Intelligent detection data calibration system based on enhancement algorithm driving

Abstract

The invention discloses an intelligent detection data calibration system based on enhancement algorithm driving, and particularly relates to the technical field of data calibration, a plurality of sensors are arranged at different positions in an environment, each position comprises a plurality of sensors of the same type, potential abnormal sensors are detected by using a redundancy or machine learning algorithm, and the data calibration accuracy is improved. The range of the abnormal sensor is quickly narrowed and the efficiency is improved; the monitoring data fluctuation curve is analyzed, an expected input cost description parameter and an expected calibration similarity are obtained through analysis, non-calibrated sensors are screened out from an abnormal sensor set based on an analysis result, and waste of calibration resources on the sensors which are not worthy of input is avoided; it is ensured that only the sensors capable of being calibrated are included in the calibration process, and the data accuracy and reliability of the whole monitoring system can be improved; detailed cost analysis about sensor maintenance or replacement is provided for decision makers, more economic and reasonable decisions can be made, and the problem of resource waste in the intelligent detection data calibration process is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data calibration, and more specifically, to an intelligent detection data calibration system driven by an enhanced algorithm. Background Art

[0002] In the process of environmental monitoring through sensors, there is a lack of intelligent detection and data calibration of sensor data, which results in the data collected by the sensors not being able to accurately reflect the actual situation; environmental monitoring sensors include at least temperature sensors, humidity sensors, air pressure sensors, and harmful gas concentration sensors; due to the diverse data sources and complex processing procedures, there may be inconsistencies between data at different time points. In order to ensure the accuracy and consistency of sensor data, data calibration is required.

[0003] However, it still has many shortcomings in actual use. For example, the existing intelligent detection data has the problem of waste of calibration resources, which is specifically reflected in the following aspects:

[0004] Many current systems rely on frequent manual intervention or resetting calibration parameters to cope with this, which is not only inefficient but also prone to waste of resources;

[0005] In a multi-sensor monitoring system, some sensors may have low calibration feasibility due to aging, damage or design defects. Unnecessary calibration of such sensors is undoubtedly a huge waste of resources.

[0006] If calibration resources (such as computing resources and energy) cannot be allocated reasonably and evenly, critical measurement ranges may not receive adequate calibration support due to insufficient resources, thus affecting the accuracy and reliability of the entire monitoring system. Summary of the invention

[0007] In order to overcome the above-mentioned defects of the prior art, the present invention provides an intelligent detection data calibration system driven by an enhanced algorithm, which avoids wasting calibration resources on sensors that are not worth investing in by screening out sensors that cannot be calibrated; ensures that only calibrable sensors are included in the calibration process, which helps to improve the data accuracy and reliability of the entire monitoring system; and provides decision makers with a detailed cost analysis of sensor repair or replacement to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: an intelligent detection data calibration system driven by an enhanced algorithm, comprising:

[0009] The abnormal sensor initial screening module sets several sensors at different locations in the environment. Each location includes several sensors of the same type. If the monitoring data of sensors of the same type are different, it indicates that the sensor at this location is abnormal, and the abnormal sensor set is screened out.

[0010] The abnormal sensor test module places the calibrated sensor and the abnormal sensor in the same environment for testing, and records the monitoring data fluctuation curve; obtains the standard monitoring data fluctuation curve A of the calibrated sensor and the abnormal monitoring data fluctuation curve B of the abnormal sensor;

[0011] The feasibility analysis module is used to analyze the monitoring data fluctuation curve, obtain the expected input cost description parameters and expected calibration similarity, and filter out the uncalibrable sensors from the abnormal sensor set based on the analysis results, including:

[0012] Apply the correction instruction to the abnormal monitoring data fluctuation curve B to maximize the expected calibration similarity between the standard curve A and the abnormal monitoring data fluctuation curve B, wherein the expected calibration similarity is an indicator for measuring the similarity between the standard monitoring data fluctuation curve A and the abnormal monitoring data fluctuation curve B of the abnormal sensor; the expected calibration similarity is recorded as Yxs;

[0013] Establish a mapping relationship between correction instructions and fault type and fault degree, and output the fault degree and type based on the correction instructions and the mapping relationship; establish a fault repair cost map, input the fault degree and type, and output the time cost, economic cost and computing resource cost of repair; based on the time cost, economic cost and computing resource cost, obtain the expected input cost description parameters;

[0014] If the expected input cost description parameter and the expected calibration similarity exceed the corresponding threshold, it indicates that the abnormal sensor is an uncalibrable sensor.

