Meteorological early warning information pushing method and device based on user behavior data and flash message

Abstract

The invention provides a meteorological early warning information pushing method and device based on user behavior data and flash information, and relates to the field of meteorological early warning, and the method comprises the steps: determining meteorological disaster related information of a plurality of sub-regions included in a target region, and determining a meteorological early warning region of the target region in combination with the meteorological disaster data of the target region; determining a plurality of early warning user sides according to the meteorological early warning area of the target area; acquiring behavior data of a plurality of early warning user terminals, and dividing the plurality of early warning user terminals into a plurality of early warning user terminal groups; determining a target meteorological disaster early warning template according to the meteorological disaster data of the target area, and generating standard meteorological early warning information in combination with the meteorological disaster data of the target area; the meteorological early warning information corresponding to the early warning user terminal group is generated according to the behavior data of the early warning user terminals included in the early warning user terminal group and the standard meteorological early warning information, and a flash message is sent to the early warning user terminals included in the early warning user terminal group, so that the meteorological early warning information can be accurately and effectively pushed.

Description

Technical Field

[0001] The present invention relates to the field of meteorological warning, and in particular to a method and device for pushing meteorological warning information based on user behavior data and flash messages. Background Art

[0002] Meteorological disasters are frequent and multiple, accounting for more than 70% of natural disasters, and often cause significant losses to economic and social development and people's lives and property. In the face of disasters, humans currently have no means to completely prevent or eliminate them, but they can take effective preventive measures to minimize the losses caused by disasters. The timely delivery of meteorological disaster warning information is crucial.

[0003] Currently, the release of meteorological warning information mainly relies on text messages. Although it can be delivered quickly, it has the limitation that users need to actively click on the text message to view the content. As a result, in highly emergency situations, the information may not attract the user's attention in time, thereby affecting the speed of emergency response.

[0004] Therefore, it is necessary to provide a method and device for pushing weather warning information based on user behavior data and flash messages, so as to achieve accurate and effective push of weather warning information. Summary of the invention

[0005] The present invention provides a method for pushing meteorological warning information based on user behavior data and flash messages, including: determining meteorological disaster-related information of multiple sub-regions included in a target region; acquiring meteorological disaster data of the target region; determining a meteorological warning region of the target region based on the meteorological disaster data of the target region and the meteorological disaster-related information of multiple sub-regions included in the target region; determining multiple warning user terminals based on the meteorological warning region of the target region; acquiring behavior data of multiple warning user terminals, and dividing the multiple warning user terminals into multiple warning user terminal groups; determining a target meteorological disaster warning template based on the meteorological disaster data of the target region; generating standard meteorological warning information based on the meteorological disaster data of the target region and the target meteorological disaster warning template; for each warning user terminal group, generating meteorological warning information corresponding to the warning user terminal group based on the behavior data of the warning user terminals included in the warning user terminal group and the standard meteorological warning information, and sending flash messages to the warning user terminals included in the warning user terminal group based on the meteorological warning information corresponding to the warning user terminal group.

[0006] Further, determining meteorological disaster association information of multiple sub-areas included in the target area includes: obtaining historical meteorological disaster information of the target area; for each meteorological disaster, calculating the disaster co-occurrence coefficient of any two sub-areas of the target area corresponding to the meteorological disaster according to the historical meteorological disaster information of the target area, determining multiple first sub-area units corresponding to the meteorological disaster according to the disaster co-occurrence coefficient of any two sub-areas of the target area corresponding to the meteorological disaster, each first sub-area unit includes two sub-areas, and determining the co-occurrence associated meteorological elements of each first sub-area unit corresponding to the meteorological disaster according to the historical meteorological disaster information of the target area; obtaining historical behavior data of the user terminal of the target area, wherein the historical behavior data includes the position of the user terminal at multiple historical time points obtained with the authorization of the user; calculating the user movement association coefficient of any two sub-areas of the target area according to the historical behavior data of the user terminal of the target area, and determining multiple second sub-area units according to the user movement association coefficient of any two sub-areas, wherein the meteorological disaster association information of the multiple sub-areas included in the target area includes at least multiple first sub-area units corresponding to each meteorological disaster, co-occurrence associated meteorological elements of the meteorological disaster corresponding to each first sub-area unit, and multiple second sub-area units.

[0007] Furthermore, according to the meteorological disaster data of the target area and the meteorological disaster correlation information of the multiple sub-areas included in the target area, the meteorological warning area of ​​the target area is determined, including: for each sub-area unit, according to the historical meteorological disaster information of the target area, a plurality of training samples corresponding to the first sub-area unit are generated, a correlation prediction model corresponding to the first sub-area unit is established, and the correlation prediction model corresponding to the first sub-area unit is trained by the multiple training samples corresponding to the first sub-area unit; according to the meteorological disaster data of the target area, the target meteorological disaster and the key sub-area are determined; according to the target meteorological disaster, the key sub-area, the meteorological disaster correlation information of the multiple sub-areas included in the target area and the correlation prediction model corresponding to each first sub-area unit, the meteorological warning area of ​​the target area is determined.

[0008] Furthermore, based on the target meteorological disaster, the key sub-region, the meteorological disaster-related information of the multiple sub-regions included in the target area, and the associated prediction model corresponding to each first sub-region unit, the meteorological warning area of ​​the target area is determined, including: determining the target first sub-region unit according to the multiple sub-region units and the key sub-region corresponding to the target meteorological disaster; predicting the disaster co-occurrence probability of the target first sub-region unit according to the historical meteorological disaster information of the target area through the associated prediction model corresponding to the target first sub-region unit; determining the first supplementary sub-region according to the disaster co-occurrence probability of each target first sub-region unit; determining the second supplementary sub-region according to the multiple second sub-region units and the key sub-region; determining the meteorological warning area of ​​the target area according to the key sub-region, the first supplementary sub-region and the second supplementary sub-region.

