CN116882765B - Disaster risk management and control method based on intelligent label - Google Patents
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Abstract
The invention relates to a disaster risk management and control method based on intelligent labels, which comprises the steps of dividing early warning areas to obtain a plurality of unit areas; acquiring risk characteristic factors of each unit area, wherein the risk characteristic factors comprise a plurality of characteristic factors; according to the real-time parameter value of each feature factor in the risk feature factors, the static label and the dynamic label of each unit area are adjusted; predicting the risk value of each unit area according to the static label, the dynamic label and the real-time parameter value of each characteristic factor; and carrying out early warning according to the risk value of each unit area. The invention can more pertinently predict and control the risk existing in the mountain.
Description
Technical Field
The application relates to the technical field of geological disaster prediction, in particular to a disaster risk management and control method based on intelligent labels.
Background
In the earth movement process, rock rings, biospheres, water rings and atmosphere rings constantly exchange substances and energy to generate various geological effects, so that the earth surface rock and soil mass is deformed and moved, and geological disasters are caused.
Wherein, mountain often receives the influence of heavy rainfall and produces collapse, landslide and mud-rock flow, and this not only can influence the resident that lives around the mountain, endangers resident's life and property, also can influence natural environment, leads to the vegetation to be destroyed to make ecological system's stability reduce. Meanwhile, ecological problems such as water and soil loss, water resource pollution and the like can be caused. Therefore, it is necessary to monitor the state of the mountain to predict the risk of the mountain.
Disclosure of Invention
The first purpose of the application is to provide a disaster risk management and control method based on intelligent labels, which has the characteristic of predicting the risk existing in mountain bodies.
The first object of the present application is achieved by the following technical solutions:
a disaster risk management and control method based on intelligent labels comprises the following steps:
dividing the early warning area to obtain a plurality of unit areas;
acquiring risk characteristic factors of each unit area, wherein the risk characteristic factors comprise a plurality of characteristic factors;
according to the real-time parameter value of each feature factor in the risk feature factors, the static label and the dynamic label of each unit area are adjusted;
predicting the risk value of each unit area according to the static label, the dynamic label and the real-time parameter value of each characteristic factor;
and carrying out early warning according to the risk value of each unit area.
By adopting the technical scheme, the early warning areas are divided, and then the risk value of each unit area is estimated according to the static label and the dynamic label of each unit area and the real-time parameter value of each characteristic factor, so that the alarm of the unit area is realized. The disaster monitoring system has the advantages that the disaster occurrence source and the disaster occurrence range can be easily determined during early warning, and monitoring and management of mountain bodies are facilitated.
The present application may be further configured in a preferred example to: the predicting the risk value of each unit area according to the static label, the dynamic label and the real-time parameter value of each characteristic factor comprises:
determining a unit area, in which the static label of the node at the same time is the same as that of the current unit area, and the dynamic label is the same as that of the current unit area, and marking the unit area as a class area;
determining a similarity region with highest similarity with the current unit region according to the real-time parameter value of each characteristic factor of each similarity region, and marking the similarity region as the most similar region;
acquiring historical data of a current unit area and historical data of a most similar area;
and determining the risk value of the current unit area according to the real-time parameter value of each characteristic factor of the current unit area and the historical data.
The present application may be further configured in a preferred example to: the determining the similar area with the highest similarity with the current unit area according to the real-time parameter value of each characteristic factor of each similar area comprises the following steps:
determining the similarity value of the real-time parameter value of each characteristic factor of each similarity region and the real-time parameter value of the corresponding characteristic factor of the current unit region one by one;
determining a weight similarity value of each similarity region and the current unit region according to a preset weight value and a similarity value of a real-time parameter value of each characteristic factor;
and selecting the similar region with the highest weight similarity value as the most similar region.
The present application may be further configured in a preferred example to: the determining the risk value of the current unit area according to the real-time parameter value of each characteristic factor of the current unit area and the historical data comprises:
determining a risk pre-estimated value of the current unit area according to the real-time parameter value of each characteristic factor of the current unit area;
determining a first risk coefficient according to the historical data of the most similar area and the historical data of the current unit area;
and determining a risk value according to the risk predicted value and the first risk coefficient.
