Method and system for intelligent management of security monitoring based on meta-model

Abstract

The application relates to a meta-model-based safety monitoring intelligent management method and system, which comprises the following steps: acquiring in-situ survey information, precipitation information and water level information of a monitored area; matching the in-situ survey information with a data model in a database, and calling a data model conforming to the monitored area; inputting the in-situ survey information, the precipitation information and the water level information into the data model to obtain an early warning level of the monitored area. The application has the effect of improving the accuracy of disaster early warning.

Description

Technical Field

[0001] This application relates to the field of disaster early warning technology, and in particular to a safety monitoring intelligent management method and system based on meta-model. Background Technology

[0002] Natural disasters include landslides, mudslides, debris flows, ground subsidence, ground fissures, and ground settlements that endanger people's lives and property, caused by natural factors or human activities.

[0003] Simple geological disasters such as ground subsidence, ground fissures, and ground settlement are difficult to predict, while natural disasters such as landslides, debris flows, and floods, which are affected by rainfall, can be predicted, thus reducing the losses they cause.

[0004] Given the predictable nature of landslides, debris flows, and floods, methods such as manual monitoring, expert experience, and physical models can achieve a certain degree of prediction, but they still cannot provide targeted and accurate monitoring for different regions and scenarios. Summary of the Invention

[0005] This application provides a meta-model-based intelligent management method and system for safety monitoring, which improves the accuracy of disaster early warning.

[0006] Firstly, this application provides a security monitoring intelligent management method based on a meta-model, employing the following technical solution:

[0007] A meta-model-based intelligent management method for security monitoring includes:

[0008] Obtain on-site survey information, precipitation information, and water level information for the monitored area;

[0009] The field survey information is matched with the data model in the database to retrieve the data model that matches the monitored area.

[0010] Based on the data model type, precipitation information, and water level information of the monitored area, determine the types of disasters that may occur in the monitored area;

[0011] Based on the on-site survey information, precipitation information, and water level information, the probability of different disasters occurring is calculated, and the corresponding early warning level is matched from the database.

[0012] By adopting the above technical solutions, different data models are determined for different monitored areas based on different field survey information. Each data model corresponds to different initial parameters and calculation coefficients, which can make targeted disaster warnings for different areas and improve the accuracy of disaster warnings.

[0013] Optionally, based on the aforementioned field survey information, precipitation information, and water level information, the probability of different disasters occurring is calculated, and the corresponding early warning level is matched from the database, including:

[0014] The initial parameters of the data model were corrected based on the soil information from the field survey.

[0015] The precipitation information and weather forecasts for each monitored area are input into the corrected data model to calculate the probability of disasters and match the corresponding warning level from the database.

[0016] Optionally, the precipitation information, weather forecasts, initial parameters, and disaster coefficients of each monitored area are input into the corrected data model to calculate the probability of a disaster, and the corresponding warning level is matched from the database, including:

[0017] Based on the revised data model, calculate the disaster coefficient of the corresponding model.

[0018] Precipitation curves are predicted based on the precipitation information and weather forecasts.

[0019] Adjust the precipitation curve based on the initial parameters, and then multiply it by the disaster coefficient to obtain the curve of disaster occurrence probability versus time.

[0020] Optionally, based on the modified data model, the disaster coefficient corresponding to the disaster occurrence in the model is retrieved, including:

[0021] Obtain historical disaster data;

[0022] The index weight of each parameter in the historical disaster data is calculated by big data algorithm, and then each parameter in the corrected data model is multiplied by its corresponding index weight to obtain the disaster coefficient.

[0023] Optionally, based on the aforementioned field survey information, precipitation information, and water level information, the probability of different disasters occurring is calculated, and the corresponding early warning level is matched from the database, including:

[0024] The initial parameters of the data model were corrected based on the terrain and river information from the field survey.

[0025] The precipitation information and weather forecasts are input into the data model to calculate the total estimated water injection, total estimated drainage, and expected precipitation for each monitored area.

[0026] Based on the estimated total water injection volume, estimated total drainage volume, and expected precipitation, the probability of a disaster occurring is calculated, and the corresponding early warning level is matched from the database.

