TWI685811B - Risk assessment method - Google Patents

Abstract

A risk assessment method is implemented by a server connected to a user terminal through a communication network, and includes the following steps: the server terminal receives a target object from the user terminal and is related to a target object After the information is obtained, a target risk assessment data corresponding to the target object data is obtained; for each consistent disaster attribute, the server end uses the risk rating score table corresponding to the disaster-causing attribute to The risk value of each risk factor is given a first-degree score to obtain a score result of the grade score of each risk factor containing the disaster-causing attribute; for each consistent disaster attribute, the server end imports the score result of the disaster-causing attribute A hazard degree classification conversion rule corresponding to each of the disaster-causing attributes obtains a target hazard level.

Description

Risk assessment method

The present invention relates to a method for obtaining multiple natural disaster risks, in particular to a risk assessment method.

Because Taiwan is in the seismic zone, and the recent discussion on soil liquefaction issues, consumers are paying more and more attention to the safety of buildings and the protection of "outside buildings". Therefore, insurance companies are also eager to promote various types of insurance related to the subject matter, trying to include the important residential elements of human life into the category of insurance commodities.

However, regardless of whether the consumer is planning the purchase of the subject matter, or the insurance company is planning the underwriting of the entire building, the consumer or insurance service personnel will treat the subject matter of the assessment (ie, the purchase or insurance Risk assessment of the surrounding environment of the building. Existing risk assessment methods often require an investigation engineer to go to the site of the object to conduct an investigation and produce a risk assessment report for the object. However, this type of assessment requires a lot of time and human resources, which is inconvenient, so it is necessary to propose a solution.

Therefore, the object of the present invention is to provide a risk assessment method that automatically obtains the hazard levels of a variety of different disasters related to a target object to effectively save time and manpower.

Therefore, the risk assessment method of the present invention is implemented by a server, the server is connected to a user end through the communication network, and the server stores multiple pieces of risk assessment data corresponding to a plurality of target objects, respectively Corresponding to various disaster-causing hazard information, each risk assessment data includes multiple risk values of multiple risk factors corresponding to each consistent disaster attribute, and each consistent disaster attribute information includes a risk grade score table, And a hazard degree classification conversion rule, the risk assessment method includes a step (A), a step (B), and a step (C).

The step (A) is that the server obtains a target object data corresponding to the target object from the risk assessment data after receiving the target object information from the user terminal and related to a target object Target risk assessment data.

The step (B) is for each consistent disaster attribute, through the server, according to the risk level score table corresponding to the disaster attribute, the risk factor of each hazard factor in the target risk assessment data The risk value is given a first grade score to obtain a score result of the grade score of each risk factor containing the hazard attribute.

In this step (C), for each consistent disaster attribute, through the server, the score result of the disaster attribute is imported into the hazard degree classification conversion rule corresponding to the disaster attribute to obtain a target hazard level.

The effect of the present invention is that: the server automatically performs a risk assessment on the target object according to the stored risk assessment data and the hazard attribute information to obtain that the target object corresponds to various hazard attributes Target hazard level.

Referring to FIG. 1, a system 100 is used to implement an embodiment of the risk assessment method of the present invention. The system 100 includes a server 1 and a user terminal 2 connected to the server 1 via a communication network 101.

The server 1 includes a server communication module 11 connected to the communication network 101, a server storage module 12, and a server side process electrically connecting the server communication module 11 and the server storage module 12 Module 13.

The server-side storage module 12 of the server-side 1 stores a plurality of pieces of risk assessment data respectively corresponding to a plurality of target objects, and a plurality of pieces of disaster-causing attribute information respectively corresponding to a plurality of different disaster-causing attributes.

Each risk assessment data includes multiple risk values of multiple risk factors corresponding to each consistent disaster attribute. Each risk factor can be classified as one of a likelihood factor and a scale factor. In this embodiment, the disaster-causing attributes include an active fault, an earthquake, a landslide, a landslide, a flood, and a soil liquefaction, but not limited to this.

Referring to Table 1 below, the risk assessment data correspond to target object addresses such as an address A, an address B, and an address C, respectively. For disaster-causing attributes belonging to the active fault, the risk assessment data includes an earthquake with a magnitude of 6.5 The probability, the distance from the fault line, and the magnification of a fault's upper and lower walls are three risk factors. Among them, the probability of an earthquake with a magnitude of 6.5 is classified as the probability factor, and the distance from the fault line is above and below the fault. The magnification is classified as the scale factor. In addition, each risk factor (for example: the probability of occurrence of a magnitude 6.5 earthquake and the distance from the fault line) corresponding to each target factor (for example: address A, address B, and address C) And the upper and lower magnifications of the fault) are exemplified in Table 1 below, but not limited to this. It is worth noting that this embodiment only exemplifies the risk assessment data corresponding to the three addresses of the address A, the address B, and the address C. However, the risk assessment data also includes more subject addresses, It is not limited to the three target object addresses exemplified in this embodiment. In addition, in this embodiment, the risk assessment data includes the target object addresses, while in other embodiments, the risk assessment data may also include The name or keyword of the target object of the target object address makes the user operating the user terminal 2 more intuitively search for the required target object information, but not limited to this. Table 1