[0009] Furthermore, based on the meteorological warning area of ​​the target area, multiple warning user terminals are determined, including: for each user terminal in the target area, a first position association parameter between the user terminal and the meteorological warning area of ​​the target area is calculated based on the behavior data of the user terminal; based on the first position association parameter between the user terminal and the meteorological warning area of ​​the target area, it is determined whether the user terminal is a warning user terminal.

[0010] Furthermore, behavioral data of multiple warning user terminals are obtained, and the multiple warning user terminals are divided into multiple warning user terminal groups, including: for each warning user terminal, determining the displacement activity coefficient of the warning user terminal according to the behavioral data of the warning user terminal; using a K-Means clustering algorithm, dividing the multiple warning user terminals into at least one warning user terminal cluster according to the displacement activity coefficient of each warning user terminal; for each warning user terminal cluster, determining a second position association parameter between the warning user terminal and each sub-region included in the meteorological warning area of ​​the target area according to the behavioral data of the warning user terminals included in the warning user terminal cluster, and using a K-Means clustering algorithm, dividing the multiple warning user terminals included in the warning user terminal cluster into at least one warning user terminal group according to the second position association parameter between the warning user terminal included in the warning user terminal cluster and each sub-region included in the meteorological warning area of ​​the target area.

[0011] Furthermore, a meteorological disaster warning template is determined based on the meteorological disaster data of the target area, including: obtaining multiple meteorological disaster warning templates and meteorological disaster characteristics corresponding to each meteorological disaster warning template, wherein the meteorological disaster characteristics include at least the meteorological disaster type and the meteorological disaster level; obtaining current meteorological disaster characteristics based on the meteorological disaster data of the target area; and determining a target meteorological disaster warning template from multiple meteorological disaster warning templates based on the current meteorological disaster characteristics and the meteorological disaster characteristics corresponding to each meteorological disaster warning template.

[0012] Furthermore, based on the meteorological disaster data of the target area and the target meteorological disaster warning template, standard meteorological warning information is generated, including: obtaining key warning information based on the meteorological disaster data of the target area, wherein the key warning information at least includes the meteorological disaster type, meteorological disaster level, expected impact time and key sub-areas; and generating standard meteorological warning information based on the key warning information and the target meteorological disaster warning template.

[0013] Furthermore, based on the behavioral data of the warning user terminals included in the warning user terminal group and the standard meteorological warning information, meteorological warning information corresponding to the warning user terminal group is generated, including: determining the meteorological warning style and response suggestions corresponding to the warning user terminal group based on the displacement activity coefficient of the warning user terminals included in the warning user terminal group; determining the warning priority of each sub-area included in the meteorological warning area of ​​the target area of ​​the corresponding warning user terminal group based on the second position association parameter of the warning user terminals included in the warning user terminal group and each sub-area included in the meteorological warning area of ​​the target area; and adjusting the standard meteorological warning information according to the meteorological warning style and response suggestions corresponding to the warning user terminal group and the warning priority of each sub-area included in the meteorological warning area of ​​the target area of ​​the corresponding warning user terminal group through the style adjustment model to generate the meteorological warning information corresponding to the warning user terminal group.

[0014] The present invention provides a device for pushing meteorological warning information based on user behavior data and flash messages, and applies the above-mentioned method for pushing meteorological warning information based on user behavior data and flash messages, including: a regional analysis module for determining meteorological disaster-related information of multiple sub-regions included in a target region; a data acquisition module for acquiring meteorological disaster data of the target region; a regional determination module for determining a meteorological warning region of the target region based on the meteorological disaster data of the target region and the meteorological disaster-related information of the multiple sub-regions included in the target region; a user analysis module for determining multiple warning user terminals based on the meteorological warning region of the target region; the user analysis module is also used to acquire the meteorological disaster data of the target region and the meteorological disaster-related information of the multiple sub-regions included in the target region. Behavior data, dividing the multiple warning user terminals into multiple warning user terminal groups; a template determination module, used to determine the target meteorological disaster warning template according to the meteorological disaster data of the target area; a meteorological warning module, used to generate standard meteorological warning information according to the meteorological disaster data of the target area and the target meteorological disaster warning template; the meteorological warning module is also used to generate meteorological warning information corresponding to the warning user terminal group for each warning user terminal group according to the behavior data of the warning user terminals included in the warning user terminal group and the standard meteorological warning information, and send flash messages to the warning user terminals included in the warning user terminal group according to the meteorological warning information corresponding to the warning user terminal group.

[0015] Compared with the prior art, the method and device for pushing weather warning information based on user behavior data and flash messages provided by the present invention have at least the following beneficial effects: 1. Flash messages have a mandatory reminder function, and users must manually click to confirm before closing, ensuring the effective reception of warning information. Through precise target area division and meteorological disaster data correlation analysis, the warning area and warning user end can be determined more accurately, thereby reducing false alarms and missed alarms. According to the meteorological disaster data of the target area and the meteorological disaster correlation information of the sub-area, the specific meteorological warning area is determined to achieve refined warning. According to the behavioral data of the warning user end, users are divided into different groups, and corresponding meteorological warning information is pushed according to the group, which enhances the personalization of warning information. Personalized warning information can better attract users' attention and increase users' attention and attention to warning information. Timely and accurate meteorological warning information can help users make preparations for disaster prevention and mitigation in advance, such as relocation and resettlement, material reserves, etc. Through effective warning and response measures, casualties and property losses caused by meteorological disasters can be significantly reduced. Long-term and frequent push of meteorological warning information can enhance the public's awareness of disaster prevention and mitigation and improve the overall disaster prevention capabilities of society.

