CN115016039A - Similar individual case recommendation system of disastrous weather based on machine learning - Google Patents
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- 238000010801 machine learning Methods 0.000 title claims abstract description 38
- 238000000034 method Methods 0.000 claims abstract description 23
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- 238000013507 mapping Methods 0.000 claims abstract description 11
- 238000003860 storage Methods 0.000 claims description 28
- 230000008859 change Effects 0.000 claims description 12
- 230000014509 gene expression Effects 0.000 claims description 5
- 238000005520 cutting process Methods 0.000 claims description 4
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- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/08—Adaptations of balloons, missiles, or aircraft for meteorological purposes; Radiosondes
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- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention relates to the technical field of meteorological monitoring, in particular to a disaster weather similar individual case recommendation system based on machine learning. The catastrophic weather database is used to store case data. And the machine learning module is used for establishing a mapping relation between the historical disastrous weather overall process meteorological data and other historical data according to the case data. The meteorological data acquisition module is used for acquiring external meteorological data. The analysis module is used for predicting the future disastrous weather based on the mapping relation. The early warning prompting module is used for prompting the historical disastrous weather case data in the case data with the future disastrous weather and the highest similarity with the analysis result of the analysis module to an administrator. The coping plan of the disastrous weather gives reference and guidance information.
Description
Technical Field
The invention relates to the technical field of meteorological monitoring, in particular to a system for recommending similar cases of disastrous weather based on machine learning.
Background
The disastrous weather refers to weather which has serious threat to lives and properties of people and can cause serious loss to industry, agriculture and transportation. Such as: strong wind, rainstorm, hail, tornado, cold tide, frost, fog and the like. Can occur in different seasons and is generally sudden.
China is wide in regions and very complex in natural conditions, and belongs to typical monsoon climate areas, so that the disastrous weather is various and has great difference in different regions.
At present, countermeasures and plans for the disastrous weather are generally empirical and have strong temporality, response speed is slow for dealing with the sudden disastrous weather, and the countermeasures and plans are not favorable for reducing the influence of the disastrous weather on normal life of people.
In view of this, the present application is specifically made.
Disclosure of Invention
The invention aims to provide a machine learning-based disaster weather similar case recommendation system which can predict future potential disaster weather according to the actual condition and the change rule of the weather state, can give an early warning to the damage condition and the influence range of the disaster weather in a targeted manner, and provides reference and guidance information for a coping plan of the disaster weather.
The embodiment of the invention is realized by the following steps:
a system for machine learning-based disaster weather-like personal recommendation, comprising: the system comprises a disastrous weather database, a machine learning module, a meteorological data acquisition module, an analysis module and an early warning prompt module.
The disaster weather database is used for storing case data of historical disaster weather, and the case data comprises historical disaster weather types, historical disaster weather overall-process weather data, historical disaster weather influence ranges, historical disaster weather influence times, historical disaster weather intensity change conditions and historical disaster situation expressions of disaster areas.
And the machine learning module is used for establishing a mapping relation between the historical disastrous weather overall process meteorological data in the case data and other historical data in the case data according to the case data.
The meteorological data acquisition module is used for acquiring external meteorological data.
The analysis module is used for predicting the future disastrous weather types, the future disastrous weather influence ranges, the future disastrous weather influence times, the future disastrous weather intensity change conditions and the future disaster situation expressions of the disaster-affected areas on the basis of the mapping relation on the basis of the external meteorological data.
The early warning prompting module is used for prompting and early warning the future disaster weather type, the future disaster weather influence range, the future disaster weather influence time, the future disaster weather intensity change situation and the future disaster situation performance of the disaster-stricken area to an administrator, and prompting the historical disaster weather case data in the case data with the highest similarity to the analysis result of the analysis module to the administrator.
Further, the historical catastrophic weather whole-process meteorological data includes: weather data m days before the occurrence of the historical disastrous weather and weather data in the occurrence process of the historical disastrous weather.
Further, the case data further includes: historical catastrophic weather derived disaster types.
The historical disastrous weather overall process meteorological data further comprises: weather data n days after the end of historical disastrous weather.
The analysis module is further configured to predict a future catastrophic weather-derived disaster type based on the mapping relationship based on the external meteorological data.
The early warning prompting module is also used for prompting and early warning the type of the future disastrous weather derived disasters to an administrator.
Further, the case data further includes: historical disastrous weather countermeasures.
