Emergency early warning method, device, equipment and medium

Abstract

The invention provides an emergency early warning method and device, equipment and a medium. The method comprises the following steps: acquiring to-be-detected data of a target area where an emergency occurs; analyzing the to-be-detected data to obtain a potential risk result of the emergency; and outputting early warning information based on the potential risk result, the early warning information being used for prompting that an emergency occurs in the target area. According to the embodiment of the invention, early warning of emergencies can be automatically realized, and the early warning accuracy of emergencies is improved.

Description

Technical Field

[0001] The present application belongs to the field of information processing technology, and in particular, relates to an early warning method, device, equipment and medium for sudden incidents. Background Art

[0002] The occurrence of emergencies often brings unpredictable disasters and adverse effects to society, such as natural disasters such as typhoons, heavy rains or ice and snow, or non-natural disasters such as sudden explosions and fires. Therefore, timely alarms and warnings are of great significance to avoid and mitigate the impact of these disasters. At present, the judgment of these disasters often relies on manual image recognition, which leads to inaccurate and untimely judgment. Summary of the invention

[0003] The embodiments of the present application provide a method, device, equipment, medium and product for early warning of emergencies, which can improve the accuracy of early warning of emergencies.

[0004] In a first aspect, an embodiment of the present application provides an early warning method for an emergency event, comprising:

[0005] Acquire the data to be detected in the target area where the emergency occurs;

[0006] Analyze the data to be tested to obtain the potential risk results of the emergency;

[0007] Based on the potential risk results, warning information is output, where the warning information is used to prompt the occurrence of emergencies in the target area.

[0008] In a second aspect, an embodiment of the present application provides an early warning device for an emergency, including:

[0009] An acquisition module, used to acquire the data to be detected in the target area where the emergency occurs;

[0010] The analysis module is used to analyze the data to be detected and obtain the potential risk results of the emergency;

[0011] The early warning module is used to output early warning information based on potential risk results, wherein the early warning information is used to prompt the occurrence of emergencies in the target area.

[0012] In a third aspect, an embodiment of the present application provides an electronic device, the device comprising:

[0013] a processor and a memory storing computer program instructions;

[0014] When the processor executes the computer program instructions, it is used to execute the emergency warning method of the first aspect.

[0015] In a fourth aspect, an embodiment of the present application provides a computer storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the emergency event warning method of the first aspect described above.

[0016] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when processed by a processor, implements the emergency event warning method of the first aspect above.

[0017] The embodiments of the present application disclose an early warning method, device, equipment, medium and product for early warning of emergencies, which can obtain the data to be detected in the target area where the emergency occurs, and analyze the data to be detected to obtain the potential risk results of the emergency. Then, based on the potential risk results, early warning information is output, thereby realizing automatic early warning of emergencies and improving the accuracy of early warning of emergencies. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 A flowchart of an emergency warning method provided for an embodiment of the present application.

[0020] Figure 2 A schematic diagram of the structure of an early warning device for an emergency event provided in one embodiment of the present application.

[0021] Figure 3 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0023] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0024] With the development of society, the impact of human activities on the natural environment often leads to some unpredictable emergencies. For example, in nature, there may be emergencies such as forest fires, volcanic eruptions or mudslides. In urban activities, there may be sudden traffic congestion or sudden explosion of electric vehicles. Therefore, it is particularly important to provide early warning and alarm based on emergencies in a timely and accurate manner.

[0025] Therefore, the embodiments of the present application provide an early warning method, device, equipment, medium and product for sudden incidents, which can obtain the potential risk results of the sudden incident based on the acquired data to be detected in the target area where the sudden incident occurs, analyze the data to be detected, and output early warning information. It can automatically warn of sudden incidents and improve the accuracy of sudden incident warnings.

[0026] like Figure 1 As shown, the embodiment of the present application provides an early warning method for an emergency event, comprising:

[0027] S110: Acquire the data to be detected in the target area where the emergency occurs.

[0028] Here, emergencies are major or sensitive events that occur unexpectedly and suddenly, in short, natural disasters or man-made disasters. Therefore, emergencies may include natural disasters, terrorist incidents or social conflicts.

[0029] The target area for an emergency event can be a certain range of areas that may be affected by the emergency event. For example, if a natural fire occurs in a forest, the target area can be locked on the area where the fire is burning and its surrounding areas.

[0030] The data to be detected may be data that can reflect the characteristics of an emergency, such as sensor data detected by different sensors, or remote sensing data obtained by satellite or drone photography, which is not limited here. Therefore, the above sensor data or remote sensing data can be obtained based on the Internet of Things or the Internet.

