A method for monitoring and predicting electrostatic phenomena

Abstract

The present invention relates to a method for monitoring and predicting electrostatic phenomena, the method comprising the following steps: constructing an algorithm model based on existing electrostatic data; collecting real-time data; analyzing and predicting through an algorithm model based on real-time data, recording, storing and updating electrostatic data; iterating the algorithm model based on updated electrostatic data; and closed-loop application of the iterated algorithm model. The present invention provides a method for monitoring and predicting electrostatic phenomena, by collecting information on the charge and environment of personnel or equipment, constructing an algorithm model based on grey correlation analysis, and predicting the electrostatic discharge risk of the acquired real-time data, and the algorithm model can be iteratively updated based on the update of the electrostatic data each time the electrostatic phenomenon is predicted to occur, and once again applied to electrostatic prediction to form a closed loop, and the accuracy of the prediction results of the algorithm model is improved as the number of predictions increases, effectively preventing dangers caused by electrostatic discharge, and solving the shortcomings of existing electrostatic prevention measures.

Description

Technical Field

[0001] The present invention relates to the technical field of electrostatic protection, and in particular to a method for monitoring and predicting electrostatic phenomena. Background Art

[0002] Static electricity is a common physical phenomenon, which refers to the imbalance of electric charge between objects due to friction or contact. It is common in production and life, and its hazards are multifaceted. It not only affects human health and production and life safety, but also may cause damage to electronic equipment and the environment. In particular, in some special occasions, if static electricity is not prevented, it will have very serious consequences, as follows: (I) Fire or explosion: Although the amount of static electricity is not large, it is easy to discharge due to its high voltage, generating static sparks. In workplaces with flammable liquids, static sparks may cause explosions.

[0003] 13. (ii) Electric shock: The static electricity carried by the human body can be as high as tens of thousands of volts. Although the electric shock caused by static electricity will not directly cause death, the human body may fall or trip due to the electric shock, causing a secondary accident.

[0004] 14. (III) Interference with production: Static electricity may interfere with production or reduce product quality.

[0005] 15. The hazards of static electricity are obvious to all. In daily life and industrial production, effective measures should be taken to prevent the generation and accumulation of static electricity. However, the existing anti-static measures are mostly to release the charge carried by the human body or equipment at one time through some active discharge equipment. However, in the subsequent process, the human body and equipment will still accumulate charge and generate static electricity. Summary of the invention

[0006] In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a method for monitoring and predicting electrostatic phenomena to solve one or more problems in the prior art.

[0007] 17. To achieve the above object, the technical solution of the present invention is as follows: A method for monitoring and predicting electrostatic phenomena, the method comprising the following steps: Construction of algorithmic models based on existing electrostatic data; Collect real-time data; Based on real-time data, the algorithm model is used to analyze and predict, record, store and update static data; Iterate the algorithm model based on updated electrostatic data; Closed-loop application of the iterative algorithm model.

[0008] 18. Furthermore, the construction of the algorithm model is based on grey correlation analysis, including the following steps: Clarify the purpose of analysis and determine the number series, wherein the number series includes a reference number series and a comparison number series; Data preprocessing, including dimensionless processing and data standardization; Calculate the correlation coefficient and correlation degree; Relevance ranking and model establishment.

[0009] 19. Furthermore, the real-time data includes the charge accumulation amount, the charge accumulation location, and environmental data including temperature and humidity.

[0010] 20. Furthermore, the reference sequence is a data sequence indicating whether static electricity occurs; the comparison sequence includes the charge accumulation amount and the environmental data.

[0011] 21. Furthermore, the said infinite tempering process is based on the initialization process of the comparison series to obtain a new data series.

[0012] 22. Furthermore, the data normalization is achieved through linear transformation based on the new data column combined with Min-Max normalization.

[0013] 23. Further, the calculation formula for calculating the correlation coefficient is as follows:

[0014] Where: ρ is the resolution coefficient, 0<ρ<1, usually 0.5, max is the maximum value of the absolute difference between all comparison series and reference series at each corresponding time or index, Δoi(t) represents the i-th comparison series X i The absolute difference with the reference series X0 at the tth moment or index.

