Dry burning prevention liquid level monitoring control system based on PCB capacitance sensing circuit

Abstract

The invention relates to the technical field of anti-dry-burning liquid level monitoring, in particular to an anti-dry-burning liquid level monitoring control system based on a PCB capacitance sensing circuit, in the anti-dry-burning liquid level monitoring control system based on the PCB capacitance sensing circuit, a signal preprocessing module adopts self-adaptive filtering to dynamically suppress noise interference in an original capacitance signal, and a reliable preprocessing signal is generated; the boiling state recognition module decomposes the signal into a steady state component and a transient state component, judges the boiling state according to the matching degree of the transient state component and a boiling feature library, and outputs a freezing instruction to maintain operation when the matching degree is high, so that mistaken shutdown is avoided; meanwhile, in combination with the judgment that the steady-state component is continuously lower than the warning line, a trigger instruction is output when the matching degree is low, and the real dry burning risk is accurately captured; the intelligent execution module responds instantly according to the instruction, guarantees continuous operation of equipment during freezing, executes power cut-off and sound-light alarm during triggering, and eliminates the potential safety hazard of dry burning; according to the process, accurate distinguishing between boiling interference and real dry burning is achieved, and equipment safety and operation continuity are synchronously optimized.

Description

Technical Field

[0001] This invention relates to the field of anti-dry-burning liquid level monitoring technology, specifically to an anti-dry-burning liquid level monitoring and control system based on a PCB capacitive sensing circuit. Background Technology

[0002] Traditional capacitive liquid level monitoring systems often face misjudgments due to liquid boiling interference in heating equipment applications. Random fluctuations in boiling bubbles can cause drastic changes in the capacitance signal, which may be misinterpreted as "dry burning," triggering fault protection and affecting continuous equipment operation. Simultaneously, subtle capacitance changes during actual dry burning are easily masked by noise, leading to missed detections. Existing technologies often rely on fixed thresholds or simple filtering, which cannot effectively distinguish between boiling interference and actual dry burning. Furthermore, the filtering parameters lack adaptability in complex noise environments, making it difficult to balance signal stability and the accuracy of feature recognition. Summary of the Invention

[0003] The purpose of this invention is to provide a liquid level monitoring and control system for preventing dry burning based on PCB capacitive sensing circuit, so as to solve the problems mentioned in the background art. The specific technical problems include how to dynamically distinguish between boiling interference and actual dry burning state, and accurately identify liquid level signal under strong noise interference, so as to solve the frequent equipment shutdown caused by boiling false triggering protection, and the equipment safety risks caused by failure to detect dry burning.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] This anti-dry-burning liquid level monitoring and control system based on PCB capacitive sensing circuit includes a signal preprocessing module, a boiling state recognition module, and an intelligent execution module, wherein:

[0006] The core task of the signal preprocessing module is to receive the raw capacitance detection signal (from the PCB capacitance sensing circuit) and perform real-time preprocessing to eliminate strong noise interference (such as environmental noise or liquid fluctuation noise), ensuring that subsequent modules can accurately extract liquid level features. The signal preprocessing module adopts an adaptive filtering method, which not only reduces the impact of noise but also optimizes signal quality, keeping the liquid level signal stable in high-interference environments. The specific process includes:

[0007] First, a signal dynamic analysis unit is established. This unit acquires the statistical characteristics of the original capacitance detection signal in real time within a sliding time window, including the rate of change of the average signal amplitude and the main energy band distribution of the noise spectrum. These statistical characteristics are used to evaluate the noise structure and the degree of signal baseline drift. Based on these statistical characteristics, the signal preprocessing module generates filter parameter adjustment instructions. When the proportion of high-frequency noise energy in the main energy band distribution of the noise spectrum exceeds a preset energy threshold, the instruction automatically reduces the upper cutoff frequency of the bandpass filter to suppress high-frequency interference. Simultaneously, when the rate of change of the average signal amplitude indicates that the low-frequency baseline drift exceeds a preset amplitude threshold, the instruction increases the cutoff slope of the high-pass filter to enhance the low-frequency noise suppression capability. Subsequently, the filter coefficients are dynamically updated using a minimum mean square error algorithm to achieve adaptive optimization of the filter.

