Method and system for processing rainstorm disaster data

Abstract

The invention relates to the technical field of meteorological disaster prediction, and provides a rainstorm disaster data processing method and system, and the method comprises the steps: collecting rainstorm disaster data from a plurality of data channels; performing data evaluation on the rainstorm disaster data and screening the data accuracy of the plurality of data channels; performing data cleaning and data fusion on the rainstorm disaster data; and constructing a rainstorm disaster data resource library according to the rainstorm disaster data. According to the method, the rainstorm disaster data of multiple data channels can be preprocessed, cleaned and fused to form a standard and normative rainstorm disaster resource library, so that the rainstorm disaster data can be conveniently inquired and called, and the accuracy of automatic identification of the rainstorm disaster can be effectively improved.

Description

Technical Field

[0001] The present invention generally relates to the technical field of meteorological disaster prediction, and in particular to a method and system for processing rainstorm disaster data. Background Art

[0003] Heavy rain is one of the most serious and common meteorological disasters, which can easily cause floods and urban waterlogging, leading to casualties and significant economic losses. Analyzing and processing various rainstorm disaster data is an important part of achieving accurate automatic identification of rainstorm disasters.

[0004] However, in the traditional processing of rainstorm disaster data, there are still great gaps and deficiencies in the integrated analysis of rainstorm disaster data. Summary of the invention

[0006] In order to at least partially solve the above problems in the prior art, the present invention proposes a rainstorm disaster data processing method, comprising the following steps: Collect rainstorm disaster data from multiple data channels; Performing data evaluation on the rainstorm disaster data and screening the data accuracy of the multiple data channels; Performing data cleaning and data fusion on the rainstorm disaster data; and A rainstorm disaster data resource library is constructed according to the rainstorm disaster data.

[0007] In one embodiment of the present invention, it is provided that the rainstorm disaster data includes: Heavy rain disaster metadata, including heavy rain disaster event metadata and heavy rain disaster process metadata; Heavy rain disaster monitoring data, including basic meteorological data, ground monitoring data, high-altitude monitoring data, radar monitoring data and satellite monitoring data; Heavy rain forecast service data, including heavy rain disaster meteorological intelligent grid data, heavy rain disaster numerical forecast data, heavy rain disaster weather forecast and warning data.

[0008] The rainstorm disaster data includes the source, time, location, impact, characteristics and auxiliary information of the rainstorm disaster; and Heavy rain thematic product data includes historical time series data of heavy rain disasters, real-time dynamic data of heavy rain disasters, and analysis data on the impact of heavy rain disasters.

[0009] In one embodiment of the present invention, it is stipulated that the data channels include: internal data of the meteorological department, global disaster platform data, emergency management department data, Internet data and social and economic data.

[0010] In one embodiment of the present invention, it is provided that the data evaluation of the rainstorm disaster data includes calculating the evaluation score of the rainstorm disaster data, which is expressed as the following formula:

[0011] in, represents the number of evaluation indicators, Represent the weight of each evaluation index, Respectively represent the evaluation scores of various evaluation indicators, where the evaluation indicators include data source, data timeliness, data audit degree and data utilization rate.

[0012] In one embodiment of the present invention, screening the data accuracy of the multiple data channels includes: by The number of data channels, Respectively represent the accuracy probability of data from each data channel, where express The probability of data accuracy for each data channel; Calculate the The probability of data accuracy for each data channel , expressed as the following formula:

[0013] in, Represents the output of a linear regression model.

[0014] In one embodiment of the present invention, data cleaning of the rainstorm disaster data includes: Perform duplicate data cleaning, in which duplicate detection is performed by pre-defining duplicate identifiers of rainstorm disaster data; Perform incomplete data cleaning, wherein key fields of rainstorm disaster data are set, rainstorm disaster data with incomplete records and no key fields are deleted, and fields are filled and retained for rainstorm disaster data with incomplete records and key fields; Perform data format cleaning, including cleaning the rainstorm disaster data with incorrect time, date, and numerical display, or unreasonable characters; Perform erroneous data cleaning, which uses statistical analysis methods to identify erroneous values ​​or outliers in the data, or uses a rule base to check outliers in the data, or uses constraints between different attributes or uses external data to detect and clean erroneous data.

