A Pressure Warning Method and System Based on Threshold and State Trend Analysis

Abstract

The present invention discloses a pressure early warning method and system based on threshold and state trend analysis. The method obtains the pressure time series data of the most recently set first time period from a real-time gas database, and uses a sliding window moving average method to calculate the mean value of the current moment in the most recent third time period, the most recent fourth time period and the most recent fifth time period; identifies an upward or downward trend based on the calculated mean value, and calculates the duration of the trend; constructs a functional relationship between the trend target value and the duration; sets the threshold interval in segments according to the actual business scenario rules, and determines whether to trigger the early warning mechanism based on the relationship between the real-time value and the threshold value.

Description

Technical Field

[0001] The present application relates to the technical field of blast furnace gas pipeline network pressure control, and specifically, to a pressure warning method and system capable of improving the timeliness and accuracy of pressure warning. Background Art

[0002] Pressure early warning monitoring is a crucial aspect of blast furnace gas pipeline network monitoring. Gas dispatchers must constantly monitor changes in pipeline pressure. When pressure is too low or too high, they must notify relevant units and make production adjustments to maintain the pressure within an appropriate range. Currently, research on blast furnace gas pressure early warning is limited, and the few studies that exist only consider the impact of threshold factors.

[0003] The pressure warning method in the prior art is mainly based on the pressure signal data collected by the sensor, and is analyzed and processed through threshold judgment conditions to determine whether the pressure during detection exceeds a preset pressure threshold.

[0004] These methods have some limitations: (1) When processing a large amount of sensor data, only one value is retained per minute to avoid the problem of high computational complexity that may be encountered when processing a large amount of data. This method leads to poor real-time performance and loss of trend information within minutes; (2) When judging whether the threshold is exceeded, only the real-time value is used as the judgment basis, which may lead to misjudgment or warning delay for complex pressure change patterns. Summary of the Invention

[0005] The purpose of this application is to provide a pressure warning method and system based on threshold and state trend analysis to improve the defects of the existing technology such as poor real-time performance, loss of trend information within minutes, and possible misjudgment or warning delay.

[0006] The embodiments of this application adopt the following technical solutions:

[0007] A pressure early warning method based on threshold and state trend analysis, characterized by comprising the following steps:

[0008] Step 1: Obtain pressure data. Obtain the pressure time series data of the most recently set first time period from the real-time database and perform outlier processing. For the processed data, take the average value and resample it every second time period.

[0009] Step 2: Use the sliding window moving average method to calculate the average of the current time in the third, fourth, and fifth time periods;

[0010] Identify the upward or downward trend based on the calculated mean and calculate the duration of the trend;

[0011] Identify rising or falling trends based on the calculated mean, calculate the duration of the trend, and construct a functional relationship between the trend target value and duration;

[0012] Step 3: Based on the functional relationship between the target value and the duration, calculate the time required to reach the target value at the current trend rate; set the threshold intervals in segments according to the actual business scenario rules, and determine whether to trigger the warning mechanism based on the relationship between the real-time value and the threshold.

[0013] In the above technical solution, in step 1, outlier processing includes jump processing and missing value processing.

[0014] In the above technical solution, in step one, the jump processing is: compare the absolute value of the difference between the current value and the previous value, and the absolute value of the difference between the next value. If the two absolute values differ too much at the same time, the pressure value at that moment is modified to the average of the two; missing values and 0 values are also filled with the average of the two jumps.

[0015] In the above technical solution, in step 1, the second time period of data resampling is much shorter than the first time period.

[0016] In the above technical solution, in step 2, in the sliding window moving average method, the relationship between the durations of each segment is that the third time period is shorter than the fourth time period, the fourth time period is shorter than the fifth time period; and the second time period is much shorter than the third time period.

[0017] Preferably, the first time period is in hours, the second time period is in seconds, and the third time period is in minutes.

[0018] In the above technical solution, in step 2, the rule for identifying an upward trend or a downward trend is configured as follows: if the current mean is greater than the previous mean, it is marked as rising; if the current mean is less than the previous mean, it is marked as falling.

[0019] In the above technical solution, in step 2, the trend target value is constructed y The functional relationship with the duration of the trend is:

[0020] ;

[0021] in x is the current real-time pressure value, y The target value set in advance, k is the pressure change rate of the two times before the current moment, is the pressure value 2 unit times before the current moment, where the unit time is the second time period; is the specific value of the duration or trend duration corresponding to x at the current moment, is the value of the variable being sought.