[0015] Preferably, the abnormal sensor initial screening module further includes identifying a set of abnormal sensors screened out in a complex environment through a deep learning model, including the following steps:

[0016] Step S11, collecting temperature data: continuously collecting temperature data from all temperature sensors, and extracting key features from the temperature data;

[0017] Step S12: Divide the collected data into a training set, a validation set, and a test set, and mark them to distinguish between data with normal temperature and data with abnormal temperature;

[0018] Step S13, select the long short-term memory network as the model, and in the training stage, use the training set data to train the LSTM model so that it can learn the patterns of normal temperature data and try to distinguish these patterns from abnormal data; use the validation set data to evaluate the performance of the model, including accuracy, recall rate, and F1 score indicators, adjust the model parameters according to the evaluation results, and optimize the model performance; use the optimized model as a sensor anomaly prediction model, ready for real-time detection;

[0019] Step S14: After the training is completed, the sensor anomaly prediction model is used to detect anomalies in sensor data in real time. When new data is input into the sensor anomaly prediction model, a prediction result is output to indicate the confidence level of whether the data is abnormal, and the abnormal sensor set is screened out.

[0020] Preferably, the key features include: time series features, statistical features and spatial features. Time series features refer to the rate, acceleration and periodic pattern of temperature change; statistical features refer to mean, variance, maximum value and minimum value, which represent information on data distribution; spatial features refer to: layout features of sensors in space, which represent information on sensor spatial layout, i.e., temperature change characteristics caused by the spatial layout of sensors.

[0021] Preferably, the abnormal sensor initial screening module also includes a data integration unit and a noise filtering unit. The data integration unit is responsible for integrating the raw data from different sensors, including time synchronization and unified data format, to ensure data consistency and comparability; the noise filtering unit uses a filtering algorithm (such as Kalman filtering, median filtering, etc.) to pre-process the sensor data to remove noise data caused by environmental interference, equipment jitter and other factors, thereby improving data quality.

[0022] Preferably, the correction instruction includes at least forward and backward movement and temperature-based correction; the forward and backward movement refers to a translation operation on the abnormal monitoring data fluctuation curve B, which is used to adjust the time offset and baseline offset; the temperature-based correction refers to adjusting the abnormal monitoring data fluctuation curve B according to the temperature change to eliminate the influence of temperature on the sensor reading.

[0023] Preferably, the calculation formula of the expected input cost description parameter YC is

[0024]

[0025] Among them, Trepair is the total time required for calibration, CT is the total cost required for calibration; EC is the economic cost, directly expressed in monetary units; TC_time is the value after the time cost is converted into monetary units, multiplying the time cost by one unit of time cost, f1 is the conversion coefficient from time cost to economic cost; TC_com is the value after the computing power resource cost is converted into monetary units, f2 is the conversion coefficient from computing power resource cost to economic cost.

[0026] Preferably, if the expected input cost description parameter and the expected calibration similarity exceed the corresponding threshold, the expected input cost description parameter YC and the expected calibration similarity Yxs are input into the calibration feasibility analysis model, and the calibration feasibility index JK is output;

[0027] The calibration feasibility analysis model satisfies the formula Where YC_norm represents the expected input cost description parameter after linear normalization, Yxs_norm represents the expected calibration similarity after linear normalization, and λ is a correction constant to avoid division by zero;

[0028] Determine whether the calibration feasibility index exceeds the threshold value THa. If the calibration feasibility index exceeds the threshold value THa, it indicates that the corresponding abnormal sensor is an uncalibrable sensor, and a second-level warning is issued to prompt the sensor to be replaced;

[0029] If the calibration feasibility index does not exceed the threshold THa, it indicates that the corresponding abnormal sensor is a calibrable sensor, and a calibrable sensor set is obtained by summarizing.

[0030] Preferably, a mapping relationship between correction instructions and fault type and fault degree is established by analyzing the commonalities and differences of correction instructions under different fault types and degrees; the fault type and degree are inferred based on the correction instructions of the mapping relationship and abnormal monitoring data fluctuation curve B.

