A water conservancy site early warning method, device and medium based on Internet of Things technology

Abstract

The present invention discloses a water conservancy site early warning method, equipment and medium based on the Internet of Things technology, which belongs to the field of water conservancy early warning technology and is used to solve the technical problems that the existing disaster early warning method between water conservancy sites has a large lag, is prone to consuming too much time cost and prediction accuracy, and the emergency dispatch timeliness of water conservancy disasters is poor. The method includes: based on the location of the water conservancy disaster, determining the first water conservancy monitoring site with the shortest geographical distance; performing an alarm analysis on the monitoring data in the first water conservancy monitoring site to obtain alarm data; performing an early warning analysis on the second water conservancy monitoring site based on the alarm data to obtain early warning data; performing a correlation analysis on the water conservancy disaster influencing factors between the alarm data and the early warning data to determine the water conservancy disaster forecast information; sending the water conservancy disaster forecast information to downstream water conservancy monitoring sites at all levels, so that each water conservancy monitoring site can formulate water conservancy disaster response measures.

Description

Technical Field

[0001] The present application relates to the field of water conservancy early warning, and in particular to a water conservancy site early warning method, equipment and medium based on Internet of Things technology. Background Art

[0002] With the development of information technology and the construction of water conservancy industry systems, the response mechanisms and measures for water disasters are becoming increasingly perfect, and the accuracy of hydrological forecast and early warning models is gradually improving. The early warning model for water disasters mainly involves water conservancy network models, spatial models, and mechanism models and critical values ​​of the correlation between hydrological elements.

[0003] The existing early warning methods for water disasters mainly rely on hydrological model forecasting and early warning methods. In some areas where it is difficult to establish hydrological forecast models, there is often a certain lag in the response measures and technical difficulties faced with water disasters. In addition, when emergency water disasters occur, the timeliness of flood peak predictions is not effective enough, and emergency dispatch solutions are relatively slow, which can easily lead to a large amount of early warning costs and a waste of a lot of response time. At the same time, water disasters will also cause certain economic losses. Summary of the invention

[0004] The embodiments of the present application provide a water conservancy site early warning method, equipment and medium based on Internet of Things technology, which are used to solve the following technical problems: the existing disaster early warning methods between water conservancy sites have a large lag, easily consume too much time cost and prediction accuracy, and the emergency dispatch timeliness of water conservancy disasters is poor.

[0005] The present application embodiment adopts the following technical solutions:

[0006] On the one hand, an embodiment of the present application provides a water conservancy site early warning method based on the Internet of Things technology, including: based on the location of the water conservancy disaster, determining the first water conservancy monitoring site with the shortest geographical distance; performing alarm analysis on the monitoring data in the first water conservancy monitoring site to obtain alarm data; based on the alarm data and based on the Internet of Things technology, performing early warning analysis on the second water conservancy monitoring site to obtain early warning data; wherein the second water conservancy monitoring site is a downstream water conservancy monitoring site of the first water conservancy monitoring site; performing a correlation analysis on the water conservancy disaster influencing factors between the alarm data and the early warning data to determine water conservancy disaster forecast information; and sending the water conservancy disaster forecast information to downstream water conservancy monitoring sites at all levels, so that each water conservancy monitoring site can formulate water conservancy disaster response measures.

[0007] The embodiment of the present application builds a forecast and early warning mechanism based on existing water conservancy monitoring stations and Internet of Things technology, which can save a lot of costs from the perspective of cost saving, thereby bringing greater economic value; from the perspective of results, this method can more effectively predict flood peaks, thereby improving the timeliness of emergency dispatch and minimizing the economic losses caused by water conservancy disasters.

[0008] In a feasible implementation, based on the location of the water disaster, the first water monitoring station with the shortest geographical distance is determined, specifically including: marking the source area of ​​the water disaster according to a preset GIS map and local meteorological information to obtain a number of marked areas; matching the coordinate positions of the several marked areas through a preset spatial distribution map of water monitoring stations to determine the location information of the water disaster; based on the coordinate information in the location information, calculating the water flow basin distance of each water monitoring station in the spatial distribution map of the water monitoring station to obtain the first water monitoring station with the shortest water flow basin distance.

