Measurement system for co 2 optical fiber sensor, and co 2 optical fiber sensor

Abstract

The present invention relates to the technical field of gas measurement. Disclosed are a measurement system for a CO2 optical fiber sensor, and a CO2 optical fiber sensor. The measurement system comprises: a basic parameter collection module, which is configured to acquire a basic operating parameter and calculate an operating reliability coefficient; a measurement mode determination module, which determines, on the basis of the operating reliability coefficient, whether to enter a CO2 measurement mode; a measurement data collection module, which is configured to acquire comprehensive measurement data when in the CO2 measurement mode; a model prediction and identification module, which predicts a CO2 concentration value on the basis of the comprehensive measurement data, and identifies a CO2 measurement state; and a measurement data display module, which classifies the comprehensive measurement data into normal data and abnormal data on the basis of the CO2 measurement state, and displays the data in order. In the present invention, abnormal operating states with poor measurement accuracy can be filtered out to avoid a decrease in CO2 concentration measurement accuracy caused by a poor operating state of the optical fiber sensor, and the CO2 concentration value in a sample to be tested can be accurately predicted, so that the negative effects caused by multi-dimensional factors such as temperature and pressure in said sample can be effectively avoided, thereby improving the CO2 concentration measurement accuracy of the optical fiber sensor to the greatest possible extent, and reducing the probability of errors in CO2 concentration measurement.

Description

A detection system for a CO2 fiber optic sensor and a CO2 fiber optic sensor. Technical Field

[0001] This invention relates to the field of gas detection technology, specifically to a detection system and a CO2 fiber optic sensor. Background Technology

[0002] Carbon dioxide sensors are machines used to detect carbon dioxide concentration. Common types of carbon dioxide detection sensors in water include ion-selective electrode sensors using electrochemical methods and sensors made using the principle of NDIR (non-dispersive infrared). However, in liquids with narrow crevices, fiber optic sensors are needed to be inserted into the liquid to detect CO2 concentration data, thereby meeting specific detection requirements.

[0003] Reference patent application CN115639167A discloses an air carbon dioxide concentration detection system, including a detection platform and several identical detection modules. Each detection module is used to acquire the carbon dioxide concentration value of a test point and send it to the detection platform. Each detection module includes an infrared light source, a chopper, a single-channel air chamber, a detector, and a processor. The infrared light source emits infrared light waves, and the chopper chops the infrared light waves to obtain a first wavelength infrared light and a second wavelength infrared light. The first wavelength infrared light has a wavelength that carbon dioxide absorbs, and the second wavelength infrared light has a wavelength that carbon dioxide does not absorb. Air is introduced into the single-channel air chamber. The first wavelength infrared light is absorbed by the detector after passing through the single-channel air chamber, and the detector obtains a first voltage signal. The second wavelength infrared light is absorbed by the detector after passing through the single-channel air chamber, and the detector obtains a second voltage signal. The processor obtains the carbon dioxide concentration value of the test point based on the first and second voltage signals and sends it to the detection platform. This system uses dual-wavelength infrared light to reduce interference from other factors, thereby improving the concentration detection accuracy.

[0004] Existing CO2 fiber optic sensors, when detecting CO2 concentration in narrow gap terrain, obtain CO2 concentration data by removing a liquid sample and directly inserting the sensor's probe into the liquid sample. However, the environmental parameters surrounding the liquid sample change randomly during removal, causing variations in the CO2 concentration data and reducing detection accuracy. Furthermore, the sensor's probe wears down over time, leading to decreased reliability. These factors combined result in low accuracy and an inability to accurately and efficiently detect CO2 concentration in narrow gap terrain.

[0005] In view of this, the present invention proposes a detection system for a CO2 fiber optic sensor and a CO2 fiber optic sensor to solve the above problems. Summary of the Invention

[0006] Purpose of the invention: To solve the above-mentioned technical problems, the present invention provides a detection system for a CO2 fiber optic sensor and a CO2 fiber optic sensor.

[0007] Technical solution: The present invention provides a detection system for a CO2 fiber optic sensor, comprising:

[0008] The basic parameter acquisition module is used to acquire the basic operating parameters of the fiber optic sensor in the startup state. The basic operating parameters include the delay response ratio and the trigger interval value, and calculate the operating reliability coefficient of the fiber optic sensor.

[0009] The detection mode determination module is used to compare the operating reliability coefficient with the preset operating reliability threshold to determine whether to enter the CO2 detection mode.

[0010] The detection data acquisition module is used to acquire comprehensive detection data from the fiber optic sensor in CO2 detection mode. The comprehensive detection data includes signal strength value, light intensity reduction value, ambient temperature compensation value, and local pressure value.

[0011] The model prediction and recognition module is used to input the comprehensive detection data into a pre-trained machine learning model to predict the CO2 concentration value and identify the CO2 detection status, which includes warning status and non-warning status.

[0012] The detection data display module is used to divide the comprehensive detection data into normal data and abnormal data according to the CO2 detection status, and to control the orderly display of the predicted CO2 concentration value, normal data and abnormal data.

[0013] Furthermore, methods for obtaining the percentage of delayed responses include:

[0014] The database is used to retrieve all data logs within a preset response period and the processing status of each data log is identified.

[0015] Record the data logs with the processed status as valid logs, and mark all the detection data in the valid logs one by one to obtain i detection data;

[0016] By querying the timestamps one by one the duration taken from the start of receiving i detection data to the end of storage, we obtain i response durations. The response durations that exceed the standard duration threshold are recorded as delay durations, thus obtaining p delay durations.

[0017] After summing up the p delay durations, compare them with the sum of the i response durations to obtain the delay response ratio.

