A microorganism detection device and method based on phonon wave frequency

Abstract

The application provides a kind of microbial detection equipment and detection method based on phonon wave frequency, including information base chip, host computer, electrode sensor, built-in microbial phonon wave frequency characteristic database and JS divergence core algorithm, electrode sensor enhances signal sensitivity through resonant cavity;The detection method is through four-stage signal refinement and three-stage progressive matching analysis, to complete the accurate identification and activity quantification of microorganism.The application can realize non-invasive detection of microorganism, without complex sampling, greatly shortening the detection cycle;Effectively separate the background noise of human body, improve the sensitivity and accuracy of early detection, can complete multi-dimensional microbial detection simultaneously;The database supports dynamic updating, the equipment structure is simple, convenient to operate, strong anti-interference, forms a standardized detection closed loop;Reduce the cost of technology upgrading and detection, adapt to the demand of on-site rapid detection, suitable for accurate detection of microorganism in many fields.

Description

Technical Field

[0001] This invention relates to the technical field of microbial detection, and in particular to a microbial detection device and method based on phonon wave frequency. Background Technology

[0002] Rapid and accurate detection of microorganisms (including bacteria, viruses, fungi, parasites, etc.) is of great significance in many fields. Existing microbial detection methods mainly include traditional culture methods, PCR detection methods, and NGS sequencing methods, but all of them have obvious limitations: traditional culture methods involve complex sampling, long detection cycles (about 3-7 days), cumbersome operation, and single target, which cannot meet the needs of rapid detection; although PCR detection methods are faster than culture methods, they rely on biomarker expression, have low early detection sensitivity, and large batch errors, making it difficult to achieve simultaneous multi-dimensional microbial detection; NGS sequencing methods have high detection costs and complex data analysis, making them unsuitable for rapid on-site detection.

[0003] Meanwhile, existing microbial detection equipment mostly relies on biochemical reactions or optical detection principles, which cannot achieve non-invasive detection of microorganisms and is difficult to capture the weak vibration signals generated by the microorganisms themselves. It is known that different types and active states of microorganisms produce unique phonon frequencies (nanoscale vibration signals), which can be used as "fingerprint features" for accurate identification of microorganisms. However, in existing detection technologies, piezoelectric technology shows a significant performance reduction in the megahertz-gigahertz high-frequency region, and Raman and Brillouin spectroscopy techniques are unable to detect the weak phonon frequency signals of microorganisms. This makes it impossible to achieve efficient and accurate acquisition and analysis of microbial phonon frequencies, thus hindering the rapid, non-invasive, and accurate detection of microorganisms.

[0004] In addition, some existing detection devices have problems such as complex structure, high operation difficulty, and poor anti-interference ability. Furthermore, they have not formed a standardized phonon wave frequency acquisition-analysis closed loop, which makes it impossible to achieve efficient, accurate, and convenient microbial detection. Therefore, developing a microbial detection device and detection method that can quickly, non-invasively, and accurately capture microbial phonon wave frequency signals, and that is simple in structure and easy to operate, has become an urgent technical problem to be solved.

[0005] In view of this, the inventors have specifically designed a microbial detection device and method based on phonon wave frequencies, which leads to this invention. Summary of the Invention

[0006] To address the shortcomings of existing microbial detection methods and equipment, such as difficult sampling, long processing time, low accuracy, cumbersome operation, inability to efficiently capture microbial phonon frequency signals, and difficulty in achieving non-invasive detection, this invention provides a phonon frequency-based microbial detection device and method. By optimizing the device structure and detection process, and utilizing the specificity of microbial phonon frequencies, it achieves rapid, non-invasive, and accurate detection of microorganisms, shortens the detection cycle, improves detection accuracy, simplifies the operation process, reduces detection difficulty, and overcomes the deficiencies of existing technologies.

[0007] To solve the above problems, the technical solution of the present invention is as follows: A microbial detection device based on phonon wave frequency includes a database chip, a main unit, and electrode sensors, wherein: The information database chip and electrode sensor are electrically connected to the host computer, respectively. The information database chip has a built-in microbial phonon frequency characteristic database; The host computer has a built-in digital signal processor, a JS divergence core algorithm module and a data storage module, which are used to receive, process phonon wave frequency signals and perform comparative analysis. The electrode sensor includes two shielded data lines and correspondingly connected acquisition rods and acquisition probes. The acquisition rods are equipped with resonant cavities to enhance the sensitivity of phonon wave frequency signal reception. The electrode sensor is used to convert the wave frequency information of the target under test into electromagnetic signals using the piezoelectric effect.