[0016] 2. By calculating the disaster co-occurrence coefficients of meteorological disasters corresponding to any two sub-regions of the target area, it is possible to identify which sub-regions are prone to the same or similar meteorological disasters in history, thus providing an important basis for determining the warning area. The calculation of the user movement correlation coefficient takes into account the movement patterns of users between different sub-regions, which helps to more accurately locate those user groups that may be affected by meteorological disasters. By using historical meteorological disaster information to generate training samples and establish an association prediction model, the co-occurrence probability of meteorological disasters between different sub-regions can be predicted, further improving the accuracy of the warning area. By comprehensively considering the target meteorological disasters, key sub-regions, meteorological disaster association information, and the results of the association prediction model, the warning area can be divided more finely to ensure that the warning information is only sent to users who may really be affected.

[0017] 3. According to the meteorological disaster data of the target area, the most appropriate template is automatically selected from multiple meteorological disaster warning templates. This can ensure that the format and content of the warning information match the current meteorological disaster characteristics and improve the readability and practicality of the information. On the basis of generating standard meteorological warning information, the warning information is dynamically adjusted according to the behavioral data and location-related parameters of the warning user end group. This includes adjusting the warning style, response suggestions, and warning priorities for different sub-regions, so that the warning information is more in line with user needs and actual conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein: Figure 1 It is a flowchart of a method for pushing weather warning information based on user behavior data and flash messages according to some embodiments of this specification; Figure 2 is a schematic diagram of a weather warning area of ​​a target area according to some embodiments of this specification; Figure 3 is a schematic diagram of a flash message according to some embodiments of this specification; Figure 4 is a schematic diagram of flash messages according to other embodiments of this specification; Figure 5 It is a module diagram of a weather warning information push device based on user behavior data and flash messages according to some embodiments of this specification. DETAILED DESCRIPTION

[0019] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0020] Figure 1 is a flow chart of a method for pushing weather warning information based on user behavior data and flash messages according to some embodiments of this specification, such as Figure 1 As shown, the method for pushing weather warning information based on user behavior data and flash messages may include the following process.

[0021] Step 110: determine meteorological disaster related information of multiple sub-areas included in the target area.

[0022] In some embodiments, step 110 specifically includes: Obtain historical meteorological disaster information in the target area; For each meteorological disaster, based on the historical meteorological disaster information of the target area, the disaster co-occurrence coefficient of any two sub-areas of the target area corresponding to the meteorological disaster is calculated, and based on the disaster co-occurrence coefficient of any two sub-areas of the target area corresponding to the meteorological disaster, a plurality of first sub-area units corresponding to the meteorological disaster are determined, each first sub-area unit includes two sub-areas, and based on the historical meteorological disaster information of the target area, the co-occurrence associated meteorological elements of each first sub-area unit corresponding to the meteorological disaster are determined; Obtaining historical behavior data of user terminals in the target area, wherein the historical behavior data includes the locations of the user terminals at multiple historical time points obtained with the user's authorization, ensuring that the user's authorization has been obtained when obtaining the historical behavior data of the user terminals, and complying with relevant privacy policies and regulations, and that user terminals whose numbers belong to the target area or have been in the target area can be regarded as user terminals in the target area; According to the historical behavior data of the user terminal in the target area, the user movement correlation coefficient of any two sub-areas of the target area is calculated, and according to the user movement correlation coefficient of any two sub-areas, a plurality of second sub-area units are determined, and the second sub-area unit includes two sub-areas, wherein the meteorological disaster association information of the plurality of sub-areas included in the target area includes at least a plurality of first sub-area units corresponding to each meteorological disaster, co-occurrence associated meteorological elements corresponding to the meteorological disaster of each first sub-area unit, and a plurality of second sub-area units.

[0023] Specifically, the historical meteorological disaster information of the target area may include relevant information of meteorological disasters that occurred in the target area in multiple historical time periods. For example, the relevant information of meteorological disasters that occurred in the historical time periods may include: 1. Types of meteorological disasters Drought: Record the time, scope, duration and impact of drought disasters that occurred in history. For example, a severe drought occurred in a certain area in a certain year, resulting in reduced crop yields, water depletion, etc.

[0024] Heavy rain and floods: Detailed records of the time, rainfall, flood range and impact of heavy rain disasters, including river flooding, urban waterlogging, mountain torrents and other disasters caused by heavy rain.

[0025] Typhoon: Record the time, location, wind force level, path and impact range of the typhoon, including the strong wind, heavy rain, storm surge and other disasters brought by the typhoon.

[0026] Low temperature and freezing damage and snow disasters: record the time, scope and impact of low temperature, frost, ice and snow disasters. For example, a certain area suffered a severe snow disaster in the winter of a certain year, resulting in traffic obstruction and crop damage.

[0027] Wind and hail disasters: record the time, scope, wind force level and hail size of disasters such as strong winds and hail, including the impact of wind and hail disasters on crops, buildings and personnel safety.

[0028] II. Impact of Disasters Agricultural losses: Assess the impact of meteorological disasters on crop yield, quality and market supply, including the affected area, degree of yield reduction, and loss of crop value.

[0029] Economic losses: Statistics on direct and indirect economic losses caused by meteorological disasters. Direct economic losses include crop losses, building damage, infrastructure damage, etc.; indirect economic losses include losses from suspension of production and business, traffic interruption, etc.

[0030] Casualties: Record the casualties caused by meteorological disasters, including the number of deaths, injuries and missing persons.

[0031] III. Disaster Response and Rescue Warning and forecast: record the disaster warning and forecast information issued by the meteorological department, as well as the effectiveness and accuracy of the warning equipment.

