A risk management system for wind power and photovoltaic power generation dual control platform

Abstract

The present invention relates to the technical field of risk analysis of power generation activities, and specifically to a risk management system for a wind power and photovoltaic power generation dual control platform. The present invention obtains conventional risk indicators based on the similarity between text data of risk inducements of different risk sources, and obtains risk similarity indicators between corresponding risk sources according to the difference between the inherent risk value and the current risk value of any two risk sources and the difference in each risk assessment indicator; obtains the difference between the risk difference indicator of each risk source and the remaining risk sources, and conducts risk assessment on the risk source in combination with the conventional risk indicator and the risk similarity indicator. The present invention conducts risk deviation assessment on the risk source based on the similarity of the risk inducements of similar risk sources and the risk assessment indicator, thereby improving the accuracy of risk management of the wind power and photovoltaic power generation dual control platform.

Description

Technical Field

[0001] The present invention relates to the technical field of risk analysis of power generation activities, and in particular to a risk management system for a wind power and photovoltaic power generation dual control platform. Background Art

[0002] With the advancement of technology and the improvement of environmental awareness, new energy forms such as wind power and photovoltaic power generation have developed rapidly due to their clean and renewable characteristics. Wind power and photovoltaic power generation are affected by similar geographical and meteorological conditions, which makes wind power and photovoltaic power generation projects have a certain degree of overlap in spatial distribution. Risk management through the dual control platform can improve the stability of energy supply and respond to various emergencies in a timely manner.

[0003] When using risk assessment methods such as operating condition hazard analysis to assess the risk level of risk sources of wind power and photovoltaic power generation dual control platforms, since the possibility of accidents at different hazard sources is obtained through the frequency of historical accidents and expert assessments, the possibility of accidents obtained by the above assessment methods is too dependent on experience values, which makes the possibility of accidents at risk sources inaccurate, causing the risk level of the risk source to be affected by the experience value, and further resulting in the risk level of the risk source obtained by the risk assessment method being inconsistent with the actual risk level, resulting in a low accuracy rate in risk management of wind power and photovoltaic power generation dual control platforms. Summary of the invention

[0004] In order to solve the technical problem that the probability of accident occurrence of risk source is too dependent on experience value, resulting in low accuracy of risk management for wind power and photovoltaic power generation dual control platform, the purpose of the present invention is to provide a risk management system for wind power and photovoltaic power generation dual control platform, and the technical scheme adopted is as follows:

[0005] The present invention proposes a risk management system for a wind power and photovoltaic power generation dual control platform, the system comprising:

[0006] The data acquisition module is used to obtain the inherent risk values, current risk values, various hazard assessment indicators and text data of risk inducements of different risk sources for risk management of wind power and photovoltaic power generation dual control platforms;

[0007] A risk general analysis module is used to obtain the risk general indicators of each risk source according to the similarity between the text data of the risk inducements of each risk source and the other risk sources;

[0008] The risk similarity analysis module is used to obtain the risk similarity index between any two risk sources according to the difference between the inherent risk values ​​of any two risk sources and the difference between each hazard assessment index;

[0009] The risk assessment module is used to perform risk assessment on each risk source based on the difference in current risk value between each risk source and other risk sources, the risk conventional index and the risk similarity index.

[0010] Furthermore, the method for obtaining the conventional risk index of each risk source includes:

[0011] Based on the difference between the text data of any two risk inducements, determine the inducement similarity index between the corresponding two risk inducements;

[0012] All risk sources are divided into different categories based on the accident types of risk sources; for each category of risk sources, the inducement conventional indicators of each risk inducement of each risk source of each category are obtained according to the inducement similarity index between each risk inducement of each risk source of each category and all risk inducements of the remaining risk sources except the risk source; the average of the inducement conventional indicators of all risk inducements of each risk source is taken as the risk conventional indicator of the corresponding risk source.

[0013] Furthermore, the method for determining the cause similarity index between two risk causes based on the difference between the text data of any two risk causes includes:

[0014] The text data of each risk inducement is converted into a vector to obtain the inducement vector of the corresponding risk inducement; the cosine similarity between the inducement vectors of any two risk inducements is used as the inducement similarity index between the corresponding two risk inducements.

[0015] Furthermore, the method for obtaining the conventional indicators of the causes of each risk source of each category includes:

[0016] Select any two risk sources from each category of risk sources and record them as analysis risk sources and target risk sources respectively; record the maximum value of the inducement similarity index between each risk inducement of the analysis risk source and all risk inducements of the target risk source as the initial conventional index between each risk inducement of the analysis risk source and the target risk source; obtain the average value of the initial conventional index between each risk inducement of the analysis risk source and the remaining risk sources of each category except the analysis risk source as the inducement conventional index of each risk inducement of the analysis risk source.