The present application may be further configured in a preferred example to: the determining the first risk coefficient according to the history data of the most similar area and the history data of the current unit area comprises:
determining the times of reaching the risk warning value and the time of reaching the risk warning value each time from the historical data of the most similar area, and determining the times of reaching the risk warning value and the time of reaching the risk warning value each time from the historical data of the current unit area;
determining the probability of reaching the risk warning value each time according to the number of reaching the risk warning value and the time of reaching the risk warning value each time in the historical data of the most similar area and the number of reaching the risk warning value and the time of reaching the risk warning value each time in the historical data of the current unit area;
determining the probability that the risk value of the current unit area reaches the risk warning value at the current time node according to the probability that the risk warning value is reached each time;
and determining a first risk coefficient according to the probability that the risk value of the current unit area reaches the risk warning value at the current time node.
The present application may be further configured in a preferred example to: the determining a risk value according to the risk pre-estimation value and the first risk coefficient comprises:
the risk value is equal to the product of the risk assessment value and the first risk coefficient.
The present application may be further configured in a preferred example to: the determining the probability that the risk value of the current unit area reaches the risk warning value at the current time node according to the probability that the risk warning value is reached each time comprises the following steps:
determining a common frequency according to the frequency that the risk value in the history data of the most similar area reaches the risk warning value and the frequency that the risk value in the history data of the current unit area reaches the risk warning value, wherein the common frequency is the smaller value of the frequency that the risk value in the history data of the most similar area reaches the risk warning value and the frequency that the risk value in the history data of the current unit area reaches the risk warning value;
forming a probability change curve according to probability fitting of each time the risk warning value is reached;
and determining the probability that the risk value of the current unit area reaches the risk warning value at the current time node according to the probability change curve.
The present application may be further configured in a preferred example to: the determining the probability of each time reaching the risk warning value according to the number of times the risk value reaches the risk warning value and the time of each time reaching the risk warning value in the historical data of the most similar area, and the number of times the risk value reaches the risk warning value and the time of each time reaching the risk warning value in the historical data of the current unit area comprises:
determining the probability of each time the risk warning value is reached in the historical data of the most similar area according to the times that the risk value reaches the risk warning value in the historical data of the most similar area and the time of each time the risk warning value is reached;
determining the probability of each time the risk warning value is reached in the historical data of the current unit area according to the times that the risk value reaches the risk warning value in the historical data of the current unit area and the time of each time the risk warning value is reached;
and calculating average probability according to the probability of each time of reaching the risk warning value in the historical data of the most similar area and the probability of each time of reaching the risk warning value in the historical data of the current unit area, and recording the average probability as the probability of each time of reaching the risk warning value.
The present application may be further configured in a preferred example to: determining the probability of reaching the risk alert value each time according to the number of times the risk value reaches the risk alert value and the time of reaching the risk alert value each time includes:
determining the times that the risk value does not reach the risk warning value between the two adjacent risk values reaches the risk warning value according to the time that the two adjacent risk values reach the risk warning value;
the probability that each unit area reaches the risk alert value is one-half the number of times the risk value does not reach the risk alert value.
The present application may be further configured in a preferred example to: the determining the risk prediction value of the current unit area according to the real-time parameter value of each characteristic factor of the current unit area comprises the following steps:
determining the risk score of each characteristic factor according to the real-time parameter value of each characteristic factor and the warning value corresponding to the real-time parameter value;
determining a second risk coefficient according to the number of the risk scores reaching a preset risk standard value;
and determining a risk pre-estimation value according to the highest value in the risk score and the second risk coefficient.
In summary, the present application includes at least one of the following beneficial technical effects:
dividing the early warning area, and then estimating the risk value of each unit area according to the static label, the dynamic label and the real-time parameter value of each characteristic factor of each unit area, thereby realizing the alarm of the unit area. The disaster monitoring system has the advantages that the disaster occurrence source and the disaster occurrence range can be easily determined during early warning, and monitoring and management of mountain bodies are facilitated.
Drawings
Fig. 1 is a flow chart of a disaster risk management and control method based on smart tags according to an embodiment of the present application.
Fig. 2 is a system schematic diagram of a smart tag-based disaster risk management and control system according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of an intelligent terminal according to an embodiment of the present application.
