Calculation method and system for multi-region risk
By identifying key risk factors and parameters and utilizing formulas for calculating local and overall risk probabilities, this approach addresses the problem of risk prediction relying on human experience in existing technologies, achieving more accurate and efficient risk assessment and enhancing the real-time nature and security of risk monitoring.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGZHOU KINTH NETWORK TECH CO LTD
- Filing Date
- 2026-04-13
- Publication Date
- 2026-05-15
AI Technical Summary
Existing risk prediction methods rely on human experience, resulting in low accuracy and efficiency in risk prediction, and making it difficult to quickly and accurately identify potential risks in complex environments.
By identifying the key risk factors and their risk parameters of the object to be evaluated, and using local and overall risk probability calculation formulas, the risk probability of multiple regions can be accurately calculated.
It improves the accuracy and efficiency of risk assessment and enhances the real-time nature and security of multi-regional risk monitoring.
Smart Images
Figure CN122046031A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data processing technology, and in particular to a calculation method and system for multi-regional risks. Background Technology
[0002] In today's complex and ever-changing social and economic environment, risks are ubiquitous and take many forms. Some scenarios have high requirements for safety and stability, and risk prediction is necessary for these scenarios in order to adjust and respond more quickly and accurately. Examples include ship navigation scenarios and ship sand mining scenarios. Therefore, risk prediction has become a key focus for ensuring the safe operation of these scenarios.
[0003] Currently, most risk prediction methods rely on staff observing video footage in the background to predict risks in target scenarios. This method is highly dependent on the staff's personal experience and subjective awareness. Risk anomalies are often only detected when danger is imminent or has already occurred. Subjective risk prediction is susceptible to various factors, such as the staff's ongoing observation of the target scenario, poor mental state during risk prediction, and different staff members having different perspectives and focuses when evaluating the same risk phenomenon. These factors can all lead to deviations in risk prediction results, even under the same conditions. Therefore, existing risk prediction methods suffer from low accuracy and efficiency. It is therefore crucial to provide a risk prediction method that improves both accuracy and efficiency. Summary of the Invention
[0004] This invention provides a method and system for calculating risks in multiple regions, which can improve the accuracy and efficiency of risk calculation for the objects to be assessed, thereby improving the real-time performance and security of risk monitoring in multiple regions.
[0005] To address the aforementioned technical problems, the first aspect of this invention discloses a method for calculating multi-regional risk, the method comprising: Identify the objects to be assessed that require multi-regional risk calculation, and determine the key risk factors and set risk parameter information for the objects to be assessed; Based on the object to be evaluated and the risk value range information corresponding to each of the key risk factors, the risk value information of each of the key risk factors is determined. Based on the set local risk probability calculation method, the set risk parameter information, and the risk value information, the local risk probability result corresponding to each key risk factor is determined; Based on the established overall risk probability calculation method, the established risk parameter information, and the local risk probability results of each of the key risk factors, the overall risk probability result of the object to be evaluated is determined.
[0006] As an optional implementation, in the first aspect of the present invention, when the object to be evaluated includes a vessel to be evaluated, the key risk factors of the vessel to be evaluated include one or more of hydrological risks, meteorological risks, waterway risks, and vessel-related risks; the set risk parameter information includes at least bias term parameter information, risk value range information corresponding to each key risk factor, mapping parameter information, and weight information; And, the determination of the key risk factors and the setting of risk parameters for the object to be evaluated includes: Identify one or more key risk factors corresponding to the object to be assessed; Based on the possible risk phenomena identified for each of the key risk factors, determine the risk value range information for each of the key risk factors; Based on the characteristic information and risk value range information of each key risk factor, the mapping parameter information of each key risk factor is determined, and the mapping parameter information includes steepness parameter information and center point parameter information. Based on the potential impact information of each identified key risk factor on the object to be evaluated, the relative importance of each key risk factor to the overall risk is determined, and based on the relative importance of each key risk factor, the weight information of each key risk factor is determined. The sum of the weights corresponding to the weight information of all key risk factors is not limited to 1. Based on the determined expected baseline risk level, determine the bias term parameter information.
[0007] As an optional implementation, in the first aspect of the present invention, the formula corresponding to the local risk probability calculation method is specifically as follows:
[0008] in, P i This is a result of local risk probability. k i For steepness parameter information, v i For risk value information, c i Center point parameter information; Furthermore, the formula corresponding to the overall risk probability calculation method is as follows:
[0009] in, P overall This represents the overall risk probability result. W iThe weight information is represented by 'Bias', which represents the bias parameter information.
[0010] As an optional implementation, in the first aspect of the present invention, determining the risk value range information of each key risk factor based on the possible occurrence of risk phenomena of each determined key risk factor includes: For each of the key risk factors, the most severe risk phenomenon and the lowest risk phenomenon are determined based on the possible risk phenomena of the identified key risk factor. Based on the first factor data value corresponding to the most severe risk phenomenon, determine the first possible risk value; and based on the second factor data value corresponding to the lowest risk phenomenon, determine the second possible risk value; based on the first and second possible risk values, determine the maximum and minimum possible risk values for the key risk factor; or... Based on the most severe risk phenomenon, determine the first risk severity, and based on the first risk severity, determine the maximum possible risk value of the key risk factor; based on the lowest risk phenomenon, determine the minimum possible risk value of the key risk factor. Based on the maximum and minimum possible risk values, the risk range information of the key risk factor is determined.
[0011] As an optional implementation, in the first aspect of the present invention, determining the minimum possible risk value of the key risk factor based on the lowest risk phenomenon includes: When the predicted probability of occurrence of the lowest risk phenomenon is used to represent that it will definitely occur, the severity of the second risk is determined based on the lowest risk phenomenon, and the minimum possible value of the key risk factor is determined based on the severity of the second risk, wherein the minimum possible value of the risk factor is greater than 0. When the predicted probability of the lowest-risk phenomenon is used to indicate that it is not certain to occur, the minimum possible risk value of the key risk factor is determined to be 0.