[0017] Furthermore, the method for obtaining the risk similarity index between any two risk sources according to the difference between the inherent risk values ​​of the two risk sources and the difference between each risk assessment index includes:

[0018] According to the difference of the same hazard assessment index of any two risk sources, the stable risk index corresponding to the two risk sources is obtained; the difference of the inherent risk value of any two risk sources and the product of the stable risk index are negatively correlated and mapped to obtain the risk similarity index between the two risk sources.

[0019] Furthermore, the method for obtaining stable risk indicators corresponding to two risk sources according to the difference of the same risk assessment indicators of any two risk sources includes:

[0020] Each risk assessment indicator is standardized to obtain the standard value of each risk assessment indicator; the absolute value of the difference between the standard values ​​of the same risk assessment indicator of any two risk sources is calculated, and recorded as the assessment difference degree of each risk assessment indicator corresponding to the two risk sources; the cumulative multiplication result of the assessment difference degrees of all risk assessment indicators of any two risk sources is recorded as the stable risk indicator of the two risk sources.

[0021] Furthermore, the method of performing risk assessment on each risk source according to the difference in current risk value between each risk source and other risk sources, the conventional risk index and the risk similarity index comprises:

[0022] According to the difference in the current risk values ​​between the analysis risk source and the target risk source, the risk similarity index and the risk conventional index of the target risk source, the local risk deviation degree of the analysis risk source relative to the target risk source is obtained; the category to which the analysis risk source belongs is recorded as the analysis category, and the average of the local analysis deviation degrees of the analysis risk source relative to the remaining risk sources of the analysis category except the analysis risk source is taken as the overall risk deviation degree of the analysis risk source relative to the analysis category; according to the risk conventional index of the analysis risk source and the overall risk deviation degree, the risk deviation index of the analysis risk source is obtained;

[0023] Obtain the current risk level of the risk source for analysis; when the risk deviation index is greater than the preset deviation threshold, there is an error in the current risk level of the risk source for analysis; when the risk deviation index is less than or equal to the preset deviation threshold, there is no error in the current risk level of the risk source for analysis.

[0024] Furthermore, the method for obtaining and analyzing the current risk level of the risk source is an operating condition hazard analysis method.

[0025] Furthermore, the risk assessment indicators include: the frequency of exposure to dangerous environments and the severity of the danger.

[0026] Furthermore, the preset deviation threshold is 0.6.

[0027] The present invention has the following beneficial effects:

[0028] In an embodiment of the present invention, since there are similarities between conventional risk sources, the risk inducements of risk sources with higher conventionality are more common, and therefore, conventional risk indicators are obtained based on the similarity between the text data of the risk inducements of each risk source and other risk sources; since the risk is mainly judged by the severity of the accident consequences, risk similarity indicators of different risk sources are obtained based on the differences in inherent risk values ​​of different risk sources and the hazard assessment indicators reflecting the risk levels of the risk sources; the risk similarity indicators and the conventional risk indicators reflect the reference value of the risk assessment results of other risk sources to the risk sources, and the current risk value presents the risk of accidents occurring in the risk sources after the control measures are taken. Risk assessment is performed based on the differences in the current risk values ​​of each risk source and other risk sources, the conventional risk indicators and the risk similarity indicators, thereby reducing the instability of human factors in the risk assessment process and improving the accuracy of risk management of the dual control platform of wind power and photovoltaic power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are 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.

[0030] Figure 1 A system structure diagram of a risk management system for a wind power and photovoltaic power generation dual control platform provided by one embodiment of the present invention;

[0031] Figure 2 A system structure diagram of a risk conventional indicator of a risk source provided by an embodiment of the present invention;

[0032] Figure 3 A system structure diagram for performing risk assessment on risk sources provided by an embodiment of the present invention;

[0033] Figure 4 A schematic diagram of a computer device for a risk management device for a wind power and photovoltaic power generation dual control platform provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a risk management system for a wind power and photovoltaic power generation dual control platform proposed by the present invention in combination 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.

[0035] 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.

[0036] The following is a detailed description of a specific solution of a risk management system for a wind power and photovoltaic power generation dual control platform provided by the present invention in conjunction with the accompanying drawings.

[0037] Embodiment 1:

[0038] See also Figure 1 , which shows a system block diagram of a risk management system for a wind power and photovoltaic power generation dual control platform provided by an embodiment of the present invention. The system includes: a data acquisition module 110, a risk routine analysis module 120, a risk similarity analysis module 130, and a risk assessment module 140.