Refer to Table 2 below. For the hazard attributes belonging to the earthquake, the risk assessment data includes two types of risk factors including a peak surface acceleration and a shear wave velocity below the surface, where the peak surface acceleration is classified as the possibility Factor, and the shear wave velocity under the surface is classified as the scale factor. In addition, each target object address (for example: address A, address B, and address C) corresponds to each risk factor (for example: peak surface acceleration, and The risk value of shear wave velocity below the surface is exemplified in Table 2 below, but not limited to this. Table 2

Refer to Table 3 below. For the hazard attributes belonging to the soil and rock flow, the risk assessment data also includes a soil and rock flow potential stream risk potential level, a soil and rock flow alert reference value, a soil and rock flow potential stream distance, and a soil and rock flow potential stream impact There are four risk factors, such as range distance, among which the risk potential level of the earth-rock flow potential stream and the warning reference value of the earth-rock flow are classified as the possibility factors, and the distance of the earth-rock flow potential stream distance and the distance range of the earth-rock flow potential stream influence range are It is classified as the scale factor. In addition, each target object address (for example: address A, address B, and address C) corresponds to each risk factor (for example: earth-rock flow potential creek risk potential level, earth-rock flow warning reference value, earth-rock flow potential The risk value of the distance of potential streams and the distance of the influence range of potential streams of earth and rocks are shown in Table 3 below, but not limited to this. table 3

Refer to Table 4 below. For the hazard attributes belonging to the landslide, the risk assessment data also includes a landslide potential, a maximum 24-hour cumulative rainfall during a recurrence period, the peak surface acceleration, a landslide type, and a landslide area, And six risk factors such as a distance from the landslide, among which the landslide potential, the maximum cumulative rainfall of 24 hours in the recurrence period, and the peak surface acceleration are classified as the probability factors, and the landslide type, the landslide The area and the distance from the landslide are classified as the scale factor. In addition, each target object address (for example: address A, address B, and address C) corresponds to each risk factor (for example: landslide potential, maximum recurrence period The risk values of cumulative rainfall, surface peak acceleration, landslide type, landslide area, and distance from landslides for 24 hours are shown in Table 4 below, but not limited to this. Table 4

Refer to Table 5 below. For the disaster-causing attributes belonging to the flood, the risk assessment data also includes a warning rain value and a flooding depth and other two risk factors, where the warning rain value is classified as the probability factor , And the flooding depth is classified as the scale factor, in addition, each target object address (for example: address A, address B, and address C) corresponds to each risk factor (for example: warning rainfall amount, and flooding depth) Examples of risk values are shown in Table 5 below, but not limited to this. table 5

Refer to Table 6 below. For the hazard attributes that belong to the soil liquefaction, the risk assessment data also includes the surface peak acceleration, a groundwater level depth, an elevation, and a stratum. Four risk factors, among which the surface peak acceleration and The depth of the groundwater table is classified as the probability factor, and the elevation and the stratum are classified as the scale factor. In addition, each target object address (eg, address A, address B, and address C) corresponds to each risk factor (Example: surface peak acceleration, groundwater depth, elevation, and stratum) The risk values are shown in Table 6 below, but not limited to this. Table 6

In this embodiment, the disaster-causing attribute information respectively corresponds to the disaster-causing attribute information, and each consistent disaster-causing attribute information includes a risk level score table and a hazard degree classification conversion rule.

The disaster-causing attribute information includes an active fault information, an earthquake information, a soil flow information, a landslide information, a flooding information, and a soil liquefaction information, but not limited to this.

The risk scale score table corresponding to each consistent disaster attribute includes the risk factors corresponding to the corresponding disaster-causing attributes, the multiple hierarchical grades corresponding to each risk factor, and the grade corresponding to each hierarchical grade Distance ratings.

The conversion rule of the hazard degree classification corresponding to each consistent disaster attribute includes a probability factor formula, a scale factor formula, and a conversion matrix, the conversion matrix having multiple probability factor steps, multiple scale factor steps, and each A probability factor rank is relative to a hazard level corresponding to each scale factor rank.