[0031] S120: Analyze the data to be detected to obtain potential risk results of the emergency.

[0032] After the data to be detected is obtained, the data to be detected may be analyzed. Here, different analysis methods may be used when analyzing the data to be detected.

[0033] The potential risk result may be information reflecting the impact of the emergency event, such as the disaster level of the emergency event, or the property loss caused.

[0034] In some examples, the detection data can be classified and analyzed based on the type of emergency event. For example, the types of emergencies include natural disasters, accidental disasters, public health incidents, and social security incidents. Therefore, different analysis models can be set based on the above four types of emergencies, and the analysis model corresponding to the emergency can be used to analyze the detection data to obtain the potential risk results of the emergency.

[0035] In some examples, the data to be detected can be analyzed based on the type of the data to be detected. For example, when the data to be detected includes two different types of sensor data and remote sensing data, a sensor data analysis model can be set up, which is mainly used to analyze the sensor data; at the same time, a remote sensing data analysis model can be set up, which is mainly used to analyze remote sensing data. The analysis results of the two analysis models can be used to obtain the potential risk results of the emergency.

[0036] S130: Outputting warning information based on the potential risk results, wherein the warning information is used to prompt the target area of ​​an emergency.

[0037] Based on the potential risk results obtained, warning information can be output. Here, the warning information can be a prompt information facing a common emergency event. Therefore, the warning information may include the issuing authority, issuing time, emergency event category, start time, possible impact range, warning level, warning matters, development of the situation, related measures, consultation telephone number, etc.

[0038] In this embodiment, by acquiring the data to be detected in the target area where the emergency occurs and analyzing it, the potential risk results of the emergency can be obtained. Furthermore, the warning information of the emergency is output, thereby realizing automatic warning of the emergency and improving the timeliness of the emergency warning. The above process does not require manual operation, thereby reducing the error of manual emergency warning and improving the accuracy of emergency warning.

[0039] In some embodiments, the data to be detected is analyzed to obtain potential risk results of the emergency event, including:

[0040] In the case where the data to be detected includes sensor data and / or remote sensing image data, the sensor data and / or remote sensing image data are analyzed to obtain potential risk results of the emergency.

[0041] The sensor data may include different types of sensor data, such as light sensor data, sound sensor data, gas sensor data, and chemical sensor data.

[0042] Here, the sensor data and / or remote sensing image data can be data detected in real time. Based on the real-time detected sensor data and / or remote sensing image data, the specific type of emergency event that is occurring can be detected, so that the potential risk results of the emergency event can be analyzed to obtain the potential risk results of the emergency event. Here, the potential risk results of the emergency event may be information such as the type of emergency event, the level of disaster, and the scope of impact.

[0043] Combining remote sensing image data and chemical sensors can detect the impact range and disaster level of sudden chemical substance leakage incidents. Combining remote sensing image data and acoustic sensors can obtain the impact range and disaster level of sudden crowd crowd incidents.

[0044] Here, when analyzing the sensor data, different thresholds may be pre-set, and the detected sensor data may be compared with the thresholds, and the emergency risk results may be obtained based on the comparison results.

[0045] In this embodiment, by analyzing the sensor data and / or remote sensing image data monitored in real time, the emergency can be detected in time, thereby improving the timeliness of the early warning of the emergency. In addition, the analysis of remote sensing data can also expand the detection area, thereby expanding the early warning capability.

[0046] In some embodiments, the data to be detected is analyzed to obtain potential risk results of the emergency event, including:

[0047] When the data to be detected includes sensor data and / or remote sensing image data and historical monitoring data of emergencies in the target area, the sensor data and / or remote sensing image data and historical monitoring data of emergencies are analyzed to obtain potential risk results of the emergencies.

[0048] When the data to be detected includes not only sensor data and / or remote sensing image data detected in real time, but also historical monitoring data of emergencies in the target area, emergencies that have not yet occurred can be detected through analysis of the above data to be detected. For example, the sensor data and / or remote sensing image data can be compared with the historical monitoring data of emergencies in the target area. When the two match, it can be predicted that an emergency may occur in the target area, thereby achieving early prediction of emergencies and obtaining potential risk results of emergencies. Here, the potential risk results can be the type of emergency that may occur or the level of disaster.

[0049] In this embodiment, by analyzing sensor data and / or remote sensing image data and historical monitoring data of emergencies in the target area, possible emergencies can be predicted. Therefore, the potential risk results obtained based on the above-mentioned data to be detected are conducive to taking corresponding measures in advance for impending emergencies and reducing the adverse effects of the emergencies.

