CN114429308B - Enterprise safety risk assessment method and system based on big data - Google Patents
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Abstract
The invention relates to an enterprise security risk assessment method and system based on big data, and belongs to the technical field of enterprise security risk assessment and early warning. The method comprises the following steps: acquiring real-time data corresponding to the risk parameters, and judging whether the real-time data is larger than a set real-time threshold value or not; if the risk is greater than the first risk early warning is carried out; if the risk parameter is not greater than the preset threshold value, calculating the data fluctuation degree corresponding to each sampling moment and the real-time risk level corresponding to the risk parameter; calculating the risk development degree corresponding to each sampling moment, and judging whether the risk development degree is larger than a set degree threshold value or not; if the risk is greater than the first risk early warning value, carrying out second risk early warning; if not, constructing a deviation accumulation sum sequence; and calculating a sequence stability index according to the deviation accumulation sum sequence, judging whether the sequence stability index is larger than a set stability threshold, and if so, carrying out third risk early warning. The invention judges whether the potential risk exists by judging the stability index of the sequence, thereby realizing the early warning of the potential risk and preventing the occurrence of accidents.
Description
Technical Field
The invention relates to the technical field of enterprise security risk assessment and early warning, in particular to an enterprise security risk assessment method and system based on big data.
Background
The enterprise security risk assessment can ensure the security production of enterprises and find out potential safety hazards in the enterprise production process; particularly, a chemical field is an aggregation area developed by chemical enterprises and is used as a huge and complex system, a large number of dangerous sources exist in the chemical field, factories in the chemical field are closely connected, conveying pipelines and the like are staggered, different raw materials in the chemical production process are mutually influenced, even chemical reactions can be generated, serious potential safety hazards are caused, and safety accidents are caused; meanwhile, a chain reaction occurs between accidents, so that immeasurable losses are caused.
These risk factors in chemical sites threaten the life and property safety of surrounding people at any time, and accidents can be caused if management is not enhanced. How to realize accurate assessment and early warning of enterprise security risk has important meaning for improving security production.
Disclosure of Invention
The invention aims to provide an enterprise security risk assessment method and system based on big data, which are used for solving the problem that the existing method cannot accurately assess and early warn the enterprise security risk.
In order to solve the problems, the technical scheme of the enterprise security risk assessment method based on big data comprises the following steps:
acquiring real-time data corresponding to the risk parameters, judging whether the real-time data is larger than a set real-time threshold value, and sequentially sampling the real-time data according to preset sampling moments to obtain the real-time data;
if the risk is greater than the first risk early warning is carried out; if the risk parameter is not greater than the real-time risk parameter, calculating the data fluctuation degree corresponding to each sampling time and the real-time risk level corresponding to the risk parameter according to the real-time data;
Calculating the risk development degree corresponding to each sampling moment according to the data fluctuation degree and the real-time risk grade, and judging whether the risk development degree is larger than a set degree threshold value or not;
if the risk is greater than the first risk early warning value, carrying out second risk early warning; if the risk development degree is not greater than the sampling time, constructing a deviation accumulation sum sequence according to the risk development degree corresponding to each sampling time, wherein each element in the deviation accumulation sum sequence is an accumulation sum of differences between each risk development degree and a risk development degree mean before the corresponding sampling time;
And calculating a sequence stability index according to the deviation accumulation sum sequence, judging whether the sequence stability index is larger than a set stability threshold, and if so, carrying out third risk early warning.
The invention also provides a technical scheme of the enterprise security risk assessment system based on big data, which comprises a memory and a processor, wherein the processor executes a computer program stored in the memory to realize the enterprise security risk assessment method based on the big data.
The evaluation method and the evaluation system have the beneficial effects that: based on the real-time data corresponding to the acquired risk parameters, judging whether the real-time data is larger than a set real-time threshold value or not and setting a primary risk early warning mechanism; when the real-time data is not greater than the set real-time threshold value, calculating the risk development degree based on the data fluctuation degree and the real-time risk grade corresponding to the risk parameters, and judging whether the risk development degree is greater than the set degree threshold value or not and setting a secondary risk early warning mechanism; when the risk development degree is not greater than the set degree threshold, a deviation accumulation sequence is constructed based on the risk development degree corresponding to each sampling time, whether potential risks exist or not is judged by judging the stability index of the sequence, early warning of the potential risks is achieved, and accidents are prevented.
