A Chemical Safety Production Management Method and System Based on Big Data

Abstract

The present invention relates to the technical field of data security management, and particularly to a chemical safety production management method and system based on big data. The method includes: obtaining data points containing chemical safety parameter values of multiple production monitoring dimensions at each moment during the chemical production process; determining the importance of each production monitoring dimension; determining the weighted distance between data points and clustering the data points; determining the time domain characteristics of each clustering cluster for evaluating the stability of the production rhythm; obtaining the neighborhood parameters of each clustering cluster for identifying abnormal working conditions and risk warnings in chemical safety production; and obtaining the anomaly score of each data point based on the neighborhood parameters by using the LOF algorithm to achieve chemical safety production management based on big data. The present invention obtains the anomaly score of data points by using the LOF algorithm based on neighborhood parameters, avoids the problem of large deviation in the results of traditional anomaly detection algorithms, and can timely discover abnormal situations in the production process.

Description

Technical Field

[0001] The present invention relates to the technical field of data security management, and in particular, to a chemical safety production management method and system based on big data. Background Art

[0002] In the field of chemical production, safety management is a key link to ensure the smooth operation of production and avoid accidents. The traditional safety management mode relies on manual inspections and judgments based on past experience. Manual inspections not only consume a large amount of manpower, material resources and time, with extremely low efficiency, but also are inevitably prone to omissions by humans, greatly reducing the accuracy. In addition, experience-based judgments are highly subjective, and the judgment criteria and ability levels of different personnel vary widely, which undoubtedly poses a hidden danger to chemical production safety.

[0003] With the development of information technology, big data technology has emerged, bringing new opportunities for the safety management of chemical enterprises. More and more chemical enterprises have begun to try to use data analysis technology to manage and monitor safety data in the production process. By collecting various sensor data information, potential safety risks can be mined to take countermeasures in advance.

[0004] However, in the actual application process, the existing big data analysis methods still face many challenges in the chemical production safety scenario. For example, the data production monitoring has a high dimension, low accuracy and insufficient real-time performance. In chemical production safety management, chemical production data has extremely high complexity, covering many different types of parameters, with a wide range of data sources and being interrelated; therefore, the high dimension of data production monitoring is a significant challenge. When traditional data analysis and anomaly detection algorithms process high-dimensional data, due to the characteristic that the distances between data points in high-dimensional space tend to be equal, the calculation accuracy decreases, and then the anomaly detection results deviate greatly, unable to accurately identify potential safety risks. Summary of the Invention

[0005] In order to solve the problem that when traditional data analysis and anomaly detection algorithms process high-dimensional data, due to the fact that in high-dimensional space, the distances between data points tend to be equal, the calculation accuracy decreases, and then the anomaly detection results are inaccurate, affecting the safety of chemical production, the present invention provides a chemical safety production management method and system based on big data.

[0006] In a first aspect, the present invention provides a chemical safety production management method based on big data, adopting the following technical solution:

[0007] A chemical safety production management method based on big data, comprising: obtaining data points of chemical safety parameter values including multiple production monitoring dimensions at each moment during the chemical production process; determining the importance of each production monitoring dimension according to the information entropy of the chemical safety parameter values in each production monitoring dimension of all data points, the chemical safety parameter values of each data point in each production monitoring dimension, the mean and standard deviation of the chemical safety parameter values of all data points within the data segment to which each data point belongs in each production monitoring dimension; for any two data points, determining the weighted distance between the two data points according to the difference in the chemical safety parameter values of the two data points in each production monitoring dimension and the importance; clustering all data points based on the weighted distance and sorting them in time sequence to obtain multiple clustering clusters; obtaining the mean and standard deviation of the intervals corresponding to all adjacent two data points within each clustering cluster to determine the time domain characteristics of each clustering cluster for evaluating the stability of the production rhythm; correcting the preset initial neighborhood parameter according to the time domain characteristics to obtain the neighborhood parameter of each clustering cluster for identifying abnormal working conditions and risk warnings in chemical safety production; based on the neighborhood parameter, obtaining the anomaly score of each data point by using the LOF algorithm to realize chemical safety production management based on big data.

