Meteorological grid-based multi-time scale prediction system

Abstract

The embodiment of the invention relates to a multi-time scale prediction system based on a meteorological grid. The system comprises a data receiving module, a database updating module, a grid database, a queue pool updating module, a full-grid queue pool and a service interface module, according to the invention, the problem that the time scale of the grid prediction data is too single can be solved, and the query richness of the grid prediction data can be improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a multi-timescale forecasting system based on meteorological grids. Background Technology

[0002] Meteorological grid forecasting (or prediction), also known as intelligent meteorological grid forecasting (or prediction), specifically refers to dividing a map into multiple unit grids with a certain grid precision, each unit grid being a meteorological grid, and performing weather forecasting processing on a unit basis. With the in-depth development of meteorological grid forecasting technology, most meteorological forecasting centers can now predict weather information for each grid in the next 24 / 7 time period. However, we have found some problems in practical applications: 1) The time scale of the 24 / 7 grid forecasting data provided by meteorological forecasting centers is too singular, supporting only a single time scale of 1 hour, which cannot meet the multi-scale requirements of different application scenarios. For example, the time scale required in personal travel applications is usually 1 hour, the time scale required in open-air production and construction applications is usually 1 or 2 hours, and the time scale required in environmental monitoring applications is usually 3, 4, 6, 8, or 12 hours, etc.; 2) Most meteorological forecasting centers do not support complex personalized query methods, such as querying by grid identifier + time scale, querying by location coordinates (or location) + time scale, etc. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of existing technologies by providing a multi-timescale forecasting system based on meteorological grids. This system includes: a data receiving module, a database updating module, a grid database, a queue pool updating module, a full-grid queue pool, and a service interface module. The data receiving module retrieves full-hour weather forecast data from all meteorological grids within the next 24 / 7 hours from a meteorological forecasting center and sends it as a forecast dataset to the database updating module. The database updating module updates the grid database based on the forecast dataset and sends a queue update command to the queue pool updating module upon completion of the update. The queue pool updating module updates all timescale queues in all first-level grid queue pools within the full-grid queue pool based on the grid database upon receiving the queue update command. The service interface module receives a first service request from any external client and prepares corresponding service feedback data based on the first service request, the grid database, and the full-grid queue pool, sending the feedback data back to the client. The full-grid queue pool of this invention can create multiple time-scale prediction data queues for each meteorological grid; the queue pool update module of this invention can refresh each time-scale queue of each meteorological grid in a timely manner each time the latest prediction dataset is received; the service interface module of this invention can provide full-grid query services, single-grid query services based on grid identifiers, and nearest-grid query services based on location, and provides time-scale parameters in each query service to meet multi-scale queries, and provides various feedback formats such as web pages, images, tables, and text when providing query results. This invention can solve the problem of the overly singular time scale of grid prediction data output by meteorological forecasting centers, and also improve the query richness of grid prediction data.

[0004] To achieve the above objectives, embodiments of the present invention provide a multi-timescale forecasting system based on meteorological grids, the system comprising: a data receiving module, a database updating module, a grid database, a queue pool updating module, a full-grid queue pool, and a service interface module;

[0005] The data receiving module is connected to an external meteorological forecasting center and also to the database update module; the grid database is connected to the database update module, the queue pool update module, and the service interface module; the full grid queue pool is connected to the queue pool update module and the service interface module; the database update module is connected to the queue pool update module; and the service interface module is connected to any external client.

[0006] The data receiving module is used to periodically obtain the whole-hour weather forecast data of all meteorological grids in the next 24×7 hours from the meteorological forecast center according to a preset data collection frequency, and send it to the database update module as the corresponding first forecast dataset.

[0007] The database update module is used to update all the first grid data lists of the grid database according to the first prediction dataset and the first grid list of the grid database; and to send a queue update instruction to the queue pool update module when the data update process is completed.

[0008] The grid database is used to store the first grid list and multiple first grid data lists;

[0009] The queue pool update module is used to perform queue update processing on all time-scale queues of all first grid queue pools in the full grid queue pool according to the grid database when the queue update instruction is received.

[0010] The full-grid queue pool is used to store multiple first-grid queue pools; each first-grid queue pool corresponds one-to-one with the meteorological grid; each first-grid queue pool includes a total number of queues N and N queues at the i-th time scale; 1 ≤ queue index i ≤ N; each i-th time scale queue corresponds to a time scale s in hours. i , 1≤s i ≤24 and s i Divisible by 24; each of the i-th time-scale queues consists of a total of M records in the queue. i The first queue consists of M records. i = (24 × 7) / s i Each of the i-th time-scale queues is used to first load the internal M during queue initialization. i Each of the first queue records is initialized as an empty record with all fields empty; each of the i-th time scale queues is also used to manage queue records according to the first-in-first-out principle of a circular queue each time a first queue record is added;

[0011] The service interface module is used to receive the first service request sent by the client; and to prepare and process the meteorological forecast service data according to the first service request, the grid database and the full grid queue pool to obtain the corresponding first service feedback data and send it back to the client.

[0012] Preferably, the first prediction dataset includes multiple first grid data subsets; each first grid data subset corresponds one-to-one with a meteorological grid; each first grid data subset includes a first grid identifier and a first prediction data sequence.

[0013] The first prediction data sequence consists of 24×7 first prediction data records arranged chronologically. Each first prediction data record includes a first future time period, first weather phenomenon prediction data, first precipitation prediction data, first humidity prediction data, first average wind force prediction data, first gust wind force prediction data, first wind direction prediction data, first maximum temperature prediction data, first minimum temperature prediction data, first minimum visibility prediction data, and first cloud cover prediction data. The first future time period consists of a pair of start and end times, with the start and end times including year, month, day, and hour information. The time interval between the start and end times of the first future time period is one hour. In the first prediction data sequence, the year, month, day, and hour information of the start time of the first future time period in the first first prediction data record corresponds to the year, month, day, and hour information of the current time. The start time of the first future period recorded in the next first prediction data corresponds to the end time of the first future period recorded in the previous first prediction data; the first weather phenomenon prediction data is a weather phenomenon phrase with one or more weather phenomenon names; the first precipitation prediction data is a precipitation value in millimeters; the first humidity prediction data is a humidity percentage; the first average wind force prediction data is a wind force range consisting of minimum and maximum wind force levels; the first gust wind force prediction data is a wind force level; the first wind direction prediction data is a wind direction name; the first maximum temperature prediction data and the first minimum temperature prediction data are each a corresponding degree Celsius value; the first minimum visibility prediction data is a visibility value in kilometers; the first cloud cover prediction data is a cloud cover percentage.

[0014] The first grid list includes multiple first grid records; each first grid record corresponds one-to-one with the meteorological grid and also one-to-one with the first grid data list; each first grid record includes a first grid identifier field, a first grid region name field, a first grid center point map coordinate field, a first grid vertex map coordinate set field, and a first grid data list identifier field; the first grid vertex map coordinate set field includes multiple first grid vertex map coordinates; the first grid data list identifier field of each first grid record matches the first data list identifier of the corresponding first grid data list.

[0015] Each first grid data list corresponds to a unique first data list identifier; the first grid data list includes multiple first grid data records; the first grid data record includes a first future time period field, a first weather phenomenon field, a first precipitation field, a first humidity field, a first average wind force field, a first gust wind force field, a first wind direction field, a first maximum temperature field, a first minimum temperature field, a first minimum visibility field, and a first cloud cover field; the first future time period field includes a first start time and a first end time, the time information of the first start time and the first end time includes year, month, day information and hour information, and the time interval from the first start time to the first end time... The first weather phenomenon field is a weather phenomenon phrase containing one or more weather phenomenon names; the first precipitation field is a precipitation value in millimeters; the first humidity field is a humidity percentage; the first average wind force field is a first wind force level range consisting of a first minimum wind force level and a first maximum wind force level; the first gust field is a first gust wind force level; the first wind direction field is a wind direction name; the first maximum temperature field and the first minimum temperature field are corresponding Celsius values; the first minimum visibility field is a visibility value in kilometers; the first cloud cover field is a cloud cover percentage.

[0016] The first queue record of each of the i-th time scale queues includes a second future time period field, a second weather phenomenon field, a second precipitation field, a second humidity field, a second average wind force field, a second gust wind force field, a second wind direction field, a second maximum temperature field, a second minimum temperature field, a second minimum visibility field, and a second cloud cover field; the second future time period field includes a second start time and a second end time, the time information of the second start time and the second end time includes year, month, day information and hour information, and the time interval from the second start time to the second end time corresponds to the time scale s. i match;

[0017] The first service application includes a first service type, a first application data, and a first feedback data type; the first service type includes a network-wide service type, a grid service type, and a location service type; the first feedback data type includes a webpage file type, an image file type, a table file type, and a text file type.

[0018] When the first service type is a network-wide service type, the corresponding first application data includes a first prediction start time, a first prediction number of days, and a first time scale; when the first service type is a grid service type, the corresponding first application data includes a second prediction start time, a second prediction number of days, a second time scale, and a first prediction grid identifier; when the first service type is a location service type, the corresponding first application data includes a third prediction start time, a third prediction number of days, a third time scale, and a first location coordinate.

[0019] Preferably, the database update module is specifically configured to, when performing data update processing on all first grid data lists of the grid database based on the first prediction dataset and the first grid list of the grid database, take each first grid data subset of the first prediction dataset as the corresponding current grid data subset; take the first grid identifier and the first prediction data sequence of the current grid data subset as the corresponding current grid identifier and current prediction data sequence; take the first grid data list corresponding to the first grid data list identifier field of the first grid record in the first grid list that matches the current grid identifier as the corresponding current grid data list; update the current grid data list based on the current prediction data sequence; and confirm the end of this data update processing when all first grid data lists corresponding to all first grid data subsets have completed data updates.

[0020] Furthermore, the database update module is specifically configured to, when updating the current grid data list according to the current predicted data sequence, traverse the first predicted data record of the current predicted data sequence; and during the traversal, use the currently traversed first predicted data record as the corresponding current predicted data record; and use the first grid data record in the current grid data list whose first future time period field matches the first future time period of the current predicted data record as the corresponding current grid data record; and when the grid data record is empty, add a new first grid data record to the current grid data list as the corresponding current grid data record and set the first future time period field of the current grid data record to the first future time period of the current predicted data record. The process involves setting the first weather phenomenon field, first precipitation field, first humidity field, first average wind force field, first gust wind force field, first wind direction field, first maximum temperature field, first minimum temperature field, first minimum visibility field, and first cloud cover field of the current grid data record as the corresponding first weather phenomenon prediction data, first precipitation prediction data, first humidity prediction data, first average wind force prediction data, first gust wind force prediction data, first wind direction prediction data, first maximum temperature prediction data, first minimum temperature prediction data, first minimum visibility prediction data, and first cloud cover prediction data in the current prediction data record. Upon completion of the traversal, the process confirms that the current grid data list has been updated.