[0008] Furthermore, the microbial phonon frequency characteristic database includes a matching database and an interference database based on the resonance principle.

[0009] Furthermore, the standard data in the microbial phonon frequency characteristic database includes basic parameters, nonlinear characteristic parameters, frequency band energy distribution characteristics, and activity-related parameters.

[0010] Furthermore, the nonlinear characteristic parameters include chaos degree and fractal dimension; The correlation parameters include the correlation coefficient between frequency band energy entropy and microbial activity.

[0011] Furthermore, the microbial phonon frequency characteristic database also includes a frequency database that excludes the influence of common environmental factors.

[0012] Furthermore, it also includes an information chip preparation component, the information chip preparation component comprising: Information carrier, used to carry specific frequency information; A transducer is used to convert electromagnetic energy into acoustic energy to generate an oscillation region at a specific frequency. The information chip enters the oscillation region and carries wave frequency information of a specific frequency.

[0013] Furthermore, the transducer can carry a frequency range of 10MHz-100MHz.

[0014] Furthermore, the information chip carrier is either wax-based or tin-based.

[0015] Furthermore, it also includes an interference shielding field, which is used to isolate external frequency information. The information library chip, the host, and the electrode sensor are located within the interference shielding field.

[0016] This invention also provides: A method for detecting microorganisms based on phonon wave frequencies, using microbial detection equipment.

[0017] The beneficial effects of this invention are as follows: The detection device of this invention adopts a human-friendly design at the acquisition end, which eliminates the need for sampling and enables non-invasive detection. It employs an adaptive preprocessing process, which can effectively separate human tissue background noise from weak target microbial signals, reduce the interference rate of human background noise, and solve the problem that traditional detection methods easily misinterpret human physiological signals as microbial signals.

[0018] By employing a three-level progressive hierarchical matching and JS divergence algorithm, the accuracy of human microbial identification is significantly improved, outperforming traditional detection methods.

[0019] In addition, the database of the information library chip supports dynamic updates, allowing for the addition of frequency data for new microorganisms; the modular design of the signal processing flow allows for rapid adjustment of feature extraction and matching parameters based on new detection sites, without requiring changes to the hardware structure, thus reducing the cost of technology upgrades. Attached Figure Description

[0020] The accompanying drawings, which are provided to further illustrate the invention and constitute a part of this invention, are illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention.

[0021] in: Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a system block diagram of the microbial phonon frequency characteristic database in Embodiment 1 of the present invention; Figure 3 This is a system block diagram of the host in Embodiment 1 of the present invention; Figure 4 This is a partial structural schematic diagram of the electrode sensor in Embodiment 1 of the present invention; Figure 5 This is a schematic diagram of the overall structure of Embodiment 3 of the present invention; Figure 6 This is a schematic diagram of the structure of the beeswax sheet in Embodiment 3 of the present invention; Figure 7 This is a schematic diagram of the application state structure of the tin sheet in Embodiment 3 of the present invention.

[0022] Label Explanation: 100. Electrode sensor; 110. Acquisition rod; 120. Acquisition probe; 200. Information chip carrier; 210. Beeswax sheet; 220. Tin sheet; 230. Medical tape; 300. Transducer; 310. Oscillation area. Detailed Implementation

[0023] To make the technical problems, solutions, and beneficial effects of this invention clearer and more understandable, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Example 1

[0024] Combination Figure 1-4 This device includes a database chip, a main unit, and electrode sensors 100. The components are electrically connected to form a complete closed loop for data acquisition, processing, analysis, and storage. The specific structure is as follows: First, the information database chip incorporates a microbial phonon frequency characteristic database, including a matching database and an interference database based on the resonance principle. The matching database stores standard phonon frequency data for over 242 pathogenic microorganisms, covering types such as bacilli, cocci, fungi, viruses (including various HPV types, Staphylococcus aureus, Escherichia coli, Listeria), and parasites, with a frequency range of 1.8Hz-41.67GHz. The database not only contains basic parameters such as peak frequency and frequency range, but also annotates the nonlinear characteristic parameters of each microorganism's phonon frequency (such as chaos degree and fractal dimension), frequency band energy distribution characteristics, and activity correlation parameters.

[0025] In addition, the interference database, based on the principle of resonance disruption, records the interference frequency information that can affect each microorganism in the aforementioned matching database.

[0026] At the same time, the frequency characteristics of physiological vibrations of common human tissues are recorded as a reference for interference removal, forming an interference removal frequency database, which provides data support for refined signal analysis.

