Online detection method and detection system for concentration of extract liquor

Abstract

The invention discloses an online detection method and system for the concentration of extract liquor, and belongs to the technical field of solution detection.The online detection method comprises the steps that a first signal output by detection equipment is received, and a second signal is generated; performing dynamic response evaluation on the detection equipment according to the second signal; constructing a sensitivity dynamic response model, and evaluating the sensitivity by analyzing the variation amplitude of an output signal of the detection equipment; the method is operated on all the detection devices to obtain the sensitivity threshold values of all the detection devices, and calibration and updating are carried out in the using process. In the implementation process of the technical scheme, the sensitivity threshold value of the detection equipment is subjected to numerical representation, the performance of the detection equipment is accurately evaluated, and it is ensured that the concentration change of the target substance can be reliably detected in the detection process of the extract liquor with different concentrations, so that the situation that the concentration of the detection equipment is not matched with the concentration of the extract liquor to be detected is prevented; and the detection efficiency and the accuracy of detection results are improved.

Description

Technical Field

[0001] The present application relates to the technical field of solution detection, and in particular to an online detection method and detection system for the concentration of an extract. Background Art

[0002] The extract is the separation liquid produced in the extraction process. Extraction refers to the use of different distribution ratios of substances in two immiscible solvents to achieve separation purposes. It is often used in the food industry, food processing, pharmaceutical industry and other fields.

[0003] In the subsequent processing steps of the extract, the concentration detection of the extract is an important processing step. For example, in the food industry, the content of specific chemical substances (such as additives, pesticide residues, heavy metals, etc.) in food is determined by detecting the concentration of the extract. In the prior art, there are many methods for detecting the concentration of the extract, such as chromatography, mass spectrometry, electrochemical detection, etc. In actual use, the appropriate method can be selected according to needs.

[0004] However, in the application of existing concentration detection methods, due to the different concentrations of different types of extracts, the sensitivity of the detection method needs to be considered when performing concentration detection. When the sample is complex or the concentration of the target substance is low, the sensitivity of the detection method is not enough to detect the low concentration of the target substance, resulting in failure to complete the detection normally. During the detection process, it is impossible to determine whether the selected detection method can meet the minimum sensitivity requirements of the corresponding extract. At the same time, when the extract contains multiple components, other components in the sample may have cross-reactions, resulting in false positives, errors, etc.

[0005] Therefore, it is necessary to provide an online detection method and detection system for the concentration of the extract to solve the above problems.

[0006] It should be noted that the above information disclosed in this background technology section is only for understanding the background technology of the present application concept, and therefore, it may contain information that does not constitute the prior art. Summary of the invention

[0007] Based on the above-mentioned problems existing in the prior art, the problem to be solved by the present application is: to provide an online detection method and detection system for the concentration of an extract, by numerically representing the sensitivity threshold of the detection equipment, accurately evaluating the performance of the detection equipment, and ensuring that the concentration changes of the target substance can be reliably detected during the detection process of extracts of different concentrations.

[0008] The technical solution adopted by the present application to solve the technical problem is: an online detection method for the concentration of an extract, the online detection method comprising:

[0009] The server receives a first signal outputted from the detection device, and pre-processes the first signal to generate a second signal, wherein the generated second signal includes a response time of the detection device and a transmission time of the first signal;

[0010] Performing a dynamic response evaluation on the detection device according to the second signal, and determining whether the detection device needs to be calibrated or maintained;

[0011] Construct a sensitivity dynamic response model, evaluate the sensitivity of the detection device by analyzing the change amplitude of the output signal of the detection device, and predict the sensitivity change of the detection device after assigning a timestamp to the model;

[0012] The aforementioned method is run on all detection devices to obtain the sensitivity thresholds of all detection devices, and calibration updates are performed during use.

[0013] During the implementation of the technical solution of the present application, the performance of the detection equipment is accurately evaluated by numerically representing the sensitivity threshold of the detection equipment, ensuring that the concentration changes of the target substance can be reliably detected during the detection of extracts of different concentrations, thereby preventing the concentration mismatch between the detection equipment and the extract to be detected, and improving the detection efficiency and the accuracy of the detection results.