[0018] The expression for the percentage of delayed response is:

[0019] ;

[0020] In the formula, This represents the percentage of delayed responses. For the a-th delay duration, This is the duration of the b-th response.

[0021] Furthermore, the methods for obtaining the trigger interval value include:

[0022] The system retrieves all access events that occurred within a preset response period of the security defense system and marks the trigger status of each access event.

[0023] Record the access event with the triggered status as the target event, obtain s target events, and query the time when the defense mechanism was first triggered by each of the s target events by timestamp, to obtain s trigger times;

[0024] The duration between the s-th trigger time and the (s+1)-th trigger time is recorded as the trigger interval duration. s-1 trigger interval durations are obtained, and the average of the s-1 trigger interval durations is calculated to obtain the trigger interval value.

[0025] The expression for the trigger interval value is:

[0026] ;

[0027] In the formula, For the trigger interval value, The duration of each of the c trigger intervals;

[0028] The expression for the operational reliability coefficient is:

[0029] ;

[0030] In the formula, The operational reliability coefficients are γ1 and γ2, which are proportional coefficients greater than 0. This represents the percentage of delayed responses.

[0031] Furthermore, the methods for determining whether to enter CO2 detection mode include:

[0032] operational reliability coefficient Compared with the preset operational reliability threshold Compare;

[0033] when Greater than or equal to When this occurs, it is determined that the CO2 detection mode has been entered;

[0034] when Less than If the condition is not met, the system will not enter CO2 detection mode.

[0035] Furthermore, methods for obtaining the light intensity reduction value include:

[0036] At time T1, detection fluorescence is emitted to the sample to be tested through a light emission source, and the signal intensity of the detection fluorescence is recorded to obtain the initial intensity;

[0037] After time T1, the signal intensity of the detected fluorescence is recorded in real time and recorded as the real-time intensity. The moment when the real-time intensity first becomes less than the initial intensity is recorded as the start time.

[0038] Starting from the initial time and using the preset reduction time as the interval standard, mark x reduction time points, and record the signal intensity of the detected fluorescence at each of the x reduction time points to obtain x detection intensity values.

[0039] The x-th detection intensity value is subtracted from the x+1-th detection intensity value to obtain x-1 sub-reduction values;

[0040] The expression for the sub-reduction value is:

[0041] ;

[0042] In the formula, For the (x-1)th sub-reduction value, For the x-th detection intensity value, This is the (x+1)th detection intensity value;

[0043] The light intensity reduction value is obtained by summing the x-1 sub-reduction values ​​and averaging them.

[0044] The expression for the light intensity reduction value is:

[0045] ;

[0046] In the formula, This is the light intensity reduction value. This is the d-th sub-reduction value.

[0047] Furthermore, methods for obtaining ambient temperature compensation values ​​include:

[0048] At x reduction time points, the temperature inside the sample under test is detected in real time by a temperature sensor to obtain x real-time temperature values. Real-time temperature values ​​that are greater than the preset calibration temperature value are recorded as excess temperature values, and q excess temperature values ​​are obtained.

[0049] The q excess temperature values ​​are successively subtracted from the preset calibration temperature value to obtain q sub-compensation values. The q sub-compensation values ​​are then summed to obtain the ambient temperature compensation value.

[0050] The expression for the ambient temperature compensation value is:

[0051] ;

[0052] In the formula, This is the ambient temperature compensation value. For the e-th excess temperature value, This is the preset calibration temperature value.

[0053] Furthermore, training methods for machine learning models include:

[0054] Multiple sets of comprehensive detection data and corresponding CO2 concentration values ​​were collected in advance;

[0055] The comprehensive detection data is converted into a set of corresponding feature vectors. The feature vectors are used as input to the machine learning model. The CO2 concentration values ​​are converted into labels corresponding to the comprehensive detection data. The CO2 concentration values ​​corresponding to each set of comprehensive detection data are used as outputs of the machine learning model. The prediction target is the CO2 concentration value. The training target is to minimize the sum of prediction errors of all training data. The machine learning model is trained until the sum of prediction errors converges and training stops.

[0056] Methods for identifying warning and non-warning states include:

[0057] Compare the predicted CO2 concentration value with the standard CO2 concentration value;

[0058] When the predicted CO2 concentration is greater than or equal to the standard CO2 concentration, an early warning status is identified.

[0059] When the predicted CO2 concentration is lower than the standard CO2 concentration, a non-warning state is identified.

[0060] Furthermore, methods for distinguishing between regular and outlier data include:

[0061] When the CO2 detection status is non-alarm status, the signal strength value, light intensity reduction value, ambient temperature compensation value, and local pressure value are all classified as routine data;

[0062] When the CO2 detection status is in the warning state, the signal strength value is compared with the strength safety value. When the signal strength value is greater than the strength safety value, the signal strength value is classified as abnormal data. When the signal strength value is less than or equal to the strength safety value, the signal strength value is classified as normal data.

[0063] The light intensity reduction value is compared with the reduction safety value. When the light intensity reduction value is greater than the reduction safety value, the light intensity reduction value is classified as abnormal data. When the light intensity reduction value is less than or equal to the reduction safety value, the light intensity reduction value is classified as normal data.

[0064] The ambient temperature compensation value is compared with the compensation safety value. When the ambient temperature compensation value is greater than the compensation safety value, the ambient temperature compensation value is classified as abnormal data. When the ambient temperature compensation value is less than or equal to the compensation safety value, the ambient temperature compensation value is classified as normal data.

[0065] The local pressure value is compared with the pressure safety value. When the local pressure value is greater than the pressure safety value, the local pressure value is classified as abnormal data. When the local pressure value is less than or equal to the pressure safety value, the local pressure value is classified as normal data.