Further, the risk parameter is storage tank pressure, medium level, temperature or gas concentration.
Further, the degree of data fluctuation corresponding to each sampling time is calculated by adopting the following calculation formula:
Wherein B i is the degree of data fluctuation corresponding to the ith sampling time, e i is the real-time data value corresponding to the ith sampling time, e i (0) is the normal data value corresponding to the ith sampling time, and e (MAX) is the maximum value of the real-time data corresponding to each sampling time.
Further, the real-time risk level corresponding to the risk parameter is calculated by adopting the following calculation formula:
Wherein τ i is the risk level of the ith sampling time corresponding to the risk parameter, l is the standard risk level corresponding to the risk parameter, e i is the real-time data value corresponding to the ith sampling time, and e i (0) is the normal data value corresponding to the ith sampling time.
Further, the risk development degree corresponding to each sampling time is calculated by adopting the following calculation formula:
Vi＝τi*Bi
Wherein V i is the risk development degree corresponding to the ith sampling time, τ i is the risk grade of the ith sampling time corresponding to the risk parameter, and B i is the data fluctuation degree corresponding to the ith sampling time.
Further, the risk development degree deviation accumulation and sequence is as follows: { S (1),..S (a),..S (m) }, wherein,A=1, 2,..m, V i is the risk development corresponding to the i-th sampling instant and m is the total number of sampling instants.
Further, the method for calculating the sequence stability index according to the risk development degree deviation accumulation sum sequence comprises the following steps:
Dividing the deviation accumulation sum sequence by adopting windows with the same size to obtain a plurality of subsequences;
Fitting each subsequence to obtain a data trend function corresponding to each subsequence;
and calculating a sequence stability index according to the deviation accumulation and each element in the sequence and the data trend function.
Further, the following calculation formula is adopted to calculate the corresponding sequence stability index:
Wherein R (w) is a sequence stability index value obtained when the subsequence is obtained in a w×w window size, t (a) is a data trend function of the subsequence corresponding to the a-th sampling moment, and w is the side length of the window.
Drawings
FIG. 1 is a flow chart of an enterprise security risk assessment method based on big data according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings in the embodiments of the present invention.
Enterprise safety risk assessment method embodiment based on big data
The embodiment aims to accurately evaluate the security risk of an enterprise, and as shown in fig. 1, the method for evaluating the security risk of the enterprise of the embodiment comprises the following steps:
1) Acquiring real-time data corresponding to the risk parameters, and judging whether the real-time data is larger than a set real-time threshold value or not;
In this embodiment, the risk parameter is a combustible gas concentration through a sensor, a monitor and other data sensing devices arranged in the chemical site, but as other embodiments, the temperature parameter, the toxic gas parameter, the weather monitoring parameter, the storage tank pressure parameter and the like of the chemical site can be monitored according to actual requirements.
The embodiment is provided with data sensing equipment at a plurality of positions in a chemical industry place, each position is a monitoring point, the embodiment adopts the same analysis method to the real-time data collected by each monitoring point, one monitoring point is sequentially sampled according to preset sampling moments to obtain the corresponding real-time data, and the collected real-time data are sequentially: e 1、e2、…em, wherein e 1 is real-time data collected at a first sampling time, e 2 is real-time data collected at a second sampling time, e m is real-time data collected at an mth sampling time, and m is the total number of sampling times.
The size of the real-time data can directly reflect whether the potential safety hazard exists at the monitoring point, and the embodiment compares the acquired real-time data with the set real-time threshold value and judges whether the potential safety hazard exists at the monitoring point through a comparison result. The set real-time threshold value, namely the safety reference value, can be set according to the actual application scene and the type of the acquired risk parameters.
2) If the risk is greater than the first risk early warning is carried out; if the risk parameter is not greater than the real-time risk parameter, calculating the data fluctuation degree corresponding to each sampling time and the real-time risk level corresponding to the risk parameter according to the real-time data;
when the real-time data of a certain acquisition moment is larger than the set real-time threshold value, the concentration value of the combustible gas is larger than the safety reference value, and danger is easy to occur, so that the embodiment performs primary early warning when the real-time data is larger than the set real-time threshold value, prompts relevant staff to detect monitoring points, and prevents potential safety hazards caused by dangerous sources.