[0039] The data acquisition module 110 is used to obtain text data of inherent risk values, current risk values, various hazard assessment indicators and risk inducements of different risk sources in the risk management of the wind power and photovoltaic power generation dual control platform.

[0040] The dual prevention mechanism refers to the safety risk classification control and hidden danger investigation and management. The safety risk classification control includes: risk point division, hazard source recognition, safety risk assessment and other steps; hidden danger investigation and management refers to the investigation and management of various risks, and the safety risk management measures of wind power and photovoltaic new energy power generation companies.

[0041] When using the dual prevention mechanism to conduct risk assessment and prevention and control on the wind power and photovoltaic dual control platform, the risk database constructed by the risk management system of the wind power and photovoltaic power generation dual control platform obtains the risk sources under different steps or units of the risk point, as well as the specific circumstances of the accidents that occurred at the risk source in the historical time. The inherent risk of the risk source is mainly guided by the consequences of the accident, and should be judged by the severity of the consequences of the accident to determine the inherent risk value of each risk source. In this embodiment, the specific method for obtaining the inherent risk value is: first, determine the inherent risk level of each risk source according to the judgment conditions in the inherent risk judgment table in Table 1; secondly, take the mean of the two endpoints of the risk value interval corresponding to the inherent risk level of each risk source in the risk level conversion table in Table 2 as the inherent risk value DG of the corresponding risk source.

[0042] As an example, if an accident occurs at a risk source and meets the judgment condition of the greater risk level in Table 1, "in a place or area where there is a risk of accidental release of energy (highly toxic, explosion, fire, collapse, geological disaster, etc.), and there are 3 or more people working or in the area", then the inherent risk level of the risk source is greater risk. The corresponding risk value interval of the greater risk level in the risk level conversion table in Table 2 is [160,320). As the inherent risk value of the risk source. It should be noted that, in the embodiment of the present invention, if the inherent risk level of a risk source is low risk, the inherent risk value of the risk source is set to 35; if the inherent risk level of a risk source is major risk, the inherent risk value of the risk source is set to 350. Table 1 and Table 2 are as follows:

[0043] Table 1, inherent risk assessment table

[0044]

[0045] Table 2, Risk level conversion table

[0046] Value at Risk Risk Level [320,+∞) Significant risks [160,320) Greater risk [70,160) General hazards [0,70) Low risk

[0047] The current risk mainly evaluates whether the existing control measures can control the inherent risks. For each risk source, control measures are taken for the risk source, and the operating condition hazard analysis method is performed on the risk source after the control measures to obtain the risk score of the risk source, and the risk score is recorded as the current risk value DN of the risk source; the risk level corresponding to the risk value of the current risk value DN of the risk source in the risk level conversion table in Table 2 is used as the current risk level of the risk source. As an example, if the current risk value of a risk source is equal to 150, the current risk level of the risk source is general hazard. At the same time, the text data of the risk inducement of each risk source is obtained. The text data is a sentence. The risk inducement is the cause of the accident at the risk source; for example, "the inlet and outlet holes of the cabinet are not equipped with fireproof plugs or the fireproof plugs fail" is a risk inducement of the svg cabinet risk source.

[0048] Preferably, in an embodiment of the present invention, for each risk source, three indicators are obtained through the operating condition hazard analysis method, namely, the possibility of an accident occurring at the risk source, the frequency of exposure to a hazardous environment, and the severity of the hazard. Because the acquisition of the possibility of an accident occurring is too dependent on empirical values, the frequency of exposure to a hazardous environment and the severity of the hazard are relatively fixed and easy to measure indicators. Therefore, the hazard assessment indicators in this embodiment include: the frequency of exposure to a hazardous environment and the severity of the hazard.

[0049] In other embodiments of the present invention, risk sources can be evaluated by other risk assessment methods. For example, the possibility of accidents occurring at risk sources and the severity of accident consequences are obtained by risk matrix analysis, and the hazard assessment indicators include: the severity of accident consequences; the possibility of accidents occurring at risk sources, the status of control measures and the frequency of exposure to dangerous environments are obtained by risk degree analysis, and the hazard assessment indicators include: the status of control measures and the frequency of exposure to dangerous environments.

[0050] Among them, the operating condition hazard analysis method, risk matrix analysis method and risk degree analysis method are all well-known technologies to those skilled in the art and will not be described in detail here.