[0012] As an optional implementation, in the first aspect of the present invention, determining the mapping parameter information of each key risk factor based on the characteristic information and risk value range information of each key risk factor includes: For each of the key risk factors, based on the characteristic information of the key risk factor, the value of the first center point parameter for medium-risk objects is determined, and / or, based on the risk value range information of the key risk factor, half of the maximum possible risk value is determined as the value of the second center point parameter. The center point parameter information of the key risk factor is determined based on the value of the first center point parameter and / or the value of the second center point parameter. Based on the characteristic information of the key risk factor, determine the risk change relationship of the key risk factor, and based on the risk change relationship, determine the target numerical type of the steepness parameter corresponding to the key risk factor, wherein the target numerical type includes positive number type or negative number type; Based on the risk value range information and the target value type, determine the steepness parameter information of the key risk factor; Based on the centroid and steepness parameters of the key risk factor, the mapping parameters of the key risk factor are determined.
[0013] As an optional implementation, in the first aspect of the present invention, determining the target numerical type of the steepness parameter corresponding to the key risk factor based on the risk change relationship includes: When the risk change relationship of the key risk factor is used to indicate that the event change value and the risk change value of the key risk factor are positively related, the target value type of the steepness parameter corresponding to the key risk factor is determined to be a positive number. When the risk change relationship of a key risk factor is used to indicate that the event change value of the key risk factor and the risk change value are inversely related, the target value type of the steepness parameter corresponding to the key risk factor is determined to be a negative number.
[0014] A second aspect of the present invention discloses a calculation system for multi-regional risks, the system comprising: The basic information determination module is used to determine the objects to be evaluated that require multi-regional risk calculation, and to determine the key risk factors and set risk parameter information of the objects to be evaluated. The risk assessment module is used to determine the risk assessment information of each key risk factor based on the object to be assessed and the risk assessment range information corresponding to each key risk factor. The local risk determination module is used to determine the local risk probability result corresponding to each of the key risk factors based on the set local risk probability calculation method, the set risk parameter information, and the risk value information. The overall risk determination module is used to determine the overall risk probability result of the object to be evaluated based on the set overall risk probability calculation method, the set risk parameter information, and the local risk probability result of each of the key risk factors.
[0015] As an optional implementation, in the second aspect of the present invention, when the object to be evaluated includes a vessel to be evaluated, the key risk factors of the vessel to be evaluated include one or more of hydrological-related risks, meteorological-related risks, waterway-related risks, and vessel-related risks; the set risk parameter information includes at least bias term parameter information, risk value range information corresponding to each key risk factor, mapping parameter information, and weight information; Furthermore, the specific methods by which the basic information determination module determines the key risk factors and sets risk parameter information for the object to be evaluated include: Identify one or more key risk factors corresponding to the object to be assessed; Based on the possible risk phenomena identified for each of the key risk factors, determine the risk value range information for each of the key risk factors; Based on the characteristic information and risk value range information of each key risk factor, the mapping parameter information of each key risk factor is determined, and the mapping parameter information includes steepness parameter information and center point parameter information. Based on the potential impact information of each identified key risk factor on the object to be evaluated, the relative importance of each key risk factor to the overall risk is determined, and based on the relative importance of each key risk factor, the weight information of each key risk factor is determined. The sum of the weights corresponding to the weight information of all key risk factors is not limited to 1. Based on the determined expected baseline risk level, determine the bias term parameter information.
[0016] As an optional implementation, in the second aspect of the present invention, the formula corresponding to the local risk probability calculation method is specifically as follows:
[0017] in, P i This is a result of local risk probability. k i For steepness parameter information, v i For risk value information, c i Center point parameter information; Furthermore, the formula corresponding to the overall risk probability calculation method is as follows:
[0018] in, P overall This represents the overall risk probability result. W iThe weight information is represented by 'Bias', which represents the bias parameter information.
[0019] As an optional implementation, in the second aspect of the present invention, the basic information determining module determines the risk value range information of each key risk factor based on the possible occurrence of risk phenomena of each key risk factor, specifically including: For each of the key risk factors, the most severe risk phenomenon and the lowest risk phenomenon are determined based on the possible risk phenomena of the identified key risk factor. Based on the first factor data value corresponding to the most severe risk phenomenon, determine the first possible risk value; and based on the second factor data value corresponding to the lowest risk phenomenon, determine the second possible risk value; based on the first and second possible risk values, determine the maximum and minimum possible risk values for the key risk factor; or... Based on the most severe risk phenomenon, determine the first risk severity, and based on the first risk severity, determine the maximum possible risk value of the key risk factor; based on the lowest risk phenomenon, determine the minimum possible risk value of the key risk factor. Based on the maximum and minimum possible risk values, the risk range information of the key risk factor is determined.
[0020] As an optional implementation, in the second aspect of the present invention, the method by which the basic information determining module determines the minimum possible value of the key risk factor based on the minimum risk phenomenon specifically includes: When the predicted probability of occurrence of the lowest risk phenomenon is used to represent that it will definitely occur, the severity of the second risk is determined based on the lowest risk phenomenon, and the minimum possible value of the key risk factor is determined based on the severity of the second risk, wherein the minimum possible value of the risk factor is greater than 0. When the predicted probability of the lowest-risk phenomenon is used to indicate that it is not certain to occur, the minimum possible risk value of the key risk factor is determined to be 0.
[0021] As an optional implementation, in the second aspect of the present invention, the basic information determining module determines the mapping parameter information of each key risk factor based on the characteristic information and risk value range information of each key risk factor in a specific manner including: For each of the key risk factors, based on the characteristic information of the key risk factor, the value of the first center point parameter for medium-risk objects is determined, and / or, based on the risk value range information of the key risk factor, half of the maximum possible risk value is determined as the value of the second center point parameter. The center point parameter information of the key risk factor is determined based on the value of the first center point parameter and / or the value of the second center point parameter. Based on the characteristic information of the key risk factor, determine the risk change relationship of the key risk factor, and based on the risk change relationship, determine the target numerical type of the steepness parameter corresponding to the key risk factor, wherein the target numerical type includes positive number type or negative number type; Based on the risk value range information and the target value type, determine the steepness parameter information of the key risk factor; Based on the centroid and steepness parameters of the key risk factor, the mapping parameters of the key risk factor are determined.