[0014] Further, preprocessing the first signal and generating the second signal specifically includes the following steps:

[0015] Filtering the first signal, wherein the filtering method includes multi-channel filtering and adaptive filtering;

[0016] Acquire at least three consecutive measurement data under a fixed detection period, and acquire signal amplitudes of the consecutive measurement data;

[0017] The signal amplitude fluctuation analysis is performed on the acquired continuous measurement data, and the stability index of the detection equipment is calculated, and the stability index is used as the second signal.

[0018] Furthermore, the calculation method of the stability index is: firstly, the standard deviation of the continuous measurement data is calculated, and then the inverse of the calculated standard deviation is taken as the stability index of the detection equipment.

[0019] Furthermore, the dynamic response evaluation of the detection equipment specifically includes the following steps:

[0020] Compare the response time of the detection equipment with the preset normal working range to determine whether it exceeds the threshold;

[0021] analyzing the signal transmission time in the second signal to evaluate the stability of the signal transmission channel;

[0022] Combine the stability index and signal transmission time to comprehensively evaluate the overall performance of the detection equipment;

[0023] Develop a calibration and maintenance plan based on the overall performance evaluation results of the testing equipment.

[0024] Furthermore, the weighting method is used to numerically represent the overall performance evaluation of the testing equipment.

[0025] Furthermore, constructing the sensitivity dynamic response model further includes the following steps:

[0026] Collect measurement data of extracts of different concentrations, analyze the relationship between signal amplitude and concentration, and establish a calibration curve between signal amplitude and concentration;

[0027] According to the calibration curve, the amplitude variation of the signal at different concentrations is calculated to determine the sensitivity threshold of the detection equipment;

[0028] Construct a sensitivity dynamic response model with timestamps, combine the timestamps with the amplitude change of the signal, and predict the sensitivity change trend of the detection equipment;

[0029] Calibrate the detection equipment regularly and adjust the calibration cycle and parameters based on the prediction results of the sensitivity dynamic response model.

[0030] Furthermore, determining the sensitivity threshold of the detection device includes the following steps: establishing a calibration equation based on the calibration curve, and calculating the signal amplitude at different concentrations based on the calibration equation, and then determining the change amplitude of the signal assignment by calculating the relative change rate, and taking the minimum value of the obtained results as the sensitivity threshold of the detection device.

[0031] Furthermore, the sensitivity threshold is the minimum concentration change that can be reliably detected by the detection device.

[0032] An online detection system for extract concentration, the online detection system comprising:

[0033] A data receiving module, used for the server to receive a first signal outputted from a detection device, and pre-process the first signal to generate a second signal, wherein the generated second signal includes a response time of the detection device and a transmission time of the first signal;

[0034] A dynamic response evaluation module, used to evaluate the dynamic response of the detection device according to the second signal, and determine whether the detection device needs to be calibrated or maintained;

[0035] A model building module is used to build a sensitivity dynamic response model, evaluate the sensitivity of the detection device by analyzing the change amplitude of the output signal of the detection device, and predict the sensitivity change of the detection device after assigning a timestamp to the model;

[0036] The calibration update module is used to run the above method on all detection devices to obtain the sensitivity thresholds of all detection devices and perform calibration updates during use.

[0037] The beneficial effects of the present application are as follows: the present application provides an online detection method and detection system for the concentration of an extract, which accurately evaluates the performance of the detection equipment by numerically representing the sensitivity threshold of the detection equipment, thereby ensuring that the concentration changes of the target substance can be reliably detected during the detection process of extracts of different concentrations, thereby preventing the concentration mismatch between the detection equipment and the extract to be detected, and improving the detection efficiency and the accuracy of the detection results.

[0038] In addition to the above-described purposes, features and advantages, the present application also has other purposes, features and advantages. The present application will be further described in detail with reference to the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings in the specification, which constitute a part of the present application, are used to provide further understanding of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute improper limitations on the present application.

[0040] In the attached picture:

[0041] Figure 1 This is a schematic diagram of the overall process of an online detection method for extract concentration in this application;

[0042] Figure 2 This is a schematic diagram of the module structure of an online detection system for extract concentration in this application. DETAILED DESCRIPTION

[0043] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0044] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0045] Embodiment 1: The present application provides an online detection method for the concentration of an extract. The online detection method is applied to the concentration detection process of a solution. In this embodiment, the extract is taken as an example to detect the concentration of the extract, thereby obtaining the content of various substances therein, and the sensitivity requirement of the extract can be numerically reflected, thereby facilitating the selection of different detection methods. In the case of a low concentration of the analyte, accurate detection can also be achieved, such as Figure 1 As shown, the method comprises the following steps:

[0046] Step A: The server receives a first signal outputted from a detection device, and pre-processes the first signal to generate a second signal, wherein the generated second signal includes a response time of the detection device and a transmission time of the first signal;

[0047] Conducting concentration detection on the extract refers to detecting the content of various substances therein. Detection requires the use of detection equipment. In this embodiment, the detection equipment is various spectrometers, such as infrared spectrometer (IR), UV-Vis spectrophotometer, atomic absorption spectrometer, etc. These detection equipment can use the principle of interaction between substances and light of a specific wavelength to determine the content of components in the solution by measuring the absorption, scattering or emission of light. For details, reference can be made to the method of detecting the concentration of a solution by various spectrometers in the prior art, which will not be described in detail in this embodiment;

[0048] The server refers to a hardware module with functions such as data reception, data analysis, and data storage, such as a computer, an industrial computer, etc. The first signal is an analog signal output by the detection device, which carries the concentration information of each component in the extract. After receiving the signal, the server needs to pre-process it and generate a second signal, wherein the generated second signal contains the response time of the detection device, which refers to the time taken from the start of detection to the server receiving the first signal. The response time can reflect the response capability of the detection device;

[0049] It can be understood that there is a signal and data transmission channel between the detection device and the server, which can be wired or wireless, and the first signal will generate a transmission time after the transmission is completed. Although the transmission time is short, generally in the millisecond level, it will affect the sensitivity digitization process of the detection device, so it is also necessary to record the transmission time of the first signal in the second signal;

[0050] Preprocessing the first signal and generating the second signal specifically includes the following steps:

[0051] Step A1: filtering the first signal, wherein the filtering methods include multi-channel filtering and adaptive filtering; the purpose of filtering is to reduce noise and interference in the signal and improve the quality of the signal, wherein multi-channel filtering refers to dividing the signal into multiple frequency channels, each channel using a different filter to meet the signal processing requirements of different frequency components, and adaptive filtering automatically adjusts the filter parameters according to the statistical characteristics of the signal to achieve the best filtering effect, both methods can be applied to the filtering process of the first signal, and the specific use method can refer to the prior art, and will not be described in detail in this embodiment;

[0052] Step A2: obtaining at least three consecutive measurement data under a fixed detection period, and obtaining the signal amplitude of the consecutive measurement data;

[0053] A fixed detection cycle refers to a set period of time when the detection device performs continuous measurement. During this period, the detection device will perform repeated measurements to ensure the accuracy and stability of the measurement results. Acquiring the signal amplitude of continuous measurement data refers to extracting the signal strength information from the continuous measurement data within a fixed detection cycle. The signal amplitude is an important parameter reflecting the substance content in the extract. By analyzing the changes in the signal amplitude, the concentration of the extract can be accurately judged.

[0054] Step A3: performing signal amplitude fluctuation analysis on the acquired continuous measurement data, and calculating the stability index of the detection equipment, and using the stability index as the second signal;

[0055] The methods used for signal amplitude fluctuation analysis of continuous measurement data include statistical analysis, frequency domain analysis, etc. The purpose is to evaluate the stability performance of the detection equipment within a fixed detection period. Signal amplitude fluctuation analysis can reveal the change law of the signal in the time series, so as to determine whether the detection equipment is running stably;

[0056] The stability index is a quantitative index obtained by analyzing the signal amplitude fluctuation of continuous measurement data. It can reflect the repeatability and reliability of the detection equipment in the continuous measurement process. The calculation method of the stability index is: first calculate the standard deviation of the continuous measurement data, and then take the inverse of the calculated standard deviation as the stability index of the detection equipment. The smaller the standard deviation, the more stable the data fluctuation. In terms of numerical expression, the larger the stability index, the more stable the detection equipment.

[0057] For example, if the standard deviation of the continuous measurement data obtained within a period of time is 0.5, the stability index of the detection device is 2, and the detection device is relatively stable. If the standard deviation of the continuous measurement data obtained is 2.5, the stability index of the detection device is 0.4, and the stability of the detection device is poor.

