CN115273439A - Sea area red tide disaster early warning method, computer equipment and storage medium - Google Patents
Sea area red tide disaster early warning method, computer equipment and storage medium Download PDFInfo
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Abstract
The application provides a sea area red tide disaster early warning method, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring monitoring data corresponding to target monitoring factors in a sea area; determining monitoring duration based on a numerical change prediction model, and determining a change numerical value of monitoring data corresponding to the target monitoring factor in the monitoring duration; and determining early warning information according to the change value of the monitoring data corresponding to the target monitoring factor, and carrying out early warning according to the early warning information. This application need not the field work personnel to go out to the sea and carries out data monitoring, reduces the cost of labor, can also acquire the data of sea area environmental factor more comprehensively and carry out the analysis simultaneously, has promoted the accuracy of sea area red tide early warning.
Description
Technical Field
The application relates to the technical field of disaster early warning, in particular to a sea area red tide disaster early warning method, computer equipment and a storage medium.
Background
At present, environmental element monitoring in offshore sea areas is mainly carried out in a field measurement mode by laying sensors for field personnel and carrying out field ship production. However, field measurement can only achieve point-to-point or on-line precision, the monitoring range is generally small, the overall environmental information of the sea cannot be completely reflected, the measurement cost is high, and the overall prediction cannot be performed, so that the accuracy of the prediction result is insufficient.
Disclosure of Invention
The present application mainly aims to provide a sea area red tide disaster early warning method, a computer device and a computer readable storage medium, aiming to improve the accuracy of sea area red tide disaster early warning.
In a first aspect, the application provides a sea area red tide disaster early warning method, which comprises the following steps:
acquiring monitoring data corresponding to target monitoring factors in a sea area;
determining monitoring duration based on a numerical value change prediction model, and determining a change numerical value of monitoring data corresponding to the target monitoring factor in the monitoring duration;
and determining early warning information according to the change value of the monitoring data corresponding to the target monitoring factor, and performing early warning according to the early warning information.
In a second aspect, the present application further provides a computer device, which includes a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program, when executed by the processor, implements the steps of the above-mentioned warning method for red tide disasters in sea area.
In a third aspect, the present application further provides a computer-readable storage medium, having a computer program stored thereon, where the computer program, when executed by a processor, implements the steps of the above-mentioned early warning method for red tide disasters in sea area.
The application provides a sea area red tide disaster early warning method, equipment and a computer readable storage medium, and the method comprises the steps of acquiring monitoring data corresponding to target detection factors in a sea area; determining monitoring duration based on the numerical value change prediction model, and determining a telephone number value of monitoring data corresponding to the target monitoring factor in the monitoring duration; and determining early warning information according to the change value of the monitoring data corresponding to the target monitoring factor, and performing early warning according to the early warning information. Data monitoring is not required to be carried out through an field ship, and the monitoring range is larger than that of a sensor arranged, so that the accuracy of disaster early warning can be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for early warning of a sea area red tide disaster according to an embodiment of the present application;
fig. 2 is a schematic diagram illustrating a sea surface temperature variation curve of a sea area according to an embodiment of the present application;
fig. 3 is a schematic diagram illustrating a sea surface salinity variation curve of a sea area according to an embodiment of the present application;
fig. 4 is a schematic diagram illustrating a chlorophyll a concentration variation curve of a sea area according to an embodiment of the present application;
fig. 5 is a block diagram illustrating a structure of a computer device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The flow diagrams depicted in the figures are merely illustrative and do not necessarily include all of the elements and operations/steps, nor do they necessarily have to be performed in the order depicted. For example, some operations/steps may be decomposed, combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The embodiment of the application provides a sea area red tide disaster early warning method, computer equipment and a computer readable storage medium. The early warning method for the sea area red tide disasters can be applied to terminal equipment, and the terminal equipment can be electronic equipment such as a tablet computer, a notebook computer and a desktop computer. The method can also be applied to a server, which can be an independent server, or a cloud server providing basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, content Delivery Network (CDN), big data and artificial intelligence platform, and the like.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for early warning of a sea area red tide disaster according to an embodiment of the present application.
As shown in fig. 1, the early warning method for red tide disasters in sea area includes steps S101 to S103.
And S101, acquiring monitoring data corresponding to the target monitoring factors in the sea area.
For example, the monitoring of the environmental elements of the sea area can be realized through the multi-source satellite sensor, specifically, the multi-source satellite sensor can monitor the environmental elements of the sea area through a remote sensing technology, so that monitoring data corresponding to the environmental elements in the sea area can be acquired.