[0027] The precipitation information and weather forecasts are input into the data model to calculate the total estimated water injection, total estimated drainage, and expected precipitation for each monitored area, including:

[0028] Based on the revised data model, calculate the safe water storage and safe drainage volumes for the monitored areas;

[0029] Based on the aforementioned safe water storage capacity, safe drainage capacity, precipitation information, and weather forecast, calculate the additional water storage capacity for the monitored area;

[0030] Calculate the expected precipitation for the monitored area based on the aforementioned weather forecast;

[0031] Based on the terrain information, the drainage direction of each monitored area is determined, and the estimated total water injection and total drainage volume per hour for each monitored area are calculated respectively.

[0032] Optionally, based on the terrain information, the drainage direction of each monitored area is determined, and the estimated total injection volume and total drainage volume per hour for each monitored area are calculated, including:

[0033] Based on the terrain information, determine the steepness of the monitored area and the total length of the boundary line of the drainage area;

[0034] Based on the steepness of the terrain in the monitored area and the total length of the boundary line of the drainage area, the drainage rate is calculated, and the total hourly drainage volume is further estimated.

[0035] Calculate the length of the drainage boundary line of each of the other monitored areas relative to the current monitored area, and calculate the total estimated water injection volume of each of the other monitored areas relative to the current monitored area.

[0036] Optionally, based on the estimated total water injection volume, estimated total drainage volume, and predicted precipitation, the probability of a disaster occurring is calculated, and the corresponding early warning level is matched from the database, including:

[0037] Retrieve the corresponding weighted indicators for total water injection forecast, total drainage forecast, and expected precipitation;

[0038] Calculate the weighted sum of the total estimated water injection, total estimated drainage, and projected precipitation as the probability of disaster occurrence;

[0039] The corresponding early warning level is matched from the database based on the probability of the disaster occurring.

[0040] Secondly, this application provides a security monitoring intelligent management system based on a meta-model, which adopts the following technical solution:

[0041] A meta-model-based intelligent management system for security monitoring includes:

[0042] The acquisition module is used to acquire on-site survey information, precipitation information, and water level information of the monitored area;

[0043] The matching module is used to match the field survey information with the data model in the database and retrieve the data model that matches the monitored area.

[0044] The calculation module is used to input the field survey information, precipitation information and water level information into the data model to obtain the early warning level of the monitored area.

[0045] In summary, this application includes at least one of the following beneficial technical effects: based on different field survey information, it determines the data model used for different monitored areas, and each data model corresponds to different initial parameters and calculation coefficients, which can make targeted disaster warnings for different areas and improve the accuracy of disaster warnings. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating one embodiment of the intelligent management method for security monitoring based on a meta-model in this application.

[0047] Figure 2 This is a schematic diagram of a meta-model-based intelligent security monitoring management system according to one embodiment of this application.

[0048] Figure 3 This is a schematic diagram of the structure of a terminal according to an embodiment of this application.

[0049] Explanation of reference numerals in the attached figures: 201, Acquisition module; 202, Matching module; 203, Calculation module; 301, CPU; 302, ROM; 303, RAM; 304, Bus; 305, I / O interface; 306, Input section; 307, Output section; 308, Storage section; 309, Communication section; 310, Driver; 311, Removable medium. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0051] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0052] The following is in conjunction with the appendix Figures 1 to 3 This application will be described in further detail.

[0053] To improve the accuracy of disaster early warning, this application provides a meta-model-based intelligent management method for safety monitoring.

[0054] Reference Figure 1 A security monitoring intelligent management method based on a meta-model includes the following steps:

[0055] S101: Obtain on-site survey information, precipitation information, and water level information for the monitored area.

[0056] Specifically, when conducting disaster prediction for a monitored area, it is necessary to first obtain on-site survey information of the monitored area to match the type of data model and calibrate the initial parameters of the model. On-site survey information includes soil information, terrain information, river information, building information, and vegetation information. Methods for obtaining on-site survey data include topographic mapping, aerial photography, satellite imagery, and soil sampling. One implementation method can be: using topographic mapping and satellite imagery to obtain terrain and building information, as well as to determine the river course and width. The vegetation coverage in the monitored area is calculated using satellite imagery; aerial photography is used to collect images of vegetated areas, and further image recognition of the aerial images is combined to determine the vegetation species. Soil sampling is conducted uniformly within the monitored area to determine the soil information of the monitored area.