Referring to Table 7 below, for the hazard attribute information corresponding to the hazard attributes belonging to the active fault, the risk level distance score table includes the probability F _{1 of} the occurrence magnitude 6.5 earthquake, the distance from the fault line M ₁ , and the Three risk factors, including the upper and lower fault magnifications M ₂ , where the probability of occurrence of an earthquake with a magnitude of 6.5 F ₁ is classified as the probability factor M ₁ , and the distance from the fault line and the upper and lower fault magnifications M ₂ are classified as The scale factor, in addition, the respective hierarchical grades corresponding to each risk factor, and the grade grade score corresponding to each hierarchical grade are shown in Table 7 below, but not limited thereto. Table 7

For the hazard attribute information corresponding to the hazard attributes belonging to the active fault, the probability factor formula corresponding to the active fault is S _{1 =} F ₁ , and the scale factor formula corresponding to the active fault is S _{2 =} M ₁ ×M ₂ The conversion matrix corresponding to this active fault is shown in Table 8 below. In addition, each hazard factor rank relative to each scale factor rank corresponds to an example of a hazard level in Table 8 below, but not limited to this . Table 8

Refer to Table 9 below. For the hazard attribute information corresponding to the hazard attributes of the earthquake, the risk scale score table includes the peak surface acceleration F ₂ and the shear wave velocity M ₃ and other two risk factors under the surface , Where the peak surface acceleration F ₂ is classified as the probability factor, and the shear wave velocity M ₃ below the surface is classified as the scale factor, in addition, each risk factor corresponds to each of these grading levels, An example of the grade score corresponding to each grade grade is shown in Table 9 below, but not limited to this. Table 9

For the hazard attribute information corresponding to the hazard attributes of the earthquake, the probability factor formula corresponding to the earthquake is S _{3 =} F ₂ , the scale factor formula corresponding to the earthquake is S _{4 =} M ₃ , and the earthquake corresponds to The conversion matrix is shown in Table 10 below. In addition, each hazard factor rank relative to each scale factor rank corresponds to an example of a hazard level in Table 10 below, but not limited to this. Table 10

Refer to Table 11 below. For the hazard attribute information corresponding to the hazard attributes of the earth and rock flow, the risk rating scale includes the potential risk level of the earth and rock flow stream F ₃ , the earth and rock flow warning reference value F ₄ , and the earth and rock flow Potential stream distance M ₄ and the potential range of the earth and rock flow potential stream distance M ₅ and other four risk factors, of which the earth and rock flow potential stream risk potential level F ₃ and the earth and rock flow warning reference value F ₄ are classified as possible Factors, and the distance M ₄ of the potential flow of the earth and rock flow and the distance M _{5 of the} influence range of the potential flow of the soil and rock flow are classified as the scale factor. In addition, each of these risk factors corresponds to each of these grades and each grade An example of the grade score corresponding to the grade distance is shown in Table 11 below, but not limited to this. Table 11

For the hazard attribute information corresponding to the hazard attribute of the earth and rock flow, the probability factor formula corresponding to the earth and rock flow is S _{5 =} F ₃ + F ₄ , and the scale factor formula corresponding to the earth and rock flow is S _{6 =} MAX{ M ₄ , M ₅ }, that is, the one with the higher intermediate distance score for both M ₄ and M ₅ , and the conversion matrix corresponding to the soil and rock flow is shown in Table 12 below. In addition, each possibility factor grade distance is relative to each scale factor grade distance. An example of a corresponding hazard level is shown in Table 12 below, but not limited to this. Table 12

Refer to Table 13 below. For the hazard attribute information corresponding to the hazard attributes of the landslide, the risk rating scale includes the landslide potential F ₅ and the maximum 24-hour cumulative rainfall F _{6 of} the recurrence period. Six types of risk factors including peak surface acceleration F ₂ , the landslide type M ₆ , the landslide area M ₇ , and the distance from the landslide M ₈ , among which the landslide potential F ₅ and the maximum recurrence period are 24 hours. The rainfall F ₆ and the peak surface acceleration F ₂ are classified as the probability factor, and the landslide type M ₆ , the landslide area M ₇ , and the distance from the landslide M ₈ are classified as the scale factor. In addition, each The respective hierarchical grades corresponding to risk factors and the grade scores corresponding to each grade are shown in Table 13 below, but not limited thereto. Table 13

For the hazard attribute information corresponding to the disaster attribute of the landslide, the probability factor formula corresponding to the landslide is S _{7 =} MAX{F ₅ +F ₆ , F ₅ +F ₂ }, that is, (F ₅ +F ₆ ) And (F ₅ + F ₂ ) who have higher intermediate distance scores, and the scale factor formula for the landslide is S _{8 =} M ₆ ×(M ₇ + M ₈ ), and the conversion matrix corresponding to the landslide is shown in Table 14 below. For each possibility factor step distance relative to each scale factor step distance, a corresponding hazard level example is shown in Table 14 below, but not limited to this. Table 14

Referring to Table 15 below, for the hazard attribute information corresponding to the flood hazard attributes, the risk scale score table includes the warning rain value F ₇ and the flood depth M ₉ and other two risk factors, of which , The warning rainfall value F ₇ is classified as the probability factor, and the flooding depth M ₉ is classified as the scale factor, in addition, each risk factor corresponds to each of these grades and each grade Examples of grade distance scores corresponding to distances are shown in Table 15 below, but not limited to this. Table 15