[0015] 24. Furthermore, the calculation formula of the correlation degree is as follows:

[0016] Where: r i represents the correlation between the ith comparison sequence and the reference sequence, mean(.) represents the function for finding the average value, ξ ij (X i ): the i-th comparison sequence X i The correlation coefficient with the reference series at the jth moment or index.

[0017] 25. Further, the acquisition of the charge accumulation amount and the charge accumulation position comprises the following steps: Establish central points based on the monitoring environment; Symmetrically distributed detection plates are arranged around the center point; Locate the charge accumulation position of the charged body and calculate the charge accumulation amount.

[0018] 26. Further, the formula for calculating the charge accumulation amount is as follows:

[0019] Where: E represents the electric field strength, k represents the Coulomb constant, q represents the charge of the point charge, and r represents the distance between the two point charges.

[0020] 27. Compared with the prior art, the beneficial technical effects of the present invention are as follows: The present invention provides a method for monitoring and predicting electrostatic phenomena. This method collects information on the charge carried by personnel or equipment and environmental information, constructs an algorithm model based on grey correlation analysis, and predicts the risk of electrostatic discharge for the acquired real-time data. Moreover, the algorithm model can be iteratively updated based on the update of electrostatic data each time an electrostatic phenomenon is predicted to occur, and is once again applied to electrostatic prediction to form a closed loop. As the number of predictions increases, the accuracy of the prediction results of the algorithm model is improved, effectively preventing dangers caused by electrostatic discharge, solving the shortcomings of existing electrostatic prevention measures, and can be coordinated with existing preventive measures for actively discharging charges to further improve the reliability of electrostatic prevention. 28. Description of the drawings Figure 1 A schematic flow chart of a method for monitoring and estimating electrostatic phenomena according to an embodiment of the present invention is shown.

[0022] 29. Figure 2 A structural schematic diagram of a method for monitoring and estimating electrostatic phenomena according to an embodiment of the present invention is shown. 30. Specific implementation methods In order to make the purpose, technical scheme and advantages of the present invention clearer, the following is a further detailed description of a monitoring and prediction method for electrostatic phenomena proposed by the present invention in combination with the accompanying drawings and specific embodiments. According to the following description, the advantages and features of the present invention will be clearer. It should be noted that the accompanying drawings adopt a very simplified form and use non-precise proportions, which are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention. In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, please refer to the accompanying drawings. It should be noted that the structure, proportion, size, etc. illustrated by the accompanying drawings of this specification are only used to match the content disclosed in the specification, so that people familiar with this technology can understand and read, and are not used to limit the limiting conditions for the implementation of the present invention, so they have no technical substantive significance. Any modification of the structure, change of the proportional relationship or adjustment of the size, without affecting the effect that the present invention can produce and the purpose that can be achieved, should still fall within the scope of the technical content disclosed by the present invention.

[0024] 31. Please refer to Figure 1 The method for monitoring and predicting electrostatic phenomena of this embodiment includes the following steps: Step 1: Construction of algorithm model based on existing electrostatic data.

[0025] 32. First, the recorded electrostatic data is called up, and the initial algorithm model is constructed through this data. The electrostatic data is the following real-time data, including the charge accumulation amount, the charge accumulation position, and the environmental data with temperature and humidity.

[0026] 33. The construction of the algorithm model is based on grey correlation analysis, which is a grey system analysis method. It measures the degree of correlation between factors according to the similarity or difference in the development trends between factors, namely "grey correlation degree", and specifically includes the following steps: Step 1.1: Clarify the purpose of analysis and determine the sequence, which includes reference sequence and comparison sequence.

[0027] 34. In this embodiment, the purpose of the analysis is to predict the occurrence of static electricity, that is, to find out the correlation between the amount of charge accumulation, environmental data and static electricity occurrence to provide a judgment standard for subsequent warnings.

[0028] 35. Further, the reference sequence is a data sequence of whether static electricity occurs. Preferably, the occurrence of static electricity can be recorded as 1, and the non-occurrence can be recorded as 0. The comparison sequence includes charge accumulation and environmental data.