In the figure, a module is divided 21; 22. an acquisition module; 23. an adjustment module; 24. a risk prediction module; 25. an early warning module; 301. a CPU; 302. a ROM; 303. a RAM; 304. a bus; 305. an I/O interface; 306. an input section; 307. an output section; 308. a storage section; 309. a communication section; 310. a driver; 311. removable media.
Detailed Description
The present application is described in further detail below with reference to the accompanying drawings.
The present embodiment is merely illustrative of the present application and is not intended to be limiting, and those skilled in the art, after having read the present specification, may make modifications to the present embodiment without creative contribution as required, but is protected by patent laws within the scope of the claims of the present application.
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In this context, unless otherwise specified, the term "/" generally indicates that the associated object is an "or" relationship.
The embodiment of the application provides a disaster risk management and control method based on intelligent labels, which can predict and manage the risk existing in mountain more pertinently.
Embodiments of the present application are described in further detail below with reference to the drawings attached hereto.
The main flow of the disaster risk management and control method based on the intelligent label provided by the embodiment of the application is described as follows.
As shown in fig. 1:
step S100: dividing the early warning area to obtain a plurality of unit areas.
The early warning area is an area in which disaster risks need to be monitored. In the embodiment of the application, the early warning area is a mountain. In other embodiments, the pre-warning area may be any area.
Specifically, an electronic map of the early warning area needs to be acquired first. Then, it is divided by a mesh division manner, thereby obtaining a plurality of unit areas. The size of the unit area can be adjusted according to the size of the early warning area.
Step S200: and acquiring risk characteristic factors of each unit area.
The risk characteristic factors are factors which can be used for evaluating the risk value, and comprise a plurality of characteristic factors. The characteristic factors may be topography, soil composition, rock composition, precipitation, water level, deformation, etc. For convenience of explanation, characteristic factors such as topography, soil composition, rock composition, precipitation, water level, deformation amount, etc. may be understood as parameter names, and their corresponding contents may be understood as parameter values. Since the values of some of the characteristic factors may change with time, the content corresponding to each characteristic factor is collectively referred to as a real-time parameter value in this application. Based on this, the actual real-time parameter values for each characteristic factor are obtained in this step.
In a specific example, for a certain unit area on a mountain, the real-time parameter value corresponding to the topography of the terrain is the topography type. The topography type can be obtained by analyzing the topography of the cell area. For example, if the topography of the unit area is generally in a state of a lower central topography and a higher peripheral topography, the topography type is a basin-type topography. The real-time parameter values corresponding to the soil components are specific components of the soil. The real-time parameter value of the precipitation amount is a specific precipitation amount. Since precipitation may be a varying value, the real-time parameter values obtained at different times are different.
Step S300: and adjusting the static label and the dynamic label of each unit area according to the real-time parameter value of each characteristic factor in the risk characteristic factors.
The static label is a characteristic factor with a constant real-time parameter value, and the dynamic label is a characteristic factor with a variable real-time parameter value. Because the real-time parameter values of the feature factors may change over time, both the feature factors corresponding to the static tags and the feature factors corresponding to the dynamic tags may change as the real-time parameter values of each feature factor change.
Specifically, taking a feature factor as an example, the rules for static and dynamic tag adjustment for each cell region are: when the real-time parameter value of the current time node of the characteristic factor is changed compared with the real-time parameter value of the previous time node, the characteristic factor of the time node is one of the characteristic factors corresponding to the dynamic label. When the real-time parameter value of the current time node of the characteristic factor is unchanged compared with the real-time parameter value of the previous time node, the characteristic factor of the time node is one of the characteristic factors corresponding to the static label.
Step S400: and predicting the risk value of each unit area according to the static label, the dynamic label and the real-time parameter value of each characteristic factor.
The risk value is used for evaluating the disaster risk existing in the unit area.
It will be appreciated that, since the risk value of each unit area is evaluated in the same manner, in the embodiment of the present application, only the risk value of one unit area is predicted, and a specific prediction method is described.
Optionally, step S400 includes the following steps (steps S410 to S440):
step S410: and determining the unit area, in which the static label of the node is the same as that of the current unit area at the same time and the dynamic label is the same as that of the current unit area, and marking the unit area as a similar area.
