An environmental monitoring instrument abnormal state identification method, device, equipment and medium

Abstract

The application discloses an environmental monitoring instrument abnormal state identification method and device, equipment and medium, and relates to the field of automatic online monitoring, which comprises the following steps: collecting parameter signal values monitored by an environmental monitoring instrument during sample testing; drawing a curve based on the parameter signal values corresponding to each key parameter to obtain a current signal value change curve; performing consistency evaluation on corresponding segments in the current signal value change curve and a standard signal value change curve to obtain a consistency index; taking the index weight as a power index, performing weighted geometric mean calculation on the consistency index to obtain a geometric mean credibility, generating a dynamic penalty factor based on the index threshold and the consistency index; calculating the product of the dynamic penalty factor and the geometric mean credibility to obtain a credibility score, and determining whether the instrument is in an abnormal state based on the size relationship between the credibility score and a credibility threshold. The application can improve the accuracy of environmental monitoring instrument abnormal state identification, and reduce the false negative rate and the false positive rate.

Description

Technical Field

[0001] This application relates to the field of automatic online monitoring, and in particular to a method, device, equipment and medium for identifying abnormal states of environmental monitoring instruments. Background Technology

[0002] In the field of environmental monitoring, automated monitoring instruments (such as particulate matter sensors, multi-parameter water quality analyzers, gas analyzers, and noise monitors) are deployed on a large scale in various complex sites to collect continuous, real-time environmental data. These instruments are key infrastructure for achieving precise environmental monitoring, early warning and forecasting, and scientific decision-making. However, in actual operation, due to factors such as harsh deployment environments, long-term continuous operation, and lack of daily human monitoring, malfunctions of monitoring instruments are unavoidable.

[0003] Traditional methods for identifying abnormal states of monitoring instruments mainly rely on periodic manual on-site inspections, manual review of instrument panel data, or simple threshold alarms. These methods have the following shortcomings: Poor timeliness: There is a significant delay between the occurrence and detection of abnormal states, and the reliance on manual inspections results in long cycles, which cannot meet the needs of rapid response to environmental events and may lead to the omission or false alarm of critical pollution events; High concealment: Many internal faults (such as slight aging of components or deviations in key parameters) do not immediately cause complete data failure in the early stages, but only manifest as decreased accuracy or abnormal data within a specific range. The abnormality patterns are extremely concealed, and there is a lack of effective means for automatic identification. This type of "sub-healthy" or "working while sick" state; strong professional dependence: anomaly judgment heavily relies on the experience and professional knowledge of operation and maintenance personnel, making it difficult to achieve large-scale (massive number of sites) automated diagnosis. Investing a lot of manpower in frequent inspections and troubleshooting will increase operation and maintenance costs, and the accessibility of remote and dangerous areas is poor; narrow identification range: traditional threshold alarm methods can usually only identify extreme abnormal values ​​(such as over-range, signal loss, and lack of liquid), and lack the ability to comprehensively judge the gradual degradation of sensor performance, complex internal faults (such as circuit abnormalities and logic errors), and anomalies under specific working conditions (such as abnormal air pressure causing slow sensor response). Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method, device, equipment, and medium for identifying abnormal states of environmental monitoring instruments, which can improve the accuracy of identifying abnormal states of environmental monitoring instruments, reduce the false alarm rate and missed alarm rate, and save labor and time costs. The specific solution is as follows:

[0005] In a first aspect, this application discloses a method for identifying abnormal states of environmental monitoring instruments, including:

[0006] The signal values ​​of various key parameters monitored by environmental monitoring instruments at different times during sample testing are collected to obtain multiple parameter signal values ​​corresponding to each key parameter; the key parameters include test environment parameters and instrument operating parameters;

[0007] Based on the multiple parameter signal values ​​corresponding to each of the key parameters, curves of their changes over time are plotted to obtain the current signal value change curve;

[0008] Consistency assessments are performed on corresponding segments of the current signal value change curve and the standard signal value change curve to obtain a consistency index for each segment; the standard signal value change curve is a curve plotted over time based on the standard signal values ​​corresponding to each of the key parameters.

[0009] The index weights corresponding to each segment are used as power exponents, and the corresponding consistency index is calculated by weighted geometric mean to obtain the geometric mean confidence level of each segment. A dynamic penalty factor is generated based on the preset index thresholds corresponding to each segment and the consistency index.

[0010] The product of the dynamic penalty factor and the geometric mean confidence level of the corresponding segment is calculated to generate the data confidence score of the corresponding segment. Based on the relationship between the multiple data confidence scores corresponding to each key parameter and the confidence threshold, it is determined whether the environmental monitoring instrument is in an abnormal state.

[0011] Optionally, the step of performing consistency evaluation on corresponding segments of the current signal value change curve and the standard signal value change curve to obtain a consistency index corresponding to each segment includes:

[0012] Calculate the similarity between the current signal value change curve and the standard signal value change curve to obtain the curve similarity value, and determine whether the curve similarity value is greater than or equal to a preset similarity threshold;

[0013] If the curve similarity values ​​corresponding to each of the key parameters are all greater than or equal to the preset similarity threshold, then the consistency evaluation is performed on the corresponding segments of the current signal value change curve and the standard signal value change curve to obtain the consistency index corresponding to each segment.