[0029] 36. Step 1.2: Data preprocessing, including dimensionless processing and data standardization.

[0030] 37. The dimensionless tempering process is based on the initialization process of the comparison series, and the data of each comparison series is divided by the first data of the corresponding series, i.e., the initial value, to obtain a new data series. Since the physical meanings of various factors in the system are different, the dimensions of the data may also be different, so dimensionless data processing is required for comparison.

[0031] 38. Further, the data normalization is achieved by linear transformation based on the new data column combined with Min-Max normalization. The Min-Max normalization maps the data to a new range between 0 and 1 by subtracting the minimum value from each value in the new data column and then dividing it by the difference between the maximum and minimum values. This transformation method preserves the relative order of the data and does not change the distribution shape of the data.

[0032] 39. Step 1.3: Calculate the correlation coefficient and degree of correlation.

[0033] 40. The calculation of correlation coefficient is to calculate the correlation coefficient of each comparison series and the reference series at each moment (each moment here refers to the information corresponding to the scene where the static electricity phenomenon occurs) by using the grey correlation analysis formula. This reflects the influence of each factor on the occurrence of static electricity at a specific moment. For a reference series X0 and several comparison series X1, X2,…, X n , the correlation coefficient ξ(X i ) can be calculated by the following formula:

[0034] Where: ρ is the resolution coefficient, 0<ρ<1, usually 0.5, Δmax is the maximum absolute difference between all comparison series and reference series at each corresponding time or index, Δoi(t) represents the value of the ith comparison series X i The absolute difference with the reference series X0 at the tth moment or index.

[0035] 41. Further, the correlation degree is to concentrate the correlation coefficients at each moment into one value, and usually take the average value as the quantitative representation of the correlation degree between the comparison series and the reference series. This reflects the average influence of each factor on the occurrence of static electricity over the entire time period. The correlation degree calculation formula is shown as follows:

[0036] Where: r i represents the correlation between the ith comparison sequence and the reference sequence, mean(.) represents the function for finding the average value, ξ ij (X i ): the i-th comparison sequence X i The correlation coefficient with the reference series at the jth moment or index.

[0037] 42. Step 1.4: Relevance ranking and model establishment.

[0038] 43. Sort the influencing factors according to the degree of correlation and find out the factors that have the greatest impact on static electricity. The greater the degree of correlation, the better the evaluation result.

[0039] 44. Further, based on the results of the correlation analysis, an algorithm model for predicting the occurrence of static electricity can be established to quantify the impact of various factors on the occurrence of static electricity, and use this as a judgment standard to predict the occurrence of static electricity phenomena, thus completing the preliminary establishment of the algorithm model.

[0040] 45. Step 2: Collect real-time data.

[0041] 46. ​​The real-time data is as described above, including charge accumulation amount, charge accumulation position, temperature data and humidity data. The temperature data and humidity data are both detected by temperature sensors, humidity sensors, etc. The acquisition of charge accumulation amount and charge accumulation position includes the following steps: Step 2.1: Establish a central point based on the monitoring environment.

[0042] 47. The central point, i.e. the origin, shall be established according to the purpose of monitoring and the surrounding monitoring environment.

[0043] 48. Step 2.2: Set up symmetrically distributed detection plates around the center point.

[0044] 49. Based on the principle of electrostatic field, the induction electrode is used to detect the change of the induced electric field caused by the change of the charge amount accumulated by the charged body, so as to obtain the information of the charged target (which can be a person or an object). Since the electric detection plate is omnidirectional to the electrostatic field, in this embodiment, four detection plates are deployed. Figure 2 shown.

[0045] 50. Step 2.3: Locate the charge accumulation position of the charged body and calculate the charge accumulation amount.

[0046] 51. The position of the charged object is accurately located by the four detection plates deployed above. The charge accumulation is reflected by the electrostatic field strength. Since the electrostatic field strength is determined by the accumulated charge, the charge of the target object can be reflected by correlating the electrostatic field strength and the distance between the target object and each detection plate. The formula for calculating the charge accumulation is as follows:

[0047] Where: E represents the electric field strength, k represents the Coulomb constant, q represents the charge of the point charge, and r represents the distance between the two point charges.