[0021] Preferably, the queue pool update module is specifically used to, when performing queue update processing on all time-scale queues of all first grid queue pools in the full grid queue pool according to the grid database, take each of the first grid queue pools in the full grid queue pool as the corresponding current grid queue pool; take the first grid data list in the grid database corresponding to the current grid queue pool as the corresponding current grid data list; extract the 24×7 most recently updated first grid data records in the current grid data list, sort them in chronological order to form the corresponding current grid data record sequence; perform queue update on the current grid queue pool according to the current grid data record sequence; and confirm the end of this queue update processing when all first grid queue pools have completed the queue update.

[0022] Furthermore, the queue pool update module is specifically used when updating the current grid queue pool according to the current grid data record sequence:

[0023] Step 61: Take the first i-th time-scale queue of the current grid queue pool as the corresponding current time-scale queue;

[0024] Step 62, and set the time scale s corresponding to the current time scale queue. i As the corresponding current time scale s * ; and the total number M of queue records corresponding to the current time scale queue. i As the corresponding current total number of queues M * ; and according to the current time scale s * Divide the next 24×7 hours into the current total number of queues M. * The third future period T j ; 1 ≤ time period index j ≤ M * ; Each of the aforementioned third future time periods T j The length of the time period and the current time scale s * Consistent;

[0025] Step 63, and then place the first future time period field in each of the third future time periods T in the current grid data record sequence. j The first grid data records are extracted to form a corresponding first record set G. j ; and for each of the first record sets G j The data fusion process is performed to obtain the corresponding first fusion record R. j Each of the first record sets G j The number of the first grid data records is related to the current time scale s. * Consistent; the first fusion record R j This includes the first time period fusion field, the first weather phenomenon fusion field, the first precipitation fusion field, the first humidity fusion field, the first average wind speed fusion field, the first gust wind speed fusion field, the first wind direction fusion field, the first highest temperature fusion field, the first lowest temperature fusion field, the first minimum visibility fusion field, and the first cloud cover fusion field; the first fusion record R j The first time period fusion field and the corresponding third future time period T j match;

[0026] Step 64, for the third future time period T j Perform the traversal; and during the traversal, the currently traversed third future time period T is... j As the corresponding current time period; and the first fusion record R corresponding to the current time period. jAs the corresponding current fusion record; and the first queue record in the current timescale queue that matches the second future time period field with the current time period as the corresponding current queue record; and identify whether the current queue record is empty; if the current queue record is empty, then the first time period fusion field, the first weather phenomenon fusion field, the first precipitation fusion field, the first humidity fusion field, the first average wind force fusion field, the first gust wind force fusion field, the first wind direction fusion field, the first maximum temperature fusion field, the first minimum temperature fusion field, the first minimum visibility fusion field, and the first cloud cover fusion field of the current fusion record are used as the corresponding second future time period field, second weather phenomenon field, second precipitation field, second humidity field, second average wind force field, second gust wind force field, second wind direction field, and second maximum temperature fusion field. The first queue record, consisting of the second minimum temperature field, the second minimum visibility field, and the second cloud cover field, is added to the current timescale queue. If the current queue record is not empty, the second weather phenomenon field, the second precipitation field, the second humidity field, the second average wind force field, the second gust wind force field, the second wind direction field, the second maximum temperature field, the second minimum temperature field, the second minimum visibility field, and the second cloud cover field of the current queue record are reset to the corresponding first weather phenomenon fusion field, first precipitation fusion field, first humidity fusion field, first average wind force fusion field, first gust wind force fusion field, first wind direction fusion field, first maximum temperature fusion field, first minimum temperature fusion field, first minimum visibility fusion field, and first cloud cover fusion field in the current fusion record.

[0027] Step 65: After the traversal is completed, identify whether the current time scale queue is the last i-th time scale queue in the current grid queue pool; if not, take the next i-th time scale queue in the current grid queue pool as the new current time scale queue and proceed to step 62; if yes, confirm that the current grid data record sequence has completed the queue update.

[0028] Furthermore, the queue pool update module is specifically used for updating each of the first record sets G. j The data fusion process is performed to obtain the corresponding first fusion record R. j At that time, each of the first record sets G j As the corresponding current record set;

[0029] and the third future time period T corresponding to the current record set j As the corresponding first set of time periods; and summing the first precipitation field of all the first grid data records in the current record set to obtain the corresponding first precipitation sum; and averaging the first humidity field of all the first grid data records in the current record set to obtain the corresponding first average humidity; and taking the first minimum wind force level with the smallest value and the first maximum wind force level with the largest value among the first average wind force fields of all the first grid data records in the current record set as the corresponding second minimum wind force level and second maximum wind force level to form a corresponding second wind force level range; and taking the first gust wind force field with the largest wind force level among all the first grid data records in the current record set as the corresponding second gust wind force level; and taking all the first grid data records in the current record set as the first set of time periods; and summing the first precipitation field of all the first grid data records in the current record set as the first average wind force field; and summing the first precipitation field of all the first grid data records in the current record set as the first average wind force field; and summing the first precipitation field of all the first grid data records in the current record set as the first average wind force field; and summing the first precipitation field of all the first grid data records in the current record set as the first average wind force field; and summing the first precipitation field of all the first grid data records in the current record set as the first average wind force field; and summing the first average wind force ... The first wind direction field with the most recent time in the record is used as the corresponding first wind direction name; the highest temperature field with the highest temperature in all the first grid data records of the current record set is used as the corresponding first highest temperature, and the lowest temperature field with the lowest temperature is used as the corresponding first lowest temperature; the lowest visibility field with the lowest visibility in all the first grid data records of the current record set is used as the corresponding first minimum visibility; the first cloud cover field of all the first grid data records of the current record set is averaged to obtain the corresponding first average cloud cover; the weather phenomenon phrases of the first weather phenomenon field of all the first grid data records of the current record set are deduplicated and merged to obtain the corresponding first phrase sequence; and the time scale s corresponding to the current record set is used as the basis for the sequence. i The first weather phenomenon phrase is obtained by fusing the first total precipitation, the first phrase sequence, the first minimum visibility, and the first average cloud cover with weather phenomenon phrases.

[0030] The first time period, the first weather phenomenon phrase, the first total precipitation, the first average humidity, the second wind force range, the second gust force, the first wind direction name, the first highest temperature, the first lowest temperature, the first minimum visibility, and the first average cloud cover are used as the corresponding first time period fusion field, first weather phenomenon fusion field, first precipitation fusion field, first humidity fusion field, first average wind force fusion field, first gust force fusion field, first wind direction fusion field, first highest temperature fusion field, first lowest temperature fusion field, first minimum visibility fusion field, and first cloud cover fusion field to form a corresponding first fusion record R.j .

[0031] Furthermore, the queue pool update module is specifically used to update the queue pool according to the time scale s corresponding to the current record set. i When the first total precipitation, the first phrase sequence, the first minimum visibility, and the first average cloud cover are fused to obtain the corresponding first weather phenomenon phrase,...

[0032] The system queries a pre-defined first correspondence table that reflects the relationship between time scales and weather parameters, and matches the first time scale field in the first correspondence table with the time scale s corresponding to the current record set. i The first weather parameter set field of the first matching correspondence record is extracted as the corresponding first weather parameter set; the first correspondence table includes multiple first correspondence records; the first correspondence record includes the first time scale field and the first weather parameter set field; the first weather parameter set field includes the range of drizzle rainfall, light rain rainfall, moderate rain rainfall, heavy rain rainfall, torrential rain rainfall, extremely heavy rain rainfall, clear sky cloud cover, cloudy sky cloud cover, overcast sky cloud cover, fog-free visibility range, light fog visibility range, moderate fog visibility range, and dense fog visibility range;

[0033] When the first total precipitation is 0, the first weather phenomenon phrase is obtained by fusing weather phenomenon phrases based on the first weather parameter set, the first average cloud cover and the first minimum visibility.

[0034] When the sum of the first precipitation is greater than 0, the weather phenomenon phrase is obtained by fusing the weather phenomenon phrases according to the first weather parameter set, the first precipitation and the first phrase sequence.

[0035] More preferably, the queue pool update module is specifically used when the first weather phenomenon phrase is obtained by fusing weather phenomenon phrases based on the first weather parameter set, the first average cloud cover, and the first minimum visibility,

[0036] The first average cloud cover is identified; if the first average cloud cover meets the sunny cloud cover range of the first weather parameter set, the corresponding first weather phenomenon keyword is set to "sunny"; if the first average cloud cover meets the cloudy cloud cover range of the first weather parameter set, the corresponding first weather phenomenon keyword is set to "cloudy"; if the first average cloud cover meets the overcast cloud cover range of the first weather parameter set, the corresponding first weather phenomenon keyword is set to "overcast".

[0037] The system identifies the first minimum visibility. If the first minimum visibility meets the fog-free visibility range of the first weather parameter set, the corresponding second weather phenomenon keyword is set to empty. If the first minimum visibility meets the light fog visibility range of the first weather parameter set, the corresponding second weather phenomenon keyword is set to "light fog". If the first minimum visibility meets the moderate fog visibility range of the first weather parameter set, the corresponding second weather phenomenon keyword is set to "fog". If the first minimum visibility meets the dense fog visibility range of the first weather parameter set, the corresponding second weather phenomenon keyword is set to "dense fog".

[0038] The system identifies whether the obtained second weather phenomenon keyword is empty; if so, it sets the corresponding first weather phenomenon phrase as the first weather phenomenon keyword; if not, it sets the corresponding first weather phenomenon phrase as a phrase formed by sequentially concatenating the first weather phenomenon keyword, a preset isolation symbol, and the second weather phenomenon keyword; the preset isolation symbol is a comma "," by default.

[0039] More preferably, the queue pool update module is specifically used when the weather phenomenon phrase is obtained by fusing weather phenomenon phrases based on the first weather parameter set, the first precipitation, and the first phrase sequence,

[0040] When the total first precipitation amount meets the drizzle rainfall range of the first weather parameter set, the corresponding first weather phenomenon phrase is set to "drizzle".

[0041] When the total first precipitation amount meets the light rain range of the first weather parameter set, the keywords in the first phrase sequence are identified; if the first phrase sequence does not contain the keywords "thunder" and "hail", the corresponding first weather phenomenon phrase is set to "light rain"; if the first phrase sequence contains the keywords "thunder" and "hail", the corresponding first weather phenomenon phrase is set to "thunderstorm with hail"; if the first phrase sequence contains the keyword "thunder" but does not contain the keyword "hail", the corresponding first weather phenomenon phrase is set to "thunderstorm".

