Lucid ganoderma extracting solution detection method based on intelligent sensing system, electronic equipment and storage medium

Abstract

The invention discloses a lucid ganoderma extracting solution detection method based on an intelligent sensing system, electronic equipment and a storage medium, and relates to the technical field of traditional Chinese medicine detection.According to the method, through a functional effective concentration correction model, a cooperative attenuation calculation mechanism of an ultraviolet aging energy accumulation value EUV and a spectral line structure disturbance score Sdrift is introduced, and the detection accuracy of a lucid ganoderma extracting solution is improved. And calculating and outputting the functional effective concentration Ceff in combination with the standard initial concentration C0, thereby realizing dynamic and continuous correction of the active concentration of the extracting solution. A response level interval set based on the result and a corresponding automatic control mechanism such as local shading and manual rechecking enable the system to have a closed-loop response capability of'real-time judgment, feedback and intervention '. According to the mechanism, the automatic early warning capability of abnormal batches on a production line is improved, unnecessary yield loss can be avoided through flexible control means such as shading curtain adjustment, and the production efficiency is considered while the quality stability is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of traditional Chinese medicine detection, and in particular to a ganoderma lucidum extract detection method based on an intelligent sensing system. Background Art

[0002] Ganoderma lucidum is a fungus of the Ganodermaceae family and the Ganoderma genus. It is also known as single ganoderma, dense-grained thin ganoderma or old wood fungus. Its components include polysaccharides, nucleosides, furans, sterols, alkaloids, triterpenes, oils, various amino acids and proteins, enzymes, organic germanium and various trace elements. It can be used to make medicinal food and can also be processed into various products such as beverages, bread and flour.

[0003] In the field of quality inspection and evaluation of active ingredients in traditional Chinese medicines, the Ganoderma lucidum extract detection method is specifically a Ganoderma lucidum extract detection method based on an intelligent sensing system that combines ultraviolet aging effect modeling and spectral structure perturbation identification; this method integrates environmental sensing, track acquisition, data normalization, concentration correction, spectral line analysis, early warning control and other modules, and is particularly suitable for dynamic detection and quality assurance of the actual effective concentration of key functional ingredients in Ganoderma lucidum extract in production scenarios with complex lighting environments or strong ultraviolet loads.

[0004] Currently, the spectral inversion method commonly used in the detection of Ganoderma lucidum extracts directly calculates the apparent concentration of the target component based solely on the absorption intensity at a specific wavelength. However, in real-world production scenarios, Ganoderma lucidum is often exposed to continuous UV light and high temperature and humidity during cultivation, drying, and extraction, causing a certain degree of structural denaturation or degradation of its active ingredients, such as triterpenes, polysaccharides, and adenosines. This "structural change without concentration reduction" situation cannot be determined by a single absorbance value, resulting in significant limitations in traditional detection methods. Conventional spectroscopy is particularly prone to "false positives" when used in locations with severe UV interference, such as greenhouses on the plateau or open cultivation in southern summer, thus affecting product stability and efficacy consistency.

[0005] The root cause of the above problems is that traditional detection methods fail to perceive and quantify the cumulative impact of external environmental energy on the structure of ingredients, lack the ability to model the long-term interactive effects of light, heat and humidity, and are unable to determine whether the ingredients have undergone photodenaturation, thermal degradation or microstructural rearrangement; when the extract ingredients lose their active functions due to structural damage but their concentration values ​​remain normal, the detection system is unable to issue an alarm, resulting in such samples being mistakenly classified as qualified batches and put into use; the consequences may include: fluctuations in the efficacy of the final product, poor batch-to-batch consistency, failure of efficacy verification, and even product recalls and brand damage due to substandard efficacy, which has become a key technical pain point in the intelligent quality control of functional Chinese medicine products.

[0006] Therefore, how to comprehensively test the Ganoderma lucidum extract and determine its efficacy is a technical problem that current technicians need to solve. Summary of the Invention

[0007] In view of the shortcomings of the existing technology, the present invention provides a Ganoderma lucidum extract detection method based on an intelligent sensing system, which solves the problems mentioned in the background technology.

[0008] To achieve the above objectives, the present invention provides a first aspect of a method for detecting a Ganoderma lucidum extract based on an intelligent sensing system, comprising the following steps:

[0009] S1. In the illumination room, real-time ultraviolet environmental data of the Ganoderma lucidum extract to be tested is collected. The ultraviolet environmental data includes ultraviolet intensity Iuv, temperature T, saturated vapor pressure Bh, initial concentration C0 of the extract, and key spectral line curve;

[0010] S2. Preprocessing the ultraviolet environment data to obtain a standard digital ultraviolet data set, wherein the standard digital ultraviolet data set includes the ultraviolet intensity Iuv(t) at time t, the temperature T(t) at time t, the saturated vapor pressure Bh(t) at time t, the initial concentration C0 of the extract, and a key spectral line curve;

[0011] S3. Based on the standard digital UV data set, calculate the UV aging energy accumulation value EUV, and perform a preliminary comparison and evaluation between the UV aging energy accumulation value EUV and the risk threshold Eth. When the UV aging energy accumulation value EUV ≥ the risk threshold Eth, it indicates that the current Ganoderma lucidum extract has entered the risk of structural degeneration, initiate the spectral line structure perturbation analysis, and execute S4.

[0012] S4, extracting the spectral response data of the Ganoderma lucidum extract to be tested, and calculating the output spectral line structure disturbance function Sdrift, the spectral response data includes the main peak position drift △X of the i-th key spectral line i and the intensity ratio difference of the i-th key spectral line △A i ;

[0013] S5. Summarize and calculate the ultraviolet aging energy accumulation value EUV and the spectral line structure perturbation function Sdrift to obtain the functional effective concentration Ceff, and perform a secondary comparative evaluation of the functional effective concentration Ceff and the judgment interval threshold to determine the effective component retention of the current sample under the influence of ultraviolet interference and structural drift.