[0032] Emergency response: Describe the emergency response measures taken by the government and relevant departments after a disaster occurs, including rescue operations, material deployment, personnel evacuation, etc.

[0033] Post-disaster recovery: Record the progress and effectiveness of post-disaster recovery work, including rebuilding homes, repairing infrastructure, and resuming agricultural production.

[0034] The target area can be divided into multiple sub-areas according to administrative management requirements. For example, a city can be divided into multiple districts, a county can be divided into multiple towns, etc.

[0035] For each sub-region of the target region, the status of the sub-region in multiple historical time periods can be encoded according to the historical meteorological disaster information of the target region. Taking drought as an example, if both sub-region A and sub-region B experience drought in a certain historical time period, the status of drought in sub-region A and sub-region B in the historical time period is coded as 1. If drought occurs in sub-region A in a certain historical time period, and drought does not occur in sub-region B in the historical time period, the status of drought in sub-region A in the historical time period is coded as 1, and the status of drought in sub-region B in the historical time period is coded as 2.

[0036] The disaster co-occurrence coefficient of meteorological disasters in two sub-regions can be calculated according to the following formula: , in, is the disaster co-occurrence coefficient of the i-th sub-region and the j-th corresponding g-th meteorological disaster, is the total number of historical time periods sampled, is the state code of the i-th sub-region corresponding to the g-th meteorological disaster in the n-th historical time period, It is the status code of the j-th sub-region corresponding to the g-th meteorological disaster in the n-th historical time period.

[0037] Two sub-regions whose disaster co-occurrence coefficients corresponding to meteorological disasters are greater than a disaster co-occurrence coefficient threshold may be regarded as a first sub-region unit.

[0038] The co-occurrence associated meteorological elements corresponding to the meteorological disaster in the first sub-regional unit can be determined according to the following process: S11. Determine multiple meteorological elements (e.g., temperature, humidity, wind speed, wind direction, rainfall, etc.); S12. For each historical time period, obtain the meteorological element value of each meteorological element at multiple historical time points included in the historical time period, and calculate the standard deviation of the meteorological element values ​​at multiple historical time points included in the historical time period; S13. For each first sub-region unit, encode according to the states of the two sub-regions included in the first sub-region unit in the historical time period. For example, when the two sub-regions included in the first sub-region unit both experience the meteorological disaster in a certain historical time period, the state code of the first sub-region unit corresponding to the historical time period is 1; if at least one of the two sub-regions included in the first sub-region unit does not experience the meteorological disaster in the historical time period, the state code of the first sub-region unit corresponding to the historical time period is 2; S14, calculating the co-occurrence influence coefficient of the meteorological element and the meteorological disaster corresponding to the first sub-regional unit according to the standard deviation of the meteorological element value in the historical time period and the state code of the first sub-regional unit in the historical time period; S15. The meteorological elements whose co-occurrence correlation influence coefficient is greater than the co-occurrence correlation influence coefficient threshold are taken as the co-occurrence correlation meteorological elements corresponding to the meteorological disaster of the first sub-region unit.

[0039] The co-occurrence impact coefficient of meteorological elements and meteorological disasters corresponding to the first sub-regional unit can be calculated according to the following formula: , in, is the co-occurrence influence coefficient between the hth meteorological element and the i-th first sub-regional unit corresponding to the g-th meteorological disaster, is the standard deviation of the meteorological element value of the hth meteorological element in the nth historical time period, The state code of the i-th first sub-region unit in the n-th historical time period.

[0040] For any two sub-areas, the number of user terminals moving in the two sub-areas in multiple historical time periods can be determined based on the historical behavior data of the user terminals in the target area. The user movement correlation coefficient of the two sub-areas is calculated based on the number of user terminals moving in the two sub-areas in each historical time period.

[0041] The user movement correlation coefficient of two sub-areas can be calculated according to the following formula: , in, is the user movement correlation coefficient between the i-th sub-area and the j-th sub-area, is the number of users moving between the ith sub-area and the jth sub-area in the nth historical time period, is the total number of users in the target area, is the total number of historical time periods sampled.

[0042] Step 120, obtaining meteorological disaster data of the target area.

[0043] Specifically, the meteorological disaster data of the target area may be relevant information of the meteorological disasters predicted to occur in the target area in the future, such as the type, duration, level, and location of the meteorological disaster.

[0044] Step 130, determining the meteorological warning area of ​​the target area according to the meteorological disaster data of the target area and the meteorological disaster related information of the multiple sub-areas included in the target area.

[0045] In some embodiments, step 130 specifically includes: For each sub-regional unit, based on the historical meteorological disaster information of the target area, multiple training samples corresponding to the first sub-regional unit are generated, and an associated prediction model corresponding to the first sub-regional unit is established. The associated prediction model corresponding to the first sub-regional unit is trained by the multiple training samples corresponding to the first sub-regional unit, wherein the associated prediction model may be a long short-term memory network model, the input of the associated prediction model includes meteorological element values ​​of co-occurring associated meteorological elements of the meteorological disaster corresponding to the first sub-regional unit at multiple consecutive time points, and the output of the associated prediction model includes the disaster co-occurrence probability of the meteorological disaster occurring in the first sub-regional unit; According to the meteorological disaster data of the target area, the target meteorological disaster and the key sub-area are determined, wherein the target meteorological disaster is the meteorological disaster predicted to occur in the key sub-area, and the key sub-area is the sub-area in the target area determined to have the meteorological disaster; The meteorological warning area of ​​the target area is determined according to the target meteorological disaster, the key sub-area, the meteorological disaster association information of multiple sub-areas included in the target area, and the association prediction model corresponding to each first sub-area unit.