[0051] As an example, the inherent risk value DG, current risk value DN, various risk assessment indicators (frequency of exposure to dangerous environment and severity of danger) and current risk level of some risk sources constitute the risk assessment data table of Table 3. Table 3 is as follows:

[0052] Table 3, Risk Assessment Data Table

[0053]

[0054]

[0055] The risk general analysis module 120 is used to obtain the risk general index of each risk source according to the similarity between the text data of the risk causes of each risk source and the other risk sources.

[0056] For different risk sources, some risks are conventional risks, and some risks are special risks, that is, some risks are similar between similar risk sources, but some risks are unique to the risk source. For a risk source, the more conventional the risk inducement of the risk source, the more reference value the risk evaluation of the risk source has for the risk evaluation of other risk sources. Therefore, according to the similarity between the text data of the risk inducements of different risk sources, the risk conventional indicators of the risk source are obtained.

[0057] See also Figure 2 , which shows a system structure diagram of a risk conventional indicator of a risk source provided by an embodiment of the present invention. The system includes: an inducement analysis unit 121 and a risk analysis unit 122.

[0058] The inducement analysis unit 121 determines the inducement similarity index between any two risk inducements based on the difference between the text data of the two risk inducements.

[0059] Preferably, in an embodiment of the present invention, the method for obtaining the inducement similarity index includes: converting the text data of each risk inducement into a vector to obtain the inducement vector of the corresponding risk inducement; and taking the cosine similarity between the inducement vectors of any two risk inducements as the inducement similarity index between the two risk inducements. When the inducement similarity index between the two risk inducements is larger, that is, closer to 1, the two risk inducements are closer.

[0060] It should be noted that in the embodiment of the present invention, the text data of each risk inducement of the risk source is converted into a sentence vector through support vector machine and natural language processing (NLP) technology, and it is recorded as the inducement vector of the corresponding risk inducement. Among them, support vector machine and natural language processing technology are well-known contents and will not be repeated here. It should be noted that the number of elements in all inducement vectors is equal.

[0061] In another embodiment of the present invention, the distance between the inducement vectors of any two risk inducements is negatively correlated to obtain the inducement similarity index between the two risk inducements. It should be noted that the distance between the two vectors can be Euclidean distance and Manhattan distance, etc.; because the smaller the difference between the data at the same position of the two inducement vectors, the closer the two inducement vectors are, so it is necessary to negatively correlate the distance between the two inducement vectors.

[0062] Risk analysis unit 122: divide all risk sources into different categories based on the accident types of risk sources; for each category of risk sources, obtain the inducement general index of each risk inducement of each risk source of each category according to the inducement similarity index between each risk inducement of each risk source of each category and all risk inducements of the remaining risk sources except the risk source; take the average of the inducement general indexes of all risk inducements of each risk source as the risk general index of the corresponding risk source.

[0063] The consequences of accidents and the causes of accidents of risk sources of the same accident type are similar, while the risk sources of different accident types are quite different; if the regularity of the risk causes is analyzed based on the risk causes of all risk sources, the accuracy of the regularity analysis of risk causes will be reduced and the amount of calculation will be increased because the consequences of accidents and the causes of accidents of risk sources of different accident types are quite different. Therefore, in the embodiment of the present invention, all risk sources are divided into different categories based on the accident type of the risk source, that is, risk sources with the same accident type are risk sources of the same category.

[0064] The inducement similarity index between two risk inducements reflects the similarity between the corresponding risk inducements. By analyzing the inducement similarity index between each risk inducement of each risk source and all risk inducements of the remaining risk sources in the category to which it belongs, the commonality of each risk inducement of each risk source relative to all risk inducements of the remaining risk sources in the category to which the risk source belongs is presented, thereby obtaining the risk commonality index of each risk inducement.

[0065] Preferably, in an embodiment of the present invention, the method for obtaining the inducement conventional indicators includes: selecting any two risk sources from each category of risk sources, and recording them as analysis risk sources and target risk sources in turn; recording the maximum value of the inducement similarity indicators between each risk inducement of the analysis risk source and all risk inducements of the target risk source as the initial conventional indicators of each risk inducement of the analysis risk source and the target risk source; obtaining the average of the initial conventional indicators of each risk inducement of the analysis risk source and the remaining risk sources of each category except the analysis risk source, as the inducement conventional indicator of each risk inducement of the analysis risk source.

[0066] It should be noted that different risk inducements of the same risk source can lead to the same type of accident. In order to reduce the amount of calculation and retain the consequences caused by the risk inducements to the greatest extent, the maximum value of the inducement similarity index between each risk inducement of the analyzed risk source and all risk inducements of the target risk source is recorded as the initial conventional index between each risk inducement of the analyzed risk source and the target risk source.