[0015] In one embodiment of the present invention, the data fusion includes fusing the rainstorm disaster data of multiple data channels through data layer fusion, feature layer fusion or decision layer fusion, which includes the following steps: Providing attribute fields of rainstorm disaster meteorological data, wherein the attribute fields include death toll, disaster-affected population, direct economic losses, collapsed houses, and disaster-affected crop area; and The similarity between the attribute fields of the rainstorm disaster data from multiple data channels is compared through the similarity matching algorithm, and the rainstorm disaster data with high similarity are fused. The fusion process is expressed as follows:

[0016] in, Indicates the similarity between the rainstorm disaster data of channel A and the rainstorm disaster data of channel B. It represents the number of intersection elements between the rainstorm disaster data of channel A and the rainstorm disaster data of channel B. Represents the number of elements in the union of the rainstorm disaster data of channel A and the rainstorm disaster data of channel B.

[0017] The present invention also provides a rainstorm disaster data processing system, which comprises: A data acquisition module, which is configured to collect rainstorm disaster data from multiple data channels; A data preprocessing module, configured to perform data evaluation on the rainstorm disaster data and screen the data accuracy of the multiple data channels; A data cleaning and fusion module, which is configured to perform data cleaning and data fusion on the rainstorm disaster data; and A rainstorm disaster data resource library is configured to store the rainstorm disaster data and provide a query directory.

[0018] The present invention also provides a computer-readable storage medium having machine-readable instructions stored thereon. When the machine-readable instructions are executed by a processor, the steps according to the method are executed.

[0019] The present invention also provides a computer system, comprising: a processor configured to execute machine-readable instructions; and A memory storing machine-readable instructions which, when executed by a processor, perform the steps according to the method.

[0020] The present invention has at least the following beneficial effects: the present invention proposes a method and system for processing rainstorm disaster data, which can pre-process, clean and fuse rainstorm disaster data from multiple data channels to form a standard rainstorm disaster resource library, which is convenient for querying and calling rainstorm disaster data, and can effectively improve the accuracy of automatic identification of rainstorm disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To further illustrate the advantages and features of various embodiments of the present invention, a more detailed description of various embodiments of the present invention will be presented with reference to the accompanying drawings. It will be understood that these drawings only depict typical embodiments of the present invention and are therefore not to be considered as limiting the scope thereof. In the accompanying drawings, for clarity, identical or corresponding parts will be represented by identical or similar reference numerals.

[0023] Figure 1 A computer system is shown that implements the system and / or method according to the present invention.

[0024] Figure 2 A schematic flow chart of a method for processing rainstorm disaster data in an embodiment of the present invention is shown.

[0025] Figure 3 A schematic diagram of the structure of a rainstorm disaster data resource library in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0027] It should be noted that the components in the various figures may be shown exaggeratedly for the sake of illustration and are not necessarily true to scale. In the various figures, identical or functionally identical components are provided with the same reference numerals.

[0028] In the present invention, unless otherwise specified, "arranged on...", "arranged above..." and "arranged above..." do not exclude the existence of an intermediate between the two. In addition, "arranged on or above..." merely indicates the relative positional relationship between two components, and in certain cases, such as after reversing the product direction, it can also be converted into "arranged below or below...", and vice versa.

[0029] In the present invention, each embodiment is only intended to illustrate the aspects of the present invention and should not be construed as limiting.

[0030] In the present invention, unless otherwise specified, the quantifiers "a", "an" and "an" do not exclude the presence of a plurality of elements.

[0031] It should also be noted that in the embodiments of the present invention, for the sake of clarity and simplicity, only a portion of the parts or components may be shown, but those of ordinary skill in the art will understand that under the teachings of the present invention, the required parts or components may be added according to the needs of the specific scenario. In addition, unless otherwise specified, the features in different embodiments of the present invention may be combined with each other. For example, a feature in the second embodiment may be used to replace a corresponding or functionally identical or similar feature in the first embodiment, and the resulting embodiment also falls within the disclosure scope or recorded scope of the present application.

[0032] It should also be noted that within the scope of the present invention, the terms "same", "equal", "equal to" and the like do not mean that the values ​​of the two are absolutely equal, but allow a certain reasonable error, that is, the terms also cover "substantially the same", "substantially equal", "substantially equal to". By analogy, in the present invention, the terms "perpendicular to", "parallel to" and the like indicating directions also cover the meanings of "substantially perpendicular to" and "substantially parallel to".

[0033] In addition, the numbering of the steps of the methods of the present invention does not limit the execution order of the method steps. Unless otherwise specified, the method steps can be executed in different orders.

