Information security sharing method for chemical storage area

Abstract

The present invention relates to the field of data processing technology, and in particular to an information security sharing method for a chemical storage area, comprising: obtaining temperature and pressure data from a chemical storage area, constructing a two-dimensional coordinate system and obtaining a temperature and pressure cluster. Calculating the cluster distance of each cluster, and identifying a normal temperature and pressure cluster according to the cluster distance and the temperature and pressure values ​​of each data point. Constructing a normal data point sequence based on the normal temperature and pressure cluster, and calculating a fitting straight line for the normal data point sequence. Obtaining a non-abnormal temperature and pressure sequence by comparing the slope of the fitting straight line and the difference of the data points. Calculating an abnormal temperature and pressure factor according to the difference of each data point in the non-abnormal temperature and pressure sequence, and obtaining a number of abnormal data points according to the abnormal temperature and pressure factor of each non-abnormal temperature and pressure sequence. The present invention accurately determines abnormal data by analyzing the pressure and temperature data of the chemical storage area.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to an information security sharing method for a chemical storage area. Background Art

[0002] When establishing an information security sharing platform in a chemical storage area, it is generally necessary to collect data information from the chemical storage area, process it locally, and then upload the data to the cloud. The cloud-based information security platform needs to conduct in-depth analysis of all data to identify potential risks. Since the data needs to be monitored in real time and the data volume is relatively large, the amount of computing power for cloud-based data analysis will increase.

[0003] Existing problem: In order to reduce the amount of computing power for cloud data analysis, it is necessary to identify abnormal data and upload it to the cloud. When the collected temperature and pressure indicators exceed a certain threshold, they are judged as abnormal data. However, when the temperature and pressure are within the threshold, a chemical reaction occurs in the chemical storage area, and all abnormal data cannot be accurately uploaded to the cloud for analysis. Summary of the invention

[0004] The present invention provides an information security sharing method for a chemical storage area to solve the existing problems.

[0005] The information security sharing method for chemical storage area of ​​the present invention adopts the following technical scheme:

[0006] An embodiment of the present invention provides a method for information security sharing in a chemical storage area, the method comprising the following steps:

[0007] Obtain the temperature data time series and pressure data time series of the chemical storage area within the same period of time;

[0008] Taking the temperature data and pressure data at the same time as the horizontal and vertical axes, construct a two-dimensional coordinate system corresponding to the temperature data time series sequence and the pressure data time series sequence; cluster all data points in the two-dimensional coordinate system to obtain several temperature and pressure clusters; obtain the cluster distance index of each temperature and pressure cluster according to the difference between the temperature values ​​and pressure values ​​of all data points in each temperature and pressure cluster; obtain the cluster data index of each temperature and pressure cluster according to the cluster distance index of each temperature and pressure cluster and the difference between the temperature values ​​and pressure values ​​of all data points; obtain the normal temperature and pressure cluster according to the cluster data index of each temperature and pressure cluster;

[0009] According to the time of all data points of the normal temperature and pressure cluster, several normal data point sequences are obtained;

[0010] According to the difference between the temperature value and pressure value of each normal data point sequence and the data point in the normal temperature and pressure cluster, the temperature and pressure correlation difference index of each normal data point sequence is obtained; according to the temperature and pressure correlation difference index of each normal data point sequence, several non-abnormal temperature and pressure sequences are obtained;

[0011] According to the differences between the temperature values ​​and pressure values ​​of adjacent data points of each non-abnormal temperature and pressure sequence, the change of pressure and temperature of each data point of the non-abnormal temperature and pressure sequence is obtained;

[0012] According to the change of pressure and temperature of each data point of each non-abnormal temperature-pressure sequence, the abnormal temperature-pressure factor of each non-abnormal temperature-pressure sequence is obtained; according to the abnormal temperature-pressure factor of each non-abnormal temperature-pressure sequence, several abnormal data points are obtained; all abnormal data points are uploaded to the information security sharing database of the chemical storage area.