[0022] As an optional implementation, in the second aspect of the present invention, the method by which the basic information determination module determines the target numerical type of the steepness parameter corresponding to the key risk factor based on the risk change relationship specifically includes: When the risk change relationship of the key risk factor is used to indicate that the event change value and the risk change value of the key risk factor are positively related, the target value type of the steepness parameter corresponding to the key risk factor is determined to be a positive number. When the risk change relationship of a key risk factor is used to indicate that the event change value of the key risk factor and the risk change value are inversely related, the target value type of the steepness parameter corresponding to the key risk factor is determined to be a negative number.
[0023] A third aspect of the present invention discloses another calculation system for multi-regional risks, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute a calculation method for multi-regional risks disclosed in the first aspect of the present invention.
[0024] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute a calculation method for multi-regional risk disclosed in the first aspect of the present invention.
[0025] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: In this embodiment of the invention, an object to be assessed requiring multi-regional risk calculation is identified, and key risk factors and set risk parameter information for the object to be assessed are determined. Based on the object to be assessed and the risk value range information corresponding to each key risk factor, the risk value information of each key risk factor is determined. According to the set local risk probability calculation method, the set risk parameter information, and the risk value information, the local risk probability result corresponding to each key risk factor is determined. According to the set overall risk probability calculation method, the set risk parameter information, and the local risk probability result of each key risk factor, the overall risk probability result of the object to be assessed is determined. Therefore, this invention can achieve risk assessment of the object to be assessed through the determination methods of key risk factors and set risk parameter information, risk value information, local risk probability results, and overall risk probability results. This is beneficial to improving the comprehensiveness and rationality of a calculation method for multi-regional risks, thereby improving the accuracy and reliability of risk assessment of the object to be assessed, improving the efficiency and convenience of risk assessment, and ultimately improving the regional security and stability of the object to be assessed. Attached Figure Description
[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0027] Figure 1 This is a flowchart illustrating a method for calculating multi-regional risks disclosed in an embodiment of the present invention; Figure 2 This is a flowchart illustrating another method for calculating multi-regional risks disclosed in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a computing system for multi-regional risks disclosed in an embodiment of the present invention; Figure 4 This is a schematic diagram of another computing system for multi-regional risks disclosed in an embodiment of the present invention. Detailed Implementation
[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0029] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.
[0030] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0031] This invention discloses a method and system for calculating multi-regional risks. It enables risk assessment of the object to be evaluated through methods for determining key risk factors and risk parameter information, risk value information, local risk probability results, and overall risk probability results. This improves the comprehensiveness and rationality of a multi-regional risk calculation method, thereby enhancing the accuracy and reliability of risk assessment, as well as its efficiency and convenience, ultimately improving the regional security and stability of the assessed object. Detailed explanations follow.
[0032] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for calculating multi-regional risk disclosed in an embodiment of the present invention. Wherein, Figure 1 The described method can be applied to a computing system targeting multi-regional risks, wherein the system may include a server, which may be a local server or a cloud server, and this embodiment of the invention is not limited thereto. Figure 1 As shown, this method for calculating multi-regional risk includes the following operations: 101. Identify the objects to be assessed that require multi-regional risk calculation, and determine the key risk factors and set risk parameter information for the objects to be assessed.
[0033] Optionally, the object to be assessed may include the vessel to be assessed. Further, the key risk factors of the vessel to be assessed may include, but are not limited to, one or more of the following: hydrological risks, meteorological risks, waterway risks, and vessel-related risks. Further, hydrological and meteorological risks may include, but are not limited to, water level risks, visibility risks, and strong wind risks. Waterway risks may include, but are not limited to, area density risks. Vessel-related risks may include, but are not limited to, deviation from the waterway risks, large vessel risks, cargo dangerous risks, cross-collision risks, certificate risks, first-time bridge crossing risks, vessel violation risks, accident level risks, vessel age risks, driving risks, crew driving experience risks, vessel historical abnormal behavior risks, crew penalty risks, crew certificate risks, and speed risks. This embodiment of the invention does not impose any limitations.
[0034] Optionally, the object to be evaluated may include the drone to be evaluated that needs to undergo flight risk assessment. Furthermore, the key risk factors of the drone to be evaluated may include, but are not limited to, one or more of wind speed, water flow, visibility, speed and altitude, etc., which are not limited in this embodiment of the invention.
[0035] Optionally, the risk parameter information may include at least bias term parameter information, risk value range information corresponding to each key risk factor, mapping parameter information, and weight information. This embodiment of the invention does not impose any limitations.
[0036] 102. Based on the information of the object to be evaluated and the risk value range corresponding to each key risk factor, determine the risk value information of each key risk factor.
[0037] Optionally, the object to be evaluated may include one or more key risk factors R1...R N Each key risk factor R i There is a risk value range [MiniVal] i MaxVal i ], among which, MiniVal i MaxVal can be 0 or any other value. i It is a positive number and represents the maximum possible value of the key risk factor. Furthermore, the higher the risk value, the more severe the risk. This embodiment of the invention does not limit this.
[0038] 103. Based on the set local risk probability calculation method, set risk parameter information and risk value information, determine the local risk probability result corresponding to each key risk factor.
[0039] Optionally, the local risk probability calculation method can be a mapping function that is monotonically increasing, that is, the higher the risk value, the higher the local risk probability; further, the mapping function can be a variant of the Sigmoid function or a linear normalization function (for nonlinear effects that do not require special emphasis on high risk), and the embodiments of the present invention are not limited thereto.
[0040] 104. Based on the established overall risk probability calculation method, the established risk parameter information, and the local risk probability results of each key risk factor, determine the overall risk probability result of the object to be evaluated.
[0041] Optionally, the model can satisfy the condition that "if a certain high risk occurs, the overall risk may be high." This means that even if other risks are low, one or a few high-risk factors may significantly increase the overall risk probability. This embodiment of the invention does not limit this.