[0050] In some embodiments, the data to be detected is analyzed to obtain potential risk results of the emergency event, including:

[0051] Use machine learning algorithms to analyze the test data and obtain the potential risk results of emergencies.

[0052] Machine learning algorithms can analyze the data to be tested based on training samples and obtain potential risk results of emergencies.

[0053] Here, the machine learning algorithm may include different types of sub-machine learning algorithms, such as decision trees, support vector machines, neural networks, etc., without specific limitation.

[0054] In some examples, different machine learning algorithms can be used for analysis based on the different types of emergencies. For example, accidents and disasters can be analyzed using decision trees, and natural disasters can be analyzed using neural networks.

[0055] In this embodiment, by using a machine learning algorithm to analyze the data to be detected, errors caused by manual analysis can be avoided, thereby improving the accuracy of emergency warnings.

[0056] In some embodiments, the machine learning algorithm includes a classification algorithm and a plurality of sub-machine learning algorithms;

[0057] The data to be tested is processed using machine learning algorithms to obtain potential risk results of emergencies, including:

[0058] The data to be detected are classified according to different types of emergencies using a classification algorithm to obtain different categories of data to be detected, wherein each category of the data to be detected has a preset corresponding relationship with the type of the emergencies.

[0059] When analyzing the acquired data to be detected, the classification algorithm in the machine learning algorithm can be used to classify the data to be detected according to the types of different emergencies. For example, it can be divided into four categories: natural disasters, accidental disasters, public health events, and social security events.

[0060] Natural disasters refer to natural phenomena that bring harm to human survival or damage the human living environment, including drought, high temperature, low temperature, cold waves, floods, etc.

[0061] Accidents and disasters are unexpected events that occur in the process of people's production and life, are directly caused by people's production and life activities, violate people's will, force activities to stop temporarily or permanently, and cause a large number of casualties, economic losses or environmental pollution.

[0062] A public health incident refers to a sudden outbreak of a major infectious disease, a mass outbreak of unexplained disease, a major food and occupational poisoning, or other incidents that seriously affect public health and cause or may cause serious damage to the health of the general public.

[0063] Social security incidents refer to mass incidents that are caused by internal conflicts among the people and have a certain organization and purpose, and which take actions such as besieging party and government organs, sit-in petitions, blocking traffic, etc., and have an impact on government management and social order.

[0064] In some examples, after the classification algorithm has divided the data to be detected into four categories, each type of emergency can be further subdivided. For example, natural disasters can be further divided into fires, floods, rainstorms and other disasters; accident disasters can be further divided into traffic accidents, factory explosions, etc., which will not be repeated here.

[0065] Based on the type of emergency, each category of data to be detected is processed using the sub-machine learning algorithm corresponding to the type of emergency to obtain the potential risk results of the emergency.

[0066] After classifying the data to be detected, it can be processed based on the types of different emergencies using sub-machine learning algorithms corresponding to the types of emergencies. Here, based on the characteristics of different sub-machine learning algorithms, different sub-machine learning algorithms can be set to process specific types of data to be detected to obtain potential risk results of emergencies.

[0067] For example, a decision tree is a method of analyzing the data to be tested and judging the feasibility of different decisions by constructing a decision tree based on the known probabilities of various situations. Therefore, a decision tree can be used to process the data to be tested corresponding to different types of known natural disasters to obtain the potential risk results of emergencies.

[0068] Neural network is a mathematical model that imitates the behavior characteristics of animal neural networks and performs distributed parallel information processing. It is highly complex and can process information by adjusting the interconnected relationships between a large number of internal nodes. It has the ability of self-learning and self-adaptation. Therefore, neural networks can be used to process the data to be detected corresponding to more complex accidents, disasters, or public health incidents.

[0069] It can be imagined that the above-mentioned sub-machine learning algorithm for processing the data to be detected corresponding to different types of emergencies is only exemplary. In actual applications, the data to be detected corresponding to the above-mentioned different types of emergencies can also be processed based on other sub-machine learning algorithms.

[0070] In this embodiment, by setting a classification model in the machine learning algorithm to classify the data to be detected according to different emergency types, massive amounts of data to be detected can be classified in order according to the emergency types, and sub-machine learning algorithms corresponding to different event types can be used to process the data to be detected of different emergency types, which can improve the accuracy of emergency warning.

[0071] In some embodiments, after outputting warning information based on the potential risk result, the method further includes:

[0072] Use the integrated media platform to release early warning information.