[0013] Furthermore, the specific calculation formula for obtaining the cluster distance index of each temperature and pressure cluster based on the differences between the temperature values ​​and pressure values ​​of all data points of each temperature and pressure cluster is:

[0014]

[0015] in, Indicates Cluster distance index of temperature and pressure clusters; Indicates The number of data points in each temperature-pressure cluster; Indicates The temperature and pressure cluster The temperature value of the data point; Indicates The mean temperature value of all data points in a temperature-pressure cluster; Indicates The temperature and pressure cluster The pressure value of each data point; Indicates The mean pressure value of all data points in a temperature-pressure cluster; Indicates absolute value.

[0016] Furthermore, the specific calculation formula for obtaining the cluster data index of each temperature and pressure cluster based on the cluster distance index of each temperature and pressure cluster and the difference between the temperature values ​​and pressure values ​​of all data points is:

[0017]

[0018] in, Indicates Cluster data indicators for temperature and pressure clusters; Indicates The maximum value of the temperature value of the data points of the temperature and pressure cluster; Indicates The minimum value of the temperature value of the data points of the temperature and pressure cluster; Indicates The number of data points in a temperature-pressure cluster; Indicates Cluster distance index of temperature and pressure clusters; Indicates The maximum value of the pressure value of the data points of the temperature and pressure cluster; Indicates The minimum value of the pressure value of the data points of the temperature and pressure cluster; Represents an exponential function with a natural constant as its base.

[0019] Furthermore, the step of obtaining a normal temperature and pressure cluster according to the cluster data index of each temperature and pressure cluster includes the following specific steps:

[0020] The temperature and pressure cluster with the largest cluster data index is recorded as the normal temperature and pressure cluster.

[0021] Furthermore, the steps of obtaining a plurality of normal data point sequences according to the time of all data points of the normal temperature and pressure cluster include the following:

[0022] Among all the data points of the normal temperature and pressure cluster, the data points that are continuous in time are sequentially combined to form a normal data point sequence.

[0023] Furthermore, the temperature-pressure correlation difference index of each normal data point sequence is obtained according to the difference between the temperature value and the pressure value of the data point in each normal data point sequence and the normal temperature-pressure cluster, and the specific steps include the following:

[0024] Obtain a target fitting straight line for a normal temperature and pressure cluster and a reference fitting straight line for each normal data point sequence;

[0025] According to the slope of the reference fitting line of each normal data point sequence and the target fitting line of the normal temperature and pressure cluster, as well as the difference between the temperature and pressure values ​​of each data point, the specific calculation formula for the temperature and pressure correlation difference index of each normal data point sequence is obtained as follows:

[0026]

[0027] in, Indicates Temperature-pressure correlation difference index for a normal data point sequence; Indicates The slope of the reference fitted line for a normal sequence of data points; represents the slope of the target fitted line for the normal temperature-pressure cluster; Indicates The mean temperature value of all data points in a normal data point sequence; represents the mean of the temperature values ​​of all data points in the normal temperature-pressure cluster; Indicates The mean of the pressure values ​​of all data points in a normal data point sequence; Represents the mean of the pressure values ​​of all data points in the normal temperature and pressure cluster; represents the linear normalization function; Indicates absolute value.

[0028] Furthermore, the method of obtaining a plurality of non-abnormal temperature and pressure sequences according to the temperature and pressure correlation difference index of each normal data point sequence includes the following specific steps:

[0029] when Greater than , then the A normal data point sequence is recorded as a non-abnormal temperature and pressure sequence; among them, is the preset empirical threshold, For the The temperature-pressure correlation difference indicator of a normal data point sequence.

[0030] Furthermore, the method of obtaining the pressure and temperature change of each data point of the non-abnormal temperature and pressure sequence according to the difference between the temperature values ​​and pressure values ​​of adjacent data points of each non-abnormal temperature and pressure sequence includes the following specific steps:

[0031] In the In a non-abnormal temperature and pressure sequence, The temperature value of the data point is subtracted from the The difference between the temperature values ​​of the data points and the The pressure value of the data point is subtracted from the The ratio of the difference in pressure values ​​of the data points is recorded as The first non-abnormal temperature-pressure sequence The changes in pressure and temperature at each data point.