[0027] The database mentioned above supports dynamic updates via the host.

[0028] Secondly, the host unit includes a core control and analysis unit, with built-in multi-module signal processing chips, a JS divergence algorithm module, a hierarchical matching decision module, and a data storage module, equipped with a display module and data interface. The multi-module signal processing chip integrates functions such as preprocessing, feature enhancement, and feature purification, adapting to the signal processing workflow; the JS divergence algorithm module combines mutual information entropy to complete feature comparison, automatically eliminating background interference features from the human body; the hierarchical matching decision module realizes the step-by-step determination of the major category, subcategory, and subtype of microorganisms; the host unit can call database data, receive raw signals collected by electrode sensors 100, and perform full-process signal processing and analysis.

[0029] In addition, the electrode sensor 100 includes two shielded data cables and correspondingly connected acquisition rod 110 with a resonant cavity and acquisition probe 120. The acquisition components are made of conductive, interference-resistant, and human-friendly metal materials. The resonant cavity can amplify the weak phonon frequency signals of microorganisms, and the shielded data cables effectively reduce external electromagnetic interference and static electricity interference from the human body surface. The acquisition frequency range is 1.8Hz-41.67GHz, and the acquisition end is rounded to ensure non-invasive human contact detection. It can completely acquire the full-dimensional phonon frequency signals of the target area, laying the hardware foundation for subsequent fine processing. Example 2

[0030] A microbial detection method based on phonon wave frequency is disclosed. This method is implemented using the detection equipment described in Example 1. The core innovation lies in refining the processing steps of the acquired phonon wave frequency signal into a four-stage process, breaking through the limitations of traditional processing methods and achieving accurate and non-invasive detection of microorganisms in the complex background of the human body. The specific steps are as follows: 1. Preparations before testing and equipment initialization Clean the area to be tested, removing surface impurities and metal deposits to avoid external signal interference; start the testing equipment, the host automatically completes initialization, calls the microbial phonon frequency characteristic database and background interference frequency data in the information library chip, sets the acquisition parameters (acquisition frequency range, signal sampling rate) according to the testing area, and checks the connection status of each component of the equipment to ensure normal operation.

[0031] 2. Detection loop formation and phonon wave frequency acquisition The acquisition rod 110 of the electrode sensor 100 is non-invasively attached to the target detection site on the human body, and the acquisition probe 120 is attached to the skin 2-3 cm around the detection site, forming a complete phonon wave frequency acquisition circuit with human tissue as the medium. When the acquisition function is activated, the electrode sensor 100 captures the mixed phonon wave frequency signal (including target microbial signal, human tissue background noise, and weak external interference) of the detection site using the piezoelectric effect principle. After being amplified by the resonant cavity, the signal is transmitted to the host.

[0032] 3. Refined processing of phonon wave frequency signals After receiving the raw phonon wave frequency mixed signal, the host performs four-stage fine processing through a multi-module signal processing chip to achieve complete separation of the target microbial characteristic signal from the human background noise and deep extraction of characteristic parameters, specifically including: Phase 1: Adaptive multi-dimensional preprocessing to achieve initial separation of human background and target signal. (1) The host uses the db5 wavelet basis to perform 5-layer wavelet packet decomposition on the original signal, decomposing the signal into 32 frequency bands; the effective frequency band is marked according to the standard frequency band range of the target microorganism in the information database chip, and the interference frequency band is marked according to the human background interference wave frequency data. The interference frequency band is completely removed by the hard threshold method, and the effective frequency band is preserved by the adaptive threshold method, which solves the problem that traditional filtering cannot distinguish between human background noise and weak target microorganism signals. (2) To address the non-stationarity of phonon wave frequency signals, empirical mode decomposition (EMD) is used to decompose the signal into multiple intrinsic mode functions (IMFs). After removing the interfering IMFs that match human physiological vibrations, the remaining effective IMFs are resampled to unify the signal time domain length to 10s, ensuring the consistency of subsequent feature extraction. (3) Dynamic gain calibration: Based on the real-time changes in signal amplitude, dynamically adjust the amplification gain (gain range 10-100 times), focus on amplifying weak target signals with amplitudes below 0.05mV, and limit the amplitude of human background noise with amplitudes above 1mV to avoid signal saturation or loss of weak signals.