[0008] The present invention enhances the influence of key production monitoring dimensions on distance calculation by calculating the importance weights of each production monitoring dimension, reduces the interference of irrelevant or noisy production monitoring dimensions, improves the accuracy of distance measurement, and overcomes the problem of distance convergence in high-dimensional spaces in traditional methods; the weighted distance based on the production monitoring dimension weights more reasonably reflects the actual differences between data points, making the clustering results more in line with the internal structure of the data, thereby improving the accuracy of anomaly detection and enabling accurate identification of potential safety risks; dynamically correcting the initial neighborhood parameter according to the mean and standard deviation of the time intervals of the data points within each clustering cluster makes the neighborhood division of the LOF algorithm more in line with the time sequence characteristics of the data, enhancing the timeliness and pertinence of anomaly detection; by integrating information entropy statistics, multi-dimensional weighted distance, time domain characteristics, and LOF anomaly scoring, comprehensively analyzing chemical production data from multiple angles, improving the processing ability of the safety management system for complex and diverse data; through more accurate and timely anomaly detection, helping operators and management discover potential safety hazards, taking timely countermeasures, ensuring production safety, and reducing the accident rate.

[0009] Further, the multiple production monitoring dimensions include a temperature dimension, a humidity dimension, a pressure dimension, and a gas concentration dimension.

[0010] Further, the importance satisfies:

[0011] ; where is the importance of the th production monitoring dimension, For the information entropy of the chemical safety parameter values of all data points in the th production monitoring dimension, is the number of data points, is the th data point's chemical safety parameter value in the th production monitoring dimension, and are respectively the mean and standard deviation of the chemical safety parameter values of all data points within the data segment to which the th data point belongs in the th production monitoring dimension, is the standard normalization function.

[0012] By combining the normalized average deviation of the information entropy to measure the dispersion and variability of the production monitoring dimension, the present invention can simultaneously reflect the complexity and local fluctuation characteristics of the data in this production monitoring dimension, avoiding the bias caused by evaluating the importance of the production monitoring dimension with a single indicator; by performing standard normalization on the information entropy and the normalized average deviation respectively, the differences in dimension and numerical range are eliminated, enabling the two indicators to have a balanced influence in the product operation, and enhancing the scientificity and stability of the evaluation of the importance of the production monitoring dimension.

[0013] Furthermore, the weighted distance satisfies:

[0014] ; where is the weighted distance between data point and data point , is the number of production monitoring dimensions, is the importance of the th production monitoring dimension, is the chemical safety parameter value of data point in the th production monitoring dimension, is the chemical safety parameter value of data point in the th production monitoring dimension, is the absolute value symbol.

[0015] By normalizing the importance of the production monitoring dimension as a weighted coefficient, the present invention makes the production monitoring dimension with high importance contribute more when calculating the distance between data points, weakening the interference of the production monitoring dimension with low importance and ensuring that the distance metric is more in line with the actual data characteristics.

[0016] Furthermore, the clustering uses the iterative self-organizing clustering algorithm.

[0017] Furthermore, the clustering uses the K-means clustering algorithm.

[0018] Furthermore, the time-domain features satisfy:

[0019] ; where is the time-domain feature of the th clustering cluster, is the mean of the intervals between the corresponding times of all adjacent pairs of data points within the th clustering cluster, is the standard deviation of the intervals between the corresponding times of all adjacent pairs of data points within the th clustering cluster, is the standard normalization function.

[0020] By calculating the mean and standard deviation of the time intervals between adjacent data points within a clustering cluster and performing standard normalization, the present invention can objectively reflect the timing distribution law of the data in the cluster, revealing the concentration degree and fluctuation of the data on the time axis; using the time-domain features as the basis for correcting the neighborhood parameters enables the neighborhood range to adapt to the timing characteristics of different clustering clusters, avoiding the timing mismatch problem caused by static neighborhood parameters, thereby improving the accuracy of local outlier detection.

[0021] Furthermore, the neighborhood parameters satisfy:

[0022] ; where is the neighborhood parameter of the th clustering cluster, is the initial neighborhood parameter, is the time-domain feature of the th clustering cluster, is a hyperparameter, is the ceiling symbol.