[0008] This adaptive filtering reduces the masking of the liquid level signal by environmental noise and transient disturbances during boiling, ensuring that the amplitude change of the preprocessed capacitor signal is closer to the actual liquid level change (such as improved signal-to-noise ratio). This solves the problem of liquid level signal distortion under strong noise interference, provides clear input data for the subsequent boiling state recognition module, and reduces the risk of misjudgment caused by noise.

[0009] The boiling state identification module is the core innovation of the invention. It is responsible for dynamically analyzing and preprocessing the capacitor signal to distinguish between boiling transient interference (high-frequency noise caused by liquid boiling) and the true dry-burning state (steady-state signal attenuation caused by a continuously low liquid level), thereby avoiding false triggering of protection. The boiling state identification module includes a signal decomposition unit and a boiling feature matching unit. These two units work together to ensure accurate identification of liquid level characteristics in a high-interference environment. The specific process includes:

[0010] The signal decomposition unit first decomposes the preprocessed capacitor signal into steady-state components (slowly changing liquid level information) and transient components (fast noise or boiling interference). This decomposition uses a wavelet packet decomposition algorithm with a preset number of layers to divide the signal into sub-signals of different frequency bands according to the logarithmic frequency scale (such as separating low-frequency steady-state information and high-frequency transient noise through multi-layer wavelet transform).

[0011] Next, the energy time distribution dispersion of each frequency band signal is calculated (i.e., the degree of fluctuation of signal energy over time). Frequency bands with dispersion below a preset dispersion threshold are selected as steady-state components (representing stable liquid levels), and frequency bands with dispersion exceeding the threshold are reconstructed as transient components (representing boiling interference). This decomposition effectively isolates the real liquid level signal from boiling noise.

[0012] Subsequently, the boiling feature matching unit analyzes the transient components to confirm whether the interference originates from boiling. First, this unit extracts waveform kurtosis parameters (measuring peak signal amplitude), zero-crossing density parameters (signal zero-crossing frequency, indicating noise activity), and energy entropy parameters (the degree of disorder in signal energy distribution) from the transient components. These three parameters are then combined into a feature vector using preset normalization coefficients. The minimum weighted spatial distance between this feature vector and each sample vector in the pre-stored boiling interference feature library is calculated. In the distance formula, the zero-crossing density parameter is given the highest weight (because it is more sensitive to rapid changes in boiling), the energy entropy parameter is given the second highest weight (measuring noise randomness), and the waveform kurtosis parameter is given a basic weight (capturing signal abrupt changes).

[0013] Next, a spatial mapping algorithm is used to linearly convert the minimum weighted spatial distance value into a matching degree value (in the range of 0 to 1). This value is monotonically negatively correlated with the distance (i.e., the smaller the distance, the higher the matching degree), which is convenient for quantifying the similarity of boiling interference.

[0014] When the matching degree exceeds the boiling characteristic threshold, a state freeze command (freeze protection operation) is output to prevent false triggering due to boiling; when the matching degree is lower than the boiling characteristic threshold and the steady-state component remains below the dry-burning warning line, a protection trigger command is output; the determination of remaining below the dry-burning warning line is achieved through a continuous sliding window decision mechanism, wherein:

[0015] Within a preset observation window, the detection point is moved and the proportion of times the steady-state component in the sampling point is lower than the warning line is counted. When the proportion exceeds the trigger threshold, it is determined to be a continuous dry burning state.

[0016] This boiling state recognition module, through multi-dimensional feature extraction and matching mechanisms, can dynamically distinguish between boiling interference (high matching degree is considered boiling, freezing operation) and true dry burning (low matching degree and continuous low liquid level trigger protection), reducing the frequency of shutdowns caused by boiling misjudgment (reduced false trigger rate). At the same time, wavelet decomposition and sliding window decision mechanisms ensure accurate capture of steady-state signals under high noise conditions, avoiding the risk of missing dry burning, thus comprehensively solving the problem of balancing equipment safety and operational continuity.

