery cells, but is also affected by the external environment. For example, drastic temperature changes, mechanical damage or excessive charging and discharging may accelerate the instability of the chemical reactions inside the battery, thereby causing self-explosion or deflagration. Current detection technology still has bottlenecks in response speed, accuracy and long-term stability, making it difficult to meet the full-cycle monitoring needs of battery safety.

[0004] Currently, the market lacks detection sensors capable of stable operation for more than ten years, and the battery pack lifespan often far exceeds that of existing sensors, limiting the long-term reliability of monitoring systems. Furthermore, the high degree of integration between the sensor and battery pack makes replacement difficult. Once a sensor fails or drifts, maintenance becomes extremely challenging, potentially requiring the replacement of the entire battery pack, increasing overall costs.

[0005] Therefore, improving the durability and adaptability of sensors has become a key challenge in battery management system (BMS) design. Furthermore, current sensor systems generally lack intelligent wake-up and self-calibration capabilities, making them unable to effectively adapt to dynamic environmental changes and struggling to meet the requirements for high precision and long-term stability. Summary of the Invention

[0006] Purpose: In order to overcome the deficiencies in the prior art, the present invention provides an autonomous intelligent wake-up gas sensing system and a working method thereof, which can increase the service life of the protected sensor.

[0007] Technical solution: To solve the above technical problems, the technical solution adopted by the present invention is:

[0008] In a first aspect, an autonomous intelligent wake-up gas sensing system includes: a temperature sensor, an expansion sensor, a shell, an air pump and a solenoid valve, a power supply, a gas sensor, and a controller.

[0009] The power supply, gas sensor and controller are arranged inside the shell, while the air pump, electromagnetic valve, temperature sensor and expansion sensor are arranged outside the shell.

[0010] One end of the pipeline extends into the shell, and the other end of the pipeline is connected to the air pump and the air outlet end of the solenoid valve.

[0011] The power supply ends of the air pump and the electromagnetic valve are connected to the power supply through circuits.

[0012] The control ends of the air pump, the electromagnetic valve, the temperature sensor, the expansion sensor and the gas sensor are connected to the controller through lines.

[0013] The power supply end of the controller is connected to the power supply through a line.

[0014] As a preferred solution, the shell wall of the shell is composed of polyurethane foam, aluminum foil and expanded polytetrafluoroethylene film from the inside to the outside.

[0015] As a preferred solution, the expansion sensor is a programmable expansion sensor.

[0016] As a preferred solution, the temperature sensor is a programmable temperature sensor.

[0017] In the second aspect, a working method of an autonomous intelligent wake-up gas sensing system specifically includes:

[0018] The controller obtains the battery pack deformation signal collected by the expansion sensor and the ambient temperature signal collected by the temperature sensor.

[0019] The controller filters the battery pack deformation signal and the ambient temperature signal to obtain processed battery pack deformation signal and ambient temperature signal.

[0020] The processed battery pack deformation signal and ambient temperature signal are compared with the preset safety threshold. When the limit is exceeded N times, the controller sends a driving signal to the air pump and solenoid valve, and the air pump and solenoid valve work to send gas into the shell.

[0021] The controller controls the gas sensor to be powered on and started, detects the gas in the shell, and sends the detection data to the controller.

[0022] As a preferred solution, the controller controls the gas sensor to be powered on and started, including:

[0023] The controller controls the gas sensor to enter the detection state after S seconds of preheating, and the gas sensor switches from sleep mode to working mode.

[0024] As a preferred solution, the controller further comprises: using an intelligent algorithm for controlling zero drift to correct the detection data of the gas sensor to obtain corrected detection data, wherein the intelligent algorithm for controlling zero drift specifically comprises:

[0025] Get the initial static output of the gas sensor , the current output , calculate zero drift deviation , zero drift deviation The expression is as follows:

[0026]

[0027] When the zero drift deviation Greater than threshold , then calculate the compensation adjustment coefficient , compensation adjustment coefficient The expression is as follows:

[0028]

[0029] in, is a natural function, k is the adjustment sensitivity factor, is the average drift threshold.