[0022] In the above technical solution, in step 3, the relationship between the real-time value and the threshold value formed by setting the threshold interval in segments is configured as follows:

[0023] Set the threshold range [attention value lower limit, attention value upper limit, alarm value lower limit]. The thresholds in the threshold range are arranged from small to large. When the real-time value is less than the attention value lower limit, the early warning mechanism is not triggered. When the attention value lower limit is less than the real-time value and less than the attention value upper limit, the early warning mechanism is triggered. When the real-time value is greater than the alarm value lower limit, the alarm information is pushed immediately.

[0024] The present invention also provides a pressure early warning system based on threshold and state trend analysis, which is characterized by comprising:

[0025] Data acquisition module, used to obtain pressure time series data from the real-time database;

[0026] A data processing module, used for processing outliers and resampling the acquired data; the data processing module includes a sliding window moving average calculation function;

[0027] The trend identification module identifies rising or falling trends based on the calculated mean, calculates the duration of the trend, and constructs a functional relationship between the trend target value and the duration. Based on this functional relationship between the target value and the duration, it calculates the time required to reach the target value at the current trend rate.

[0028] The early warning judgment module sets the threshold interval in segments according to the actual business scenario rules, and determines whether to trigger the early warning mechanism based on the relationship between the real-time value and the threshold.

[0029] The present invention also provides a computer-readable storage medium, characterized in that it stores a computer program, and when the computer program is executed, it is used to implement the above-mentioned pressure warning method based on threshold value and state trend analysis.

[0030] In summary, the present invention provides a pressure trend warning method and system based on thresholds and duration of state, aiming to address the problem of false warnings in existing technologies and improve the timeliness and accuracy of pressure warnings. In particular, because pipeline pressure is affected by both production and consumption, and pressure changes frequently and complexly, existing threshold warning models fail to consider pressure states, such as distinguishing between rising and falling trends, leading to delayed and false alarms.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] This invention combines preset pressure thresholds, pressure trend identification, and pressure trend duration to form a pressure early warning method based on threshold and state trend analysis. The method includes data acquisition and preprocessing, trend state identification and statistics, and a segmented threshold trend early warning algorithm. The system includes a data acquisition module, a data processing module, a trend identification module, and an early warning judgment module. This can effectively improve the timeliness and accuracy of the pressure trend early warning model, addressing the gaps and deficiencies in research on blast furnace gas pressure early warning methods.

[0033] The present invention is not only applicable to trend warning of pipeline gas pressure, but is also widely applicable to physical quantity warning scenarios related to thresholds and trend warnings, such as water pressure and voltage.

[0034] The present invention aims to solve the problem of false warning in the prior art. By combining pressure trend identification and pressure trend duration, it solves the problem that the prior art only retains one value per minute to avoid large data processing volume, resulting in poor real-time performance and easy loss of trend information within one minute.

[0035] By resampling the pressure to obtain the average value over a period of time, data fluctuations can be alleviated, and combined with the pressure trend to further accurately determine whether an alarm is needed, the problems of false alarms and untimely alarms can be solved, making the warning results more accurate and reliable, and improving the timeliness and accuracy of pressure warnings.

[0036] Early warning: Based on the combined assessment of mean pressure, trend, and duration, this technology generates an alarm message one and a half minutes earlier than existing technologies, giving users time to react and prepare measures, and further accurately determining whether an alarm is necessary. Furthermore, by comprehensively considering trend and pressure balance, it effectively mitigates the impact of pressure fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0038] Figure 1 This is the overall flow chart of Example 1 of the present application, including data acquisition, trend status identification and statistics, and segmented threshold trend warning. DETAILED DESCRIPTION

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Generally, the components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0040] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for protection, but merely represents selected embodiments of the present application. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in the present application without creative work are within the scope of protection of the present application.

[0041] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0042] In the description of this application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, or are the orientations or positional relationships in which the product of this application is typically placed when in use. These terms are intended only to facilitate the description of this application and simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0043] Furthermore, terms such as "horizontal," "vertical," and "overhanging" do not necessarily imply that a component must be absolutely horizontal or overhanging, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but rather that it can be slightly tilted.