[0017] The intelligent execution module executes operations based on instructions from the boiling state recognition module, ensuring timely protection of the equipment when dry burning is detected and maintaining operation during boiling interference, thereby preventing frequent equipment shutdowns and safety hazards. The specific process includes:

[0018] When a status freeze command is received (high boiling matching degree), the module maintains the equipment's operating state (e.g., without interrupting heating) to avoid unnecessary shutdowns. When a protection trigger command is received (low matching degree and liquid level continuously below the warning line), the module immediately activates the dry-burn protection program. This program includes immediately cutting off the main heating power supply to prevent overheating; forcibly reducing the heating power to a safe maintenance level (e.g., keep-alive state); and triggering the audible and visual alarm device to send a warning signal to monitoring personnel to ensure rapid manual intervention.

[0019] This intelligent execution module makes decisions in real time, which can effectively prevent equipment from overheating or causing fire risks when the system is in a dry-burning state, while avoiding unnecessary power adjustments when boiling (maintaining operation and optimizing equipment efficiency). This solves the equipment safety risks caused by missed dry-burning detection and reduces the overall maintenance cost through intelligent processing.

[0020] Compared with the prior art, the beneficial effects of the present invention are:

[0021] 1. This invention effectively suppresses environmental noise and signal drift through the dynamic adaptive filtering of the signal preprocessing module, providing a stable signal base for liquid level monitoring; the boiling state identification module uses a multi-scale signal decomposition and multi-dimensional feature matching mechanism to accurately distinguish between transient interference of liquid boiling and the actual dry burning state.

[0022] 2. By combining the real-time decision-making and response of the intelligent execution module, the problem of frequent equipment shutdowns caused by boiling false triggers in traditional liquid level monitoring is solved. At the same time, it ensures rapid execution of power-off protection and alarm operations in dry-burning conditions, thereby greatly improving the continuity of equipment operation and eliminating safety hazards caused by dry-burning missed detection. Ultimately, it achieves highly reliable liquid level monitoring in strong interference environments, taking into account both equipment safety and usage efficiency, and reducing maintenance costs. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the overall modules of the present invention;

[0024] Figure 2 This is a schematic diagram of the boiling state recognition module unit of the present invention.

[0025] In the diagram: 100, Signal preprocessing module; 200, Boiling state recognition module; 201, Signal decomposition unit; 202, Boiling feature matching unit; 300, Intelligent execution module. Detailed Implementation

[0026] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Next, please refer to Figure 1 This invention provides a technical solution: a liquid level monitoring and control system for preventing dry burning based on a PCB capacitance sensing circuit, comprising a signal preprocessing module 100, a boiling state identification module 200, and an intelligent execution module 300. The signal preprocessing module 100 is the fundamental component of the entire system, responsible for receiving the raw capacitance detection signal from the capacitance detection device. This raw capacitance detection signal typically originates from an internal sensor, but during actual acquisition, due to environmental electromagnetic interference, component aging, or random noise, the signal inevitably contains a large number of non-ideal fluctuation components. If these components are not processed, they will seriously affect the accuracy of subsequent modules. Therefore, the signal preprocessing module 100 employs an adaptive filtering method to purify the signal. The core of the adaptive filtering method lies in its dynamic adjustment characteristics, specifically including:

[0028] First, a signal dynamic analysis unit is established, which acquires the statistical characteristics of the original capacitance detection signal in real time within a sliding time window (including the rate of change of the average amplitude of the signal and the distribution of the main energy band of the noise spectrum).

[0029] Filter parameter adjustment instructions are generated based on statistical characteristics. When the proportion of high-frequency noise energy exceeds a preset energy threshold based on the main energy band distribution of the noise spectrum, the upper cutoff frequency of the bandpass filter is reduced. When the low-frequency baseline drift exceeds a preset amplitude threshold based on the rate of change of the average signal amplitude, the cutoff slope of the high-pass filter is increased.