[0046] In some embodiments, according to the target meteorological disaster, the key sub-area, the meteorological disaster association information of the multiple sub-areas included in the target area, and the association prediction model corresponding to each first sub-area unit, the meteorological warning area of ​​the target area is determined, including: Determine a target first sub-region unit according to multiple sub-region units and key sub-regions corresponding to the target meteorological disaster, for example, a first sub-region unit of two sub-regions, one of which is a key sub-region and the other is not a key sub-region, as the first sub-region unit; By using the association prediction model corresponding to the first sub-region unit of the target, and according to the historical meteorological disaster information of the target area, the disaster co-occurrence probability of the first sub-region unit of the target is predicted; Determine the first supplementary sub-region according to the disaster co-occurrence probability of each target first sub-region unit, for example, take the target first sub-region unit whose disaster co-occurrence probability is greater than the disaster co-occurrence probability threshold as the supplementary first sub-region unit, and take the sub-region in the supplementary first sub-region unit that is not a key sub-region as the first supplementary sub-region; Determine a second supplementary sub-region according to the plurality of second sub-region units and the key sub-region, for example, use a second sub-region unit, one of the two sub-regions being a key sub-region and the other being a non-key sub-region, as a target second sub-region unit, and use a sub-region of the target second sub-region unit that is not a key sub-region as a second supplementary sub-region; According to the key sub-region, the first supplementary sub-region and the second supplementary sub-region, the weather warning region of the target region is determined. For example, the first supplementary sub-region and the second supplementary sub-region are combined, and the union of the first supplementary sub-region and the second supplementary sub-region is combined with the key sub-region to determine the weather warning region of the target region. Figure 2 As shown, the area within the red dotted box is the weather warning area of ​​the target area of ​​the example.

[0047] Step 140, determining a plurality of warning user terminals according to the weather warning area of ​​the target area.

[0048] In some embodiments, step 140 specifically includes: For each user terminal in the target area, the first position association parameter between the user terminal and the meteorological warning area of ​​the target area is calculated based on the user terminal's behavior data, and whether the user terminal is a warning user terminal is determined based on the first position association parameter between the user terminal and the meteorological warning area of ​​the target area.

[0049] Specifically, based on the behavior data of the user terminal, the position of the user terminal at multiple time points in the current period (for example, the last three months) is determined.

[0050] A location sequence of the user terminal is generated based on the location of the user terminal at multiple time points in the current period (for example, the last 3 months), where an element of the location sequence represents the location state of the user terminal at a time point. When the user terminal is located in the meteorological warning area of ​​the target area at a time point, the value of the element is 2, otherwise the value of the element is 1.

[0051] The first position association parameter between the user terminal and the meteorological warning area of ​​the target area is calculated according to the position sequence of the user terminal by the following formula: , in, is the first position association parameter between the i-th user terminal and the meteorological warning area of ​​the target area, is the value of the tth element of the position sequence, is the total number of time points sampled in the current cycle, is the preset parameter, Greater than 0.

[0052] When the first position association parameter between the user terminal and the meteorological warning area of ​​the target area is greater than the first position association parameter threshold, the user terminal is determined to be a warning user terminal.

[0053] Step 150: Obtain behavior data of multiple warning user terminals, and divide the multiple warning user terminals into multiple warning user terminal groups.

[0054] In some embodiments, step 150 specifically includes: For each warning user terminal, a displacement activity coefficient of the warning user terminal is determined according to the behavior data of the warning user terminal; By using the K-Means clustering algorithm, multiple warning user terminals are divided into at least one warning user terminal cluster according to the displacement activity coefficient of each warning user terminal; For each warning user terminal cluster, the second position association parameter between the warning user terminal and each sub-area included in the meteorological warning area of ​​the target area is determined based on the behavioral data of the warning user terminals included in the warning user terminal cluster. Through the K-Means clustering algorithm, the multiple warning user terminals included in the warning user terminal cluster are divided into at least one warning user terminal group based on the second position association parameters between the warning user terminals included in the warning user terminal cluster and each sub-area included in the meteorological warning area of ​​the target area.

[0055] Specifically, for each early warning user terminal, the position of the early warning user terminal at multiple time points in the current period (for example, the last three months) can be encoded. For example, the early warning user terminal is located in sub-area A of the target area at the first time point, and its position is encoded as 1; at the second time point, it is located in sub-area B of the target area, and its position is encoded as 2; at the nth time point, it is located outside the target area, and its position is encoded as K+1, where K is the total number of sub-areas included in the target area.

[0056] The displacement activity coefficient of the warning user end can be calculated according to the following formula: , in, is the displacement activity coefficient of the i-th warning user end, is the position code of the i-th warning user terminal at the t-th time point in the current cycle, The total number of time points sampled in the current cycle.

[0057] The method of determining the second position association parameter between the warning user terminal and each sub-area included in the meteorological warning area of ​​the target area is similar to the method of determining the first position association parameter between the warning user terminal and the meteorological warning area of ​​the target area, and will not be repeated here.

[0058] The clustering distance between any two warning users included in the warning user cluster can be calculated according to the following formula: , in, is the clustering distance between the i-th warning user terminal and the j-th warning user terminal included in the warning user terminal cluster, is the second position association parameter between the i-th warning user terminal and the f-th sub-area included in the meteorological warning area of ​​the target area, is the second position association parameter between the j-th warning user terminal and the f-th sub-area included in the meteorological warning area of ​​the target area, is the total number of sub-areas included in the meteorological warning area of ​​the target area, is the preset parameter, Greater than 0.

[0059] By using the K-Means clustering algorithm, the multiple warning clients included in the warning client cluster are divided into at least one warning client group according to the clustering distance between any two warning clients included in the warning client cluster.