[0057] Furthermore, precipitation information within the monitored area is determined by rain gauges within the area, water level information is determined by water level gauges in the river channel, and weather forecasts are determined by satellite imagery. In one implementation scenario, precipitation data can be collected within the monitored area using uniform sampling or five-point sampling methods. For river water level measurement, water level gauges can be installed upstream and downstream of the monitored area, and multiple sampling points can be set up along the riverbank to measure river water level information. Simultaneously, the safe water level line of the river in the monitored area should also be retrieved.

[0058] S102: Match the field survey information with the data model in the database and retrieve the data model that matches the monitored area.

[0059] Once the field survey information of the monitored area is obtained, it is matched against the database to find data models with the same or similar tags as those in the field survey information. Specifically, the matching method involves extracting keywords from soil, terrain, river, building, and vegetation information as tags, and then finding the data model in the database that has the most corresponding tags. Each data model stores initial parameters, corresponding to each parameter in the field survey information.

[0060] In one implementation scenario, the process of matching data models requires training with big data and manual verification of the matching results. When matching errors occur, feedback and adjustments are made in a timely manner to improve the accuracy of model matching.

[0061] In an implementation scenario, when the soil information is loess, with high vegetation coverage, few buildings, and gentle terrain, the matched model may be a plain forest model; while when the soil information has a high content of stones and sandy soil and steep terrain, the matched model is a mountain model.

[0062] S103: Based on the data model type, precipitation information, and water level information, determine the types of disasters that may occur in the monitored area.

[0063] Specifically, within the environment corresponding to a data model, the types of disasters that can occur are limited. For example, landslides are unlikely to occur in flat areas; therefore, only floods are likely to occur in such areas. In mountainous areas, however, heavy rainfall can lead not only to flash floods but also to landslides and debris flows. Therefore, based on data models, precipitation information, and water level information, the types of potential disasters can be determined. Different calculation methods are used for different types of disasters.

[0064] S104: Based on on-site survey information, precipitation information, and water level information, calculate the probability of different disasters occurring, and match the corresponding warning level from the database.

[0065] Specifically, in actual calculations, since the initial parameters in the data model cannot be completely consistent with the field survey information, it is necessary to correct the initial parameters of the data model based on the field survey information to improve the accuracy of the calculation. Each parameter in the field survey information is input into the data model one by one to obtain the corrected data model.

[0066] After the data model is revised, the precipitation information and weather forecasts of the monitored area are then input into the revised data model to calculate the probability of disasters.

[0067] In one implementation scenario, the calculation method for the probability of landslide disasters is as follows: Based on the modified data model, the disaster coefficient k of the landslide disaster in the corresponding model is recalculated. The value of k is calculated by first calculating the index weight of each parameter in the historical disaster data using big data algorithms, and then multiplying each parameter in the modified data model by its corresponding index weight to obtain the disaster coefficient. Then, the precipitation curve is predicted based on precipitation information and weather forecasts, and the precipitation curve is adjusted according to the initial parameters and multiplied by the disaster coefficient to obtain the curve of disaster probability versus time. Based on the precipitation at the time of disasters in the historical disaster data, the precipitation and the number of disasters are fitted using the data model to obtain the relationship coefficient h between precipitation m and the number of disasters. Thus, a comprehensive coefficient k*h is obtained for the early warning of landslide disasters, and m*k*h can be used as the probability of landslide disasters. Furthermore, the early warning level corresponding to the disaster probability is matched from the data.