For a static label, it is determined whether the real-time parameter values of the same characteristic factors of the two unit areas are the same, and whether the main contrast types are the same. For example, whether the topography types are the same or whether the soil composition is the same.
For the dynamic tag, the judgment standard for determining whether the real-time parameter values of the same characteristic factors of the two unit areas are the same is whether the magnitudes to which the real-time parameter values of the same characteristic factors belong are the same.
Step S420: and determining the similarity region with the highest similarity with the current unit region according to the real-time parameter value of each characteristic factor of each similarity region, and marking the similarity region as the most similar region.
It can be appreciated that, due to the number of unit areas, the number of characteristic factors for representing each unit area is limited, so that a plurality of class areas can appear in the early warning area. However, the cell region most similar to the current cell region can provide a more powerful reference value.
The method for determining the most similar region comprises the following steps:
first, the similarity value of the real-time parameter value of each characteristic factor of each similarity region and the real-time parameter value of the corresponding characteristic factor of the current cell region is determined one by one.
For a dynamic tag, the similarity value of a certain feature factor is (1-parameter value difference/real-time parameter value of the current cell area) ×100%, where the parameter value difference is the absolute value of the difference between two real-time parameter values of the same feature factor.
For static labels, different analysis of different characteristic factors is required. For example, if the feature factor is a terrain type, it is necessary to analyze the difference in ground potential, gradient, etc., and then calculate the feature factor in a manner that the dynamic tag calculates the similarity value of the feature factor. For example, if the characteristic factor is a soil component, the ratio of each element in the soil component needs to be determined, and then the characteristic factor can be calculated according to the ratio of the component with the highest ratio and the similar value of the characteristic factor can be calculated according to the dynamic label. Of course, in other embodiments, the similarity value for each characteristic factor may be calculated in other ways.
And then, determining the weight similarity value of each similarity region and the current unit region according to the preset weight value and the similarity value of the real-time parameter value of each characteristic factor.
The weight value can be determined according to the influence degree of each characteristic factor on the risk existing in the mountain. For example, the degree of influence of precipitation on the risk of mountain presence is greater than the degree of influence of soil components on the risk of mountain presence, and the degree of influence of soil components on the risk of mountain presence is greater than the degree of influence of other characteristic factors on the risk of mountain presence, so if the characteristic factors have 7, the weight value is 3:2:1:1:1:1:1. The weight of 3 is the weight of precipitation, the weight of 2 is the weight of soil components, and the weight of 1 is the weight of other characteristic factors. Of course, the foregoing is merely an example, and the weight values are not limited thereto, and in other embodiments, the weight values may be adaptively adjusted according to requirements.
Further, after determining the similarity value of the weight value and the real-time parameter value of each feature factor, the weight similarity value may be determined according to the similarity value of the weight value and the real-time parameter value of each feature factor. The calculation method for calculating the weight similarity value according to the weight value is a common technical means for those skilled in the relevant art, so the detailed description of the calculation method is omitted here.
And finally, selecting the similar region with the highest weight similarity value as the most similar region.
Step S430: the history data of the current cell area and the history data of the most similar area are acquired.
Wherein the history data includes a change over time of each characteristic factor of each unit area, and a change over time of the risk value. Of course, the risk values here are not predicted in real time, but rather at intervals.
Step S440: and determining the risk value of the current unit area according to the real-time parameter value and the historical data of each characteristic factor of the current unit area.
Optionally, step S440 includes the steps of:
step S441: and determining a risk pre-estimation value of the current unit area according to the real-time parameter value of each characteristic factor of the current unit area.
The risk prediction value is a preliminary prediction of the risk value existing for the current cell region. The specific prediction method comprises the following steps:
first, a risk score for each characteristic factor is determined based on the real-time parameter value for each characteristic factor and the alert value corresponding thereto. Specifically, the risk score is 100% of the real-time parameter value/alert value. When the real-time parameter value of each characteristic factor reaches the warning value, natural disasters may occur. It should be noted that this step only calculates the risk scores of the feature factors whose real-time parameter values may vary. The warning value is a preset value. In some specific embodiments, the adaptation may be made according to actual requirements.