[0033] Phase Two: Nonlinear Feature Enhancement to Uncover Core Microbial Fingerprint Features (1) Based on Hilbert-Huang transform (HHT), time-frequency analysis is performed on the preprocessed signal to generate Hilbert spectrum; according to the distribution law of microbial phonon wave frequencies in the standard database, the time domain position and frequency band range of the target resonance peak are automatically identified, and the energy of the target resonance peak is increased by 20-50 times through frequency band energy weighting to further suppress the human background signal in the non-resonance peak region. (2) Extract the core nonlinear features of the signal, including: chaos degree (calculated using Lyapunov exponent), which reflects the disorder of microbial phonon vibration; fractal dimension (calculated using box dimension), which reflects the spatial complexity of the signal; frequency band energy entropy, which reflects the uniformity of energy distribution of the signal in each frequency band; and combine the traditional frequency peak, frequency range, and vibration amplitude to form a 6-dimensional feature vector, breaking through the limitation of the single dimension of traditional feature extraction.

[0034] Phase 3: Feature Refinement, Eliminating Redundant Features and Enhancing Core Differences (1) Calculate the mutual information entropy between each feature and the microbial species in the 6-dimensional feature vector, and remove redundant features with mutual information entropy lower than 0.6 (retain ≥4 core features) to reduce the interference of invalid features on subsequent comparative analysis; (2) Using the JS divergence algorithm, the core features after screening are normalized based on the mean value of similar microorganisms in the standard database, and the feature values ​​are mapped to the [0,1] interval. Compared with the traditional JS divergence, this invention introduces an activity weight coefficient. Based on the correlation between the feature and the activity of the microorganism (e.g., the correlation between the frequency band energy entropy and the activity is 0.85), different features are assigned differentiated weights to strengthen the feature differences that are directly related to the type and activity of the microorganism.

[0035] Phase 4: Feature vector reconstruction to generate standardized analysis samples The purified core features are sorted according to fixed dimensions such as frequency peak, chaos degree, fractal dimension, and frequency band energy entropy, and reconstructed into standardized feature vectors, which serve as the core basis for subsequent comparative analysis.

[0036] The hierarchical comparative analysis and result determination host uses a hierarchical matching decision module to perform a three-level progressive comparative analysis between standardized feature vectors and standard data in the information database chip, achieving accurate identification of microorganisms. (1) First level: Category matching: Compare the feature vector with the standard feature mean of the microbial categories (bacilli, cocci, fungi, viruses, parasites), calculate the improved JS divergence value (the smaller the divergence value, the higher the similarity), and select the category with the smallest divergence value; (2) Second level: Subclass matching: In the selected major classes, the feature vectors are compared with the standard data of the subclasses under the major class to further lock the microbial subclasses; (3) Level 3: Precise matching: Within the subclass range, the feature vector is compared with the standard data of the specific microorganism, and the similarity is calculated. Similarity = 1 - JS divergence value A similarity score of ≥95% indicates a successful, accurate match. Simultaneously, based on the activity-related parameters in the feature vector, the activity level (high, medium, low) and relative content of the microorganisms are quantified. Finally, the host computer generates a standardized testing report containing data on the major microbial category, subcategory, specific species (subtype), activity level, relative content, and feature comparison.

[0037] After the device reset and data storage test are completed, the acquisition function is turned off, and the acquisition component of the electrode sensor 100 is cleaned and reset with medical-grade sterile cleaning. The host stores the original phonon wave frequency signal, the intermediate data after each stage of processing, and the final test report to the data storage module. The entire process data can be exported through the data interface to complete the test. Example 3

[0038] Combination Figure 5-7 An information chip preparation component, comprising: Information carrier 200 is used to carry specific frequency information; Transducer 300 is used to convert electromagnetic energy into acoustic energy to generate an oscillation region 310 at a specific frequency; The information chip enters the oscillation region 310 and carries wave frequency information of a specific frequency.

[0039] Furthermore, the transducer 300 can carry a frequency range of 10MHz-100MHz, and the information chip carrier 200 is either wax-based or tin-based.

[0040] In this embodiment, beeswax is used as the information carrier 200. Its melting point is 62-67℃, and the measured minimum melting temperature is above 55℃. It has good human biocompatibility, is not easily decomposed in the human body, and can be used as a good ingestible information carrier.

[0041] The transducer 300 is a device that uses the piezoelectric effect to convert electromagnetic energy into acoustic energy. To transmit the transducer 300, a specific frequency wave information is applied to the oscillation region 310. Then, beeswax is melted and dripped into the oscillation region 310 of the transducer 300 in a molten state. During the solidification process, it is affected by the specific frequency. The beeswax sheet 210 formed after solidification will record the corresponding frequency information. The frequency information is continuously applied for 10-15 minutes to complete the preparation of the information sheet.