Further, the historical catastrophic weather whole-process meteorological data includes: meteorological data of different altitudes throughout historical catastrophic weather.
Further, the system for recommending the disaster weather similar case based on the machine learning further comprises: the sonde can be recovered.
The meteorological data acquisition module is also used for acquiring meteorological data acquired by the recoverable sounding device.
Further, the retrievable sonde includes: sounding balloon, first lifting rope, descending control subassembly, second lifting rope and sonde.
The first lifting rope is connected between the sounding balloon and the landing control assembly, and the second lifting rope is connected between the landing control assembly and the sounding instrument.
The landing control assembly is used for cutting off the connection between the first lifting rope and the landing control assembly at a preset time point so as to enable the sonde to fall.
Further, the drop control assembly includes: parachute, storage cylinder, lid, locking rope, timing cutter, fly leaf and elastic component.
The bottom end of the elastic piece is connected with the inner bottom wall of the storage cylinder, the top end of the elastic piece is connected with the movable plate, and the movable plate is slidably accommodated in the storage cylinder.
The parachute cord of parachute connects in the fly leaf, and the parachute can be accomodate in the storage cylinder.
The cover body is used for covering the opening part of the storage cylinder, and the locking rope is used for binding the cover body on the storage cylinder, so that the parachute is packaged in the storage cylinder, and the elastic piece is compressed.
The timing cutter is used for cutting off the locking rope at a preset time point.
The bottom end of the first lifting rope is connected with a lifting ring, and the lifting ring is sleeved on the locking rope. The second lifting rope is connected to the bottom of the storage cylinder.
Furthermore, one side edge of the cover body is hinged to the edge of the opening of the storage cylinder, and the cover body is matched with a torsion spring which is used for driving the cover body to open.
Furthermore, the top of the cover body is provided with a groove matched with the locking rope, and the groove is arranged along the radial direction of the cover body. The middle part of recess is still offered and is used for holding the holding tank of rings.
The technical scheme of the embodiment of the invention has the beneficial effects that:
in the using process of the disaster weather similar individual case recommendation system based on machine learning provided by the embodiment of the invention, the early warning prompt module can predict the future weather conditions according to the analysis result of the analysis module, so that the disaster weather which is likely to occur in the future can be predicted, and meanwhile, the development process of the potential disaster weather in the future can be predicted.
On the other hand, according to the analysis result of the analysis module, the case data of the disaster weather most similar to the current weather condition can be found in the case data, and the case data of the disaster weather most similar to the current weather condition is prompted to the administrator, so that the administrator can use the case data of the disaster weather most similar to the current weather condition as a reference sample to give reference to the specific conditions and the influence conditions of the disaster weather which may possibly occur in the future, and thus, the specific and intuitive reference can be better given to the disaster preventive measures, the counter measures to the potential disaster weather in the future are closer to the reality, the practicability and pertinence of the counter measures are also ensured, and the practical effect of the counter measures is further improved.
In general, the system for recommending similar cases of disastrous weather based on machine learning provided by the embodiment of the invention can predict the potential future disastrous weather according to the actual conditions and the change rules of the meteorological states, can perform early warning on the damage conditions and the influence ranges of the disastrous weather in a targeted manner, and provides reference and guidance information for coping plans of the disastrous weather.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic configuration diagram of a recommendation system for similar examples of disastrous weather based on machine learning according to an embodiment of the present invention;
fig. 2 is a schematic overall structure diagram of a recoverable sounding device of a recommendation system for similar cases of disastrous weather based on machine learning according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram illustrating a landing control component of a recoverable sounding device of a recommendation system for disaster-like weather based on machine learning according to an embodiment of the present invention in a first operating state;
fig. 4 is a schematic structural diagram illustrating a landing control component of a recoverable sounding device of a recommendation system for disaster-like weather based on machine learning according to an embodiment of the present invention in a second operating state;
fig. 5 is a schematic structural diagram illustrating a landing control component of a recoverable sounding device of a recommendation system for disaster-like weather based on machine learning according to an embodiment of the present invention in a third operating state;
fig. 6 is a schematic structural diagram illustrating a fourth operating state of a descent control component of a recoverable sounding device of a recommendation system for disaster-like weather based on machine learning according to an embodiment of the present invention.