[0066] Further, methods for controlling the orderly display include:

[0067] When the comprehensive test data is divided into regular data, a first display information sheet is constructed, and the first regular position and the second regular position are marked in the first display information sheet respectively.

[0068] The predicted CO2 concentration value and conventional data are imported into the first conventional position and the second conventional position respectively to generate a conventional display information sheet, and the conventional display information sheet is sent to the display module for orderly display.

[0069] When the comprehensive detection data is divided into normal data and abnormal data, a second display information sheet is constructed, and the first abnormal position, the second abnormal position and the third abnormal position are marked in the second display information sheet respectively.

[0070] The predicted CO2 concentration value, normal data, and abnormal data are imported into the first abnormal position, the second abnormal position, and the third abnormal position, respectively, to generate an abnormality display information sheet. The abnormality display information sheet is then sent to the display module for orderly display.

[0071] A CO2 fiber optic sensor is used in a CO2 fiber optic sensor detection system. The CO2 fiber optic sensor includes a display screen, a sensor body, a main control module, an optical fiber, and an optical fiber probe. The main control module consists of a basic parameter acquisition module, a detection mode determination module, a detection data acquisition module, a model prediction determination module, and a detection data display module in the CO2 fiber optic sensor detection system.

[0072] Beneficial Effects: Compared with existing technologies, the CO2 fiber optic sensor detection system and CO2 fiber optic sensor of this invention have the following technical effects and advantages: In the startup state, this invention acquires the basic operating parameters of the fiber optic sensor and calculates its operational reliability coefficient. The operational reliability coefficient is compared with a preset operational reliability threshold to determine whether to enter CO2 detection mode. In CO2 detection mode, comprehensive detection data from the fiber optic sensor is acquired and input into a pre-trained machine learning model to predict the CO2 concentration value and identify the CO2 detection status. Based on the CO2 detection status, the comprehensive detection data is divided into normal data and abnormal data, and the predicted CO2 concentration value, normal data, and abnormal data are controlled to... The introduction demonstrates that, compared to existing technologies, by acquiring basic operating parameters and calculating the operational reliability coefficient, the operational reliability of fiber optic sensors can be effectively identified, and abnormal operating states with poor detection accuracy can be filtered out. This provides an efficient and reliable foundation for subsequent CO2 concentration detection, avoiding the phenomenon of decreased CO2 concentration detection accuracy due to poor fiber optic sensor operation. At the same time, by collecting diverse and multi-dimensional comprehensive detection data and combining it with machine learning models, the CO2 concentration value in the sample can be accurately predicted. This effectively avoids the negative impact of multi-dimensional factors such as temperature and pressure in the sample, thereby maximizing the accuracy of CO2 concentration detection by the fiber optic sensor and reducing the probability of CO2 concentration detection errors. Attached Figure Description

[0073] Figure 1 is a schematic diagram of a CO2 fiber optic sensor detection system provided in Embodiment 1 of the present invention;

[0074] Figure 2 is a schematic diagram of the structure of a CO2 fiber optic sensor provided in Embodiment 2 of the present invention. Embodiments of the present invention

[0075] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] Example 1: Referring to Figure 1, the CO2 fiber optic sensor detection system described in this example includes:

[0077] The basic parameter acquisition module, when started, acquires the basic operating parameters of the fiber optic sensor and calculates the operating reliability coefficient of the fiber optic sensor.

[0078] The startup state refers to the state in which the fiber optic sensor is powered on and can randomly perform CO2 detection, thereby ensuring that the fiber optic sensor is in a fully ready operating state before performing CO2 detection, and providing a good preparatory foundation for subsequent CO2 detection.

[0079] When fiber optic sensors are used for a long time or subjected to interference factors such as external impact, their detection performance will degrade, resulting in a decrease in CO2 detection accuracy. In order to ensure that fiber optic sensors can maintain high detection accuracy when performing CO2 detection, it is necessary to collect the basic operating parameters that affect the reliable operation of the fiber optic sensor, and calculate the reliability of the fiber optic sensor based on the collected basic operating parameters, thereby representing the detection accuracy of the fiber optic sensor.

[0080] Basic operating parameters include the percentage of delayed response and the trigger interval.

[0081] The delay response ratio refers to the ratio between the time that the fiber optic sensor exceeds the standard response time when processing data in the startup state and the total time. It can be used to represent the performance of the fiber optic sensor in processing data. When the delay response ratio is larger, it means that the ratio between the time that exceeds the standard response time when the fiber optic sensor processes data in the startup state and the total time is larger. In this case, the fiber optic sensor has a lower operational reliability and a smaller operational reliability coefficient.

[0082] Methods for obtaining the percentage of delayed response include:

[0083] The system retrieves all data logs from a pre-defined response period from the database and identifies the processing status of each log. The pre-defined response period is the maximum duration of data logs that could negatively impact the reliability of the fiber optic sensor, thus limiting the data log collection time and ensuring a reasonable and sufficient quantity of collected data logs, avoiding both excessive and insufficient data logs. The processing status indicates whether all data in the logs has been processed and serves as the basis for determining whether a log is valid. The processing status includes processed and unprocessed.

[0084] Record the data logs with the processed status as valid logs, and mark all the detection data in the valid logs one by one to obtain i detection data;

[0085] By querying the timestamps of each i detection data point from the start of reception to the end of storage, i response times are obtained. Response times exceeding the standard time threshold are recorded as delay times, resulting in p delay times. The standard time threshold refers to the maximum time for processing detection data under normal operating conditions of the fiber optic sensor, and thus serves as the data basis for identifying delay times.

[0086] The sum of p delay durations is compared with the sum of i response durations to obtain the delay response ratio.

[0087] The expression for the percentage of delayed response is:

[0088] ;

[0089] In the formula, This represents the percentage of delayed responses. For the a-th delay duration, This is the duration of the b-th response.