And then, determining a second risk coefficient according to the number of the risk scores reaching a preset risk standard value.
It will be appreciated that the risk criterion value is a value below the alert value. When the risk score reaches the risk standard value, the unit area is meant to have a certain risk. Similarly, the risk standard value is also a preset value, and can be adaptively adjusted according to actual requirements.
It will be appreciated that there is a possibility of interaction between the plurality of characteristic factors, so that when the risk score reaches the greater number of risk criterion values, the greater the risk is indicated and the risks are superimposed. Therefore, it is necessary to set different second risk coefficients according to the number of risk scores reaching the preset risk standard value.
In a specific embodiment, if the number of risk scores reaches the preset risk standard value is 0 or 1, the second risk coefficient is 1, if the number of risk scores reaches the preset risk standard value is not more than 4, the second risk coefficient is 1.2, and if the number of risk scores reaches the preset risk standard value is 5, the second risk coefficient is 1.5.
And finally, determining a risk pre-estimation value according to the highest value in the risk score and the second risk coefficient.
The risk assessment value is the product of the highest value in the risk score and the second risk coefficient.
Step S442: determining a first risk coefficient according to the history data of the most similar area and the history data of the current unit area;
it is understood that when a disaster occurs repeatedly in a certain cell area, each disaster may affect the characteristic factors of the cell area. For example, when a debris flow occurs in a certain unit area, the soil is more easily eroded due to the influence of the debris flow, and the stability of the ecosystem in the unit area is reduced, so that more debris flows may be induced. Therefore, in addition to the risk prediction value determined according to the real-time parameter value of each characteristic factor of the current cell area, it is necessary to consider the hidden trouble caused by the disaster occurring before. The first risk factor is used to evaluate the impact of a disaster that occurred before.
Specifically, the first risk factor determining method includes:
first, the number of times the risk value reaches the risk guard value and the time of reaching the risk guard value each time are determined from the history data of the most similar area, and the number of times the risk value reaches the risk guard value and the time of reaching the risk guard value each time are determined from the history data of the current unit area.
Wherein the risk warning value is a preset value. When the risk value reaches the risk warning value, namely the current unit area has higher risk, natural disasters such as landslide, debris flow and the like can occur. Otherwise, when the risk value does not reach the risk warning value, the current unit area is indicated to have a certain risk, but natural disasters cannot occur. In some embodiments, the risk alert value may be adaptively adjusted according to actual needs.
And then, determining the probability of reaching the risk warning value each time according to the times of reaching the risk warning value and the time of reaching the risk warning value each time in the historical data of the most similar area, and the times of reaching the risk warning value and the time of reaching the risk warning value each time in the historical data of the current unit area.
Specifically, the implementation manner of the steps is that the probability of reaching the risk warning value in each time in the historical data of the most similar area is determined according to the times of reaching the risk warning value in the historical data of the most similar area and the time of reaching the risk warning value each time. And determining the probability of each time the risk warning value is reached in the historical data of the current unit area according to the times that the risk value reaches the risk warning value in the historical data of the current unit area and the time of each time the risk warning value is reached. And calculating the average probability according to the probability of reaching the risk warning value each time in the historical data of the most similar area and the probability of reaching the risk warning value each time in the historical data of the current unit area, namely the probability of reaching the risk warning value each time.
The probability of each reaching the risk alert value in the history data of the most similar area and the probability of each reaching the risk alert value in the history data of the current unit area are the same, so the two steps can be collectively referred to as determining the probability of each reaching the risk alert value according to the number of times the risk value reaches the risk alert value and the time of each reaching the risk alert value. The specific implementation mode is as follows: and determining the times that the risk value does not reach the risk warning value between the two adjacent risk values reaches the risk warning value according to the time that the two adjacent risk values reach the risk warning value. The probability that each unit area reaches the risk warning value is the number of times that the risk value does not reach the risk warning value plus one-half. For ease of understanding, an example is provided below.