[0014] Optionally, the formula for generating the dynamic penalty factor is:

[0015] ;

[0016] In the formula, The consistency index represents any segment corresponding to the i-th key parameter. P represents the preset exponential threshold for any segment corresponding to the i-th key parameter, P is the preset penalty intensity coefficient, and PF represents the dynamic penalty factor.

[0017] Optionally, if the key parameter is temperature, then the consistency evaluation of corresponding segments in the current signal value change curve and the standard signal value change curve, respectively, to obtain the consistency index corresponding to each segment, includes:

[0018] According to the preset segmentation marks, the initial segment, rising segment, stationary segment and falling segment in the current signal value change curve and the standard signal value change curve are evaluated in sequence to obtain the consistency index corresponding to each segment.

[0019] Optionally, a consistency evaluation is performed on the rising / falling segments of the current signal value change curve and the standard signal value change curve to obtain a consistency index corresponding to the rising / falling segments, including:

[0020] The slopes of the rising / falling segments in the current signal value change curve and the standard signal value change curve are fitted using the least squares method to obtain the first slope and the second slope.

[0021] The slope ratio is obtained by calculating the ratio of the first slope to the second slope, and the absolute error of the slope ratio to 1 is calculated.

[0022] The consistency index corresponding to the absolute error is determined based on the preset mapping relationship between the absolute error value and the consistency index value.

[0023] Optionally, determining whether the environmental monitoring instrument is in an abnormal state based on the relationship between the multiple data credibility scores corresponding to each of the key parameters and the credibility threshold includes:

[0024] Determine whether the multiple data credibility scores corresponding to each of the key parameters are all greater than the credibility threshold;

[0025] If any of the multiple data credibility scores corresponding to the key parameter is less than the credibility threshold, then the environmental monitoring instrument is determined to be in an abnormal state.

[0026] If the data credibility scores corresponding to each of the key parameters are all greater than the credibility threshold, then the environmental monitoring instrument is determined to be in normal condition.

[0027] Optionally, the method for identifying abnormal states of environmental monitoring instruments further includes:

[0028] Determine the target credibility score that is not greater than the credibility threshold among all the data credibility scores corresponding to each of the key parameters;

[0029] Based on the target credibility score and the corresponding key parameters and segments, an early warning suggestion is generated indicating that the environmental monitoring instrument is in an abnormal state, and the early warning suggestion is sent to the corresponding user terminal.

[0030] Secondly, this application discloses an abnormal state identification device for environmental monitoring instruments, comprising:

[0031] The acquisition module is used to acquire the signal values ​​of various key parameters monitored by the environmental monitoring instrument at different times during sample testing, and to obtain multiple parameter signal values ​​corresponding to each key parameter; the key parameters include test environment parameters and instrument operating parameters;

[0032] The curve plotting module is used to plot curves that change over time based on multiple parameter signal values ​​corresponding to each of the key parameters, thereby obtaining the current signal value change curve.

[0033] The evaluation module is used to evaluate the consistency of corresponding segments in the current signal value change curve and the standard signal value change curve, respectively, and obtain the consistency index corresponding to each segment; the standard signal value change curve is a curve that changes over time based on the standard signal values ​​corresponding to each key parameter.

[0034] The first calculation module is used to use the index weights corresponding to each segment as power exponents, calculate the weighted geometric average of the corresponding consistency index, obtain the geometric average confidence level of each segment, and generate a dynamic penalty factor based on the preset index thresholds corresponding to each segment and the consistency index.

[0035] The second calculation module is used to calculate the product of the dynamic penalty factor and the geometric mean confidence level of the corresponding segment to generate the data confidence score of the corresponding segment.

[0036] An abnormal state determination module is used to determine whether the environmental monitoring instrument is in an abnormal state based on the relationship between the multiple data credibility scores corresponding to each of the key parameters and the credibility threshold.

[0037] Thirdly, this application discloses an electronic device, including a processor and a memory; wherein, when the processor executes a computer program stored in the memory, it implements the aforementioned method for identifying abnormal states of environmental monitoring instruments.

[0038] Fourthly, this application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for identifying abnormal states of environmental monitoring instruments.