[0009] In a feasible implementation manner, an alarm analysis is performed on the monitoring data in the first water conservancy monitoring site to obtain alarm data, specifically including: real-time data collection of water conservancy data is performed through multiple groups of water conservancy sensors in the first water conservancy monitoring site to obtain monitoring data; wherein the monitoring data includes at least: water level information, water quality information and unit water flow information; based on a first preset threshold, a threshold judgment is performed on the information parameters in the monitoring data; if the information parameter is less than the first preset threshold, the monitoring data at the current time node is determined as low-risk alarm data; if the information parameter is greater than or equal to the first preset threshold, the monitoring data at the current time node is determined as medium-risk alarm data; if the information parameter is much greater than the first preset threshold, the monitoring data at the current time node is determined as high-risk alarm data; wherein the alarm data includes: the low-risk alarm data, the medium-risk alarm data and the high-risk alarm data.

[0010] In a feasible implementation manner, according to the alarm data and based on the Internet of Things technology, an early warning analysis is performed on the second water conservancy monitoring site to obtain early warning data, specifically including: sending the alarm data to the second water conservancy monitoring site through the Internet of Things technology; performing water conservancy data analysis on the water flow basin distance between the second water conservancy monitoring site and the first water conservancy monitoring site through the spatial distribution map of the water conservancy monitoring site to obtain a gradient decreasing curve function graph; according to the gradient decreasing curve function graph, performing correlation matching on various data parameters in the alarm data to obtain an early warning monitoring curve function graph; extracting text content from the early warning monitoring curve function graph to obtain early warning monitoring data; performing early warning threshold judgment on the early warning monitoring data to determine the early warning data; wherein, the early warning data includes: low-risk early warning data, medium-risk early warning data and high-risk early warning data.

[0011] In a feasible implementation manner, the text content of the early warning monitoring curve function diagram is extracted to obtain early warning monitoring data, specifically including: obtaining the image width of the early warning monitoring curve function diagram, and determining the horizontal axis definition area corresponding to the early warning monitoring curve function according to the image width; wherein the horizontal axis definition area is the distance parameter of the water disaster in different water flow basins; within the horizontal axis definition area, obtaining the horizontal axis coordinate of the early warning monitoring curve function; based on the early warning monitoring curve function, determining the vertical axis coordinate corresponding to the horizontal axis coordinate; the vertical axis coordinate is the disaster intensity parameter of the water disaster; according to the horizontal axis coordinate and the coordinate position of the various data parameters determined by the vertical axis coordinate, and according to the preset step value, determining the specific data parameters within the horizontal axis definition area; performing data text selection conversion on the horizontal axis definition area and the specific data parameters to obtain text content data; performing data preprocessing on the text content data to obtain the early warning monitoring data.

[0012] In a feasible implementation manner, a correlation analysis of water disaster influencing factors is performed between the alarm data and the early warning data to determine water disaster forecast information, specifically including: extracting a first key value from the alarm data, and extracting a second key value from the early warning data; wherein the first key value and the second key value are both water disaster influencing factors at different water monitoring stations; wherein the water disaster influencing factors include at least: water level information, water quality information, local hydrological information, river basin spatial relationship and unit water flow information; through a preset correlation function, the first key value and the second key value are subjected to a correlation analysis to determine the association relationship at the current time node; according to the association relationship and based on the water flow basin distance between the second water monitoring station and the first water monitoring station in the spatial distribution map of the water monitoring stations, data mapping processing is performed between the alarm data and the early warning data, and water disaster forecast information between the first water monitoring station and the second water monitoring station is generated.

[0013] In a feasible implementation, the water disaster forecast information is sent to downstream water monitoring stations at all levels so that each water monitoring station can formulate water disaster response measures, specifically including: sending the water disaster forecast information to a third water monitoring station through the Internet of Things technology; wherein the third water monitoring station is a downstream water monitoring station of the second water monitoring station; based on the water disaster forecast information, generating early warning disaster information of the third water monitoring station, and sending the early warning disaster information to downstream water monitoring stations at all levels step by step; based on the early warning disaster information in each downstream water monitoring station, determining the water disaster response measures of each downstream water monitoring station.

[0014] In a feasible implementation, the information communication connection between each downstream water monitoring station is completed through the Internet of Things technology in the preset Internet of Things platform; wherein the information communication includes at least: Lora technology, Mesh technology and 5G technology.

[0015] In the second aspect, an embodiment of the present application also provides a water conservancy site early warning device based on Internet of Things technology, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, so that the at least one processor can execute a water conservancy site early warning method based on Internet of Things technology as described in any of the above embodiments.