[0042] As can be seen, the calculation system for multi-regional risks described in this embodiment of the invention can achieve risk assessment of the object to be assessed through methods for determining key risk factors and set risk parameter information, methods for determining risk value information, methods for determining local risk probability results, and methods for determining overall risk probability results. This is beneficial to improving the comprehensiveness and rationality of a calculation method for multi-regional risks, thereby improving the accuracy and reliability of risk assessment of the object to be assessed, improving the efficiency and convenience of risk assessment, and ultimately improving the regional security and stability of the object to be assessed.
[0043] Example 2 Please see Figure 2 , Figure 2 This is a flowchart illustrating another method for calculating multi-regional risk disclosed in an embodiment of the present invention. Wherein, Figure 2 The described method can be applied to a computing system targeting multi-regional risks, wherein the system may include a server, which may be a local server or a cloud server, and this embodiment of the invention is not limited thereto. Figure 2 As shown, this method for calculating multi-regional risk includes the following operations: 201. Identify the objects to be assessed that require multi-regional risk calculation, and identify one or more key risk factors corresponding to the objects to be assessed.
[0044] 202. Based on the possible risk phenomena of each identified key risk factor, determine the risk value range information for each key risk factor.
[0045] Optionally, the possible risk phenomena may include one or more specific phenomena. Further, the severity risk representation value corresponding to each specific phenomenon can be calculated, and the risk value range information of the key risk factor can be generated based on one or more severity risk representation values. This embodiment of the invention does not limit the scope of the risk phenomenon.
[0046] 203. Based on the characteristic information and risk value range information of each key risk factor, determine the mapping parameter information of each key risk factor. The mapping parameter information includes the steepness parameter information and the center point parameter information.
[0047] Optionally, the steepness parameter and center point parameter of the Sigmoid function can be determined based on the characteristics of each risk factor and the definition of high risk. Furthermore, the experience of business experts or historical data can be used for fitting. This embodiment of the invention does not impose any limitations.
[0048] Optionally, the steepness parameter is used to control the rate of rise of the curve. The larger the value of the steepness parameter, the steeper the curve is near the center point, which means that a small change in the risk value will lead to a significant change in the probability result. This embodiment of the invention does not limit this.
[0049] Optionally, the center point parameter is used to indicate that the local risk probability is approximately 0.5 when the risk value reaches the center point parameter value. This embodiment of the invention does not limit this.
[0050] 204. Based on the potential impact information of each identified key risk factor on the object to be evaluated, determine the relative importance of each key risk factor to the overall risk, and based on the relative importance of each key risk factor, determine the weight information of each key risk factor. The sum of the weight information of all key risk factors is not limited to 1.
[0051] Optionally, each risk factor can be assigned a corresponding weight based on its potential impact on the business or system. The specific value of the weight can be determined based on the importance of the business or historical data analysis. The sum of the weights does not necessarily need to be 1, as they are used for weighted cumulative terms. The weights reflect the relative importance of the key risk factor to the overall risk. This embodiment of the invention does not impose any limitations on this.
[0052] 205. Based on the determined expected baseline risk level, determine the bias term parameter information.
[0053] Optionally, when the expected baseline risk level is used to represent that the overall risk probability result is also close to 0 when all local risk probability results are 0, that is, to ensure that the overall risk probability is also low when the risk of all key risk factors is low, the bias term parameter can be set to a negative number or 0. This embodiment of the invention does not limit this.
[0054] Optionally, by adjusting the steepness parameter, center point parameter, and bias term parameter, different business scenarios and risk preferences can be adapted, and the embodiments of the present invention are not limited thereto.
[0055] 206. Based on the information of the object to be evaluated and the risk value range corresponding to each key risk factor, determine the risk value information of each key risk factor.
[0056] 207. Based on the set local risk probability calculation method, set risk parameter information and risk value information, determine the local risk probability result corresponding to each key risk factor.
[0057] 208. Based on the established overall risk probability calculation method, the established risk parameter information, and the local risk probability results of each key risk factor, determine the overall risk probability result of the object to be evaluated.
[0058] In this embodiment of the invention, for other descriptions of steps 201-208, please refer to the other detailed descriptions of steps 101-104 in Embodiment 1. These descriptions will not be repeated in this embodiment of the invention.
[0059] As can be seen, the embodiments of the present invention can achieve risk assessment of the object to be assessed through methods for determining key risk factors and set risk parameter information, methods for determining risk value information, methods for determining local risk probability results, and methods for determining overall risk probability results. This is beneficial to improving the comprehensiveness and rationality of a calculation method for multi-regional risks, thereby improving the accuracy and reliability of risk assessment of the object to be assessed, improving the efficiency and convenience of risk assessment, and thus improving the regional security and stability of the object to be assessed. Furthermore, it can also provide methods for determining key risk factors and set risk parameter information of the object to be assessed, which is beneficial to improving the comprehensiveness and rationality of the method for determining key risk factors, thereby improving the diversity, flexibility, accuracy, and relevance of the determined key risk factors. In addition, it is beneficial to improving the comprehensiveness and rationality of the method for determining set risk parameter information, thereby improving the diversity, flexibility, accuracy, and relevance of the determined set risk parameter information.
[0060] In an optional embodiment, the formula for calculating the local risk probability is as follows:
[0061] in, P i This is a result of local risk probability. k i For steepness parameter information, v i For risk value information, ci This refers to the center point parameter information.
[0062] It is evident that this optional embodiment can provide a formula for calculating the local risk probability result, which is beneficial to improving the rationality and feasibility of the local risk probability calculation method, thereby improving the creativity and scientificity of the method for determining the local risk probability result, and improving the effectiveness, rationality and accuracy of the determined local risk probability result.
[0063] In another optional embodiment, the formula for calculating the overall risk probability is as follows:
[0064] in, P overall This represents the overall risk probability result. W i The weight information is represented by 'Bias', which represents the bias parameter information.
[0065] Optionally, when any local risk approaches 1, the overall risk probability will also approach 1, perfectly satisfying the risk monitoring requirement that "if a certain high risk occurs, the overall risk may be high." This embodiment of the invention does not impose any limitations.