[0090] The trigger interval value refers to the duration during which the security defense system triggers the defense mechanism when the fiber optic sensor is in the startup state. It can be used to represent the security performance of the data processed by the fiber optic sensor. When the trigger interval value is larger, it means that the duration during which the security defense system triggers the defense mechanism when the fiber optic sensor is in the startup state is larger, and the fiber optic sensor has a higher degree of operational reliability and a larger operational reliability coefficient.

[0091] Methods for obtaining the trigger interval value include:

[0092] The system retrieves all access events from the security defense system within a preset response period and marks the trigger status of each event. The trigger status indicates whether the access event has triggered the security defense mechanism. The trigger status includes triggered and not triggered, thus accurately identifying each access event.

[0093] Record the access event with the triggered status as the target event, obtain s target events, and query the time when the defense mechanism was first triggered by each of the s target events by timestamp, to obtain s trigger times;

[0094] The duration between the s-th trigger time and the (s+1)-th trigger time is recorded as the trigger interval duration. s-1 trigger interval durations are obtained, and the average of the s-1 trigger interval durations is calculated to obtain the trigger interval value.

[0095] The expression for the trigger interval value is:

[0096] ;

[0097] In the formula, For the trigger interval value, The duration of each of the c trigger intervals is [number].

[0098] Once the delay response ratio and trigger interval values ​​are obtained, the operational reliability coefficient can be calculated based on these values, allowing the operational reliability coefficient to represent the reliability of the CO2 detection accuracy of the fiber optic sensor.

[0099] The expression for the operational reliability coefficient is:

[0100] ;

[0101] In the formula, The operational reliability coefficients are γ1 and γ2, which are proportional coefficients greater than 0. This represents the percentage of delayed responses.

[0102] In this case, γ1+γ2=1. The settings of γ1 and γ2 are to balance the proportion of the delay response ratio and the trigger interval in the operational reliability coefficient, so that when the delay response ratio and the trigger interval change, the corresponding magnitude of the operational reliability coefficient changes, thereby improving the accuracy of the operational reliability coefficient calculation.

[0103] The detection mode determination module compares the operating reliability coefficient with the preset operating reliability threshold to determine whether to enter the CO2 detection mode.

[0104] Once the operational reliability coefficient of the fiber optic sensor is calculated, the actual detection performance and accuracy of the fiber optic sensor can be compared based on the magnitude of the operational reliability coefficient. Based on the comparison results, it can be determined whether the fiber optic sensor meets the subsequent CO2 detection requirements. Only when the subsequent CO2 detection requirements are met can the CO2 detection mode be entered.

[0105] The methods for determining whether to enter CO2 detection mode include:

[0106] operational reliability coefficient Compared with the preset operational reliability threshold The preset operational reliability threshold refers to the minimum operational reliability coefficient that can meet the subsequent CO2 detection requirements of the fiber optic sensor. It limits the minimum operational reliability coefficient when entering the CO2 detection mode, ensuring the accuracy of the determination of whether to enter the CO2 detection mode. The preset operational reliability threshold is obtained by collecting a large number of minimum operational reliability coefficients from historical CO2 detection modes and then averaging them.

[0107] when Greater than or equal to When the fiber optic sensor's reliability coefficient is greater than or equal to the preset reliability threshold, it indicates that the fiber optic sensor can meet the subsequent CO2 detection requirements, and thus it is determined to enter the CO2 detection mode.

[0108] when Less than If the operating reliability coefficient of the fiber optic sensor is less than the preset operating reliability threshold, it means that the fiber optic sensor cannot meet the subsequent CO2 detection requirements, and therefore it is determined not to enter the CO2 detection mode.

[0109] It should be noted that when the system is in CO2 detection mode, the fiber optic sensor can be directly used to detect CO2 concentration, ensuring high accuracy and reliability of the detected CO2 data. Conversely, if the system is not in CO2 detection mode, it indicates that the fiber optic sensor's detection accuracy and reliability are low and cannot meet subsequent CO2 detection requirements. In this case, it is necessary to repeatedly collect basic operating parameters of the fiber optic sensor and calculate and compare the operating reliability coefficient until the subsequent CO2 detection requirements are met.

[0110] The detection data acquisition module acquires comprehensive detection data from the fiber optic sensor in CO2 detection mode. The comprehensive detection data includes signal strength value, light intensity reduction value, ambient temperature compensation value, and local pressure value.

[0111] When the CO2 detection mode is entered, the fiber optic probe on the fiber optic sensor can be inserted into the sample of the liquid or object to be tested. This allows the detection component on the fiber optic probe to collect comprehensive CO2 detection data of the sample to be tested, which will serve as the basis for subsequent calculation of CO2 concentration values. It can also provide a comprehensive representation of various factors such as the sample itself and the environment in which the sample is located.

[0112] The comprehensive test data includes signal strength value, light intensity reduction value, ambient temperature compensation value, and local pressure value;

[0113] The signal strength value refers to the signal intensity of the fluorescence signal when the fiber optic probe of the fiber optic sensor is inserted into the sample to detect CO2. It represents the intensity of the fluorescence signal emitted by the fiber optic sensor and provides a baseline value for the subsequent CO2 concentration detection result. The signal strength value is obtained by querying the signal strength database within the fiber optic sensor.

[0114] The light intensity reduction value refers to the reduction in the intensity of the fluorescence signal emitted by the fiber optic probe of the fiber optic sensor after being absorbed by CO2 gas. It can be used to represent the magnitude of the change in fluorescence signal intensity. The larger the light intensity reduction value, the higher the concentration of CO2 gas in the sample to be tested, the more fluorescence energy the CO2 gas absorbs, and the higher the CO2 concentration value.