For the hazard attribute information corresponding to the flooding hazard attributes, the probability factor formula corresponding to the flooding is S _{9 =} F ₇ , and the scale factor formula corresponding to the flooding is S _{10 =} M ₉ , and the The conversion matrix corresponding to flooding is shown in Table 16 below. In addition, each hazard factor rank relative to each scale factor rank corresponds to an example of a hazard level in Table 16 below, but not limited to this. Table 16

Referring to Table 17 below, for the hazard attribute information corresponding to the hazard attributes of the soil liquefaction, the risk level distance score table includes the surface peak acceleration F ₂ , the groundwater level depth F ₈ , the elevation M ₁₀ , and the formation M ₁₁ and other four risk factors, wherein the peak acceleration surface F ₂ F ₈ with the depth of water table is classified as the likelihood factor, and the elevation of the M ₁₀ and M ₁₁ are classified as the formation of scale factors, in addition Each of these risk factors corresponds to the grading grades, and the grading scores corresponding to each grading grade are shown in Table 17 below, but not limited to this. Table 17

For the hazard attribute information corresponding to the hazard attributes of the soil liquefaction, the probability factor formula corresponding to the soil liquefaction is S _{11 =} F ₂ + F ₈ , and the scale factor formula corresponding to the soil liquefaction is S _{12 =} M ₁₀ + M ₁₁ , and the conversion matrix corresponding to the soil liquefaction is shown in Table 18 below. In addition, each possibility factor grade distance corresponding to each scale factor grade distance corresponds to an example of a hazard level in Table 18 below, but not as limit. Table 18

The user terminal 2 includes a user terminal communication module 21 connected to the communication network 101, a user terminal input module 22, and a user terminal processing electrically connecting the user terminal communication module 21 and the user terminal input module 22 Module 23.

In this embodiment, the implementation of the server 1 is, for example, a personal computer, a server, or a cloud host, but it is not limited thereto. The implementation of the user terminal 2 is, for example, a tablet computer, a notebook computer, a smartphone or a personal computer, but it is not limited thereto.

Referring to FIG. 1 and FIG. 2, the operation details of the components of the server 1 and the use end 2 will be described below by the embodiment of the risk assessment method of the present invention. The embodiment of the risk assessment method includes the following steps: a Step 61, a step 62, a step 63, a step 64, and a step 65.

In step 61, the user terminal processing module 23 generates target object information related to a target object according to the input signal from the user terminal input module 22, and communicates the target object information through the user terminal The module 21 is transmitted to the server 1 through the communication network 101. It is worth noting that in this embodiment, the target object information is, for example, the address of the target object, but the name or keyword related to the target object can also be used to achieve the same effect.

In step 62, after receiving the target object information through the server-side communication module 11, the server-side processing module 13 obtains a value from the risk assessment data stored in the server-side storage module 12 The target risk assessment data corresponding to the target object data.

In step 63, for each consistent disaster attribute, the server-side processing module 13 stores the target risk assessment data according to the risk rating score table corresponding to the disaster-causing attribute stored in the server-side storage module 12 The risk value of each risk factor of the disaster-causing attribute is given a first-degree score to obtain a score result of the grade score of each risk factor containing the disaster-causing attribute.

Referring to FIG. 3, it is worth noting that in this embodiment, step 63 further includes a sub-step 631 and a sub-step 632 detailed flow.

In sub-step 631, for each consistent disaster attribute, the server-side processing module 13 obtains the target risk assessment data according to the risk rating score table corresponding to the disaster-causing attribute stored in the server-side storage module 12 The risk value of each risk factor of the disaster-causing attribute in the belongs to a hierarchical level.

In sub-step 632, for each consistent disaster attribute, the server-side processing module 13 obtains the target risk assessment data according to the risk rating score table corresponding to the disaster-causing attribute stored in the server-side storage module 12 The rating score corresponding to the grading level to which the risk value of each risk factor of the disaster-causing attribute in is obtained to obtain a scoring result of the rating score containing each risk factor of the disaster-causing attribute.

In step 64, for each consistent disaster attribute, the server-side processing module 13 imports the score result of the disaster-causing attribute into the hazard level corresponding to each of the disaster-causing attributes stored in the server-side storage module 12 Conversion rules to obtain a target hazard level.

Referring to FIG. 4, it is worth noting that, in this embodiment, step 64 further includes detailed processes of sub-step 641, sub-step 642, and sub-step 643.

In sub-step 641, for each consistent disaster attribute, the server-side processing module 13 is based on the rank score of the probability factor and the disaster attribute of the disaster-causing attribute stored in the server-side storage module 12 Probability factor formula, calculate a probability factor score, and calculate a scale based on the scale score of the scale factor in the disaster attribute stored in the server-side storage module 12 and the scale factor formula of the disaster attribute Factor score.