It can be understood that based on remote sensing, large-scale measurement can be realized for the sea area, so that the data of the sea area environment elements can be determined more comprehensively. And whether red tide disasters possibly occur in the sea area can be pre-warned through data analysis of sea area environment elements, so that the problems of ship-out measurement of field workers, range limitation of sensor arrangement and the like are avoided.
For example, in some cases, when a red tide disaster occurs, some sea environment elements may not be changed, that is, the occurrence of the red tide disaster is not strongly correlated with the sea environment elements, and when the red tide disaster is predicted, the sea environment elements may be excluded to improve the accuracy of prediction.
Specifically, the environmental elements are represented by the monitoring factors, the monitoring data corresponding to the monitoring factors in the sea area can be acquired, the monitoring factors are screened, the target monitoring factors and the corresponding monitoring data are determined, and early warning of red tide disasters is carried out according to the monitoring data corresponding to the target monitoring factors, so that the accuracy of the early warning is improved.
In some embodiments, before obtaining monitoring data corresponding to target monitoring in the sea area, the method further comprises: acquiring historical red tide disaster information and historical monitoring data of a plurality of monitoring factors in a sea area; and determining a target detection factor from the multiple monitoring factors according to the historical red tide disaster information and historical monitoring data corresponding to the multiple monitoring factors based on a data correlation analysis algorithm.
For example, it may be determined which monitoring factors may be related to the red tide disaster through historical red tide disaster information and historical monitoring data of multiple monitoring factors in the sea area, so as to determine that the target monitoring factor performs early warning on the red tide disaster in the sea area. As can be appreciated, the historical red tide disaster information includes a time period during which a red tide disaster occurred within the historical time period, and a time period during which no red tide disaster occurred.
For example, the target monitoring factor may be determined from a plurality of monitoring factors by a data correlation analysis algorithm.
In some embodiments, the determining, by the data correlation analysis algorithm, a target monitoring factor from a plurality of monitoring factors according to the historical red tide disaster information and historical monitoring data corresponding to the plurality of monitoring factors includes: determining a first historical time period in which the red tide disasters occur and a second historical time period in which the red tide disasters do not occur according to the historical red tide disaster information; determining a first change trend of historical monitoring data corresponding to the multiple monitoring factors in the first historical time period and a second change trend of the historical monitoring data corresponding to the multiple monitoring factors in the second historical time period; determining the monitoring factor with different first variation trend and second variation trend in the plurality of monitoring factors as a target monitoring factor.
Illustratively, in the historical red tide disaster information, a time period in which the red tide disaster occurs and a time period in which the red tide disaster does not occur are included, so that a first historical time period in which the red tide disaster occurs and a second historical time period in which the red tide disaster does not occur can be determined through the historical red tide disaster information.
For example, after the first historical time period and the second historical time period are determined, a first change trend of historical monitoring data corresponding to the first historical time period is determined from the historical monitoring data of a plurality of monitoring factors in the sea area; determining a second change trend of historical monitoring data corresponding to a second historical time period from the historical monitoring data of a plurality of monitoring factors in the sea area; and determining which monitoring factors are related to the red tide disasters according to the first variation trend and the second variation trend.
Specifically, when the monitoring factors include the sea surface temperature, the sea surface salinity and the chlorophyll a concentration in the sea area, which of the sea surface temperature, the sea surface salinity and the chlorophyll a concentration in the sea area is related to the red tide disaster can be determined through historical monitoring data corresponding to the sea surface temperature, historical monitoring data corresponding to the sea surface salinity and monitoring data corresponding to the chlorophyll a concentration.
For example, taking the rongeur sea area as an example, acquiring historical data corresponding to the sea surface temperature monitoring factor, wherein, as shown in fig. 2 (SST in fig. 2 is used for indicating the sea surface temperature), the historical data may include monitoring data corresponding to months 11 to 2020, months 11 to 2021 and months 11 to 2022 of 2021, and it can be understood that the first historical time of occurrence of red tide disasters in the sea area is from months 2020 to months 1 of 2021 and months 11 to 12 of 2021; and the second historical time when no red tide disaster occurs is 11 months to 2020 months in 2019; in the historical data corresponding to the sea surface temperature, a first change trend corresponding to the monitoring data of the sea surface temperature in the first historical time (11 months to 2 months in 2021; 11 months to 2 months in 2021) is the same as a second change trend corresponding to the monitoring data of the sea surface temperature in the second historical time (11 months to 2 months in 2019), and both trends are seasonal reduction trends.