[0031] Preferably, it further includes an abnormal sensor calibration module, which is used to calibrate the calibrable sensor set, including:

[0032] The area division unit is used to divide the sensor data into several areas according to the range and number them, with n representing the number of areas and i representing the area number;

[0033] The calibration requirement analysis unit obtains the amplitude and frequency of the data deviation of each area based on the standard monitoring data fluctuation curve A and the abnormal monitoring data fluctuation curve B of the abnormal sensor; constructs a calibration requirement index based on the amplitude and frequency of the data deviation, and outputs the calibration requirement index of each area;

[0034] The calibration resource allocation unit assigns calibration priority to each area based on the calibration demand index; and reasonably allocates calibration resources according to the calibration demand index of each area.

[0035] The model selection unit corrects the abnormal monitoring data fluctuation curve B of each area through a polynomial function, and uses a statistical method to find the polynomial function corresponding to each area;

[0036] The iterative calibration unit makes the intelligent detection data meet the preset accuracy requirements through iterative calibration; the iterative calibration refers to the preliminary calibration of the calibrable sensor, and then collecting monitoring data in actual application to evaluate the calibration accuracy, and fine-tuning the calibration parameters according to the evaluated accuracy, and repeating this process until a satisfactory calibration accuracy is achieved.

[0037] Preferably, the calibration requirement index Jx satisfies the formula

[0038]

[0039] Among them, pcf_avg and pcp_avg represent the average data deviation amplitude and data deviation frequency of the region respectively; pcf_max and pcp_max represent the maximum deviation amplitude and the highest deviation frequency of the region respectively; α and β are weight coefficients used to adjust the relative importance of the deviation amplitude and frequency in the index; the maximum deviation amplitude and the highest deviation frequency are the maximum values ​​of all regions and are used for normalization processing.

[0040] Preferably, it also includes a calibration quality management module, which records the calibration process data and analyzes it to obtain the calibration stability parameter and the calibration accuracy parameter, wherein the calibration process data includes the actual calibration time, the interval time between two adjacent calibration behaviors and the actual error after calibration; the calibration stability parameter and the calibration accuracy parameter are input into the calibration quality assessment model, and the calibration quality assessment is output. If the calibration quality assessment index is lower than the threshold THb, an early warning is issued to prompt attention to the calibration behavior and calibration instructions; the calibration quality assessment model satisfies the formula JP=Form(Jw*Jz), wherein Jw represents the calibration stability parameter, Jz represents the calibration accuracy parameter, and Form(·) represents the linear normalization function.

[0041] Preferably, the calibration stability parameter satisfies the formula

[0042]

[0043] Among them, t 1i represents the actual time consumed for calibration of the i-th region, t 2i represents the interval between two consecutive calibration actions of the ith region, t 10 Indicates the preset calibration time, t 20 Indicates the preset calibration interval; the weight coefficient K of the i-th area is expressed by the ratio of the range of the i-th area to the total range. i; The calibration accuracy parameter satisfies the formula

[0044]

[0045] Among them, d i represents the actual error after calibration of the i-th region, and d0 represents the preset error.

[0046] Technical effects and advantages of the present invention:

[0047] (1) The intelligent detection data calibration system provided by the present invention uses redundancy or machine learning algorithms to detect potential abnormal sensors, which can quickly narrow the scope of abnormal sensors and improve efficiency. By screening out sensors that cannot be calibrated, it avoids wasting calibration resources on sensors that are not worth investing in. It ensures that only calibrable sensors are included in the calibration process, which helps to improve the data accuracy and reliability of the entire monitoring system. It provides decision makers with a detailed cost analysis of sensor repair or replacement, which helps to make more economical and reasonable decisions and solves the problem of resource waste in the intelligent detection data calibration process.

[0048] (2) The intelligent detection data calibration system provided by the present invention analyzes the calibration stability parameters and calibration accuracy parameters through the calibration quality management module provided, which helps the system administrator or operator to better understand the calibration status, find problems in time and take corresponding measures, so as to ensure the accuracy and reliability of the monitoring data; by issuing early warning prompts to pay attention to calibration behaviors and calibration instructions, the system can also promote the continuous optimization and improvement of the calibration work. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flowchart of the intelligent detection data calibration system based on feasibility analysis of the present invention.