[0014] Preferably, in S1, a sliding track is arranged on the roof of the illumination room in a direction crosswise to the X-axis and the Y-axis, and the sliding track is provided with collection points in both the X-axis and the Y-axis directions;

[0015] A sensor group is arranged around a platform for placing the ganoderma lucidum to be detected, the sensor group including an ultraviolet intensity probe, a temperature sensor, a vapor pressure sensor, a micro-spectral sensor and a micro-flow cuvette;

[0016] The ultraviolet intensity probe is arranged at the collection point of the sliding track, and collects the ultraviolet intensity Iuv of the Ganoderma lucidum extract to be tested by moving; the temperature sensor and the vapor pressure sensor are used to respectively collect the temperature T and saturated vapor pressure Bh around the Ganoderma lucidum extract in real time;

[0017] The micro-circulation colorimetric cell includes an integrated MEMS micro-spectral sensor, which is used to collect the key spectral line curves of the Ganoderma lucidum extract and infer the concentration of the active ingredients in the Ganoderma lucidum solution by combining the Lambert-Beer law to obtain the initial concentration C0 of the extract, wherein the key spectral line curves include the key spectral line curves of triterpenes, polysaccharides, nucleosides, phenolic derivatives and pigments.

[0018] Preferably, in S2, the preprocessing includes timestamp alignment processing and normalization processing;

[0019] The timestamp alignment process includes: defining a global time axis, setting a unified time interval △t, and using linear interpolation and sliding average methods to perform a unified timestamp step and interpolation on the original data to obtain the ultraviolet intensity Iuv(t) at time t, the temperature T(t) at time t, and the saturated vapor pressure Bh(t) at time t of the unified timestamp;

[0020] The normalization process includes: using Min-Max normalization to normalize the ultraviolet intensity Iuv(t) at time t, the temperature T(t) at time t, the saturated vapor pressure Bh(t) at time t, and the initial concentration C0 of the extract after the timestamp alignment, scaling all parameters to the range of [0,1] to eliminate the influence of unit dimension; and using the logarithmic compression method to extract the key spectral line curve, perform spectral skew data normalization processing, and compress the key spectral line curve to a unified range.

[0021] Preferably, in S3, the calculation formula of the ultraviolet aging energy accumulation value EUV is:

[0022]

[0023] Where t1 represents the end time, t0 represents the start time, dt represents the time integral variable, ln represents the natural logarithm function, w represents the sensitivity coefficient of the Ganoderma lucidum strain, a1 represents the temperature correction coefficient, and a2 represents the vapor pressure correction coefficient. All the above parameters are dimensionless.

[0024] Preferably, the preliminary comparative evaluation of the ultraviolet aging energy accumulation value EUV and the risk threshold Eth further includes:

[0025] When the ultraviolet aging energy accumulation value EUV is less than the risk threshold Eth, it indicates that the current Ganoderma lucidum extract sample is in a normal state and the test continues;

[0026] Among them, the risk threshold Et is calculated and determined based on the ultraviolet aging energy accumulation value EUV of all historical Ganoderma lucidum extract samples.

[0027] Preferably, in S4, the calculation formula of the spectral line structure disturbance function Sdrift is:

[0028]

[0029] Where n represents the total number of key spectral line curves, w i and p i They represent the peak position perturbation weight of the i-th key spectral line curve and the absorption intensity perturbation weight of the i-th key spectral line curve, respectively. The above parameters are dimensionless.

[0030] Among them, w i ·|△X i | represents the main peak shift caused by structural denaturation; p i ·(△A i ) 2 Represents a measure of relative strength changes.

[0031] Preferably, in S5, the calculation formula of the functional effective concentration Ceff is:

[0032] Ceff=C0·exp(-N·EUV·Sdrift);

[0033] Where exp represents the exponential function, and N represents the comprehensive degradation sensitivity coefficient, which is set according to the overall sensitivity to light aging and structural drift.

[0034] exp(-N·EUV·Sdrift) represents the exponential decay structure, which is used to analyze the decrease in the functional concentration of Ganoderma lucidum extract.

[0035] Preferably, in S5, the judgment interval threshold includes a first judgment threshold F1 and a second judgment threshold F2, and the judgment interval threshold is set according to the relative concentration of the initial concentration C0 of the extract;

[0036] The functional effective concentration Ceff is subjected to a secondary comparative evaluation with the judgment interval threshold, including:

[0037] When the functional effective concentration Ceff ≥ the first judgment threshold F1, it means that the function of the Ganoderma lucidum extract is completed, and this is classified as a first-level response. At the same time, the Ganoderma lucidum extract flows normally without intervention;

[0038] When the second judgment threshold F2 ≤ functional effective concentration Ceff < first judgment threshold F1, it indicates that the Ganoderma lucidum extract is in a metastable state, and this is classified as a secondary response, and local shading is activated at the same time, by shading the overexposed points in the ultraviolet energy distribution heat map;

[0039] When the functional effective concentration Ceff is less than the second judgment threshold F2, it indicates that the degradation of the Ganoderma lucidum extract is abnormal, indicating that the current batch of Ganoderma lucidum extract is abnormal, and this is divided into the third level response, and a manual re-inspection is prompted.

[0040] A second aspect of the present application provides an electronic device, including:

[0041] processor; and

[0042] The memory stores executable codes thereon, and when the executable codes are executed by the processor, the processor is caused to execute the method described above.

[0043] A third aspect of the present application provides a storage medium having executable code stored thereon. When the executable code is executed by a processor of an electronic device, the processor is caused to execute the method described above.