When the collected real-time data is not greater than the set real-time threshold value, only the fact that the collected real-time concentration value of the combustible gas is not greater than the safety reference value can be indicated, but the fact that the monitoring point is normal is not meant, and safety risks are avoided, because risks are possibly in development, and only the fact that the real-time concentration value is not shown in a mode that the real-time concentration value is greater than the safety reference value is not indicated. Therefore, in this embodiment, when the collected real-time data is not greater than the set real-time threshold, the data fluctuation degree corresponding to each sampling time and the real-time risk level corresponding to the risk parameter are calculated, so as to predict the risk development degree through the data fluctuation degree and the real-time risk level.
The data fluctuation degree mainly reflects the difference between the acquired actual data and the corresponding normal data, and the embodiment specifically adopts the following calculation formula to calculate the data fluctuation degree corresponding to each sampling moment:
wherein B i is the data fluctuation degree corresponding to the ith sampling moment; e i is a real-time data value corresponding to the ith sampling time; e i (0) is a normal data value corresponding to the ith sampling time, namely a data value acquired for the same monitoring point under the condition of no safety risk; e (MAX) is the maximum value of real-time data corresponding to each sampling time.
In this embodiment, the above formula is used to calculate the data fluctuation degree corresponding to each sampling time, and as other embodiments, other calculation formulas may be used to calculate the data fluctuation degree corresponding to each sampling time, but it should be satisfied that the data fluctuation degree corresponding to each sampling time and |e i-ei (0) | form a positive correlation.
Considering that the normal data value corresponding to the i-th sampling time is not necessarily a numerical value in the actual chemical industry place, but may be a section, for example, the normal data range corresponding to the i-th sampling time is (x, y), the average value of x and y may be taken as e i (0), or the value in the normal data range with the smallest difference from e i may be taken as e i (0).
In order to comprehensively analyze the risk development degree, the embodiment also refers to the risk level of the acquired parameters, and when the risk level of the acquired parameters is higher, the risk development degree under the same fluctuation degree is higher; when the risk level of the acquired parameters is low, the risk development degree under the same fluctuation degree is small. In order to accurately analyze the influence of the risk level of the acquired parameters on the risk development degree, the embodiment also corrects the risk level of the acquired parameters by combining the acquired real-time data, and adjusts the risk level of the acquired parameters according to the fluctuation amplitude of the acquired real-time data relative to the normal data value; the embodiment specifically adopts the following calculation formula to calculate the real-time risk level corresponding to the risk parameter:
Wherein τ i is the risk level of the ith sampling time corresponding to the risk parameter; l is a standard risk level corresponding to the risk parameter, and when the risk parameter is determined, the corresponding standard risk level is a fixed value; e i is a real-time data value corresponding to the ith sampling time; e i (0) is the normal data value corresponding to the i-th sampling time.
In this embodiment, the real-time risk level corresponding to the risk parameter is calculated by using the above formula, and as other embodiments, the real-time risk level corresponding to the risk parameter may be calculated by using other calculation formulas, but it should be satisfied that the real-time risk level corresponding to each risk parameter and the standard risk level corresponding to the risk parameter and |e i-ei (0) | are both in positive correlation.
3) Calculating the risk development degree corresponding to each sampling moment according to the data fluctuation degree and the real-time risk grade, and judging whether the risk development degree is larger than a set degree threshold value or not;
On the basis of calculating the fluctuation degree of the data and the real-time risk level, the risk development degree corresponding to each sampling moment is calculated by adopting the following calculation formula:
Vi＝τi*Bi
wherein V i is the risk development degree corresponding to the ith sampling time.
In this embodiment, the risk development degree corresponding to each sampling time is calculated by adopting the above formula, and as other embodiments, the risk development degree corresponding to each sampling time may be calculated by using other calculation formulas, but it should be satisfied that the risk development degree corresponding to each sampling time, the data fluctuation degree and the real-time risk level all form a positive correlation relationship.