[0039] As can be seen, this application first collects the signal values ​​of each key parameter monitored by the environmental monitoring instrument at different times during sample testing, obtaining multiple parameter signal values ​​corresponding to each key parameter; the key parameters include test environment parameters and instrument operating parameters; curves of change over time are plotted based on the multiple parameter signal values ​​corresponding to each key parameter to obtain the current signal value change curve; consistency evaluation is performed on the corresponding segments of the current signal value change curve and the standard signal value change curve to obtain the consistency index corresponding to each segment; the standard signal value change curve is a curve of change over time plotted based on the standard signal values ​​corresponding to each key parameter; the index weight corresponding to each segment is used as a power exponent, and a weighted geometric mean is calculated on the corresponding consistency index to obtain the geometric mean confidence level corresponding to each segment, and a dynamic penalty factor is generated based on the preset index threshold corresponding to each segment and the consistency index; the product of the dynamic penalty factor and the geometric mean confidence level of the corresponding segment is calculated to generate the data confidence score of the corresponding segment, and the relationship between the multiple data confidence scores corresponding to each key parameter and the confidence threshold is used to determine whether the environmental monitoring instrument is in an abnormal state. This application first assesses the consistency between the current signal value change curve of the environmental monitoring instrument and corresponding segments of the standard signal value change curve, obtaining a consistency index for each segment. By comparing the change curves of key parameters in the current testing process with the standard curve, the reliability of the test data currently monitored by the environmental monitoring instrument can be ensured. Furthermore, compared to the overall consistency assessment, the segmented approach further improves the accuracy of the assessment. Next, the index weights corresponding to each segment are used as power exponents, and a weighted geometric mean is calculated for the corresponding consistency indices to obtain the geometric mean confidence level for each segment. This confidence level is then based on a preset index threshold for each segment. The consistency index generates a dynamic penalty factor. Finally, the product of the dynamic penalty factor and the geometric mean confidence level of the corresponding segment is calculated to generate the data confidence score of the corresponding segment. Based on the data confidence score, it is determined whether the instrument is in an abnormal state. By calculating the weighted geometric mean of the consistency index, a slight decrease in the consistency index can cause an exponential decay in the geometric mean confidence level. This makes the final calculated data confidence score more accurately reflect the confidence level of each segment, thereby improving the accuracy of instrument abnormal state identification and reducing the false alarm rate and missed alarm rate. In addition, the above method realizes automated abnormal state identification, thereby saving labor and time costs. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0041] Figure 1 This is a flowchart of an abnormal state identification method for environmental monitoring instruments disclosed in this application;

[0042] Figure 2 This is a schematic diagram of temperature changes during a specific sample testing process disclosed in this application;

[0043] Figure 3 This application discloses a flowchart of a specific method for identifying abnormal states of environmental monitoring instruments.

[0044] Figure 4 This is a schematic diagram of the structure of an abnormal state identification device for an environmental monitoring instrument disclosed in this application;

[0045] Figure 5 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0046] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] This application discloses a method for identifying abnormal states of environmental monitoring instruments. (See also...) Figure 1 As shown, the method includes:

[0048] Step S11: Collect the signal values ​​of each key parameter monitored by the environmental monitoring instrument at different times during the sample testing process, and obtain multiple parameter signal values ​​corresponding to each key parameter; the key parameters include test environment parameters and instrument operating parameters.

[0049] Understandably, environmental monitoring instruments are core equipment for acquiring environmental quality data, and their continuous operation and data quality directly affect the scientific nature of environmental management decisions. Furthermore, environmental monitoring instruments rely on various physical / chemical sensors (such as optical, electrochemical, catalytic combustion, and electrode methods). These sensors have inherent characteristics such as zero-point drift, range drift, sensitivity decay, response time variations, and nonlinear errors, and are easily affected by environmental factors (temperature, humidity, air pressure, and cross-interference from pollutants). Therefore, in order to identify abnormal states of environmental monitoring instruments, it is necessary to collect signal values ​​of key parameters, including test environment parameters (such as ambient temperature, light intensity, humidity, and air pressure) and instrument operating parameters (such as pump pressure, light source intensity, internal temperature, and power supply voltage).

[0050] In this embodiment, during the testing of samples (such as particulate matter samples, water quality samples, gas samples, etc.) by environmental monitoring instruments (such as particulate matter sensors, water quality multi-parameter analyzers, gas analyzers, noise monitors, etc.), the signal values ​​of key parameters (such as temperature, light intensity, potential, measurement signals, etc.) at different times throughout the entire testing process are collected in real time. For non-core testing procedures / non-critical parameters, a larger allowable error range can be set.

[0051] Step S12: Plot curves of changes over time based on the multiple parameter signal values ​​corresponding to each of the key parameters to obtain the current signal value change curve.

[0052] In this embodiment, after collecting multiple parameter signal values ​​corresponding to different key parameters during the environmental monitoring instrument testing process, further, the multiple parameter signal values ​​of each key parameter at different times are plotted on the vertical axis and time (t) is plotted on the horizontal axis to obtain the current signal value change curve corresponding to each key parameter.

[0053] Step S13: Perform consistency evaluation on the corresponding segments of the current signal value change curve and the standard signal value change curve respectively to obtain the consistency index corresponding to each segment; the standard signal value change curve is a curve drawn based on the standard signal values ​​corresponding to each key parameter and changing over time.

[0054] In this embodiment, after plotting the current signal value change curves corresponding to each key parameter, the current signal value change curves can be compared with the corresponding segments of a standard signal value change curve plotted over time based on a preset segmentation rule to obtain the consistency index corresponding to each segment. By evaluating the consistency of the two curves, the reliability of the currently obtained test data (i.e., signal values) can be ensured.

[0055] The number of segments for each curve can be selected according to actual application needs and is related to the distribution characteristics of each key parameter during the test. The actual number of segments of the curve can be specifically selected based on different test indicators and test requirements.