[0032] Furthermore, the abnormal temperature pressure factor of each non-abnormal temperature pressure sequence is obtained according to the change of pressure and temperature of each data point of each non-abnormal temperature pressure sequence, and the specific steps include the following:

[0033] Divide each non-abnormal temperature and pressure sequence into a front non-abnormal temperature and pressure sequence and a rear non-abnormal temperature and pressure sequence; obtain a piecewise fitting straight line of the front non-abnormal temperature and pressure sequence and the rear non-abnormal temperature and pressure sequence of each non-abnormal temperature and pressure sequence;

[0034] According to the change of pressure and temperature of each data point of each non-abnormal temperature and pressure sequence and the difference between the slopes of the segmented fitting straight lines of the front non-abnormal temperature and pressure sequence and the back non-abnormal temperature and pressure sequence, the specific calculation formula of the abnormal temperature and pressure factor of each non-abnormal temperature and pressure sequence is obtained as follows:

[0035]

[0036] in, Indicates The abnormal temperature pressure factor of a non-abnormal temperature pressure sequence; Indicates The slope of the piecewise fitting straight line of the first non-abnormal temperature and pressure sequence; Indicates The slope of the piecewise fitting straight line of the latter part of the non-abnormal temperature and pressure sequence; Indicates The number of data points of a non-anomalous temperature-pressure series; Indicates The first non-abnormal temperature-pressure sequence The changes of pressure and temperature at each data point; Indicates The mean value of the pressure and temperature changes of all data points in a non-abnormal temperature and pressure sequence; represents the linear normalization function; Indicates absolute value.

[0037] According to the time of all data points in the normal temperature and pressure cluster, several normal data point sequences are obtained; according to the difference between the temperature value and pressure value of each normal data point sequence and the data point in the normal temperature and pressure cluster, the temperature and pressure correlation difference index of each normal data point sequence is obtained, which reduces the interference of the abnormal data screening due to the reaction of chemicals. According to the temperature and pressure correlation difference index of each normal data point sequence, several non-abnormal temperature and pressure sequences are obtained; according to the difference between the temperature value and pressure value of the adjacent data points of each non-abnormal temperature and pressure sequence, the pressure and temperature change of each data point of the non-abnormal temperature and pressure sequence is obtained, which accurately reflects the temperature and pressure change of each data point. According to the pressure and temperature change of each data point of each non-abnormal temperature and pressure sequence, the abnormal temperature and pressure factor of each non-abnormal temperature and pressure sequence is obtained, and the abnormal data is screened out more accurately. According to the abnormal temperature and pressure factor of each non-abnormal temperature and pressure sequence, several abnormal data points are obtained; all abnormal data points are uploaded to the information security sharing database of the chemical storage area. The present invention accurately judges abnormal data by analyzing the pressure and temperature data of the chemical storage area. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 The present invention is a flowchart of the steps of the information security sharing method for chemical storage areas. DETAILED DESCRIPTION

[0040] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the information security sharing method for chemical storage area proposed by the present invention, its specific implementation method, structure, characteristics and effects are described in detail as follows in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0041] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0042] The specific scheme of the information security sharing method for chemical storage area provided by the present invention is described in detail below with reference to the accompanying drawings.

[0043] See also Figure 1 , which shows a flow chart of the steps of a method for information security sharing in a chemical storage area provided by an embodiment of the present invention, the method comprising the following steps:

[0044] Step S001: Obtain the temperature data time series and pressure data time series of the chemical storage area within the same period of time.

[0045] Temperature and pressure sensors are installed in the chemical storage area to collect temperature and pressure data of the current chemical storage area 24 hours a day to achieve continuous monitoring of environmental parameters. Data is collected synchronously every minute to ensure real-time tracking of environmental conditions in the reservoir area. is a preset time threshold. The preset time threshold in this embodiment is The value is 1, and this example is used for description.

[0046] Step S002: With the temperature data and pressure data at the same time as the horizontal and vertical axes, construct a two-dimensional coordinate system corresponding to the temperature data time series sequence and the pressure data time series sequence; cluster all data points in the two-dimensional coordinate system to obtain a number of temperature and pressure clusters; obtain a cluster distance index of each temperature and pressure cluster based on the difference between the temperature values ​​and pressure values ​​of all data points in each temperature and pressure cluster; obtain a cluster data index of each temperature and pressure cluster based on the cluster distance index of each temperature and pressure cluster and the difference between the temperature values ​​and pressure values ​​of all data points; obtain a normal temperature and pressure cluster based on the cluster data index of each temperature and pressure cluster.