[0042] When the total first precipitation amount satisfies the range of moderate rain, heavy rain, torrential rain, heavy downpour, or extremely heavy downpour in the first weather parameter set, keywords in the first phrase sequence are identified; if the first phrase sequence does not contain the keywords "thunder" and "hail," the corresponding first weather phenomenon phrase is set to "moderate rain," "heavy rain," "torrential rain," "heavy downpour," or "extreme downpour"; if the first phrase sequence contains the keywords "thunder" and "hail,"... If the first weather phenomenon phrase is set to "moderate rain with thunder and hail", "heavy rain with thunder and hail", "torrential rain with thunder and hail", "heavy rain with thunder and hail", or "extremely heavy rain with thunder and hail", then the corresponding first weather phenomenon phrase is set to "moderate rain with thunder and hail", "heavy rain with thunder and hail", "torrential rain with thunder and hail", or "extremely heavy rain with thunder and hail".

[0043] Preferably, the i-th timescale queue is specifically used for managing queue records according to the first-in-first-out principle of a circular queue each time a first queue record is added. When a new first queue record is added, the currently added first queue record is taken as the corresponding currently added record; the number of empty records in the current i-th timescale queue where all fields are empty is counted to obtain the corresponding current total number of empty records Q; and the current total number of empty records Q is identified; if the current total number of empty records Q is greater than 0, then the (M)-th record in the current i-th timescale queue is removed from the queue. i -Q+1) of the first queue records are used as the corresponding current modified records; if the total number of current empty records Q is equal to 0, then the first queue record with the earliest time of the second future time period field in the current i-th time scale queue is used as the corresponding current modified record; and each corresponding field in the current modified record is set based on each field of the currently added record.

[0044] Preferably, the service interface module is specifically used to extract the corresponding first service type, first application data, and first feedback data type from the first service application when the first service feedback data is prepared and processed according to the first service application, the grid database, and the full grid queue pool to be sent back to the client.

[0045] And identify the first service type;

[0046] If the first service type is a network-wide service type, then the corresponding first prediction start time, first prediction days, and first time scale are extracted from the first application data; the first prediction end time is obtained by adding the first prediction start time and the first prediction days; the first prediction start time and the first prediction end time form the corresponding first prediction period; and the time scale s in each of the first grid queue pools of the full grid queue pool is used to... i The i-th time scale queue that matches the first time scale is taken as the corresponding first grid matching queue; and the first queue records in each first grid matching queue that have an intersection between the second future time period field and the first prediction time period are extracted and arranged in chronological order to form a corresponding first grid prediction list; and all the obtained first grid prediction lists form a corresponding first prediction list set;

[0047] If the first service type is a grid service type, then the corresponding second prediction start time, second prediction days, second time scale, and first prediction grid identifier are extracted from the first application data; the second prediction end time is obtained by adding the second prediction start time and the second prediction days; the second prediction start time and the second prediction end time form the corresponding second prediction period; and the first grid queue pool corresponding to the first prediction grid identifier in the full grid queue pool is used as the corresponding first matching grid pool; and the time scale s in the first matching grid pool is... i The i-th timescale queue that matches the second timescale is used as the corresponding second grid matching queue; and the first queue records in the second grid matching queue that have an intersection between the second future time period field and the second prediction time period are extracted and arranged in chronological order to form a corresponding first grid prediction list; and the obtained first grid prediction list forms the corresponding first prediction list set;

[0048] If the first service type is a location service type, then the corresponding third prediction start time, third prediction days, third time scale, and first location coordinates are extracted from the first application data; the third prediction end time is obtained by adding the third prediction start time and the third prediction days; the third prediction start time and the third prediction end time form the corresponding third prediction period; the straight-line distance between the first grid center point map coordinate field and the first location coordinates of each first grid record in the first grid list of the grid database is calculated to obtain the corresponding first straight-line distance; and the first grid record whose first straight-line distance is less than a preset straight-line distance threshold is taken as the corresponding first candidate grid record; the inclusion and non-inclusion relationship between the polygon map area enclosed by all the first grid vertex map coordinates in the first grid vertex map coordinate set field of each first candidate grid record and the first location coordinate is identified to obtain the corresponding first identification result; and the first candidate grid record corresponding to the only first identification result that is specifically inclusion is taken as the corresponding first matching grid record; and the first grid queue pool in the full grid queue pool corresponding to the first matching grid record is taken as the corresponding second matching grid pool; and the time scale s in the second matching grid pool is taken as the first matching grid pool. i The queue of the i-th time scale that matches the third time scale is taken as the corresponding third grid matching queue; and the first queue records in the third grid matching queue that have an intersection between the second future time period field and the third prediction time period are extracted and arranged in chronological order to form a corresponding first grid prediction list; and the obtained first grid prediction list forms a corresponding first grid prediction list set; the first identification result includes inclusion relationship and non-inclusion relationship;

[0049] The system identifies the data type of the first feedback. If the first feedback data type is a webpage file, all the first grid prediction lists in the obtained first grid prediction list set are converted into webpage files using a preset webpage template to obtain a corresponding first webpage file, which is then used as the corresponding first service feedback data. If the first feedback data type is an image file, each of the first grid prediction lists in the obtained first grid prediction list set is converted into a table image to obtain a corresponding first table image, which is then used as the corresponding first image file. If the first feedback data type is a table file, each of the first grid prediction lists in the obtained first grid prediction list set is converted into a spreadsheet to obtain a corresponding first spreadsheet, which is then used as the corresponding first table file, which is then used as the corresponding first service feedback data. If the first feedback data type is a text file, each of the first grid prediction lists in the obtained first grid prediction list set is converted into text information to obtain a corresponding first list text, which is then used as the corresponding first text file, which is then used as the corresponding first service feedback data.

[0050] The obtained first service feedback data is then sent back to the client.

[0051] This invention provides a multi-timescale forecasting system based on meteorological grids. The system includes: a data receiving module, a database updating module, a grid database, a queue pool updating module, a full-grid queue pool, and a service interface module. The data receiving module retrieves full-hour weather forecast data from all meteorological grids within the next 24 / 7 hours from a meteorological forecasting center and sends it as a forecast dataset to the database updating module. The database updating module updates the grid database based on the forecast dataset and sends a queue update instruction to the queue pool updating module upon completion of the update. The queue pool updating module updates all timescale queues in all first-scale queue pools of the full-grid queue pool based on the grid database upon receiving the queue update instruction. The service interface module receives a first service request from any external client and prepares corresponding service feedback data based on the first service request, the grid database, and the full-grid queue pool, sending the feedback data back to the client. The full-grid queue pool of this invention can create multiple time-scale prediction data queues for each meteorological grid; the queue pool update module of this invention can refresh each time-scale queue of each meteorological grid in a timely manner each time the latest prediction dataset is received; the service interface module of this invention can provide full-grid query services, single-grid query services based on grid identifiers, and nearest-grid query services based on location, and provides time-scale parameters in each query service to meet multi-scale queries, and provides various feedback formats such as web pages, images, tables, and text when providing query results. This invention not only solves the problem of the overly singular time scale of grid prediction data output by meteorological forecasting centers, but also improves the query richness of grid prediction data. Attached Figure Description

[0052] Figure 1 This is a module structure diagram of a multi-timescale prediction system based on meteorological grids provided in an embodiment of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0054] This invention provides a multi-timescale forecasting system based on meteorological grids, such as... Figure 1The module structure diagram of a multi-timescale prediction system based on meteorological grids provided in this embodiment of the invention is shown. The multi-timescale prediction system 1 based on meteorological grids mainly includes: a data receiving module 11, a database update module 12, a grid database 13, a queue pool update module 14, a full grid queue pool 15, and a service interface module 16.

[0055] like Figure 1 As shown, the connection relationships of the modules within the system are as follows: the data receiving module 11 is connected to the external meteorological forecasting center 2 and also to the database update module 12; the grid database 13 is connected to the database update module 12, the queue pool update module 14, and the service interface module 16; the full grid queue pool 15 is connected to the queue pool update module 14 and the service interface module 16; the database update module 12 is connected to the queue pool update module 14; and the service interface module 16 is connected to any external client 3. Here, in this embodiment of the invention, the client 3 is any interface, module, device, equipment, server / server, system, or platform that needs to query information through the service interface module 16.

[0056] (a) Data receiving module 11:

[0057] The data receiving module 11 is used to periodically obtain the whole-hour weather forecast data of all meteorological grids in the next 24×7 hours from the meteorological forecasting center 2 according to the preset data acquisition frequency, and send it to the database update module 12 as the corresponding first forecast dataset.

[0058] Here, the data acquisition frequency is a preset time frequency parameter; under normal circumstances, the time period (1 / data acquisition frequency) corresponding to this data acquisition frequency should be less than 1 hour. For example, the data acquisition frequency is once every 5 minutes, and the corresponding time period is 5 minutes.

[0059] The data format of the first prediction dataset in this embodiment of the invention is as follows:

[0060] The first prediction dataset includes multiple subsets of the first grid data;

[0061] The first grid data subset corresponds one-to-one with the meteorological grid;

[0062] The first grid data subset includes the first grid identifier and the first predicted data sequence;

[0063] The first grid identifier is the unique grid identifier of the corresponding meteorological grid;

[0064] The first forecast data sequence consists of 24×7 first forecast data records arranged chronologically. Each first forecast data record includes the first future time period, the first weather phenomenon forecast data, the first precipitation forecast data, the first humidity forecast data, the first average wind speed forecast data, the first gust wind speed forecast data, the first wind direction forecast data, the first maximum temperature forecast data, the first minimum temperature forecast data, the first minimum visibility forecast data, and the first cloud cover forecast data. The first future time period consists of a pair of start and end times, with the start and end times including year, month, day, and hour information. The time interval between the start and end times of the first future time period is one hour. In the first forecast data sequence, the year, month, day, and hour information of the start time of the first future time period of the first first forecast data record corresponds to the year, month, day, and hour information of the current time. The start time of the first future time period of the subsequent first forecast data record corresponds to the start time of the previous first forecast data record. The first forecast data corresponds to the end time of the first future period; the first weather phenomenon forecast data is a weather phenomenon phrase with one or more weather phenomenon names, such as "sunny", "partly cloudy", "overcast", "light rain", "thunderstorm", "moderate rain with thunder and hail", etc.; the first precipitation forecast data is a precipitation value in millimeters; the first humidity forecast data is a humidity percentage; the first average wind speed forecast data is a wind speed range consisting of the minimum and maximum wind speed levels; the first gust wind speed forecast data is a wind speed level; the first wind direction forecast data is a wind direction name, such as "east", "south", "west", "north", "west of northwest", "south of southeast", etc.; the first maximum temperature forecast data and the first minimum temperature forecast data are each a corresponding Celsius value; the first minimum visibility forecast data is a visibility value in kilometers; and the first cloud cover forecast data is a cloud cover percentage.

[0065] (II) Database Update Module 12:

[0066] The database update module 12 is used to update all the first grid data lists of the grid database 13 according to the first prediction dataset and the first grid list of the grid database 13; and to send a queue update instruction to the queue pool update module 14 when the data update process is completed.