[0044] The technical solution provided by this application may have the following beneficial effects:

[0045] This method, by constructing an intelligent sensing system integrating multi-dimensional environmental perception and spectral perturbation recognition, introduces for the first time a coupled modeling mechanism of "UV aging energy and structural changes" in the detection of Ganoderma lucidum extracts. This enables real-time identification of functional component failure caused by overexposure to light or structural degradation, without relying on chemically destructive reagents and in a non-contact manner. Compared to traditional methods that rely solely on spectral absorbance to invert concentration, this method comprehensively considers the dual effects of environmental stress and structural characteristics on concentration, significantly improving the accuracy and traceability of functional testing, resolving the problem of misjudgment of "normal concentration but degraded ingredients," and ensuring the consistency of the efficacy of traditional Chinese medicine products in actual use.

[0046] (2) This method uses a functional effective concentration correction model, introduces a collaborative attenuation calculation mechanism of the ultraviolet aging energy accumulation value EUV and the spectral line structure disturbance score Sdrift, and combines the standard initial concentration C0 to calculate the output functional effective concentration Ceff, thereby achieving dynamic and continuous correction of the active concentration of the extract. The response level range set based on this result and its corresponding automatic control mechanism, such as local shading and manual review, enable the system to have a closed-loop response capability of "real-time judgment feedback intervention". This mechanism not only improves the automatic early warning capability of abnormal batches on the production line, but also avoids unnecessary production losses through flexible control measures such as shading curtain adjustment, ensuring stable quality while taking into account production efficiency.

[0047] (3) The spectral perturbation function constructed by this method extracts the difference in the main peak position drift and intensity ratio of key active ingredients, combines the spectral perturbation weights for weighted modeling, and forms a quantitative score Sdrift for structural changes. This method can not only significantly enhance the system's ability to identify small structural perturbations of various functional ingredients such as triterpenes, polysaccharides, and adenosine, but can also detect potential degradation risks through spectral drift before traditional absorbance differences appear, thus possessing the characteristic of "forward-looking identification". Combined with the trigger mechanism of ultraviolet aging energy modeling, it ensures that the analysis process is only activated when there is a real environmental stress risk, effectively saving computing resources and improving system efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0049] Figure 1 Schematic diagram of the steps of the Ganoderma lucidum extract detection method of the present invention;

[0050] Figure 2 is the ultraviolet energy distribution thermal map of the present invention;

[0051] Figure 3 Schematic diagram of the structure of the electronic device of the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0053] Example 1

[0054] The present invention provides a method for detecting Ganoderma lucidum extract based on an intelligent sensing system. Figure 1 , including the following steps:

[0055] S1, by setting up collection points in the Ganoderma lucidum lighting room and installing sensor groups, real-time ultraviolet environment data is collected, and an intelligent sensing system is built to transmit the ultraviolet environment data to the intelligent sensor system;

[0056] S2. Preprocessing the UV environment data in the intelligent sensor system to obtain a standard digital UV data set, and constructing a relational database to classify and store the standard digital UV data set;

[0057] S3. Extract the standard digital UV data set, calculate and output the UV aging energy accumulation value EUV, and set the risk threshold Eth for preliminary comparative evaluation;

[0058] S4. Triggering spectral line structure perturbation analysis through preliminary comparative evaluation, extracting spectral response data, and calculating the output spectral line structure perturbation function Sdrift;

[0059] S5. Perform summary calculation based on the ultraviolet aging energy accumulation value EUV and the spectral line structure perturbation function Sdrift, output the functional effective concentration Ceff, set the judgment interval threshold and perform secondary comparative evaluation with the functional effective concentration Ceff, and execute the relevant response mechanism based on the secondary comparative evaluation results.

[0060] In this embodiment, the method realizes comprehensive perception of key influencing factors such as ultraviolet intensity, temperature, saturated vapor pressure, initial concentration and spectral response by constructing a multi-point distributed ultraviolet collection point array in the Ganoderma lucidum illumination room, combining a track-type mobile probe with an environmental parameter collection unit, and constructing an ultraviolet environment perception with spatial resolution and time synchronization; then, the collected data is pre-processed by timestamp alignment, normalization and logarithmic compression, and standardized data storage and efficient management are achieved by constructing a relational database to obtain a standard digital ultraviolet data set; on this basis, the system calculates the ultraviolet intensity, temperature and vapor pressure according to the data. The data is collected, the ultraviolet aging energy accumulation value EUV is calculated, and a preliminary risk assessment is made using the risk threshold Eth. When the sample is in a high-risk state, the spectral line structure perturbation analysis is automatically initiated to extract the spectral main peak drift and intensity ratio difference, construct the spectral line structure perturbation function Sdrift, and determine whether there is structural degeneration. Finally, based on the ultraviolet aging energy accumulation value EUV and the spectral line structure perturbation function Sdrift, the initial concentration of the extract C0 is integrated to calculate the functional effective concentration Ceff, and a secondary assessment is performed by setting a concentration judgment interval. Response mechanisms including automatic labeling, light-shielding intervention, and manual review are implemented. Through the above series of steps, the present invention establishes a closed-loop intelligent detection system covering "data collection, modeling analysis, result judgment, and feedback control", breaking through the technical shortcomings of traditional detection methods in terms of structural degeneration that are invisible, unjudgable, and uncontrollable, significantly improving the quality judgment accuracy of the active ingredients of Ganoderma lucidum extract, the environmental risk response capability, and the level of automatic control in the production process, providing stable and reliable technical support for the quality monitoring, digital cultivation, and intelligent extraction of functional Chinese medicinal materials.

[0061] Example 2

[0062] See also Figure 1 ,Specifically: S1 includes S11 and S12;

[0063] S11. Sliding tracks are arranged crosswise in the X-axis direction and the Y-axis direction on the roof of the Ganoderma lucidum illumination room. The sliding tracks are provided with a collection point every 5 meters in the X-axis direction and the Y-axis direction, and a sensor group is arranged around the Ganoderma lucidum to collect ultraviolet environmental data around the Ganoderma lucidum in real time.