[0048] 52. Step 3: Analyze and predict based on real-time data through algorithm model, record, store and update electrostatic data.

[0049] 53. After the initial algorithm model is constructed, the real-time data obtained is predicted by the algorithm model to be static electricity or static electricity discharge is not predicted. The static electricity data predicted to be static electricity is stored and updated. Preferably, in this embodiment, after the algorithm model predicts the occurrence of static electricity, it can be notified by warning so that measures can be taken to protect people or objects.

[0050] 54. Step 4: Iterate the algorithm model based on the updated electrostatic data.

[0051] 55. Apply the updated electrostatic data stored in step 3 to the reference series and comparison series in step 1, and iterate the initially constructed algorithm model after each update of the electrostatic data to further improve the accuracy of the algorithm model.

[0052] 56. Step 5: Closed-loop application of the iterated algorithm model.

[0053] 57. The algorithm model after each iteration is applied again in step 3 to form a closed loop, reducing the limitations caused by the amount of data during prediction and effectively improving the accuracy of the algorithm model prediction.

[0054] 58. The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0055] 59. The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be based on the attached claims.

Claims

1. A method for monitoring and predicting electrostatic phenomena, characterized in that: The method comprises the following steps: Construction of algorithmic models based on existing electrostatic data; Collect real-time data; Based on real-time data, the algorithm model is used to analyze and predict, record, store and update static data; Iterate the algorithm model based on updated electrostatic data; Closed-loop application of the iterative algorithm model.

2. The method for monitoring and predicting electrostatic phenomena according to claim 1, characterized in that: The construction of the algorithm model is based on grey relational analysis and includes the following steps: Clarify the purpose of analysis and determine the number series, wherein the number series includes a reference number series and a comparison number series; Data preprocessing, including dimensionless processing and data standardization; Calculate the correlation coefficient and correlation degree; Relevance ranking and model establishment.

3. The method for monitoring and predicting electrostatic phenomena according to claim 2, characterized in that: The real-time data includes a charge accumulation amount, a charge accumulation location, and environmental data including temperature and humidity.

4. The method for monitoring and predicting electrostatic phenomena according to claim 3, characterized in that: The reference sequence is a data sequence indicating whether static electricity occurs; the comparison sequence includes the charge accumulation amount and the environmental data.

5. The method for monitoring and predicting electrostatic phenomena according to claim 4, characterized in that: The said infinite tempering process is based on the initialization process of the comparison series to obtain a new data series.

6. The method for monitoring and predicting electrostatic phenomena according to claim 5, characterized in that: The data normalization is achieved through linear transformation based on new data columns combined with Min-Max normalization.

7. The method for monitoring and predicting electrostatic phenomena according to claim 6, characterized in that: The calculation formula for calculating the correlation coefficient is as follows: Where: ρ is the resolution coefficient, 0<ρ<1, usually 0.5, max is the maximum value of the absolute difference between all comparison series and reference series at each corresponding time or index, Δoi(t) represents the i-th comparison series X i The absolute difference with the reference series X0 at the tth moment or index.

8. The method for monitoring and predicting electrostatic phenomena according to claim 7, characterized in that: The calculation formula of the correlation degree is shown as follows: Where: r i represents the correlation between the ith comparison sequence and the reference sequence, mean(.) represents the function for finding the average value, ξ ij (X i ): the i-th comparison sequence X i The correlation coefficient with the reference series at the jth moment or index.

9. A method for monitoring and predicting electrostatic phenomena as claimed in claim 8, characterized in that: The acquisition of the charge accumulation amount and the charge accumulation position comprises the following steps: Establish central points based on the monitoring environment; Symmetrically distributed detection plates are arranged around the center point; Locate the charge accumulation position of the charged body and calculate the charge accumulation amount.

10. The method for monitoring and predicting electrostatic phenomena according to claim 9, characterized in that: The formula for calculating the charge accumulation amount is as follows: Where: E represents the electric field strength, k represents the Coulomb constant, q represents the charge of the point charge, and r represents the distance between the two point charges.

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