Mine sensor detection data adaptive optimization method

Abstract

The present application relates to a kind of mining sensor detection data adaptive optimization method, belong to sensor technical field, including the following steps: S1: set mining sensor output detection original data queue as X [n];S2: sensor first time work, single output detection original data is d, while the element of original data queue X [n] is all assigned d;S3: from second data, data d is sequentially stored in the last bit of original data queue X [n], and the first data is removed from X [n], the ratio between elements in original data queue X [n] is calculated, i.e.data stability factor;S4: data stability factor is analyzed, let the number of greater than 1 be C1, the number of equal to 1 be C2, the number of less than 1 be C3;According to the numerical relationship of C1, C2, C3 different processing functions are handled, and real-time update is carried out, and the final detection data is calculated.

Description

Technical Field

[0001] This invention belongs to the field of sensor technology and relates to an adaptive optimization method for detection data of mining sensors. Background Technology

[0002] As a crucial component of safety monitoring systems, mine sensors are primary environmental sensing devices in coal mine roadways. Effective monitoring of explosive and toxic gases in coal mines is vital, making timely and reliable sensors a fundamental guarantee for safe coal mine production. With the development of coal mine equipment technology, the widespread use of high-power and wireless equipment has led to a complex electromagnetic environment in mine roadways. This environment easily interferes with the weak signals output by sensors, resulting in reduced measurement accuracy and insensitive response. Slow sensor response can lead to untimely data acquisition, impacting production efficiency; low measurement accuracy can cause errors in production decisions, affecting safe production. Currently, commonly used sensors for carbon monoxide, oxygen, and wind speed employ sensitive elements with weak output signals, occasionally exhibiting "large values" or insufficient response time in coal mine roadways, severely reducing the stability and reliability of monitoring systems. Therefore, improving the measurement accuracy and response speed of mine sensors is an urgent problem to be solved.

[0003] Currently, existing technical solutions for improving sensor response time and data accuracy mainly involve using technologies such as structural protection, hardware circuit compensation, and temperature compensation to improve the measurement data accuracy and response time of sensors. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide an adaptive method to improve the response time and data accuracy of mining sensors. By continuously collecting the raw detection data output by the sensor, the method performs real-time trend prediction on the changing patterns of the raw detection data sequence, and adopts an adaptive data processing method, thereby ensuring high-precision measurement by the sensor, further accelerating the sensor's response speed, and improving the stability and accuracy of the sensor's operation.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] An adaptive optimization method for detection data from mining sensors includes the following steps:

[0007] S1: Let the output detection raw data queue of the mining sensor be X[n], where the value range of n is A≤n≤B, and A and B are preset constants;

[0008] S2: When the sensor works for the first time, the single output detection raw data is d, and at the same time, each element in the raw data queue X[n] is assigned the value d;

[0009] S3: Starting from the second data point, store the collected data d sequentially into the last position of the original data queue X[n], remove the first data point from X[n], and calculate the ratio between elements in the original data queue X[n], i.e., the data stability factor θ. n-1 =X n / X n-1 In the formula X n Let X be the nth data item in the original data queue. n-1 This is the (n-1)th data item in the original data queue;

[0010] S4: Calculate the data stability factors θ1, θ2...θ n-1 Perform data analysis, let the number of values ​​greater than 1 be C1, the number of values ​​equal to 1 be C2, and the number of values ​​less than 1 be C3;

[0011] If C3 > C1 + C2, it is determined that the sensor output data is in a rapidly decreasing mode. It is predicted that the single output detection raw data in the next processing cycle will decrease, and the data processing function f(X,n-C3) is used for data processing.

[0012] If C2>C1+C3, the sensor output data is determined to be in a stable detection mode. It is inferred that the original data of the single output detection in the next processing cycle will remain basically unchanged. Data processing function f(X,n) is used for data processing.

[0013] If C1>C3+C2, it is determined that the sensor output data is in a rapid increasing mode. It is predicted that the single output detection raw data of the next processing cycle will increase. The data processing function f(X,n-C1) is used for data processing.

[0014] S5: Repeat steps S3-S4 to continuously update the single output detection raw data d and the raw data queue X[n], determine and predict the data change trend, determine the data processing function parameters, and calculate the final detection data.

[0015] Furthermore, in step S4, let the sum of C1, C2, and C3 be n-1.