[0058] Step B: evaluating the dynamic response of the detection device according to the second signal, and determining whether the detection device needs to be calibrated or maintained;

[0059] After the second signal is generated, the second signal includes the stability index of the detection device and the signal transmission time, and the detection device itself also has a response time, which reflects whether the detection device is within the normal working range and is combined with the second signal to perform a dynamic response evaluation on the detection device. Specifically, the dynamic response evaluation of the detection device includes the following steps:

[0060] Step B1: Compare the response time of the detection device with the preset normal working range to determine whether it exceeds the threshold;

[0061] The preset normal working range refers to the response time that the detection equipment should have under ideal conditions. This range can be set according to the performance parameters of the equipment and actual application requirements. If the response time of the detection equipment exceeds the preset normal working range, it means that the equipment may have a fault or performance degradation, and further inspection and maintenance are required;

[0062] Step B2: analyzing the signal transmission time in the second signal to evaluate the stability of the signal transmission channel;

[0063] Signal transmission time refers to the time taken from the detection device outputting the first signal to the server receiving the signal. Fluctuations in signal transmission time may indicate that there are unstable factors in the signal transmission channel, such as interference, signal attenuation, etc. By analyzing the changes in signal transmission time, the stability of the signal transmission channel can be evaluated, and corresponding measures can be taken to ensure the reliability of data transmission. Among them, the methods used to evaluate the stability of the signal transmission channel include delay analysis, bit error rate testing, etc., in order to ensure the integrity and accuracy of the signal during the transmission process. For details, please refer to the existing technology;

[0064] Step B3: Combine the stability index and signal transmission time to comprehensively evaluate the overall performance of the detection equipment;

[0065] Comprehensive evaluation of the overall performance of the testing equipment requires consideration of two aspects: stability index and signal transmission time. The stability index reflects the repeatability and reliability of the testing equipment during continuous measurement, while the signal transmission time reflects the stability of the signal transmission channel. By combining these two parameters, the overall performance of the testing equipment can be more comprehensively evaluated, thereby providing a basis for the calibration, maintenance or replacement of the equipment. Specifically, if the stability index is high and the signal transmission time is short, it indicates that the testing equipment is operating well and has high overall performance, and no immediate calibration or maintenance is required. On the contrary, if the stability index is low or the signal transmission time is long, or even abnormal fluctuations occur, it indicates that the testing equipment may have performance degradation or unstable signal transmission channels, and timely inspection and adjustment are required;

[0066] In addition, the overall performance evaluation of the detection equipment can also be numerically expressed using the weighting method. For example, the weights of the stability index and signal transmission time can be set to 0.6 and 0.4 respectively, and then a comprehensive performance index can be calculated based on the actual measurement values. This index can be used as an intuitive indicator for evaluating the overall performance of the detection equipment.

[0067] Step B4: Develop a calibration and maintenance plan based on the overall performance evaluation results of the testing equipment;

[0068] If the overall performance evaluation results of the testing equipment indicate that calibration or maintenance is required, a corresponding plan should be formulated. The calibration plan includes determining the calibration parameters, calibration methods and calibration cycles, etc., to ensure that the testing equipment can accurately measure the solution concentration. The maintenance plan includes replacing wearing parts, cleaning the detection head and sensors of the testing equipment, etc., to maintain the normal operation of the equipment. For equipment that needs to be replaced, select a suitable replacement model based on actual needs and ensure that the new equipment can meet the testing requirements.

[0069] Through the above steps, it can be ensured that the detection equipment has good performance and accuracy when performing online detection of the extract concentration, thereby providing reliable data support for the production process;

[0070] Step C: construct a sensitivity dynamic response model, and evaluate its sensitivity by analyzing the change amplitude of the output signal of the detection device, and after assigning a timestamp to the model, predict the sensitivity change of the detection device;

[0071] In the prior art, the sensitivity of the detection equipment is usually expressed by the signal-to-noise ratio, but the signal-to-noise ratio cannot fully reflect the sensitivity performance of the detection equipment in practical applications. Therefore, it is particularly important to build a sensitivity dynamic response model, which can evaluate the sensitivity of the detection equipment according to the change amplitude of the output signal of the detection equipment, and after assigning a timestamp, the sensitivity change of the detection equipment can be predicted, so as to discover potential problems in advance and take corresponding measures. Specifically, building a sensitivity dynamic response model further includes the following steps:

[0072] Step C1: Collect measurement data of extracts of different concentrations, analyze the relationship between signal amplitude and concentration, and establish a calibration curve between signal amplitude and concentration.