[0050] Figure 2 This is a structural block diagram of the intelligent detection data calibration system driven by the enhanced algorithm of the present invention. DETAILED DESCRIPTION

[0051] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0052] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0053] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present application, its application, or uses.

[0054] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0055] Example 1, see Figure 1 The present invention provides a flowchart of an intelligent detection data calibration system based on feasibility analysis. Figure 1 An intelligent detection data calibration system driven by an enhanced algorithm is shown, comprising:

[0056] The abnormal sensor initial screening module sets several sensors at different locations in the environment. Each location includes several sensors of the same type. If the monitoring data of sensors of the same type are different, it indicates that the sensor at this location is abnormal, and the abnormal sensor set is screened out.

[0057] It needs to be further explained in the embodiment of the present invention that the abnormal sensor preliminary screening module also includes a data integration unit and a noise filtering unit. The data integration unit is responsible for integrating the raw data from different sensors, including time synchronization and data format unification, to ensure data consistency and comparability; the noise filtering unit: uses a filtering algorithm (such as Kalman filtering, median filtering, etc.) to pre-process the sensor data to remove noise data caused by environmental interference, equipment jitter and other factors, and improve data quality;

[0058] Explanation: Several sensors are set up at different locations in the environment, and each location includes several sensors of the same type. The purpose of this is to use redundancy to detect potential anomalies. For each location, the monitoring data of sensors of the same type are compared. If these data are different, this is usually a strong signal that one or some sensors at the location may be abnormal. Based on the above comparison results, a collection of abnormal sensors can be preliminarily screened out. The abnormal sensors will require further testing or calibration, quickly narrowing the scope of abnormal sensors and improving efficiency.

[0059] The abnormal sensor test module places the calibrated sensor and the abnormal sensor in the same environment for testing, and records the monitoring data fluctuation curve; obtains the standard monitoring data fluctuation curve A of the calibrated sensor and the abnormal monitoring data fluctuation curve B of the abnormal sensor;

[0060] Explanation: The monitoring data fluctuation curve of the calibrated qualified sensor is recorded as the standard monitoring data fluctuation curve A; the monitoring data fluctuation curve of the abnormal sensor is recorded as the abnormal monitoring data fluctuation curve B;

[0061] The feasibility analysis module is used to analyze the monitoring data fluctuation curve, obtain the expected input cost description parameters and expected calibration similarity, and filter out the uncalibrable sensors from the abnormal sensor set based on the analysis results, including:

[0062] Apply the correction instruction to the abnormal monitoring data fluctuation curve B to maximize the expected calibration similarity between the standard curve A and the abnormal monitoring data fluctuation curve B, wherein the expected calibration similarity is an indicator for measuring the similarity between the standard monitoring data fluctuation curve A and the abnormal monitoring data fluctuation curve B of the abnormal sensor; the expected calibration similarity is recorded as Yxs;

[0063] Establish a mapping relationship between correction instructions and fault type and fault degree, and output the fault degree and type based on the correction instructions and the mapping relationship; establish a fault repair cost map, input the fault degree and type, and output the time cost, economic cost and computing resource cost of repair; based on the time cost, economic cost and computing resource cost, obtain the expected input cost description parameters;

[0064] Explanation: Collect historical maintenance records, including maintenance time, economic expenditure (such as spare parts cost, labor cost) and computing resource usage under different fault types and degrees; use the collected data to establish a fault maintenance cost map. The fault maintenance cost map is a multi-dimensional database or model, which contains the corresponding relationship between fault type, fault degree and maintenance time cost, economic cost and computing resource cost; input the fault type and degree, and output the maintenance time cost, economic cost and computing resource cost.

[0065] If the expected input cost description parameter and the expected calibration similarity exceed the corresponding threshold, it indicates that the abnormal sensor is an uncalibrable sensor;

[0066] Explanation: The higher the calibration cost of an abnormal sensor, the less necessary it is to calibrate it; in order to save costs, non-calibrable sensors are screened out to save costs; by screening out non-calibrable sensors, the system can avoid wasting calibration resources on sensors that are not worth investing in; ensuring that only calibrable sensors are included in the calibration process helps to improve the data accuracy and reliability of the entire monitoring system; providing decision makers with a detailed cost analysis of sensor repair or replacement helps to make more economical and reasonable decisions.