In a specific example, assuming that the risk value reaches the risk alert value on the 1 st day, the risk value does not reach the risk alert value on the 2 nd to 8 th days, the risk value reaches the risk alert value on the 9 th day, the risk value does not reach the risk alert value on the 10 th to 12 th days, the risk value reaches the risk alert value again on the 13 th day, the probability that the second risk value reaches the risk alert value is 1/7, and the probability that the third risk value reaches the risk alert value is 1/4.
It should be noted that the probability that the risk value reaches the risk alert value each time in different unit areas may be different, and in order to obtain a more accurate probability, an average probability needs to be calculated according to the probability that the risk alert value reaches each time in the history data of the most similar area and the probability that the risk alert value reaches each time in the history data of the current unit area. The average probability can be regarded as the probability that each time the risk value reaches the risk alert value per unit area.
The probability that the risk value reaches the risk alert value is also affected to some extent as the number of natural disasters occurring in the same cell area increases.
Further, the probability that the risk value of the current unit area reaches the risk warning value at the current time node is determined according to the probability that the risk warning value is reached each time.
Specifically, the common number of times needs to be determined according to the number of times the risk value reaches the risk guard value in the history data of the most similar area and the number of times the risk value reaches the risk guard value in the history data of the current unit area. The common number is the smaller of the number of times the risk value reaches the risk guard value in the history data of the most similar area and the number of times the risk value reaches the risk guard value in the history data of the current unit area. For example, the number of times the risk value reaches the risk guard value in the history data of the most similar area is 3, and the number of times the risk value reaches the risk guard value in the history data of the current cell area is 5, and the total number of times is 3. It should be noted that, since there may be different time nodes for each unit area to start precipitation and different time nodes for each unit area to end precipitation, natural disasters may occur in different unit areas at different time nodes, and the number of times of occurrence of natural disasters may also be different due to different real-time parameter values of characteristic factors of the unit areas.
And after the sharing times are determined, a probability change curve is formed according to probability fitting of each time the risk warning value is reached.
In a specific example, assuming that the probability that the 2 nd risk value reaches the risk alert value is 10%, the probability that the 3 rd risk value reaches the risk alert value is 12%, and the probability that the 4 th risk value reaches the risk alert value is 15%, a probability change curve can be formed according to the probability fit that the third risk value reaches the risk alert value. The probability change curve may reflect the probability of each time the risk value reaches the risk alert value.
After determining the number of times that the risk value of the current unit area reaches the risk warning value, the risk value of the current time node can be defaulted to reach the risk warning value. At this time, based on the probability change curve, the probability corresponding to the number of times that the risk value of the current unit area reaches the risk warning value added by one can be determined, and the probability is the probability that the risk value of the current unit area reaches the risk warning value at the current time node.
And finally, determining a first risk coefficient according to the probability that the risk value of the current unit area reaches the risk warning value at the current time node. It will be appreciated that the higher the probability that the risk value reaches the risk alert value, the higher the corresponding first risk factor. In a specific embodiment, the first risk factor is 1 when the probability that the risk value reaches the risk alert value does not exceed 50%, 1.1 when the probability that the risk value reaches the risk alert value does not exceed 75% and is higher than 50%, 1.2 when the probability that the risk value reaches the risk alert value does not exceed 90% and is higher than 75%, and 1.5 when the probability that the risk value reaches the risk alert value is higher than 90%. In other embodiments, the correspondence between the first risk coefficient and the probability that the risk value reaches the risk alert value may be adaptively adjusted according to the actual requirement.
Step S443: and determining a risk value according to the risk predicted value and the first risk coefficient.
In this application, the risk value is the product of the risk and the estimate and the first risk factor.
Step S500: and carrying out early warning according to the risk value of each unit area.
When the risk value of a certain unit area reaches the risk warning value, the unit area needs to be pre-warned. Otherwise, when the risk value of the unit area does not reach the risk warning value, early warning is not needed.
Fig. 2 is a schematic diagram of a disaster risk management and control system based on smart tags according to an embodiment of the present application.
The disaster risk management and control system based on intelligent labels as shown in fig. 2 comprises a dividing module 21, an obtaining module 22, an adjusting module 23, a risk prediction module 24 and an early warning module 25, wherein:
the dividing module 21 is configured to divide the early warning area to obtain a plurality of unit areas.
An obtaining module 22, configured to obtain risk feature factors of each of the unit areas, where the risk feature factors include a plurality of feature factors.