[0030] Finally, the filter coefficients are dynamically updated using the minimum mean square error algorithm to process the original capacitance detection signal, ensuring that the suppression strength against electromagnetic pulse interference increases with the increase of noise energy, and the correction amplitude for sensor drift increases with the increase of offset.

[0031] The output after this processing is the preprocessed capacitor signal, which significantly reduces noise levels, improves the signal-to-noise ratio, and exhibits smoother and more stable characteristics. The key to this process is that the generated preprocessed capacitor signal has high purity and consistency, providing a solid input foundation for the subsequent signal decomposition and spectrum matching of the boiling state identification module 200. Specifically, the adaptive filtering method ensures that the signal is not affected by external interference, enabling the boiling state identification module 200 to accurately separate steady-state and transient components, avoiding misjudgments caused by signal noise, thereby improving the robustness and reliability of the entire system. Finally, the output of the signal preprocessing module 100 is a prerequisite for the efficient operation of subsequent modules, ensuring the timeliness and accuracy of equipment monitoring.

[0032] Please see Figure 2 The signal decomposition unit 201 in the boiling state identification module 200 receives the purified pre-processed capacitance signal output from the signal pre-processing module 100 and undertakes the core judgment function of distinguishing normal liquid level changes, boiling interference and real dry burning danger. In order to achieve this goal, the signal decomposition unit 201 decomposes the received pre-processed capacitance signal into two independent sub-signal components in real time, including steady-state components and transient components. The steady-state components mainly reflect the relatively slow and stable trend of liquid level change or the capacitance value in a stable state. The transient components capture the rapid and violent fluctuations in the signal. These fluctuations are usually caused by boiling phenomena such as liquid turbulence and steam jet caused by the violent generation, detachment and rupture of bubbles at the bottom of the pot.

[0033] The signal decomposition process specifically includes:

[0034] A wavelet packet decomposition algorithm with a preset number of layers is used to divide the preprocessed capacitance signal into different frequency band sub-signals according to the logarithmic frequency scale; the energy time distribution dispersion of each frequency band signal is calculated, and frequency bands with dispersion below a preset dispersion threshold are selected as steady-state components; frequency bands with energy time distribution dispersion exceeding the preset dispersion threshold are selected, and transient components representing rapid disturbances are reconstructed.

[0035] The core of the boiling feature matching unit 202 in the boiling state identification module 200 lies in processing transient components. It obtains a pre-stored boiling interference feature library from a public database, which contains unique patterns of transient components under various typical boiling states. Then, the boiling feature matching unit 202 calculates the matching degree between the currently detected transient component and each feature in the boiling interference feature library in real time. When the calculated matching degree exceeds the preset boiling feature threshold, the current state is intense boiling. At this time, the interference generated by boiling (intense capacitance fluctuation) is easily misjudged as low liquid level (reduced capacitance value). To prevent such misjudgment from triggering unnecessary protection actions, the boiling feature matching unit 202 outputs a state freeze command. This command tells the intelligent execution module 300 to temporarily maintain the current operating state, ignore the current low capacitance signal, and not use it as a basis for dry burning.

[0036] The specific process of calculating the matching degree includes:

[0037] First, three characteristic parameters with clear physical definitions are extracted from the transient components, including waveform kurtosis parameter (quantifying the sharpness of a single pulse), zero-crossing density parameter (statistically counting the frequency of the signal crossing the baseline per unit time), and energy entropy parameter (calculating the dispersion of energy distribution within the pre-division band).

[0038] Next, the three parameters are combined into a feature vector according to the preset normalization coefficients. The minimum weighted spatial distance between this vector and each sample vector in the pre-stored boiling interference feature library is calculated. The distance formula gives the zero-crossing density parameter the highest weight, the energy entropy parameter the second highest weight, and the waveform kurtosis parameter the basic weight.