In sub-step 642, for each consistent disaster attribute, the server-side processing module 13 obtains the probability factor of the disaster-causing attribute according to the conversion matrix corresponding to the disaster-causing attribute stored in the server-side storage module 12 A probability factor scale to which the score belongs, and a scale factor scale to which the scale factor score of the disaster-causing attribute belongs.

In sub-step 643, for each consistent disaster attribute, the server-side processing module 13 obtains the probability that the disaster attribute belongs to according to the conversion matrix corresponding to the disaster attribute stored in the server-side storage module 12 The factor scale is relative to the target hazard level corresponding to the scale factor scale to which the scale factor score of the disaster-causing attribute belongs.

In step 65, the server obtains a plurality of objects that are all related to the target and correspond to a variety of different types of disasters according to the respective target hazard levels corresponding to each consistent disaster attribute stored in the server-side storage module 12 The risk assessment level, and transmit the risk assessment levels to the user end 2 through the server-side communication module 11. It is worth noting that the disasters corresponding to each disaster type are related to at least consistent disaster attributes. In this embodiment, the server-side processing module 13 obtains an earthquake with a disaster type of earthquake according to the target hazard level belonging to the hazard attribute of the active fault and the target hazard level belonging to the hazard attribute of the earthquake Risk assessment level. The server-side processing module 13 obtains an earth-rock flow risk assessment level whose disaster type is earth-rock flow according to the target hazard level belonging to the disaster-causing attribute of the earth-rock flow. The server-side processing module 13 obtains a landslide risk assessment level whose disaster type is landslide according to the target hazard level belonging to the disaster-causing attribute of the landslide. The servo-side processing module 13 obtains a flood risk assessment level with a flood type of flood according to the target hazard level belonging to the disaster-causing attribute of the flood. The servo-side processing module 13 obtains a soil liquefaction risk assessment level with a disaster type of soil liquefaction according to the target hazard level belonging to the hazard attribute of the soil liquefaction. It is worth mentioning that the server-side processing module 13 is based on the target hazard level belonging to the hazard attribute of the active fault, and the higher of the target hazard level belonging to the hazard attribute of the earthquake as the earthquake The risk assessment level, and the target hazard level belonging to the hazard attribute of the earth and rock flow, the target hazard level belonging to the hazard attribute of the landslide, the target hazard level belonging to the hazard attribute of flooding and the hazard attribute of the soil liquefaction The target hazard grades are taken as the risk assessment grade of the earth and rock flow, the landslide risk assessment grade, the flooding risk assessment grade and the soil liquefaction risk assessment grade, respectively.

The following describes an embodiment of the risk assessment method of the present invention with an application example. In this application example, the above Tables 1 to 6 are used as the risk assessment data, and at the same time, the address A is used as the address of the target object.

As shown in step 61, the user terminal processing module 23 generates target object information related to a target object according to the input signal from the user terminal input module 22 (eg, 91 Xueshi Road, North District, Taichung City) , And the object information of the target is transmitted to the server 1 through the communication network 101 through the user-end communication module 21. It is worth noting that in this application example, the information of the target object is described by address, but the name of the target object (eg, China Medical University) or keywords (eg, hospital, University) as the target object information.

As shown in step 62, after receiving the target object information through the server-side communication module 11, the server-side processing module 13 is obtained from the risk assessment data stored in the server-side storage module 12 A target risk assessment data (that is, all risk factors related to address A in Tables 1 to 6 and the risk values corresponding to all risk factors), where address A corresponding to the target risk assessment data and the target subject matter The addresses indicated by the information are consistent.

As shown in sub-step 631, for each consistent disaster attribute, the server-side processing module 13 obtains the target risk assessment according to the risk rating score table corresponding to the disaster-causing attribute stored in the server-side storage module 12 The risk value of each risk factor of the disaster-causing attribute in the data belongs to a grading step (the following grading step in Table 19 to Table 22).

表20

表21

表22

As shown in sub-step 632, for each consistent disaster attribute, the server-side processing module 13 obtains the target risk assessment according to the risk rating score table corresponding to the disaster-causing attribute stored in the server-side storage module 12 The rating score corresponding to the grading grade (the grading grade in Table 19~Table 22 below) to which the risk value of each risk factor of the hazard attribute in the data belongs (the following in Table 19~Table 22) Grade distance score) to obtain a score result of the grade score of each risk factor containing the hazard attribute. Table 19