[0047] It should be noted that: since the temperature and pressure data of a general chemical storage area are stable, the data of a normal chemical storage area will generally remain in a stable data interval. Therefore, the DBSCAN clustering algorithm can be used to determine the different point clusters to which the temperature and pressure values ​​collected at historical moments belong. Among them, the point clusters with the largest number and the highest temperature and pressure density are generally the more normal data in the chemical storage area.

[0048] In the temperature data time series and the pressure data time series, a two-dimensional coordinate system is constructed with the temperature data and the pressure data at the same time, wherein the temperature data is the horizontal axis and the pressure data is the vertical axis; the DBSCAN clustering algorithm is used to calculate all data points in the two-dimensional coordinate system to obtain several temperature and pressure clusters. The DBSCAN clustering algorithm is a well-known technology, and the specific method is not introduced here.

[0049] First For example, the temperature and pressure cluster The specific calculation formula of the cluster distance index of the temperature and pressure cluster is:

[0050]

[0051] in, Indicates Cluster distance index of temperature and pressure clusters; Indicates The number of data points in each temperature-pressure cluster; Indicates The temperature and pressure cluster The temperature value of the data point; Indicates The mean temperature value of all data points in a temperature-pressure cluster; Indicates The temperature and pressure cluster The pressure value of each data point; Indicates The mean pressure value of all data points in a temperature-pressure cluster; Indicates absolute value.

[0052] What needs to be explained is: is the distance from the data point of the temperature and pressure cluster to the center point, indicating the degree of aggregation of the data points of the temperature and pressure cluster. The larger the value of The smaller the probability that the data points in the temperature and pressure clusters are normal data.

[0053] The first The specific calculation formula for the cluster data index of a temperature and pressure cluster is:

[0054]

[0055] in, Indicates Cluster data indicators for temperature and pressure clusters; Indicates The maximum value of the temperature value of the data points of the temperature and pressure cluster; Indicates The minimum value of the temperature of the data points in the temperature and pressure cluster; Indicates The number of data points in each temperature-pressure cluster; Indicates Cluster distance index of temperature and pressure clusters; Indicates The maximum value of the pressure value of the data points of the temperature and pressure cluster; Indicates The minimum value of the pressure value of the data points of the temperature and pressure cluster; Represents an exponential function with a natural constant as its base.

[0056] What needs to be explained is: and Indicates The range of temperature and pressure values ​​of the data points in the temperature and pressure clusters, and The larger the value, the The greater the fluctuation of the internal data of a temperature and pressure cluster, the The smaller the probability that the data points in the temperature and pressure clusters are normal data.

[0057] Through the above process, we obtain Cluster data indicators for temperature and pressure clusters.

[0058] Each temperature and pressure cluster is calculated according to the above process to obtain the cluster data index of each temperature and pressure cluster.

[0059] The temperature and pressure cluster with the largest cluster data index is recorded as the normal temperature and pressure cluster.

[0060] The data points of the temperature and pressure clusters other than the normal temperature and pressure clusters are determined as abnormal data points.

[0061] Step S003: Obtain several normal data point sequences according to the time of all data points of the normal temperature and pressure cluster.

[0062] It should be noted that after determining the normal temperature and pressure cluster of the chemical storage area, under normal circumstances, normal data will not appear alone, but will appear in a certain time series segment, so several time series segments can be obtained in the normal temperature and pressure cluster.

[0063] Among all the data points of the normal temperature and pressure cluster, the data points that are continuous in time are sequentially combined to form a normal data point sequence.

[0064] Through the above process, several normal data point sequences are obtained.

[0065] In the two-dimensional coordinate system, the least squares method is used to perform straight line fitting on all data points in the normal temperature and pressure cluster to obtain the target fitting line; the least squares method is used to perform straight line fitting on all data points in each normal data point sequence to obtain the reference fitting line. Among them, the least squares fitting algorithm is a well-known technology, and the specific method is not introduced here.