[0056] In one specific implementation, the step of performing consistency evaluation on corresponding segments of the current signal value change curve and the standard signal value change curve to obtain a consistency index corresponding to each segment may specifically include: calculating the similarity between the current signal value change curve and the standard signal value change curve to obtain a curve similarity value, and determining whether the curve similarity value is greater than or equal to a preset similarity threshold; if the curve similarity values ​​corresponding to each of the key parameters are all greater than or equal to the preset similarity threshold, then performing consistency evaluation on corresponding segments of the current signal value change curve and the standard signal value change curve to obtain a consistency index corresponding to each segment. In this embodiment, see... Figure 2 As shown, Figure 2 The curves showing the change of the current signal value and the standard signal value corresponding to the temperature parameter are displayed. First, a similarity fitting is performed on the two curves to obtain the curve similarity value. The specific calculation formula is as follows:

[0057] ;

[0058] In the formula, This represents the i-th signal value in the curve representing the current signal value change of a key parameter (such as temperature). This represents the i-th signal value in the standard signal value change curve corresponding to a certain key parameter (such as temperature).

[0059] Next, determine the curve similarity value. Whether it is greater than or equal to a preset similarity threshold, if the curve similarity value of the key parameter is... If the similarity value is less than the preset similarity threshold, it indicates that the difference between the two curves corresponding to the key parameter (such as temperature) is large, suggesting that the test data (i.e., parameter signal values) is unreliable and may not reflect the true situation; if the curve similarity value of the key parameter is less than the preset similarity threshold, it indicates that the difference between the two curves is large, suggesting that the test data (i.e., parameter signal values) is unreliable and may not reflect the true situation; If the similarity threshold is greater than or equal to the preset threshold, it indicates that the difference between the two curves corresponding to the key parameter (such as temperature parameter) is small, and the test data (i.e. parameter signal value) is reliable. At this time, the subsequent process can continue, that is, to evaluate the consistency of each segment in the two curves separately, that is, to judge the consistency of the two curves segment by segment.

[0060] In one specific implementation, if the key parameter is temperature, then the consistency evaluation of corresponding segments in the current signal value change curve and the standard signal value change curve to obtain a consistency index corresponding to each segment can specifically include: sequentially evaluating the consistency of the initial segment, rising segment, stable segment, and falling segment in the current signal value change curve and the standard signal value change curve according to preset segmentation marks, and obtaining a consistency index corresponding to each segment. In this embodiment, if the key parameter is temperature, the current signal value change curve and the standard signal value change curve can be divided according to preset segmentation marks to obtain four different segments including the initial segment, rising segment, stable segment, and falling segment, which correspond to different stages of the entire test process.

[0061] The calculation process for the consistency index of the initial segment (corresponding to the initial stage of the sample measurement process, this segment reflects the test environment and standby state of the instrument, and the relevant parameter information (i.e., signal values) of this segment reflects the stability of the test environment and the initial state of the instrument) specifically includes the following steps: First, it is preliminarily determined whether each temperature value in this segment exceeds the upper limit of the temperature required by the test environment (e.g., the optimal test condition for the instrument is below 40℃). If there is a temperature value exceeding 40℃ in this segment, a temperature anomaly warning message is generated or the linkage control system adjusts the temperature in the station; if none of the temperature values ​​in this segment exceed 40℃, the absolute error of the two temperature values ​​at each time point in the two segmented curves is calculated separately. The specific calculation formula is as follows:

[0062] ;

[0063] In the formula, This represents the temperature values ​​within the initial segment of the current signal value change curve. This represents the temperature values ​​within the initial segment of the standard signal value change curve.

[0064] Next, based on the preset error threshold (like =10°C), calculate the consistency ratio (Indicates absolute error) At the error threshold The percentage of temperature points within a certain range), specifically calculated using the following formula:

[0065] ;

[0066] In the formula, For absolute error at the error threshold The total number of temperature points within the initial segment, M represents the total number of temperature points within the initial segment.

[0067] Finally, based on the consistency ratio Determine the consistency index for the initial segmentation, for example, when When the percentage is ≥95% (the preset percentage threshold), the signal value collected in that segment is considered consistent with the actual situation, and the consistency index of the initial segment is set accordingly. Set to 1; for example, when 90% ≤ If the accuracy is less than 95%, the signal value collected in that segment is considered consistent with the actual situation, and the consistency index of the initial segment is adjusted accordingly. Set it to 0.9, and so on. If the value is less than the preset percentage threshold (e.g., 50%), it is considered that the signal value collected in that segment is inconsistent with the actual situation. At this point, the current abnormal state identification process can be terminated directly, and the corresponding temperature abnormality alarm information can be generated.

[0068] The calculation process for the consistency index of stationary segments includes the following steps: First, calculate the absolute error of two temperature values ​​at the same time point within the segment. The specific calculation formula is as follows: ;

[0069] In the formula, This represents the temperature values ​​within the stationary segment of the current signal value change curve. This represents the temperature values ​​within the stable segments of the standard signal value change curve.

[0070] Next, the mean absolute error within the stationary segment is calculated. The specific calculation formula is as follows:

[0071] ;

[0072] In the formula, L represents the total number of temperature points within the stationary segment.