[0066] It is evident that this optional embodiment can provide a formula for calculating the overall risk probability result, which is beneficial to improving the rationality and feasibility of the calculation method for the overall risk probability result, thereby improving the creativity and feasibility of the method for determining the overall risk probability result, and improving the effectiveness, rationality and accuracy of the determined overall risk probability result.
[0067] In another optional embodiment, the above-mentioned determination of the risk range information for each key risk factor based on the possible occurrence of each key risk factor may include: For each key risk factor, based on the possible risk phenomena of that key risk factor, determine the most severe risk phenomenon and the lowest risk phenomenon. Based on the first factor data value corresponding to the most severe risk phenomenon, determine the first possible risk value; and based on the second factor data value corresponding to the lowest risk phenomenon, determine the second possible risk value; based on the first and second possible risk values, determine the maximum and minimum possible risk values for this key risk factor; or... Based on the most severe risk phenomenon, determine the first risk severity level, and based on the first risk severity level, determine the maximum possible risk value of the key risk factor; based on the lowest risk phenomenon, determine the minimum possible risk value of the key risk factor. Based on the maximum and minimum possible risk values, determine the risk range information for this key risk factor.
[0068] Optionally, the risk value range of the key risk factor can be the entire range of values with the maximum and minimum possible risk values as endpoints, or it can be generated with multiple endpoints to form an endpoint range value. This embodiment of the invention does not limit the range.
[0069] Optionally, the data values of the second factor corresponding to the key risk factor and its lowest risk phenomenon, and the data values of the first factor corresponding to the most severe risk phenomenon, can be illustrated as follows: For an object to be evaluated as a drone, when the key risk factor is wind speed, the data value of the second factor corresponding to the lowest risk phenomenon can be 0 m / s, and the data value of the first factor corresponding to the most severe risk phenomenon can be 20 m / s. For another example, when the key risk factor is visibility, the data value of the second factor corresponding to the lowest risk phenomenon can be 1000 m (or more), and the data value of the first factor corresponding to the most severe risk phenomenon can be 0 m. In addition, for an object to be evaluated as a ship, when the key risk factor is visibility risk, the data value of the second factor corresponding to the lowest risk phenomenon can be 5 kilometers (or more), and the data value of the first factor corresponding to the most severe risk phenomenon can be 0 m. This embodiment of the invention does not impose any limitations.
[0070] Optionally, determining the first possible risk value based on the first factor data value corresponding to the most severe risk phenomenon may include: determining the first factor data value corresponding to the most severe risk phenomenon as the first possible risk value. This embodiment of the invention does not limit this.
[0071] Optionally, determining the second possible risk value based on the second factor data value corresponding to the lowest risk phenomenon may include: determining the second factor data value corresponding to the lowest risk phenomenon as the second possible risk value. This embodiment of the invention does not limit this.
[0072] Optionally, the above-mentioned determination of the first risk severity based on the most severe risk phenomenon, and determination of the maximum possible risk value of the key risk factor based on the first risk severity, for example: the maximum possible risk value here can be set according to actual needs, and needs to be different from the actual risk value corresponding to other risk phenomena besides the most severe risk phenomenon. Different risk severity corresponds to different risk values, which is not limited in this embodiment of the invention.
[0073] As can be seen, this optional embodiment can determine the maximum and minimum possible risk values based on the most severe and lowest possible risk phenomena among the possible risk phenomena, thereby determining the risk value range information of key risk factors. This is beneficial to improving the diversity, flexibility, and pertinence of the method for determining the possible risk values, and thus improving the accuracy and reliability of the determined possible risk values. In addition, it is beneficial to improving the comprehensiveness and rationality of the method for determining the risk value range information of key risk factors, and thus improving the accuracy and reliability of the determined risk value range information.
[0074] In another optional embodiment, determining the minimum possible risk value of the key risk factor based on the lowest-risk phenomenon may include: When the predicted probability of the lowest risk phenomenon is used to represent that it will definitely occur, the severity of the second risk is determined based on the lowest risk phenomenon, and the minimum possible value of the key risk factor is determined based on the severity of the second risk, and the minimum possible value of the risk factor is greater than 0. When the predicted probability of the lowest-risk phenomenon is used to indicate that it is not certain to occur, the minimum possible risk value of the key risk factor is set to 0.
[0075] It is evident that this optional embodiment can match the predicted probability of occurrence of the lowest risk phenomenon with the corresponding minimum risk possible value determination method, which is conducive to improving the comprehensiveness and rationality of the minimum risk possible value determination method, and is conducive to improving the diversity, flexibility and pertinence of the minimum risk possible value determination method, thereby improving the accuracy and reliability of the determined minimum risk possible value.
[0076] In another optional embodiment, determining the mapping parameter information of each key risk factor based on the characteristic information and risk value range information of each key risk factor may include: For each key risk factor, based on the characteristic information of the key risk factor, determine the value of the first center point parameter for medium-risk objects, and / or, based on the risk value range information of the key risk factor, determine half of the maximum possible risk value as the value of the second center point parameter. The center point parameter information of the key risk factor is determined based on the values of the first center point parameter and / or the second center point parameter. Based on the characteristic information of the key risk factor, determine the risk change relationship of the key risk factor, and based on the risk change relationship, determine the target value type of the steepness parameter corresponding to the key risk factor. The target value type includes positive or negative numbers. Based on the risk value range information and the target value type, determine the steepness parameter information of the key risk factor; Based on the centroid and steepness parameters of the key risk factor, the mapping parameters of the key risk factor are determined.
[0077] Optionally, the center point parameter of the key risk factor can be set to half of the maximum value in the risk range of the key risk factor, or a "medium risk" threshold can be set according to business experience. This embodiment of the invention does not limit the setting.
[0078] Further optionally, the determination of the centroid parameter information of the key risk factor based on the values of the first centroid parameter and / or the second centroid parameter may include: The value of the first centroid parameter or the value of the second centroid parameter are determined as the centroid parameter information of the key risk factor; or, the values of the first centroid parameter and the second centroid parameter are added together and averaged to obtain the mean value, which is used as the centroid parameter information of the key risk factor.