[0067] In the embodiment of the present invention, the conventional index of the inducement is expressed by the formula:

[0068] Where NR is the conventional indicator of each risk inducement of the risk source under analysis; A is the total number of risk sources in the category to which the risk source under analysis belongs; B a YS is the risk inducement set of the ath risk source in the category to which the risk source to be analyzed belongs, excluding the risk source to be analyzed. The risk inducement set is composed of all risk inducements of the ath risk source in the category to which the risk source to be analyzed belongs, excluding the risk source to be analyzed. a,b is the risk similarity index between each risk inducement of the analyzed risk source and the b-th risk inducement of the a-th risk source in the category to which the analyzed risk source belongs, excluding the analyzed risk source; max(YS a,b ,b∈B a ) is the initial conventional indicator of each risk cause of the analyzed risk source and the ath risk source in the category to which the analyzed risk source belongs, excluding the analyzed risk source; max() is the maximum value function.

[0069] It should be noted that if the risk inducement similarity index between each risk inducement of the risk source analyzed and the risk inducements of the remaining risk sources of the category to which the risk source analyzed belongs, that is, YS a,bThe larger the value is, the more similar the risk inducements of the risk source in the category to which the risk source is analyzed are, and the more common the risk inducement of the risk source is. The method for obtaining the common inducement indicators of all risk inducements of each risk source is the same as the method for obtaining the common inducement indicators of each risk inducement of the risk source being analyzed.

[0070] Afterwards, the regularity of the risk inducement of the risk source affects the regularity of the risk source, and the regularity of accidents occurring at the risk source is determined by combining the regularity of all risk inducements of the risk source. Therefore, the average of the inducement regularity index of all risk inducements of each risk source is used as the risk regularity index of the corresponding risk source. The risk evaluation of a risk source with a larger risk regularity index has more reference value for the risk evaluation of risk sources of the category to which the risk source belongs.

[0071] The risk similarity analysis module 130 is used to obtain the risk similarity index between any two risk sources according to the difference between the inherent risk values ​​of any two risk sources and the difference between each risk assessment index.

[0072] Since the risk is mainly judged by the severity of the consequences of the accident, the risk source is analyzed by the operating condition hazard analysis method to obtain the possibility of accidents, the frequency of exposure to dangerous environments and the severity of the danger, and the danger level of the risk source, i.e. the risk size, is evaluated by three indicators. Because the frequency of exposure to dangerous environments and the severity of danger are relatively fixed and easy to measure indicators, the possibility of accidents is not easy to determine, so the hazard assessment indicators include: the frequency of exposure to dangerous environments and the severity of danger, and combined with the inherent risk value that presents the risk size of the risk source before taking control measures, the risk size between different risk sources, i.e. the risk similarity index, is obtained.

[0073] Preferably, in an embodiment of the present invention, the method for obtaining a risk similarity index includes: obtaining a stable risk index corresponding to two risk sources based on the difference in the same hazard assessment index of any two risk sources; and negatively correlating the difference in the inherent risk values ​​of any two risk sources with the product of the stable risk index to obtain a risk similarity index between the corresponding two risk sources.

[0074] In an embodiment of the present invention, each risk assessment indicator is standardized to obtain a standard value of each risk assessment indicator; the absolute value of the difference between the standard values ​​of the same risk assessment indicator of any two risk sources is calculated and recorded as the assessment difference of each risk assessment indicator of the corresponding two risk sources; the cumulative multiplication result of the assessment difference of all risk assessment indicators of any two risk sources is recorded as the stable risk indicator of the two risk sources. In other embodiments of the present invention, the cumulative sum of the assessment difference of all risk assessment indicators of any two risk sources can also be recorded as the stable risk indicator of the two risk sources.

[0075] Since different risk assessment indicators have different dimensions, the risk assessment indicators need to be standardized before analysis. In the embodiment of the present invention, the range standardization method is used for standardization. In other embodiments of the present invention, other methods can also be used for standardization, such as Z score standardization, decimal calibration standardization, and sum normalization standardization.

[0076] Taking the xth risk source and the yth risk source as an example for analysis, the formula for the risk similarity index between the two risk sources is expressed as:

[0077] Where SD(x, y) is the risk similarity index between the x-th risk source and the y-th risk source; DG x is the inherent risk value of the x-th risk source; DG y is the inherent risk value of the yth risk source; WP x，h WP is the standard value of the hth hazard assessment indicator of the xth risk source; y，h is the standard value of the hth risk assessment indicator of the yth risk source; H is the total number of risk assessment indicators for each risk source, and in this embodiment, H takes the empirical value of 2; is the stable risk index of the x-th risk source and the y-th risk source; | | is the absolute value function; exp is the exponential function with the natural constant e as the base. That is, in this formula, negative correlation mapping is performed by the exponential function with the natural constant e as the base. In other embodiments of the present invention, other negative correlation mapping methods can also be selected, which are technical means well known to those skilled in the art and will not be elaborated or limited here.

