Meteorological information targeted publishing system and method

Abstract

The invention discloses a meteorological information targeted publishing system and method, and relates to the technical field of meteorological information publishing. The method comprises the following steps: performing system initialization; the application layer is used for collecting login information, performing service authentication, and receiving a short message task after the service authentication is passed; performing authority verification on the staff, and entering the next step after the authority verification is passed; performing user screening according to the short message task to generate a target user ID list; according to the short message task, generating short message content, and issuing the short message content to all users in the target user ID list; and collecting feedback information published by the short message content, and updating the user screening decision model in the short message task management module according to the feedback information to obtain an updated user screening decision model. The problems that audience distinguishing is difficult, publishing efficiency is low, information is lack of accuracy, and the intelligent level is insufficient in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of meteorological information dissemination technology, and in particular to a targeted meteorological information dissemination system and method. Background Technology

[0002] In the field of meteorological information dissemination, traditional SMS delivery methods have many problems. They lack effective differentiation of target audiences, resulting in poor information targeting and causing a large number of irrelevant users to receive information while potentially overlooking those who truly need it. Furthermore, SMS content lacks personalization and precision, leading to a poor reading experience and failing to meet users' actual needs. In addition, large-scale SMS sending is expensive, and SMS channels are prone to congestion, severely impacting the timeliness of information delivery.

[0003] In terms of meteorological services, although infrastructure such as meteorological monitoring stations is in place, there are significant shortcomings in the accurate transmission of meteorological information to specific areas and populations. The positioning and big data capabilities of operator base stations have not been fully utilized, making it impossible to provide refined services for users in different scenarios (such as construction sites, tourist attractions, and transportation hubs).

[0004] The main drawbacks of existing technologies include:

[0005] 1) Difficulty in audience segmentation: Traditional messaging is mostly based on administrative regions and is sent out in bulk across the entire network or in specific areas, failing to effectively utilize the operator's precise positioning and user profiling capabilities. This results in a large number of users in non-risk areas receiving redundant warning information, causing information interference and wasting resources; while core users may be overwhelmed by massive amounts of information and unable to meet their needs for precise services.

[0006] 2) Low delivery efficiency: Traditional SMS channels are prone to congestion, making it difficult to guarantee the timeliness of information delivery. At the same time, indiscriminate mass sending leads to high SMS costs, resulting in low cost-effectiveness and making it difficult to reach the users who need the information most in a timely and economical manner.

[0007] 3) Lack of precision in information: Traditional methods struggle to tailor information content to the user's specific location, identity characteristics, and context. For example, sending the same general warning message to tourists in scenic areas and construction workers on construction sites lacks targeted defense guidance, resulting in poor information usability and low user reading experience and adoption rates.

[0008] 4) Insufficient level of intelligence: The selection process for target users relies heavily on human experience and lacks data-driven intelligent decision support. This is not only inefficient and unable to cope with sudden and rapidly evolving weather events, but also unable to form an effective feedback loop to continuously optimize future release strategies.

[0009] Therefore, how to build a meteorological information dissemination technology that can achieve high-precision targeting, intelligent decision-making, and closed-loop optimization is a technical problem that urgently needs to be solved in the current meteorological service field. Summary of the Invention

[0010] This invention provides a targeted meteorological information dissemination system and method, which solves the problems of difficulty in audience differentiation, low dissemination efficiency, lack of information accuracy, and insufficient intelligence level in existing technologies.

[0011] In a first aspect, embodiments of the present invention provide a meteorological information targeted dissemination system, the system comprising: a data acquisition layer, a data processing layer, a business logic layer, and an application layer;

[0012] The data acquisition layer communicates with both the operator's server and the meteorological server.

[0013] The data processing layer includes a data cleaning and noise reduction module, a data association and integration module, and a data caching and update module, and the data processing layer is connected to an in-memory database;

[0014] The business logic layer includes a user grid management module, an access control module, an SMS service area creation module, an SMS template management module, an SMS task management module, and a population heat map and profile query module.

[0015] The SMS task management module communicates with the operator's server.

[0016] In one optional implementation, the data acquisition layer is used to collect raw geographic data and raw user data from the operator server and raw meteorological data from the meteorological server. After preliminary processing and labeling of the collected raw data, the preliminary processed data is transmitted to the data processing layer.

[0017] The data processing layer is used to process the data transmitted from the data acquisition layer after initial processing, to obtain preprocessed geographic data, preprocessed user data, and preprocessed meteorological data. The preprocessed user data and preprocessed meteorological data are then converted into standard user data and standard meteorological data and stored in the memory database and / or cache database.

[0018] The business logic layer is used to construct several user grids based on the preprocessed geographic data and preprocessed user data output from the data processing layer; perform business authentication on staff login information and permission verification on SMS tasks collected by the application layer; create several SMS service areas based on the preprocessed geographic data; create several SMS templates to be reviewed and review them to obtain several SMS templates; filter users and generate a target user ID list based on the target SMS service area and targeted delivery mode of the SMS task; query target standard user data in the cache database and / or memory database based on the target SMS service area and generate the target summary corresponding to the target user data; generate SMS content based on the target meteorological information and target SMS template of the SMS task, and send the SMS content, target user ID list, and targeted delivery parameters to the operator server.

[0019] The application layer is used to collect staff login information and SMS tasks.