[0030] According to the current output and compensation adjustment coefficient , calculate the corrected detection data , the corrected detection data The expression is as follows:

[0031] .

[0032] As a preferred solution, the controller further includes optimizing the current output value of the gas sensor using a Kalman filter algorithm to obtain an optimized current output value of the gas sensor, specifically including:

[0033] System estimate of the gas sensor based on k-1 time steps , calculate the system estimate of the gas sensor at k time steps ,in, The expression is as follows:

[0034]

[0035] in, represents the state transition matrix, represents the control input matrix, represents the control input vector at time step k.

[0036] The estimated error covariance matrix based on k-1 time steps , calculate the estimated error covariance matrix for k time steps ,in, The expression is as follows:

[0037]

[0038] in, represents the process noise covariance matrix, and T represents the transposed matrix.

[0039] The estimated error covariance matrix according to k time steps , calculate the Kalman gain for k time steps ,in, The expression is as follows:

[0040]

[0041] in, represents the observation matrix, represents the measurement noise covariance matrix.

[0042] Kalman gain according to k time steps , the system estimated value of the gas sensor , and the output value of the gas sensor , calculate the current output value of the optimized gas sensor , The expression is as follows:

[0043] .

[0044] As a preferred solution, the preset safety threshold includes an expansion threshold and a temperature threshold; wherein the expansion threshold is 2 mm and the temperature threshold is 80°C.

[0045] As a preferred solution, the controller adopts an Arduino main control board.

[0046] Beneficial Effects: The present invention provides an autonomous intelligent wake-up gas sensing system and its working method. The present invention aims to solve the zero drift and life problems faced by common sensors through the "intelligent wake-up gas sensing system". In particular, in key applications such as battery packs, the stability of the sensor is crucial. The system detects the expansion or temperature change of the battery pack through intelligent algorithms and a self-awakening control mechanism. The single-chip microcomputer is connected to the core sensor, the external excitation sensor, and the solenoid valve inside the protection device. When it receives abnormal information from the external battery pack, it will control the solenoid valve to open the gas path and wake up the dormant core sensor for high-precision detection, thereby achieving effective protection for the sensor in harsh environments while ensuring its long-term stability.

[0047] Compared with the existing technology, it has the following beneficial effects:

[0048] (1) This invention introduces an intelligent wake-up mechanism that uses an expansion sensor or temperature sensor to monitor abnormal battery pack conditions, such as overheating or expansion. When an abnormal condition is detected, the microcontroller control system triggers the sensor to start, achieving on-demand wake-up and avoiding unnecessary energy waste.

[0049] (2) The present invention adopts an intelligent self-calibration algorithm based on historical drift trends, which can regularly detect and adjust the baseline value of the sensor, thereby effectively compensating for zero drift and improving measurement accuracy. Considering the potential risk of battery pack self-explosion or deflagration, the intelligent monitoring system of the present invention can issue an early warning before an abnormal situation occurs, take timely countermeasures, and reduce the possibility of accidents. During long-term use, the battery pack may cause thermal runaway due to overcharging, overheating, internal short circuit or material aging, and existing detection methods still face great challenges in long-term stability. The high-precision sensors of this system are combined with intelligent algorithms to monitor the internal status of the battery pack in real time, and predict potential risks through data analysis to improve the safety of the system.

[0050] (3) At the same time, there is a lack of sensors on the market that can operate stably for more than ten years. The present invention enhances its long-term reliability by optimizing the sensor structure and protection mechanism. Since sensors are usually highly integrated with battery packs, replacement costs are high and operation is difficult, the intelligent self-calibration algorithm of this system can reduce maintenance requirements and extend the effective service life of the sensor. To enhance the stability of the sensor in complex environments, the system uses optimized protective materials, including a composite structure of expanded polytetrafluoroethylene (PTFE) membrane, aluminum foil and polyurethane foam. These materials can effectively isolate external heat, moisture and air pollutants, ensuring that the sensor can operate stably for a long time in harsh environments. In addition, through the intelligent control of the single-chip microcomputer main control board and the solenoid valve, the system can achieve dynamic management to ensure that the sensor only works when necessary, thereby improving the reliability and service life of the overall system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a structural diagram of an autonomous intelligent wake-up gas sensing system of the present invention.