[0115] Methods for obtaining light intensity reduction values ​​include:

[0116] At time T1, detection fluorescence is emitted to the sample to be tested through a light emission source, and the signal intensity of the detection fluorescence is recorded to obtain the initial intensity;

[0117] After time T1, the signal intensity of the detected fluorescence is recorded in real time and recorded as the real-time intensity. The moment when the real-time intensity first becomes less than the initial intensity is recorded as the start time.

[0118] Starting from the initial time and using a preset reduction interval as the standard, mark... The reduction time points were recorded one by one, and the fluorescence detection was recorded at each time point. The signal strength at each reduced time point is obtained. Each detection strength value; the preset reduction time refers to the minimum time span between two adjacent reduction time points, thereby ensuring that the signal strength at two adjacent reduction time points has enough time to change, and ensuring that the signal strength at each reduction time point is independent of each other;

[0119] The x-th detection intensity value is subtracted from the x+1-th detection intensity value to obtain x-1 sub-reduction values;

[0120] The expression for the sub-reduction value is:

[0121] ;

[0122] In the formula, For the (x-1)th sub-reduction value, For the x-th detection intensity value, This is the (x+1)th detection intensity value;

[0123] The light intensity reduction value is obtained by summing the x-1 sub-reduction values ​​and averaging them.

[0124] The expression for the light intensity reduction value is:

[0125] ;

[0126] In the formula, This is the light intensity reduction value. This is the d-th sub-reduction value.

[0127] The ambient temperature compensation value refers to the extent to which the temperature inside the sample being tested, where the fiber optic probe of the fiber optic sensor is located, exceeds the calibrated temperature. It can be used to represent the detection temperature of the sample. The larger the ambient temperature compensation value, the greater the extent to which the temperature inside the sample exceeds the calibrated temperature. At this time, the higher the concentration of CO2 gas, the higher the CO2 concentration value.

[0128] Methods for obtaining ambient temperature compensation values ​​include:

[0129] At x reduction time points, the temperature inside the sample under test is detected in real time by a temperature sensor to obtain x real-time temperature values. Real-time temperature values ​​that are greater than the preset calibration temperature value are recorded as excess temperature values, resulting in q excess temperature values. The preset calibration temperature value refers to the maximum value of the temperature value preset by the fiber optic sensor under normal conditions, and is used as the data basis for subsequent comparison with the real-time temperature values. The preset calibration temperature value is obtained by averaging the minimum values ​​of a large number of real-time temperature values ​​that were identified as excess temperature values ​​in history.

[0130] The q excess temperature values ​​are successively subtracted from the preset calibration temperature value to obtain q sub-compensation values. The q sub-compensation values ​​are then summed to obtain the ambient temperature compensation value.

[0131] The expression for the ambient temperature compensation value is:

[0132] ;

[0133] In the formula, This is the ambient temperature compensation value. For the first One excess temperature value, This is the preset calibration temperature value.

[0134] Local pressure value refers to the air pressure intensity at a local location within the sample being tested, where the fiber optic probe of the fiber optic sensor is located. It can be used to represent the air pressure intensity of the sample being tested. When the local pressure value is higher, it indicates that the local air pressure intensity within the sample being tested is higher, and at this time, the concentration of CO2 gas is higher, and the CO2 concentration value is higher. The local pressure value is obtained by monitoring the air pressure sensor built into the fiber optic sensor.

[0135] The model prediction and recognition module inputs the comprehensive detection data into a pre-trained machine learning model to predict the CO2 concentration value and identify the CO2 detection status, which includes warning status and non-warning status.

[0136] Once the signal strength value, light intensity reduction value, ambient temperature compensation value, and local pressure value are obtained, the CO2 concentration value can be predicted based on these values. This allows the fiber optic sensor to accurately analyze and predict the CO2 concentration value of the sample under test. To obtain accurate prediction results for CO2 concentration, it is necessary to use a large amount of historical signal strength values, light intensity reduction values, ambient temperature compensation values, local pressure values, and corresponding CO2 concentration values ​​to train a machine learning model that can predict CO2 concentration based on comprehensive detection data, thereby meeting subsequent prediction needs.

[0137] The CO2 concentration value is a numerical representation of the CO2 concentration corresponding to the comprehensive detection data obtained when the fiber optic sensor detects the sample to be tested, and is used as the output of the machine learning model. It is obtained by the calculation module built into the fiber optic sensor that integrates a carbon dioxide fitting algorithm.

[0138] Training methods for machine learning models include:

[0139] Multiple sets of comprehensive detection data and corresponding CO2 concentration values ​​were collected in advance;

[0140] The comprehensive detection data is converted into a set of corresponding feature vectors. The feature vectors are used as input to the machine learning model. The CO2 concentration values ​​are converted into labels corresponding to the comprehensive detection data. The CO2 concentration values ​​corresponding to each set of comprehensive detection data are used as outputs of the machine learning model. The prediction target is the CO2 concentration value. The training objective is to minimize the sum of prediction errors of all training data. The machine learning model is trained until the sum of prediction errors converges and training stops.

[0141] For example, the machine learning model is either a CNN neural network model or AlexNet;

[0142] The formula for calculating prediction error is:

[0143] ;

[0144] In the formula, zk is the prediction error, k is the group number of the feature vector; ak is the predicted state value corresponding to the k-th feature vector, and wk is the actual state value corresponding to the k-th training data.

[0145] After the collected comprehensive detection data is input into the machine learning model, the corresponding CO2 concentration value can be predicted. Based on the predicted CO2 concentration value, the CO2 detection status of the sample to be tested can be identified, thereby indicating whether the CO2 concentration value in the sample to be tested exceeds the normal CO2 concentration value, and facilitating the subsequent display of relevant data on the CO2 concentration value of the sample to be tested.