[0034] The present invention will be further described below in conjunction with specific embodiments with reference to the accompanying drawings.

[0035] Figure 1 FIG. 1 shows a computer system 100 for implementing the system and / or method according to the present invention. Unless otherwise specified, the method and / or system according to the present invention may be implemented in Figure 1 The present invention may be implemented in the computer system 100 shown in the figure to achieve the purpose of the present invention, or the present invention may be implemented in a distributed manner in multiple computer systems 100 according to the present invention through a network, such as a local area network or the Internet. The computer system 100 of the present invention may include various types of computer systems, such as handheld devices, laptop computers, personal digital assistants (PDAs), multiprocessor systems, microprocessor-based or programmable consumer electronic devices, network PCs, minicomputers, mainframes, network servers, tablet computers, etc.

[0036] like Figure 1 As shown, the computer system 100 includes a processor 111, a system bus 102, a system memory 102, a video adapter 105, an audio adapter 107, a hard disk drive interface 109, an optical drive interface 113, a network interface 114, and a universal serial bus (USB) interface 112. The system bus 101 can be any of several types of bus structures, such as a memory bus or a memory controller, a peripheral bus, and a local bus using various types of bus architectures. The system bus 101 is used for communication between various bus devices. In addition to Figure 1In addition to the bus devices or interfaces shown in , other bus devices or interfaces are also conceivable. The system memory 102 includes a read-only memory (ROM) 103 and a random access memory (RAM) 104, wherein the ROM 103 can, for example, store basic input / output system (BIOS) data for implementing basic routines for information transmission at startup, and the RAM 104 is used to provide the system with a running memory with a faster access speed. The computer system 100 also includes a hard disk drive 109 for reading and writing to the hard disk 110, an optical drive interface 113 for reading and writing to optical media such as CD-ROMs, and the like. The hard disk 110 can, for example, store an operating system and application programs. The drive and its associated computer-readable medium provide the computer system 100 with non-volatile storage of computer-readable instructions, data structures, program modules, and other data. The computer system 100 can also include a video adapter 105 for image processing and / or image output, which is used to connect output devices such as a display 106. The computer system 100 may further include an audio adapter 107 for audio processing and / or audio output, which is used to connect output devices such as speakers 108. In addition, the computer system 100 may further include a network interface 114 for network connection, wherein the network interface 114 may be connected to the Internet 116 through a network device such as a router 115, wherein the connection may be wired or wireless. In addition, the computer system 100 may further include a universal serial bus interface (USB) 112 for connecting peripheral devices, wherein the peripheral devices include, for example, a keyboard 117, a mouse 118, and other peripheral devices, such as a microphone, a camera, etc.

[0037] When the present invention Figure 1 When implemented on the computer system 100, the rainstorm disaster data from multiple data channels can be preprocessed, cleaned, and integrated to form a standard rainstorm disaster resource library, which is convenient for querying and calling rainstorm disaster data, thereby effectively improving the accuracy of automatic identification of rainstorm disasters.

[0038] Furthermore, the embodiments may be provided as a computer program product that may include one or more machine-readable media having machine-executable instructions stored thereon, which, when executed by one or more machines such as a computer, a computer network or other electronic device, may cause the one or more machines to perform operations according to the embodiments of the present invention. The machine-readable medium may include, but is not limited to, a floppy disk, an optical disk, a CD-ROM (compact disk read-only memory) and a magneto-optical disk, a ROM (read-only memory), a RAM (random access memory), an EPROM (erasable programmable read-only memory), an EEPROM (electrically erasable programmable read-only memory), a magnetic or optical card, a flash memory, or other types of media / machine-readable media suitable for storing machine-executable instructions.

[0039] In addition, various embodiments may be downloaded as a computer program product, wherein the program may be transmitted from a remote computer (e.g., a server) to a requesting computer (e.g., a client) via a communication link (e.g., a modem and / or a network connection) using one or more data signals implemented and / or modulated by a carrier wave or other propagation medium. Thus, a machine-readable medium as used herein may include such a carrier wave, but this is not required.