[0064] Ultraviolet environment data include ultraviolet intensity Iuv, temperature T, saturated vapor pressure Bh, initial concentration of extract C0 and key spectral line curve;

[0065] The sensor group includes a UV intensity probe, a temperature sensor, a vapor pressure sensor, a micro-spectral sensor and a micro-flow colorimetric cell;

[0066] Among them, a motor-driven sliding track is used, and a track-type ultraviolet intensity probe is set on the sliding track. Each movement covers a collection point of 5 meters × 5 meters. At the same time, the daily time period is divided into three periods to collect ultraviolet intensity Iuv in the morning, noon and evening. The collected band range is 315nm-400nm ultraviolet intensity Iuv;

[0067] The temperature T and saturated vapor pressure Bh around the Ganoderma lucidum are collected in real time by the temperature sensor and vapor pressure sensor set around the Ganoderma lucidum;

[0068] The Ganoderma lucidum extract is passed through a micro-circulation colorimetric cell, and the key spectral line response is collected by an integrated MEMS micro-spectral sensor. The concentration of the active ingredient in the Ganoderma lucidum solution is then inferred by combining the Lambert-Beer law to obtain the initial concentration C0 of the extract.

[0069] Key spectral curves include those of triterpenes, polysaccharides, nucleosides, phenolic derivatives and pigments;

[0070] S12. Through the wireless communication module LoRa built into the sensor group, a wireless connection is established with the smart sensor system through a local wireless network, and the real-time ultraviolet environment data is transmitted to the smart sensor system.

[0071] In this embodiment, the method achieves high-density perception of key aging factors in the Ganoderma lucidum cultivation lighting environment by constructing a spatial acquisition mechanism based on sliding tracks and a highly timely sensor network. Specifically, by laying a track system arranged in an X and Y direction crosswise on the roof of the Ganoderma lucidum lighting room and setting up multi-point acquisition units at 5-meter intervals, the track-type motor-driven probe has the ability to scan across the entire space. Combined with a time-sharing sampling strategy for three time periods each day, ultraviolet intensity data from the 315nm to 400nm ultraviolet band is obtained. At the same time, ultraviolet intensity probes, temperature sensors, vapor pressure sensors, micro-spectral sensors and flow-through colorimetric cells are set around each acquisition point to respectively collect ultraviolet intensity Iuv, temperature T, saturated vapor pressure Bh, initial concentration C0 of the extract and key spectral line curves related to ultraviolet aging, and the original concentration C0 of the extract is deduced by combining the Lambert-Beer law. The above-mentioned sensor group is wirelessly connected to the intelligent sensor system via a built-in LoRa wireless communication module, ensuring that multi-point data can be quickly aggregated and uploaded in real time in the local area network. Through this structured, automated, and distributed ultraviolet environmental data acquisition mechanism, the present invention realizes full monitoring of multi-source environmental parameters and key spectral data that affect the stability of Ganoderma lucidum's active ingredients. It not only improves the temporal and spatial resolution of sampling, but also avoids misjudgments caused by local blind spots, manual delays, or data transmission lags in traditional detection methods.

[0072] Example 3

[0073] See also Figure 1 , specifically: S2 includes S21 and S22;

[0074] S21. Receive ultraviolet environment data in real time in the intelligent sensor system, and pre-process the ultraviolet environment data to obtain a standard digital ultraviolet data set;

[0075] Preprocessing includes timestamp alignment and normalization;

[0076] The standard digital UV data set includes the UV intensity Iuv(t) at time t, the temperature T(t) at time t, the saturated vapor pressure Bh(t) at time t, the initial concentration of the extract C0 and the key spectral line curve;

[0077] Timestamp alignment is achieved by defining a global time axis, setting a unified time interval △t, and using linear interpolation and sliding average methods to unify the timestamp step and interpolation padding for the raw data of all sensors, obtaining the ultraviolet intensity Iuv(t) at time t, the temperature T(t) at time t, and the saturated vapor pressure Bh(t) at time t of the unified timestamp;

[0078] Normalization processing: By using Min-Max normalization, the UV intensity Iuv(t) at time t, the temperature T(t) at time t, the saturated vapor pressure Bh(t) at time t, and the initial concentration of the extract C0 after the timestamps are aligned are normalized, and all parameters are scaled to the range of [0,1] to eliminate the unit dimension effect;

[0079] At the same time, the logarithmic compression method is used to extract key spectral line curves and perform spectral skew data normalization processing, compressing the key spectral line curves into a unified range, compressing abnormal high values, enhancing the model's ability to identify weak signals, and preventing the main peak from dominating the clustering;

[0080] S22. By constructing a relational database, setting a sensor meta-information table, a raw measurement data table, and a pre-processed parameter table, the ultraviolet environment data collected by the corresponding sensor information and the standard digital ultraviolet data set obtained after pre-processing are stored in the corresponding sensor meta-information table, the raw measurement data table, and the pre-processed parameter table.

[0081] In this embodiment, the method constructs a unified, high-quality, and callable standard digital data foundation by preprocessing and structured storage of ultraviolet environmental data collected in real time. Specifically, the multi-channel asynchronous sensor data is timestamp aligned based on a unified global time axis, the linear interpolation method is used to fill the asynchronous data, and the sliding average algorithm is used to achieve synchronized resampling to ensure that all parameters are output with a unified time step; then, the aligned ultraviolet environmental data is dimensionlessly standardized by the Min-Max normalization method, and the skew compression method is used for the key spectral line response data to reduce the dominant effect of strong signals and enhance the model's sensitivity to weak signals and subtle disturbances, thereby forming a standard digital ultraviolet data set with model input consistency and data training adaptability. Based on the relational database architecture, three core data table structures are designed: sensor metadata table, original measurement data table, and preprocessed parameter table, which respectively realize the hierarchical storage of sensor device mapping, original data evidence, and standard data results. By building this structured database, all data related to collection, processing, and modeling can be efficiently traced, batch-called, and version-controlled, providing stable basic data support for subsequent modeling analysis, decision execution, response strategy triggering, and other operations. Through the implementation of this step, the present invention not only significantly improves the collaborative consistency of multi-source perception data at the temporal and dimensional levels, but also enhances the model's adaptability to spectral anomaly patterns. Furthermore, through the structured database, it optimizes data security, queryability, and lifecycle management, overall enhancing data governance capabilities, model training robustness, and operational controllability, providing a high-quality support foundation for subsequent aging modeling and spectral line perturbation analysis.