[0146] CO2 detection status includes warning status and non-warning status; warning status indicates that the CO2 concentration in the sample exceeds the normal CO2 concentration value, while non-warning status indicates that the CO2 concentration in the sample does not exceed the normal CO2 concentration value.

[0147] Methods for identifying warning and non-warning states include:

[0148] The predicted CO2 concentration value is compared with the standard CO2 concentration value. The standard CO2 concentration value refers to the minimum CO2 concentration value when the warning state is identified. This provides a numerical comparison basis for the identification of the warning state and the non-warning state and ensures the accuracy of the identification of the warning state and the non-warning state.

[0149] When the predicted CO2 concentration value is greater than or equal to the standard CO2 concentration value, the CO2 concentration value of the sample detected by the fiber optic sensor exceeds the normal CO2 concentration value. At this time, the CO2 concentration value in the sample will show an abnormal phenomenon, and an early warning is required. The early warning state is then identified.

[0150] When the predicted CO2 concentration is less than the standard CO2 concentration, the CO2 concentration detected by the fiber optic sensor does not exceed the normal CO2 concentration. In this case, the CO2 concentration in the sample will not show any abnormality, and no warning is required. Thus, the non-warning state is identified.

[0151] The detection data display module divides the comprehensive detection data into normal data and abnormal data according to the CO2 detection status, and controls the orderly display of the predicted CO2 concentration value, normal data and abnormal data.

[0152] When the CO2 detection status is identified, the specific data in the comprehensive detection data is distinguished according to the real-time CO2 detection status, and the different data types are divided to realize the orderly display and processing of the comprehensive detection data and the predicted CO2 concentration value, so as to facilitate the transmission of relevant inspection data to the fiber optic sensor for display.

[0153] When the comprehensive test data is divided, it will be divided into normal data and abnormal data. Normal data means that the corresponding comprehensive test data is of moderate size, without being too large or too small. Abnormal data means that the corresponding comprehensive test data varies greatly in size, with some data being too large or too small.

[0154] Methods for dividing regular data and outlier data include:

[0155] When the CO2 detection status is non-alarm status, there are no data in the comprehensive detection data that are too large or too small than normal values. Therefore, the comprehensive detection data are all normal data. At this time, the signal strength value, light intensity reduction value, ambient temperature compensation value and local pressure value are all classified as normal data.

[0156] When the CO2 detection status is in the warning state, if there are data in the comprehensive detection data that are too large or too small than the normal value, it is necessary to compare the comprehensive detection data with the corresponding safety value one by one.

[0157] Compare the signal strength value with the safe strength value; the safe strength value refers to the maximum value when the signal strength value is classified as normal data, which limits the upper limit of the signal strength value under normal circumstances.

[0158] When the signal strength value is greater than the safe value, the signal strength value will have a negative impact on the CO2 detection status, and the signal strength value will be classified as abnormal data. When the signal strength value is less than or equal to the safe value, the signal strength value will not have a negative impact on the CO2 detection status, and the signal strength value will be classified as normal data.

[0159] Compare the light intensity reduction value with the reduction safety value; the reduction safety value refers to the maximum value when the light intensity reduction value is classified as normal data, which limits the upper limit of the light intensity reduction value under normal circumstances.

[0160] When the light intensity reduction value is greater than the reduction safety value, the light intensity reduction value will have a negative impact on the CO2 detection status, and the light intensity reduction value will be classified as abnormal data. When the light intensity reduction value is less than or equal to the reduction safety value, the light intensity reduction value will not have a negative impact on the CO2 detection status, and the light intensity reduction value will be classified as normal data.

[0161] Compare the ambient temperature compensation value with the compensation safety value; the compensation safety value refers to the maximum value when the ambient temperature compensation value is classified as normal data, which limits the upper limit of the ambient temperature compensation value under normal circumstances.

[0162] When the ambient temperature compensation value is greater than the compensation safety value, the ambient temperature compensation value will have a negative impact on the CO2 detection status, and the ambient temperature compensation value will be classified as abnormal data. When the ambient temperature compensation value is less than or equal to the compensation safety value, the ambient temperature compensation value will not have a negative impact on the CO2 detection status, and the ambient temperature compensation value will be classified as normal data.

[0163] Compare the local pressure value with the pressure safety value; the pressure safety value refers to the maximum value when the local pressure value is classified as normal data, which limits the upper limit of the local pressure value under normal conditions.

[0164] When the local pressure value is greater than the pressure safety value, the local pressure value will have a negative impact on the CO2 detection status, and the local pressure value will be classified as abnormal data. When the local pressure value is less than or equal to the pressure safety value, the local pressure value will not have a negative impact on the CO2 detection status, and the local pressure value will be classified as normal data.

[0165] Once the normal data and abnormal data are divided, the predicted CO2 concentration values, normal data and abnormal data can be summarized accordingly, so that the summarized predicted CO2 concentration values, normal data and abnormal data can be displayed in different ways and transmitted to the display module of the fiber optic sensor for orderly display.

[0166] Methods for controlling orderly display include:

[0167] When the comprehensive test data is divided into regular data, a first display information sheet is constructed, and the first regular position and the second regular position are marked in the first display information sheet respectively.

[0168] The predicted CO2 concentration value and conventional data are imported into the first conventional position and the second conventional position respectively to generate a conventional display information sheet, and the conventional display information sheet is sent to the display module for orderly display.

[0169] When the comprehensive detection data is divided into normal data and abnormal data, a second display information sheet is constructed, and the first abnormal position, the second abnormal position and the third abnormal position are marked in the second display information sheet respectively.