In some embodiments, determining a first trend of the historical monitoring data corresponding to the plurality of monitoring factors in the first historical time period and a second trend of the historical monitoring data corresponding to the plurality of monitoring factors in the second historical time period when the monitoring factors include the sea surface temperature includes: acquiring a first historical sea surface temperature based on a medium-resolution imaging spectrometer; obtaining a second historical poster temperature based on the mixed coordinate ocean model; calculating a difference between the first historical sea surface temperature and the second historical sea surface temperature; and when the difference is smaller than a difference threshold value, determining a first change trend of the sea surface temperature in the first historical time period and a second change trend of the sea surface temperature in the second historical time period according to the first historical sea surface temperature or the second historical sea surface temperature.
For example, when determining The historical monitoring data corresponding to The sea surface temperature, a first historical sea surface temperature may be obtained through a mode-resolution Imaging spectrometer (MODIS), a second historical sea surface temperature may be obtained through a Hybrid Coordinate Ocean Model (HYCOM), after obtaining The first historical sea surface temperature and The second historical sea surface temperature, a difference between The first historical sea surface temperature and The second historical sea surface temperature may be calculated, and a first change trend of The sea surface temperature in The first historical time period and a second change trend of The sea surface temperature in The second historical time period may be determined according to The difference.
Specifically, when the difference is smaller than the difference threshold, the first historical sea surface temperature or the second historical sea surface temperature may be used as monitoring data corresponding to the sea surface temperature, and a first change trend of the sea surface temperature in the first historical time period and a second change trend of the sea surface temperature in the second historical time period may be determined according to the first historical sea surface temperature or the second historical sea surface temperature.
When the difference is greater than or equal to the difference threshold, an average value of the first historical sea surface temperature and the second historical sea surface temperature can be calculated, and a first change trend of the sea surface temperature in the first historical time period and a second change trend of the sea surface temperature in the second historical time period are determined according to the average value.
It can be understood that historical monitoring data of the sea surface temperature can be obtained in different modes, so that the accuracy of the historical monitoring data of the sea surface temperature is improved, and the accuracy of judging whether the sea surface temperature is a target monitoring factor is improved.
For another example, historical monitoring data corresponding to the sea surface salinity is obtained, and it can be understood that the historical monitoring data corresponding to the sea surface salinity includes monitoring data corresponding to months 11 to 2020 and 2, months 2020 and 11 to 2021 and 2, and months 11 to 2022 and 2, respectively; as shown in fig. 3 (SSS in fig. 3 is used to indicate corresponding monitoring data of sea surface salinity), as described above, the first historical time of red tide disasters in the sea area is 12 months to 1 month of 2021 year 2020, and 11 months to 12 months of 2021 year; and the second historical time when no red tide disaster occurs is 11 months to 2020 months in 2019; in the historical data corresponding to the sea surface salinity, a first change trend corresponding to the monitoring data of the sea surface salinity at the first historical time (11 months to 2 months from 2021; 11 months to 2 months from 2021) and a second change trend corresponding to the monitoring data of the sea surface salinity at the second historical time (11 months to 2 months from 2019) are obviously different, so that the sea surface salinity and the red tide disaster are considered to be related, and the sea surface salinity is determined as a target monitoring factor.
It is understood that historical monitoring data of sea surface salinity can also be obtained by a Hybrid Coordinate Ocean Model (HYCOM).
For another example, historical monitoring data corresponding to the concentration of chlorophyll a is obtained, and it can be understood that the historical monitoring data corresponding to the concentration of chlorophyll a includes monitoring data corresponding to months 11 to 2020 and 2, months 11 to 2021 and 2, and months 11 to 2022 and 2, respectively, in 2019; as shown in fig. 4, as described above, the first historical time of red tide disasters in the sea area is 2020, 12 months to 2021, 1 month, and 2021, 11 months to 12 months; and the second historical time when no red tide disaster occurs is 11 months to 2020 months in 2019; in the historical data corresponding to the chlorophyll a concentration, a first change trend corresponding to the monitoring data of the chlorophyll a concentration in the first historical time (11 months to 2 months at 2021; and 11 months to 2022 months at 2022) is not the same as a second change trend corresponding to the monitoring data of the chlorophyll a concentration in the second historical time (11 months to 2 months at 2019), so that the chlorophyll a concentration and the red tide disaster can be considered to have correlation, and the chlorophyll a concentration is determined as a target monitoring factor.
It is understood that, in the case that the monitoring factors include sea surface temperature, sea surface salinity and chlorophyll a concentration, taking the honored sea area as an example, the sea surface salinity and chlorophyll a concentration can be determined as the target monitoring factors by the above-mentioned manner.