[0023] By dynamically calculating the neighborhood parameters based on the initial neighborhood parameter, combining the time-domain features and adjusting the hyperparameters, the present invention enables the neighborhood range to flexibly change according to the timing characteristics of different clustering clusters, avoiding the deficiencies brought by fixed neighborhood parameters; the adaptive neighborhood parameters can more reasonably delimit the neighborhood range of each clustering cluster, enabling the LOF algorithm to effectively identify outliers in clusters with different time distribution densities, and improving the reliability and discrimination of the outlier scores.

[0024] Further, the method for obtaining the anomaly score of each data point by using the LOF algorithm to implement chemical industrial safety production management based on big data includes: for any data point, using the LOF algorithm to take the neighborhood parameter of the cluster where the data point is located as the neighborhood parameter of the data point, and obtaining the anomaly score of the data point; in response to the anomaly score being greater than a preset anomaly threshold, determining that the data point is abnormal, storing the data point separately, and issuing a warning prompt, thereby completing chemical industrial safety production management based on big data.

[0025] In a second aspect, the present invention provides a chemical industrial safety production management system based on big data, adopting the following technical solution:

[0026] A chemical industrial safety production management system based on big data includes: a processor and a memory, where the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned method for chemical industrial safety production management based on big data is implemented.

[0027] By adopting the above technical solution, the above-mentioned method for chemical industrial safety production management based on big data is generated into a computer program and stored in the memory to be loaded and executed by the processor, so as to manufacture a terminal device according to the memory and the processor, which is convenient to use.

[0028] The present invention has the following technical effects:

[0029] (1) Aiming at the problems of high production monitoring dimensions and strong complexity of chemical production data, the present invention determines the importance of each production monitoring dimension by comprehensively considering the information entropy of the chemical safety parameter values of all data points in each production monitoring dimension, the chemical safety parameter values of each data point in each production monitoring dimension, the mean and standard deviation of the chemical safety parameter values of the data segment to which each data point belongs in each production monitoring dimension, which can fully explore the potential information of each production monitoring dimension in high-dimensional data, avoid the problem of decreased calculation accuracy caused by the characteristic that the distances between data points in high-dimensional space tend to be equal in traditional algorithms, and effectively improve the processing ability of high-dimensional data.

[0030] (2) When determining the distance between data points, the weighted distance is calculated according to the difference in the chemical safety parameter values of two data points in each production monitoring dimension and the importance of each production monitoring dimension, making the calculation of the distance more reasonable, being able to more accurately reflect the actual similarity degree between data points, providing a more reliable basis for subsequent clustering analysis, and helping to more accurately divide data categories.

[0031] (3) Obtain the mean and standard deviation of the time intervals corresponding to adjacent data points within each cluster to determine the time-domain characteristics of the cluster, and modify the preset initial neighborhood parameters based on these characteristics. This fully considers the time-series characteristics of the data, enabling the neighborhood parameters to better adapt to the data distribution of different clusters and improving the rationality and effectiveness of the parameters.

[0032] (4) Based on the modified neighborhood parameters, use the LOF algorithm to obtain the anomaly score of each data point, effectively avoiding the problem of large deviations in the results of traditional anomaly detection algorithms. It can more accurately identify potential safety risk data points in the chemical production process, promptly detect abnormal situations during production, provide stronger guarantees for chemical production safety, help reduce the occurrence of safety accidents, and reduce production risks and losses. Brief Description of the Drawings

[0033] Figure 1 It is the flowchart of the method in an embodiment of a chemical production safety management method based on big data according to the present invention. Specific Embodiments

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0035] An embodiment of the present invention discloses a chemical production safety management method based on big data. Refer to Figure 1 , including steps S1 - S6:

[0036] S1: Obtain data points containing chemical safety parameter values in multiple production monitoring dimensions at each moment during the chemical production process.

[0037] It should be noted that the data points at each moment during the chemical production process refer to various types of information and data related to production safety during the chemical production process, including but not limited to equipment production process parameters, environmental data, equipment operating status, etc. By collecting and analyzing these data points, real-time monitoring of the production process can be achieved, potential risks can be promptly discovered, and the system can respond in a timely manner.