[0067] Explanation: The embodiment of the present invention does not specifically limit the calculation method of the expected calibration similarity. The expected calibration similarity may be a mean square error, a correlation coefficient, or other indicators for measuring the similarity between two curves.

[0068] What needs to be further explained in the embodiments of the present invention is that the correction instruction at least includes forward and backward movement and temperature-based correction; the forward and backward movement refers to a translation operation on the abnormal monitoring data fluctuation curve B, which is used to adjust the time offset and baseline offset, such as zero drift; temperature-based correction refers to adjusting the abnormal monitoring data fluctuation curve B according to the change in temperature to eliminate the influence of temperature on the sensor reading, such as temperature drift.

[0069] It is necessary to further explain in the embodiment of the present invention that the calculation formula of the expected input cost description parameter YC is:

[0070]

[0071] Among them, Trepair is the total time required for calibration, CT is the total cost required for calibration; EC is the economic cost, directly expressed in monetary units; TC_time is the value after the time cost is converted into monetary units, multiplying the time cost by one unit of time cost, f1 is the conversion coefficient from time cost to economic cost; TC_com is the value after the computing power resource cost is converted into monetary units, f2 is the conversion coefficient from computing power resource cost to economic cost.

[0072] It is necessary to further explain in the embodiment of the present invention that if the expected input cost description parameter and the expected calibration similarity exceed the corresponding threshold value, the expected input cost description parameter YC and the expected calibration similarity Yxs are input into the calibration feasibility analysis model, and the calibration feasibility index JK is output;

[0073] The calibration feasibility analysis model satisfies the formula Where YC_norm represents the expected input cost description parameter after linear normalization, Yxs_norm represents the expected calibration similarity after linear normalization, and λ is a correction constant to avoid division by zero;

[0074] Determine whether the calibration feasibility index exceeds the threshold THa. If the calibration feasibility index exceeds the threshold THa, it indicates that the corresponding abnormal sensor is an uncalibratable sensor, and a secondary warning is issued to prompt the sensor to be replaced. If the calibration feasibility index does not exceed the threshold THa, it indicates that the corresponding abnormal sensor is a calibrable sensor, and a set of calibrable sensors is obtained.

[0075] Explanation: The larger the calibration feasibility index is, the higher the feasibility of sensor calibration is. The lower the calibration feasibility index is, the more difficult the calibration of the sensor device is and the lower the calibration benefit is. The sensor device is marked as unusable.

[0076] What needs to be further explained in the embodiments of the present invention is that a mapping relationship between correction instructions and fault type and fault degree is established by analyzing the commonalities and differences of correction instructions under different fault types and degrees; the fault type and degree are inferred based on the correction instructions of the mapping relationship and the abnormal monitoring data fluctuation curve B.

[0077] BACKGROUND OF THE INVENTION Assume that a plurality of temperature sensors are deployed in an industrial automation environment. The temperature sensors are distributed on different machines and pipelines and are used to monitor the temperature of the equipment to prevent failures caused by overheating.

[0078] In a possible embodiment, the abnormal sensor initial screening module further includes identifying a set of abnormal sensors screened out in a complex environment by using a deep learning model, including the following steps:

[0079] Step S11, collecting temperature data: continuously collecting temperature data from all temperature sensors, and extracting key features from the temperature data;

[0080] Step S12: Divide the collected data into a training set, a validation set, and a test set, and mark them to distinguish between data with normal temperature and data with abnormal temperature;

[0081] Step S13, select the long short-term memory network as the model, and in the training stage, use the training set data to train the LSTM model so that it can learn the patterns of normal temperature data and try to distinguish these patterns from abnormal data; use the validation set data to evaluate the performance of the model, including accuracy, recall rate, and F1 score indicators, adjust the model parameters according to the evaluation results, and optimize the model performance; use the optimized model as a sensor anomaly prediction model, ready for real-time detection;

[0082] Step S14: After the training is completed, the sensor anomaly prediction model is used to detect anomalies in sensor data in real time. When new data is input into the sensor anomaly prediction model, a prediction result is output to indicate the confidence level of whether the data is abnormal, and the abnormal sensor set is screened out.

[0083] Furthermore, the key features include: time series features, statistical features and spatial features. Time series features refer to the rate, acceleration and periodic pattern of temperature change; statistical features refer to mean, variance, maximum value and minimum value, which represent information on data distribution; spatial features refer to: layout features of sensors in space, which represent information on sensor spatial layout, i.e., temperature change features caused by the spatial layout of sensors.