[0040] In the present invention, each module of the system according to the present invention can be implemented using software, hardware, firmware or a combination thereof. When the module is implemented using software, the function of the module can be implemented through a computer program flow, for example, the module can be implemented by a code segment (such as a code segment of a language such as C, C++, etc.) stored in a storage device (such as a hard disk, memory, etc.), wherein when the code segment is executed by a processor, the corresponding function of the module can be implemented. When the module is implemented using hardware, the function of the module can be implemented by setting a corresponding hardware structure, for example, the function of the module can be implemented by hardware programming a programmable device such as a field programmable gate array (FPGA), or by designing an application-specific integrated circuit (ASIC) including electronic devices such as multiple transistors, resistors and capacitors to implement the function of the module. When the module is implemented using firmware, the function of the module can be written into a read-only memory such as an EPROM or EEPROM of the device in the form of a program code, and when the program code is executed by a processor, the corresponding function of the module can be implemented. In addition, some functions of the module may need to be implemented by separate hardware or by collaboration with the hardware, for example, the detection function is implemented by corresponding sensors (such as proximity sensors, acceleration sensors, gyroscopes, etc.), the signal transmission function is implemented by corresponding communication devices (such as Bluetooth devices, infrared communication devices, baseband communication devices, Wi-Fi communication devices, etc.), the output function is implemented by corresponding output devices (such as displays, speakers, etc.), and so on.

[0041] Figure 2 FIG. 2 is a flow chart of a method for processing rainstorm disaster data in one embodiment of the present invention. Figure 2 As shown, the method may include the following steps: Step 201: Collect rainstorm disaster data from multiple data channels.

[0042] Step 202: perform data evaluation on the rainstorm disaster data and screen the data accuracy of the multiple data channels.

[0043] Step 203: performing data cleaning and data fusion on the rainstorm disaster data.

[0044] Step 204: construct a rainstorm disaster data resource library based on the rainstorm disaster data.

[0045] The method first collects rainstorm disaster data from multiple channels, and the collection channels of the rainstorm disaster data can include internal data of the meteorological department, global disaster data, emergency management department data, and Internet rainstorm disaster data. Furthermore, the rainstorm disaster data can be cleaned, quality controlled, and fused to form a rainstorm disaster data resource library, which can include rainstorm disaster metadata, rainstorm disaster monitoring data, rainstorm disaster forecast service data, socio-economic data, and rainstorm disaster data. Based on the above data, a complete meteorological disaster event information chain can be formed, which can provide rich data support for automatic identification of rainstorm disasters and effectively improve the accuracy of automatic identification of rainstorm disasters.

[0046] Each step of the method is described in detail below with reference to embodiments.

[0047] In this method, it is first necessary to collect a variety of rainstorm disaster resource data, including rainstorm disaster metadata, rainstorm disaster monitoring data, rainstorm forecast service data, rainstorm disaster data, social and economic data, and rainstorm special product data.

[0048] The rainstorm disaster metadata includes rainstorm disaster event metadata and rainstorm disaster process metadata. The rainstorm disaster monitoring data includes meteorological basic data, ground monitoring data, high-altitude monitoring data, radar monitoring data and satellite monitoring data of rainstorm disasters. The rainstorm forecast service data includes rainstorm disaster meteorological intelligent grid data, rainstorm disaster numerical forecast data, rainstorm disaster weather forecast and warning data. The rainstorm disaster data includes the source, time, location, impact, characteristics and auxiliary information of rainstorm disasters. The rainstorm thematic product data includes rainstorm disaster time history series data, real-time dynamic data and impact analysis data.

[0049] Furthermore, based on machine learning and artificial intelligence, data evaluation and data accuracy screening are performed on the rainstorm disaster resource data.

[0050] The data evaluation of the rainstorm disaster resource data includes calculating the evaluation score of the rainstorm disaster resource data, which is expressed as the following formula:

[0051] in, represents the number of evaluation indicators, Represent the weight of each evaluation index, Respectively represent the evaluation scores of various evaluation indicators, where the evaluation indicators include data source, data timeliness, data audit degree and data utilization rate.

[0052] Screening the data accuracy of the rainstorm disaster resource data includes: by Indicates the number of source channels for rainstorm disaster data. Respectively represent the accuracy probability of data from each source channel, where Indicates The probability of data accuracy from each source channel; Calculate the The probability of data accuracy from each source channel , expressed as the following formula:

[0053] in, Represents the output of the linear regression model. The source channels of rainstorm disaster data can include internal data of the meteorological department, global disaster platform data, rainstorm disaster data of the Ministry of Emergency Management, rainstorm disaster data on the Internet, and socio-economic data, etc.

[0054] Furthermore, the rainstorm disaster data may be cleaned.