[0082] Example 4

[0083] See also Figure 1 and Figure 2 ,Specifically: S3 includes S31 and S32;

[0084] S31, extracting the ultraviolet intensity Iuv(t) at time t, the temperature T(t) at time t, and the saturated vapor pressure Bh(t) at time t from the relational database, performing time integration calculation to output the ultraviolet aging energy accumulation value EUV, analyzing the total amount of structural aging stimulation received by the Ganoderma lucidum extract over a period of time, and storing the ultraviolet aging energy accumulation value EUV in the relational database, constructing an ultraviolet energy distribution heat map based on the ultraviolet aging energy accumulation value EUV, and identifying aging areas;

[0085] The UV aging energy accumulation value EUV is calculated and output by the following algorithm formula;

[0086]

[0087] Where, t1 represents the end time, t0 represents the start time, dt represents the time integral variable, ln represents the natural logarithm function, w represents the sensitivity coefficient of the Ganoderma lucidum strain. Different strains have different UV sensitivities. a1 represents the temperature correction coefficient, which is used to control the contribution of temperature to aging. a2 represents the vapor pressure correction coefficient, which represents the degree to which humidity inhibits the aging rate. All values ​​are dimensionless and are obtained by the user's initial preset input.

[0088] (1+a1·lnT(t))-a2·Bh(t) This is a modulation function that approximates the nonlinear interaction of three factors: ultraviolet, heat, and humidity. The rate at which ultraviolet rays break chemical bonds increases logarithmically with temperature. The greater the vapor pressure, the more unstable the solution. However, partial volatilization may also take away energy, thus playing a buffering or heat dissipation role. It is processed by subtraction, and ultimately the entire product term represents an actual aging rate function, which is accumulated over different time periods.

[0089] S32. Extract the historical accumulated ultraviolet aging energy values ​​EUV of all Ganoderma lucidum extracts from the relational database. The 90th percentile of the historical accumulated ultraviolet aging energy values ​​EUV of all Ganoderma lucidum extracts, representing the 10% most severely exposed samples, belongs to the potential risk group and is set as the risk threshold Eth. A preliminary comparative assessment is performed between the real-time accumulated ultraviolet aging energy values ​​EUV and the risk threshold Eth to determine the risk of structural degeneration caused by ultraviolet radiation. The specific assessment content is as follows;

[0090] When the accumulated ultraviolet aging energy value EUV ≥ the risk threshold Eth, it indicates that the current Ganoderma lucidum extract is at risk of structural degeneration, and the spectral line structure perturbation analysis is initiated.

[0091] When the ultraviolet aging energy accumulation value EUV is less than the risk threshold Eth, it indicates that the current Ganoderma lucidum extract sample is in a normal state and the test is continued.

[0092] In this example, a method constructs a UV aging energy modeling and risk perception assessment mechanism to identify whether a Ganoderma lucidum extract enters a structural degeneration risk zone due to environmental stress. Specifically, three key environmental parameters (UV intensity Iuv(t) at time t, temperature T(t) at time t, and saturated vapor pressure Bh(t) at time t) are extracted from a relational database at a unified timestamp. Combined with a time integration method, a multi-factor nonlinear interactive modulation formula is used to calculate the UV aging energy accumulation value (EUV). This value comprehensively reflects the intensity of structural aging stimulation experienced by the sample due to UV, heat, and humidity within a specific time window. Parameters are personalized and modified using the strain sensitivity coefficient w, temperature adjustment coefficient a1, and vapor pressure suppression factor a2. The calculated UV aging energy accumulation value (EUV) is stored in the database, and a two-dimensional UV distribution heat map is constructed to intuitively identify local areas of high aging risk. Further statistical modeling based on historical datasets is performed, and the 90th percentile value of the historical UV aging energy accumulation value (EUV) of the entire Ganoderma lucidum extract is used as the risk threshold (Eth). This threshold reflects the upper limit of the most severely aged sample in a long-term environment. The ultraviolet aging energy accumulation value EUV of the current batch of samples is compared with the risk threshold Eth in real time. If it is higher than the threshold, it is regarded as a potential structural degeneration risk, and the spectral line perturbation analysis mechanism is automatically triggered; if it is lower than the threshold, the sample enters the routine process without intervention. Through the implementation of this step, the present invention realizes the early identification and quantitative early warning from "environmental energy stimulation" to "structural degeneration of functional components", which not only breaks the bottleneck of the traditional detection method's imperceptible impact on the environment, but also, by introducing a risk threshold comparison strategy, has the ability to identify environmental loads, focus on regional anomalies, and determine risks in batches. It improves the intelligent control accuracy and front-end data driving capabilities of the extraction process as a whole, and lays a solid data foundation and algorithmic support for the quality visualization, risk control automation, and standardization of Ganoderma lucidum Chinese medicinal materials.

[0093] Example 5

[0094] See also Figure 1 , specifically: S4 includes S41 and S42;

[0095] S41. After the initial comparative evaluation triggers the start of the spectral line structure perturbation analysis, feature extraction is performed based on the key spectral line curve to obtain spectral response data, and the spectral response data is normalized to eliminate the dimension effect of the spectral response data;

[0096] The spectral response data includes the main peak position drift △X of the i-th key spectral line i and the intensity ratio difference of the i-th key spectral line △Ai ;

[0097] The main peak position drift of the i-th key spectrum line △X i Based on the key spectral lines of each known target, the absorption peak under standard conditions, such as 230nm and 235nm for triterpenes and 265nm for adenosine, the main peak is located in the key spectral line curve collected in real time using a sliding window and first-order derivative method, and the main peak position of the current sample of each located key spectral line curve is calculated by difference with the absorption peak under standard conditions;

[0098] The difference in the ratio of the intensity of the i-th key spectral line △A i The actual absorbance is obtained by shifting △X at the main peak position of each key spectral line curve, allowing a ±2nm wavelength window, and then the actual absorbance is calculated by ratioing the absorbance of the standard Ganoderma lucidum extract.