Description of reference numerals:
a machine learning based disaster weather similar case recommendation system 1000; a catastrophic weather database 100; a machine learning module 200; a meteorological data acquisition module 300; an analysis module 400; an early warning prompt module 500; the sounding device 600 can be recovered; a sounding balloon 610; a first lifting rope 620; a hanging ring 621; a drop control assembly 630; a parachute 631; a storage cylinder 632; a cover 633; grooves 633 a; the housing groove 633 b; locking cord 634; a timing cutter 635; a movable plate 636; an elastic member 637; a second lifting rope 640; a sonde 650.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. 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 invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings or the orientations or positional relationships that the products of the present invention are conventionally placed in use, and are only used for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
Furthermore, the terms "parallel," "perpendicular," and the like do not require that the components be absolutely parallel or perpendicular, but may be slightly inclined. For example, "parallel" merely means that the directions are more parallel relative to "perpendicular," and does not mean that the structures are necessarily perfectly parallel, but may be slightly tilted.
Furthermore, the terms "horizontal", "vertical", "overhang" and the like do not imply that the components are required to be absolutely horizontal or overhang, but may be slightly inclined. For example, "horizontal" merely means that the direction is more horizontal than "vertical" and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
The terms "substantially", "essentially", and the like are intended to indicate that the relative terms are not required to be absolutely exact, but may have some deviation. For example: "substantially equal" does not mean absolute equality, but it is difficult to achieve absolute equality in actual production and operation, and some deviation generally exists. Thus, in addition to absolute equality, "substantially equal" also includes the above-described case where there is some deviation. In this case, unless otherwise specified, terms such as "substantially", and the like are used in a similar manner to those described above.
In the description of the present invention, it should also be noted that, unless otherwise explicitly specified or limited, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Examples
Referring to fig. 1, the present embodiment provides a system 1000 for recommending disaster-like weather based on machine learning, which includes: the system comprises a disastrous weather database 100, a machine learning module 200, a meteorological data acquisition module 300, an analysis module 400 and an early warning prompt module 500.
The disastrous weather database 100 is used for storing case data of historical disastrous weather, wherein the case data comprises historical disastrous weather types, historical disastrous weather overall process weather data, historical disastrous weather influence ranges, historical disastrous weather influence times, historical disastrous weather intensity change conditions and historical disaster manifestations in disaster areas.
The machine learning module 200 is configured to establish a mapping relationship between the weather data of the historical disastrous weather in the case data and other historical data in the case data according to the case data.
The meteorological data acquisition module 300 is used for acquiring external meteorological data.
The analysis module 400 is configured to predict a future catastrophic weather type, a future catastrophic weather influence range, a future catastrophic weather influence time, a future catastrophic weather intensity change condition, and a future disaster situation performance of the damaged area based on the mapping relationship on the basis of the external meteorological data.
The early warning prompting module 500 is configured to prompt the administrator about the future type of the catastrophic weather, the future range of the catastrophic weather, the future time of the catastrophic weather, the future change of the catastrophic weather intensity, and the future disaster performance of the damaged area, and prompt the administrator about the historical catastrophic weather case data in the case data with the highest similarity to the analysis result of the analysis module 400.
In the using process, the early warning module 500 can predict the future weather conditions according to the analysis result of the analysis module 400, so that the possible future disastrous weather can be predicted, and the development process of the potential future disastrous weather can be predicted at the same time.
On the other hand, according to the analysis result of the analysis module 400, the case data of the disastrous weather which is most similar to the current weather condition can be found in the case data, and the case data of the disastrous weather which is most similar to the current weather condition is prompted to the administrator, so that the administrator can use the case data of the disastrous weather which is most similar as a reference sample to give reference to the specific conditions and the influence conditions of the disastrous weather which may occur in the future, and thus, the specific and intuitive reference can be better given to the disaster prevention measures, the countermeasures to the potential disastrous weather in the future are closer to the reality, the practicability and pertinence of the countermeasures are also ensured, and the practical effect of the countermeasures is further improved.
In general, the similar individual example recommendation system 1000 for the disastrous weather based on machine learning can predict the potential future disastrous weather according to the actual conditions and the change rules of the meteorological states, can perform early warning on the damage conditions and the influence ranges of the disastrous weather in a targeted manner, and provides reference and guidance information for the coping plans of the disastrous weather.
In this embodiment, the historical catastrophic weather overall process meteorological data includes: weather data m days before the occurrence of the historical disastrous weather and weather data in the occurrence process of the historical disastrous weather.