[0038] Use corresponding sensors to obtain data such as temperature values, humidity values, pressure values, gas concentration values, etc., and use the chemical safety parameter values in all production monitoring dimensions collected at the same moment as a data point.

[0039] Implementers can set the collection frequency according to specific implementation situations. For example, 20Hz.

[0040] Specifically, the multiple production monitoring dimensions include a temperature dimension, a humidity dimension, a pressure dimension, and a gas concentration dimension.

[0041] S2: Determine the importance of each production monitoring dimension.

[0042] It should be noted that in the chemical production process, due to the large number of characteristic production monitoring dimensions involved, if all the collected data is directly input into the anomaly monitoring model, not only will the computational complexity increase significantly, but also the LOF algorithm realizes the anomaly monitoring of data points based on the Euclidean distance, and the Euclidean distance between different production monitoring dimensions will affect the anomaly detection result. To reduce the influence of the distance between different production monitoring dimensions on the anomaly detection result, the present invention calculates the importance of any production monitoring dimension to facilitate further analysis based on the importance result.

[0043] Determine the importance of each production monitoring dimension according to the information entropy of the chemical safety parameter values of all data points in each production monitoring dimension, the chemical safety parameter values of each data point in each production monitoring dimension, and the mean and standard deviation of the chemical safety parameter values of all data points in each production monitoring dimension within the data segment to which each data point belongs.

[0044] The implementer can set the length of the data segment according to the specific implementation situation. For example, 21, including the data point and the previous 20 data points.

[0045] Specifically, the importance satisfies:

[0046] ;

[0047] In the formula, is the importance of the th production monitoring dimension, is the information entropy of the chemical safety parameter values of all data points in the th production monitoring dimension, is the number of data points, is the th data point in the th production monitoring dimension, and are respectively the mean and standard deviation of the chemical safety parameter values of all data points in the th data point within the data segment to which it belongs in the th production monitoring dimension, is the standard normalization function.

[0048] Among them, in chemical production, for any production monitoring dimension, during normal operation, it stabilizes within a certain range, but in abnormal situations, there will be violent fluctuations, that is, there may be potential safety anomalies. Therefore, for any production monitoring dimension, the greater the data fluctuation of this production monitoring dimension, the more attention needs to be paid to this production monitoring dimension, that is, the higher the importance of this production monitoring dimension. represents the distribution of all data of this production monitoring dimension. The larger this value is, the larger the data distribution range of this production monitoring dimension is, and the more uniform the data value distribution is, the greater the data fluctuation of this production monitoring dimension is, the more likely there are potential safety anomalies, and the higher the importance is; however, information entropy cannot take into account the local changes of the data of this production monitoring dimension. Therefore, is introduced. By calculating the difference between all data points and the data values within their respective data segments, the local difference situation is obtained. The larger this value is, the more obvious the local data fluctuation of this production monitoring dimension is, the more likely there are potential safety anomalies, and the higher the importance is.

[0049] S3: Determine the weighted distance between data points and cluster the data points.

[0050] It should be noted that since the amount of data for directly performing anomaly detection on all data is huge, the computational complexity is too high, and the time consumption is too long. Therefore, in the present invention, by clustering the data, it is convenient to analyze each cluster separately. When clustering, both the processing efficiency can be improved, and the weight of the production monitoring dimension with higher importance can be increased, thereby improving the accuracy. Since different production monitoring dimensions have different degrees of influence on chemical safety production management, the production monitoring dimension with higher importance is more critical for judging the similarity between data points. Therefore, the weighted distance between data points is obtained in combination with the importance, and the Euclidean distance in conventional clustering is replaced by the weighted distance to improve the accuracy of the clustering result.

[0051] For any two data points, according to the difference in the chemical safety parameter values of the two data points in each production monitoring dimension and the importance, the weighted distance between the two data points is determined.

[0052] Specifically, the weighted distance satisfies:

[0053] ;

[0054] In the formula, is the weighted distance between data point and data point , is the number of production monitoring dimensions, is the importance of the th production monitoring dimension, is data point The chemical safety parameter value in the th production monitoring dimension, is the data point The chemical safety parameter value in the th production monitoring dimension, is the absolute value symbol, .