An adjustment module 23, configured to adjust the static label and the dynamic label of each unit area according to the real-time parameter value of each of the risk characteristic factors.
The risk prediction module 24 is configured to predict a risk value of each unit area according to the static label, the dynamic label, and the real-time parameter value of each feature factor.
And the early warning module 25 is used for carrying out early warning according to the risk value of each unit area.
Fig. 3 shows a schematic structural diagram of a smart terminal suitable for implementing embodiments of the present application.
As shown in fig. 3, the smart terminal includes a Central Processing Unit (CPU) 301 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage section into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data required for the system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other through a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
The following components are connected to the I/O interface 305: an input section 306 including a keyboard, a mouse, and the like; an output portion 307 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 308 including a hard disk or the like; and a communication section 309 including a network interface card such as a LAN card, a modem, or the like. The communication section 309 performs communication processing via a network such as the internet. The drive 310 is also connected to the I/O interface 305 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 310 as needed, so that a computer program read out therefrom is installed into the storage section 308 as needed.
In particular, according to embodiments of the present application, the process described above with reference to flowchart fig. 1 may be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a machine-readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 309, and/or installed from the removable medium 311. The above-described functions defined in the system of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 301.
It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units or modules described in the embodiments of the present application may be implemented by software, or may be implemented by hardware. The described units or modules may also be provided in a processor, for example, as: a processor comprising: the system comprises a dividing module 21, an acquiring module 22, an adjusting module 23, a risk prediction module 24 and an early warning module 25. The names of these units or modules do not limit the units or modules themselves in some cases, and the dividing module 21 may be described as "a module for dividing an early warning area into a plurality of unit areas", for example.
As another aspect, the present application also provides a computer-readable storage medium, which may be included in the intelligent terminal described in the above embodiment; or may exist alone without being assembled into the smart terminal. The computer readable storage medium stores one or more programs that when executed by one or more processors perform the smart tag-based disaster risk management method described herein.
The foregoing description is only of the preferred embodiments of the present application and is presented as a description of the principles of the technology being utilized. It will be appreciated by persons skilled in the art that the scope of the application referred to in this application is not limited to the specific combinations of features described above, but it is intended to cover other embodiments in which any combination of features described above or their equivalents is possible without departing from the spirit of the application. Such as the above-mentioned features and the technical features having similar functions (but not limited to) applied for in this application are replaced with each other.
Claims (7)
1. The disaster risk management and control method based on the intelligent label is characterized by comprising the following steps of:
dividing the early warning area to obtain a plurality of unit areas;
acquiring risk characteristic factors of each unit area, wherein the risk characteristic factors comprise a plurality of characteristic factors;
according to the real-time parameter value of each feature factor in the risk feature factors, the static label and the dynamic label of each unit area are adjusted;
predicting the risk value of each unit area according to the static label, the dynamic label and the real-time parameter value of each characteristic factor;
early warning is carried out according to the risk value of each unit area;
the predicting the risk value of each unit area according to the static label, the dynamic label and the real-time parameter value of each characteristic factor comprises:
determining a unit area, in which the static label of the node at the same time is the same as that of the current unit area, and the dynamic label is the same as that of the current unit area, and marking the unit area as a class area;
determining a similarity region with highest similarity with the current unit region according to the real-time parameter value of each characteristic factor of each similarity region, and marking the similarity region as the most similar region;
acquiring historical data of a current unit area and historical data of a most similar area, wherein the historical data comprises time variation of each characteristic factor of each unit area and time variation of a risk value;
determining a risk value of the current unit area according to the real-time parameter value of each characteristic factor of the current unit area and the historical data;
the determining the risk value of the current unit area according to the real-time parameter value of each characteristic factor of the current unit area and the historical data comprises:
determining a risk pre-estimated value of the current unit area according to the real-time parameter value of each characteristic factor of the current unit area;
determining a first risk coefficient according to the historical data of the most similar area and the historical data of the current unit area, wherein the first risk coefficient is used for evaluating the influence caused by the disaster occurring before;
determining a risk value according to the risk pre-estimated value and the first risk coefficient;
the determining the risk prediction value of the current unit area according to the real-time parameter value of each characteristic factor of the current unit area comprises the following steps:
determining the risk score of each characteristic factor according to the real-time parameter value of each characteristic factor and the warning value corresponding to the real-time parameter value;
determining a second risk coefficient according to the number of the risk scores reaching a preset risk standard value, wherein the second risk coefficient is used for evaluating the influence among a plurality of characteristic factors;
and determining a risk pre-estimation value according to the highest value in the risk score and the second risk coefficient.