[0044] It should also be noted that, in the description of this application, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0045] In this application, unless otherwise expressly specified or limited, a first feature being "above" or "below" a second feature may include the first and second features being in direct contact, or may include the first and second features being in contact not directly but through another feature between them. Moreover, a first feature being "above," "above," and "above" a second feature may include the first feature being directly above or obliquely above the second feature, or may simply mean that the first feature is higher in level than the second feature. A first feature being "below," "below," and "below" a second feature may include the first feature being directly below or obliquely below the second feature, or may simply mean that the first feature is lower in level than the second feature.

[0046] The features and performance of the present application are further described in detail below with reference to the embodiments.

[0047] Example 1

[0048] The pressure warning method based on threshold value and state trend analysis in Example 1 of the present application is as follows Figure 1 As shown, the following steps are included:

[0049] Step 1: Obtain pressure data. Obtain the pressure time series data of the most recently set first time period from the real-time database and perform outlier processing. For the processed data, take the average value and resample it every second time period.

[0050] Step 2: Use the sliding window moving average method to calculate the average of the current time in the third, fourth, and fifth time periods;

[0051] Identify the upward or downward trend based on the calculated mean and calculate the duration of the trend;

[0052] Identify rising or falling trends based on the calculated mean, calculate the duration of the trend, and construct a functional relationship between the trend target value and duration;

[0053] Step 3: Based on the functional relationship between the target value and the duration, calculate the time required to reach the target value at the current trend rate; set the threshold intervals in segments according to the actual business scenario rules, and determine whether to trigger the warning mechanism based on the relationship between the real-time value and the threshold.

[0054] The specific steps are explained as follows:

[0055] Step 1: Pressure data acquisition and preprocessing

[0056] 1.1 Data acquisition and outlier processing

[0057] To analyze the pressure change trend, the pressure time series data (one pressure value every 2 seconds) of the last hour (set as the first time period) is obtained from the real-time database. Due to transmission communication problems, the original data may jump, be missing, or have a value of 0. Therefore, after obtaining the original data, preprocessing is performed for data anomalies in different scenarios: (1) Jump: Compare the absolute value of the difference between the current value and the previous value, and the absolute value of the difference between the next value. If the two absolute values differ too much at the same time, the pressure value at that moment is modified to ; (2) Missing and 0 values: Use Filling. This method of processing abnormal data through linear interpolation can preserve the trend in the original data.

[0058] 1.2 Data Resampling

[0059] Because the raw pressure data is collected at a 2-second interval, this short interval can result in two adjacent values being identical, leading to errors in identifying trend changes. Therefore, the preprocessed data is resampled, averaging the values every 10 seconds (set as the second time period). This balances data interval accuracy with data availability, improving the timeliness and accuracy of pressure data.

[0060] Step 2: Identification and statistics of rising / falling trend states

[0061] 2.1 Mean calculation of different windows

[0062] Moving average method using sliding window , where n is set to 6, 18, and 30 respectively, to calculate the average of the current time within the last minute (set to the third time period), the last 3 minutes (set to the fourth time period), and the last 5 minutes (set to the fifth time period). 、 、 2.2 Trend Identification

[0063] Rules: If and , it rises and is marked as 1;

[0064] like and , then it drops and is marked as -1;

[0065] Otherwise, mark it as 0.

[0066] According to the above calculation rules, the state at each moment is identified to obtain the state column at each moment, marked as S, such as [1,1,1,1,0,0,-1,-1,1,1,1,1,1,1].

[0067] 2.3 Trend duration statistics

[0068] Set the initial trend duration = 0.

[0069] Get the current state currentState. For example, if currentState = 1, it is in an upward state. For the state column S obtained in 2.2, count from the back to the previous. If the element is the same as currentState, then "trend duration + 1" is added. This continues until the first different element is encountered, then the accumulation is terminated. For example, when currentState = 1 and S = [1,1,1,1,0,0,-1,-1,1,1,1,1,1,1], the calculated trend duration = 6.