Table 20

Table 21

Table 22

As shown in sub-step 641, for each consistent disaster attribute, the server-side processing module 13 according to the rank score belonging to the possibility factor of the disaster attribute stored in the server-side storage module 12 and the disaster attribute Formula of probability factor, calculate a possibility factor score (S ₁ , S ₃ , S ₅ , S ₇ , S ₉ , and S _{11 in} Table 23 below), and store the data in the server-side storage module 12 according to the Calculate a scale factor score (S ₂ , S ₄ , S ₆ , S ₈ , S ₁₀ , and S _{12 in} Table 23 below) from the scale score of the disaster attribute and the scale factor formula of the disaster attribute ). Here, taking the disaster-causing attribute as the description of the earth and rock flow (see Table 20), the corresponding probability factor formula of the earth and rock flow is S _{5 =} F ₃ + F ₄ , where F ₃ is the earth and rock flow potential creek risk potential level and its grade rating Is 30, F ₄ is the base value of earth and rock flow warning, and its grade score is 10, so the probability factor score corresponding to the earth and rock flow is S ₅₌ 40, and the scale factor formula corresponding to the earth and rock flow is S _{6 =} MAX{ M ₄ , M ₅ }, where M ₄ is the distance from the potential flow of the earth and rock flow to a grade of 5, and M ₅ is the influence range of the potential flow of the soil and rock flow to a grade of 60, so the scale factor score S _{6 =} 60 corresponding to the soil and rock flow is shown in Table 23 below .

As shown in sub-step 642, for each consistent disaster attribute, the server-side processing module 13 obtains the possibility of the disaster-causing attribute according to the conversion matrix corresponding to the disaster-causing attribute stored in the server-side storage module 12 A probability factor scale to which the factor score belongs, and a scale factor scale to which the scale factor score of the disaster-causing attribute belongs. Here, taking the disaster-causing attribute as the description of the earth and rock flow (see Table 12), the probability factor corresponding to the earth and rock flow is S ₅₌ 40, so the corresponding probability factor is 40~70, and the corresponding scale of the earth and rock flow The factor score S _{6 =} 60, so the corresponding scale factor scale is 40~70.

As shown in sub-step 643, for each consistent disaster attribute, the server-side processing module 13 obtains the possibility that the disaster attribute belongs to based on the conversion matrix corresponding to the disaster attribute stored in the server-side storage module 12 The scale of the sex factor is relative to the target hazard level corresponding to the scale of the scale factor to which the scale factor score of the hazard attribute belongs. Here, taking the disaster-causing attribute as the description of the soil and rock flow (see Table 12), the probability factor is 40~70, and the scale factor corresponding to the soil and rock flow is 40~70, so the corresponding target hazard level is 3 See Table 23 below. Table 23

As shown in step 65, the server-side processing module 13 according to the target hazard level belonging to the disaster-causing attribute of the active fault is 1 (see Table 23), and the target hazard level belonging to the disaster-causing attribute of the earthquake is 1 (See Table 23), the seismic risk assessment level obtained is 1. The server-side processing module 13 has a target hazard level of 3 (see Table 23) according to the hazard attributes of the earth and rock flow, and the risk evaluation level of the earth and rock flow is 3. The server-side processing module 13 has a target hazard level of 2 according to the disaster-causing attribute of the landslide (see Table 23), and the landslide risk assessment level obtained is 2. According to the target hazard level belonging to the disaster-causing attribute of the flooding, the server-side processing module 13 is 1 (see Table 23), and the flooding risk evaluation level obtained is 1. The servo-side processing module 13 has a target hazard level of 3 (see Table 23) according to the hazard attributes of the soil liquefaction, and the obtained soil liquefaction risk assessment level is 3. Finally, the servo-side processing module 13 passes the earthquake risk assessment level, the earth and rock flow risk assessment level, the landslide risk assessment level, the flooding risk assessment level, and the soil liquefaction risk assessment level through the servo-end communication module 11 It is transmitted to the user terminal 2 through the communication network 101.

In summary, in the risk assessment method of the present invention, the server-side processing module 13 obtains the target risk assessment data related to the subject matter according to the risk assessment data stored by the server-side storage module 12 The risk value of each risk factor of the risk, and then through the risk-level distance score table stored in the server-side storage module 12 to obtain the risk level of each risk factor in the target risk assessment data The distance and the grade distance are scored, and finally through these hazard degree classification conversion rules, the target hazard level corresponding to each consistent disaster attribute is obtained. As a result, the target object can be quickly and correctly provided for the disaster types Risk assessment level, which in turn reduces the labor and time costs of risk assessment. Therefore, the purpose of cost invention can indeed be achieved.

However, the above are only examples of the present invention, and the scope of implementation of the present invention cannot be limited by this, any simple equivalent changes and modifications made according to the scope of the patent application of the present invention and the content of the patent specification are still classified as Within the scope of the invention patent.