2. The smart tag-based disaster risk management method according to claim 1, wherein the determining a class area having the highest similarity to the current unit area according to the real-time parameter value of each feature factor of each class area comprises:
determining the similarity value of the real-time parameter value of each characteristic factor of each similarity region and the real-time parameter value of the corresponding characteristic factor of the current unit region one by one;
determining a weight similarity value of each similarity region and the current unit region according to a preset weight value and a similarity value of a real-time parameter value of each characteristic factor;
and selecting the similar region with the highest weight similarity value as the most similar region.
3. The smart tag-based disaster risk management method of claim 1, wherein the determining a first risk factor from the historical data of the most similar area and the historical data of the current unit area comprises:
determining the times of reaching the risk warning value and the time of reaching the risk warning value each time from the historical data of the most similar area, and determining the times of reaching the risk warning value and the time of reaching the risk warning value each time from the historical data of the current unit area;
determining the probability of reaching the risk warning value each time according to the number of reaching the risk warning value and the time of reaching the risk warning value each time in the historical data of the most similar area and the number of reaching the risk warning value and the time of reaching the risk warning value each time in the historical data of the current unit area;
determining the probability that the risk value of the current unit area reaches the risk warning value at the current time node according to the probability that the risk warning value is reached each time;
and determining a first risk coefficient according to the probability that the risk value of the current unit area reaches the risk warning value at the current time node.
4. The smart tag-based disaster risk management method according to claim 3, wherein the determining a risk value according to the risk prediction value and the first risk coefficient comprises:
the risk value is equal to the product of the risk assessment value and the first risk coefficient.
5. A disaster risk management method based on intelligent labels according to claim 3, wherein said determining the probability that the risk value of the current cell area reaches the risk alert value at the current time node according to the probability that the risk alert value is reached each time comprises:
determining a common frequency according to the frequency that the risk value in the history data of the most similar area reaches the risk warning value and the frequency that the risk value in the history data of the current unit area reaches the risk warning value, wherein the common frequency is the smaller value of the frequency that the risk value in the history data of the most similar area reaches the risk warning value and the frequency that the risk value in the history data of the current unit area reaches the risk warning value;
forming a probability change curve according to probability fitting of each time the risk warning value is reached;
and determining the probability that the risk value of the current unit area reaches the risk warning value at the current time node according to the probability change curve.
6. A disaster risk management method based on intelligent labels according to claim 3, wherein said determining the probability of each time a risk alert value is reached based on the number of times a risk value reaches a risk alert value and the time each time a risk alert value is reached in the history data of the most similar area, and the number of times a risk value reaches a risk alert value and the time each time a risk alert value is reached in the history data of the current cell area comprises:
determining the probability of each time the risk warning value is reached in the historical data of the most similar area according to the times that the risk value reaches the risk warning value in the historical data of the most similar area and the time of each time the risk warning value is reached;
determining the probability of each time the risk warning value is reached in the historical data of the current unit area according to the times that the risk value reaches the risk warning value in the historical data of the current unit area and the time of each time the risk warning value is reached;
and calculating average probability according to the probability of each time of reaching the risk warning value in the historical data of the most similar area and the probability of each time of reaching the risk warning value in the historical data of the current unit area, and recording the average probability as the probability of each time of reaching the risk warning value.
7. The smart tag-based disaster risk management method of claim 6, wherein determining the probability of reaching the risk alert value each time according to the number of times the risk value reaches the risk alert value and the time of reaching the risk alert value each time comprises:
determining the times that the risk value does not reach the risk warning value between the two adjacent risk values reaches the risk warning value according to the time that the two adjacent risk values reach the risk warning value;
the probability that each unit area reaches the risk alert value is one-half the number of times the risk value does not reach the risk alert value.
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