[0068] In another implementation scenario, the probability of flooding is calculated as follows: First, the initial parameters of the data model are corrected based on terrain and river information from on-site surveys. Then, precipitation information and weather forecasts are input into the data model to calculate the total estimated water injection, total estimated drainage, and expected precipitation for each monitored area. The calculation method for the total estimated drainage is similar to that for the total estimated water injection; both first calculate the safe water storage and safe drainage volumes for the monitored areas. These safe water storage and safe drainage volumes are determined by soil, terrain, and vegetation information, and are calculated using big data algorithms to analyze the impact of various parameters from historical data on safe water storage. Finally, the safe water level of the river is considered to calculate the safe water storage and safe drainage volumes for the river. When the predicted precipitation calculated from rainfall information and meteorological forecasts exceeds the safe water storage and safe drainage capacity, the direction of the additional water storage is determined based on terrain information. The estimated total drainage volume per hour from the current monitored area to other monitored areas, and the estimated total water injection volume per hour from other monitored areas to the current monitored area are then calculated. The calculation methods for the total water injection and drainage estimates include: determining the terrain steepness of the monitored area and the total length of the drainage area boundary based on terrain information; then calculating the drainage rate based on the terrain steepness of other monitored areas and the total length of the drainage area boundary, further obtaining the estimated total drainage volume per hour. The water flow velocity is positively correlated with the terrain steepness. Furthermore, the length of the drainage boundary line from other monitored areas to the current monitored area is calculated, and the estimated total water injection volume from each other monitored area to the current monitored area is also calculated. When the difference between the total estimated water injection and total estimated drainage in the monitored area within a certain period exceeds a preset safety value, or when any one of the total estimated water injection, total estimated drainage, and expected precipitation exceeds the corresponding safety value, or when it exceeds the safety value within a future period, it indicates a risk of disaster. The specific calculation method involves retrieving the weights of the corresponding total estimated water injection, total estimated drainage, and expected precipitation, and then calculating the weighted sum as the probability of risk. Further, a warning level corresponding to the risk probability is matched from the data.

[0069] In one implementation scenario, soil information also includes soil moisture content. Based on the soil moisture content, the soil's water absorption capacity can be calculated more accurately, which can improve the accuracy of the calculation of safe water storage capacity.

[0070] In another implementation scenario, considerations for landslide disasters during the above process also include monitoring soil displacement. This includes: in areas prone to landslide disasters, micro-deformation radar is installed to monitor soil displacement. When soil deformation is detected to reach the corresponding threshold, an early warning is issued based on the warning level of the corresponding threshold.

[0071] This application provides a security monitoring intelligent management system based on a meta-model, which adopts the following technical solution:

[0072] Reference Figure 2 A meta-model-based intelligent management system for security monitoring includes:

[0073] The acquisition module 201 is used to acquire on-site survey information, precipitation information and water level information of the monitored area;

[0074] The matching module 202 is used to match the field survey information with the data model in the database and retrieve the data model that matches the monitored area.

[0075] The calculation module 203 is used to determine the types of disasters that may occur in the monitored area based on the type of data model, precipitation information and water level information of the monitored area; and to calculate the probability of different disasters from the field survey information, precipitation information and water level information, and to match the corresponding warning level from the database.

[0076] Figure 3 A schematic diagram of a terminal suitable for implementing embodiments of this application is shown.

[0077] like Figure 3 As shown, the terminal includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 302 or programs loaded from storage into Random Access Memory (RAM) 303. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0078] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), and speakers, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card and a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to I / O interface 305 as needed. A removable medium 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 310 as needed so that computer programs read from it can be installed into storage section 308 as needed.

[0079] Specifically, according to embodiments of this application, the above reference flow Figure 1 The described process can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a machine-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the functions defined in the system of this application.

[0080] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, register file (RF), etc., or any suitable combination thereof.

[0081] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0082] The units or modules described in the embodiments of this application can be implemented in software or hardware. The described units or modules can also be housed in a processor; for example, a processor can be described as including an acquisition module 201, a matching module 202, and a calculation module 203. The names of these units or modules do not necessarily constitute a limitation on the unit or module itself.

[0083] In another aspect, this application also provides a computer-readable storage medium, which may be included in the terminal described in the above embodiments; or it may exist independently and not assembled into the terminal. The aforementioned computer-readable storage medium stores one or more programs, which are used by one or more processors to execute the data encryption transmission method described in this application.

[0084] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A security monitoring intelligent management method based on a meta-model, characterized in that, include: Obtain on-site survey information, precipitation information, and water level information for the monitored area; The field survey information is matched with the data model in the database to retrieve the data model that matches the monitored area. Based on the data model type, precipitation information, and water level information that match the monitored area, determine the types of disasters that may occur in the monitored area. Based on the on-site survey information, precipitation information, and water level information, the probability of different disasters occurring is calculated, and the corresponding early warning level is matched from the database. The initial parameters of the data model that conforms to the monitored area are corrected based on the soil information from the field survey. The precipitation information and weather forecasts for each monitored area are input into the corrected data model to calculate the probability of disasters and match the corresponding warning level from the database. Based on the revised data model, calculate the disaster coefficient of the corresponding model. Acquire historical disaster data; calculate the index weight of each parameter in the historical disaster data using big data algorithms, and then multiply each parameter in the corrected data model by its corresponding index weight to obtain the disaster coefficient; Precipitation curves are predicted based on the precipitation information and weather forecasts. Adjust the precipitation curve based on the initial parameters, and then multiply it by the disaster coefficient to obtain the curve of disaster occurrence probability versus time.