[0078] It should be noted that the hazard assessment index is an index to measure the degree of hazard of the risk source obtained through the hazard analysis method of operating conditions. The closer the hazard assessment index of the xth and yth risk sources are, the higher the hazard assessment index of the xth and yth risk sources are. The smaller it is, the more similar the risk levels between the two risk sources are, and the larger the risk similarity index SD(x, y) is. x -DG y The smaller | is, the closer the risks of the x-th and y-th risk sources are before control measures are taken, and the larger the risk similarity index SD(x, y).

[0079] The risk assessment module 140 is used to perform risk assessment on each risk source according to the difference in the current risk value between each risk source and other risk sources, the risk conventional index and the risk similarity index.

[0080] When the risk source is assessed for the current risk through the operating condition hazard analysis method, if the number of hazard sources in a certain step or unit of the wind power and photovoltaic power generation dual control platform is large, the accident probability obtained by the operating condition hazard analysis method will be distorted. At the same time, due to the different probability and deviation of accidents at risk sources, there are large differences between the risk scores calculated for different risk sources, resulting in inaccurate assessment of the current risk, and thus errors in risk assessment. The current risk value presents the risk of accidents at the risk source after control measures, and is analyzed as the main body of the risk deviation process. The risk causes that lead to accidents at the risk source, i.e., risk similarity indicators, and the normal degree of accidents at the risk source, i.e., risk normal indicators, are combined to improve the accuracy of risk deviations at different risk sources.

[0081] See also Figure 3 , which shows a system structure diagram for performing risk assessment on risk sources provided by an embodiment of the present invention. The system includes: a risk deviation analysis unit 141 and a risk assessment unit 142.

[0082] Risk deviation analysis unit 141: used to obtain the local risk deviation degree of the analysis risk source relative to the target risk source based on the difference in the current risk values ​​between the analysis risk source and the target risk source, the risk similarity index and the risk general index of the target risk source; record the category to which the analysis risk source belongs as the analysis category, and take the average of the local analysis deviation degrees of the analysis risk source relative to the analysis category and the remaining risk sources except the analysis risk source as the overall risk deviation degree of the analysis risk source relative to the analysis category; obtain the risk deviation index of the analysis risk source based on the risk general index and the overall risk deviation degree of the analysis risk source.

[0083] Preferably, the formula for the risk deviation index for each risk source in each category is expressed as:

[0084] In the formula, F i,k is the risk deviation index of the kth risk source in the ith category; NR i,k is the risk general indicator of the k-th risk source of the ith category; H i is the total number of risk sources of the ith category; DN i，k is the current risk value of the kth risk source in the ith category; DN i,n is the current risk value of the nth risk source of the ith category except the kth risk source; NR i,n is the risk general indicator of the nth risk source of the ith category except the kth source; SD(k,n) i is the risk similarity index between the k-th risk source of the i-th category and the n-th risk source except the k-th risk source; |DN i，k -DN i，n|×NR i，n ×SD(k,n) i is the local risk deviation of the k-th risk source in the ith category relative to the n-th risk source excluding the k-th risk source; is the overall risk deviation of the kth risk source in the ith category relative to the ith category; | | is the absolute value function; and the Sigmoid function is used for normalization.

[0085] It should be noted that if the risk source is a special risk, the risk assessment conditions of the special risk are relatively special, and the risk assessment conditions of other risk sources are not applicable to the special risk source, so that the smaller the impact of the risk assessment conditions of other risk sources on the special risk source, the more accurate the risk assessment result of the special risk source. Therefore, the more conventional the risk inducement of the risk source, the greater the reference value of the risk assessment data of other similar risk sources to the risk source, and the greater the error degree of the assessment result of the risk source. i，k When NR is larger, the risk regularity of the k-th risk source in the i-th category is stronger, and the risk regularity index is obtained based on the similarity of the risk inducements of all risk sources in each category, that is, similar risk sources. Then the risk assessment data of the i-th category except the k-th risk source has a greater reference value for the k-th risk source; NR i,n With NR i,k Similar to the analysis of , when both are larger, the value of mutual reference of risk assessment data of the i-th category of risk source is greater, and the error degree of risk source assessment result is greater, and the risk deviation index P is i，k When SD(k,n) i When the value is larger, the risk of the k-th risk source in the ith category is more similar to the risk of the n-th risk source except the k-th risk source, the reference value of the n-th risk source to the k-th risk source is greater, the error degree of the evaluation result of the k-th risk source is greater, and the risk deviation index P is i，k . |DN i，k -DN i，n | is the subject that measures the risk deviation between the k-th risk source of the ith category and the n-th risk source excluding the k-th risk source. When |DN i，k -DN i，n When | is larger, it directly indicates the risk difference between the two risk sources. The risk deviation index P i，k The bigger.