[0170] The predicted CO2 concentration value, normal data, and abnormal data are imported into the first abnormal position, the second abnormal position, and the third abnormal position, respectively, to generate an abnormality display information sheet. The abnormality display information sheet is then sent to the display module for orderly display.

[0171] It should be noted that by constructing regular and abnormal display information sheets, all data under different CO2 detection states and different data division conditions can be displayed in an orderly and comprehensive manner, enabling the fiber optic sensor to display the CO2 concentration data of the sample to be tested, making it convenient for users to observe, understand and use the CO2 concentration data.

[0172] In this embodiment, by acquiring the basic operating parameters of the fiber optic sensor during startup and calculating its operational reliability coefficient, the reliability coefficient is compared with a preset operational reliability threshold to determine whether to enter CO2 detection mode. In CO2 detection mode, comprehensive detection data from the fiber optic sensor is acquired and input into a pre-trained machine learning model to predict CO2 concentration values ​​and identify the CO2 detection status. Based on the CO2 detection status, the comprehensive detection data is divided into normal data and abnormal data, and the predicted CO2 concentration values, normal data, and abnormal data are displayed in an orderly manner. Compared to existing technologies, by acquiring basic operating parameters and calculating... Calculating the operational reliability coefficient allows for the effective identification of the reliability of the fiber optic sensor, filtering out abnormal operating states with poor detection accuracy. This provides an efficient and reliable foundation for subsequent CO2 concentration detection, avoiding the decrease in CO2 concentration detection accuracy due to poor fiber optic sensor operation. Furthermore, by collecting diverse and multi-dimensional comprehensive detection data and combining it with machine learning models, the CO2 concentration value in the sample can be accurately predicted. This effectively avoids the negative impact of multi-dimensional factors such as temperature and pressure in the sample, thereby maximizing the accuracy of CO2 concentration detection by the fiber optic sensor and reducing the probability of CO2 concentration detection errors.

[0173] Example 2: Please refer to Figure 2. For the parts not described in detail in this example, please refer to the description in Example 1. A CO2 fiber optic sensor is provided and applied to the above-mentioned CO2 fiber optic sensor detection system. The CO2 fiber optic sensor includes a display screen 1, a sensor body 2, a main control module 3, an optical fiber 4, and an optical fiber probe 5. The main control module 3 consists of a basic parameter acquisition module, a detection mode determination module, a detection data acquisition module, a model prediction determination module, and a detection data display module in the above-mentioned CO2 fiber optic sensor detection system.

[0174] The display screen 1 is fixedly installed on the sensor body 2 to display various data information sent to the display module, so that users can easily observe the CO2 detection results on the fiber optic sensor. The main control module 3 is built into the sensor body 2. One end of the fiber optic 4 is connected to the main control module 3 and the other end is connected to the fiber optic probe 5, so that the fiber optic probe 5 can be inserted into the liquid sample in the narrow slit terrain without having to take the liquid sample out of the narrow slit terrain, thereby avoiding the phenomenon of liquid sample being contaminated when the liquid sample is taken out of the narrow slit terrain.

[0175] The main control module 3, through the basic parameter acquisition module, detection mode determination module, detection data acquisition module, model prediction determination module, and detection data display module, can effectively collect, analyze, calculate, and predict CO2 detection data in liquid samples. Based on the detection results, it formulates corresponding display information and finally sends the display information to the display module, which displays it to the user through the display screen 1, facilitating accurate and convenient detection and processing of CO2 concentration data in liquid samples within narrow gaps.

[0176] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A detection system for a CO2 fiber optic sensor, characterized in that, include: The basic parameter acquisition module is used to acquire the basic operating parameters of the fiber optic sensor in the startup state. The basic operating parameters include the delay response ratio and the trigger interval value, and calculate the operating reliability coefficient of the fiber optic sensor. Methods for obtaining the percentage of delayed response include: The database is used to retrieve all data logs within a preset response period and the processing status of each data log is identified. Record the data logs with the processed status as valid logs, and mark all the detection data in the valid logs one by one to obtain i detection data; By querying the timestamps one by one the duration taken from the start of receiving i detection data to the end of storage, we obtain i response durations. The response durations that exceed the standard duration threshold are recorded as delay durations, thus obtaining p delay durations. After summing up the p delay durations, compare them with the sum of the i response durations to obtain the delay response ratio. The expression for the percentage of delayed response is: ； In the formula, This represents the percentage of delayed responses. For the a-th delay duration, This is the duration of the b-th response; Methods for obtaining the trigger interval value include: The system retrieves all access events that occurred within a preset response period of the security defense system and marks the trigger status of each access event. Record the access event with the triggered status as the target event, obtain s target events, and query the time when the defense mechanism was first triggered by each of the s target events by timestamp, to obtain s trigger times; The duration between the s-th trigger time and the (s+1)-th trigger time is recorded as the trigger interval duration. s-1 trigger interval durations are obtained, and the average of the s-1 trigger interval durations is calculated to obtain the trigger interval value. The expression for the trigger interval value is: ； In the formula, For the trigger interval value, The duration of each of the c trigger intervals is [number]. The expression for the operational reliability coefficient is: ； In the formula, The operational reliability coefficients are γ1 and γ2, which are proportionality coefficients greater than 0, and γ1 + γ2 = 1. This represents the percentage of delayed responses. The detection mode determination module is used to compare the operating reliability coefficient with the preset operating reliability threshold to determine whether to enter the CO2 detection mode. The detection data acquisition module is used to acquire comprehensive detection data from the fiber optic sensor in CO2 detection mode. The comprehensive detection data includes signal strength value, light intensity reduction value, ambient temperature compensation value, and local pressure value. The model prediction and recognition module is used to input the comprehensive detection data into a pre-trained machine learning model to predict the CO2 concentration value and identify the CO2 detection status, which includes warning status and non-warning status. The detection data display module is used to divide the comprehensive detection data into normal data and abnormal data according to the CO2 detection status, and to control the orderly display of the predicted CO2 concentration value, normal data and abnormal data.