[0073] After receiving the early warning information, in order to inform the public in time and reduce the adverse effects of the emergency, we can choose to use the integrated media platform to release the early warning information. Here, the integrated media platform is a platform that makes full use of advanced technologies such as cloud computing, big data, and artificial intelligence to unify different media such as radio, television, and newspapers to achieve joint publicity.

[0074] Therefore, releasing early warning information through the integrated media platform can maximize the media's propaganda role, quickly disseminate early warning information, improve information coverage and dissemination effects, and provide important technical support for disaster response and emergency rescue.

[0075] In some embodiments, after acquiring the data to be detected in the target area where the emergency occurs, the method further includes:

[0076] Preprocessing the data to be detected to obtain processed data to be detected;

[0077] Analyze the data to be tested to obtain the potential risk results of the emergency, including:

[0078] The processed data to be tested are analyzed to obtain the potential risk results of the emergency.

[0079] Since the data to be detected may be of different types, it is necessary to preprocess the data to be detected, and different types of data to be detected may be preprocessed in different ways.

[0080] In some examples, when preprocessing sensor data, smoothing filtering may be used for denoising. Also, since there are many types of sensors and the same type of sensor may have different models, it may be necessary to convert the format of some sensor data.

[0081] In some examples, filtering and denoising can also be performed when preprocessing remote sensor data. At the same time, atmospheric correction can be used to eliminate radiation errors caused by clouds, haze, etc.

[0082] In some examples, the remote sensing data preprocessing method may also include at least one of the following processing methods: image registration, atmospheric correction, image fusion, mosaicking, and cropping, which will not be described in detail here.

[0083] In this embodiment, by preprocessing the data to be detected, it is possible to facilitate subsequent analysis of the data to be detected, thereby obtaining potential risk results of emergencies and improving the accuracy of emergency warnings.

[0084] like Figure 2 As shown, the embodiment of the present application provides an early warning device for an emergency, including:

[0085] Acquisition module 201, for acquiring the target area where the emergency occurs to be detected data.

[0086] The analysis module 202 is used to analyze the data to be detected to obtain the potential risk results of the emergency.

[0087] The early warning module 203 is used to output early warning information based on the potential risk results, wherein the early warning information is used to prompt the target area of ​​an emergency.

[0088] In some embodiments, the analysis module 202 may be specifically used to:

[0089] In the case where the data to be detected includes sensor data and / or remote sensing image data, the sensor data and / or remote sensing image data are analyzed to obtain potential risk results of the emergency.

[0090] In some embodiments, the analysis module 202 may also be used to:

[0091] When the data to be detected includes sensor data and / or remote sensing image data and historical monitoring data of emergencies in the target area, the sensor data and / or remote sensing image data and historical monitoring data of emergencies are analyzed to obtain potential risk results of the emergencies.

[0092] In some embodiments, the analysis module 202 may also be used to:

[0093] Use machine learning algorithms to analyze the test data and obtain the potential risk results of emergencies.

[0094] In some embodiments, the machine learning algorithm includes a classification algorithm and a plurality of sub-machine learning algorithms;

[0095] The analysis module 202 may also be used to:

[0096] The data to be detected are classified according to different types of emergencies using a classification algorithm to obtain different types of data to be detected, wherein each type of data to be detected has a preset corresponding relationship with the type of the emergencies;

[0097] Based on the type of emergency, each category of data to be detected is processed using the sub-machine learning algorithm corresponding to the type of emergency to obtain the potential risk results of the emergency.

[0098] In some embodiments, the apparatus further comprises:

[0099] The release module is used to release early warning information using the integrated media platform.

[0100] In some embodiments, the apparatus further comprises:

[0101] The processing module is used to pre-process the data to be detected to obtain processed data to be detected.

[0102] The analysis module 202 can also be used to analyze the processed data to be detected to obtain potential risk results of emergencies.

[0103] The device of the above embodiment is used to implement the corresponding emergency warning method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0104] Figure 3A schematic diagram of the hardware structure of an electronic device provided in an application embodiment.

[0105] The electronic device 300 may include a processor 301 and a memory 302 storing computer program instructions.

[0106] Specifically, the processor 301 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0107] The memory 302 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 302 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 302 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 302 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 302 is a non-volatile solid-state memory.

[0108] In certain embodiments, memory 302 includes a read-only memory (ROM). The ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of the above, where appropriate.

[0109] The memory may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, typically, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to the first aspect of the present application.

[0110] The processor 301 reads and executes the computer program instructions stored in the memory 302 to implement any one of the emergency warning methods in the above embodiments.