[0052] Figure 2 It is a schematic diagram of the cross-sectional structure of the shell wall of the shell in the present invention.

[0053] Figure 3 The figure is a flow chart of a working method of an autonomous intelligent wake-up gas sensing system in the present invention.

[0054] Figure 4 This is a flow chart of the zero drift control algorithm in the present invention. DETAILED DESCRIPTION

[0055] The following is a clear and complete description of the technical solutions in the examples of the present invention, in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work are within the scope of protection of the present invention.

[0056] The present invention will be further described below with reference to specific embodiments.

[0057] Unless otherwise specifically stated, the relative arrangement, numerical expressions and numerical values ​​of the parts and steps set forth in these embodiments do not limit the scope of the present invention. Meanwhile, it should be understood that, for ease of description, the sizes of the various parts shown in the accompanying drawings are not drawn according to actual proportional relationships. Technology, methods and equipment known to those of ordinary skill in the relevant art may not be discussed in detail, but in appropriate cases, the technology, methods and equipment should be considered as a part of the specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments may also include different values. It should be noted that similar numbers and letters represent similar items in the following drawings, and therefore, once an item is defined in an accompanying drawing, it does not need to be further discussed in subsequent drawings.

[0058] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are 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 cannot be understood as a limitation on the protection content of the present invention.

[0059] Example 1:

[0060] This embodiment introduces an autonomous intelligent wake-up gas sensing system, such as Figure 1 As shown, it includes: a temperature sensor 1, an expansion sensor 2, a housing 3, an air pump and a solenoid valve 4, a power supply 5, a gas sensor 6, and a controller 7.

[0061] The power supply, gas sensor and controller are arranged inside the shell, while the air pump, electromagnetic valve, temperature sensor and expansion sensor are arranged outside the shell.

[0062] One end of the pipeline extends into the shell, and the other end of the pipeline is connected to the air pump and the air outlet end of the solenoid valve.

[0063] The power supply ends of the air pump and the electromagnetic valve are connected to the power supply through circuits.

[0064] The control ends of the air pump, the electromagnetic valve, the temperature sensor, the expansion sensor and the gas sensor are connected to the controller through lines.

[0065] The power supply end of the controller is connected to a power supply.

[0066] like Figure 2 As shown, further, the shell wall of the shell 3 is composed of polyurethane foam 301, aluminum foil 302 and expanded polytetrafluoroethylene (PTFE) film 303 from the inside to the outside.

[0067] During use, the selected expansion sensor is precisely mounted on the battery pack to ensure real-time and accurate monitoring of the battery pack's expansion status, used to assess battery safety. A temperature sensor also monitors the battery pack's operating temperature, triggering the sensor to detect battery status in the event of high temperature or overheating. The programmable expansion sensor accurately detects battery pack expansion. Available sensor types include capacitive and piezoresistive expansion sensors, with a measurement range of 0-10 mm and adjustable to the actual expansion range of the battery pack. The sensor offers a resolution of up to 0.01 mm and an accuracy of ±0.05 mm. Operating voltages include 3.3V or 5V, the sensor outputs include I²C, SPI, and a 0-3.3V analog signal. The sampling frequency is programmable between 10Hz and 100Hz, and the operating temperature range extends from -20°C to 85°C. Communication interfaces utilize I²C / SPI. The accompanying programmable temperature sensor monitors the battery pack's temperature, detecting high temperatures or overheating. Once the temperature exceeds the set threshold, the sensor triggers the battery status check. The selected temperature sensor has a measurement range of -40°C to 125°C, with an accuracy of ±0.5°C from -10°C to 85°C. It operates at 3.3V or 5V, outputs an SPI signal, has a sampling frequency of 1 Hz to 10 Hz, and has a response time of less than 1 second. Under normal circumstances, the sensor is dormant to maximize its lifespan. However, if the battery pack overheats or expands, the system receives the sensor signal via the microcontroller main control board and controls the opening and closing of the solenoid valve to determine whether to allow the sensor to operate.