[0039] Then, a spatial mapping algorithm is used to linearly convert the minimum weighted spatial distance value into a matching degree value in the range of zero to one. Finally, the matching degree is compared with the boiling feature threshold. When the matching degree exceeds the boiling feature threshold, it is determined that the fluctuation characteristics of the transient component conform to the boiling interference mode. The entire process ensures the objectivity and reproducibility of boiling state identification through the physical measurability of feature parameters, the preset weight coefficients, and the numericalization of matching criteria. The spatial mapping algorithm is used to map the minimum weighted spatial distance value to the matching degree value in the range of zero to one through a preset linear transformation rule. The matching degree value is monotonically negatively correlated with the minimum weighted spatial distance value. That is, the smaller the minimum weighted spatial distance value, the higher the output matching degree value, and the larger the minimum weighted spatial distance value, the lower the output matching degree value. Finally, it ensures that the matching degree value objectively quantifies the similarity between the current transient component and the boiling interference mode.

[0040] On the other hand, if the calculated matching degree is lower than the boiling feature threshold (indicating that the boiling interference is not significant or does not exist), and the steady-state component is detected to be lower than the preset dry burning warning line, then its persistence needs to be further evaluated (to avoid misjudgment of short-term fluctuations). At this time, the calculated matching degree initiates the continuous sliding window decision mechanism.

[0041] Specifically, it includes:

[0042] By defining a preset observation window and continuously moving the detection point within this window, the proportion of times the steady-state component is below the dry-burning warning line is counted among all sampling points within the observation window. When this proportion exceeds a preset trigger threshold, it is determined that the steady-state component is continuously below the dry-burning warning line. If both the transient component matching degree is below the boiling characteristic threshold and the steady-state component is determined to be continuously below the dry-burning warning line are met simultaneously, a protection trigger command is output, indicating that the system confirms the existence of a dry-burning risk state.

[0043] As the execution terminal of system instructions, the intelligent execution module 300 performs corresponding operations based on the control instructions issued by the boiling feature matching unit 202. When it receives the state freeze instruction output by the boiling feature matching unit 202, the instruction indicates that there is high-intensity boiling interference in the current state of the system, and the preprocessing capacitor signal is caused by boiling and should not be regarded as a real dry burning signal. Based on this analysis result, the intelligent execution module 300 will maintain the current operating state of the equipment (such as maintaining the original heating power setting, temperature control set point, etc.) and will not perform any protective actions to ensure that the equipment works normally in the boiling state without being disturbed by false interference.

[0044] Conversely, when a protection trigger command is received from the boiling characteristic matching unit 202, the command indicates that the system has eliminated boiling interference factors and, based on the continuous sliding window judgment, confirmed that the steady-state component is consistently below the dry-burning warning line, ultimately determining a real dry-burning risk. The intelligent execution module 300 immediately responds to the confirmation command and initiates the preset dry-burning protection program. The specific actions of the dry-burning protection program typically include, but are not limited to, immediately cutting off the main heating power supply of the equipment, forcibly reducing the heating power to a safe maintenance level, and possibly simultaneously triggering an audible and visual alarm device to issue a warning signal to the monitoring personnel. The core purpose of this series of actions is to forcibly terminate the heating process that may cause safety hazards, protect the equipment from overheating damage, and prevent fire risks. The intelligent execution module 300 operates according to the command provided by the boiling characteristic matching unit 202, which is derived through rigorous signal processing and logical judgment, thereby achieving precise dry-burning protection in complex cooking environments.

[0045] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A liquid level monitoring and control system for preventing dry burning based on a PCB capacitive sensing circuit, characterized in that, It includes a signal preprocessing module (100), a boiling state recognition module (200), and an intelligent execution module (300), wherein: The signal preprocessing module (100) receives the original capacitance detection signal and outputs the preprocessed capacitance signal through an adaptive filtering method; The boiling state identification module (200) decomposes the preprocessed capacitor signal into steady-state components and transient components, calculates the matching degree between the transient components and the pre-stored boiling interference feature library, and outputs a state freeze command when the matching degree exceeds the boiling feature threshold. When the matching degree is lower than the boiling feature threshold and the steady-state component is continuously lower than the dry burning warning line, it outputs a protection trigger command. The intelligent execution module (300) maintains the operation of the device according to the status freeze command and starts the dry burning protection program according to the protection trigger command.