[0111] In one example, the electronic device may further include a communication interface 303 and a bus 310. Figure 3The processor 301, the memory 302, and the communication interface 303 are connected via a bus 310 and communicate with each other.

[0112] The communication interface 303 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0113] Bus 310 includes hardware, software or both, and the parts of online data flow billing equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-end bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 310 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0114] The electronic device of the above embodiment is used to implement the corresponding emergency warning method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0115] In addition, in combination with the early warning method for emergencies in the above embodiments, the embodiments of the present application may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any of the early warning methods for emergencies in the above embodiments is implemented.

[0116] In addition, in combination with the emergency warning method in the above embodiment, the present application embodiment can provide a computer program product to implement. When the computer program product instructions are executed by a processor of an electronic device, any emergency warning method in the above embodiment is implemented.

[0117] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0118] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0119] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or devices based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0120] The above reference is according to the method of the embodiment of the present application, the flow chart of the device (device) and the computer program product and / or the block diagram described various aspects of the present application.It should be understood that each square box in the flow chart and / or the block diagram and the combination of each square box in the flow chart and / or the block diagram can be realized by computer program instructions.These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the realization of the function / action specified in one or more square boxes of the flow chart and / or the block diagram.Such a processor can be but is not limited to a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit.It can also be understood that each square box in the block diagram and / or the flow chart and the combination of the square boxes in the block diagram and / or the flow chart can also be realized by the dedicated hardware that performs the specified function or action, or can be realized by the combination of dedicated hardware and computer instructions.

[0121] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described devices, modules and units can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application.

Claims

1. A method for early warning of emergencies, characterized in that: include: Acquire the data to be detected in the target area where the emergency occurs; Analyze the data to be detected to obtain the potential risk results of the emergency; Based on the potential risk result, warning information is output, wherein the warning information is used to prompt the occurrence of an emergency in the target area.

2. The method for early warning of an emergency according to claim 1, characterized in that: The analyzing the data to be detected to obtain the potential risk result of the emergency event includes: In the case where the data to be detected includes sensor data and / or remote sensing image data, the sensor data and / or remote sensing image data are analyzed to obtain the potential risk result of the emergency.

3. The method for early warning of an emergency according to claim 1, characterized in that: The analyzing the data to be detected to obtain the potential risk result of the emergency event includes: In the case where the data to be detected includes the sensor data and / or remote sensing image data and the historical monitoring data of emergencies in the target area, the sensor data and / or remote sensing image data and the historical monitoring data of emergencies are analyzed to obtain the potential risk results of the emergencies.

4. The method for early warning of an emergency according to claim 1, characterized in that: The analyzing the data to be detected to obtain the potential risk result of the emergency event includes: The machine learning algorithm is used to analyze the data to be detected to obtain the potential risk results of the emergency.

5. The method for early warning of an emergency according to claim 4, characterized in that: The machine learning algorithm includes a classification algorithm and a plurality of sub-machine learning algorithms; The method of processing the data to be detected by using a machine learning algorithm to obtain a potential risk result of the emergency event includes: Using the classification algorithm to classify the data to be detected according to different types of emergencies, different categories of data to be detected are obtained, wherein each category of the data to be detected has a preset corresponding relationship with the type of the emergencies; Based on the type of the emergency event, each category of the data to be detected is processed using the sub-machine learning algorithm corresponding to the type of the emergency event to obtain the potential risk results of the emergency event.

6. The method for early warning of an emergency according to claim 1, characterized in that: After outputting warning information based on the potential risk result, the method further includes: Use the integrated media platform to publish the above-mentioned warning information.

7. The method for early warning of an emergency according to claim 1, characterized in that: After acquiring the data to be detected in the target area where the emergency occurs, the method further includes: Preprocessing the data to be detected to obtain processed data to be detected; The analyzing the data to be detected to obtain the potential risk result of the emergency event includes: The processed data to be detected are analyzed to obtain the potential risk results of the emergency event.

8. An early warning device for emergencies, characterized in that: include: An acquisition module, used to acquire the data to be detected in the target area where the emergency occurs; An analysis module is used to analyze the data to be detected to obtain the potential risk results of the emergency; The early warning module is used to output early warning information based on the potential risk results, wherein the early warning information is used to prompt the occurrence of an emergency in the target area.

9. An electronic device, characterized in that: The device comprises: a processor, and a memory storing computer program instructions; The processor reads and executes the computer program instructions to implement the emergency warning method according to any one of claims 1 to 7.

10. A readable storage medium, characterized in that: The readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method for early warning of an emergency event as claimed in any one of claims 1 to 7 is implemented.

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