[0099] S42, based on the main peak position drift △X of the i-th key spectrum line curve in the spectral response data i The difference between the intensity ratio of the i-th key spectral line curve and △A i , perform correlation calculation to output the spectral line structure disturbance function Sdrift, and analyze the degree of deviation between the spectral line and the normal structure type;

[0100] The line structure perturbation function Sdrift is calculated and outputted by the following algorithm formula;

[0101]

[0102] Where n represents the total number of key spectral line curves, w i and p i They represent the peak position perturbation weight of the i-th key spectral line curve and the absorbance intensity perturbation weight of the i-th key spectral line curve, respectively. Their specific values ​​are set by the user and are dimensionless.

[0103] w i ·|△X i | represents the main peak drift caused by structural denaturation. The larger the drift, the more chemical bond changes occur in the component structure, such as oxidation and polymerization. Weighting is to ensure that components with high structural criticality have a greater impact.

[0104] p i ·(△A i ) 2 It measures the change in relative intensity. The change in absorbance may come from the decrease in concentration, purity, the appearance of interference peaks, etc. The secondary difference indicates that both the upper and lower deviations are meaningful.

[0105] The significance of the formula is to provide a quantitative criterion for determining whether structural denaturation of Ganoderma lucidum extract occurs under high-risk conditions.

[0106] In this embodiment, the method further identifies whether the functional components in the extract have been damaged by the microstructure due to ultraviolet aging stress by constructing a mapping mechanism of "spectral response shift-structural denaturation". Specifically, in S41, when the system determines that the sample has entered a high-risk state based on the ultraviolet aging energy accumulation value EUV, the spectral line structure perturbation analysis module is automatically started, and the spectral data feature extraction is performed based on the standard absorption lines of known target components, such as triterpenes 230nm, 235nm, adenosine 265nm, etc. The main peak of the spectral data collected in real time is located by the sliding window and the first-order derivative method, and the main peak position drift △X of the i-th key spectral line of each key spectral line is extracted. i , and obtain the current absorbance within the window of ±2nm of the main peak position, compare it with the absorbance of the standard sample, and obtain the difference △A of the intensity ratio of the i-th key spectral line i , and finally form a standardized and normalized spectral response data vector. Taking the drift value and intensity difference of each key spectral line as input features, a spectral line structure perturbation function Sdrift is constructed. This function comprehensively evaluates the degree of deviation of the sample spectral line compared with the standard structure in two dimensions of position and intensity, and can quantify the risk of structural alienation induced by light, especially for structurally sensitive active ingredients such as triterpenes and polysaccharides. The perturbation score is used for further concentration correction and grade judgment, and is the core intermediate variable for the system to perceive structural denaturation. Through the implementation of this step, the present invention has significantly broken through the technical limitations of traditional ultraviolet detection technology of "only detecting concentration and not perceiving structural changes", and realized quantitative modeling and spectral line-level perception of microstructural changes. While ensuring the accuracy of concentration assessment, this mechanism greatly improves the ability to judge the activity stability of Ganoderma lucidum extract under high stress environment, provides a reliable basis for subsequent concentration correction and extraction scheduling, and enhances the depth of intelligent diagnosis and refined risk grading capabilities.

[0107] Example 6

[0108] See also Figure 1 , specifically: S5 includes S51, S52 and S53;

[0109] S51, based on the obtained spectral line structure perturbation function Sdrift and the ultraviolet aging energy accumulation value EUV, combined with the initial concentration C0 of the extract in the relational database, a comprehensive calculation is performed to output the functional effective concentration Ceff, and the functional attenuation caused by ultraviolet aging and structural perturbation of the Ganoderma lucidum extract is comprehensively analyzed;

[0110] The functional effective concentration Ceff is calculated and output by the following algorithm formula;

[0111] Ceff=C0·exp(-N·EUV·Sdrift);

[0112] Where exp represents the exponential function, and N represents the comprehensive degradation sensitivity coefficient, which is dimensionless and can be set by the user according to the overall sensitivity to light aging and structural drift.

[0113] exp(-N·EUV·Sdrift) represents the exponential decay structure, due to the decreasing engineering of the functional concentration of the Ganoderma lucidum extract, but when the environmental and structural perturbations increase, the product term increases and the exponential term decreases;

[0114] The purpose of using the product is to express that the environmental influence on aging and the molecular structure influence on denaturation have a multiplicative synergistic effect, that is, when the UV intensity is high and the structural damage is severe, the functional degradation is more severe.

[0115] S52. Setting a judgment interval threshold value based on the relative concentration of the initial concentration C0 of the extract, for example, 90% of the initial concentration C0 of the extract is set as a first judgment threshold value F1, and 70% of the initial concentration C0 of the extract is set as a second judgment threshold value F2, where 90% indicates almost no functional loss, and 70% indicates the lowest acceptable lower limit. The judgment interval threshold value includes the first judgment threshold value F1 and the second judgment threshold value F2. A secondary comparative evaluation is performed between the judgment interval threshold value and the functional effective concentration Ceff obtained in real time to determine the retention of the active ingredients of the current sample under the influence of ultraviolet interference and structural drift, and a response level classification is performed based on the secondary comparative evaluation results. The specific evaluation content is as follows;

[0116] When the functional effective concentration Ceff ≥ the first judgment threshold F1, it is classified as a first-level response;

[0117] When the second judgment threshold F2 ≤ functional effective concentration Ceff < first judgment threshold F1, the secondary response is divided;

[0118] When the functional effective concentration Ceff is less than the second judgment threshold F2, it is classified as a third-level response.