2. The detection system for a CO2 fiber optic sensor according to claim 1, characterized in that, The method for determining whether to enter CO2 detection mode includes: operational reliability coefficient Compared with the preset operational reliability threshold Compare; when Greater than or equal to When this occurs, it is determined that the CO2 detection mode has been entered; when Less than If the condition is not met, the system will not enter CO2 detection mode.

3. The detection system for a CO2 fiber optic sensor according to claim 2, characterized in that, The method for obtaining the light intensity reduction value includes: At time T1, detection fluorescence is emitted to the sample to be tested through a light emission source, and the signal intensity of the detection fluorescence is recorded to obtain the initial intensity; After time T1, the signal intensity of the detected fluorescence is recorded in real time and recorded as the real-time intensity. The moment when the real-time intensity first becomes less than the initial intensity is recorded as the start time. Starting from the initial time and using the preset reduction time as the interval standard, mark x reduction time points, and record the signal intensity of the detected fluorescence at each of the x reduction time points to obtain x detection intensity values. The x-th detection intensity value is subtracted from the x+1-th detection intensity value to obtain x-1 sub-reduction values; The light intensity reduction value is obtained by summing the x-1 sub-reduction values ​​and averaging them.

4. The detection system for a CO2 fiber optic sensor according to claim 3, characterized in that, The method for obtaining the ambient temperature compensation value includes: At x reduction time points, the temperature inside the sample under test is detected in real time by a temperature sensor to obtain x real-time temperature values. Real-time temperature values ​​that are greater than the preset calibration temperature value are recorded as excess temperature values, and q excess temperature values ​​are obtained. The q excess temperature values ​​are successively subtracted from the preset calibration temperature value to obtain q sub-compensation values. The q sub-compensation values ​​are then summed to obtain the ambient temperature compensation value.

5. The detection system for a CO2 fiber optic sensor according to claim 4, characterized in that, The training methods for the machine learning model include: Multiple sets of comprehensive detection data and corresponding CO2 concentration values ​​were collected in advance; The comprehensive detection data is converted into a set of corresponding feature vectors. The feature vectors are used as input to the machine learning model. The CO2 concentration values ​​are converted into labels corresponding to the comprehensive detection data. The CO2 concentration values ​​corresponding to each set of comprehensive detection data are used as outputs of the machine learning model. The prediction target is the CO2 concentration value. The training target is to minimize the sum of prediction errors of all training data. The machine learning model is trained until the sum of prediction errors converges and training stops. Methods for identifying warning and non-warning states include: Compare the predicted CO2 concentration value with the standard CO2 concentration value; When the predicted CO2 concentration is greater than or equal to the standard CO2 concentration, an early warning status is identified. When the predicted CO2 concentration is lower than the standard CO2 concentration, a non-warning state is identified.

6. The detection system for a CO2 fiber optic sensor according to claim 5, characterized in that, The methods for classifying regular data and abnormal data include: When the CO2 detection status is non-alarm status, the signal strength value, light intensity reduction value, ambient temperature compensation value, and local pressure value are all classified as routine data; When the CO2 detection status is in the warning state, the signal strength value is compared with the strength safety value. When the signal strength value is greater than the strength safety value, the signal strength value is classified as abnormal data. When the signal strength value is less than or equal to the strength safety value, the signal strength value is classified as normal data. The light intensity reduction value is compared with the reduction safety value. When the light intensity reduction value is greater than the reduction safety value, the light intensity reduction value is classified as abnormal data. When the light intensity reduction value is less than or equal to the reduction safety value, the light intensity reduction value is classified as normal data. The ambient temperature compensation value is compared with the compensation safety value. When the ambient temperature compensation value is greater than the compensation safety value, the ambient temperature compensation value is classified as abnormal data. When the ambient temperature compensation value is less than or equal to the compensation safety value, the ambient temperature compensation value is classified as normal data. The local pressure value is compared with the pressure safety value. When the local pressure value is greater than the pressure safety value, the local pressure value is classified as abnormal data. When the local pressure value is less than or equal to the pressure safety value, the local pressure value is classified as normal data.

7. The detection system for a CO2 fiber optic sensor according to claim 6, characterized in that, The method for controlling the orderly display includes: When the comprehensive test data is divided into regular data, a first display information sheet is constructed, and the first regular position and the second regular position are marked in the first display information sheet respectively. The predicted CO2 concentration value and conventional data are imported into the first conventional position and the second conventional position respectively to generate a conventional display information sheet, and the conventional display information sheet is sent to the display module for orderly display. When the comprehensive detection data is divided into normal data and abnormal data, a second display information sheet is constructed, and the first abnormal position, the second abnormal position and the third abnormal position are marked in the second display information sheet respectively; The predicted CO2 concentration value, normal data, and abnormal data are imported into the first abnormal position, the second abnormal position, and the third abnormal position, respectively, to generate an abnormality display information sheet. The abnormality display information sheet is then sent to the display module for orderly display.

8. A CO2 fiber optic sensor, applied to a detection system of a CO2 fiber optic sensor according to any one of claims 1-7, the CO2 fiber optic sensor comprising a display screen (1), a sensor body (2), a main control module (3), an optical fiber (4), and an optical fiber probe (5), characterized in that, The main control module (3) consists of a basic parameter acquisition module, a detection mode determination module, a detection data acquisition module, a model prediction determination module, and a detection data display module in the detection system of the CO2 fiber optic sensor according to any one of claims 1-9.

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