[0055] Based on the weighted distance, all data points are clustered and sorted in time sequence to obtain multiple clusters.

[0056] Specifically, the clustering uses the iterative self-organizing clustering algorithm.

[0057] In another embodiment, the clustering uses the K-means clustering algorithm.

[0058] S4: Determine the time-domain features of each cluster for evaluating the stability of the production rhythm.

[0059] It should be noted that in order to improve the efficiency of anomaly detection, the present invention clusters the collected data to obtain multiple clusters, and performs anomaly detection on each cluster separately using the LOF algorithm. The traditional LOF algorithm directly presets a fixed distance parameter, that is, the neighborhood parameter, and completes anomaly detection according to this parameter. However, in the present invention, since the clustering is completed according to the weighted distance between data points in different clusters, the time interval factor is not considered. For example, if the time interval in a certain cluster is far and the distance between data points is large, using a fixed distance parameter will result in a high anomaly score for the data points in this cluster. Therefore, by calculating the time interval in the cluster to obtain the eigenvalue of this cluster, that is, the time-domain feature, and then selecting an appropriate distance parameter according to the eigenvalue.

[0060] Obtain the mean and standard deviation of the intervals corresponding to the moments of all adjacent two data points in each cluster, and determine the time-domain features of each cluster for evaluating the stability of the production rhythm.

[0061] Specifically, the time-domain features satisfy:

[0062] ;

[0063] In the formula, is the time-domain feature of the th cluster, is the mean of the intervals corresponding to the moments of all adjacent two data points in the th cluster, is the standard deviation of the intervals corresponding to the moments of all adjacent two data points in the th cluster, is the standard normalization function.

[0064] Among them, The larger the value of, the wider the distribution range of the data points in the cluster from the time series perspective, that is, the stronger the time domain feature. On the contrary, the smaller its value, the smaller the distribution range of the data points in the cluster from the time series perspective, that is, the weaker the time domain feature; reflects the distribution uniformity of all time intervals within the cluster. The smaller this value, the more uniform the time interval distribution, and the stronger the time domain feature, and vice versa.

[0065] S5: Obtain the neighborhood parameters for each cluster to identify abnormal working conditions and risk warnings in chemical production safety.

[0066] It should be noted that for any cluster, if the time domain feature of the cluster is stronger, it means that the data points in the cluster are more discrete in the time series, and a larger range of neighborhood parameters needs to be selected to avoid the influence of local noise caused by too small a parameter range.

[0067] Modify the preset initial neighborhood parameters according to the time domain feature to obtain the neighborhood parameters for each cluster to identify abnormal working conditions and risk warnings in chemical production safety.

[0068] Specifically, the neighborhood parameters satisfy:

[0069] ;

[0070] In the formula, is the neighborhood parameter of the th cluster, is the initial neighborhood parameter, is the time domain feature of the th cluster, is the hyperparameter, is the ceiling symbol.

[0071] Among them, represents the time domain feature reference value. If the time domain feature of the cluster is greater than , that is, , it means that the time domain feature is too large, and the neighborhood parameters need to be appropriately enlarged. On the contrary, the neighborhood parameters need to be appropriately reduced.

[0072] Implementers can set the initial neighborhood parameters and hyperparameters according to the specific implementation situation. For example, the initial neighborhood parameter is 10 and the hyperparameter is 0.5.

[0073] S6: Based on the neighborhood parameters, use the LOF algorithm to obtain the anomaly score of each data point to achieve chemical production safety management based on big data.

[0074] Specifically, the method of obtaining the anomaly score of each data point by using the LOF algorithm to implement chemical safety production management based on big data includes:

[0075] For any data point, use the LOF algorithm to take the neighborhood parameter of the cluster where the data point is located as the neighborhood parameter of the data point, and obtain the anomaly score of the data point;

[0076] In response to the anomaly score being greater than a preset anomaly threshold, determine that the data point is abnormal, store the data point separately, and issue a warning prompt to complete chemical safety production management based on big data.

[0077] Implementers can set the anomaly threshold according to the specific implementation situation. For example, 1.

[0078] The embodiment of the present invention also discloses a chemical safety production management system based on big data, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a chemical safety production management method based on the present invention is implemented.