[0084] For example, suppose that at a certain point in time, the temperature reading of a temperature sensor suddenly rises sharply and exceeds the normal operating range. After data processing and feature extraction, this abnormal temperature change pattern is input into the LSTM model. Based on the normal temperature pattern learned previously, the model finds that this new data point does not match the normal pattern, so it outputs a high-confidence abnormal signal. The abnormal signal is captured by the system and triggers the corresponding alarm or fault handling mechanism, thereby achieving accurate detection of abnormal situations and identifying and filtering out abnormal sensor sets in complex environments through deep learning models.

[0085] Embodiment 2: The difference between the embodiment of the present invention and embodiment 1 is that the intelligent detection data calibration system further includes an abnormal sensor calibration module.

[0086] The abnormal sensor calibration module is used to calibrate the calibrable sensor set, including:

[0087] The area division unit is used to divide the sensor data into several areas according to the range and number them, with n representing the number of areas and i representing the area number;

[0088] Explanation: Since sensors may exhibit different error characteristics within different measuring ranges, dividing the measuring range into multiple regions can more accurately identify and handle the calibration requirements of each region. According to the real-time changes of sensor data, the regional division boundaries are dynamically adjusted to ensure that the data characteristics within each region are relatively stable, thereby improving the calibration accuracy.

[0089] The calibration requirement analysis unit obtains the amplitude and frequency of the data deviation of each area based on the standard monitoring data fluctuation curve A and the abnormal monitoring data fluctuation curve B of the abnormal sensor; constructs a calibration requirement index based on the amplitude and frequency of the data deviation, and outputs the calibration requirement index of each area;

[0090] The calibration resource allocation unit assigns calibration priorities to each region based on the calibration demand index; according to the calibration demand index of each region, it rationally allocates calibration resources, such as time, manpower, equipment, etc., to ensure that regions with higher calibration demand index are given priority;

[0091] The model selection unit corrects the abnormal monitoring data fluctuation curve B of each area through a polynomial function, and uses a statistical method to find the polynomial function corresponding to each area;

[0092] Explanation: Polynomial functions are simple and computationally efficient, and are suitable for areas where the deviation relationship is relatively smooth and changes continuously. If the polynomial function cannot be calibrated, a deviation prediction model is built by training a neural network, inputting historical data, and outputting the corresponding deviation prediction value. The output value of the sensor is adjusted according to the deviation prediction value to improve the calibration accuracy.

[0093] The iterative calibration unit makes the intelligent detection data meet the preset accuracy requirements through iterative calibration; the iterative calibration refers to the preliminary calibration of the calibrable sensor, and then collecting monitoring data in actual application to evaluate the calibration accuracy, and fine-tuning the calibration parameters according to the evaluated accuracy, and repeating this process until a satisfactory calibration accuracy is achieved.

[0094] It is necessary to further explain in the embodiment of the present invention that the calibration requirement index Jx satisfies the formula

[0095]

[0096] Among them, pcf_avg and pcp_avg represent the average data deviation amplitude and data deviation frequency of the region respectively; pcf_max and pcp_max represent the maximum deviation amplitude and the highest deviation frequency of the region respectively; α and β are weight coefficients used to adjust the relative importance of the deviation amplitude and frequency in the index; the maximum deviation amplitude and the highest deviation frequency are the maximum values ​​of all regions and are used for normalization processing.

[0097] Example 3, see Figure 2 The structure block diagram of the intelligent detection data calibration system based on enhanced algorithm driving is shown in FIG. 1 . The difference between the embodiment of the present invention and embodiment 2 is that the intelligent detection data calibration system further includes a calibration quality management module.