When V i is larger, the risk development degree at the ith sampling moment is larger, and the risk of the monitoring point is more likely. In the embodiment, the risk development degree corresponding to each sampling time is compared with the set degree threshold value, and whether the risk development degree is larger than the set degree threshold value is judged, so that whether the monitoring point has a larger risk is judged according to the comparison result.
4) If the risk is greater than the first risk early warning value, carrying out second risk early warning; if the deviation is not greater than the risk development degree corresponding to each sampling time, constructing a deviation accumulation sum sequence;
When the risk development degree is greater than the set degree threshold value, the risk is larger at the monitoring point, and secondary early warning is carried out on the monitoring point, so that relevant staff is prompted to detect the monitoring point, and potential safety hazards caused by a dangerous source are prevented.
When the risk development degree is not greater than the set degree threshold, in order to realize early warning of risks, the embodiment also constructs a deviation accumulation sum sequence according to the risk development degree corresponding to each acquisition time, so as to analyze potential risks of the monitoring points by analyzing the deviation accumulation sum sequence.
For a sequence V= { V 1,V2,...Vm } formed by the risk development degrees corresponding to each acquisition time, constructing a corresponding risk development degree deviation accumulation sum sequence as follows: { S (1),..S (a),..S (m) }, wherein,a＝1，2，...，m。
5) And calculating a sequence stability index according to the deviation accumulation sum sequence, judging whether the sequence stability index is larger than a set stability threshold, and if so, carrying out third risk early warning.
When the deviation is accumulated and each element in the sequence is more disordered and unstable, the risk development of the monitoring point is more unstable, and the risk possibility of the monitoring point is higher; in the embodiment, whether potential risks exist at the monitoring points is judged by analyzing the sequence stability indexes of the deviation accumulation sum sequence; the specific process is as follows:
① Selecting a window with a fixed size to divide the deviation accumulation and the sequence, ensuring that the windows do not overlap when the sequence is divided, namely dividing the sequence by adopting a plurality of windows with identical sizes and mutually disjoint, wherein the size of the window is w multiplied by w, and the number of the corresponding windows is as follows:
Considering that the sequence length cannot be fully guaranteed to be an integer multiple of the window size, partial data information at the end of the sequence cannot be fully utilized, so that the evaluation effect of the system is reduced. Therefore, in this embodiment, in order to ensure the integrity of the sequence information, from the end of the sequence, the sequence data is divided once through a plurality of windows with equal size, so as to obtain 2×q subsequences with equal length;
② Fitting the obtained data in each subsequence to obtain a corresponding subsequence data trend function t (a), a=1, 2, &..m, fitting the data trend function of the subsequence by using a polynomial fitting method, a least square method and other methods, performing data fitting by using a least square method to obtain the data trend function of each subsequence, and then analyzing the stability of the bias accumulation sum sequence according to the change of the local subsequence relative to the whole subsequence, and constructing a sequence stability analysis model based on the bias accumulation sum sequence and the subsequence data trend function for detecting the stability of the sequence, wherein the sequence stability analysis model specifically comprises the following steps:
Wherein R (w) is a sequence stability index value obtained when the subsequence is obtained in a w×w window size, and w is a side length of the window.
③ And respectively dividing the data of the sequences with different window sizes, calculating sequence stability indexes based on different window sizes, and finally taking the average value of all the sequence stability indexes under different window sizes as a final sequence stability index R j.
Setting a stability threshold according to the risk prediction index model aiming at the corresponding prediction index of each sequence, setting three-level early warning for the system when the prediction index is higher than the stability threshold, and considering that the risk parameter has higher risk, and timely taking corresponding measures by staff to prevent risk accidents; the size of the stability threshold can be set according to actual needs.
In this embodiment, a plurality of windows with equal sizes and mutually exclusive are adopted to divide the sequence, R j is further calculated after R (w) is obtained, and as other embodiments, the sequence may be divided by sliding the window with the set size, where the calculated R (w) may be directly used as a sequence stability indicator.