In some embodiments, the monitoring factors include sea surface temperature and/or salinity; the acquiring historical monitoring data of a plurality of monitoring factors in the sea area comprises: acquiring data to be processed of sea surface temperature and/or data to be processed of salinity in the sea area based on the mixed coordinate sea model; and performing conversion calculation on the data to be processed of the sea surface temperature and/or the data to be processed of the salinity based on a preset conversion algorithm to obtain historical monitoring data of the sea surface temperature and/or the historical monitoring data of the salinity in the sea area.
Exemplarily, after obtaining the data to be processed of the sea surface temperature and/or the data to be processed of the salinity in the sea area through the mixed coordinate ocean model, performing conversion calculation on the data to be processed of the sea surface temperature and/or the data to be processed of the salinity through a preset conversion algorithm, so as to obtain historical monitoring data of the sea surface temperature and/or the historical monitoring data of the salinity in the sea area; it can be understood that the data to be processed needs to be converted to obtain historical monitoring data of the monitoring factors which can be used for performing the data correlation analysis algorithm, so that the accuracy of determining the target monitoring factors is improved.
In some embodiments, the performing conversion calculation on the sea surface temperature to be processed and/or the salinity information to be processed based on a preset conversion algorithm to obtain the sea surface temperature information and the salinity information of the target sea area includes: performing conversion calculation on the sea surface temperature to be treated and/or the salinity information to be treated based on the following calculation formula:
SST＝Water_Temp×0.001+20
SSS＝Water_Salinitly×0.001+20
the SST is used for indicating historical monitoring data of the sea surface temperature, and the Water _ Temp is used for indicating to-be-processed data of the sea surface temperature; the SSS is used for indicating historical monitoring data of salinity, and the Water _ Salinitly is used for indicating to-be-processed data of salinity.
Illustratively, the conversion calculation may be performed by the above formula to obtain historical monitoring data of the sea surface temperature and historical monitoring data of the salinity.
It can be understood that, after the target monitoring factor is determined and the to-be-processed data corresponding to the target monitoring factor is obtained, the data conversion processing may be performed on the target monitoring factor to obtain the monitoring data corresponding to the target monitoring factor.
In some embodiments, the monitoring factors further include chlorophyll a concentration, and the obtaining historical monitoring data for a plurality of monitoring factors in the sea area includes: and acquiring historical monitoring data corresponding to the chlorophyll a concentrations of the adjacent sea areas of the target sea area.
For example, since the chlorophyll-a concentration in the sea area may be influenced by the chlorophyll-a concentrations of the neighboring sea areas, it may be determined whether the chlorophyll-a concentration is the target monitoring factor by acquiring historical monitoring data corresponding to the chlorophyll-a concentrations of the neighboring sea areas of the target sea area.
For example, determining whether the chlorophyll-a concentration is the target monitoring factor may be performed in the manner provided in the above embodiments, and will not be described herein.
Step S102, based on a numerical value change prediction model, determining monitoring duration, and determining a change numerical value of monitoring data corresponding to the target monitoring factor in the monitoring duration.
For example, after determining the target monitoring factor and the corresponding monitoring data in the sea area, the monitoring duration corresponding to the target monitoring factor may be determined through a numerical change prediction model, and the change numerical value of the monitoring data corresponding to the target monitoring factor may be determined in real time within the monitoring duration, so as to perform early warning on the red tide disaster in the sea area.
For example, taking the rongcheng sea area as an example of the target sea area, and determining that the target monitoring factors include the sea surface salinity and the chlorophyll a concentration through the above embodiment, the monitoring data of the sea surface salinity of the target sea area can be obtained; and acquiring monitoring data of chlorophyll a concentration of adjacent sea areas of the target sea area, determining monitoring duration, and determining a change value of the monitoring data within the monitoring duration to predict the red tide disaster.
In a specific implementation process, a monitoring duration and a monitoring data acquisition time interval may be preset, for example, the preset monitoring duration is one week, the monitoring data acquisition time interval of the chlorophyll a concentration may be 2 days, the monitoring data acquisition time interval of the sea surface salinity may be 3 hours, that is, the monitoring data acquisition of the sea surface salinity is performed once every 3 hours, and the monitoring data of the chlorophyll a concentration is acquired every 2 days, wherein the monitoring data of the chlorophyll a concentration may include the monitoring data of the chlorophyll a concentration of a target sea area and the monitoring data of the chlorophyll a concentration of a sea area adjacent to the target sea area; and determining the change value of the monitoring data acquired in one week. Therefore, early warning of the red tide disasters in the target sea area is carried out according to the change numerical value.