[0016] In the third aspect, an embodiment of the present application also provides a non-volatile computer storage medium, characterized in that the storage medium is a non-volatile computer-readable storage medium, and the non-volatile computer-readable storage medium stores at least one program, each of which includes instructions, and when the instructions are executed by the terminal, the terminal executes a water conservancy site early warning method based on Internet of Things technology described in any of the above-mentioned embodiments.

[0017] The present application provides a water conservancy site early warning method, device and medium based on the Internet of Things technology. Compared with the prior art, the embodiments of the present application have the following beneficial technical effects:

[0018] 1. Rapid response: By determining the first water conservancy monitoring site with the shortest geographical distance, relevant monitoring data can be quickly obtained to improve the timeliness of early warning.

[0019] 2. Accurate warning: The monitoring data of the first water conservancy monitoring station is analyzed to obtain warning data, which helps to accurately identify potential water conservancy disaster threats.

[0020] 3. Early warning: Based on the Internet of Things technology, early warning analysis is carried out on downstream water conservancy monitoring stations, so that downstream stations can make response preparations in advance and reduce disaster losses.

[0021] 4. Correlation analysis: Conduct correlation analysis on water disaster influencing factors between alarm data and early warning data to determine more accurate water disaster forecast information.

[0022] 5. Comprehensive response: Sending water disaster forecast information to downstream water monitoring stations at all levels will help each station develop targeted water disaster response measures and improve overall response capabilities.

[0023] 6. Scientific decision-making: Provide scientific data support to water management departments to help them make more reasonable decisions and optimize water resources management and the layout of water conservancy facilities.

[0024] 7. Improve safety: Timely and effective early warning and response measures can increase the safety of water conservancy systems and protect people’s lives and property.

[0025] 8. Resource optimization: It helps to rationally allocate water resources and improve the efficiency of water resource utilization.

[0026] 9. Accumulate experience: Continuously accumulate experience in water disaster forecasting and response, improve early warning methods and measures, and improve the management level of water conservancy sites. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0028] Figure 1 A flow chart of a water conservancy site early warning method based on Internet of Things technology provided in an embodiment of the present application;

[0029] Figure 2 A schematic diagram of the structure of a water conservancy site early warning device based on Internet of Things technology provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0031] The present application embodiment provides a water conservancy site early warning method based on Internet of Things technology, such as Figure 1 As shown, the water conservancy site early warning method based on the Internet of Things technology specifically includes steps S101-S105:

[0032] It should be noted that the information communication connection between various downstream water conservancy monitoring stations is completed through the Internet of Things technology in the preset Internet of Things platform; among them, the information communication includes at least: Lora technology, Mesh technology and 5G technology.

[0033] S101. Based on the location of the water disaster, determine the first water monitoring site with the shortest geographical distance.

[0034] Specifically, it is first necessary to mark the source areas of water disasters according to the preset GIS map and local area meteorological information to obtain a number of marked areas.

[0035] Furthermore, the coordinate positions of several marked areas are matched through the preset spatial distribution map of water conservancy monitoring sites to determine the location information of the water conservancy disaster.

[0036] Furthermore, based on the coordinate information in the occurrence location information, the water flow basin distance of each water conservancy monitoring station in the spatial distribution map of the water conservancy monitoring station is calculated to obtain the first water conservancy monitoring station with the shortest water flow basin distance.

[0037] As a feasible implementation method, when a flood disaster occurs in a river basin, the monitoring station in the downstream impact area closest to the source of the disaster obtains monitoring data, that is, the water basin distance of each water monitoring station in the spatial distribution map of the water monitoring stations is calculated, and finally the first water monitoring station closest to the source of the disaster is determined.

[0038] S102: Perform alarm analysis on the monitoring data in the first water conservancy monitoring station to obtain alarm data.

[0039] Specifically, the water conservancy data is collected in real time through multiple groups of water conservancy sensors in the first water conservancy monitoring station to obtain monitoring data, wherein the monitoring data at least includes: water level information, water quality information and unit water flow information.

[0040] Furthermore, based on the first preset threshold, a threshold value judgment is performed on the information parameter in the monitoring data:

[0041] If the information parameter is less than the first preset threshold, the monitoring data at the current time node is determined as low-risk warning data.

[0042] If the information parameter is greater than or equal to the first preset threshold, the monitoring data at the current time node is determined as medium-risk warning data.