The case data also includes: historical catastrophic weather derived disaster types.
The historical disastrous weather overall process meteorological data further comprises: weather data n days after the end of historical disastrous weather.
The analysis module 400 is further configured to predict a future catastrophic weather-derived disaster type based on the mapping relationship based on the external meteorological data.
The early warning prompting module 500 is further configured to prompt the administrator about the type of the future disastrous weather-derived disasters.
The case data also includes: historical disastrous weather countermeasures.
Through the design, more comprehensive and accurate reference data can be provided conveniently. Wherein, the specific values of m and n can be flexibly adjusted according to the actual situation.
Further, the historical catastrophic weather whole-process meteorological data includes: meteorological data of different altitudes throughout historical catastrophic weather.
Specifically, please refer to fig. 2 to 6, the system 1000 for recommending disaster weather similar cases based on machine learning further includes: the sounding device 600 can be recovered.
The meteorological data obtaining module 300 is further configured to obtain meteorological data collected by the recoverable sounding device 600.
Wherein, recoverable sounding device 600 includes: sounding balloon 610, first lifting rope 620, descent control assembly 630, second lifting rope 640, and sonde 650.
A first lifting line 620 is connected between the sounding balloon 610 and the landing control assembly 630, and a second lifting line 640 is connected between the landing control assembly 630 and the sonde 650.
The landing control assembly 630 is used to disconnect the first lifting rope 620 from the landing control assembly 630 at a preset time point so that the sonde 650 falls.
Further, the landing control assembly 630 includes: parachute 631, storage cylinder 632, cover 633, locking cord 634, timing cutter 635, movable plate 636, and elastic member 637.
The bottom end of the elastic member 637 is connected to the inner bottom wall of the storage cylinder 632, the top end of the elastic member 637 is connected to the movable plate 636, and the movable plate 636 is slidably received in the storage cylinder 632.
The parachute cord of the parachute 631 is connected to the movable plate 636, and the parachute 631 can be received in the storage cylinder 632.
The cover 633 is used to cover the opening of the storage cylinder 632, and the locking rope 634 is used to bind the cover 633 to the storage cylinder 632, thereby enclosing the parachute 631 in the storage cylinder 632 and compressing the elastic member 637.
Wherein, the bottom end of the first lifting rope 620 is connected with a lifting ring 621, and the lifting ring 621 is sleeved on the locking rope 634. A second lifting cord 640 is connected to the bottom of the storage cylinder 632.
One side edge of the cover 633 is hinged to the edge of the opening of the storage cylinder 632, and the cover 633 is matched with a torsion spring for driving the cover 633 to open.
The top of the cover 633 is provided with a groove 633a adapted to the locking rope 634, and the groove 633a is disposed along the radial direction of the cover 633. The middle of the groove 633a is also opened with a holding groove 633b for holding the hanging ring 621.
During use, locking cord 634 may be controlled to be cut at different heights, depending on the setting of different preset times. When locking rope 634 is cut off, cover 633 is opened, elastic member 637 pushes out parachute 631 via movable plate 636, and parachute 631 is opened. In addition, as the locking line 634 is cut, the locking line 634 is disengaged from the lifting eye 621 and the sounding balloon 610 is detached from the descent control assembly 630. Parachute 631 falls with sonde 650.
During the falling process, the sonde 650 can perform secondary detection on the high altitude meteorological data within the corresponding height, so that the monitoring accuracy is improved.
When parachute 631 is opened, elastic member 637 can also act as a buffer when parachute 631 is just opened, improving the stability of sonde 650.
The timer 635 may be composed of a timer, a cutter and a battery box, wherein the timer can set a timing time, and the meteorological data within a predetermined altitude can be detected according to the average ascending speed of the balloon. After the timer has cleared, it outputs a control signal to the cutter which severs locking cord 634. The structure of the timing cutter 635 is not limited thereto.