[0119] S53. Execute relevant response mechanisms based on the response levels determined by the secondary comparative evaluation;

[0120] When the response level is classified as level one, it means that the function of the Ganoderma lucidum extract is complete and normal flow does not require intervention;

[0121] When the response level is classified as a secondary response, it means that the Ganoderma lucidum extract is judged to be in a metastable state, and local shading is activated at this time;

[0122] Partial shading is achieved by opening a shading curtain at the overexposed points in the UV energy distribution thermal map, with the shading area of ​​the shading curtain being less than or equal to 25%, ensuring that production does not decrease;

[0123] When the response level is classified as level three response, it means that the degradation of Ganoderma lucidum extract is judged to be abnormal. At this time, the batch of Ganoderma lucidum extract in the area covered by the current collection point is abnormal and manual re-inspection is prompted.

[0124] In this embodiment, the method achieves accurate judgment and intelligent adjustment of the quality status of Ganoderma lucidum extract by constructing a functional effective concentration evaluation mechanism and a multi-level response control strategy. After comprehensively calling the ultraviolet aging energy accumulation value EUV obtained by the previous calculation, the spectral line structure perturbation function Sdrift, and the initial concentration of the extract C0 recorded in the database, the functional effective concentration Ceff is calculated and output through an exponential decay model. This model integrates the combined effects of ultraviolet aging and structural drift, and with the help of a dimensionless comprehensive sensitivity coefficient N and a multiplicative control method, it accurately simulates the dynamic decay trend of the actual functional concentration, overcoming the limitations of traditional methods that only rely on absorbance estimation, and truly reflects the degree of functional retention of the active ingredients in the sample. After setting the judgment interval threshold according to the relative ratio of the initial concentration C0 of the extract, the functional effective concentration Ceff is compared with the judgment interval threshold twice, thereby dividing the current sample into three states with three response levels, realizing hierarchical and classified management of the functional status of the sample. Then, the corresponding control mechanism is implemented according to the response level: under the first-level response, the samples flow normally; the second-level response automatically starts the local shading module, and performs regional shading intervention through the over-exposure points in the shading heat map, effectively reducing the risk of local aging and controlling the coverage area to no more than 25%; the third-level response triggers a manual re-inspection prompt and implements quality marking on the batch of extracts to ensure that abnormal samples do not flow into the downstream links.

[0125] Regarding the method in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment of the method, and will not be elaborated again here.

[0126] See also Figure 3 , the electronic device 1000 includes a memory 1010 and a processor 1020.

[0127] The processor 1020 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0128] The memory 1010 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. ROM may store static data or instructions required by the processor 1020 or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that retains stored instructions and data even when the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as the permanent storage device. In other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as dynamic random access memory. The system memory may store some or all instructions and data required by the processor during operation. In addition, the memory 1010 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and magnetic disks and / or optical disks may also be used. In some embodiments, the memory 1010 may include a readable and / or writable removable storage device, such as a compact disc (CD), a read-only digital versatile disc (e.g., DVD-ROM, double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (e.g., SD card, mini SD card, Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0129] The memory 1010 stores executable codes. When the executable codes are processed by the processor 1020 , the processor 1020 may execute part or all of the above-mentioned methods.

[0130] The scheme of the present application has been described in detail above with reference to the accompanying drawings. In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. Those skilled in the art should also be aware that the actions and modules involved in the description are not necessarily required for this application. In addition, it is understood that the steps in the method of the embodiment of the present application can be adjusted in sequence, merged and deleted according to actual needs, and the modules in the device of the embodiment of the present application can be merged, divided and deleted according to actual needs.

[0131] In addition, the method according to the present application may also be implemented as a computer program or a computer program product, which includes computer program code instructions for executing some or all of the steps in the above method of the present application.

[0132] Alternatively, the present application can also be implemented as a non-transitory machine-readable storage medium (or computer-readable storage medium, or machine-readable storage medium) on which executable code (or computer program, or computer instruction code) is stored. When the executable code (or computer program, or computer instruction code) is executed by a processor of an electronic device (or electronic device, server, etc.), the processor executes part or all of the steps of the above-mentioned method according to the present application.

[0133] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems and methods according to multiple embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or code, and the part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0134] While embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations may be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for detecting Ganoderma lucidum extract based on an intelligent sensing system, characterized in that: The following steps are involved: S1. In an illumination room, collecting ultraviolet environmental data of the Ganoderma lucidum extract to be tested in real time, wherein the ultraviolet environmental data includes ultraviolet intensity Iuv, temperature T, saturated vapor pressure Bh, initial concentration C0 of the extract, and key spectral line curve; S2. Preprocessing the ultraviolet environment data to obtain a standard digital ultraviolet data set, wherein the standard digital ultraviolet data set includes ultraviolet intensity Iuv(t) at time t, temperature T(t) at time t, saturated vapor pressure Bh(t) at time t, initial concentration C0 of the extract, and key spectral line curves; S3. Based on the standard digital ultraviolet data set, calculate the ultraviolet aging energy accumulation value EUV, and perform a preliminary comparison and evaluation between the ultraviolet aging energy accumulation value EUV and the risk threshold Eth. When the ultraviolet aging energy accumulation value EUV is greater than or equal to the risk threshold Eth, it indicates that the current Ganoderma lucidum extract is at risk of structural degeneration, initiate spectral line structure perturbation analysis, and execute S4. S4, extracting the spectral response data of the Ganoderma lucidum extract to be tested, and calculating the output spectral line structure disturbance function Sdrift, wherein the spectral response data includes the main peak position drift △X of the i-th key spectral line i and the intensity ratio difference of the i-th key spectral line △A i ; S5. Summarize and calculate the ultraviolet aging energy accumulation value EUV and the spectral line structure perturbation function Sdrift to obtain the functional effective concentration Ceff, and perform a secondary comparative evaluation of the functional effective concentration Ceff with the judgment interval threshold to determine the effective component retention of the current sample under the influence of ultraviolet interference and structural drift.