[0073] Finally, based on the mean absolute error , compared with the preset average error threshold (like The relationship between (=1°C) determines the consistency index corresponding to the stationary segmentation. For example, when ≤ At that time, it was considered that the passage was consistent with the actual situation, and the consistency index was 1. =1; For example, when > At that time, it was considered that the passage was consistent with the actual situation, and the consistency index was 1. =0.5.

[0074] In this embodiment, a consistency evaluation is performed on the rising / falling segments of the current signal value change curve and the standard signal value change curve to obtain a consistency index corresponding to the rising / falling segments. Specifically, this may include: fitting the slopes of the rising / falling segments of the current signal value change curve and the standard signal value change curve using the least squares method to obtain a first slope and a second slope; calculating the ratio of the first slope to the second slope to obtain a slope ratio, and calculating the absolute error of the slope ratio with respect to 1; determining the consistency index corresponding to the absolute error based on a preset mapping relationship between the absolute error value and the consistency index value. In this embodiment, the same consistency index acquisition method is used for both the rising and falling segments. For example, for the rising segment, the slope of the rising segment of the current signal value change curve and the standard signal value change curve is first fitted using the least squares method to obtain a first slope. Second slope Among them, the first slope It can be obtained through the following formula:

[0075] ;

[0076] in, This represents the temperature values ​​within the rising segment of the current signal value change curve, when t=0. = ;

[0077] Second slope It can be obtained through the following formula:

[0078] ;

[0079] in, This represents the temperature values ​​within the rising segment of the standard signal value change curve, when t=0. = .

[0080] Next, calculate the first slope. With the second slope The ratio of the slope values ​​is obtained by the slope ratio. ,Right now And calculate the slope ratio The absolute error of 1 Then, based on the preset mapping relationship between the absolute error value and the consistency index value, the current absolute error is determined. The corresponding consistency index, for example, if the absolute error ≤ (like If the value is 0.1, then the segment is considered consistent with the actual situation, and the consistency index is 0.1. =1; For example, when ≤ ≤ (like When the coefficient of variation is 0.2, the segment is considered consistent with the actual situation, and the consistency index is 0.2. =0.8; and so on. Similarly, the consistency index of the descent segment can be determined by calculating the slope ratio.

[0081] Step S14: Using the index weights corresponding to each segment as power exponents, calculate the weighted geometric mean of the corresponding consistency index to obtain the geometric mean confidence level of each segment, and generate a dynamic penalty factor based on the preset index thresholds corresponding to each segment and the consistency index.

[0082] In this embodiment, consistency evaluation is performed on the corresponding segments of the two signal value change curves corresponding to each key parameter. After obtaining the consistency index corresponding to each segment, the index weights corresponding to each segment can be... As a power exponent, for the corresponding consistency index The weighted geometric mean (GMC) is calculated to obtain the geometric mean confidence level (GMC) for each segment. The specific calculation formula is as follows:

[0083] ;

[0084] This scheme calculates the geometric mean confidence level (GMC), which is equivalent to introducing a nonlinear "bottleneck effect." This makes the final result highly dependent on the segment with the worst consistency, and the consistency index... A slight decrease in the value will trigger an exponential decay of GMC. Then, based on the preset exponential threshold corresponding to each segment... and consistency index Generate a dynamic penalty factor PF.

[0085] Specifically, the formula for generating the dynamic penalty factor is:

[0086] ;

[0087] In the formula, The consistency index represents any segment corresponding to the i-th key parameter. P represents the preset exponential threshold for any segment corresponding to the i-th key parameter, P is the preset penalty intensity coefficient, and PF represents the dynamic penalty factor.

[0088] Step S15: Calculate the product of the dynamic penalty factor and the geometric mean confidence level of the corresponding segment to generate the data confidence score of the corresponding segment, and determine whether the environmental monitoring instrument is in an abnormal state based on the relationship between the multiple data confidence scores corresponding to each key parameter and the confidence threshold.

[0089] In this embodiment, the product of the dynamic penalty factor PF and the geometric mean confidence level GMC of the corresponding segment is calculated, and the calculation result is used as the data confidence score CCS of the corresponding segment (this score comprehensively reflects the data confidence level corresponding to the corresponding key parameter, and the value range is [0,1]). The specific calculation formula is as follows: Then, it is determined whether the multiple data credibility scores corresponding to each key parameter are greater than the credibility threshold. If the data credibility scores corresponding to all key parameters are greater than the credibility threshold, the environmental monitoring instrument is determined to be in normal condition. If there is a score among the multiple data credibility scores corresponding to any key parameter that is not greater than the credibility threshold, the environmental monitoring instrument is determined to be in abnormal condition.

[0090] Specifically, determining whether the environmental monitoring instrument is in an abnormal state based on the relationship between the multiple data credibility scores corresponding to each key parameter and the credibility threshold may include: determining whether all the multiple data credibility scores corresponding to each key parameter are greater than the credibility threshold; if there is a score among the multiple data credibility scores corresponding to the key parameter that is less than the credibility threshold, then the environmental monitoring instrument is determined to be in an abnormal state; if all the multiple data credibility scores corresponding to each key parameter are greater than the credibility threshold, then the environmental monitoring instrument is determined to be in a normal state. That is, determining whether all data credibility scores corresponding to each key parameter are greater than the credibility threshold; if so, the environmental monitoring instrument is determined to be in a normal state; if not (i.e., there are key parameters whose data credibility scores are not greater than the credibility threshold), then the environmental monitoring instrument is determined to be in an abnormal state.