[0073] In order to construct a sensitivity dynamic response model, it is first necessary to collect a series of measurement data of extracts of different concentrations. By analyzing these data, a calibration curve between signal amplitude and concentration can be established. The establishment of the calibration curve helps to understand the quantitative relationship between signal amplitude and substance content, and provides a basis for subsequent sensitivity evaluation. The calibration curve is implemented using a fitting tool, which can quickly determine the functional relationship between signal amplitude and concentration. The fitting tool can be selected from MATLAB, GraphPad Prism, etc., and residual analysis is used for verification after fitting to ensure that the residuals between the fitted curve and the actual data points are randomly distributed and have no systematic deviations.

[0074] Step C2: Calculate the signal amplitude variation at different concentrations based on the calibration curve to determine the sensitivity threshold of the detection device;

[0075] After obtaining the calibration curve, the amplitude variation of the signal at different concentrations can be calculated; through these variation amplitudes, the sensitivity threshold of the detection device can be determined. The sensitivity threshold refers to the minimum concentration change that the detection device can reliably detect, and is an important parameter for evaluating the sensitivity of the detection device. Specifically, the determination of the sensitivity threshold needs to consider the needs in practical applications and the performance limitations of the detection device. Generally, the lower the sensitivity threshold, the higher the sensitivity of the detection device to concentration changes, and the more subtle the concentration differences can be detected.

[0076] Wherein, determining the sensitivity threshold of the detection device includes the following steps: establishing a calibration equation according to the calibration curve, and calculating the signal amplitude at different concentrations according to the calibration equation, and then determining the change amplitude of the signal assignment by calculating the relative change rate, and taking the minimum value of the obtained results as the sensitivity threshold of the detection device;

[0077] For example, when the calibration equation corresponding to the standard curve is a linear equation, the concentration is substituted as the independent variable to calculate the signal amplitude, and then for each pair of adjacent concentration values ​​n and n+1, the corresponding signal amplitudes s and s+1 are calculated, and then the relative rate of change or percentage change between them is calculated, and finally the minimum value among the obtained results is used as the sensitivity threshold of the detection device, indicating that the detection device cannot operate below this sensitivity;

[0078] Step C3: construct a sensitivity dynamic response model with a timestamp, combine the timestamp with the amplitude change of the signal, and predict the sensitivity change trend of the detection equipment;

[0079] In order to better evaluate the sensitivity of the detection equipment, it is necessary to build a sensitivity dynamic response model with a timestamp. This model combines the timestamp with the amplitude change of the signal. By analyzing historical data and real-time data, the sensitivity change trend of the detection equipment can be predicted. In this way, the change value of the sensitivity of the detection equipment can be discovered in time, and corresponding calibration or maintenance measures can be taken.

[0080] Step C4: Regularly calibrate the detection equipment and adjust the calibration cycle and parameters according to the prediction results of the sensitivity dynamic response model;

[0081] In order to ensure that the sensitivity of the detection equipment remains at the best state, regular calibration is required. According to the prediction results of the sensitivity dynamic response model, the calibration cycle and parameters can be adjusted to adapt to the actual operation of the detection equipment. In this way, the service life of the detection equipment can be effectively extended and the accuracy of the detection data can be improved;

[0082] Step D: Run the above method on all detection devices to obtain the sensitivity thresholds of all detection devices, and perform calibration updates during use.

[0083] Since different detection devices have different detection methods and detection principles, their sensitivity thresholds can be obtained respectively through the above steps. When selecting the detection method, the corresponding detection device can be selected according to the properties of the extract, historical data, etc., and the sensitivity threshold of the detection device is automatically updated during each use, because with the use of the equipment, the sensitivity of the detection equipment will also undergo slight changes, thereby affecting the accuracy of the test results.

[0084] Embodiment 2: The present application also proposes an online detection system for the concentration of an extract, which runs the detection method in Embodiment 1, and comprises:

[0085] A data receiving module, used for the server to receive a first signal outputted from a detection device, and pre-process the first signal to generate a second signal, wherein the generated second signal includes a response time of the detection device and a transmission time of the first signal;

[0086] A dynamic response evaluation module, used to evaluate the dynamic response of the detection device according to the second signal, and determine whether the detection device needs to be calibrated or maintained;

[0087] A model building module is used to build a sensitivity dynamic response model, evaluate the sensitivity of the detection device by analyzing the change amplitude of the output signal of the detection device, and predict the sensitivity change of the detection device after assigning a timestamp to the model;

[0088] The calibration update module is used to run the above method on all detection devices to obtain the sensitivity thresholds of all detection devices and perform calibration updates during use.