[0016] Furthermore, in step S4, the data processing function f(X,n) is calculated as follows:

[0017] Initialize and define three variables P n R0, m, where P n R0 is the process error, m is the average value of the raw data, and Xn is the average value of the raw data for this period.

[0018] Obtain the first measurement data X1, and process the data according to the following formula: X1′=(X1-m)*P0 / (P0+R0)+m;

[0019] Calculate the next filtering parameter P1: P1 = (1 - P0 / (P0 + R0)) * P0;

[0020] Obtain the second measurement data X2, and process the data according to the following formula: X2′=(X2-X1′)*P1 / (P1+R0)+X1′;

[0021] Calculate the next filtering parameter P2: P2 = (1 - P1 / (P1 + R0)) * P1;

[0022] Obtain the third measurement data X3, and process the data according to the following formula: X3′=(X3-X2′)*P2 / (P2+R0)+X2′;

[0023] Calculate the next filtering parameter P3: P3 = (1 - P2 / (P2 + R0)) * P2;

[0024] This process continues uninterrupted, involving trend analysis and data processing, i.e., X. n ′ is the precise value obtained after the original data queue has undergone data processing functions.

[0025] Furthermore, if C3 > C1 + C2, the calculated result of Xn is:

[0026] If C2 > C1 + C3, the result of Xn calculation is:

[0027] If C1 > C3 + C2, the result of Xn calculation is:

[0028] The beneficial effects of this invention are as follows: This invention focuses on the changing patterns of the original data sequence of sensor measurements, uses data change trend prediction and adaptive data processing methods to improve the sensor measurement accuracy, further reduces the sensor response time under the same environment, improves the sensor intelligence level, and reduces the sensor maintenance workload of coal miners.

[0029] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0030] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0031] Figure 1 Flowchart of an adaptive optimization method for detection data from mining sensors. Detailed Implementation

[0032] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0033] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0034] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0035] Please see Figure 1 This invention provides an adaptive optimization method for detection data from mining sensors, including...

[0036] S1: Let the sensor output detection raw data queue be X[n], where the value of n is in the range A≤n≤B. In this embodiment, the value of A is 10, the value of B is 200, and the initial value of n is 40.

[0037] S2: When the sensor works for the first time, the single output detection raw data is d, and at the same time, each element in the raw data queue X[n] is assigned the value d to ensure the accuracy of trend prediction;

[0038] S3: Starting from the second data point, store the collected data d sequentially into the last position of the original data queue X[n], remove the first data point from X[n], and calculate the ratio between elements in the original data queue X[n], i.e., the data stability factor θ. n-1 =X n / X n- 1.

[0039] S4: Calculate the data stability factors θ1, θ2...θ n-1 If we perform data analysis, and the number of terms greater than 1 is C1, the number of terms equal to 1 is C2, and the number of terms less than 1 is C3, then the sum of C1, C2, and C3 is n-1.

[0040] S5: If C3>C1+C2, it can be determined that the sensor output data is in a rapidly decreasing mode. It can be predicted that the single output detection raw data in the next processing cycle will decrease, and the data processing function f(X,n-C3)) is used for data processing.

[0041] S6: If C2>C1+C3, it can be determined that the sensor output data is in a stable detection mode. It can be inferred that the single output detection raw data in the next processing cycle will remain basically unchanged, and the data processing function f(X,n)) is used for data processing.

[0042] S7: If C1>C3+C2, it can be determined that the sensor output data is in a rapid increasing mode. It can be predicted that the single output detection raw data in the next processing cycle will increase, and the data processing function f(X,n-C1)) is used for data processing.

[0043] S8: Repeat steps S3, S4, S5, S6, and S7 to continuously update the original data d and the original data queue X[n] for each output detection, determine and predict the data change trend, determine the data processing function parameters, and calculate the final detection data.

[0044] In steps S5, S6, and S7, the data processing function f(X,n) is calculated as follows:

[0045] Initialize and define three variables P n R0, m, where P n R0 is the process error, m is the average value of the raw data (usually the average value of the raw data from the first sensor detection), and Xn is the average value of the raw data for the current period.