[0086] The risk assessment unit 142 is used to obtain the current risk level of the risk source for analysis; when the risk deviation index is greater than the preset deviation threshold, there is an error in the current risk level of the risk source for analysis; when the risk deviation index is less than or equal to the preset deviation threshold, there is no error in the current risk level of the risk source for analysis.

[0087] If there is an error in the analysis of the current risk level of the risk source, the risk source will be marked and a message will be sent to remind the risk assessment project team that the risk assessment result of the risk source may have an error, and ask whether a second assessment is needed; the risk assessment project team will conduct a second review and re-evaluate; if the risk level result of the second assessment of the risk source is different from the risk level result of the first assessment, that is, the current risk level, the assessment result of the risk source will be modified. If the risk level result of the second assessment of the risk source is the same as the risk level result of the first assessment, that is, the current risk level, the first assessment result of the risk source will be directly stored in the database. If there is no error in the analysis of the current risk level of the risk source, the current risk level of the risk source will be directly stored in the database. The database is a database such as MySQL or Oracle.

[0088] It should be noted that the current risk level of the risk source has been acquired in the data acquisition module 110; the error judgment method of the current risk level of all risk sources is the same as the error judgment method of the current risk level of the analysis risk source.

[0089] So far, the present invention is completed.

[0090] In summary, in an embodiment of the present invention, a conventional risk index is obtained based on the similarity between the text data of the risk inducements of different risk sources, and a risk similarity index between corresponding risk sources is obtained based on the difference between the inherent risk value and the current risk value of any two risk sources and the difference in each risk assessment index; the difference between the risk difference index of each risk source and the remaining risk sources is obtained, and the risk source is risk assessed by combining the conventional risk index and the risk similarity index. The present invention performs risk deviation assessment on the risk source based on the similarity of the risk inducements of similar risk sources and the risk assessment index, thereby improving the accuracy of risk management of the wind power and photovoltaic power generation dual control platform.

[0091] Embodiment 2:

[0092] Figure 4 A computer device schematic diagram of a risk management device for a wind power and photovoltaic power generation dual control platform provided by an embodiment of the present invention. For example, Figure 4 As shown, the computer device includes: a memory 201, a processor 202, and a computer program 203 stored in the memory 201 and running on the processor 202, wherein when the processor 202 executes the computer program 203, the computer device can execute any one of the risk management systems for wind power and photovoltaic power generation dual control platforms introduced above.

[0093] In addition, an embodiment of the present application also protects a device, which may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to execute a risk management system for a wind power and photovoltaic power generation dual control platform provided by an embodiment of the present application.

[0094] In this embodiment, the functional modules of the device can be divided according to the above method example. For example, each functional module can be corresponded, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0095] It should be understood that the device provided in this embodiment is used to execute the above-mentioned risk management system for a wind power and photovoltaic power generation dual-control platform, and thus can achieve the same effect as the above-mentioned implementation method.

[0096] In the case of an integrated unit, the device may include a processing module and a storage module. When the device is applied to a device, the processing module may be used to control and manage the actions of the device. The storage module may be used to support the device to execute mutual program codes, etc.

[0097] The processing module may be a processor or a controller, which may implement or execute various exemplary logic blocks, modules and circuits disclosed in conjunction with the present application. The processor may also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of a digital signal processing (DSP) and a microprocessor, etc. The storage module may be a memory.

[0098] Embodiment 3:

[0099] This embodiment also provides a computer-readable storage medium, in which a computer program code is stored. When the computer program code is executed on a computer, the computer executes the above-mentioned related method steps to implement a risk management system for a wind power and photovoltaic power generation dual-control platform provided in the above embodiment.

[0100] Among them, the device and computer-readable storage medium provided in this embodiment are used to execute the corresponding system provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding system provided above, and will not be repeated here.