[0079] Optionally, the steepness parameter can be adjusted according to the definition of "high risk," for example, if it is desired that the risk value reaches MaxVal i When the probability is ×0.8, it is already very close to 1. Therefore, the steepness parameter value can be determined by iteration or presetting. This embodiment of the invention does not impose any limitations.
[0080] As can be seen, this optional embodiment can provide a method for determining center point parameter information and a method for determining steepness parameter information, which is beneficial to improving the comprehensiveness and rationality of the method for determining center point parameter information, thereby improving the accuracy and reliability of the determined center point parameter information. In addition, it is beneficial to improve the comprehensiveness and rationality of the method for determining steepness parameter information, thereby improving the accuracy and reliability of the determined steepness parameter information, and further improving the accuracy and reliability of the determined mapping parameter information.
[0081] In another optional embodiment, the target numerical type of determining the steepness parameter corresponding to the key risk factor based on the risk change relationship may include: When the risk change relationship of the key risk factor is used to indicate that the event change value and the risk change value of the key risk factor are positively related, the target value type of the steepness parameter corresponding to the key risk factor is determined to be a positive number. When the risk change relationship of a key risk factor is used to indicate that the event change value of the key risk factor and the risk change value are inversely related, the target value type of the steepness parameter corresponding to the key risk factor is determined to be a negative number.
[0082] Optionally, taking a ship as an example, the key risk factor is visibility. The risk of visibility is inverse, that is, the lower the visibility, the higher the risk. In other words, the event change value of visibility and the risk change value are inversely related. In this case, the steepness parameter of visibility is set to a negative number (i.e., a negative value). Another example is the risk of deviation from the channel. The greater the deviation from the channel, the greater the risk. In other words, the event change value of deviation from the channel risk and the risk change value are positively related. In this case, the steepness parameter of deviation from the channel risk is set to a positive number (i.e., a positive value). This embodiment of the invention is not limited.
[0083] Further optional, taking a ship as the object to be evaluated as an example, assuming that the key risk factor is visibility and the steepness parameter of visibility is less than 0, the local risk will decrease as the risk value of the key risk factor increases. Therefore, it is necessary to regard the "high risk" value of the key risk factor with the inverse relationship of risk change as a "low value", or to convert the risk value of the key risk factor into its inverse value. For example, the risk is moderate when the visibility is 5 kilometers, high risk when the visibility is 1 kilometer, and low risk when the visibility is 8 kilometers, etc. This embodiment of the invention does not limit the scope.
[0084] As can be seen, this optional embodiment can match the corresponding target value type determination method to the risk change relationship of key risk factors to represent positive or negative relationships, which is conducive to improving the comprehensiveness and rationality of the target value type determination method, the diversity, flexibility and pertinence of the target value type determination method, and thus the accuracy, reliability and fit of the determined target value type.
[0085] Example 3 Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a computational system for multi-regional risks disclosed in an embodiment of the present invention. Wherein, Figure 3 The described system may include a server, which may be a local server or a cloud server; this embodiment of the invention does not limit the scope. Figure 3 As shown, this calculation system for multi-regional risks may include: The basic information determination module 301 is used to determine the objects to be assessed that require multi-regional risk calculation, and to determine the key risk factors and set risk parameter information for the objects to be assessed.
[0086] The risk assessment module 302 is used to determine the risk assessment information of each key risk factor based on the object to be assessed and the risk assessment range information corresponding to each key risk factor.
[0087] The local risk determination module 303 is used to determine the local risk probability result corresponding to each key risk factor based on the set local risk probability calculation method, set risk parameter information and risk value information.
[0088] The overall risk determination module 304 is used to determine the overall risk probability result of the object to be evaluated based on the set overall risk probability calculation method, the set risk parameter information, and the local risk probability result of each key risk factor.
[0089] It is evident that implementation Figure 3 The described calculation system for multi-regional risks can achieve risk assessment of the object to be assessed through methods for determining key risk factors and risk parameter information, risk value information, local risk probability results, and overall risk probability results. This is beneficial to improving the comprehensiveness and rationality of a calculation method for multi-regional risks, thereby improving the accuracy and reliability of risk assessment of the object to be assessed, as well as the efficiency and convenience of risk assessment, and ultimately improving the regional security and stability of the object to be assessed.
[0090] In an optional embodiment, when the object to be evaluated includes a vessel to be evaluated, the key risk factors of the vessel to be evaluated include one or more of the following: hydrological risk, meteorological risk, waterway risk, and vessel-related risk; the risk parameter information includes at least bias term parameter information, risk value range information corresponding to each key risk factor, mapping parameter information, and weight information. Furthermore, the basic information determination module 301 determines the key risk factors of the object to be evaluated and sets risk parameter information in the following specific ways: Identify one or more key risk factors for the object to be assessed; Based on the potential risk phenomena identified for each key risk factor, determine the risk value range information for each key risk factor; Based on the characteristic information and risk value range information of each key risk factor, the mapping parameter information of each key risk factor is determined. The mapping parameter information includes steepness parameter information and center point parameter information. Based on the potential impact information of each identified key risk factor on the object to be evaluated, the relative importance of each key risk factor to the overall risk is determined, and based on the relative importance of each key risk factor, the weight information of each key risk factor is determined. The sum of the weight information of all key risk factors is not limited to 1. Based on the determined expected baseline risk level, determine the bias term parameter information.
[0091] It is evident that implementation Figure 3The described system can also provide methods for determining key risk factors and setting risk parameter information for the object to be evaluated, which helps to improve the comprehensiveness and rationality of the key risk factor determination method, thereby improving the diversity, flexibility, accuracy and relevance of the determined key risk factors. In addition, it helps to improve the comprehensiveness and rationality of the method for setting risk parameter information, thereby improving the diversity, flexibility, accuracy and relevance of the determined risk parameter information.
[0092] In another optional embodiment, the formula for calculating the local risk probability is as follows:
[0093] in, P i This is a result of local risk probability. k i For steepness parameter information, v i For risk value information, c i This refers to the center point parameter information.
[0094] It is evident that implementation Figure 3 The described system can also provide a formula for calculating local risk probability results, which helps to improve the rationality and feasibility of the local risk probability calculation method, thereby improving the creativity and scientific nature of the method for determining local risk probability results, and improving the effectiveness, rationality and accuracy of the determined local risk probability results.