[0066] Step S004: According to the difference between the temperature value and pressure value of each normal data point sequence and the data points in the normal temperature and pressure cluster, a temperature-pressure correlation difference index of each normal data point sequence is obtained; according to the temperature-pressure correlation difference index of each normal data point sequence, several non-abnormal temperature and pressure sequences are obtained.

[0067] It should be noted that in an ideal gas, the relationship between temperature and pressure can be described by the ideal gas law. From the ideal gas law, it can be seen that temperature and pressure are proportional. In the chemical storage area, after the temperature or pressure begins to deviate abnormally, the chemicals will produce exothermic or endothermic reactions. In the exothermic process, as the pressure increases, the temperature will rise suddenly, and the rate of temperature increase will increase compared to normal conditions; in the endothermic process, as the pressure increases, the temperature may increase slowly. If the heat absorbed by the chemical reaction is too large, it may even cause the temperature to drop.

[0068] First As an example, the normal data point sequence The specific calculation of the temperature-pressure correlation difference index of a normal data point sequence is:

[0069]

[0070] in, Indicates Temperature-pressure correlation difference index for a normal data point sequence; Indicates The slope of the reference fitted line for a normal sequence of data points; represents the slope of the target fitted line for the normal temperature-pressure cluster; Indicates The mean temperature value of all data points in a normal data point sequence; represents the mean of the temperature values ​​of all data points in the normal temperature-pressure cluster; Indicates The mean of the pressure values ​​of all data points in a normal data point sequence; Represents the mean of the pressure values ​​of all data points in the normal temperature and pressure cluster; represents the linear normalization function; Indicates absolute value.

[0071] What needs to be explained is: The target fitting line and the first The difference between the slopes of the reference fitted lines for a sequence of normal data points, The larger the value, the The greater the possibility of endothermic or exothermic occurrence in a normal data point sequence, The larger the value of The temperature and pressure data of a normal data point sequence deviate greatly from the overall data center.

[0072] when Greater than , then the A normal data point sequence is recorded as a non-abnormal temperature and pressure sequence; Less than or equal to When The data points of a normal data point sequence are recorded as abnormal data points. is a preset experience threshold. The preset experience threshold in this embodiment is The value is 0.8, and this example is used for description.

[0073] Through the above process, several non-abnormal temperature and pressure sequences are obtained.

[0074] Step S005: according to the differences between the temperature values ​​and pressure values ​​of adjacent data points of each non-abnormal temperature and pressure sequence, the change of the pressure and temperature of each data point of the non-abnormal temperature and pressure sequence is obtained.

[0075] It should be noted that: through the above, abnormal data with relatively large impact of chemical reaction on pressure and temperature in the chemical storage area can be screened out, but when an abnormality occurs, it is impossible to distinguish the abnormal data through the temperature-pressure correlation difference index of the non-abnormal temperature-pressure sequence. In particular, the front part of the non-abnormal temperature-pressure sequence is normal data, but due to the influence of other factors in the chemical storage area, the temperature and pressure in the non-abnormal temperature-pressure sequence have slight changes, and there are abnormal data in the latter part.

[0076] It is further necessary to explain that: since the non-abnormal temperature and pressure sequence has a slight change at a certain point in time, when fitting the entire non-abnormal temperature and pressure sequence, a large error will result, and it will be impossible to distinguish that an abnormality has occurred in the non-abnormal temperature and pressure sequence. Therefore, it is necessary to determine the turning point of the non-abnormal temperature and pressure sequence, and to determine whether there is an abnormality in the non-abnormal temperature and pressure sequence by comparing the two parts before and after the turning point.

[0077] First As an example, the APCA data segmentation algorithm is used to segment the non-abnormal temperature and pressure sequence. The non-abnormal temperature and pressure sequence is calculated to obtain the The non-abnormal temperature and pressure sequence of the front section and the non-abnormal temperature and pressure sequence of the rear section. The APCA data segmentation algorithm is a well-known technology, and the specific method is not introduced here. The number of segments set in this embodiment is 2, and this example is used for description.