[0070] 2.4 Functional Relationship between Target Value and Duration

[0071] Through steps 2.1-2.3, we can get: current real-time value x, trend status (up or down), and trend duration. Then we can construct target value y and x , the functional relationship of the trend duration:

[0072] ;

[0073] Where y is the target value set in advance, x is the real-time value of pressure, k is the pressure change rate two times before the current moment, is the value (pressure value) two unit times before the current moment (the "unit time" is the second time period mentioned above), for x The corresponding duration (the specific value of the trend duration at the current moment), is the value of the variable to be sought. k The duration of the trend can be calculated by the two changes in the real-time value corresponding to the duration of the trend. The time required to reach the target value can be calculated using the above formula.

[0074] For example, setting the target value to y =14, real-time value x =12, =11, =7, the duration of the trend to reach the set target value . Then first find , then substitute the known conditions into the formula You can get .

[0075] Means: According to the current rate of change, the target value will be reached when the duration changes to 11, which is a long time from now. Unit time.

[0076] Step 3: Segmented Threshold Trend Warning Algorithm

[0077] 3.1 Interval Threshold Setting

[0078] Based on actual business scenario rules, set the threshold range [Note Down, Note Up, Report Down]. The thresholds are arranged from smallest to largest. Note Down represents the lower limit of the caution value, Note Up represents the upper limit of the caution value, and Report Down represents the lower limit of the alarm value. For simplicity, this example only illustrates a single-side high-pressure warning problem.

[0079] 3.2 Rules within different threshold ranges

[0080] When the real-time value is less than the note, the early warning mechanism is not triggered.

[0081] When the bet value is less than the real-time value and less than the bet value, the warning mechanism is triggered. First, identify the rising / falling trend through step 2.2. If it is in a falling trend, no alarm is triggered;

[0082] If it is in an upward trend, the duration of the trend is further obtained through step 2.3. Only when the trend continues for a period of time will the warning information be pushed, and step 2.4 is used to give how long it will take for the pressure value to reach the alarm value based on the existing working conditions.

[0083] When the real-time value > the reported value, the alarm information will be pushed immediately.

[0084] Example 2

[0085] The method of the present invention solves the problem of false alarms. Principle: 1. By resampling the pressure to obtain the average value over a period of time, data fluctuations are alleviated. 2. Under the conditions of pressure discrimination, the pressure trend is combined to further accurately determine whether an alarm is needed.

[0086] Table 1 Examples of solving false alarm problems

[0087]

[0088] Table 1 shows the comparative effects of the second embodiment of the present invention and the existing simple threshold method in solving the false alarm problem.

[0089] From the data in the 7th and 8th columns shown in Table 1, it can be seen that the prior art predicts an alarm, while the alarm condition of this embodiment is: the pressure value is lower than 7. After analysis, it is confirmed that no alarm is needed.

[0090] In view of the above situation, if the existing threshold alarm model is used, an alarm message will be pushed. If the method of the present invention is used, no alarm will be displayed.

[0091] According to the pressure change after the threshold model alarm, it can be seen that the low pressure value at the alarm moment is caused by pressure fluctuations. After a short change, it returns to normal and no longer meets the alarm condition.

[0092] Therefore, the alarm of the threshold model can be considered as a false alarm. Using the method of the present invention, since the current value of the processed data in the 7th column is 7.39 and the trend is in an upward state, the non-alarm is a correct judgment.

[0093] Example 3

[0094] By using the method of the present invention, an early warning is given.

[0095] Principle: 1. Determine based on mean pressure, trend, and duration. Alarm condition in this embodiment: pressure value is lower than 7.

[0096] Table 2 below shows the comparative effect of early warning given by Example 3 of the present invention compared with the existing simple threshold method.

[0097] Table 2 Early warning examples

[0098]

[0099] As shown in Table 2, if the existing simple threshold method is used, the first alarm condition is at 8:25 on August 12, 2024, and there are missed alarms due to pressure fluctuations after the alarm.

[0100] The first alarm using this method occurred at 8:23 AM on August 12, 2024. The alarm conditions were a one-minute pressure average less than 7 and a downward pressure trend that persisted for a period of time. By combining this pressure and trend approach, this embodiment can generate an alarm two minutes earlier and mitigate the impact of pressure fluctuations.

[0101] In other examples not shown, the alarm is given 1 minute 30 seconds and 1 minute 40 seconds in advance. It can be seen that the effect of the present invention in giving early warning is very obvious.