100‧‧‧System 101‧‧‧Communication network 1‧‧‧Servo terminal 11‧‧‧Servo terminal communication module 12‧‧‧Servo terminal storage module 13‧‧‧Servo terminal processing module 2‧‧‧Using terminal 21‧‧‧Usage end communication module 22‧‧‧Usage end input module 23‧‧‧Usage end processing module 61~65‧‧‧Steps 631, 632‧‧‧Substeps 641~643‧‧‧Substep

Other features and functions of the present invention will be clearly presented in the embodiment with reference to the drawings, in which: FIG. 1 is a block diagram illustrating a system for implementing an embodiment of the risk assessment method of the present invention; FIG. 2 is a A flowchart illustrating the process steps of the embodiment of the risk assessment method of the present invention; FIG. 3 is a flowchart illustrating how the embodiment of the risk assessment method of the present invention obtains a scoring result; and FIG. 4 is a flowchart illustrating the present invention. How does this embodiment of the invention risk assessment method obtain a target hazard level.

61~65‧‧‧Step

Claims (10)

A risk assessment method is implemented by a server, which is connected to a user end via a communication network, the server end stores multiple pieces of risk assessment data corresponding to multiple objects, and multiple pieces correspond to Disaster attribute information of a variety of different disaster attributes, where the risk assessment data respectively correspond to the addresses of multiple target objects, and each risk assessment data includes multiple risk values of multiple risk factors corresponding to each consistent disaster attribute, Each consistent disaster attribute information includes a risk grade score table and a hazard classification conversion rule. The risk assessment method includes the following steps: (A) through the server, after receiving a After the target object information of the target object, obtain a target risk assessment data corresponding to the target object data according to the addresses of the target objects in the risk assessment data; (B) For each consistent disaster attribute, by the The server side, according to the risk level score table corresponding to the disaster-causing attribute, gives the first-level score to the risk value of each risk factor of the disaster-causing attribute in the target risk assessment data to obtain a file containing the disaster-causing attribute (C) For each consistent disaster attribute, through the server, the score result of the disaster attribute is imported into the hazard degree classification conversion corresponding to the disaster attribute for each consistent disaster attribute. Rules to obtain a target hazard level; and (D) through the server, according to the target hazard level of each consistent disaster attribute, send multiple risk assessment levels corresponding to the multiple hazard attributes to the user end. According to the risk assessment method described in claim 1, the risk grade score table includes the risk factors corresponding to the corresponding disaster-causing attributes, the multiple grade grades corresponding to each risk factor, and each grade grade Step (B) includes the following steps: (B-1) For each consistent disaster attribute, through the server, according to the risk level score table corresponding to the disaster-causing attribute, obtain the A hierarchical level to which the risk value of each risk factor of the hazard attribute in the target risk assessment data belongs; and (B-2) For each consistent disaster attribute, the server side corresponds to the hazard attribute according to The risk grade score table of the target risk assessment data to obtain the grade score corresponding to the grade grade to which the risk value of each risk factor of the hazard attribute in the target risk assessment data is to obtain a The scoring result of the grade score of each risk factor. The risk assessment method according to claim 1, wherein, in step (B), the hazard attribute information includes an active fault information, an earthquake information, a debris flow information, a landslide information, a flooding information, and 1. Soil liquefaction information. According to the risk assessment method described in claim 3, multiple risk factors corresponding to each consistent disaster attribute can be classified into one of a probability factor and a scale factor, and the hazard degree corresponding to each consistent disaster attribute is converted The rule includes a probability factor formula, a scale factor formula, and a conversion matrix, the conversion matrix has multiple probability factor steps, multiple scale factor steps, and each possibility factor step is relative to each scale A hazard level corresponding to each of the factor levels, where step (C) includes the following steps: (C-1) For each consistent disaster attribute, through the server, a probability factor score is calculated according to the grade score of the probability factor belonging to the disaster attribute and the probability factor formula of the disaster attribute, and Calculate a scale factor score according to the scale score of the disaster attribute belonging to the scale factor and the scale factor formula of the disaster attribute; (C-2) For each consistent disaster attribute, by the server, according to the disaster The conversion matrix corresponding to the attribute, to obtain a probability factor grade to which the probability factor score of the disaster-causing attribute belongs, and a scale factor grade to which the scale factor score of the disaster-causing attribute belongs; and (C-3) for each Consistent disaster attribute, through the server, according to the conversion matrix corresponding to the disaster attribute, obtain the probability factor rank of the disaster attribute relative to the scale factor grade of the scale factor score of the disaster attribute Corresponding to the target hazard level. According to the risk assessment method described in claim 4, when the disaster-causing attribute is an active fault, the risk factors of the disaster-causing attribute include a probability classified as the probability factor and related to the probability of occurrence of a magnitude 6.5 