[0043] If the information parameter is much larger than the first preset threshold, the monitoring data at the current time node is determined as high-risk alarm data.

[0044] Among them, the alarm data includes: low-risk alarm data, medium-risk alarm data and high-risk alarm data.

[0045] S103: Perform early warning analysis on the second water conservancy monitoring site according to the alarm data and based on the Internet of Things technology to obtain early warning data. The second water conservancy monitoring site is a downstream water conservancy monitoring site of the first water conservancy monitoring site.

[0046] Specifically, it is necessary to first use the Internet of Things technology to send the alarm data to the second water conservancy monitoring site. Through the spatial distribution map of the water conservancy monitoring site, the water conservancy data analysis is performed on the water flow basin distance between the second water conservancy monitoring site and the first water conservancy monitoring site to obtain a gradient decreasing curve function diagram. Then, according to the gradient decreasing curve function diagram, the data parameters in the alarm data are correlated and matched to obtain the early warning monitoring curve function diagram.

[0047] Furthermore, the text content of the early warning monitoring curve function graph is extracted and processed to obtain early warning monitoring data. The image width of the early warning monitoring curve function graph can be obtained, and the horizontal axis definition area corresponding to the early warning monitoring curve function can be determined according to the image width. Among them, the horizontal axis definition area is the distance parameter of the water disaster in different water flow basins. At the same time, within the horizontal axis definition area, the horizontal axis coordinate of the early warning monitoring curve function needs to be obtained.

[0048] Furthermore, based on the early warning monitoring curve function, the vertical axis coordinate corresponding to the horizontal axis coordinate is determined. The vertical axis coordinate is the disaster intensity parameter of the water disaster. Then, according to the coordinate positions of various data parameters determined by the horizontal axis coordinate and the vertical axis coordinate, the specific data parameters in the horizontal axis definition area are determined according to the preset step value. The horizontal axis definition area and the specific data parameters are converted into data text to obtain text content data.

[0049] In one embodiment, after extracting the text content of the early warning monitoring curve function, the data text that needs to be converted can be determined based on the horizontal and vertical coordinates of the curve function, thereby completing the conversion and finally obtaining the text content data.

[0050] Furthermore, the text content data is preprocessed to obtain early warning monitoring data. Data preprocessing includes data cleaning, data correction, data filling, etc.

[0051] Furthermore, the early warning monitoring data is subjected to early warning threshold judgment, that is, the early warning monitoring data is classified under the threshold value, and then the early warning data can be determined. Among them, the early warning data includes: low-risk early warning data, medium-risk early warning data and high-risk early warning data.

[0052] In one embodiment, the extracted text content data, that is, the early warning monitoring data predicted according to the early warning monitoring curve function trend, needs to be further judged by threshold value to predict the early warning data of the second water conservancy monitoring station at the current time node. Since the alarm data and the corresponding early warning data at different time nodes will change, it is necessary to obtain the time node, that is, the real-time early warning data and alarm data, so as to accurately grasp the trend or degree of harm of water conservancy disasters.

[0053] S104: Conduct correlation analysis on water disaster influencing factors between the alarm data and the early warning data to determine water disaster forecast information.

[0054] Specifically, it is necessary to first extract the first key value in the alarm data and the second key value in the early warning data. The first key value and the second key value are both water disaster impact factors at different water monitoring stations. The water disaster impact factors include at least: water level information, water quality information, local hydrological information, basin spatial relationship and unit water flow information.

[0055] Furthermore, a correlation analysis is performed on the first key value and the second key value through a preset correlation function to determine the association relationship at the current time node.

[0056] Furthermore, according to the association relationship and based on the water flow basin distance between the second water conservancy monitoring station and the first water conservancy monitoring station in the spatial distribution map of the water conservancy monitoring stations, data mapping processing is then performed between the alarm data and the early warning data, and water disaster forecast information between the first water conservancy monitoring station and the second water conservancy monitoring station is generated.

[0057] In one embodiment, by analyzing the correlation between the second water conservancy monitoring station and the first water conservancy monitoring station, the dangerous situation of the water conservancy monitoring station affected by the current water disaster at the current time node can be accurately obtained, that is, the data mapping between the alarm data and the early warning data, and then it can be progressively passed to the subsequent downstream water conservancy monitoring stations. The water conservancy disaster forecast information between the first water conservancy monitoring station and the second water conservancy monitoring station lays a data foundation for forecasting water disasters for the downstream water conservancy monitoring stations below.