[0091] As can be seen, this embodiment first evaluates the consistency between the current signal value change curve of the environmental monitoring instrument and the corresponding segments of the standard signal value change curve to obtain the consistency index corresponding to each segment. By evaluating / comparing the consistency of the change curves of key parameters in the current test process with the standard curve, the reliability of the test data currently monitored by the environmental monitoring instrument can be ensured. Moreover, compared with the overall consistency evaluation, the accuracy of the evaluation is further improved by segmentation. Next, the index weights corresponding to each segment are used as power exponents, and the corresponding consistency indices are calculated by weighted geometric mean to obtain the geometric mean confidence level corresponding to each segment. Based on the preset index corresponding to each segment... Thresholds and consistency indices generate dynamic penalty factors. Finally, the product of the dynamic penalty factor and the geometric mean confidence level of the corresponding segment is calculated to generate the data confidence score for the corresponding segment. The data confidence score is then used to determine whether the instrument is in an abnormal state. By using a weighted geometric mean to calculate the consistency index, a slight decrease in the consistency index can cause an exponential decay in the geometric mean confidence level. This makes the final calculated data confidence score more accurate in reflecting the confidence level of each segment, thereby improving the accuracy of instrument abnormal state identification and reducing the false alarm and missed alarm rates. Furthermore, the above method achieves automated abnormal state identification, thus saving labor and time costs. In addition, compared with the traditional simple, static, and single-point threshold judgment method that sets a fixed threshold (which cannot maintain the best judgment performance throughout the entire equipment life cycle, may become too lenient after the equipment ages, leading to missed detections in the later stages of the equipment, or false alarms in specific scenarios, and the judgment accuracy decreases over time and with environmental changes), this application can realize dynamic abnormal state recognition in different scenarios by generating a dynamic penalty factor. This fundamentally solves the problems of high missed detections, high false alarms, difficult diagnosis, and poor adaptability, and realizes a more timely, accurate, and reliable automated assessment of the abnormal state of the monitoring instrument itself.

[0092] This application discloses a specific method for identifying abnormal states of environmental monitoring instruments. (See also...) Figure 3 As shown, the method includes:

[0093] Step S21: Collect the signal values ​​of each key parameter monitored by the environmental monitoring instrument at different times during the sample testing process, and obtain multiple parameter signal values ​​corresponding to each key parameter; the key parameters include test environment parameters and instrument operating parameters.

[0094] Step S22: Plot curves of changes over time based on the multiple parameter signal values ​​corresponding to each of the key parameters to obtain the current signal value change curve.

[0095] Step S23: Perform consistency evaluation on the corresponding segments in the current signal value change curve and the standard signal value change curve respectively to obtain the consistency index corresponding to each segment; the standard signal value change curve is a curve drawn based on the standard signal values ​​corresponding to each key parameter and changing over time.

[0096] Step S24: Using the index weights corresponding to each segment as power exponents, perform a weighted geometric mean calculation on the corresponding consistency index to obtain the geometric mean confidence level corresponding to each segment, and generate a dynamic penalty factor based on the preset index thresholds corresponding to each segment and the consistency index.

[0097] Step S25: Calculate the product of the dynamic penalty factor and the geometric mean confidence level of the corresponding segment to generate the data confidence score of the corresponding segment, and determine whether the environmental monitoring instrument is in an abnormal state based on the relationship between the multiple data confidence scores corresponding to each key parameter and the confidence threshold.

[0098] Step S26: Determine the target credibility score that is not greater than the credibility threshold among all the data credibility scores corresponding to each key parameter.

[0099] In this embodiment, if the environmental monitoring instrument is determined to be in an abnormal state, the target confidence score that is not greater than the confidence threshold among all data confidence scores (CCS) corresponding to each key parameter is taken as the cause of the abnormal state.

[0100] Step S27: Based on the target credibility score and the corresponding key parameters and segments, generate an early warning suggestion that the environmental monitoring instrument is in an abnormal state, and send the early warning suggestion to the corresponding user terminal.

[0101] In this embodiment, after finding the cause of the anomaly, corresponding early warning suggestions can be generated based on the cause of the anomaly. For example, early warning suggestions can be generated based on the target credibility score causing the anomaly, the corresponding key parameters, and the specific anomaly segment. The early warning suggestions can then be sent to the corresponding user terminals so that the relevant technical personnel / maintenance personnel can quickly know that the current environmental monitoring instrument is in an abnormal state. At the same time, they can accurately know the cause of the abnormal state, thereby quickly locating the cause of the anomaly so that corresponding measures can be taken to deal with the anomaly.

[0102] For more detailed processing procedures of steps S21 to S25, please refer to the relevant content disclosed in the foregoing embodiments, which will not be repeated here.