earthquake The first factor of, a second factor classified as the scale factor and related to the distance from the fault line, and a third factor classified as the scale factor and related to the upper and lower magnification of the fault, where: In step (C-1), for the hazard attributes belonging to the active fault, the server calculates the likelihood factor score S ₁ according to the first factor's grade score F ₁ and the following probability factor formula: S1=F ₁ , and calculate the scale factor score S ₂ according to the scale score M ₁ of the second factor, the scale score M _{2 of} the second factor, and the following scale factor formula: S ₂ =M ₁ ×M ₂ . According to the risk assessment method described in claim 4, when the disaster-causing attribute is an earthquake, the risk factors of the disaster-causing attribute also include a fourth categorized as the likelihood factor and related to the peak surface acceleration Factor, and a fifth factor that is classified as the scale factor and is related to the shear wave velocity below the surface, where: in step (C-1), for the hazard attributes belonging to the active fault, the servo At the end, the probability factor score S ₃ is calculated according to the grade factor score F _{2 of} the fourth factor, and the following possibility factor formula: S ₃ =F ₂ , and the grade factor score M ₃ according to the fifth factor, and the following Scale factor formula, calculate the scale factor score S ₄ : S ₄ =M ₃ . According to the risk assessment method described in claim 4, when the hazard attribute is a debris flow, the risk factors of the hazard attribute include a risk potential that is classified as the probability factor and is related to the potential of the debris flow The first factor of the grade, a second factor classified as the likelihood factor and related to the baseline value of the earth and rock flow alert, a third factor classified as the scale factor and related to the potential stream distance of the earth and rock flow, and a The fourth factor that is classified as the scale factor and is related to the distance range of the potential flow of earth and rock flows, where: in step (C-1), for the hazard attributes belonging to the earth and rock flows, by the server, according to the The grade factor score F ₃ of the first factor, the grade factor score F _{4 of} the second factor, and the following probability factor formula calculate the probability factor score S ₅ : S ₅ =F ₃ +F ₄ , and according to the third The scale score M ₄ of the factor, the scale score M _{5 of} the second factor, and the following scale factor formula is to calculate the scale factor score S ₆ : S ₆ =MAX{M ₄ ,M ₅ }, which is M ₄ and M _{5 have} higher intermediate distance scores. According to the risk assessment method described in claim 4, when the disaster-causing attribute is a landslide, the risk factors of the disaster-causing attribute include a first factor classified as the possibility factor and related to the potential of the landslide 1. A second factor that is classified as the likelihood factor and related to the accumulated rainfall during the maximum twenty-four hour recurrence period, a third factor that is classified as the likelihood factor and related to the surface peak acceleration, a A fourth factor that is classified as the scale factor and related to the type of landslide, a fifth factor that is classified as the scale factor and related to the area of the landslide, and a factor that is classified as the scale factor and related to the potential of the landslide The sixth factor, wherein: in step (C-1), for the disaster-causing attribute belonging to the landslide, the servo end is used according to the first factor's grade score F ₅ and the second factor's grade score F ₆ , the grade score F _{2 of} the third factor, and the following probability factor formula to calculate the likelihood factor score S ₇ : S ₇ =MAX{F ₅ +F ₆ ,F ₅ +F ₂ }, that is (F ₅ +F ₆ ) and (F ₅ +F ₂ ) who have higher intermediate distance scores, according to the fourth factor grade score M ₆ , the fifth factor grade score M ₇ , the first The six-factor grade score M ₈ and the following scale factor formula is to calculate the scale factor score S ₈ : S ₈ =M ₆ ×(M ₇ +M ₈ ). According to the risk assessment method described in claim 4, when the disaster-causing attribute is a flood, the risk factors of the disaster-causing attribute include a first classifier that is classified as the likelihood factor and is related to the warning rainfall value Factor, a second factor that is categorized as the scale factor and related to the flooding depth, where: in step (C-1), for the disaster-causing attributes that belong to the flooding, by the server, according to the The grade factor score F _{7 of the} first factor and the following probability factor formula calculate the probability factor score S ₉ : S ₉ =F ₇ , and according to the grade factor score M _{9 of} the second factor, and the following scale factor formula is , Calculate the scale factor score S ₁₀ : S ₁₀ =M ₉ . According to the risk assessment method described in claim 4, when the hazard attribute is a soil liquefaction, the risk factors of the hazard attribute include a first categorized as the likelihood factor and related to the peak acceleration of the surface Factor, a second factor that is classified as the likelihood factor and related to the depth of the groundwater level, a third factor that is classified as the scale factor and related to elevation, and a third factor that is classified as the scale factor and related to The fourth factor of the stratum, where: in step (C-1), for the hazard attributes belonging to the soil liquefaction, the servo end is used to score F ₂ according to the first factor’s step distance, and the second factor’s The grade score F ₈ and the following probability factor formula calculate the possibility factor score S ₁₁ : S ₁₁ =F ₂ +F ₈ , and according to the grade factor score M _{10 of} the third factor, the grade factor of the second factor The score M ₁₁ and the following scale factor formula are to calculate the scale factor score S ₁₂ : S ₁₂ =M ₁₀ +M ₁₁ .

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