[0068] To protect the sensor's long-term stability in extreme environments, the system utilizes a special protective material. This material is composed of expanded polytetrafluoroethylene (EPTFE) film, aluminum foil, and polyurethane foam. This combination of materials offers excellent thermal, moisture, and air insulation properties. The expanded PTFE film effectively blocks external heat and moisture, protecting the sensor from environmental fluctuations. The aluminum foil prevents the intrusion of moisture and oxygen from the air, further ensuring the sensor's long-term use. The polyurethane foam not only provides excellent mechanical cushioning properties but also effectively isolates external airflow, preventing contaminants and gases from interfering with the sensor. This composite material ensures the sensor can operate stably and for extended periods in high-temperature, high-humidity, and other harsh environmental conditions.

[0069] This system utilizes a variety of optimized designs to ensure the sensor's efficiency, stability, and long-term reliability. First, the system uses an expansion sensor to monitor the battery pack's expansion in real time, thereby determining whether there are any safety hazards within the battery. Furthermore, a temperature sensor monitors the battery pack's operating temperature. High temperatures or overheating trigger the sensor to detect battery status. To enhance sensor stability in harsh environments, the system utilizes a multi-layer composite protective material, including expanded polytetrafluoroethylene (PTFE) membrane, aluminum foil, and polyurethane foam. The PTFE membrane effectively insulates and allows for adequate gas diffusion. The aluminum foil acts as a moisture barrier to prevent moisture intrusion. The polyurethane foam provides mechanical cushioning, minimizing the effects of external physical impact on the gas sensor. This material combination significantly enhances the sensor's durability in extreme environments, extending its service life and ensuring reliable data acquisition.

[0070] Example 2:

[0071] This embodiment introduces a working method of an autonomous intelligent wake-up gas sensing system. Figure 3 As shown, specifically including:

[0072] The expansion sensor monitors the battery pack deformation in real time with an accuracy of ±0.1mm and transmits the data to the Arduino main control board via a 4-20mA analog signal. At the same time, the temperature sensor collects ambient temperature data every 10 seconds and transmits the digital signal via the bus.

[0073] The ADC module built into the main control board converts the analog signal into a digital quantity with 12-bit precision and performs sliding average filtering on the original data to eliminate sudden noise interference.

[0074] The preprocessed data is compared in real time with preset safety thresholds (expansion <2mm, temperature <80°C). If the limit is exceeded three times in a row, an alarm is triggered. The main control board then outputs a 5V PWM signal through the GPIO pin to drive the air pump and solenoid valve, opening the protective layer channel within 50ms and energizing the internal electrochemical gas sensor. After a 20-second warm-up, it enters the detection state. At the same time, the system flag switches from sleep mode (0x01) to working mode (0x02).

[0075] In terms of algorithms, such as Figure 4 As shown, the present invention designs an intelligent algorithm for controlling zero drift. Based on the modeling of the physical characteristics of the battery pack during thermal expansion, and combined with the influence of environmental disturbances on sensor accuracy, a set of adaptive dynamic compensation and response mechanisms are established. In practical applications, the sensor works in a high temperature, high humidity and complex electromagnetic interference environment for a long time, and it is very easy to have a "zero drift" phenomenon, that is, the sensor output is offset under the condition of no external excitation, thereby affecting the accuracy of battery status judgment. The differential calibration algorithm adopted by this system is based on the steady-state drift modeling assumption. It takes the initial state as the reference standard and compares the offset between the current sensor output and the initial normal output value. It is believed that the offset value is the zero drift caused by environmental changes. By constructing an adaptive drift weight function, the system can dynamically adjust the compensation intensity within different time scales, so that the calibration algorithm not only has immediate response capabilities, but also can adapt to long-term trend changes to avoid over-compensation or compensation lag. The principle is as follows:

[0076] Assume that the initial static output of the sensor is , the current output is , the zero drift deviation is:

[0077]

[0078] The system is based on the threshold Determine whether to enter the compensation state:

[0079]

[0080] Compensation adjustment coefficient Dynamically adjusted to:

[0081]

[0082] Where k is the adjustment sensitivity factor, is the system statistical average drift threshold. The final output compensation is:

[0083]

[0084] Furthermore, the Kalman filter algorithm optimizes sensor drift compensation through a prediction model, measurement updates, and optimal estimation. The system first predicts the current measurement value based on historical data. It then updates the system state based on the actual measurement value and achieves more accurate drift compensation through optimal weight adjustment. To further improve the system's accuracy in detecting sensor data anomalies, the Kalman filter algorithm is introduced, utilizing a prediction and update mechanism to dynamically estimate sensor outputs. The Kalman filter model models the current sensor state as a linear Gaussian process, assuming a linear transformation relationship between the system state and the measurement value. The optimal state estimation is achieved through the coordinated optimization of historical measurement data and current measurement errors. This algorithm not only effectively filters out high-frequency noise but also limits sudden changes in error, significantly improving the temporal stability of sensor data.

[0085] Using the standard one-dimensional linear Kalman filter model, the system state is updated as follows:

[0086] Prediction equation:

[0087]

[0088] Using the optimal estimate at k-1 time and a known control input , predict the state of the system at time k through the system model The specific variables are explained as follows: : The current time step index. For example, k=1 means the first moment, k=2 means the second moment, and so on. : The state estimate (optimal estimate) obtained after the measurement update at time step k-1. This is our best guess at the state of the system at time k-1. : At time step k, the predicted estimate of the current state k is calculated using only information from k-1 and before (prediction step), which is based on the system model. : State transition matrix. It describes how the system state naturally evolves from time k-1 to time k (without control input and noise). : Control input matrix. It describes the external control input How to influence the evolution of system state. : The control input vector applied to the system at time step k. This is a known, actively applied action.

[0089]

[0090] This equation is used to predict the uncertainty of the state estimate at time k ( ). It consists of two parts: 1. : The uncertainty at time k-1 ( ) propagates through the system model to time k. The model itself may amplify or reduce uncertainty. 2. Q: Adds additional uncertainty introduced by process noise. This represents the impact of model imperfections and unknown disturbances. The specific variables are explained as follows: : The covariance matrix of the prediction error at time step k, using only information from k-1 and before (prediction step). It quantifies the prediction state The uncertainty comes from the uncertainty of the previous moment ( ) and the uncertainty in system modeling (Q). : The estimated error covariance matrix obtained after the measurement update is completed at time step k-1. It quantifies the state estimate The uncertainty (confidence) of the value. Large values ​​indicate high uncertainty (unreliable estimate), while small values ​​indicate low uncertainty (reliable estimate). In the one-dimensional case, P is a scalar variance value. : The process noise covariance matrix (system noise). It models the uncertainty of the system model or external disturbances. In the one-dimensional case, Q is a scalar (variance).