[0060] Step 160, determining a target meteorological disaster warning template based on the meteorological disaster data of the target area.

[0061] In some embodiments, step 160 specifically includes: Acquire multiple meteorological disaster warning templates and meteorological disaster characteristics corresponding to each meteorological disaster warning template, wherein the meteorological disaster characteristics at least include a meteorological disaster type and a meteorological disaster level; According to the meteorological disaster data of the target area, obtain the current meteorological disaster characteristics; A target meteorological disaster warning template is determined from multiple meteorological disaster warning templates according to the current meteorological disaster characteristics and the meteorological disaster characteristics corresponding to each meteorological disaster warning template.

[0062] Specifically, a meteorological disaster warning template with consistent meteorological disaster type and meteorological disaster level may be used as a target meteorological disaster warning template.

[0063] Step 170, generating standard meteorological warning information according to the meteorological disaster data of the target area and the target meteorological disaster warning template.

[0064] In some embodiments, step 170 specifically includes: According to the meteorological disaster data of the target area, key warning information is obtained, where the key warning information at least includes the type of meteorological disaster, the level of meteorological disaster, the expected impact time and key sub-areas, for example, the type of meteorological disaster (such as rainstorm, typhoon, drought, etc.), the level of meteorological disaster (such as level 1, level 2, level 3, etc.), the expected impact time (such as XX month XX day XX to XX month XX day XX), key sub-areas (such as XX district, XX county, etc.); Generate standard meteorological warning information based on key warning information and target meteorological disaster warning templates.

[0065] Specifically, the standard format of warning information includes title, text, issuing unit, release time, etc. The target meteorological disaster warning template contains placeholders for subsequent insertion of key warning information. Fill the key warning information into the placeholders of the selected template. For example, title: generated according to the type and level of meteorological disasters, such as "Blue Warning for Heavy Rain". Text: Detailed description of the type, level, expected impact time and key sub-areas of meteorological disasters, and may include disaster prevention and mitigation suggestions. Issuing unit: Fill in the name of the unit that issued the warning information. Release time: Fill in the release time of the warning information.

[0066] Step 180, for each warning user terminal group, generate meteorological warning information corresponding to the warning user terminal group based on the behavioral data of the warning user terminals included in the warning user terminal group and standard meteorological warning information, and send a flash message to the warning user terminals included in the warning user terminal group based on the meteorological warning information corresponding to the warning user terminal group.

[0067] In some embodiments, step 180 specifically includes: According to the displacement activity coefficient of the warning user terminal included in the warning user terminal group, the meteorological warning style and response suggestions corresponding to the warning user terminal group are determined; Determine the warning priority of each sub-region included in the meteorological warning area of ​​the target area of ​​the corresponding warning user terminal group according to the second position association parameter of the warning user terminal included in the warning user terminal group and each sub-region included in the meteorological warning area of ​​the target area, for example, average the second position association parameters of the warning user terminal included in the warning user terminal group and each sub-region included in the meteorological warning area of ​​the target area to obtain the mean value of the second position association parameters of the warning user terminal group and the sub-region, and the sub-region with a larger mean value of the second position association parameter has a higher warning priority; Through the style adjustment model, the standard meteorological warning information is adjusted according to the meteorological warning style and response suggestions corresponding to the warning user terminal group and the warning priority of each sub-area included in the meteorological warning area of ​​the target area of ​​the corresponding warning user terminal group, and the meteorological warning information corresponding to the warning user terminal group is generated. Among them, the style adjustment model can be a convolutional neural network model. In the meteorological warning information corresponding to the warning user terminal group, the warning prompts of the sub-areas included in the meteorological warning area of ​​the target area with a higher warning priority are placed in the front, for example, Figure 3 In the meteorological warning information corresponding to the warning user terminal group A shown in FIG. 1 , the warning priority of the A1 sub-area is higher than that of the A2 sub-area, and the warning priority of the A2 sub-area is higher than that of the A3 sub-area. Figure 3 In the warning user terminal group B shown, the warning priority of the A2 sub-area is higher than that of the A1 sub-area, and the warning priority of the A1 sub-area is higher than that of the A3 sub-area.

[0068] Specifically, multiple thresholds of different stages may be set to divide the early warning user end group into a high displacement active group, a medium displacement active group, and a low displacement active group. For example, a first threshold and a second threshold are set, wherein the first threshold is less than the second threshold, the displacement active coefficients of the early warning user ends included in the early warning user end group are averaged to obtain the displacement active coefficient average, the early warning user end group whose displacement active coefficient average is less than the first threshold is regarded as a low displacement active group, the early warning user end group whose displacement active coefficient average is greater than or equal to the first threshold and less than the second threshold is regarded as a medium displacement active group, and the early warning user end group whose displacement active coefficient average is greater than or equal to the second threshold is regarded as a high displacement active group.

[0069] Different meteorological warning styles and response suggestions are set for the high-displacement active group, medium-displacement active group and low-displacement active group respectively.

[0070] As an example only, the weather warning styles and response suggestions corresponding to the high displacement active group, the medium displacement active group and the low displacement active group may be: High displacement active group: Warning style: Since users move frequently and may often be in different geographical locations, warning information should be concise and clear, highlighting key information, such as "Emergency! Heavy rain is about to occur in XX area, please find a safe shelter immediately."

[0071] Response suggestions: It is recommended that users install the weather warning app and enable the real-time push function to ensure that they can receive warning information in a timely manner no matter where they are. At the same time, users are reminded to carry necessary emergency items with them, such as raincoats, flashlights, etc.