[0067] In a specific implementation of this invention, the database update module 12 is specifically configured to, when performing data update processing on all first grid data lists of the grid database 13 according to the first prediction dataset and the first grid list of the grid database 13, take each first grid data subset of the first prediction dataset as the corresponding current grid data subset; take the first grid identifier and the first prediction data sequence of the current grid data subset as the corresponding current grid identifier and current prediction data sequence; take the first grid data list identifier field of the first grid record in the first grid list that matches the current grid identifier as the corresponding current grid data list; update the current grid data list according to the current prediction data sequence; and confirm the end of this data update processing when all first grid data lists corresponding to all first grid data subsets have completed data update.

[0068] In another specific implementation of this invention, the database update module 12 is specifically configured to, when updating the current grid data list according to the current predicted data sequence, traverse the first predicted data record of the current predicted data sequence; and during the traversal, use the currently traversed first predicted data record as the corresponding current predicted data record; and use the first grid data record in the current grid data list whose first future time period field matches the first future time period of the current predicted data record as the corresponding current grid data record; and when the grid data record is empty, add a new first grid data record to the current grid data list as the corresponding current grid data record and set the first future time period field of the current grid data record to the current predicted data record. The first future time period of the measured data is recorded; and the first weather phenomenon field, first precipitation field, first humidity field, first average wind force field, first gust wind force field, first wind direction field, first maximum temperature field, first minimum temperature field, first minimum visibility field, and first cloud cover field of the current grid data record are set as the corresponding first weather phenomenon prediction data, first precipitation prediction data, first humidity prediction data, first average wind force prediction data, first gust wind force prediction data, first wind direction prediction data, first maximum temperature prediction data, first minimum temperature prediction data, first minimum visibility prediction data, and first cloud cover prediction data in the current prediction data record; and at the end of the traversal, it is confirmed that the current grid data list has completed the data update.

[0069] (III) Grid Database 13:

[0070] The grid database 13 is used to store the first grid list and multiple first grid data lists.

[0071] Here, the grid database 13 of this embodiment of the invention stores the identifier, name and location information of all meteorological grids based on the first grid list; and stores the time-series grid prediction data of each meteorological grid obtained from the meteorological forecasting center 2 based on each first grid data list.

[0072] The first grid list includes multiple first grid records; each first grid record corresponds one-to-one with a meteorological grid and also one-to-one with the first grid data list; each first grid record includes a first grid identifier field, a first grid region name field, a first grid center point map coordinate field, a first grid vertex map coordinate set field, and a first grid data list identifier field.

[0073] Here, the first grid identifier field is used to store the unique grid identifier of the corresponding meteorological grid; the first grid region name field is used to store the region name information of the corresponding meteorological grid; the first grid center point map coordinate field is used to store the map coordinates of the grid center position of the corresponding meteorological grid; the first grid vertex map coordinate set field includes multiple first grid vertex map coordinates, and the first grid vertex map coordinate set field is used to store the map coordinates of all grid vertices of the corresponding meteorological grid, with each grid vertex corresponding to one first grid vertex map coordinate; the first grid data list identifier field of each first grid record matches the first data list identifier of the corresponding first grid data list.

[0074] Each first grid data list corresponds to a unique first data list identifier.

[0075] Each first grid data list includes multiple first grid data records; each first grid data record includes a first future time period field, a first weather phenomenon field, a first precipitation field, a first humidity field, a first average wind speed field, a first gust wind speed field, a first wind direction field, a first maximum temperature field, a first minimum temperature field, a first minimum visibility field, and a first cloud cover field; the first future time period field includes a first start time and a first end time, the time information for the first start time and the first end time includes year, month, day, and hour information, and the time interval between the first start time and the first end time is one hour; the first weather phenomenon... The first field is a weather phenomenon phrase containing one or more weather phenomenon names; the first precipitation field is a precipitation value in millimeters; the first humidity field is a humidity percentage; the first average wind force field is a first wind force range consisting of a first minimum wind force level and a first maximum wind force level; the first gust wind force field is a first gust wind force level; the first wind direction field is a wind direction name; the first maximum temperature field and the first minimum temperature field are corresponding values ​​in degrees Celsius; the first minimum visibility field is a visibility value in kilometers; and the first cloud cover field is a cloud cover percentage.

[0076] (iv) Queue Pool Update Module 14:

[0077] The queue pool update module 14 is used to perform queue update processing on all time-scale queues of all first grid queue pools 151 in the full grid queue pool 15 according to the grid database 13 when a queue update instruction is received.

[0078] In another specific implementation of this invention, the queue pool update module 14 is specifically used to, when performing queue update processing on all time-scale queues of all first grid queue pools 151 in the full grid queue pool 15 according to the grid database 13, take each first grid queue pool 151 of the full grid queue pool 15 as the corresponding current grid queue pool; take the first grid data list in the grid database 13 corresponding to the current grid queue pool as the corresponding current grid data list; extract the 24×7 most recently updated first grid data records in the current grid data list, sort them in chronological order to form the corresponding current grid data record sequence; perform queue update on the current grid queue pool according to the current grid data record sequence; and confirm the end of this queue update processing when all first grid queue pools 151 have completed the queue update.

[0079] In another specific implementation of this invention, the queue pool update module 14 is specifically used to update the current grid queue pool according to the current grid data record sequence:

[0080] Step A1: Take the first i-th time-scale queue 1511 of the current grid queue pool as the corresponding current time-scale queue;

[0081] Step A2, and set the time scale s corresponding to the current time scale queue. i As the corresponding current time scale s * ; and the total number of queue records M corresponding to the current time scale queue. i As the corresponding current total number of queues M * ; and according to the current time scale s * Divide the next 24 x 7 hours into the current total number of queues M. * The third future period T j ;

[0082] Where 1 ≤ time period index j ≤ M * ; Each third future time period T j The length of the time period and the current time scale s * Consistent;

[0083] Step A3, and place the first future time period field in each third future time period T in the current grid data record sequence. jThe first grid data records are extracted to form a corresponding first record set G. j ; and for each first record set G j The data fusion process is performed to obtain the corresponding first fusion record R. j ;

[0084] Among them, each first record set G j The number of data records in the first grid is related to the current time scale s * Consistent; First fusion record R j This includes the first time period fusion field, the first weather phenomenon fusion field, the first precipitation fusion field, the first humidity fusion field, the first average wind speed fusion field, the first gust wind speed fusion field, the first wind direction fusion field, the first highest temperature fusion field, the first lowest temperature fusion field, the first minimum visibility fusion field, and the first cloud cover fusion field; the first fusion record R j The first time period fusion field and the corresponding third future time period T j match;

[0085] Step A4, for the third future time period T j Perform the traversal; and during the traversal, set the third future time interval T of the current traversal. j As the corresponding current time period; and the first fusion record R corresponding to the current time period. j As the corresponding current fusion record; and take the first queue record in the current timescale queue that matches the second future time period field with the current time period as the corresponding current queue record; and identify whether the current queue record is empty;

[0086] If the current queue record is empty, then the first time period fusion field, the first weather phenomenon fusion field, the first precipitation fusion field, the first humidity fusion field, the first average wind force fusion field, the first gust wind force fusion field, the first wind direction fusion field, the first maximum temperature fusion field, the first minimum temperature fusion field, the first minimum visibility fusion field, and the first cloud cover fusion field of the current fusion record are used as the corresponding second future time period field, the second weather phenomenon field, the second precipitation field, the second humidity field, the second average wind force field, the second gust wind force field, the second wind direction field, the second maximum temperature field, the second minimum temperature field, the second minimum visibility field, and the second cloud cover field to form a corresponding first queue record and add it to the current time scale queue;

[0087] If the current queue record is not empty, then reset the second weather phenomenon field, second precipitation field, second humidity field, second average wind force field, second gust wind force field, second wind direction field, second maximum temperature field, second minimum temperature field, second minimum visibility field, and second cloud cover field of the current queue record to the corresponding first weather phenomenon fusion field, first precipitation fusion field, first humidity fusion field, first average wind force fusion field, first gust wind force fusion field, first wind direction fusion field, first maximum temperature fusion field, first minimum temperature fusion field, first minimum visibility fusion field, and first cloud cover fusion field in the current fusion record;

[0088] Step A5: After the traversal is complete, identify whether the current time scale queue is the last i-th time scale queue 1511 in the current grid queue pool; if not, take the next i-th time scale queue 1511 in the current grid queue pool as the new current time scale queue and go to step A2; if yes, confirm that the current grid data record sequence has completed the queue update.

[0089] In another specific implementation of this invention, the queue pool update module 14 is specifically used to update each first record set G. j The data fusion process is performed to obtain the corresponding first fusion record R. j hour:

[0090] Step B1, set each first record set G j As the corresponding current record set;

[0091] Step B2, and set the third future time period T corresponding to the current record set. jAs the corresponding first set of time periods; and summing the first precipitation field of all first grid data records in the current record set to obtain the corresponding first precipitation sum; and averaging the first humidity field of all first grid data records in the current record set to obtain the corresponding first average humidity; and taking the smallest first minimum wind force level and the largest first maximum wind force level from the first average wind force field of all first grid data records in the current record set as the corresponding second minimum wind force level and second maximum wind force level to form a corresponding second wind force level range; and taking the first gust wind force field with the largest wind force level from all first grid data records in the current record set as the corresponding second gust wind force level; and taking all first grid data records in the current record set as the corresponding second gust wind force level; The first wind direction field, which is the most recent in the record, is used as the corresponding first wind direction name; the highest temperature field among all first grid data records in the current record set is used as the corresponding first highest temperature, and the lowest temperature field is used as the corresponding first lowest temperature; the lowest visibility field among all first grid data records in the current record set is used as the corresponding first minimum visibility; the first cloud cover field of all first grid data records in the current record set is averaged to obtain the corresponding first average cloud cover; the weather phenomenon phrases in the first weather phenomenon field of all first grid data records in the current record set are deduplicated and merged to obtain the corresponding first phrase sequence; and the time scale s corresponding to the current record set is used as the basis for the analysis. i The first weather phenomenon phrase is obtained by fusing the first total precipitation, the first phrase sequence, the first minimum visibility, and the first average cloud cover.

[0092] Step B3, using the obtained first time period, first weather phenomenon phrase, first total precipitation, first average humidity, second wind force range, second gust force, first wind direction name, first highest temperature, first lowest temperature, first minimum visibility, and first average cloud cover as the corresponding first time period fusion field, first weather phenomenon fusion field, first precipitation fusion field, first humidity fusion field, first average wind force fusion field, first gust force fusion field, first wind direction fusion field, first highest temperature fusion field, first lowest temperature fusion field, first minimum visibility fusion field, and first cloud cover fusion field to form a corresponding first fusion record R. j .