[0101] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0102] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0103] 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 risk management system for a wind power and photovoltaic power generation dual control platform, characterized in that: The system includes: The data acquisition module is used to obtain the inherent risk values, current risk values, various hazard assessment indicators and text data of risk inducements of different risk sources for risk management of wind power and photovoltaic power generation dual control platforms; A risk general analysis module is used to obtain the risk general indicators of each risk source according to the similarity between the text data of the risk inducements of each risk source and the other risk sources; The risk similarity analysis module is used to obtain the risk similarity index between any two risk sources according to the difference between the inherent risk values ​​of any two risk sources and the difference between each hazard assessment index; A risk assessment module, used to perform risk assessment on each risk source according to the difference in current risk value between each risk source and other risk sources, the risk conventional index and the risk similarity index; The method for obtaining the conventional risk index of each risk source includes: Based on the difference between the text data of any two risk inducements, determine the inducement similarity index between the corresponding two risk inducements; All risk sources are divided into different categories based on the accident types of risk sources; for each category of risk sources, the inducement conventional index of each risk inducement of each risk source of each category is obtained according to the inducement similarity index between each risk inducement of each risk source of each category and all risk inducements of the remaining risk sources except the risk source; the average of the inducement conventional index of all risk inducements of each risk source is taken as the risk conventional index of the corresponding risk source; The method for obtaining the conventional indicators of the causes of each risk source of each category includes: Select any two risk sources from each category of risk sources and record them as analysis risk sources and target risk sources in turn; record the maximum value of the similarity index between each risk inducement of the analysis risk source and all risk inducements of the target risk source as the initial conventional index between each risk inducement of the analysis risk source and the target risk source; obtain the average value of the initial conventional index between each risk inducement of the analysis risk source and the remaining risk sources of each category except the analysis risk source as the conventional index of each risk inducement of the analysis risk source; The method for obtaining the risk similarity index between any two risk sources according to the difference between the inherent risk values ​​of the two risk sources and the difference between each risk assessment index comprises: According to the difference of the same hazard assessment index of any two risk sources, the stable risk index corresponding to the two risk sources is obtained; the difference of the inherent risk value of any two risk sources and the product of the stable risk index are negatively correlated to obtain the risk similarity index between the two risk sources; The method for obtaining stable risk indicators corresponding to two risk sources according to the difference of the same risk assessment indicators of any two risk sources includes: Each risk assessment indicator is standardized to obtain a standard value for each risk assessment indicator; the absolute value of the difference between the standard values ​​of the same risk assessment indicator of any two risk sources is calculated, and recorded as the assessment difference degree of each risk assessment indicator corresponding to the two risk sources; the cumulative multiplication result of the assessment difference degrees of all risk assessment indicators of any two risk sources is recorded as the stable risk indicator of the two risk sources; The method of performing risk assessment on each risk source according to the difference between the current risk values ​​of each risk source and the remaining risk sources, the conventional risk index and the risk similarity index comprises: According to the difference in the current risk values ​​between the analysis risk source and the target risk source, the risk similarity index and the risk conventional index of the target risk source, the local risk deviation degree of the analysis risk source relative to the target risk source is obtained; the category to which the analysis risk source belongs is recorded as the analysis category, and the average of the local analysis deviation degrees of the analysis risk source relative to the remaining risk sources of the analysis category except the analysis risk source is taken as the overall risk deviation degree of the analysis risk source relative to the analysis category; according to the risk conventional index of the analysis risk source and the overall risk deviation degree, the risk deviation index of the analysis risk source is obtained; Obtain the current risk level of the risk source for analysis; when the risk deviation index is greater than the preset deviation threshold, there is an error in the current risk level of the risk source for analysis; when the risk deviation index is less than or equal to the preset deviation threshold, there is no error in the current risk level of the risk source for analysis.

2. A risk management system for a wind power and photovoltaic power generation dual control platform according to claim 1, characterized in that: The method for determining the cause similarity index between two risk causes based on the difference between the text data of any two risk causes includes: The text data of each risk inducement is converted into a vector to obtain the inducement vector of the corresponding risk inducement; the cosine similarity between the inducement vectors of any two risk inducements is used as the inducement similarity index between the corresponding two risk inducements.

3. A risk management system for a wind power and photovoltaic power generation dual control platform according to claim 1, characterized in that: The method for obtaining and analyzing the current risk level of risk sources is an operating condition hazard analysis method.

4. A risk management system for a wind power and photovoltaic power generation dual control platform according to claim 1, characterized in that: The risk assessment indicators include: the frequency of exposure to dangerous environments and the severity of the danger.

5. A risk management system for a wind power and photovoltaic power generation dual control platform according to claim 1, characterized in that: The preset deviation threshold is 0.6.

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