[0078] According to the method of obtaining the reference fitting straight line of the normal data point sequence, we can obtain The piecewise fitting straight line of the front non-abnormal temperature and pressure sequence and the back non-abnormal temperature and pressure sequence of a non-abnormal temperature and pressure sequence.

[0079] Through the above process, the front non-abnormal temperature and pressure sequence and the rear non-abnormal temperature and pressure sequence of each non-abnormal temperature and pressure sequence and the corresponding piecewise fitting straight line are obtained.

[0080] First The first non-abnormal temperature-pressure sequence Take the data point as an example. The temperature value of the data point is subtracted from the The difference between the temperature values ​​of the data points and the The pressure value of the data point is subtracted from the The ratio of the difference in pressure values ​​of the data points is recorded as The first non-abnormal temperature-pressure sequence The changes in pressure and temperature at each data point.

[0081] What needs to be explained is: The change in pressure and temperature of the last data point of a non-abnormal temperature-pressure sequence is not calculated.

[0082] Through the above process, the change of pressure and temperature of each data point of each non-abnormal temperature-pressure sequence is obtained.

[0083] Step S006: Obtain the abnormal temperature-pressure factor of each non-abnormal temperature-pressure sequence according to the change of pressure-temperature of each data point of each non-abnormal temperature-pressure sequence; obtain a number of abnormal data points according to the abnormal temperature-pressure factor of each non-abnormal temperature-pressure sequence; upload all abnormal data points to the information security sharing database of the chemical storage area.

[0084] It should be noted that: under normal conditions in chemical storage areas, the pressure changes caused by changes in ambient temperature are stable, while after chemical products react, the pressure changes caused by heat release or heat absorption are irregular and fluctuate greatly. At the same time, after the non-abnormal temperature and pressure sequence is segmented, under normal conditions, the changes between the temperature and pressure before and after the turning point are relatively small, while under abnormal conditions, the changes between the temperature and pressure before and after the turning point are relatively large.

[0085] Still For example, a non-abnormal temperature and pressure sequence The specific calculation formula of the abnormal temperature pressure factor of a non-abnormal temperature pressure sequence is:

[0086]

[0087] in, Indicates The abnormal temperature pressure factor of a non-abnormal temperature pressure sequence; Indicates The slope of the piecewise fitting straight line of the first non-abnormal temperature and pressure sequence; Indicates The slope of the piecewise fitting straight line of the latter part of the non-abnormal temperature and pressure sequence; Indicates The number of data points of a non-anomalous temperature-pressure series; Indicates The first non-abnormal temperature-pressure sequence The changes of pressure and temperature at each data point; Indicates The mean value of the pressure and temperature changes of all data points in a non-abnormal temperature and pressure sequence; represents the linear normalization function; Indicates absolute value.

[0088] What needs to be explained is: The larger the value of The possibility of heat absorption or heat release occurring in a non-abnormal temperature-pressure sequence is relatively high. Reflect the The pressure and temperature fluctuations of a non-abnormal temperature-pressure sequence, The larger the value, the The greater the instability of pressure and temperature in a non-abnormal temperature-pressure sequence.

[0089] Through the above process, we obtain The abnormal temperature pressure factor of a non-abnormal temperature pressure sequence.

[0090] Each non-abnormal temperature and pressure sequence is calculated according to the above process to obtain the abnormal temperature and pressure factor of each non-abnormal temperature and pressure sequence.

[0091] when Greater than When All data points of the non-abnormal temperature and pressure sequence are determined to be abnormal data points. is the preset second experience threshold. The second experience threshold preset in this embodiment is The value is 0.8, and this example is used for description.

[0092] Uploading all abnormal data points to the chemical storage area's information security shared database for analysis can achieve timely detection and response to potential dangerous situations, helping to ensure the safety of the chemical storage area. The information security shared database can collect a large amount of data and analyze it in combination with historical data to identify any abnormal patterns or trends.

[0093] Once the information security shared database determines that a dangerous situation exists, it can send notifications to relevant personnel, allowing them to take timely actions to respond to potential dangers or emergencies. This centralized data analysis and alarm system can greatly improve the safety of chemical storage areas and ensure the safety of personnel and facilities.