[0095] In yet another optional embodiment, the formula for calculating the overall risk probability is as follows:
[0096] in, P overall This represents the overall risk probability result. W i The weight information is represented by 'Bias', which represents the bias parameter information.
[0097] It is evident that implementation Figure 3 The described system can also provide a formula for calculating the overall risk probability result, which helps to improve the rationality and feasibility of the calculation method for the overall risk probability result, thereby improving the creativity and feasibility of the method for determining the overall risk probability result, and improving the effectiveness, rationality and accuracy of the determined overall risk probability result.
[0098] In another optional embodiment, the basic information determination module 301 determines the risk value range information of each key risk factor based on the possible occurrence of each key risk factor, specifically including: For each key risk factor, based on the possible risk phenomena of that key risk factor, determine the most severe risk phenomenon and the lowest risk phenomenon. Based on the first factor data value corresponding to the most severe risk phenomenon, determine the first possible risk value; and based on the second factor data value corresponding to the lowest risk phenomenon, determine the second possible risk value; based on the first and second possible risk values, determine the maximum and minimum possible risk values for this key risk factor; or... Based on the most severe risk phenomenon, determine the first risk severity level, and based on the first risk severity level, determine the maximum possible risk value of the key risk factor; based on the lowest risk phenomenon, determine the minimum possible risk value of the key risk factor. Based on the maximum and minimum possible risk values, determine the risk range information for this key risk factor.
[0099] It is evident that implementation Figure 3 The described system can also determine the maximum and minimum possible risk values based on the most severe and lowest possible risk phenomena, thereby determining the risk value range information of key risk factors. This helps to improve the diversity, flexibility, and pertinence of the methods for determining the possible risk values, which in turn helps to improve the accuracy and reliability of the determined possible risk values. In addition, it helps to improve the comprehensiveness and rationality of the methods for determining the risk value range information of key risk factors, which in turn helps to improve the accuracy and reliability of the determined risk value range information.
[0100] In another optional embodiment, the basic information determination module 301 determines the minimum possible risk value of the key risk factor based on the lowest risk phenomenon in the following specific ways: When the predicted probability of the lowest risk phenomenon is used to represent that it will definitely occur, the severity of the second risk is determined based on the lowest risk phenomenon, and the minimum possible value of the key risk factor is determined based on the severity of the second risk, and the minimum possible value of the risk factor is greater than 0. When the predicted probability of the lowest-risk phenomenon is used to indicate that it is not certain to occur, the minimum possible risk value of the key risk factor is set to 0.
[0101] It is evident that implementation Figure 3The described system can also match the predicted probability of occurrence of the lowest risk phenomenon with the corresponding minimum risk possible value determination method, which is conducive to improving the comprehensiveness and rationality of the minimum risk possible value determination method, and to improving the diversity, flexibility and pertinence of the minimum risk possible value determination method, thereby improving the accuracy and reliability of the determined minimum risk possible value.
[0102] In another optional embodiment, the basic information determination module 301 determines the mapping parameter information of each key risk factor based on the characteristic information and risk value range information of each key risk factor in the following specific ways: For each key risk factor, based on the characteristic information of the key risk factor, determine the value of the first center point parameter for medium-risk objects, and / or, based on the risk value range information of the key risk factor, determine half of the maximum possible risk value as the value of the second center point parameter. The center point parameter information of the key risk factor is determined based on the values of the first center point parameter and / or the second center point parameter. Based on the characteristic information of the key risk factor, determine the risk change relationship of the key risk factor, and based on the risk change relationship, determine the target value type of the steepness parameter corresponding to the key risk factor. The target value type includes positive or negative numbers. Based on the risk value range information and the target value type, determine the steepness parameter information of the key risk factor; Based on the centroid and steepness parameters of the key risk factor, the mapping parameters of the key risk factor are determined.
[0103] It is evident that implementation Figure 3 The described system can also provide methods for determining center point parameter information and steepness parameter information, which helps to improve the comprehensiveness and rationality of the method for determining center point parameter information, thereby improving the accuracy and reliability of the determined center point parameter information. In addition, it helps to improve the comprehensiveness and rationality of the method for determining steepness parameter information, thereby improving the accuracy and reliability of the determined steepness parameter information, and further improving the accuracy and reliability of the determined mapping parameter information.
[0104] In another optional embodiment, the basic information determination module 301 determines the target numerical type of the steepness parameter corresponding to the key risk factor based on the risk change relationship in the following specific ways: When the risk change relationship of the key risk factor is used to indicate that the event change value and the risk change value of the key risk factor are positively related, the target value type of the steepness parameter corresponding to the key risk factor is determined to be a positive number. When the risk change relationship of a key risk factor is used to indicate that the event change value of the key risk factor and the risk change value are inversely related, the target value type of the steepness parameter corresponding to the key risk factor is determined to be a negative number.
[0105] It is evident that implementation Figure 3 The described system can also match the corresponding target value type determination method for the risk change relationship of key risk factors to represent positive or negative relationships, which helps to improve the comprehensiveness and rationality of the target value type determination method, as well as the diversity, flexibility and pertinence of the target value type determination method, and thus helps to improve the accuracy, reliability and fit of the determined target value type.
[0106] Example 4 Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of another computing system for multi-regional risks disclosed in an embodiment of the present invention. Wherein, Figure 4 The described system may include a server, which may be a local server or a cloud server; this embodiment of the invention does not limit the scope. Figure 4 As shown, the system may include: Memory 401 storing executable program code; Processor 402 coupled to memory 401; Furthermore, it may also include an input interface 403 coupled to the processor 402 and an output interface 404; The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the calculation method for multi-region risk described in Embodiment 1 or Embodiment 2.
[0107] Example 5 This invention discloses a computer storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to execute steps in a multi-region risk calculation method described in Embodiment 1 or Embodiment 2.
[0108] Example 6 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in a calculation method for multi-regional risk described in Embodiment 1 or Embodiment 2.
[0109] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0110] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.