[0103] As can be seen, when the environmental monitoring instrument is identified as being in an abnormal state, this application's embodiment generates corresponding early warning suggestions based on the cause of the abnormal state and sends these suggestions to the corresponding user terminals. This not only detects the instrument's abnormal state but also quickly and accurately identifies the root cause, providing decision support for rapid and precise repairs and improving operational efficiency. Furthermore, compared to the traditional method of fault location where maintenance personnel rely on extensive on-site troubleshooting and testing based on personal experience after receiving an alarm, this solution directly provides early warning suggestions containing the cause of the abnormality, thereby reducing the mean time to repair (MTTR) and operational costs. Furthermore, by calculating the product of the dynamic penalty factor and the geometric mean confidence level of the corresponding segments, and identifying abnormal states based on the generated data confidence scores, it can promptly identify progressive faults and performance degradation trends that traditional threshold methods cannot detect, greatly reducing the false negative rate. Moreover, through multi-source information fusion and cross-validation (i.e., segmented judgment of parameter signal values ​​of multiple key parameters), it can effectively distinguish between real faults and environmental interference, significantly improving alarm confidence. Additionally, the evaluation benchmark can be dynamically updated as the equipment (i.e., environmental monitoring instruments) and environment change, ensuring the effectiveness of abnormal state identification throughout the entire lifecycle. Furthermore, since no human intervention is required, unattended continuous monitoring and identification can be achieved, reducing reliance on manpower, making it suitable for large-scale monitoring networks, and lowering long-term operation and maintenance costs.

[0104] Accordingly, this application also discloses an abnormal state identification device for environmental monitoring instruments, see [link to relevant documentation]. Figure 4 As shown, the device includes:

[0105] The acquisition module 11 is used to acquire the signal values ​​of various key parameters monitored by the environmental monitoring instrument at different times during the sample testing process, and to obtain multiple parameter signal values ​​corresponding to each key parameter; the key parameters include test environment parameters and instrument operating parameters;

[0106] The curve plotting module 12 is used to plot curves that change over time based on multiple parameter signal values ​​corresponding to each of the key parameters, so as to obtain the current signal value change curve.

[0107] Evaluation module 13 is used to evaluate the consistency of corresponding segments in the current signal value change curve and the standard signal value change curve respectively, and obtain the consistency index corresponding to each segment; the standard signal value change curve is a curve that changes over time based on the standard signal values ​​corresponding to each key parameter;

[0108] The first calculation module 14 is used to use the index weights corresponding to each segment as power exponents, perform weighted geometric average calculation on the corresponding consistency index to obtain the geometric average confidence level corresponding to each segment, and generate a dynamic penalty factor based on the preset index thresholds corresponding to each segment and the consistency index.

[0109] The second calculation module 15 is used to calculate the product of the dynamic penalty factor and the geometric mean confidence of the corresponding segment to generate the data confidence score of the corresponding segment.

[0110] The abnormal state determination module 16 is used to determine whether the environmental monitoring instrument is in an abnormal state based on the relationship between the multiple data credibility scores corresponding to each key parameter and the credibility threshold.

[0111] The specific workflow of each of the above modules can be found in the relevant content disclosed in the foregoing embodiments, and will not be repeated here.

[0112] Furthermore, embodiments of this application also disclose an electronic device, Figure 5 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0113] Figure 5 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the environmental monitoring instrument abnormality identification method disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0114] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0115] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0116] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the environmental monitoring instrument abnormality identification method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include a computer program capable of performing other specific tasks.

[0117] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for identifying abnormal states of environmental monitoring instruments. The specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0118] Furthermore, embodiments of this application also disclose a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the environmental monitoring instrument abnormal state identification method disclosed above.

[0119] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0120] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0121] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0122] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0123] The above provides a detailed description of the method, apparatus, equipment, and medium for identifying abnormal states of environmental monitoring instruments provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for identifying abnormal states of environmental monitoring instruments, characterized in that, include: The signal values ​​of various key parameters monitored by environmental monitoring instruments at different times during sample testing are collected to obtain multiple parameter signal values ​​corresponding to each key parameter; the key parameters include test environment parameters and instrument operating parameters; Based on the multiple parameter signal values ​​corresponding to each of the key parameters, curves of their changes over time are plotted to obtain the current signal value change curve; Consistency assessments are performed on corresponding segments of the current signal value change curve and the standard signal value change curve to obtain a consistency index for each segment; the standard signal value change curve is a curve plotted over time based on the standard signal values ​​corresponding to each of the key parameters. The index weights corresponding to each segment are used as power exponents, and the corresponding consistency index is calculated by weighted geometric mean to obtain the geometric mean confidence level of each segment. A dynamic penalty factor is generated based on the preset index thresholds corresponding to each segment and the consistency index. The product of the dynamic penalty factor and the geometric mean confidence level of the corresponding segment is calculated to generate the data confidence score of the corresponding segment. Based on the relationship between the multiple data confidence scores corresponding to each key parameter and the confidence threshold, it is determined whether the environmental monitoring instrument is in an abnormal state. The formula for generating the dynamic penalty factor is: ； In the formula, This represents the preset index threshold for the i-th segment. The consistency index corresponding to the i-th segment is represented by P, which is a preset penalty intensity coefficient, and PF represents the dynamic penalty factor. The formula for calculating the geometric mean confidence level (GMC) is as follows: ； In the formula, Let be the exponential weight corresponding to the i-th segment.