For example, when the change value of the monitored data of the chlorophyll a concentration in the sea area adjacent to the target sea area is monitored to be large, the monitoring duration of the chlorophyll a concentrations in the target sea area and the sea area adjacent to the target sea area may be shortened, for example, the monitoring duration is shortened to 5 days for one week; thereby improving the accuracy of the red tide disaster prediction.
It can be understood that when the salinity or the sea surface temperature monitored in the preset monitoring time period has a large change value, the monitoring time period corresponding to the salinity and/or the monitoring time period corresponding to the sea surface temperature can be adjusted.
The above embodiments are all exemplified by taking the honor sea area as the target sea area, and the determination of the target monitoring factor and the determination of the monitoring time period are not limited.
In some embodiments, the determining a monitoring duration based on the numerical change prediction model, and determining a change value of the monitoring data corresponding to the target monitoring factor in the monitoring duration, includes: acquiring environmental weather information of a target sea area; and determining monitoring duration according to the environmental meteorological information based on a numerical change prediction model, and determining a change numerical value of monitoring data corresponding to the target monitoring factor in the monitoring duration.
Illustratively, the monitoring duration can also be adjusted by acquiring environmental weather information and based on the numerical change prediction model and the environmental weather information.
It can be understood that, because the acquisition of the monitoring data is acquired by the remote sensing technology, the acquired monitoring data can be influenced by the environmental weather of the target sea area, so that the environmental weather information can be acquired, and the monitoring time can be adjusted according to the environmental weather information.
And S103, determining early warning information according to the change value of the monitoring data corresponding to the target monitoring factor, and performing early warning according to the early warning information.
For example, after determining the change value of the monitoring data corresponding to the target monitoring factor, it may be determined whether the monitoring data of the target monitoring factor is abnormal according to the change value, so as to perform early warning of the red tide disaster.
Specifically, a threshold interval may be set to perform early warning of red tide disasters according to the monitoring data of the target monitoring factor and the threshold interval.
In some embodiments, determining the early warning information according to a change value of the monitoring data corresponding to the target detection factor includes: and determining a threshold interval in which the change value of the monitoring data corresponding to the target monitoring factor is positioned, and determining early warning information according to the early warning level corresponding to the threshold interval.
Illustratively, a plurality of threshold intervals can be preset, and each threshold interval has a corresponding early warning level, so that the corresponding early warning level can be determined according to the threshold interval in which the change value of the monitoring data corresponding to the target monitoring factor is located, and the corresponding early warning information is determined to be output.
For example, the threshold interval may be determined according to the historical monitoring data of the target monitoring factor in the first historical time period and the second historical time period, for example, by the average of the historical monitoring data in the first historical time period and the second historical time period.
Specifically, taking salinity as an example, according to historical monitoring data in a first historical time period and historical monitoring data in a second historical time period, determining a maximum difference value in the corresponding historical time period; and determining a threshold interval according to the maximum difference value and the historical monitoring data in the second historical time period.
For example, as described above, historical monitoring data corresponding to the sea surface salinity includes monitoring data corresponding to months 11 to 2020 and 2 in 2019, months 11 to 2021 and 2 in 2020 and 11 to 2022 in 2021 and 2 in 2022; the first historical time corresponding to the occurrence of red tide disasters is 2020, 12 months to 2021, 1 month, and 2021, 11 months to 12 months; and the second historical time when no red tide disaster happens is 11 months to 2020 months in 2019; calculating the difference value between the monitoring data of the sea surface salinity at the first historical time (11 months to 2 months from 2020 to 2021; 11 months to 2 months from 2021 to 2022) and the monitoring data of the sea surface salinity at the corresponding second historical time (11 months to 2 months from 2019) respectively, determining the maximum difference value to be 0.9 when determining the maximum difference value, for example, the difference value between 11 months from 2020 to 11 months from 2019 is 0.8, and the difference value between 11 months from 2021 to 11 months from 2019 is 0.9, and determining the threshold interval according to the mean value and the maximum difference value of the monitoring data at the second historical time.
For example, the threshold interval may be determined as follows
S1＝Smean–Dif*0.2
S2＝Smean–Dif*0.4
S3＝Smean–Dif*0.6
S4＝Smean–Dif*0.8
The monitoring method comprises the following steps that S1, S2, S3 and S4 are used for indicating corresponding early warning levels, smean is used for indicating the mean value of monitoring data of a second historical time, and Dif is used for indicating the maximum difference value of the monitoring data of a first historical time and the monitoring data of the second historical time.
For example, when the change value of the monitoring data corresponding to the target monitoring factor is in the corresponding threshold interval, the corresponding early warning level may be determined, and early warning information may be generated.