[0094] So far, the present invention is completed.

[0095] In summary, in an embodiment of the present invention, temperature and pressure data are obtained from a chemical storage area, a two-dimensional coordinate system is constructed, and a temperature and pressure cluster is obtained. The cluster distance of each cluster is calculated, and the normal temperature and pressure cluster is identified based on the cluster distance and the temperature and pressure values ​​of each data point. A normal data point sequence is constructed based on the normal temperature and pressure cluster, and a fitting straight line of the normal data point sequence is calculated. A non-abnormal temperature and pressure sequence is obtained by comparing the slope of the fitting straight line and the difference of the data points. According to the difference of each data point of the non-abnormal temperature and pressure sequence, an abnormal temperature and pressure factor is calculated, and according to the abnormal temperature and pressure factor of each non-abnormal temperature and pressure sequence, a number of abnormal data points are obtained. The present invention accurately judges abnormal data by analyzing the pressure and temperature data of the chemical storage area.

[0096] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for sharing information security in a chemical storage area, characterized in that: The method comprises the following steps: Obtain the temperature data time series and pressure data time series of the chemical storage area within the same period of time; Using temperature data and pressure data at the same time as the horizontal and vertical axes, a two-dimensional coordinate system corresponding to the temperature data time series and the pressure data time series is constructed; all data points in the two-dimensional coordinate system are clustered to obtain several temperature and pressure clusters; based on the differences between the temperature and pressure values ​​of all data points in each temperature and pressure cluster, a cluster distance index for each temperature and pressure cluster is obtained; based on the cluster distance index of each temperature and pressure cluster and the differences between the temperature and pressure values ​​of all data points, a cluster data index for each temperature and pressure cluster is obtained; based on the cluster data index of each temperature and pressure cluster, a normal temperature and pressure cluster is obtained; According to the time of all data points of the normal temperature and pressure cluster, several normal data point sequences are obtained; Based on the differences between the temperature and pressure values ​​of each normal data point sequence and the data points in the normal temperature and pressure cluster, a temperature-pressure correlation difference index of each normal data point sequence is obtained; based on the temperature-pressure correlation difference index of each normal data point sequence, several non-abnormal temperature and pressure sequences are obtained; According to the differences between the temperature and pressure values ​​of adjacent data points of each non-abnormal temperature and pressure sequence, the pressure and temperature changes of each data point of the non-abnormal temperature and pressure sequence are obtained; Based on the pressure and temperature changes of each data point in each non-abnormal temperature and pressure sequence, the abnormal temperature and pressure factor of each non-abnormal temperature and pressure sequence is obtained; based on the abnormal temperature and pressure factor of each non-abnormal temperature and pressure sequence, a number of abnormal data points are obtained; all abnormal data points are uploaded to the information security sharing database of the chemical storage area; The specific calculation formula for obtaining the cluster distance index of each temperature and pressure cluster based on the differences between the temperature and pressure values ​​of all data points of each temperature and pressure cluster is: in, Indicates the Cluster distance index of temperature and pressure clusters; Indicates the The number of data points in each temperature and pressure cluster; Indicates the The temperature and pressure cluster The temperature value of each data point; Indicates the The mean temperature value of all data points in a temperature and pressure cluster; Indicates the The temperature and pressure cluster The pressure value of each data point; Indicates the The mean pressure value of all data points in a temperature-pressure cluster; Indicates absolute value; The specific calculation formula for obtaining the cluster data index of each temperature and pressure cluster based on the cluster distance index of each temperature and pressure cluster and the difference between the temperature and pressure values ​​of all data points is: in, Indicates the Cluster data indicators of temperature and pressure clusters; Indicates the The maximum temperature value of the data points in the temperature and pressure cluster; Indicates the The minimum temperature value of the data points in the temperature and pressure cluster; Indicates the The maximum value of the pressure value of the data points of the temperature and pressure cluster; Indicates the The minimum pressure value of the data points in the temperature and pressure cluster; represents an exponential function with a natural constant as its base; The specific steps of obtaining a normal temperature and pressure cluster based on the cluster data indicators of each temperature