[0093] In another specific implementation of this invention, the queue pool update module 14 is specifically used to update the queue pool according to the time scale s corresponding to the current record set. iWhen the first weather phenomenon phrase is obtained by fusing the first total precipitation, the first phrase sequence, the first minimum visibility, and the first average cloud cover:

[0094] Step C1: Query the preset first correspondence table that reflects the relationship between time scales and weather parameters, and match the first time scale field in the first correspondence table with the time scale s corresponding to the current record set. i The first weather parameter set field of the first matching record is extracted as the corresponding first weather parameter set.

[0095] The first correspondence table includes multiple first correspondence records; each first correspondence record includes a first time scale field and a first weather parameter set field; the first weather parameter set field includes the range of drizzle rainfall, light rain rainfall, moderate rain rainfall, heavy rain rainfall, torrential rain rainfall, extremely heavy rain rainfall, clear sky cloud cover, partly cloudy cloud cover, overcast sky cloud cover, fog-free visibility range, light fog visibility range, moderate fog visibility range, and dense fog visibility range.

[0096] Step C2: When the first total precipitation is 0, the weather phenomenon phrase is fused according to the first weather parameter set, the first average cloud cover and the first minimum visibility to obtain the corresponding first weather phenomenon phrase.

[0097] Step C3: When the total first precipitation is greater than 0, the weather phenomenon phrase is fused according to the first weather parameter set, the first precipitation and the first phrase sequence to obtain the corresponding first weather phenomenon phrase.

[0098] In another specific implementation of this invention, the queue pool update module 14 is specifically used to obtain the corresponding first weather phenomenon phrase by fusing weather phenomenon phrases based on the first weather parameter set, the first average cloud cover, and the first minimum visibility:

[0099] Step D1: Identify the first average cloud cover; if the first average cloud cover meets the sunny cloud cover range of the first weather parameter set, then set the corresponding first weather phenomenon keyword to "sunny"; if the first average cloud cover meets the cloudy cloud cover range of the first weather parameter set, then set the corresponding first weather phenomenon keyword to "cloudy"; if the first average cloud cover meets the overcast cloud cover range of the first weather parameter set, then set the corresponding first weather phenomenon keyword to "overcast".

[0100] Step D2, and identify the first minimum visibility; if the first minimum visibility meets the fog-free visibility range of the first weather parameter set, then set the corresponding second weather phenomenon keyword to empty; if the first minimum visibility meets the light fog visibility range of the first weather parameter set, then set the corresponding second weather phenomenon keyword to "light fog"; if the first minimum visibility meets the moderate fog visibility range of the first weather parameter set, then set the corresponding second weather phenomenon keyword to "fog"; if the first minimum visibility meets the dense fog visibility range of the first weather parameter set, then set the corresponding second weather phenomenon keyword to "dense fog".

[0101] Step D3, and identify whether the obtained second weather phenomenon keyword is empty; if so, set the corresponding first weather phenomenon phrase as the first weather phenomenon keyword; if not, set the corresponding first weather phenomenon phrase as a phrase formed by sequentially concatenating the first weather phenomenon keyword, the preset isolation symbol, and the second weather phenomenon keyword.

[0102] The default separator is the comma ",".

[0103] For example, if the keyword for the first weather phenomenon is "sunny" and the keyword for the second weather phenomenon is empty, then the resulting phrase for the first weather phenomenon is "sunny". Another example: if the keyword for the first weather phenomenon is "cloudy" and the keyword for the second weather phenomenon is "dense fog", and the default separator is a comma, then the resulting phrase for the first weather phenomenon is "cloudy, dense fog".

[0104] In another specific implementation of this invention, the queue pool update module 14 is specifically used to obtain the corresponding first weather phenomenon phrase by fusing weather phenomenon phrases according to the first weather parameter set, the first precipitation, and the first phrase sequence:

[0105] Step E1: When the total first precipitation meets the drizzle rainfall range of the first weather parameter set, set the corresponding first weather phenomenon phrase to "drizzle".

[0106] Step E2: When the total first precipitation amount meets the light rain range of the first weather parameter set, the keywords in the first phrase sequence are identified; if the first phrase sequence does not contain the keywords "thunder" and "hail", the corresponding first weather phenomenon phrase is set to "light rain"; if the first phrase sequence contains the keywords "thunder" and "hail", the corresponding first weather phenomenon phrase is set to "thunderstorm with hail"; if the first phrase sequence contains the keyword "thunder" but does not contain the keyword "hail", the corresponding first weather phenomenon phrase is set to "thunderstorm".

[0107] Step E3: When the total first precipitation amount satisfies the range of moderate rain, heavy rain, torrential rain, extremely heavy rain, or exceptionally heavy rain in the first weather parameter set, the keywords in the first phrase sequence are identified. If the first phrase sequence does not contain the keywords "thunder" and "hail," the corresponding first weather phenomenon phrase is set to "moderate rain," "heavy rain," "torrential rain," "extremely heavy rain," or "exceptively heavy rain." If the first phrase sequence contains the keywords "thunder" and "hail," the corresponding... The first weather phenomenon phrase is set to the corresponding "moderate rain with thunder and hail", "heavy rain with thunder and hail", "torrential rain with thunder and hail", "heavy rain with thunder and hail", or "extremely heavy rain with thunder and hail". If the first phrase sequence contains the keyword "thunder" but not the keyword "hail", then the corresponding first weather phenomenon phrase is set to the corresponding "moderate rain with thunder", "heavy rain with thunder", "torrential rain with thunder", "heavy rain with thunder", or "extremely heavy rain with thunder".

[0108] (V) Full Grid Queue Pool 15:

[0109] The full grid queue pool 15 is used to store multiple first grid queue pools 151.

[0110] Here, the first grid queue pool 151 in this embodiment of the invention corresponds one-to-one with the meteorological grid.

[0111] Each first grid queue pool 151 in this embodiment of the invention includes a total number of queues N and N queues 1511 at the i-th time scale; 1 ≤ queue index i ≤ N. Each queue 1511 at the i-th time scale corresponds to a time scale s in hours. i , 1≤s i ≤24 and s i It is divisible by 24. Each queue at time scale i, 1511, records a total of M. i The first queue consists of M records. i = (24 × 7) / s i .

[0112] Here, the total number N of each first grid queue pool 151 in this embodiment of the invention is actually the total number of time scales required for the corresponding meteorological grid. The total number N of queues can be customized according to the number of time scales required for the corresponding meteorological grid.

[0113] For example, if a meteorological grid A covering highways only requires a specific time scale of 1 hour, then the total number of queues in the first grid queue pool 151 corresponding to meteorological grid A is N = 1. This first grid queue pool 151 includes only one queue 1511 for the i-th time scale, where the value of i is only 1, and the time scale s corresponding to the first time scale queue 1511 is...i＝1 = 1 hour, and the first timescale queue 1511 consists of a total queue record M. i＝1 = (24 × 7) / s i＝1 = Consists of 168 records from the first queue;

[0114] For example, if meteorological grid B covering the dock operation area requires two specific time scales of 1 and 2 hours, then the total number of queues in the first grid queue pool 151 corresponding to meteorological grid B is N = 2. This first grid queue pool 151 includes two queues 1511 of the i-th time scale, where the value of i is only {1, 2}, and the time scale s corresponding to the first time scale queue 1511 is... i＝1 = 1 hour, the time scale s corresponding to queue 1511 of the second time scale i＝2 = 2 hours, and the first time scale queue 1511 is recorded by the total number of queue records M. i＝1 = (24 × 7) / s i＝1 = Consists of 168 records in the first queue, and the second timescale queue 1511 consists of the total number of queue records M. i＝2 = (24 × 7) / s i＝2 = Consists of 84 records from the first queue;

[0115] For example, if a meteorological grid C covering a marine environmental monitoring area requires three time scales of 1, 3, and 8 hours, then the total number of queues in the first grid queue pool 151 corresponding to meteorological grid C is N = 3. This first grid queue pool 151 includes three queues 1511 at the i-th time scale, where the value of i is only {1, 2, 3}, and the time scale s corresponding to the first time scale queue 1511 is... i＝1 = 1 hour, the time scale s corresponding to queue 1511 of the second time scale i＝2 =3 hours, the time scale s corresponding to the third time scale queue 1511 i＝3 =8 hours, and the first time scale queue 1511 is recorded by the total number of queue records M. i＝1 = (24 × 7) / s i＝1 = Consists of 168 records in the first queue, and the second timescale queue 1511 consists of the total number of queue records M. i＝2 = (24 × 7) / s i＝2 = Consists of 56 records from the first queue, and the third timescale queue 1511 consists of the total number of queue records M. i＝2 = (24 × 7) / s i＝3 = It consists of 21 records from the first queue.

[0116] The first queue record of each i-th timescale queue 1511 in this embodiment of the invention includes a second future time period field, a second weather phenomenon field, a second precipitation field, a second humidity field, a second average wind force field, a second gust wind force field, a second wind direction field, a second maximum temperature field, a second minimum temperature field, a second minimum visibility field, and a second cloud cover field; the second future time period field includes a second start time and a second end time, the time information of the second start time and the second end time includes year, month, day information and hour information, and the time interval from the second start time to the second end time corresponds to the timescale s. i match.

[0117] Each i-th time-scale queue 1511 is used to first load the internal M during queue initialization. i Each record in the first queue is initialized as an empty record with all fields empty.

[0118] Here, the total number of queues N and the corresponding N time scales s for each meteorological grid in the embodiments of the present invention are as follows. i Once determined, the first grid queue pool 151 corresponding to each meteorological grid will be initialized in the full grid queue pool 15. Specifically, all i-th time-scale queues 1511 in each first grid queue pool 151 will be initialized. The method for initializing each i-th time-scale queue 1511 is to first initialize the M in the current queue. i Each record in the first queue is initialized as an empty record with all fields empty.

[0119] Each i-th timescale queue 1511 is also used to manage queue records according to the first-in-first-out principle of a circular queue each time a first queue record is added.

[0120] In another specific implementation of this invention, the i-th timescale queue 1511 is specifically used to manage queue records according to the first-in-first-out principle of a circular queue each time a first queue record is added. When a new first queue record is added, the newly added first queue record is taken as the corresponding currently added record; the number of empty records in the current i-th timescale queue 1511 with all fields empty is counted to obtain the corresponding current total number of empty records Q; and the current total number of empty records Q is identified; if the current total number of empty records Q is greater than 0, then the (M)-th record in the current i-th timescale queue 1511 is removed from the queue. i -Q+1) records in the first queue are used as the corresponding current modified records; if the total number of empty records Q is equal to 0, then the first queue record with the earliest time of the second future time period field in the current i-th time scale queue 1511 is used as the corresponding current modified record; and the corresponding fields in the current modified record are set based on the fields of the currently added record.

[0121] (vi) Service Interface Module 16:

[0122] The service interface module 16 is used to receive the first service request sent by the client 3; and to prepare and process the meteorological forecast service data according to the first service request, the grid database 13 and the full grid queue pool 15 to obtain the corresponding first service feedback data and send it back to the client 3.