[0046] Obtain the first measurement data X1, and process the data according to the following formula: X1′=(X1-m)*P0 / (P0+R0)+m;

[0047] Calculate the next filtering parameter P1: P1 = (1 - P0 / (P0 + R0)) * P0;

[0048] Obtain the second measurement data X2, and process the data according to the following formula: X2′=(X2-X1′)*P1 / (P1+R0)+X1′;

[0049] Calculate the next filtering parameter P2: P2 = (1 - P1 / (P1 + R0)) * P1;

[0050] Obtain the third measurement data X3, and process the data according to the following formula: X3′=(X3-X2′)*P2 / (P2+R0)+X2′;

[0051] Calculate the next filtering parameter P3: P3 = (1 - P2 / (P2 + R0)) * P2;

[0052] This process continues uninterrupted, involving trend analysis and data processing, i.e., X. n ′ is the precise value obtained after the original data queue has undergone data processing functions, that is, the high-precision measurement value of the sensor after data processing. P n The initial value is set to 1, R0 is determined according to the error of different sensors, and is generally between 2 and 7. m is the average value of the first series.

[0053] If the calculation result of Xn adopts the requirement of S5 in claim 1, then

[0054] If the calculation result of Xn adopts the requirement of S6 in claim 1, then

[0055] If the calculation result of Xn adopts the requirement of S7 in claim 1, then

[0056] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium. When the program is executed, it can implement the steps of the method. The storage medium may be, for example, ROM / RAM, magnetic disk, optical disk, etc.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An adaptive optimization method for detection data from mining sensors, characterized in that: Includes the following steps: S1: Let the output detection raw data queue of the mining sensor be X[n], where the value range of n is A≤n≤B, and A and B are preset constants; S2: When the sensor works for the first time, the single output detection raw data is d, and at the same time, each element in the raw data queue X[n] is assigned the value d; S3: Starting from the second data point, store the collected data d sequentially into the last position of the original data queue X[n], remove the first data point from X[n], and calculate the ratio between elements in the original data queue X[n], i.e., the data stability factor θ. n-1 =X n / X n-1 In the formula X n Let X be the nth data item in the original data queue. n-1 This is the (n-1)th data item in the original data queue; S4: Calculate the data stability factors θ1, θ2...θ n-1 Perform data analysis, let the number of values ​​greater than 1 be C1, the number of values ​​equal to 1 be C2, and the number of values ​​less than 1 be C3; If C3 > C1 + C2, it is determined that the sensor output data is in a rapidly decreasing mode. It is predicted that the single output detection raw data in the next processing cycle will decrease, and the data processing function f(X, n-C3) is used for data processing. If C2>C1+C3, the sensor output data is determined to be in a stable detection mode. It is inferred that the original data of the single output detection will remain basically unchanged in the next processing cycle. The data processing function f(X, n) is used for data processing. If C1 > C3 + C2, it is determined that the sensor output data is in a rapid increasing mode. It is predicted that the single output detection raw data in the next processing cycle will increase, and the data processing function f(X, n-C1) is used for data processing. Let the sum of C1, C2, and C3 be n-1; The calculation method for the data processing function f(X, n) is as follows: Initialize and define three variables P n R0, m, where P n R0 is the process error, m is the average value of the raw data, and Xn is the average value of the raw data for this period. Obtain the first measurement data X1, and process the data according to the following formula. = (X1-m)*P0 / (P0+R0)+m; Calculate the next filtering parameter P1: P1 = (1 - P0 / (P0 + R0)) * P0; Obtain the second measurement data X2, and process the data according to the following formula. = (X2- )*P1 / (P1+R0)+ ; Calculate the next filtering parameter P2: P2 = (1 - P1 / (P1 + R0)) * P1; Obtain the third measurement data X3, and process the data according to the following formula. = (X3- )*P2 / (P2+R0)+ ; Calculate the next filtering parameter P3: P3 = (1 - P2 / (P2 + R0)) * P2; This process continues uninterrupted, enabling trend analysis and data processing. It is the precise value obtained after the original data queue has undergone data processing functions; If C3 > C1 + C2, the result of Xn calculation is: ; If C2 > C1 + C3, the result of Xn calculation is: ; If C1 > C3 + C2, the result of Xn calculation is: ; S5: Repeat steps S3-S4 to continuously update the single output detection raw data d and the raw data queue X[n], determine and predict the data change trend, determine the data processing function parameters, and calculate the final detection data.

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