Based on the real-time data corresponding to the acquired risk parameters, the embodiment firstly judges whether the real-time data is larger than a set real-time threshold value and sets a primary risk early warning mechanism; when the real-time data is not greater than the set real-time threshold value, calculating the risk development degree based on the data fluctuation degree and the real-time risk grade corresponding to the risk parameters, and judging whether the risk development degree is greater than the set degree threshold value or not and setting a secondary risk early warning mechanism; when the risk development degree is not greater than the set degree threshold, a deviation accumulation sequence is constructed based on the risk development degree corresponding to each sampling time, whether potential risks exist or not is judged by judging the stability index of the sequence, early warning of the potential risks is achieved, and accidents are prevented.
Enterprise safety risk assessment system embodiment based on big data
The big data based enterprise security risk assessment system of the present embodiment includes a memory and a processor executing a computer program stored by the memory to implement the big data based enterprise security risk assessment method as described in the big data based enterprise security risk assessment method embodiment.
Since the embodiment of the enterprise security risk assessment method based on big data has already been described, the description thereof will not be repeated here.
It should be noted that while the preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
Claims (2)
1. The enterprise security risk assessment method based on big data is characterized by comprising the following steps:
acquiring real-time data corresponding to the risk parameters, judging whether the real-time data is larger than a set real-time threshold value, and sequentially sampling the real-time data according to preset sampling moments to obtain the real-time data;
if the risk is greater than the first risk early warning is carried out; if the risk parameter is not greater than the real-time risk parameter, calculating the data fluctuation degree corresponding to each sampling time and the real-time risk level corresponding to the risk parameter according to the real-time data;
Calculating the risk development degree corresponding to each sampling moment according to the data fluctuation degree and the real-time risk grade, and judging whether the risk development degree is larger than a set degree threshold value or not;
if the risk is greater than the first risk early warning value, carrying out second risk early warning; if the risk development degree is not greater than the sampling time, constructing a deviation accumulation sum sequence according to the risk development degree corresponding to each sampling time, wherein each element in the deviation accumulation sum sequence is an accumulation sum of differences between each risk development degree and a risk development degree mean before the corresponding sampling time;
calculating a sequence stability index according to the deviation accumulation sum sequence, judging whether the sequence stability index is larger than a set stability threshold, and if so, performing a third risk early warning;
the risk parameters are storage tank pressure, medium liquid level, temperature or gas concentration;
The data fluctuation degree corresponding to each sampling time is calculated by adopting the following calculation formula:
Wherein, For the data fluctuation degree corresponding to the ith sampling moment,/>For the real-time data value corresponding to the ith sampling moment,/>For the normal data value corresponding to the ith sampling moment,/>The maximum value of the real-time data corresponding to each sampling moment;
and calculating the real-time risk level corresponding to the risk parameter by adopting the following calculation formula:
Wherein, Is the risk level of the ith sampling moment corresponding to the risk parameter,/>Is the standard risk level corresponding to the risk parameter,/>For the real-time data value corresponding to the ith sampling moment,/>The normal data value corresponding to the ith sampling moment;
the risk development degree corresponding to each sampling moment is calculated by adopting the following calculation formula:
Wherein, Is the risk development degree corresponding to the ith sampling moment,/>Is the risk level of the ith sampling moment corresponding to the risk parameter,/>The data fluctuation degree corresponding to the ith sampling moment is obtained;
the risk development degree deviation accumulation and sequence is as follows: Wherein, the method comprises the steps of, wherein, ，/>，/>，/>Is the risk development corresponding to the ith sampling instant,The total number of sampling moments;
the method for calculating the sequence stability index according to the risk development degree deviation accumulation sum sequence comprises the following steps:
Dividing the deviation accumulation sum sequence by adopting windows with the same size to obtain a plurality of subsequences;
Fitting each subsequence to obtain a data trend function corresponding to each subsequence;
Calculating a sequence stability index according to the deviation accumulation and each element in the sequence and the data trend function;
the corresponding sequence stability index is calculated by adopting the following calculation formula:
Wherein, To/>Sequence stability index value obtained when sub-sequence is acquired by window size,/>For/>Data trend function of subsequence corresponding to each sampling moment,/>Is the side length of the window.
2. A big data based enterprise security risk assessment system comprising a memory and a processor, wherein the processor executes a computer program stored in the memory to implement the big data based enterprise security risk assessment method of claim 1.
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| CN117009831B (en) * | 2023-10-07 | 2023-12-08 | 山东世纪阳光科技有限公司 | Fine chemical accident risk prediction assessment method |
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