2. The anti-dry-burning liquid level monitoring and control system based on PCB capacitive sensing circuit according to claim 1, characterized in that, The process of the adaptive filtering method specifically includes: A signal dynamic analysis unit is established to acquire the statistical characteristics of the original capacitance detection signal in real time within a sliding time window; filter parameter adjustment instructions are generated based on the statistical characteristics; and the filter coefficients are dynamically updated using a minimum mean square error algorithm.

3. The anti-dry-burning liquid level monitoring and control system based on PCB capacitive sensing circuit according to claim 2, characterized in that, The statistical characteristics include the rate of change of the average amplitude of the signal and the distribution of the main energy band of the noise spectrum.

4. The anti-dry-burning liquid level monitoring and control system based on PCB capacitive sensing circuit according to claim 2, characterized in that, The process of generating the filter parameter adjustment command specifically includes: When the proportion of high-frequency noise energy exceeds a preset energy threshold based on the main energy band distribution of the noise spectrum, the upper cutoff frequency of the bandpass filter is reduced; when the low-frequency baseline drift exceeds a preset amplitude threshold based on the rate of change of the average signal amplitude, the cutoff slope of the high-pass filter is increased.

5. The anti-dry-burning liquid level monitoring and control system based on PCB capacitive sensing circuit according to claim 1, characterized in that, The boiling state identification module (200) includes a signal decomposition unit (201), which uses a wavelet packet decomposition algorithm with a preset number of layers to divide the preprocessed capacitor signal into sub-signals of different frequency bands according to the logarithmic frequency scale. Calculate the energy time distribution dispersion of each frequency band signal, select frequency bands with dispersion below a preset dispersion threshold as steady-state components, and select frequency bands with dispersion exceeding the preset dispersion threshold to reconstruct transient components.

6. The anti-dry-burning liquid level monitoring and control system based on PCB capacitive sensing circuit according to claim 1, characterized in that, The boiling state identification module (200) includes a boiling feature matching unit (202), which calculates the matching degree between the transient component and a pre-stored boiling interference feature library, specifically including: The waveform kurtosis parameter, zero-crossing density parameter, and energy entropy parameter are extracted from the transient component; the three parameters are combined into a feature vector according to the preset normalization coefficient; the minimum weighted spatial distance between the feature vector and each sample vector in the pre-stored boiling interference feature library is calculated; the numerical value of the minimum weighted spatial distance is linearly converted into the matching degree value using a spatial mapping algorithm.

7. The anti-dry-burning liquid level monitoring and control system based on PCB capacitive sensing circuit according to claim 6, characterized in that, In the formula for calculating the minimum weighted spatial distance, the zero-crossing density parameter is assigned the highest weight, the energy entropy parameter is assigned the second highest weight, and the waveform kurtosis parameter is assigned the basic weight.

8. The anti-dry-burning liquid level monitoring and control system based on PCB capacitive sensing circuit according to claim 6, characterized in that, The spatial mapping algorithm maps the minimum weighted spatial distance value to a matching degree value in the range of zero to one through a preset linear transformation rule. The matching degree value is monotonically negatively correlated with the minimum weighted spatial distance value.

9. The anti-dry-burning liquid level monitoring and control system based on PCB capacitive sensing circuit according to claim 6, characterized in that, The boiling feature matching unit (202) determines whether the steady-state component is continuously below the dry-burning warning line through a continuous sliding window decision mechanism. This continuous sliding window decision mechanism specifically includes: By defining a preset observation window, the detection point is moved within the observation window, and the proportion of times the steady-state component in the sampling point is lower than the dry burning warning line is counted; when the proportion exceeds the trigger threshold, it is determined to be continuously lower than the dry burning warning line.

10. The anti-dry-burning liquid level monitoring and control system based on PCB capacitive sensing circuit according to claim 1, characterized in that, The intelligent execution module (300) initiates the dry-burn protection program by immediately cutting off the main heating power supply of the equipment, forcibly reducing the heating power to a safe maintenance level, and triggering an audible and visual alarm device to send a warning signal to the monitoring personnel.

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