2. The method for detecting Ganoderma lucidum extract based on an intelligent sensing system according to claim 1, characterized in that: In S1, a sliding track is arranged on the roof of the illumination room in a direction intersecting the X-axis and the Y-axis, and the sliding track is provided with collection points in both the X-axis and the Y-axis directions; A sensor group is arranged around a platform for placing the ganoderma lucidum to be detected, wherein the sensor group includes an ultraviolet intensity probe, a temperature sensor, a vapor pressure sensor, a micro-spectral sensor and a micro-flow cuvette; The ultraviolet intensity probe is arranged at the collection point of the sliding track, and collects the ultraviolet intensity Iuv of the Ganoderma lucidum extract to be tested by moving; the temperature sensor and the vapor pressure sensor are used to respectively collect the temperature T and saturated vapor pressure Bh around the Ganoderma lucidum extract in real time; The micro-circulation colorimetric cell includes an integrated MEMS micro-spectral sensor, which is used to collect the key spectral line curves of the Ganoderma lucidum extract and infer the concentration of the active ingredients in the Ganoderma lucidum solution by combining the Lambert-Beer law to obtain the initial concentration C0 of the extract, wherein the key spectral line curves include the key spectral line curves of triterpenes, polysaccharides, nucleosides, phenolic derivatives and pigments.

3. The method for detecting Ganoderma lucidum extract based on an intelligent sensing system according to claim 1, characterized in that: In S2, the preprocessing includes timestamp alignment processing and normalization processing; The timestamp alignment process includes: defining a global time axis, setting a unified time interval Δt, and performing a unified timestamp step and interpolation padding on the original data using linear interpolation and sliding average methods to obtain the ultraviolet intensity Iuv(t) at time t, the temperature T(t) at time t, and the saturated vapor pressure Bh(t) at time t of the unified timestamp; The normalization processing includes: using Min-Max normalization to normalize the ultraviolet intensity Iuv(t) at time t, the temperature T(t) at time t, the saturated vapor pressure Bh(t) at time t, and the initial concentration C0 of the extract after the timestamp alignment, scaling all parameters to the interval range of [0,1] to eliminate the influence of unit dimension; and using the logarithmic compression method to extract the key spectral line curve, perform spectral skew data normalization processing, and compress the key spectral line curve to a unified range interval.

4. The method for detecting Ganoderma lucidum extract based on an intelligent sensing system according to claim 1, characterized in that: In S3, the calculation formula of the ultraviolet aging energy accumulation value EUV is: Where t1 represents the end time, t0 represents the start time, dt represents the time integral variable, ln represents the natural logarithm function, w represents the sensitivity coefficient of the Ganoderma lucidum strain, a1 represents the temperature correction coefficient, and a2 represents the vapor pressure correction coefficient. All the above parameters are dimensionless.

5. The method for detecting Ganoderma lucidum extract based on an intelligent sensing system according to claim 1, characterized in that: The preliminary comparative evaluation of the ultraviolet aging energy accumulation value EUV and the risk threshold Eth further includes: When the ultraviolet aging energy accumulation value EUV is less than the risk threshold Eth, it indicates that the current Ganoderma lucidum extract sample is in a normal state, and the test continues; The risk threshold Et is determined by calculation based on the accumulated ultraviolet aging energy EUV of all historical Ganoderma lucidum extract samples.

6. The method for detecting Ganoderma lucidum extract based on an intelligent sensing system according to claim 1, characterized in that: In S4, the calculation formula of the spectral line structure disturbance function Sdrift is: Where n represents the total number of key spectral line curves, w i and p i They represent the peak position perturbation weight of the i-th key spectral line curve and the absorption intensity perturbation weight of the i-th key spectral line curve, respectively. The above parameters are dimensionless. Among them, w i ·|△X i | represents the main peak shift caused by structural denaturation; p i ·(△A i ) 2 Represents a measure of relative strength changes.

7. The method for detecting Ganoderma lucidum extract based on an intelligent sensing system according to claim 1, characterized in that: In S5, the calculation formula of the functional effective concentration Ceff is: Ceff=C0·exp(-N·EUV·Sdrift); Wherein, exp represents an exponential function, and N represents a comprehensive degradation sensitivity coefficient, which is set according to the overall sensitivity to light aging and structural drift; exp(-N·EUV·Sdrift) represents the exponential decay structure, which is used to analyze the decrease in the functional concentration of Ganoderma lucidum extract.

8. The method for detecting Ganoderma lucidum extract based on an intelligent sensing system according to claim 1, characterized in that: In said S5, said judgment interval threshold comprises a first judgment threshold F1 and a second judgment threshold F2, said judgment interval threshold being set according to the relative concentration of the initial concentration C0 of the extract; The second comparative evaluation of the functional effective concentration Ceff and the judgment interval threshold comprises: When the functional effective concentration Ceff ≥ the first judgment threshold F1, it means that the function of the Ganoderma lucidum extract is completed, and this is classified as a first-level response. At the same time, the Ganoderma lucidum extract flows normally without intervention; When the second judgment threshold F2 ≤ functional effective concentration Ceff < first judgment threshold F1, it indicates that the Ganoderma lucidum extract is in a metastable state, and this is classified as a secondary response, and local shading is activated at the same time, by shading the overexposed points in the ultraviolet energy distribution heat map; When the functional effective concentration Ceff is less than the second judgment threshold F2, it indicates that the degradation of the Ganoderma lucidum extract is abnormal, indicating that the current batch of Ganoderma lucidum extract is abnormal, and this is divided into the third level response, and a manual re-inspection is prompted.

9. An electronic device, characterized in that: include: processor; as well as A memory having executable codes stored thereon, which, when executed by the processor, causes the processor to execute the method according to any one of claims 1 to 8.

10. A storage medium having executable code stored thereon, wherein when the executable code is executed by a processor of an electronic device, the processor is caused to execute the method according to any one of claims 1 to 8.

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