[0058] S105. Sending water disaster forecast information to downstream water monitoring stations at all levels, so that each water monitoring station can formulate water disaster response measures.

[0059] Specifically, it is necessary to continue to use the Internet of Things technology to send water disaster forecast information to the third water monitoring station, where the third water monitoring station is a downstream water monitoring station of the second water monitoring station.

[0060] Furthermore, based on the water disaster forecast information, the early warning disaster information of the third water conservancy monitoring station is generated, and the early warning disaster information is sent to the downstream water conservancy monitoring stations at all levels step by step. That is, step by step, the water disaster forecast information of the upstream water conservancy monitoring station is used to complete the forecast generation processing of the water disaster forecast information of the downstream water conservancy monitoring station.

[0061] Furthermore, based on the early warning disaster information in each downstream water conservancy monitoring station, the water conservancy disaster response measures of each downstream water conservancy monitoring station are determined.

[0062] In addition, the embodiment of the present application also provides a water conservancy site early warning device based on the Internet of Things technology, such as Figure 2As shown, the water conservancy site early warning device 200 based on the Internet of Things technology specifically includes:

[0063] At least one processor 201. And a memory 202 in communication with the at least one processor 201. The memory 202 stores instructions that can be executed by the at least one processor 201, so that the at least one processor 201 can execute:

[0064] Based on the location of the water disaster, determine the first water monitoring site with the shortest geographical distance;

[0065] Performing alarm analysis on monitoring data at the first water conservancy monitoring station to obtain alarm data;

[0066] According to the alarm data and based on the Internet of Things technology, the second water conservancy monitoring station is subjected to early warning analysis to obtain early warning data; wherein the second water conservancy monitoring station is a downstream water conservancy monitoring station of the first water conservancy monitoring station;

[0067] Conduct correlation analysis on water disaster influencing factors between alarm data and early warning data to determine water disaster forecast information;

[0068] The water disaster forecast information is sent to downstream water monitoring stations at all levels so that each water monitoring station can formulate water disaster response measures.

[0069] The embodiment of the present application builds a forecast and early warning mechanism based on existing water conservancy monitoring stations and Internet of Things technology, which can save a lot of costs from the perspective of cost saving, thereby bringing greater economic value; from the perspective of results, this method can more effectively predict flood peaks, thereby improving the timeliness of emergency dispatch and minimizing the economic losses caused by water conservancy disasters.

[0070] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0071] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0072] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0073] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0074] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0075] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0076] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0077] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0078] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0079] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0080] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A water conservancy site early warning method based on Internet of Things technology, characterized in that: The method comprises: Based on the location of the water disaster, determine the first water monitoring site with the shortest geographical distance; Performing alarm analysis on the monitoring data in the first water conservancy monitoring station to obtain alarm data; According to the alarm data, a warning analysis is performed on a second water conservancy monitoring site to obtain warning data; wherein the second water conservancy monitoring site is a downstream water conservancy monitoring site of the first water conservancy monitoring site; Performing a correlation analysis on water disaster influencing factors between the alarm data and the early warning data to determine water disaster forecast information specifically includes: Extracting a first key value from the alarm data, and extracting a second key value from the early warning data; wherein the first key value and the second key value are both water disaster impact factors at different water monitoring stations; wherein the water disaster impact factors include at least: water level information, water quality information, local hydrological information, watershed spatial relationship, and unit water flow information; By using a preset correlation function, the first key value and the second key value are subjected to correlation analysis to determine the correlation relationship at the current time node; According to the association relationship and based on the water flow basin distance between the second water conservancy monitoring station and the first water conservancy monitoring station in the spatial distribution map of water conservancy monitoring stations, data mapping processing is performed between the alarm data and the early warning data, and water conservancy disaster forecast information between the first water conservancy monitoring station and the second water conservancy monitoring station is generated; The water disaster forecast information is sent to downstream water monitoring stations at all levels so that each water monitoring station can formulate water disaster response measures, including: The water disaster forecast information is sent to a third water conservancy monitoring site through the Internet of Things technology; wherein the third water conservancy monitoring site is a downstream water conservancy monitoring site of the second water conservancy monitoring site; Based on the water disaster forecast information, generate early warning disaster information of the third water conservancy monitoring station, and send the early warning disaster information to downstream water conservancy monitoring stations at all levels step by step; Based on the early warning disaster information in the downstream water conservancy monitoring stations at all levels, the water conservancy disaster response measures of each downstream water conservancy monitoring station are determined.