It can be understood that different target monitoring factors correspond to different threshold intervals, the threshold intervals correspond to salinity only, and threshold intervals corresponding to chlorophyll a concentration also can exist, when the early warning level is determined, the threshold interval corresponding to the salinity where the change value of the salinity monitoring data is located is determined, and the threshold interval corresponding to the chlorophyll a concentration where the change value of the chlorophyll a concentration monitoring data is located is determined, so that the early warning level is determined.
It can be understood that the early warning information can be determined according to the high early warning level, for example, when the change value of the monitoring data of salinity determines that the early warning level is the second level, and the change value of the monitoring data of chlorophyll a determines that the early warning level is the third level, the early warning level in the early warning information is the third level; under the condition that the early warning system comprises a plurality of target monitoring factors, different weights can be set for different target monitoring factors to determine early warning levels in output early warning information, and the weights can be determined according to the relevance of the target monitoring factors and red tide disasters. For example, the weight of the chlorophyll a concentration is 0.6, the weight of the salinity is 0.3, and the weight of the sea surface temperature is 0.1, when the early warning level is determined to be two levels according to the change value of the monitoring data of the chlorophyll a concentration, the early warning level is determined to be three levels according to the change value of the monitoring data of the salinity, and the early warning level is determined to be four levels according to the change value of the monitoring data of the sea surface temperature, the early warning level is calculated through the weights, for example, 2 × 0.6+3 × 0.3+1 × 0.1=2.5, so that the early warning level in the early warning information is determined to be three levels.
It should be noted that the determination of the early warning level and the determination of the early warning level in the early warning information are exemplary descriptions, and other determination manners may also be available, which is not limited in the present application.
According to the early warning method for the red tide disasters in the sea area, the monitoring data corresponding to the target monitoring factors in the sea area are obtained; determining monitoring duration based on a numerical change prediction model, and determining a change numerical value of monitoring data corresponding to the target monitoring factor in the monitoring duration; and determining early warning information according to the change value of the monitoring data corresponding to the target monitoring factor, and performing early warning according to the early warning information. Need not the field work personnel to go to the sea and measure and lay the sensor, less cost of labor and the monitoring data that can be more comprehensive obtains the target monitoring factor in sea area to promote the accuracy of red tide calamity prediction.
Referring to fig. 5, fig. 5 is a schematic block diagram of a computer device according to an embodiment of the present disclosure. The computer device may be a server or a terminal.
As shown in fig. 5, the computer device includes a processor, a memory and a network interface connected by a system bus, wherein the memory may include a storage medium and an internal memory.
The storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause a processor to perform any one of the methods for warning of red tide disasters in sea areas.
The processor is used for providing calculation and control capability and supporting the operation of the whole computer equipment.
The internal memory provides an environment for running a computer program in a storage medium, and the computer program can make a processor execute any one of the methods for early warning of red tide disasters in sea areas when being executed by the processor.
The network interface is used for network communication, such as sending assigned tasks and the like. It will be appreciated by those skilled in the art that the configuration shown in fig. 5 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computing device to which the present application may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
It should be understood that the Processor may be a Central Processing Unit (CPU), and the Processor may be other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, etc. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Wherein, in one embodiment, the processor is configured to execute a computer program stored in the memory to implement the steps of:
acquiring monitoring data corresponding to target monitoring factors in a sea area;
determining monitoring duration based on a numerical change prediction model, and determining a change numerical value of monitoring data corresponding to the target monitoring factor in the monitoring duration;
and determining early warning information according to the change value of the monitoring data corresponding to the target monitoring factor, and carrying out early warning according to the early warning information.
In one embodiment, when the processor implements the sea area red tide disaster warning method, the processor is configured to implement:
acquiring historical red tide disaster information and historical monitoring data of a plurality of monitoring factors in a sea area;
and determining a target monitoring factor from the multiple monitoring factors according to the historical red tide disaster information and the historical monitoring data corresponding to the multiple monitoring factors based on a data correlation analysis algorithm.
In one embodiment, the processor, when implementing a data correlation analysis algorithm based on the determination of the target monitoring factor from the plurality of monitoring factors according to the historical red tide disaster information and the historical monitoring data corresponding to the plurality of monitoring factors, is configured to implement:
determining a first historical time period in which the red tide disasters occur and a second historical time period in which the red tide disasters do not occur according to the historical red tide disaster information;
determining a first change trend of historical monitoring data corresponding to the multiple monitoring factors in the first historical time period and a second change trend of the historical monitoring data corresponding to the multiple monitoring factors in the second historical time period;
determining the first change trend and the second change trend of the plurality of monitoring factors to be different monitoring factors as target monitoring factors.