[0123] The first service application includes the first service type, the first application data, and the first feedback data type; the first service type includes the network-wide service type, the grid service type, and the location service type; the first feedback data type includes web page file type, image file type, table file type, and text file type.

[0124] When the first service type is a network-wide service type, the corresponding first application data includes the first prediction start time, the first prediction number of days, and the first time scale; when the first service type is a grid service type, the corresponding first application data includes the second prediction start time, the second prediction number of days, the second time scale, and the first prediction grid identifier; when the first service type is a location service type, the corresponding first application data includes the third prediction start time, the third prediction number of days, the third time scale, and the first location coordinates.

[0125] In another specific implementation of this invention, the service interface module 16 is specifically used to send back the corresponding first service feedback data to the client 3 after preparing and processing the meteorological forecast service data according to the first service request, the grid database 13, and the full grid queue pool 15:

[0126] Step F1: Extract the corresponding first service type, first application data, and first feedback data type from the first service application;

[0127] Step F2, and identify the first service type;

[0128] Step F3: If the first service type is a network-wide service type, then extract the corresponding first prediction start time, first prediction days, and first time scale from the first application data; and add the first prediction start time and the first prediction days to obtain the corresponding first prediction end time; and combine the first prediction start time and the first prediction end time to form the corresponding first prediction period; and combine the time scales s in each of the first grid queue pools 151 of the full grid queue pool 15. iThe i-th timescale queue 1511 that matches the first timescale is taken as the corresponding first grid matching queue; and the first queue records in each first grid matching queue that have the intersection of the second future time period field and the first prediction time period are extracted and arranged in chronological order to form a corresponding first grid prediction list; and all the obtained first grid prediction lists form the corresponding first prediction list set;

[0129] Here, the service interface module 16 of this embodiment provides full-grid query service when the first service type is a full-network service type, and provides multi-scale query based on the first time scale;

[0130] Step F4: If the first service type is a grid service type, extract the corresponding second prediction start time, second prediction days, second time scale, and first prediction grid identifier from the first application data; add the second prediction start time and the second prediction days to obtain the corresponding second prediction end time; and combine the second prediction start time and the second prediction end time to form the corresponding second prediction period; and use the first grid queue pool 151 in the full grid queue pool 15 corresponding to the first prediction grid identifier as the corresponding first matching grid pool; and set the time scale s in the first matching grid pool as the first matching grid pool. i The i-th timescale queue 1511 that matches the second timescale is used as the corresponding second grid matching queue; and the first queue records in the second grid matching queue that have the intersection of the second future time period field and the second prediction time period are extracted and arranged in chronological order to form a corresponding first grid prediction list; and the obtained first grid prediction list forms the corresponding first prediction list set;

[0131] Here, in this embodiment of the invention, the service interface module 16 provides a single grid query service based on the first predicted grid identifier when the first service type is a grid service type, and provides multi-scale queries based on the second time scale;

[0132] Step F5: If the first service type is a location service type, extract the corresponding third prediction start time, third prediction days, third time scale, and first location coordinates from the first application data; add the third prediction start time and the third prediction days to obtain the corresponding third prediction end time; and combine the third prediction start time and the third prediction end time to form the corresponding third prediction period; calculate the straight-line distance between the map coordinate field of the first grid center point of each first grid record in the first grid list of the grid database 13 and the first location coordinates to obtain the corresponding first straight-line distance; and take the first grid record whose first straight-line distance is less than the preset straight-line distance threshold as the corresponding first candidate grid record; identify the inclusion and non-inclusion relationship between the polygon map area enclosed by all the first grid vertex map coordinates in the first grid vertex map coordinate set field of each first candidate grid record and the first location coordinates to obtain the corresponding first identification result; and take the first candidate grid record corresponding to the only first identification result that is specifically inclusion as the corresponding first matching grid record; and take the first grid queue pool 151 in the full grid queue pool 15 corresponding to the first matching grid record as the corresponding second matching grid pool; and take the time scale s in the second matching grid pool as the first candidate grid record. i The i-th timescale queue 1511 that matches the third timescale is used as the corresponding third grid matching queue; and the first queue records in the third grid matching queue that have the intersection of the second future time period field and the third prediction time period are extracted and arranged in chronological order to form a corresponding first grid prediction list; and the obtained first grid prediction list forms the corresponding first prediction list set;

[0133] The first identification result includes inclusion relationships and non-inclusion relationships;

[0134] Here, the straight-line distance threshold is a pre-set distance parameter; the service interface module 16 of this embodiment provides the nearest grid query service based on the first location coordinates when the first service type is the location service type, and provides multi-scale query based on the third time scale;

[0135] Step F6 involves identifying the first feedback data type. If the first feedback data type is a webpage file, all first grid prediction lists in the obtained first grid prediction list set are input into a preset webpage template for webpage file conversion to obtain the corresponding first webpage file, and the obtained first webpage file is used as the corresponding first service feedback data. If the first feedback data type is an image file, each first grid prediction list in the obtained first grid prediction list set is converted into a table image to obtain the corresponding first table image, and all the obtained first table images form the corresponding first image file, and the obtained first image file is used as the corresponding first service feedback data. If the first feedback data type is a table file, each first grid prediction list in the obtained first grid prediction list set is converted into a spreadsheet to obtain the corresponding first spreadsheet, and all the obtained first spreadsheets form the corresponding first table file, and the obtained first table file is used as the corresponding first service feedback data. If the first feedback data type is a text file, each first grid prediction list in the obtained first grid prediction list set is converted into text information to obtain the corresponding first list text, and all the obtained first list text forms the corresponding first text file, and the obtained first text file is used as the corresponding first service feedback data.

[0136] Here, the webpage template is a pre-set webpage template that can be customized according to actual application needs and / or the type of client 3; when preparing feedback data, the service interface module 16 of this embodiment prepares corresponding webpage, image, table or text data based on the first feedback data type;

[0137] Step F7, and send the obtained first service feedback data back to client 3.

[0138] It should be noted that the division of the various modules in the above system is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing element calls; they can be fully implemented in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware. For example, the data receiving module can be a separate processing element, or it can be integrated into a chip in the aforementioned device. Alternatively, it can be stored as program code in the memory of the aforementioned device, and called and executed by a processing element of the system. The implementation of other modules is similar. Moreover, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, the method steps of the aforementioned method or the processing steps of the modules of the aforementioned system can be completed through hardware integrated logic circuits in the processor element or software instructions.

[0139] For example, these modules in the above system can be one or more integrated circuits configured to implement the aforementioned methods, such as one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs). As another example, when a module in the above system is implemented through processing element scheduler code, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together to implement a system-on-a-chip (SOC).

[0140] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the foregoing method embodiments are generated. The computer described above can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The aforementioned computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the aforementioned computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, Bluetooth, microwave, etc.) means. The aforementioned computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The aforementioned available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0141] This invention provides a multi-timescale forecasting system based on meteorological grids. The system includes: a data receiving module, a database updating module, a grid database, a queue pool updating module, a full-grid queue pool, and a service interface module. The data receiving module retrieves full-hour weather forecast data from all meteorological grids within the next 24 / 7 hours from a meteorological forecasting center and sends it as a forecast dataset to the database updating module. The database updating module updates the grid database based on the forecast dataset and sends a queue update instruction to the queue pool updating module upon completion of the update. The queue pool updating module updates all timescale queues in all first-scale queue pools of the full-grid queue pool based on the grid database upon receiving the queue update instruction. The service interface module receives a first service request from any external client and prepares corresponding service feedback data based on the first service request, the grid database, and the full-grid queue pool, sending the feedback data back to the client. The full-grid queue pool of this invention can create multiple time-scale prediction data queues for each meteorological grid; the queue pool update module of this invention can refresh each time-scale queue of each meteorological grid in a timely manner each time the latest prediction dataset is received; the service interface module of this invention can provide full-grid query services, single-grid query services based on grid identifiers, and nearest-grid query services based on location, and provides time-scale parameters in each query service to meet multi-scale queries, and provides various feedback formats such as web pages, images, tables, and text when providing query results. This invention not only solves the problem of the overly singular time scale of grid prediction data output by meteorological forecasting centers, but also improves the query richness of grid prediction data.

[0142] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0143] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0144] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-timescale forecasting system based on meteorological grids, characterized in that, The system includes: a data receiving module, a database update module, a grid database, a queue pool update module, a full grid queue pool, and a service interface module; The data receiving module is connected to an external meteorological forecasting center and also to the database update module; the grid database is connected to the database update module, the queue pool update module, and the service interface module; the full grid queue pool is connected to the queue pool update module and the service interface module; the database update module is connected to the queue pool update module; and the service interface module is connected to any external client. The data receiving module is used to periodically obtain the whole-hour weather forecast data of all meteorological grids in the next 24×7 hours from the meteorological forecast center according to a preset data collection frequency, and send it to the database update module as the corresponding first forecast dataset. The database update module is used to update all the first grid data lists of the grid database according to the first prediction dataset and the first grid list of the grid database; and to send a queue update instruction to the queue pool update module when the data update process is completed. The grid database is used to store the first grid list and multiple first grid data lists; The queue pool update module is used to perform queue update processing on all time-scale queues of all first grid queue pools in the full grid queue pool according to the grid database when the queue update instruction is received. The full-grid queue pool is used to store multiple first-grid queue pools; each first-grid queue pool corresponds one-to-one with the meteorological grid; each first-grid queue pool includes a total number of queues N and N queues at the i-th time scale; 1 ≤ queue index i ≤ N; each i-th time scale queue corresponds to a time scale s in hours. i , 1≤s i ≤24 and s i Divisible by 24; each of the i-th time-scale queues consists of a total of M records in the queue. i The first queue consists of M records. i = (24 × 7) / s i Each of the i-th time-scale queues is used to first load the internal M during queue initialization. i Each of the first queue records is initialized as an empty record with all fields empty; each of the i-th time scale queues is also used to manage queue records according to the first-in-first-out principle of a circular queue each time a first queue record is added; The service interface module is used to receive the first service request sent by the client; and to prepare and process the meteorological forecast service data according to the first service request, the grid database and the full grid queue pool to obtain the corresponding first service feedback data and send it back to the client.