and pressure cluster are as follows: The temperature and pressure cluster with the largest cluster data index is recorded as the normal temperature and pressure cluster; The specific steps of obtaining a plurality of normal data point sequences based on the time of all data points of the normal temperature and pressure cluster are as follows: Among all the data points in the normal temperature and pressure cluster, the data points that are continuous in time are sequentially grouped to form a normal data point sequence; The step of obtaining the temperature-pressure correlation difference index of each normal data point sequence based on the difference between the temperature value and the pressure value of the data points in the normal temperature-pressure cluster and each normal data point sequence includes the following specific steps: Obtain the target fitting line of the normal temperature and pressure cluster and the reference fitting line of each normal data point sequence; According to the slope of the reference fitting line of each normal data point sequence and the target fitting line of the normal temperature and pressure cluster, as well as the difference between the temperature and pressure values ​​of each data point, the specific calculation formula for the temperature and pressure correlation difference index of each normal data point sequence is obtained as follows: in, Indicates the Temperature-pressure correlation difference index for a normal data point sequence; Indicates the The slope of the reference fitted line for a sequence of normal data points; represents the slope of the target fitted line for the normal temperature-pressure cluster; Indicates the The mean temperature value of all data points in a normal data point sequence; represents the mean temperature value of all data points in the normal temperature-pressure cluster; Indicates the The mean of the pressure values ​​of all data points in a normal data point sequence; represents the mean pressure value of all data points in the normal temperature and pressure cluster; represents the linear normalization function; The specific steps of obtaining a number of non-abnormal temperature and pressure sequences based on the temperature and pressure correlation difference index of each normal data point sequence are as follows: when Greater than When A normal data point sequence is recorded as a non-abnormal temperature and pressure sequence; among them, is the preset experience threshold; The method of obtaining the pressure and temperature change of each data point in the non-abnormal temperature and pressure sequence based on the differences between the temperature and pressure values ​​of adjacent data points in each non-abnormal temperature and pressure sequence comprises the following specific steps: In the In a non-abnormal temperature and pressure sequence, The temperature value of the data point is subtracted from the The difference between the temperature values ​​of the data points and the The pressure value of the data point is subtracted from the The ratio of the difference between the pressure values ​​of the data points is recorded as The first non-abnormal temperature-pressure sequence The changes in pressure and temperature of each data point; The method of obtaining the abnormal temperature pressure factor of each non-abnormal temperature pressure sequence according to the change of pressure and temperature of each data point of each non-abnormal temperature pressure sequence includes the following specific steps: Dividing each non-abnormal temperature and pressure sequence into a front non-abnormal temperature and pressure sequence and a rear non-abnormal temperature and pressure sequence; obtaining a piecewise fitting straight line of the front non-abnormal temperature and pressure sequence and the rear non-abnormal temperature and pressure sequence of each non-abnormal temperature and pressure sequence; According to the change of pressure and temperature of each data point of each non-abnormal temperature and pressure sequence and the difference between the slopes of the segmented fitting lines of the first segment and the second segment, the specific calculation formula of the abnormal temperature and pressure factor of each non-abnormal temperature and pressure sequence is obtained as follows: in, Indicates the Abnormal temperature pressure factor of a non-abnormal temperature pressure sequence; Indicates the The slope of the piecewise fitting straight line of the first segment of the non-abnormal temperature and pressure sequence; Indicates the The slope of the piecewise fitting straight line of the latter part of the non-abnormal temperature and pressure sequence; Indicates the The number of data points in a non-anomalous temperature-pressure series; Indicates the The first non-abnormal temperature-pressure sequence The changes in pressure and temperature of each data point; Indicates the The mean of the pressure and temperature changes of all data points in a non-abnormal temperature and pressure series; represents the linear normalization function.

2. The information security sharing method for chemical storage area according to claim 1 is characterized in that: The specific steps of obtaining a number of abnormal data points based on the abnormal temperature pressure factor of each non-abnormal temperature pressure sequence are as follows: when Greater than When All data points of the non-abnormal temperature and pressure series are determined to be abnormal data points; among them, is the preset second experience threshold.

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