2. According to claim 1, a water conservancy site early warning method based on Internet of Things technology is characterized in that: Based on the location of the water disaster, determine the first water monitoring station with the shortest geographical distance, including: According to the preset GIS map and local regional meteorological information, the source formation area of ​​the water disaster is marked to obtain several marked areas; Through the preset spatial distribution map of water conservancy monitoring sites, coordinate position matching is performed on a number of the marked areas to determine the location information of the water conservancy disaster; Based on the coordinate information in the occurrence location information, the water flow basin distance of each water conservancy monitoring station in the spatial distribution map of the water conservancy monitoring stations is calculated to obtain the first water conservancy monitoring station with the shortest water flow basin distance.

3. According to the water conservancy site early warning method based on Internet of Things technology as described in claim 1, it is characterized in that: Performing alarm analysis on the monitoring data at the first water conservancy monitoring station to obtain alarm data, specifically including: Through the multiple groups of water conservancy sensors in the first water conservancy monitoring station, water conservancy data is collected in real time to obtain monitoring data; wherein the monitoring data at least includes: water level information, water quality information and unit water flow information; Based on a first preset threshold, performing a threshold judgment on the information parameter in the monitoring data; If the information parameter is less than the first preset threshold, the monitoring data at the current time node is determined as low-risk warning data; If the information parameter is greater than or equal to the first preset threshold, the monitoring data at the current time node is determined as medium-risk warning data; If the information parameter is much greater than the first preset threshold, the monitoring data at the current time node is determined as high-risk warning data; The warning data includes: the low-risk warning data, the medium-risk warning data and the high-risk warning data.

4. The water conservancy site early warning method based on Internet of Things technology according to claim 1 is characterized in that: According to the alarm data, an early warning analysis is performed on the second water conservancy monitoring station to obtain early warning data, specifically including: By means of the Internet of Things technology, the alarm data is sent to the second water conservancy monitoring site; Through the spatial distribution map of water conservancy monitoring stations, water conservancy data analysis is performed on the water flow basin distance between the second water conservancy monitoring station and the first water conservancy monitoring station to obtain a gradient reduction curve function map; According to the gradient decreasing curve function diagram, correlation matching is performed on various data parameters in the alarm data to obtain an early warning monitoring curve function diagram; Extracting text content from the early warning monitoring curve function graph to obtain early warning monitoring data; The warning monitoring data is subjected to a warning threshold judgment to determine the warning data; wherein the warning data includes: low-risk warning data, medium-risk warning data and high-risk warning data.

5. The water conservancy site early warning method based on Internet of Things technology according to claim 4 is characterized in that: Extracting text content from the early warning monitoring curve function graph to obtain early warning monitoring data specifically includes: Obtaining the image width of the early warning monitoring curve function graph, and determining the horizontal axis definition area corresponding to the early warning monitoring curve function according to the image width; wherein the horizontal axis definition area is the distance parameter of the water disaster in different water flow basins; In the horizontal axis definition area, obtaining the horizontal axis coordinate of the early warning monitoring curve function; Based on the early warning monitoring curve function, determine the vertical axis coordinate corresponding to the horizontal axis coordinate; wherein the vertical axis coordinate is the disaster intensity parameter of the water disaster; The coordinate positions of the data parameters are determined according to the horizontal axis coordinates and the vertical axis coordinates, and the specific data parameters are determined in the horizontal axis definition area according to the preset step value; Performing a data text selection conversion on the horizontal axis definition area and the specific data parameter to obtain text content data; The text content data is preprocessed to obtain the early warning monitoring data.

6. The water conservancy site early warning method based on Internet of Things technology according to claim 1 is characterized in that: Through the Internet of Things technology in the preset Internet of Things platform, the information communication connection between each downstream water conservancy monitoring station is completed; wherein, the information communication includes at least: Lora technology, Mesh technology and 5G technology.

7. A water conservancy site early warning device based on Internet of Things technology, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, so that the at least one processor can execute the water conservancy site early warning method based on the Internet of Things technology according to any one of claims 1-6.

8. A non-volatile computer storage medium, characterized in that: The storage medium is a non-volatile computer-readable storage medium, which stores at least one program. Each of the programs includes instructions. When the instructions are executed by the terminal, the terminal executes a water conservancy site early warning method based on Internet of Things technology according to any one of claims 1-6.

Images