In one embodiment, the processor, in implementing determining a first trend of the historical monitoring data corresponding to the plurality of monitoring factors over the first historical time period and a second trend of the historical monitoring data corresponding to the plurality of monitoring factors over the second historical time period, is configured to implement:
acquiring a first historical sea surface temperature based on a medium-resolution imaging spectrometer;
acquiring a second historical sea surface temperature based on the mixed coordinate ocean model;
calculating a difference between the first historical sea surface temperature and the second historical sea surface temperature;
and when the difference is smaller than a difference threshold value, determining a first change trend of the sea surface temperature in the first historical time period and a second change trend of the sea surface temperature in the second historical time period according to the first historical sea surface temperature or the second historical sea surface temperature.
In one embodiment, the processor, when enabled to obtain historical monitoring data for a plurality of monitoring factors in a sea area, is configured to enable:
acquiring data to be processed of sea surface temperature and/or data to be processed of salinity in the sea area based on the mixed coordinate sea model;
and performing conversion calculation on the data to be processed of the sea surface temperature and/or the data to be processed of the salinity based on a preset conversion algorithm to obtain historical monitoring data of the sea surface temperature in the sea area and/or historical monitoring data of the salinity.
In one embodiment, when the processor performs conversion calculation on the to-be-processed data of the sea surface temperature and/or the to-be-processed data of the salinity based on a preset conversion algorithm to obtain historical monitoring data of the sea surface temperature and/or the historical monitoring data of the salinity in the sea area, the processor is configured to:
and performing conversion calculation on the data to be processed of the sea surface temperature and/or the information to be processed of the salinity based on the following calculation formula:
SST＝Water_Temp×0.001+20
SSS＝Water_Salinitly×0.001+20
the SST is used for indicating historical monitoring data of the sea surface temperature, and the Water _ Temp is used for indicating to-be-processed data of the sea surface temperature; the SSS is used for indicating historical monitoring data of salinity, and the Water _ Salinitly is used for indicating to-be-processed data of salinity.
In one embodiment, when determining the warning information according to the variation value of the monitoring data corresponding to the target monitoring factor, the processor is configured to:
and determining a threshold interval in which the change value of the monitoring data corresponding to the target monitoring factor is located, and determining early warning information according to an early warning level corresponding to the threshold interval.
In one embodiment, the processor, in implementing the determining the monitoring duration based on a numerical change prediction model, and the determining the change value of the monitoring data corresponding to the target monitoring factor in the monitoring duration, is configured to implement:
acquiring environmental weather information of a target sea area;
and determining monitoring duration according to the environmental meteorological information based on a numerical value change prediction model, and determining a change numerical value of monitoring data corresponding to the target monitoring factor in the monitoring duration.
It should be noted that, as will be clearly understood by those skilled in the art, for convenience and simplicity of description, the specific working process of the sea area red tide disaster early warning may refer to the corresponding process in the embodiment of the sea area red tide disaster early warning method, and details are not repeated herein.
Embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, where the computer program includes program instructions, and when the program instructions are executed, a method implemented by the computer program may refer to the various embodiments of the sea area red tide disaster warning method in this application.
The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the computer device.
It is to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or system comprising the element.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments. While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A sea area red tide disaster early warning method is characterized by comprising the following steps:
acquiring monitoring data corresponding to target monitoring factors in a sea area;
determining monitoring duration based on a numerical change prediction model, and determining a change numerical value of monitoring data corresponding to the target monitoring factor in the monitoring duration;
and determining early warning information according to the change value of the monitoring data corresponding to the target monitoring factor, and carrying out early warning according to the early warning information.
2. The early warning method for red tide disasters in sea area according to claim 1, wherein the method further comprises:
acquiring historical red tide disaster information and historical monitoring data of a plurality of monitoring factors in a sea area;
and determining a target monitoring factor from the multiple monitoring factors according to the historical red tide disaster information and the historical monitoring data corresponding to the multiple monitoring factors based on a data correlation analysis algorithm.
3. The early warning method for red tide disasters in sea area according to claim 2, wherein the determining a target monitoring factor from the plurality of monitoring factors according to the historical red tide disaster information and the historical monitoring data corresponding to the plurality of monitoring factors based on the data correlation analysis algorithm comprises:
determining a first historical time period when the red tide disasters occur and a second historical time period when the red tide disasters do not occur according to the historical red tide disaster information;
determining a first change trend of historical monitoring data corresponding to the multiple monitoring factors in the first historical time period and a second change trend of the historical monitoring data corresponding to the multiple monitoring factors in the second historical time period;
determining the monitoring factor with different first variation trend and second variation trend in the plurality of monitoring factors as a target monitoring factor.