[0091] Measurement Update:

[0092]

[0093] This equation is the core of the Kalman filter equation, the numerator : Indicates the uncertainty of the predicted state ( ) is mapped to the measurement space( ) after the uncertainty. It represents the predicted value Reliability when mapped to a measured value. : represents the uncertainty of the predicted mapped value plus the uncertainty of the measurement itself. This represents the total uncertainty of the predicted measured value. Meaning: If the sensor is very accurate (R is small), the denominator is mainly composed of If the prediction is also reliable ( small), then Moderate size; if the forecast is unreliable ( big), big, It will approach 1 / H (close to 1 in one dimension), indicating that the new measurement value is more trusted If the sensor noise is very large (R is large), the denominator is mainly dominated by R, will become very small (approaching 0), indicating that the new measurement value is not trusted. , more trust in the predicted value In short, Balances the reliability of model predictions and sensor measurements. The larger it is, the greater the weight given to the measurement value when updating; The smaller it is, the greater the weight given to the predicted value. : The measurement matrix. It describes how to map the true state of the system into the domain of measurements. It tells the filter what we expect to measure. In the one-dimensional case, if the state and measurement are the same quantity, H is usually a scalar 1. : The measurement noise covariance matrix (observation noise). It models the noise level of the sensor. The larger R is, the less precise the sensor measurement (the noisier it is), and the less trust we can place in a single measurement. In the one-dimensional case, R is a scalar (variance). : Kalman gain. This is the heart of the Kalman filter. It is a weight coefficient that determines how much we should trust the prediction and how much we should trust the new measurement during the update step. Dynamically calculated based on prediction uncertainty and measurement uncertainty.

[0094]

[0095] This equation means using the Kalman gain Fusion prediction value ( )) and measured values ​​( ), get the optimal state estimate at time k .in, : At time step k, the corrected state estimate (optimal estimate) is obtained by combining the prediction and the current measurement. This is the final output of the Kalman filter at time k. : The observation value actually measured by the sensor at time step k.

[0096]

[0097] This equation is used to calculate the final state estimate after integrating the measurement information. Uncertainty ( ).in, : The corrected estimate error covariance matrix obtained by combining the prediction and the current measurement at time step k. It quantifies the final state estimate After integrating the measurement information, this uncertainty is usually smaller than the prediction uncertainty ( ). : The identity matrix. Its dimensions match the state dimensions. In the one-dimensional case, It is the scalar 1.

[0098] Through this recursive "prediction-update" process, the Kalman filter can effectively fuse model predictions and actual measurements in a noisy environment, continuously providing the optimal estimate of the system state.

[0099] In terms of algorithms, the present invention designs an intelligent algorithm for controlling zero drift. Traditional sensors are often affected by environmental changes, resulting in zero drift, which will cause the output value of the sensor to shift and cause measurement errors. In order to effectively control this phenomenon, the present invention adopts a differential calibration algorithm and a Kalman filter algorithm. The basic steps of the differential calibration algorithm include initial sampling, drift detection, compensation adjustment and periodic calibration. The system first obtains the initial normal detection value of the sensor, and then regularly compares the current normal value with the initial value to calculate the drift amount, and adjusts the sensor output according to the measured drift amount. At fixed intervals, the system recalculates the drift trend and adjusts the parameters to ensure the continued effectiveness of the calibration. The Kalman filter algorithm optimizes the zero drift compensation of the sensor through prediction models, measurement updates and optimal estimation. The system first predicts the current measurement value based on historical data, then updates the system state in combination with the actual measurement value, and achieves more accurate zero drift compensation through optimal weight adjustment.

[0100] This invention also incorporates a "wake-up sensor" mechanism, enabling the sensor to automatically activate upon demand based on an external signal. While the battery pack is operating normally, the sensor remains dormant, reducing energy consumption and extending its lifespan. If the battery temperature or expansion sensors detect an anomaly, the microcontroller control system receives the signal and controls the opening of the solenoid valve, allowing the sensor, located within the protective material, to begin operation. This "wake-up" process ensures that the sensor is functional when needed while preventing damage from environmental factors during prolonged use.

[0101] In summary, this system has been optimized in terms of sensor selection, protection material design, intelligent control and self-calibration algorithms, enabling it to achieve efficient, accurate and long-term reliable operation in scenarios such as battery monitoring and industrial safety monitoring.