2. The multi-timescale forecasting system based on meteorological grids according to claim 1, characterized in that, The first prediction dataset includes multiple first grid data subsets; each first grid data subset corresponds one-to-one with a meteorological grid; each first grid data subset includes a first grid identifier and a first prediction data sequence. The first prediction data sequence consists of 24×7 first prediction data records arranged chronologically. Each first prediction data record includes a first future time period, first weather phenomenon prediction data, first precipitation prediction data, first humidity prediction data, first average wind speed prediction data, first gust wind speed prediction data, first wind direction prediction data, first maximum temperature prediction data, first minimum temperature prediction data, first minimum visibility prediction data, and first cloud cover prediction data. The first future time period consists of a pair of start and end times, with the start and end times including year, month, day, and hour information. The time interval between the start and end times of the first future time period is one hour. In the first prediction data sequence, the year, month, day, and hour information of the start time of the first future time period in the first first prediction data record corresponds to the year, month, day, and hour information of the current time, and the start time of the first future time period in the subsequent first prediction data record corresponds to the end time of the first future time period in the preceding first prediction data record. The first weather phenomenon prediction data is a weather phenomenon phrase containing one or more weather phenomenon names. The first precipitation forecast data is a precipitation value in millimeters; the first humidity forecast data is a humidity percentage; the first average wind speed forecast data is a wind speed range consisting of a minimum wind speed level and a maximum wind speed level; the first gust wind speed forecast data is a wind speed level; the first wind direction forecast data is a wind direction name; the first maximum temperature forecast data and the first minimum temperature forecast data are each a corresponding value in degrees Celsius; the first minimum visibility forecast data is a visibility value in kilometers; the first cloud cover forecast data is a cloud cover percentage. The first grid list includes multiple first grid records; each first grid record corresponds one-to-one with the meteorological grid and also one-to-one with the first grid data list; each first grid record includes a first grid identifier field, a first grid region name field, a first grid center point map coordinate field, a first grid vertex map coordinate set field, and a first grid data list identifier field; the first grid vertex map coordinate set field includes multiple first grid vertex map coordinates; the first grid data list identifier field of each first grid record matches the first data list identifier of the corresponding first grid data list. Each of the first grid data lists corresponds to a unique first data list identifier; The first grid data list includes multiple first grid data records; The first grid data record includes a first future time period field, a first weather phenomenon field, a first precipitation field, a first humidity field, a first average wind force field, a first gust wind force field, a first wind direction field, a first maximum temperature field, a first minimum temperature field, a first minimum visibility field, and a first cloud cover field; the first future time period field includes a first start time and a first end time, the time information of the first start time and the first end time includes year, month, day information and hour information, and the time interval from the first start time to the first end time is one hour; the first weather phenomenon field is a weather phenomenon phrase with one or more weather phenomenon names; The first precipitation field is a precipitation value in millimeters; the first humidity field is a humidity percentage; the first average wind force field is a first wind force level range consisting of a first minimum wind force level and a first maximum wind force level; the first gust wind force field is a first gust wind force level; the first wind direction field is a wind direction name; the first maximum temperature field and the first minimum temperature field are corresponding values ​​in degrees Celsius; the first minimum visibility field is a visibility value in kilometers; the first cloud cover field is a cloud cover percentage. The first queue record of each of the i-th time scale queues includes a second future time period field, a second weather phenomenon field, a second precipitation field, a second humidity field, a second average wind force field, a second gust wind force field, a second wind direction field, a second maximum temperature field, a second minimum temperature field, a second minimum visibility field, and a second cloud cover field; the second future time period field includes a second start time and a second end time, the time information of the second start time and the second end time includes year, month, day information and hour information, and the time interval from the second start time to the second end time corresponds to the time scale s. i match; The first service application includes a first service type, a first application data, and a first feedback data type; the first service type includes a network-wide service type, a grid service type, and a location service type; the first feedback data type includes a webpage file type, an image file type, a table file type, and a text file type. When the first service type is a network-wide service type, the corresponding first application data includes a first prediction start time, a first prediction number of days, and a first time scale; when the first service type is a grid service type, the corresponding first application data includes a second prediction start time, a second prediction number of days, a second time scale, and a first prediction grid identifier; when the first service type is a location service type, the corresponding first application data includes a third prediction start time, a third prediction number of days, a third time scale, and a first location coordinate.

3. The multi-timescale forecasting system based on meteorological grids according to claim 2, characterized in that, The database update module is specifically used to, when performing data update processing on all first grid data lists of the grid database based on the first prediction dataset and the first grid list of the grid database, take each first grid data subset of the first prediction dataset as the corresponding current grid data subset; The first grid identifier and the first predicted data sequence of the current grid data subset are used as the corresponding current grid identifier and current predicted data sequence; The first grid data list corresponding to the first grid data list identifier field of the first grid record that matches the current grid identifier in the first grid list is taken as the corresponding current grid data list; and the current grid data list is updated according to the current predicted data sequence. The data update process is confirmed to be complete when all first grid data lists corresponding to all first grid data subsets have been updated.

4. The multi-timescale forecasting system based on meteorological grids according to claim 3, characterized in that, The database update module is specifically used to: traverse the first predicted data record of the current predicted data sequence when updating the current grid data list according to the current predicted data sequence; during the traversal, use the currently traversed first predicted data record as the corresponding current predicted data record; use the first grid data record in the current grid data list whose first future time period field matches the first future time period of the current predicted data record as the corresponding current grid data record; and when the grid data record is empty, add a new first grid data record in the current grid data list as the corresponding current grid data record and set the first future time period field of the current grid data record to the first future time period of the current predicted data record. The first weather phenomenon field, first precipitation field, first humidity field, first average wind force field, first gust wind force field, first wind direction field, first maximum temperature field, first minimum temperature field, first minimum visibility field, and first cloud cover field of the current grid data record are set as the corresponding first weather phenomenon prediction data, first precipitation prediction data, first humidity prediction data, first average wind force prediction data, first gust wind force prediction data, first wind direction prediction data, first maximum temperature prediction data, first minimum temperature prediction data, first minimum visibility prediction data, and first cloud cover prediction data in the current prediction data record; and at the end of the traversal, it is confirmed that the current grid data list has completed the data update.

5. The multi-timescale forecasting system based on meteorological grids according to claim 2, characterized in that, The queue pool update module is specifically used to treat each of the first grid queue pools in the full grid queue pool as the corresponding current grid queue pool when performing queue update processing on all time-scale queues of all first grid queue pools in the full grid queue pool according to the grid database. The first grid data list corresponding to the current grid queue pool in the grid database is taken as the corresponding current grid data list; the 24×7 most recently updated first grid data records in the current grid data list are extracted and sorted in chronological order to form the corresponding current grid data record sequence; the current grid queue pool is updated according to the current grid data record sequence; and the queue update process is confirmed to be completed when all first grid queue pools have completed the queue update.

6. The multi-timescale forecasting system based on meteorological grids according to claim 5, characterized in that, The queue pool update module is specifically used when updating the current grid queue pool according to the current grid data record sequence: Step 61: Take the first i-th time-scale queue of the current grid queue pool as the corresponding current time-scale queue; Step 62, and set the time scale s corresponding to the current time scale queue. i As the corresponding current time scale s * ; and the total number M of queue records corresponding to the current time scale queue. i As the corresponding current total number of queues M * ; and according to the current time scale s * Divide the next 24×7 hours into the current total number of queues M. * The third future period T j ; 1 ≤ time period index j ≤ M * ; Each of the aforementioned third future time periods T j The length of the time period and the current time scale s * Consistent; Step 63, and then place the first future time period field in each of the third future time periods T in the current grid data record sequence. j The first grid data records are extracted to form a corresponding first record set G. j ; and for each of the first record sets G j The data fusion process is performed to obtain the corresponding first fusion record R. j Each of the first record sets G j The number of the first grid data records is related to the current time scale s. * Consistent; the first fusion record R j This includes the first time period fusion field, the first weather phenomenon fusion field, the first precipitation fusion field, the first humidity fusion field, the first average wind speed fusion field, the first gust wind speed fusion field, the first wind direction fusion field, the first highest temperature fusion field, the first lowest temperature fusion field, the first minimum visibility fusion field, and the first cloud cover fusion field; the first fusion record R j The first time period fusion field and the corresponding third future time period T j match; Step 64, for the third future time period T j Perform the traversal; and during the traversal, the currently traversed third future time period T is... j As the corresponding current time period; and the first fusion record R corresponding to the current time period j As the corresponding current fusion record; The first queue record in the current timescale queue that matches the second future time period field with the current time period is taken as the corresponding current queue record; and the system identifies whether the current queue record is empty. If the current queue record is empty, then the first time period fusion field, the first weather phenomenon fusion field, the first precipitation fusion field, the first humidity fusion field, the first average wind force fusion field, the first gust wind force fusion field, the first wind direction fusion field, the first highest temperature fusion field, the first lowest temperature fusion field, the first minimum visibility fusion field, and the first cloud cover fusion field of the current fusion record are used as the corresponding second future time period field, the second weather phenomenon field, the second precipitation field, the second humidity field, the second average wind force field, the second gust wind force field, the second wind direction field, the second highest temperature field, the second lowest temperature field, the second minimum visibility field, and the second cloud cover field to form a corresponding first queue record and add it to the current time scale queue; If the current queue record is not empty, then the second weather phenomenon field, the second precipitation field, the second humidity field, the second average wind force field, the second gust wind force field, the second wind direction field, the second highest temperature field, the second lowest temperature field, the second minimum visibility field, and the second cloud cover field of the current queue record are reset to the corresponding first weather phenomenon fusion field, first precipitation fusion field, first humidity fusion field, first average wind force fusion field, first gust wind force fusion field, first wind direction fusion field, first highest temperature fusion field, first lowest temperature fusion field, first minimum visibility fusion field, and first cloud cover fusion field in the current fusion record; Step 65: After the traversal is completed, identify whether the current time scale queue is the last i-th time scale queue in the current grid queue pool; if not, take the next i-th time scale queue in the current grid queue pool as the new current time scale queue and proceed to step 62; if yes, confirm that the current grid data record sequence has completed the queue update.

7. The multi-timescale forecasting system based on meteorological grids according to claim 6, characterized in that, The queue pool update module is specifically used for updating each of the first record sets G. j The data fusion process is performed to obtain the corresponding first fusion record R. j At that time, each of the first record sets G j As the corresponding current record set; and the third future time period T corresponding to the current record set j As the corresponding first set of time periods; and summing the first precipitation field of all the first grid data records in the current record set to obtain the corresponding first precipitation sum; and calculating the average of the first humidity field of all the first grid data records in the current record set to obtain the corresponding first average humidity; and taking the smallest first minimum wind force level and the largest first maximum wind force level among the first average wind force fields of all the first grid data records in the current record set as the corresponding second minimum wind force level and second maximum wind force level to form a corresponding second wind force level range; and taking the first gust wind force field with the largest wind force level among all the first grid data records in the current record set as the corresponding second gust wind force level; and taking the current record set as the first set of time periods; and summing the first precipitation field of all the first grid data records in the current record set to obtain the corresponding first precipitation sum; and summing the first precipitation field of all the first grid data records in the current record set to obtain the corresponding first precipitation sum; and summing the first humidity field of all the first grid data records in the current record set as the first gust wind level ... The first wind direction field with the most recent time among all the first grid data records in the current record set is taken as the corresponding first wind direction name; the first maximum temperature field with the highest temperature among all the first grid data records in the current record set is taken as the corresponding first maximum temperature, and the first minimum temperature field with the lowest temperature is taken as the corresponding first minimum temperature; the first minimum visibility field with the lowest visibility among all the first grid data records in the current record set is taken as the corresponding first minimum visibility; the first cloud cover field of all the first grid data records in the current record set is averaged to obtain the corresponding first average cloud cover; and the weather phenomenon phrases of the first weather phenomenon field of all the first grid data records in the current record set are deduplicated and merged to obtain the corresponding first phrase sequence. And according to the time scale s corresponding to the current record set i The first weather phenomenon phrase is obtained by fusing the first total precipitation, the first phrase sequence, the first minimum visibility, and the first average cloud cover with weather phenomenon phrases. The first time period, the first weather phenomenon phrase, the first total precipitation, the first average humidity, the second wind force range, the second gust force, the first wind direction name, the first highest temperature, the first lowest temperature, the first minimum visibility, and the first average cloud cover are used as the corresponding first time period fusion field, first weather phenomenon fusion field, first precipitation fusion field, first humidity fusion field, first average wind force fusion field, first gust force fusion field, first wind direction fusion field, first highest temperature fusion field, first lowest temperature fusion field, first minimum visibility fusion field, and first cloud cover fusion field to form a corresponding first fusion record R. j .