[0089] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An online detection method for extract concentration, characterized in that: The online detection method includes: The server receives a first signal outputted from the detection device, and pre-processes the first signal to generate a second signal, wherein the generated second signal includes a response time of the detection device and a transmission time of the first signal; Performing a dynamic response evaluation on the detection device according to the second signal, and determining whether the detection device needs to be calibrated or maintained; Construct a sensitivity dynamic response model, evaluate the sensitivity of the detection device by analyzing the change amplitude of the output signal of the detection device, and predict the sensitivity change of the detection device after assigning a timestamp to the model; The aforementioned method is run on all detection devices to obtain the sensitivity thresholds of all detection devices, and calibration updates are performed during use.

2. The method for online detection of extract concentration according to claim 1, characterized in that: Preprocessing the first signal and generating the second signal specifically includes the following steps: Filtering the first signal, wherein the filtering method includes multi-channel filtering and adaptive filtering; Acquire at least three consecutive measurement data under a fixed detection period, and acquire signal amplitudes of the consecutive measurement data; The signal amplitude fluctuation analysis is performed on the acquired continuous measurement data, and the stability index of the detection equipment is calculated, and the stability index is used as the second signal.

3. The method for online detection of extract concentration according to claim 2, characterized in that: The calculation method of the stability index is: first calculate the standard deviation of the continuous measurement data, and then take the inverse of the calculated standard deviation as the stability index of the detection equipment.

4. The method for online detection of extract concentration according to claim 1, characterized in that: The dynamic response evaluation of the detection equipment specifically includes the following steps: Compare the response time of the detection equipment with the preset normal working range to determine whether it exceeds the threshold; analyzing the signal transmission time in the second signal to evaluate the stability of the signal transmission channel; Combine the stability index and signal transmission time to comprehensively evaluate the overall performance of the detection equipment; Develop a calibration and maintenance plan based on the overall performance evaluation results of the testing equipment.

5. The method for online detection of extract concentration according to claim 4, characterized in that: The overall performance evaluation of the testing equipment is numerically expressed using the weighting method.

6. The method for online detection of extract concentration according to claim 1, characterized in that: Building the sensitivity dynamic response model further includes the following steps: Collect measurement data of extracts of different concentrations, analyze the relationship between signal amplitude and concentration, and establish a calibration curve between signal amplitude and concentration; According to the calibration curve, the amplitude variation of the signal at different concentrations is calculated to determine the sensitivity threshold of the detection equipment; Construct a sensitivity dynamic response model with timestamps, combine the timestamps with the amplitude change of the signal, and predict the sensitivity change trend of the detection equipment; Calibrate the detection equipment regularly and adjust the calibration cycle and parameters based on the prediction results of the sensitivity dynamic response model.

7. The method for online detection of extract concentration according to claim 6, characterized in that: Determining the sensitivity threshold of the detection device includes the following steps: establishing a calibration equation based on the calibration curve, and calculating the signal amplitude at different concentrations based on the calibration equation, and then determining the change amplitude of the signal assignment by calculating the relative change rate, and taking the minimum value of the obtained results as the sensitivity threshold of the detection device.

8. The method for online detection of extract concentration according to claim 7, characterized in that: The sensitivity threshold is the minimum concentration change that can be reliably detected by the detection device.

9. An online detection system for extract concentration, characterized in that: The online detection system includes: A data receiving module, used for the server to receive a first signal outputted from a detection device, and pre-process the first signal to generate a second signal, wherein the generated second signal includes a response time of the detection device and a transmission time of the first signal; A dynamic response evaluation module, used to evaluate the dynamic response of the detection device according to the second signal, and determine whether the detection device needs to be calibrated or maintained; A model building module is used to build a sensitivity dynamic response model, and evaluate the sensitivity of the detection device by analyzing the change amplitude of the output signal of the detection device, and after assigning a timestamp to the model, predict the sensitivity change of the detection device; The calibration update module is used to run the above process on all detection devices, obtain the sensitivity thresholds of all detection devices, and perform calibration updates during use.

10. The online detection system for extract concentration according to claim 9, characterized in that: Used to implement the online detection method for extract concentration as described in any one of claims 1 to 8.

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