[0111] Finally, it should be noted that the calculation method and system for multi-regional risks disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for calculating multi-regional risk, characterized in that, The method includes: Identify the objects to be assessed that require multi-regional risk calculation, and determine the key risk factors and set risk parameter information for the objects to be assessed; Based on the object to be evaluated and the risk value range information corresponding to each of the key risk factors, the risk value information of each of the key risk factors is determined. Based on the set local risk probability calculation method, the set risk parameter information, and the risk value information, the local risk probability result corresponding to each key risk factor is determined; Based on the established overall risk probability calculation method, the established risk parameter information, and the local risk probability results of each of the key risk factors, the overall risk probability result of the object to be evaluated is determined.
2. The method for calculating multi-regional risk according to claim 1, characterized in that, When the object to be evaluated includes a vessel to be evaluated, the key risk factors of the vessel to be evaluated include one or more of the following: hydrological risk, meteorological risk, waterway risk, and vessel-related risk; the set risk parameter information includes at least bias term parameter information, risk value range information corresponding to each of the key risk factors, mapping parameter information, and weight information. And, the determination of the key risk factors and the setting of risk parameters for the object to be evaluated includes: Identify one or more key risk factors corresponding to the object to be assessed; Based on the possible risk phenomena identified for each of the key risk factors, determine the risk value range information for each of the key risk factors; Based on the characteristic information and risk value range information of each key risk factor, the mapping parameter information of each key risk factor is determined, and the mapping parameter information includes steepness parameter information and center point parameter information. Based on the potential impact information of each identified key risk factor on the object to be evaluated, the relative importance of each key risk factor to the overall risk is determined, and based on the relative importance of each key risk factor, the weight information of each key risk factor is determined. The sum of the weights corresponding to the weight information of all key risk factors is not limited to 1. Based on the determined expected baseline risk level, determine the bias term parameter information.
3. The method for calculating multi-regional risk according to claim 2, characterized in that, The formula corresponding to the method for calculating the probability of local risk is as follows: in, P i This is a result of local risk probability. k i For steepness parameter information, v i For risk value information, c i Center point parameter information; Furthermore, the formula corresponding to the overall risk probability calculation method is as follows: in, P overall This represents the overall risk probability result. W i The weight information is represented by 'Bias', which represents the bias parameter information.
4. The method for calculating multi-regional risk according to claim 2, characterized in that, The step of determining the risk range information for each key risk factor based on the possible occurrence of risk phenomena for each identified key risk factor includes: For each of the key risk factors, the most severe risk phenomenon and the lowest risk phenomenon are determined based on the possible risk phenomena of the identified key risk factor. Based on the first factor data value corresponding to the most severe risk phenomenon, determine the first possible risk value; and based on the second factor data value corresponding to the lowest risk phenomenon, determine the second possible risk value; based on the first and second possible risk values, determine the maximum and minimum possible risk values for the key risk factor; or... Based on the most severe risk phenomenon, determine the first risk severity, and based on the first risk severity, determine the maximum possible risk value of the key risk factor; based on the lowest risk phenomenon, determine the minimum possible risk value of the key risk factor. Based on the maximum and minimum possible risk values, the risk range information of the key risk factor is determined.
5. The method for calculating multi-regional risk according to claim 4, characterized in that, The step of determining the minimum possible risk value of the key risk factor based on the lowest risk phenomenon includes: When the predicted probability of occurrence of the lowest risk phenomenon is used to represent that it will definitely occur, the severity of the second risk is determined based on the lowest risk phenomenon, and the minimum possible value of the key risk factor is determined based on the severity of the second risk, wherein the minimum possible value of the risk factor is greater than 0. When the predicted probability of the lowest-risk phenomenon is used to indicate that it is not certain to occur, the minimum possible risk value of the key risk factor is determined to be 0.
6. The method for calculating multi-regional risk according to claim 2, characterized in that, The step of determining the mapping parameter information for each key risk factor based on its characteristic information and risk value range information includes: For each of the key risk factors, based on the characteristic information of the key risk factor, the value of the first center point parameter for medium-risk objects is determined, and / or, based on the risk value range information of the key risk factor, half of the maximum possible risk value is determined as the value of the second center point parameter. The center point parameter information of the key risk factor is determined based on the value of the first center point parameter and / or the value of the second center point parameter. Based on the characteristic information of the key risk factor, determine the risk change relationship of the key risk factor, and based on the risk change relationship, determine the target numerical type of the steepness parameter corresponding to the key risk factor, wherein the target numerical type includes positive number type or negative number type; Based on the risk value range information and the target value type, determine the steepness parameter information of the key risk factor; Based on the centroid and steepness parameters of the key risk factor, the mapping parameters of the key risk factor are determined.
7. The method for calculating multi-regional risk according to claim 6, characterized in that, The step of determining the target numerical type of the steepness parameter corresponding to the key risk factor based on the risk change relationship includes: When the risk change relationship of the key risk factor is used to indicate that the event change value and the risk change value of the key risk factor are positively related, the target value type of the steepness parameter corresponding to the key risk factor is determined to be a positive number. When the risk change relationship of a key risk factor is used to indicate that the event change value of the key risk factor and the risk change value are inversely related, the target value type of the steepness parameter corresponding to the key risk factor is determined to be a negative number.
8. A calculation system for multi-regional risks, characterized in that, The system includes: The basic information determination module is used to determine the objects to be evaluated that require multi-regional risk calculation, and to determine the key risk factors and set risk parameter information of the objects to be evaluated. The risk assessment module is used to determine the risk assessment information of each key risk factor based on the object to be assessed and the risk assessment range information corresponding to each key risk factor. The local risk determination module is used to determine the local risk probability result corresponding to each of the key risk factors based on the set local risk probability calculation method, the set risk parameter information, and the risk value information. The overall risk determination module is used to determine the overall risk probability result of the object to be evaluated based on the set overall risk probability calculation method, the set risk parameter information, and the local risk probability result of each of the key risk factors.
9. A calculation system for multi-regional risks, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute a calculation method for multi-region risk as described in any one of claims 1-7.
10. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute a calculation method for multi-regional risk as described in any one of claims 1-7.