[0098] Used to record calibration process data and analyze to obtain calibration stability parameters and calibration accuracy parameters, wherein the calibration process data includes actual calibration time, interval between two adjacent calibration actions and actual error after calibration;

[0099] Inputting the calibration stability parameter and the calibration accuracy parameter into a calibration quality assessment model, and outputting a calibration quality assessment; the calibration quality assessment model satisfies the formula JP=Form(Jw*Jz), wherein Jw represents the calibration stability parameter, Jz represents the calibration accuracy parameter, and Form(·) represents a linear normalization function;

[0100] If the calibration quality evaluation index is lower than the threshold THb, an early warning is issued to prompt attention to the calibration behavior and calibration instructions;

[0101] It is necessary to further explain in the embodiment of the present invention that the calibration stability parameter satisfies the formula

[0102]

[0103] Among them, t 1i represents the actual time consumed for calibration of the i-th region, t 2irepresents the interval between two consecutive calibration actions of the ith region, t 10 Indicates the preset calibration time, t 20 Indicates the preset calibration interval; the weight coefficient K of the i-th area is expressed by the ratio of the range of the i-th area to the total range. i ; The calibration accuracy parameter satisfies the formula

[0104]

[0105] Among them, d i represents the actual error after calibration of the i-th region, and d0 represents the preset error.

[0106] Explanation: Based on the difference between the actual calibration time and the preset calibration time, the calibration stability parameter is evaluated and divided by the converted calibration requirement index to eliminate the influence of the calibration requirement on the calibration stability. In this way, the calibration stability parameter can more purely reflect the stability of the calibration process itself without being affected by the amplitude and frequency of data deviations. Based on the actual error after calibration and the preset error, the calibration accuracy parameter is evaluated and divided by the converted calibration requirement index to eliminate the influence of the calibration requirement on the calibration accuracy.

[0107] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. Intelligent detection data calibration system driven by enhanced algorithm, characterized by: include: The abnormal sensor initial screening module sets several sensors at different locations in the environment. Each location includes several sensors of the same type. If the monitoring data of sensors of the same type are different, it indicates that the sensor at this location is abnormal, and the abnormal sensor set is screened out. The abnormal sensor test module places the calibrated sensor and the abnormal sensor in the same environment for testing, and records the monitoring data fluctuation curve; obtains the standard monitoring data fluctuation curve A of the calibrated sensor and the abnormal monitoring data fluctuation curve B of the abnormal sensor; The feasibility analysis module is used to analyze the monitoring data fluctuation curve, obtain the expected input cost description parameters and expected calibration similarity, and filter out the uncalibrable sensors from the abnormal sensor set based on the analysis results, including: Apply the correction instruction to the abnormal monitoring data fluctuation curve B to maximize the expected calibration similarity between the standard curve A and the abnormal monitoring data fluctuation curve B, wherein the expected calibration similarity is an indicator for measuring the similarity between the standard monitoring data fluctuation curve A and the abnormal monitoring data fluctuation curve B of the abnormal sensor; the expected calibration similarity is recorded as Yxs; Establish a mapping relationship between correction instructions and fault type and fault degree, and output the fault degree and type based on the correction instructions and the mapping relationship; establish a fault repair cost map, input the fault degree and type, and output the time cost, economic cost and computing resource cost of repair; based on the time cost, economic cost and computing resource cost, obtain the expected input cost description parameters; If the expected input cost description parameter and the expected calibration similarity exceed the corresponding threshold, it indicates that the abnormal sensor is an uncalibrable sensor.

2. The intelligent detection data calibration system based on enhanced algorithm driving according to claim 1 is characterized in that: The correction instruction at least includes forward and backward movement and temperature-based correction; the forward and backward movement refers to a translation operation performed on the abnormal monitoring data fluctuation curve B, which is used to adjust the time offset and baseline offset; Temperature-based correction refers to adjusting the abnormal monitoring data fluctuation curve B according to temperature changes to eliminate the impact of temperature on sensor readings.

3. The intelligent detection data calibration system based on enhanced algorithm driving according to claim 2 is characterized in that: The calculation formula of the expected input cost description parameter YC is: Among them, Trepair is the total time required for calibration, CT is the total cost required for calibration; EC is the economic cost, directly expressed in monetary units; TC_time is the value after the time cost is converted into monetary units, multiplying the time cost by one unit of time cost, f1 is the conversion coefficient from time cost to economic cost; TC_com is the value after the computing power resource cost is converted into monetary units, f2 is the conversion coefficient from computing power resource cost to economic cost.