[0072] Medium displacement active group: Warning style: For this type of users, the warning information can be more detailed, including the type, level, expected impact time and scope of the meteorological disaster, such as "Typhoon XX is expected to land in XX City tonight, with wind speeds reaching above level 12. Please prepare wind-proof reinforcement measures."

[0073] Response suggestions: Users are advised to pay attention to the warning information issued by the local meteorological department and take preventive measures in advance, such as reinforcing doors and windows, clearing debris from the balcony, etc. At the same time, users are reminded to avoid going to dangerous areas to ensure personal safety.

[0074] Low displacement active group: Warning style: Since users move less frequently, they may be more concerned about long-term or regional meteorological changes. Warning information can be more in-depth, including the background of meteorological disasters, historical data, possible impacts, etc. For example, "There has been frequent rainfall in XX area recently, and the soil moisture content is close to saturation, which is prone to secondary disasters such as landslides. Residents are requested to strengthen prevention."

[0075] Figure 5 is a schematic diagram of a module of a weather warning information push device based on user behavior data and flash messages according to some embodiments of this specification, such as Figure 5 As shown, the weather warning information push device based on user behavior data and flash messages may include a region analysis module, a data acquisition module, a region determination module, a user analysis module, a template determination module and a weather warning module.

[0076] A regional analysis module is used to determine meteorological disaster related information of multiple sub-regions included in the target area; A data acquisition module is used to acquire meteorological disaster data in a target area; A region determination module, used to determine a meteorological warning region of a target region based on meteorological disaster data of the target region and meteorological disaster association information of a plurality of sub-regions included in the target region; A user analysis module is used to determine multiple warning user terminals according to the meteorological warning area of ​​the target area; The user analysis module is also used to obtain the behavior data of multiple warning user terminals and divide the multiple warning user terminals into multiple warning user terminal groups; A template determination module is used to determine a target meteorological disaster warning template according to the meteorological disaster data of the target area; A meteorological warning module is used to generate standard meteorological warning information based on the meteorological disaster data of the target area and the target meteorological disaster warning template; The meteorological warning module is also used to generate meteorological warning information corresponding to the warning user terminal group for each warning user terminal group based on the behavioral data of the warning user terminals included in the warning user terminal group and the standard meteorological warning information, and send flash messages to the warning user terminals included in the warning user terminal group based on the meteorological warning information corresponding to the warning user terminal group.

[0077] The weather warning information push device based on user behavior data and flash messages can be used to execute the weather warning information push method based on user behavior data and flash messages, which will not be repeated here.

[0078] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A method for pushing weather warning information based on user behavior data and flash messages, characterized in that: include: Determine meteorological disaster related information of multiple sub-areas included in the target area; Obtain meteorological disaster data in the target area; Determine the meteorological warning area of ​​the target area according to the meteorological disaster data of the target area and the meteorological disaster related information of multiple sub-areas included in the target area; Determine multiple warning user terminals according to the meteorological warning area of ​​the target area; Acquire behavior data of a plurality of warning user terminals, and divide the plurality of warning user terminals into a plurality of warning user terminal groups; Determining a target meteorological disaster warning template according to the meteorological disaster data of the target area; Generate standard meteorological warning information according to the meteorological disaster data of the target area and the target meteorological disaster warning template; For each warning user terminal group, meteorological warning information corresponding to the warning user terminal group is generated based on the behavioral data of the warning user terminals included in the warning user terminal group and standard meteorological warning information, and flash messages are sent to the warning user terminals included in the warning user terminal group based on the meteorological warning information corresponding to the warning user terminal group.

2. The method for pushing weather warning information based on user behavior data and flash messages according to claim 1 is characterized in that: Determine the meteorological disaster related information of multiple sub-areas included in the target area, including: Obtain historical meteorological disaster information in the target area; For each meteorological disaster, based on the historical meteorological disaster information of the target area, the disaster co-occurrence coefficient of any two sub-areas of the target area corresponding to the meteorological disaster is calculated, and based on the disaster co-occurrence coefficient of any two sub-areas of the target area corresponding to the meteorological disaster, a plurality of first sub-area units corresponding to the meteorological disaster are determined, each first sub-area unit includes two sub-areas, and based on the historical meteorological disaster information of the target area, the co-occurrence associated meteorological elements of each first sub-area unit corresponding to the meteorological disaster are determined; Acquire historical behavior data of a user terminal in a target area, wherein the historical behavior data includes the location of the user terminal at multiple historical time points acquired with the authorization of the user; According to the historical behavior data of the user terminal in the target area, the user movement correlation coefficient of any two sub-areas of the target area is calculated, and according to the user movement correlation coefficient of any two sub-areas, a plurality of second sub-area units are determined, wherein the second sub-area unit includes two sub-areas, wherein the meteorological disaster association information of the plurality of sub-areas included in the target area includes at least a plurality of first sub-area units corresponding to each meteorological disaster, co-occurrence associated meteorological elements corresponding to the meteorological disaster of each first sub-area unit, and a plurality of second sub-area units.

3. The method for pushing weather warning information based on user behavior data and flash messages according to claim 2 is characterized in that: According to the meteorological disaster data of the target area and the meteorological disaster related information of multiple sub-areas included in the target area, the meteorological warning area of ​​the target area is determined, including: For each sub-region unit, based on the historical meteorological disaster information of the target area, a plurality of training samples corresponding to the first sub-region unit are generated, an associated prediction model corresponding to the first sub-region unit is established, and the associated prediction model corresponding to the first sub-region unit is trained by using the plurality of training samples corresponding to the first sub-region unit; Determine the target meteorological disasters and key sub-areas based on the meteorological disaster data of the target area; The meteorological warning area of ​​the target area is determined according to the target meteorological disaster, the key sub-area, the meteorological disaster related information of the multiple sub-areas included in the target area, and the related prediction model corresponding to each first sub-area unit.