[0102] The embodiments described above are part of the embodiments of the present application, rather than all of the embodiments. The detailed description of the embodiments of the present application is not intended to limit the scope of the present application for protection, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

Claims

1. A pressure warning method based on threshold and state trend analysis, characterized in that: The following steps are involved: Step 1: Obtain pressure data, obtain the pressure time series data of the most recently set first time period from the real-time database, and perform outlier processing; For the processed data, the mean value is taken and resampled every second time period; Step 2: Use the sliding window moving average method to calculate the average of the current time in the third, fourth, and fifth time periods; Identify the upward or downward trend based on the calculated mean, calculate the duration of the trend and the pressure change rate, and construct a functional relationship between the pressure trend target value and duration; Step 3: Based on the functional relationship between the pressure trend target value and the duration, calculate the time required to reach the pressure trend target value at the current pressure change rate; According to the actual business scenario rules, the threshold intervals are set in segments, and the relationship between the real-time value and the threshold is used to determine whether to trigger the early warning mechanism; The relationship between the real-time value and the threshold value of the segmented threshold interval is configured as follows: Set the threshold range [lower limit of attention value, upper limit of attention value, lower limit of alarm value], and arrange the thresholds in the threshold range from small to large. When the real-time value is less than the lower limit of attention value, the warning mechanism will not be triggered; When the lower limit of the attention value is less than the real-time value and the upper limit of the attention value, the warning mechanism is triggered: if it is in a downward trend, no alarm is given; if it is in an upward trend, the warning information is pushed after the trend continues for a period of time, and the time it takes for the pressure value to reach the alarm value under the existing working conditions is calculated; When the real-time value is greater than the lower limit of the alarm value, the alarm information will be pushed immediately.

2. The stress warning method based on threshold and state trend analysis according to claim 1 is characterized in that: In step 1, outlier processing includes jump processing and missing value processing.

3. The stress warning method based on threshold and state trend analysis according to claim 1 is characterized in that: In step 1, the jump processing is: compare the absolute value of the difference between the current value and the previous value, and the absolute value of the difference between the next value. If the two absolute values differ too much at the same time, the pressure value at that moment is modified to the average of the two; missing and 0 values are also filled with the average of the two jumps.

4. The stress warning method based on threshold and state trend analysis according to claim 1 is characterized in that: In step 1, the second time period of data resampling is much shorter than the first time period.

5. The stress warning method based on threshold and state trend analysis according to claim 1 is characterized in that: In step 2, in the sliding window moving average method, the relationship between the durations of the segments is that the third time period is shorter than the fourth time period, the fourth time period is shorter than the fifth time period; and the second time period is much shorter than the third time period.

6. The stress warning method based on threshold and state trend analysis according to claim 1 is characterized in that: In step 2, the rule for identifying an upward trend or a downward trend is configured as follows: if the current mean is greater than the previous mean, it is marked as an upward trend; if the current mean is less than the previous mean, it is marked as a downward trend.

7. The stress warning method based on threshold and state trend analysis according to claim 1 is characterized in that: In step 2, the functional relationship between the pressure trend target value and the trend duration is constructed as follows: ； in x is the current real-time pressure value, y The trend target value set in advance, k is the pressure change rate of the two times before the current moment, is the pressure value 2 unit times before the current moment, where the unit time is the second time period; is the specific value of the duration or trend duration corresponding to x at the current moment, is the value of the variable being sought.

8. A stress warning system based on threshold and state trend analysis, characterized in that: A pressure warning method based on threshold and state trend analysis for implementing any one of claims 1 to 7, comprising: Data acquisition module, used to obtain pressure time series data from the real-time database; A data processing module, used for processing outliers and resampling the acquired data; the data processing module includes a sliding window moving average calculation function; The trend identification module identifies rising or falling trends based on the calculated mean, calculates the duration of the trend, and constructs a functional relationship between the trend target value and the duration. Based on this functional relationship between the target value and the duration, it calculates the time required to reach the target value at the current trend rate. The early warning judgment module sets the threshold interval in segments according to the actual business scenario rules, and determines whether to trigger the early warning mechanism based on the relationship between the real-time value and the threshold.

9. A computer-readable storage medium, characterized in that A computer program is stored, and when the computer program is executed, it is used to implement the pressure warning method based on threshold value and state trend analysis as described in any one of claims 1 to 7.

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