4. The intelligent detection data calibration system based on enhanced algorithm driving according to claim 3 is characterized in that: If the expected input cost description parameter and the expected calibration similarity exceed the corresponding threshold, the expected input cost description parameter YC and the expected calibration similarity Yxs are input into the calibration feasibility analysis model, and the calibration feasibility index JK is output; The calibration feasibility analysis model satisfies the formula Where YC_norm represents the expected input cost description parameter after linear normalization, Yxs_norm represents the expected calibration similarity after linear normalization, and λ is a correction constant to avoid division by zero; Determine whether the calibration feasibility index exceeds the threshold value THa. If the calibration feasibility index exceeds the threshold value THa, it indicates that the corresponding abnormal sensor is an uncalibrable sensor, and a second-level warning is issued to prompt the sensor to be replaced; If the calibration feasibility index does not exceed the threshold THa, it indicates that the corresponding abnormal sensor is a calibrable sensor, and a calibrable sensor set is obtained by summarizing.

5. The intelligent detection data calibration system based on enhanced algorithm driving according to claim 1 is characterized in that: By analyzing the commonalities and differences of correction instructions under different fault types and degrees, a mapping relationship between correction instructions and fault types and fault degrees is established; the fault type and degree are inferred based on the correction instructions of the mapping relationship and abnormal monitoring data fluctuation curve B.

6. The intelligent detection data calibration system based on enhanced algorithm driving according to claim 1 is characterized in that: Also included is an abnormal sensor calibration module, which is used to calibrate the calibrable sensor set, including: The area division unit is used to divide the sensor data into several areas according to the range and number them, with n representing the number of areas and i representing the area number; The calibration requirement analysis unit obtains the amplitude and frequency of the data deviation of each area based on the standard monitoring data fluctuation curve A and the abnormal monitoring data fluctuation curve B of the abnormal sensor; constructs a calibration requirement index based on the amplitude and frequency of the data deviation, and outputs the calibration requirement index of each area; The calibration resource allocation unit assigns calibration priorities to each region based on the calibration demand index; and reasonably allocates calibration resources according to the calibration demand index of each region; The model selection unit corrects the abnormal monitoring data fluctuation curve B of each area through a polynomial function, and uses a statistical method to find the polynomial function corresponding to each area; The iterative calibration unit makes the intelligent detection data meet the preset accuracy requirements through iterative calibration; the iterative calibration refers to the preliminary calibration of the calibrable sensor, and then collecting monitoring data in actual application to evaluate the calibration accuracy, and fine-tuning the calibration parameters according to the evaluated accuracy, and repeating this process until a satisfactory calibration accuracy is achieved.

7. The intelligent detection data calibration system based on enhanced algorithm driving according to claim 6 is characterized in that: The calibration requirement index Jx satisfies the formula Among them, pcf_avg and pcp_avg represent the average data deviation amplitude and data deviation frequency of the region respectively; pcf_max and pcp_max represent the maximum deviation amplitude and the highest deviation frequency of the region respectively; α and β are weight coefficients used to adjust the relative importance of the deviation amplitude and frequency in the index; the maximum deviation amplitude and the highest deviation frequency are the maximum values ​​of all regions and are used for normalization processing.

8. The intelligent detection data calibration system based on enhanced algorithm driving according to claim 6 is characterized in that: It also includes a calibration quality management module, which records the calibration process data and analyzes the calibration stability parameter and the calibration accuracy parameter, wherein the calibration process data includes the actual calibration time, the interval time between two adjacent calibration behaviors and the actual error after calibration; the calibration stability parameter and the calibration accuracy parameter are input into the calibration quality assessment model, and the calibration quality assessment is output. If the calibration quality assessment index is lower than the threshold THb, an early warning is issued to prompt attention to the calibration behavior and calibration instructions; the calibration quality assessment model satisfies the formula JP=Form(Jw*Jz), wherein Jw represents the calibration stability parameter, Jz represents the calibration accuracy parameter, and Form(·) represents the linear normalization function.

9. The intelligent detection data calibration system based on enhanced algorithm driving according to claim 1 is characterized in that: The calibration stability parameter satisfies the formula Among them, t 1i represents the actual time consumed for calibration of the i-th region, t 2i represents the interval between two consecutive calibration actions of the ith region, t 10 Indicates the preset calibration time, t 20 Indicates the preset calibration interval; the weight coefficient K of the i-th area is expressed by the ratio of the range of the i-th area to the total range. i ; The calibration accuracy parameter satisfies the formula Among them, d i represents the actual error after calibration of the i-th region, and d0 represents the preset error.

Images