8. The multi-timescale forecasting system based on meteorological grids according to claim 7, characterized in that, The queue pool update module is specifically used to update the time scale s corresponding to the current record set. i When the first total precipitation, the first phrase sequence, the first minimum visibility, and the first average cloud cover are fused to obtain the corresponding first weather phenomenon phrase,... The system queries a pre-defined first correspondence table that reflects the relationship between time scales and weather parameters, and matches the first time scale field in the first correspondence table with the time scale s corresponding to the current record set. i The first weather parameter set field of the first matching correspondence record is extracted as the corresponding first weather parameter set; the first correspondence table includes multiple first correspondence records; the first correspondence record includes the first time scale field and the first weather parameter set field; the first weather parameter set field includes the range of drizzle rainfall, light rain rainfall, moderate rain rainfall, heavy rain rainfall, torrential rain rainfall, extremely heavy rain rainfall, clear sky cloud cover, cloudy sky cloud cover, overcast sky cloud cover, fog-free visibility range, light fog visibility range, moderate fog visibility range, and dense fog visibility range; When the first total precipitation is 0, the first weather phenomenon phrase is obtained by fusing weather phenomenon phrases based on the first weather parameter set, the first average cloud cover and the first minimum visibility. When the sum of the first precipitation is greater than 0, the weather phenomenon phrase is obtained by fusing the weather phenomenon phrases according to the first weather parameter set, the first precipitation and the first phrase sequence.

9. The multi-timescale forecasting system based on meteorological grids according to claim 8, characterized in that, The queue pool update module is specifically used when the first weather phenomenon phrase is obtained by fusing weather phenomenon phrases based on the first weather parameter set, the first average cloud cover, and the first minimum visibility. The first average cloud cover is identified; if the first average cloud cover meets the sunny cloud cover range of the first weather parameter set, the corresponding first weather phenomenon keyword is set to "sunny"; if the first average cloud cover meets the cloudy cloud cover range of the first weather parameter set, the corresponding first weather phenomenon keyword is set to "cloudy"; if the first average cloud cover meets the overcast cloud cover range of the first weather parameter set, the corresponding first weather phenomenon keyword is set to "overcast". The system identifies the first minimum visibility. If the first minimum visibility meets the fog-free visibility range of the first weather parameter set, the corresponding second weather phenomenon keyword is set to empty. If the first minimum visibility meets the light fog visibility range of the first weather parameter set, the corresponding second weather phenomenon keyword is set to "light fog". If the first minimum visibility meets the moderate fog visibility range of the first weather parameter set, the corresponding second weather phenomenon keyword is set to "fog". If the first minimum visibility meets the dense fog visibility range of the first weather parameter set, the corresponding second weather phenomenon keyword is set to "dense fog". It also identifies whether the obtained second weather phenomenon keyword is empty; if so, it sets the corresponding first weather phenomenon phrase as the first weather phenomenon keyword; if not, it sets the corresponding first weather phenomenon phrase as a phrase formed by sequentially concatenating the first weather phenomenon keyword, the preset isolation symbol, and the second weather phenomenon keyword. The default isolation symbol is a comma ",".

10. The multi-timescale forecasting system based on meteorological grids according to claim 8, characterized in that, The queue pool update module is specifically used when the corresponding first weather phenomenon phrase is obtained by fusing weather phenomenon phrases based on the first weather parameter set, the first precipitation, and the first phrase sequence. When the total first precipitation amount meets the drizzle rainfall range of the first weather parameter set, the corresponding first weather phenomenon phrase is set to "drizzle". When the total first precipitation amount meets the light rain range of the first weather parameter set, the keywords in the first phrase sequence are identified; if the first phrase sequence does not contain the keywords "thunder" and "hail", the corresponding first weather phenomenon phrase is set to "light rain"; if the first phrase sequence contains the keywords "thunder" and "hail", the corresponding first weather phenomenon phrase is set to "thunderstorm with hail"; if the first phrase sequence contains the keyword "thunder" but does not contain the keyword "hail", the corresponding first weather phenomenon phrase is set to "thunderstorm". When the total first precipitation amount satisfies the range of moderate rain, heavy rain, torrential rain, heavy torrential rain, or extremely heavy torrential rain defined in the first weather parameter set, keywords in the first phrase sequence are identified. If the first phrase sequence does not contain the keywords "thunder" and "hail," the corresponding first weather phenomenon phrase is set to "moderate rain," "heavy rain," "torrential rain," "heavy torrential rain," or "extremely heavy torrential rain." If the first phrase sequence contains the keywords "thunder" and "hail,"... If the first weather phenomenon phrase is set to "moderate rain with thunder and hail", "heavy rain with thunder and hail", "torrential rain with thunder and hail", "very heavy rain with thunder and hail", or "extremely heavy rain with thunder and hail", then the corresponding first weather phenomenon phrase is set to "moderate rain with thunder and hail", "heavy rain with thunder and hail", "torrential rain with thunder and hail", or "extremely heavy rain with thunder and hail".

11. The multi-timescale forecasting system based on meteorological grids according to claim 2, characterized in that, The i-th time scale queue is specifically used when managing queue records according to the first-in-first-out principle of a circular queue each time a first queue record is added, and when a new first queue record is added, the first queue record added at that time is taken as the corresponding currently added record; The total number of empty records Q is obtained by counting the number of empty records in the queue at the i-th time scale where all fields are empty; and the total number of empty records Q is identified. If the total number of empty records Q is greater than 0, then the Mth record in the i-th time scale queue will be removed. i -Q+1) records from the first queue are used as the corresponding current modification records; If the total number of current empty records Q is equal to 0, then the first queue record with the earliest time of the second future time period field in the current i-th time scale queue is taken as the corresponding current modified record; and each corresponding field in the current modified record is set based on each field of the currently added record.

12. The multi-timescale forecasting system based on meteorological grids according to claim 2, characterized in that, The service interface module is specifically used to extract the corresponding first service type, first application data, and first feedback data type from the first service application when the first service feedback data is prepared and processed according to the first service application, the grid database, and the full grid queue pool to be sent back to the client. And identify the first service type; If the first service type is a network-wide service type, then the corresponding first prediction start time, first prediction days, and first time scale are extracted from the first application data; the first prediction end time is obtained by adding the first prediction start time and the first prediction days; the first prediction start time and the first prediction end time form the corresponding first prediction period; and the time scale s in each of the first grid queue pools of the full grid queue pool is used to... i The i-th time scale queue that matches the first time scale is used as the corresponding first grid matching queue; The records of the first queues that intersect with the first prediction time period in each of the first grid matching queues are extracted and arranged in chronological order to form a corresponding first grid prediction list; and all the obtained first grid prediction lists form a corresponding first prediction list set. If the first service type is a grid service type, then the corresponding second prediction start time, second prediction days, second time scale, and first prediction grid identifier are extracted from the first application data; and the corresponding second prediction end time is obtained by adding the second prediction start time and the second prediction days; and the corresponding second prediction period is formed by the second prediction start time and the second prediction end time; and the first grid queue pool in the full grid queue pool that corresponds to the first prediction grid identifier is used as the corresponding first matching grid pool. and the time scale s in the first matching grid pool i The i-th timescale queue that matches the second timescale is used as the corresponding second grid matching queue; and the first queue records in the second grid matching queue that have an intersection between the second future time period field and the second prediction time period are extracted and arranged in chronological order to form a corresponding first grid prediction list; and the obtained first grid prediction list forms the corresponding first prediction list set; If the first service type is a location service type, then the corresponding third prediction start time, third prediction number of days, third time scale, and first location coordinates are extracted from the first application data; the third prediction end time is obtained by adding the third prediction start time and the third prediction number of days; the third prediction start time and the third prediction end time form the corresponding third prediction period; the straight-line distance between the map coordinate field of the first grid center point of each first grid record in the first grid list of the grid database and the first location coordinates is calculated to obtain the corresponding first straight-line distance; the first grid record whose first straight-line distance is less than a preset straight-line distance threshold is taken as the corresponding first candidate grid record; the inclusion and non-inclusion relationship between the polygon map region enclosed by all the map coordinates of the first grid vertex map coordinate set field of each first candidate grid record and the first location coordinates is identified to obtain the corresponding first identification result; the first candidate grid record corresponding to the only first identification result that is specifically inclusion is taken as the corresponding first matching grid record; and the first grid queue pool in the full grid queue pool corresponding to the first matching grid record is taken as the corresponding second matching grid pool. and the time scale s in the second matching grid pool i The queue of the i-th time scale that matches the third time scale is taken as the corresponding third grid matching queue; and the first queue records in the third grid matching queue that have an intersection between the second future time period field and the third prediction time period are extracted and arranged in chronological order to form a corresponding first grid prediction list; and the obtained first grid prediction list forms a corresponding first grid prediction list set; the first identification result includes inclusion relationship and non-inclusion relationship; The system identifies the data type of the first feedback. If the first feedback data type is a webpage file, all the first grid prediction lists in the obtained first grid prediction list set are converted into webpage files using a preset webpage template to obtain a corresponding first webpage file, which is then used as the corresponding first service feedback data. If the first feedback data type is an image file, each of the first grid prediction lists in the obtained first grid prediction list set is converted into a table image to obtain a corresponding first table image, which is then used as the corresponding first image file. If the first feedback data type is a table file, each of the first grid prediction lists in the obtained first grid prediction list set is converted into a spreadsheet to obtain a corresponding first spreadsheet, which is then used as the corresponding first table file, which is then used as the corresponding first service feedback data. If the first feedback data type is a text file, each of the first grid prediction lists in the obtained first grid prediction list set is converted into text information to obtain a corresponding first list text, which is then used as the corresponding first text file, which is then used as the corresponding first service feedback data. The obtained first service feedback data is then sent back to the client.

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