[0079] The above system also includes other components well-known to those skilled in the art such as a communication bus and a communication interface. Their settings and functions are known in the art, so they will not be elaborated here.

[0080] The above are all the preferred embodiments of the present invention. The protection scope of the present invention is not limited by this. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A chemical safety production management method based on big data, characterized in that, Including: Obtaining data points of chemical safety parameter values including multiple production monitoring dimensions at each moment during the chemical production process; Determining the importance of each production monitoring dimension according to the information entropy of the chemical safety parameter values of all data points in each production monitoring dimension, the chemical safety parameter values of each data point in each production monitoring dimension, and the mean and standard deviation of the chemical safety parameter values of all data points within the data segment to which each data point belongs in each production monitoring dimension; The importance satisfies: ； In the formula, is the importance of the th production monitoring dimension, is the information entropy of the chemical safety parameter values of all data points in the th production monitoring dimension, is the number of data points, is the th chemical safety parameter value of the th data point in the th production monitoring dimension, and are respectively the mean and standard deviation of the chemical safety parameter values of all data points in the data segment to which the th data point belongs in the th production monitoring dimension; For any two data points, determining the weighted distance between the two data points according to the difference in the chemical safety parameter values of the two data points in each production monitoring dimension and the said importance; based on the weighted distance, clustering all data points and sorting them in time sequence to obtain multiple clustering clusters; Obtaining the mean and standard deviation of the intervals corresponding to every two adjacent data points within each clustering cluster to determine the time-domain characteristics of each clustering cluster for evaluating the stability of the production rhythm; Correcting the preset initial neighborhood parameter according to the time-domain characteristics to obtain the neighborhood parameter of each clustering cluster for identifying abnormal working conditions and risk warnings in chemical production safety; Based on the neighborhood parameter, using the LOF algorithm to obtain the anomaly score of each data point to achieve chemical production safety management based on big data.

2. The chemical industrial safety production management method based on big data according to claim 1, characterized in that The multiple production monitoring dimensions include a temperature dimension, a humidity dimension, a pressure dimension, and a gas concentration dimension.

3. The chemical industrial safety production management method based on big data according to claim 1 is characterized in that, The weighted distance satisfies: ； Wherein, is the weighted distance between and data point ; is the number of production monitoring dimensions, is the importance of the th production monitoring dimension, is the chemical safety parameter value of data point in the th production monitoring dimension, is the chemical safety parameter value of data point in the th production monitoring dimension, is the absolute value symbol.

4. A chemical safety production management method based on big data according to claim 1, characterized in that, The clustering uses the iterative self-organizing clustering algorithm.

5. A chemical safety production management method based on big data according to claim 1, characterized in that, The clustering uses the K-means clustering algorithm.

6. The chemical industrial safety production management method based on big data according to claim 1, characterized in that, The time-domain characteristics satisfy: ； Wherein, is the time domain feature of the th clustering cluster, is the mean of the intervals between the corresponding times of all adjacent pairs of data points within the th clustering cluster, is the standard deviation of the intervals between the corresponding times of all adjacent pairs of data points within the th clustering cluster, is the standard normalization function.

7. A chemical safety production management method based on big data according to claim 1, characterized in that The neighborhood parameter satisfies: ； Wherein, is the neighborhood parameter of the th clustering cluster, is the initial neighborhood parameter, is the time domain feature of the th clustering cluster, is a hyperparameter, is the ceiling symbol.

8. A chemical safety production management method based on big data according to claim 1, characterized in that, The using the LOF algorithm to obtain the anomaly score of each data point to achieve chemical production safety management based on big data includes: For any data point, using the LOF algorithm to take the neighborhood parameter of the clustering cluster where the data point is located as the neighborhood parameter of the data point and obtaining the anomaly score of the data point; In response to the anomaly score being greater than a preset anomaly threshold, determining that the data point is abnormal, storing the data point separately, and sending a warning prompt to complete chemical production safety management based on big data.

9. A chemical safety production management system based on big data, characterized in that, Including: A processor and a memory, where the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a chemical production safety management method according to any one of claims 1-8 is implemented.

Images