[0042] Using the above operations, the information carrier 200 carrying specific frequency information can be swallowed into the human body. Since the human body is essentially a water-containing environment, after the beeswax tablet 210 carrying a certain amount of energy enters the human body, it will continuously release the wave frequency information it carries. Target microorganisms that are sensitive to this wave frequency information will be affected accordingly. There is also a corresponding energy source in the human body, which enables the beeswax tablet 210 to continuously exert the influence of a specific frequency during its existence in the human body. Finally, it is expelled from the human body, achieving a good intervention process.

[0043] The specific frequency can be a single-band frequency information that targets interference from a single microorganism, or it can be a composite frequency signal that carries composite frequency information, corresponding to interference information from multiple microorganisms.

[0044] In addition, tin-based materials can be used to prepare tin sheets 220 in the same manner as described above. These sheets carry specific frequency information and can exert influence on specific areas by external attachment. The tin sheets 220 can be attached to the corresponding interference target surface using medical tape 230.

[0045] Furthermore, it also includes an interference shielding field, which is used to isolate external frequency information. The information library chip, the host, and the electrode sensor 100 are located within the interference shielding field.

[0046] In this embodiment, the interference shielding field is an anechoic chamber, an electromagnetic anechoic chamber, or other forms of shielding space, which shields external frequency information and avoids interference and influence caused by external frequency signals during the detection or preparation of information chips.

[0047] The present invention has been described above by way of example with reference to the accompanying drawings. Obviously, the specific implementation of the present invention is not limited to the above-described manner. Any non-substantial improvements made using the inventive concept and technical solution of the present invention, or the direct application of the inventive concept and technical solution of the present invention to other occasions without modification, are all within the protection scope of the present invention.

Claims

1. A microorganism detection device based on phonon wave frequencies, characterized by, Includes a database chip, a host computer, and electrode sensors (100), wherein: The information database chip and the electrode sensor (100) are electrically connected to the host computer, respectively. The information database chip has a built-in microbial phonon frequency characteristic database; The host computer has a built-in digital signal processor, a JS divergence core algorithm module and a data storage module, which are used to receive, process phonon wave frequency signals and perform comparative analysis. The electrode sensor (100) includes two shielded data lines and correspondingly connected acquisition rod (110) and acquisition probe (120). The acquisition rod (110) is provided with a resonant cavity to enhance the sensitivity of phonon wave frequency signal reception. The electrode sensor (100) is used to convert the wave frequency information of the target under test into electromagnetic signals using the piezoelectric effect.

2. The microbial detection device based on phonon wave frequency according to claim 1, characterized in that, The microbial phonon frequency characteristic database includes a matching database and an interference database based on the resonance principle.

3. The microbial detection device based on phonon wave frequency according to claim 1, characterized in that, The standard data in the microbial phonon wave frequency characteristic database includes basic parameters, nonlinear characteristic parameters, frequency band energy distribution characteristics, and activity-related parameters.

4. The microbial detection device based on phonon wave frequency according to claim 3, characterized in that, The nonlinear characteristic parameters include chaos degree and fractal dimension; The correlation parameters include the correlation coefficient between frequency band energy entropy and microbial activity.

5. A microbial detection device based on phonon wave frequency according to claim 2, characterized in that, The microbial phonon frequency characteristic database also includes a frequency database that excludes the influence of common environmental factors.

6. The microbial detection device based on phonon wave frequency according to claim 1, characterized in that, It also includes an information chip preparation component, which includes: Information carrier (200) is used to carry specific frequency information; A transducer (300) is used to convert electromagnetic energy into acoustic energy to generate an oscillation region (310) of a specific frequency; The information chip enters the oscillation region (310) and carries wave frequency information of a specific frequency.

7. The microbial detection device based on phonon wave frequency according to claim 1, characterized in that, The transducer (300) can carry a frequency range of 10MHz-100MHz.

8. The microbial detection device based on phonon wave frequency according to claim 1, characterized in that, The information chip carrier (200) is either wax-based or tin-based.

9. A microbial detection device based on phonon wave frequency according to claim 1, characterized in that, It also includes an interference shielding field, which is used to isolate external frequency information. The information library chip, host and electrode sensor (100) are located in the interference shielding field.

10. A method for detecting microorganisms based on phonon wave frequencies, characterized in that, The detection is performed using the microbial detection device as described in any one of claims 1-9.

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