2. The intelligent management method for safety monitoring based on a meta-model according to claim 1, characterized in that, Based on the aforementioned field survey information, precipitation information, and water level information, the probability of different disasters occurring is calculated, and the corresponding early warning levels are matched from the database, including: The initial parameters of the data model that conforms to the monitored area are corrected based on the terrain and river information in the field survey data. The precipitation information and weather forecasts are input into the data model to calculate the total estimated water injection, total estimated drainage, and expected precipitation for each monitored area. Based on the estimated total water injection volume, estimated total drainage volume, and expected precipitation, the probability of a disaster occurring is calculated, and the corresponding early warning level is matched from the database.

3. The intelligent management method for safety monitoring based on a meta-model according to claim 2, characterized in that, The precipitation information and weather forecasts are input into the data model to calculate the total estimated water injection, total estimated drainage, and expected precipitation for each monitored area, including: Based on the revised data model, calculate the safe water storage and safe drainage volumes for the monitored areas; Based on the aforementioned safe water storage capacity, safe drainage capacity, precipitation information, and weather forecast, calculate the additional water storage capacity for the monitored area; Calculate the expected precipitation for the monitored area based on the aforementioned weather forecast; Based on the terrain information, the drainage direction of each monitored area is determined, and the estimated total water injection and total drainage volume per hour for each monitored area are calculated respectively.

4. The intelligent management method for safety monitoring based on a meta-model according to claim 3, characterized in that, Based on the terrain information, the drainage direction for each monitored area is determined, and the estimated total water injection and drainage volume per hour for each monitored area are calculated, including: Based on the terrain information, determine the steepness of the monitored area and the total length of the boundary line of the drainage area; Based on the steepness of the terrain in the monitored area and the total length of the boundary line of the drainage area, the drainage rate is calculated, and the total hourly drainage volume is further estimated. Calculate the length of the drainage boundary line of each of the other monitored areas relative to the current monitored area, and calculate the total estimated water injection volume of each of the other monitored areas relative to the current monitored area.

5. The intelligent management method for safety monitoring based on a meta-model according to claim 4, characterized in that, Based on the estimated total water injection, estimated total drainage, and predicted precipitation, the probability of a disaster occurring is calculated, and the corresponding early warning level is matched from the database, including: Retrieve the corresponding weighted indicators for total water injection forecast, total drainage forecast, and expected precipitation; Calculate the weighted sum of the total estimated water injection, total estimated drainage, and projected precipitation as the probability of disaster occurrence; The corresponding early warning level is matched from the database based on the probability of the disaster occurring.

6. A safety monitoring intelligent management system based on a meta-model, characterized in that, include: The acquisition module (201) is used to acquire on-site survey information, precipitation information and water level information of the monitored area; The matching module (202) is used to match the field survey information with the data model in the database and retrieve the data model that matches the monitored area; The calculation module (203) is used to determine the types of disasters that may occur in the monitored area based on the type of data model that conforms to the monitored area, precipitation information and water level information; calculate the probability of different disasters occurring based on the field survey information, precipitation information and water level information, and match the corresponding early warning level from the database; The initial parameters of the data model that conforms to the monitored area are corrected based on the soil information from the field survey; the precipitation information and weather forecast of each monitored area are input into the corrected data model to calculate the probability of disaster and match the corresponding warning level from the database; Based on the revised data model, calculate the disaster coefficient of the corresponding model. Acquire historical disaster data; calculate the index weight of each parameter in the historical disaster data using big data algorithms, and then multiply each parameter in the corrected data model by its corresponding index weight to obtain the disaster coefficient; Precipitation curves are predicted based on the precipitation information and weather forecasts. Adjust the precipitation curve based on the initial parameters, and then multiply it by the disaster coefficient to obtain the curve of disaster occurrence probability versus time.

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