[0055] The data cleaning of the rainstorm disaster data includes: Duplicate data cleaning is performed, wherein duplicate detection can be performed by pre-defining duplicate identifiers of heavy rain disaster data, thereby reducing redundant information in heavy rain disaster data.

[0056] Incomplete data cleaning is performed, wherein key fields of the rainstorm disaster data can be set, the rainstorm disaster data with incomplete records and no key fields can be deleted, and the rainstorm disaster data with incomplete records and key fields can be filled in and retained.

[0057] Perform data format cleaning, which can clean data that does not conform to the format and content of the regulations. For example, data with incorrect time, date, and numerical display, or with unreasonable characters can be cleaned.

[0058] Perform erroneous data cleaning, where statistical analysis methods can be used to identify erroneous values ​​or outliers in the data. For example, deviation analysis methods can be used to identify values ​​in the data that do not conform to the distribution or regression equation. Rule libraries can also be used to check outliers in the data. Alternatively, constraints between different attributes or external data can be used to detect and clean erroneous data.

[0059] Furthermore, for the rainstorm disaster data from multiple channels, it is necessary to verify their relevance. If contradictions are found between the related data during the data analysis process, the related data needs to be adjusted or removed to make the related data consistent. The cross-check accuracy can be calculated by the following formula, which is expressed as follows:

[0060] in, Indicates the number of true and accurate samples, represents the number of true error samples, Indicates the actual number of samples that need to be cleaned, Indicates the number of error samples that need to be cleaned.

[0061] Furthermore, the rainstorm disaster data from multiple channels can be integrated, including the internal data of the meteorological department, the global disaster platform data, the emergency management department data, the Internet rainstorm disaster data and the social and economic data. The fusion method can be data layer fusion, feature layer fusion or decision layer fusion. Through data fusion, the comprehensive use value of rainstorm disaster meteorological data can be improved. The data fusion process can include the following steps: Provides attribute fields of rainstorm disaster meteorological data, which may include death toll, disaster-affected population, direct economic losses, collapsed houses, disaster-affected area of ​​crops, etc.

[0062] The similarity between the attribute fields of the rainstorm disaster data from multiple channels is compared through the similarity matching algorithm, and the rainstorm disaster data with high similarity are fused. The fusion process can be expressed as follows:

[0063] in, Indicates the similarity between the rainstorm disaster data of channel A and the rainstorm disaster data of channel B. It represents the number of intersection elements between the rainstorm disaster data of channel A and the rainstorm disaster data of channel B. Represents the number of elements in the union of the rainstorm disaster data of channel A and the rainstorm disaster data of channel B.

[0064] Furthermore, a rainstorm disaster data resource library can be generated based on the rainstorm disaster data after data processing, data cleaning and data fusion. Figure 3The schematic diagram of the structure of a rainstorm disaster data resource library in one embodiment of the present invention is shown. The rainstorm disaster data resource library may include a rainstorm disaster metadata resource library, a rainstorm disaster monitoring data resource library, a rainstorm forecast service product resource library, a rainstorm disaster situation resource library, a social and economic database, and a rainstorm special product resource library. The resource library can provide a standard rainstorm disaster resource library directory to facilitate the query and call of rainstorm disaster data, thereby effectively improving the accuracy of automatic identification of rainstorm disasters.

[0065] In one embodiment of the present invention, a rainstorm disaster data processing system is also provided, comprising: A data acquisition module, which is configured to collect rainstorm disaster data from multiple data channels; A data preprocessing module, configured to perform data evaluation on the rainstorm disaster data and screen the data accuracy of the multiple data channels; A data cleaning and fusion module, which is configured to perform data cleaning and data fusion on the rainstorm disaster data; and A rainstorm disaster data resource library is configured to store the rainstorm disaster data and provide a query directory.

[0066] Although various embodiments of the present invention are described above, it should be understood that they are presented as examples only and not as limitations. It is obvious to those skilled in the relevant art that various combinations, modifications and changes can be made thereto without departing from the spirit and scope of the present invention. Therefore, the breadth and scope of the present invention disclosed herein should not be limited by the exemplary embodiments disclosed above, but should only be defined according to the attached claims and their equivalents.

Claims

1. A method for processing rainstorm disaster data, characterized in that: The following steps are involved: Collect rainstorm disaster data from multiple data channels; Performing data evaluation on the rainstorm disaster data and screening the data accuracy of the multiple data channels; Performing data cleaning and data fusion on the rainstorm disaster data; as well as A rainstorm disaster data resource library is constructed according to the rainstorm disaster data.