4. The early warning method for red tide disasters in sea area according to claim 3, wherein the monitoring factor comprises sea surface temperature; the determining a first trend of the historical monitoring data corresponding to the plurality of monitoring factors in the first historical time period and a second trend of the historical monitoring data corresponding to the plurality of monitoring factors in the second historical time period includes:
acquiring a first historical sea surface temperature based on a medium-resolution imaging spectrometer;
acquiring a second historical sea surface temperature based on the mixed coordinate ocean model;
calculating a difference between the first historical sea surface temperature and the second historical sea surface temperature;
and when the difference is smaller than a difference threshold value, determining a first change trend of the sea surface temperature in the first historical time period and a second change trend of the sea surface temperature in the second historical time period according to the first historical sea surface temperature or the second historical sea surface temperature.
5. The early warning method for red tide disasters in sea area according to claim 2, wherein the monitoring factors comprise sea surface temperature and/or salinity; the acquiring historical monitoring data of a plurality of monitoring factors in the sea area comprises:
acquiring data to be processed of sea surface temperature and/or data to be processed of salinity in the sea area based on the mixed coordinate sea model;
and performing conversion calculation on the data to be processed of the sea surface temperature and/or the data to be processed of the salinity based on a preset conversion algorithm to obtain historical monitoring data of the sea surface temperature in the sea area and/or historical monitoring data of the salinity.
6. The sea area red tide disaster early warning method as claimed in claim 5, wherein the step of performing conversion calculation on the data to be processed of the sea surface temperature and/or the data to be processed of the salinity based on a preset conversion algorithm to obtain historical monitoring data of the sea surface temperature and/or the historical monitoring data of the salinity in the sea area comprises:
and performing conversion calculation on the data to be processed of the sea surface temperature and/or the information to be processed of the salinity based on the following calculation formula:
SST＝Water_Temp×0.001+20
SSS＝Water_Salinitly×0.001+20
the SST is used for indicating historical monitoring data of the sea surface temperature, and the Water _ Temp is used for indicating to-be-processed data of the sea surface temperature; the SSS is used for indicating historical monitoring data of salinity, and the Water _ Salinitly is used for indicating to-be-processed data of salinity.
7. The early warning method for red tide disasters in sea areas according to any one of claims 1 to 6, wherein the determining early warning information according to the variation value of the monitoring data corresponding to the target monitoring factor comprises:
and determining a threshold interval in which the change value of the monitoring data corresponding to the target monitoring factor is located, and determining early warning information according to an early warning level corresponding to the threshold interval.
8. The early warning method for red tide disasters in sea area according to any one of claims 1 to 6, wherein the determining a monitoring duration based on a numerical change prediction model and the determining a change value of the monitoring data corresponding to the target monitoring factor in the monitoring duration comprises:
acquiring environmental weather information of a target sea area;
and determining monitoring duration according to the environmental meteorological information based on a numerical value change prediction model, and determining a change numerical value of monitoring data corresponding to the target monitoring factor in the monitoring duration.
9. A computer device, characterized in that the computer device comprises a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program, when executed by the processor, implements the steps of the sea area red tide disaster warning method as claimed in any one of claims 1 to 8.
10. A computer-readable storage medium, having a computer program stored thereon, wherein the computer program, when being executed by a processor, implements the steps of the method for warning of red tide disasters in sea areas according to any one of claims 1 to 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115965295A (en) * | 2023-03-16 | 2023-04-14 | 百鸟数据科技(北京)有限责任公司 | Wetland ecosystem monitoring method, computer equipment and storage medium |
| CN116384284A (en) * | 2023-05-08 | 2023-07-04 | 国家海洋环境预报中心 | Red tide gridding forecasting method and system |
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2022
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115965295A (en) * | 2023-03-16 | 2023-04-14 | 百鸟数据科技(北京)有限责任公司 | Wetland ecosystem monitoring method, computer equipment and storage medium |
| CN115965295B (en) * | 2023-03-16 | 2023-06-13 | 百鸟数据科技(北京)有限责任公司 | Wetland ecosystem monitoring method, computer equipment and storage medium |
| CN116384284A (en) * | 2023-05-08 | 2023-07-04 | 国家海洋环境预报中心 | Red tide gridding forecasting method and system |
| CN116384284B (en) * | 2023-05-08 | 2024-05-14 | 国家海洋环境预报中心 | Red tide gridding forecasting method and system |
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