4. The method for pushing weather warning information based on user behavior data and flash messages according to claim 3 is characterized in that: Determining a meteorological warning area of ​​the target area according to the target meteorological disaster, the key sub-area, meteorological disaster association information of multiple sub-areas included in the target area, and the association prediction model corresponding to each first sub-area unit, includes: Determine a target first sub-region unit according to a plurality of sub-region units and key sub-regions corresponding to the target meteorological disaster; By using the association prediction model corresponding to the first sub-region unit of the target, and according to the historical meteorological disaster information of the target area, the disaster co-occurrence probability of the first sub-region unit of the target is predicted; Determine the first supplementary sub-region according to the disaster co-occurrence probability of each target first sub-region unit; Determine a second supplementary sub-region according to the plurality of second sub-region units and the key sub-region; A meteorological warning area of ​​the target area is determined according to the key sub-area, the first supplementary sub-area and the second supplementary sub-area.

5. The method for pushing weather warning information based on user behavior data and flash messages according to claim 3 is characterized in that: According to the meteorological warning area of ​​the target area, multiple warning user terminals are determined, including: For each user terminal in the target area, the first position association parameter between the user terminal and the meteorological warning area of ​​the target area is calculated based on the user terminal's behavior data, and whether the user terminal is a warning user terminal is determined based on the first position association parameter between the user terminal and the meteorological warning area of ​​the target area.

6. The method for pushing weather warning information based on user behavior data and flash messages according to claim 1, characterized in that: Acquiring behavior data of a plurality of warning users and dividing the plurality of warning users into a plurality of warning user groups includes: For each warning user terminal, a displacement activity coefficient of the warning user terminal is determined according to the behavior data of the warning user terminal; Using a K-Means clustering algorithm, the plurality of warning user terminals are divided into at least one warning user terminal cluster according to the displacement activity coefficient of each warning user terminal; For each warning user terminal cluster, the second position association parameter between the warning user terminal and each sub-area included in the meteorological warning area of ​​the target area is determined based on the behavioral data of the warning user terminals included in the warning user terminal cluster. Through the K-Means clustering algorithm, the multiple warning user terminals included in the warning user terminal cluster are divided into at least one warning user terminal group based on the second position association parameters between the warning user terminals included in the warning user terminal cluster and each sub-area included in the meteorological warning area of ​​the target area.

7. The method for pushing weather warning information based on user behavior data and flash messages according to any one of claims 1 to 6, characterized in that: According to the meteorological disaster data of the target area, a meteorological disaster warning template is determined, including: Acquire multiple meteorological disaster warning templates and meteorological disaster characteristics corresponding to each meteorological disaster warning template, wherein the meteorological disaster characteristics at least include a meteorological disaster type and a meteorological disaster level; Acquiring current meteorological disaster characteristics according to the meteorological disaster data of the target area; A target meteorological disaster warning template is determined from multiple meteorological disaster warning templates according to the current meteorological disaster characteristics and the meteorological disaster characteristics corresponding to each meteorological disaster warning template.

8. The method for pushing weather warning information based on user behavior data and flash messages according to any one of claims 1 to 6, characterized in that: Generate standard meteorological warning information based on the meteorological disaster data of the target area and the target meteorological disaster warning template, including: Acquire key warning information according to the meteorological disaster data of the target area, wherein the key warning information at least includes the type of meteorological disaster, the level of meteorological disaster, the expected impact time and the key sub-area; Standard meteorological warning information is generated based on the key warning information and the target meteorological disaster warning template.

9. The method for pushing weather warning information based on user behavior data and flash messages according to claim 6, characterized in that: According to the behavior data of the warning user terminals included in the warning user terminal group and the standard weather warning information, weather warning information corresponding to the warning user terminal group is generated, including: Determining the meteorological warning style and response suggestions corresponding to the warning user terminal group according to the displacement activity coefficients of the warning user terminals included in the warning user terminal group; Determine the warning priority of each sub-region included in the meteorological warning area of ​​the target area of ​​the corresponding warning user terminal group according to the second position association parameter of each sub-region included in the meteorological warning area of ​​the target area of ​​the warning user terminal group; Through the style adjustment model, the standard meteorological warning information is adjusted according to the meteorological warning style and response suggestions corresponding to the warning user terminal group and the warning priority of each sub-area included in the meteorological warning area of ​​the target area corresponding to the warning user terminal group, and the meteorological warning information corresponding to the warning user terminal group is generated.

10. Weather warning information push device based on user behavior data and flash messages, characterized in that: The method for pushing weather warning information based on user behavior data and flash messages as described in any one of claims 1 to 9 comprises: A regional analysis module is used to determine meteorological disaster related information of multiple sub-regions included in the target area; A data acquisition module is used to acquire meteorological disaster data in a target area; A region determination module, used to determine a meteorological warning region of a target region based on meteorological disaster data of the target region and meteorological disaster association information of a plurality of sub-regions included in the target region; A user analysis module is used to determine multiple warning user terminals according to the meteorological warning area of ​​the target area; The user analysis module is also used to obtain behavior data of multiple warning user terminals and divide the multiple warning user terminals into multiple warning user terminal groups; A template determination module, used to determine a target meteorological disaster warning template according to the meteorological disaster data of the target area; A meteorological warning module, used to generate standard meteorological warning information according to the meteorological disaster data of the target area and the target meteorological disaster warning template; The meteorological warning module is also used to generate meteorological warning information corresponding to each warning user terminal group based on the behavioral data of the warning user terminals included in the warning user terminal group and standard meteorological warning information, and send flash messages to the warning user terminals included in the warning user terminal group based on the meteorological warning information corresponding to the warning user terminal group.

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