In summary, the system 1000 for recommending similar cases of disastrous weather based on machine learning according to the embodiment of the present invention can predict the potential future disastrous weather according to the actual conditions and the change rules of the meteorological states, can perform early warning on the damage conditions and the influence ranges of the disastrous weather in a targeted manner, and simultaneously provide reference and guidance information for the coping plans of the disastrous weather.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A system for recommending disaster weather similar cases based on machine learning is characterized by comprising: the system comprises a disastrous weather database, a machine learning module, a meteorological data acquisition module, an analysis module and an early warning prompt module;
the disaster weather database is used for storing case data of historical disaster weather, and the case data comprises historical disaster weather types, historical disaster weather overall process weather data, historical disaster weather influence ranges, historical disaster weather influence times, historical disaster weather intensity change conditions and historical disaster situation expressions of disaster areas;
the machine learning module is used for establishing a mapping relation between historical weather full-process meteorological data in the case data and other historical data in the case data according to the case data;
the meteorological data acquisition module is used for acquiring external meteorological data;
the analysis module is used for predicting future disastrous weather types, future disastrous weather influence ranges, future disastrous weather influence times, future disastrous weather intensity changes and future disaster situation expressions of the disaster-affected areas on the basis of the external meteorological data based on the mapping relation;
the early warning prompting module is used for prompting and early warning the future disastrous weather types, the future disastrous weather influence ranges, the future disastrous weather influence times, the future disastrous weather intensity change conditions and the future disaster situation expressions of the disaster areas to an administrator, and prompting historical disastrous weather case data in the case data with the highest similarity to the analysis results of the analysis module to the administrator.
2. The machine learning-based disastrous weather similar personal recommendation system according to claim 1, wherein the historical disastrous weather full process meteorological data comprises: weather data m days before the occurrence of the historical disastrous weather and weather data in the occurrence process of the historical disastrous weather.
3. The machine learning-based disastrous weather-like personal recommendation system according to claim 2, wherein the case data further comprises: historical catastrophic weather derived disaster types;
the historical catastrophic weather full process meteorological data further comprises: weather data of n days after the historical disastrous weather is finished;
the analysis module is further configured to predict a future catastrophic weather-derived disaster type based on the external meteorological data based on the mapping;
the early warning prompting module is also used for prompting and early warning the future disastrous weather derived disaster type to an administrator.
4. The machine learning-based disastrous weather-like personal recommendation system according to claim 1, wherein the case data further comprises: historical disastrous weather countermeasures.
5. The machine learning-based disastrous weather similar personal recommendation system according to claim 1, wherein the historical disastrous weather full process meteorological data comprises: meteorological data of different altitudes throughout the course of historical disastrous weather.
6. The machine-learning based disastrous weather-like personal recommendation system of claim 5, further comprising: the sounding device can be recovered;
the meteorological data acquisition module is also used for acquiring meteorological data acquired by the recoverable sounding device.
7. The machine learning-based disastrous weather-like personal recommendation system according to claim 6, wherein said recoverable sounding device comprises: the sounding balloon, the first lifting rope, the landing control assembly, the second lifting rope and the sounding instrument;
the first lifting rope is connected between the sounding balloon and the landing control assembly, and the second lifting rope is connected between the landing control assembly and the sonde;
the landing control assembly is used for cutting off the connection between the first lifting rope and the landing control assembly at a preset time point so as to enable the sonde to fall.
8. The machine learning-based disastrous weather-like personal recommendation system of claim 7, wherein the fall control component comprises: the parachute, the storage cylinder, the cover body, the locking rope, the timing cutter, the movable plate and the elastic piece;
the bottom end of the elastic piece is connected with the inner bottom wall of the storage cylinder, the top end of the elastic piece is connected with the movable plate, and the movable plate is slidably accommodated in the storage cylinder;
the parachute ropes of the parachute are connected to the movable plate, and the parachute can be accommodated in the storage cylinder;
the cover body is used for covering the opening part of the storage barrel, and the locking rope is used for binding the cover body on the storage barrel so as to enclose the parachute in the storage barrel and enable the elastic piece to be compressed;
the timing cutter is used for cutting off the locking rope at a preset time point;
the bottom end of the first lifting rope is connected with a lifting ring, and the lifting ring is sleeved on the locking rope; the second lifting rope is connected to the bottom of the storage cylinder.
9. The system for recommending disaster weather similar cases based on machine learning according to claim 8, wherein one side edge of said cover body is hinged to the edge of the mouth of said storage cylinder, and said cover body is fitted with a torsion spring for driving said cover body to open.
10. The system for recommending disaster weather similar cases based on machine learning according to claim 8, wherein a groove for fitting with the locking rope is opened at the top of the cover body, and the groove is arranged along the radial direction of the cover body; the middle part of recess still seted up and be used for holding the holding tank of rings.
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