[0102] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An autonomous intelligent wake-up gas sensing system, characterized by: include: Temperature sensor, expansion sensor, housing, air pump and solenoid valve, power supply, gas sensor, controller; The power supply, gas sensor and controller are arranged inside the shell; the air pump and electromagnetic valve, temperature sensor and expansion sensor are arranged outside the shell; One end of the pipeline extends into the shell, and the other end of the pipeline is connected to the air pump and the air outlet of the solenoid valve; The power supply ends of the air pump and the solenoid valve are connected to the power supply through a circuit; The control ends of the air pump, the solenoid valve, the temperature sensor, the expansion sensor and the gas sensor are connected to the controller via lines; The power supply end of the controller is connected to the power supply through a line.

2. The autonomous intelligent wake-up gas sensing system according to claim 1, characterized in that: The shell wall of the shell is composed of polyurethane foam, aluminum foil and expanded polytetrafluoroethylene film from the inside to the outside.

3. The autonomous intelligent wake-up gas sensing system according to claim 1, characterized in that: The expansion sensor is a programmable expansion sensor.

4. The autonomous intelligent wake-up gas sensing system according to claim 1, characterized in that: The temperature sensor is a programmable temperature sensor.

5. The operating method of the autonomous intelligent wake-up gas sensing system according to any one of claims 1 to 4, characterized in that: Specifically include: The controller obtains the battery pack deformation signal collected by the expansion sensor and the ambient temperature signal collected by the temperature sensor; The controller filters the battery pack deformation signal and the ambient temperature signal to obtain processed battery pack deformation signal and ambient temperature signal; The processed battery pack deformation signal and ambient temperature signal are compared with the preset safety threshold. When the limit is exceeded N times, the controller sends a drive signal to the air pump and solenoid valve, and the air pump and solenoid valve work to send gas into the shell; The controller controls the gas sensor to be powered on and started, detects the gas in the shell, and sends the detection data to the controller.

6. The working method according to claim 5, characterized in that: The controller controls the gas sensor to be powered on and started, including: The controller controls the gas sensor to enter the detection state after S seconds of preheating, and the gas sensor switches from sleep mode to working mode.

7. The working method according to claim 5, characterized in that: Also includes: The controller uses an intelligent algorithm for controlling zero drift to correct the detection data of the gas sensor to obtain the corrected detection data. The intelligent algorithm for controlling zero drift specifically includes: Get the initial static output of the gas sensor , the current output , calculate zero drift deviation , zero drift deviation The expression is as follows: ； When the zero drift deviation Greater than threshold , then calculate the compensation adjustment coefficient , compensation adjustment coefficient The expression is as follows: ； in, is a natural function, k is the adjustment sensitivity factor, is the average drift threshold; According to the current output and compensation adjustment coefficient , calculate the corrected detection data , the corrected detection data The expression is as follows: 。 8. The working method according to claim 5, characterized in that: Also includes: The controller uses the Kalman filter algorithm to optimize the current output value of the gas sensor to obtain the optimized current output value of the gas sensor, which specifically includes: System estimate of the gas sensor based on k-1 time steps , calculate the system estimate of the gas sensor at k time steps ,in, The expression is as follows: ； in, represents the state transition matrix, represents the control input matrix, represents the control input vector at time step k; The estimated error covariance matrix based on k-1 time steps , calculate the estimated error covariance matrix for k time steps ,in, The expression is as follows: ； in, represents the process noise covariance matrix, T represents the transposed matrix; The estimated error covariance matrix according to k time steps , calculate the Kalman gain for k time steps ,in, The expression is as follows: ； in, represents the observation matrix, represents the measurement noise covariance matrix; Kalman gain according to k time steps , the system estimated value of the gas sensor , and the output value of the gas sensor , calculate the current output value of the optimized gas sensor , The expression is as follows: 。 9. The working method according to claim 5, characterized in that: The preset safety thresholds include an expansion threshold and a temperature threshold; wherein the expansion threshold is 2 mm and the temperature threshold is 80°C.

10. The working method according to claim 5, characterized in that: The controller adopts an Arduino main control board.

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