2. The method for processing rainstorm disaster data according to claim 1, characterized in that: The rainstorm disaster data include: Heavy rain disaster metadata, including heavy rain disaster event metadata and heavy rain disaster process metadata; Heavy rain disaster monitoring data, including basic meteorological data, ground monitoring data, high-altitude monitoring data, radar monitoring data and satellite monitoring data; Heavy rain forecast service data, including heavy rain disaster meteorological intelligent grid data, heavy rain disaster numerical forecast data, heavy rain disaster weather forecast and warning data; heavy rain disaster data, including the source, time, location, impact, characteristics and auxiliary information of heavy rain disasters; and Heavy rain thematic product data includes historical time series data of heavy rain disasters, real-time dynamic data of heavy rain disasters, and analysis data on the impact of heavy rain disasters.

3. The method for processing rainstorm disaster data according to claim 2, characterized in that: The data channels include: internal data of meteorological departments, global disaster platform data, emergency management department data, Internet data and socio-economic data.

4. The method for processing rainstorm disaster data according to claim 3, characterized in that: The data evaluation of the rainstorm disaster data includes calculating the evaluation score of the rainstorm disaster data, which is expressed as the following formula: in, represents the number of evaluation indicators, Represent the weight of each evaluation index, Respectively represent the evaluation scores of various evaluation indicators, where the evaluation indicators include data source, data timeliness, data audit degree and data utilization rate.

5. The method for processing rainstorm disaster data according to claim 4, characterized in that: Screening the data accuracy of the multiple data channels includes: by The number of data channels, Respectively represent the accuracy probability of data from each data channel, where Indicates The probability of data accuracy for each data channel; Calculate the The probability of data accuracy for each data channel , expressed as the following formula: in, Represents the output of a linear regression model.

6. The method for processing rainstorm disaster data according to claim 5, characterized in that: Data cleaning of the rainstorm disaster data includes: Perform duplicate data cleaning, in which duplicate detection is performed by pre-defining duplicate identifiers of rainstorm disaster data; Perform incomplete data cleaning, wherein key fields of rainstorm disaster data are set, rainstorm disaster data with incomplete records and no key fields are deleted, and fields are filled and retained for rainstorm disaster data with incomplete records and key fields; Perform data format cleaning, including cleaning the rainstorm disaster data with incorrect time, date, and numerical display, or unreasonable characters; Perform erroneous data cleaning, which uses statistical analysis methods to identify erroneous values ​​or outliers in the data, or uses a rule base to check outliers in the data, or uses constraints between different attributes or uses external data to detect and clean erroneous data.

7. The method for processing rainstorm disaster data according to claim 6, characterized in that: The data fusion includes fusing the rainstorm disaster data from multiple data channels through data layer fusion, feature layer fusion or decision layer fusion, which includes the following steps: Providing attribute fields of rainstorm disaster meteorological data, the attribute fields include death toll, disaster-affected population, direct economic losses, collapsed houses, and disaster-affected area of ​​crops; as well as The similarity between the attribute fields of the rainstorm disaster data from multiple data channels is compared through the similarity matching algorithm, and the rainstorm disaster data with high similarity are fused. The fusion process is expressed as follows: in, Indicates the similarity between the rainstorm disaster data of channel A and the rainstorm disaster data of channel B. It represents the number of intersection elements between the rainstorm disaster data of channel A and the rainstorm disaster data of channel B. Represents the number of elements in the union of the rainstorm disaster data of channel A and the rainstorm disaster data of channel B.

8. A rainstorm disaster data processing system, characterized in that: include: A data acquisition module, which is configured to collect rainstorm disaster data from multiple data channels; A data preprocessing module, configured to perform data evaluation on the rainstorm disaster data and screen the data accuracy of the multiple data channels; A data cleaning and fusion module, which is configured to perform data cleaning and data fusion on the rainstorm disaster data; as well as A rainstorm disaster data resource library is configured to store the rainstorm disaster data and provide a query directory.

9. A computer-readable storage medium, characterized in that: Machine-readable instructions are stored thereon, and when the machine-readable instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are performed.

10. A computer system, characterized in that: include: a processor configured to execute machine-readable instructions; and A memory storing machine-readable instructions, which, when executed by a processor, perform the steps of the method according to any one of claims 1 to 7.

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