2. The method for identifying abnormal states of environmental monitoring instruments according to claim 1, characterized in that, The process of evaluating the consistency of corresponding segments in the current signal value change curve and the standard signal value change curve to obtain a consistency index for each segment includes: Calculate the similarity between the current signal value change curve and the standard signal value change curve to obtain the curve similarity value, and determine whether the curve similarity value is greater than or equal to a preset similarity threshold; If the curve similarity values ​​corresponding to each of the key parameters are all greater than or equal to the preset similarity threshold, then the consistency evaluation is performed on the corresponding segments of the current signal value change curve and the standard signal value change curve to obtain the consistency index corresponding to each segment.

3. The method for identifying abnormal states of environmental monitoring instruments according to claim 1, characterized in that, If the key parameter is temperature, then the consistency evaluation of corresponding segments in the current signal value change curve and the standard signal value change curve is performed respectively to obtain the consistency index corresponding to each segment, including: According to the preset segmentation marks, the initial segment, rising segment, stationary segment and falling segment in the current signal value change curve and the standard signal value change curve are evaluated in sequence to obtain the consistency index corresponding to each segment.

4. The method for identifying abnormal states of environmental monitoring instruments according to claim 3, characterized in that, A consistency evaluation is performed on the rising / falling segments of the current signal value change curve and the standard signal value change curve to obtain a consistency index corresponding to the rising / falling segment, including: The slopes of the rising / falling segments in the current signal value change curve and the standard signal value change curve are fitted using the least squares method to obtain the first slope and the second slope. The slope ratio is obtained by calculating the ratio of the first slope to the second slope, and the absolute error of the slope ratio to 1 is calculated. The consistency index corresponding to the absolute error is determined based on the preset mapping relationship between the absolute error value and the consistency index value.

5. The method for identifying abnormal states of environmental monitoring instruments according to claim 1, characterized in that, The determination of whether the environmental monitoring instrument is in an abnormal state based on the relationship between the multiple data credibility scores corresponding to each of the key parameters and the credibility threshold includes: Determine whether the multiple data credibility scores corresponding to each of the key parameters are all greater than the credibility threshold; If any of the multiple data credibility scores corresponding to the key parameter is less than the credibility threshold, then the environmental monitoring instrument is determined to be in an abnormal state. If the data credibility scores corresponding to each of the key parameters are all greater than the credibility threshold, then the environmental monitoring instrument is determined to be in normal condition.

6. The method for identifying abnormal states of environmental monitoring instruments according to any one of claims 1 to 5, characterized in that, Also includes: Determine the target credibility score that is not greater than the credibility threshold among all the data credibility scores corresponding to each of the key parameters; Based on the target credibility score and the corresponding key parameters and segments, an early warning suggestion is generated indicating that the environmental monitoring instrument is in an abnormal state, and the early warning suggestion is sent to the corresponding user terminal.

7. An abnormal state identification device for environmental monitoring instruments, characterized in that, include: The acquisition module is used to acquire the signal values ​​of various key parameters monitored by the environmental monitoring instrument at different times during sample testing, and to obtain multiple parameter signal values ​​corresponding to each key parameter; the key parameters include test environment parameters and instrument operating parameters; The curve plotting module is used to plot curves that change over time based on multiple parameter signal values ​​corresponding to each of the key parameters, thereby obtaining the current signal value change curve. The evaluation module is used to evaluate the consistency of corresponding segments in the current signal value change curve and the standard signal value change curve, respectively, and obtain the consistency index corresponding to each segment; the standard signal value change curve is a curve that changes over time based on the standard signal values ​​corresponding to each key parameter. The first calculation module is used to use the index weights corresponding to each segment as power exponents, calculate the weighted geometric average of the corresponding consistency index, obtain the geometric average confidence level of each segment, and generate a dynamic penalty factor based on the preset index thresholds corresponding to each segment and the consistency index. The second calculation module is used to calculate the product of the dynamic penalty factor and the geometric mean confidence level of the corresponding segment to generate the data confidence score of the corresponding segment. An abnormal state determination module is used to determine whether the environmental monitoring instrument is in an abnormal state based on the relationship between the multiple data credibility scores corresponding to each of the key parameters and the credibility threshold. The formula for generating the dynamic penalty factor is: ； In the formula, This represents the preset index threshold for the i-th segment. The consistency index corresponding to the i-th segment is represented by P, which is a preset penalty intensity coefficient, and PF represents the dynamic penalty factor. The formula for calculating the geometric mean confidence level (GMC) is as follows: ； In the formula, Let be the exponential weight corresponding to the i-th segment.

8. An electronic device, characterized in that, It includes a processor and a memory; wherein, when the processor executes a computer program stored in the memory, it implements the environmental monitoring instrument abnormality identification method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, Used to store computer programs; wherein, when the computer programs are executed by a processor, they implement the environmental monitoring instrument abnormality identification method as described in any one of claims 1 to 6.

Images