[0020] In one optional implementation, the data cleaning and denoising module is used to clean and denoise the data after the initial processing transmitted from the data acquisition layer, so as to obtain preprocessed geographic data, preprocessed user data, and preprocessed meteorological data.

[0021] The data association and integration module is used to associate and integrate the preprocessed user data and meteorological data into the user grid built by the user grid management module based on the business logic layer, so as to obtain standard user data and standard meteorological data with unified spatial scale, and store the standard user data and standard meteorological data in the memory database.

[0022] The data caching and update module is used to store standard user data and standard meteorological data into the cache database according to the preset data caching mechanism, and to update the cache database periodically.

[0023] The user grid management module is used to construct several user grids based on the preprocessed geographic data and preprocessed user data output from the data processing layer.

[0024] The permission management module is used to perform business authentication on login information collected by the application layer, and to verify permissions on SMS tasks collected by the application layer.

[0025] The SMS service area creation module is used to create several SMS service areas based on preprocessed geographic data.

[0026] The SMS template management module is used to create several SMS templates to be approved and to review these templates to obtain a total of several SMS templates.

[0027] The SMS task management module is equipped with a response and feedback prediction model and a user screening decision model. It is used to screen users based on the target SMS service area and targeted delivery mode of the SMS task, and generate a target user ID list. Based on the target weather information and target SMS template of the SMS task, it generates SMS content and sends the SMS content, target user ID list and targeted delivery parameters to the operator server.

[0028] The population heat map and profile query module is equipped with an intelligent profile analysis model, which is used to query target standard user data in the cache database and / or memory database based on the target SMS service area, and use the intelligent profile analysis model to generate target summaries corresponding to the target user data.

[0029] Secondly, embodiments of the present invention provide a method for targeted dissemination of meteorological information, based on a targeted meteorological information dissemination system, the method comprising:

[0030] Initialize the targeted meteorological information dissemination system;

[0031] Using the application layer, collect the login information of the staff, and call the permission management module of the business logic layer to perform business authentication on the login information. After the business authentication is successful, receive the SMS task entered by the staff.

[0032] SMS tasks include target SMS service areas, target weather information, target user groups, target SMS templates, targeted delivery parameters, and targeted delivery modes;

[0033] Based on the SMS task, the permission management module of the business logic layer is called to verify the permissions of the staff. After the permission verification is successful, the process proceeds to the next step.

[0034] Based on the target SMS service area, target weather information, and targeted delivery mode of the SMS task, the SMS task management module of the business logic layer is invoked to filter users and generate a list of target user IDs.

[0035] Based on the target weather information and target SMS template of the SMS task, generate SMS content and, according to the targeted release parameters, release the SMS content to all users in the target user ID list;

[0036] The system collects feedback information on SMS content releases and updates the user screening decision model in the SMS task management module based on this feedback information, resulting in an updated user screening decision model.

[0037] In one optional implementation, the meteorological information targeted dissemination system is initialized, including:

[0038] The data acquisition layer collects raw geographic data and raw user data from the operator's server and raw meteorological data from the meteorological server. After preliminary processing and labeling of the collected raw data, the preliminary processed data is transmitted to the data processing layer.

[0039] The data processing layer is used to process the initially sorted data to obtain preprocessed geographic data, preprocessed user data, and preprocessed meteorological data. The preprocessed user data and preprocessed meteorological data are then converted into standard user data and standard meteorological data and stored in an in-memory database and / or a cache database.

[0040] User data includes user distribution heat map information, user profile information, and user location data;

[0041] Using the business logic layer, several user grids are constructed based on the preprocessed geographic data and preprocessed user data output from the data processing layer, and several SMS service areas are created based on the preprocessed geographic data.

[0042] Using the business logic layer, we initialize legitimate identities and build a legitimate identity database that includes dynamic business authentication factors and user permission policies for several legitimate personnel.

[0043] In one optional implementation, the application layer collects staff login information and calls the permission management module of the business logic layer to perform business authentication on the login information. After successful business authentication, the system receives SMS tasks input by the staff, including:

[0044] Using the application layer, collect staff login information and extract the user ID, timestamp, and geographic region information corresponding to the login information;

[0045] Based on user ID, timestamp, and geographic region information, the permission management module of the business logic layer is invoked to generate dynamic business authentication factors for staff.

[0046] Based on the SM2 public key, the dynamic business authentication factor of the staff is encrypted to obtain the encrypted dynamic business authentication factor, which is then transmitted to the legitimate identity database.

[0047] In the legitimate identity database, the encrypted dynamic business authentication factor is decrypted based on the SM2 private key to obtain the decrypted dynamic business authentication factor.

[0048] The business authentication is performed on the decrypted dynamic business authentication factor using the legitimate identity database. If there is a legitimate person's dynamic business authentication factor that matches the decrypted dynamic business authentication factor, the business authentication is successful.

[0049] After business authentication is successful, the system receives SMS tasks input by staff.

[0050] In one optional implementation, based on the SMS task, the permission management module of the business logic layer is invoked to verify the staff member's permissions. After successful permission verification, the process proceeds to the next step, including:

[0051] Extract the user permission policies corresponding to the staff from the legitimate identity database;

[0052] Generate the corresponding business attribute vector based on the SMS task;

[0053] Based on the business attribute vector, the staff member's private key is used to perform attribute matching and decryption. If the business attribute vector meets the user permission policy, the preset symmetric key is generated, the permission verification is passed, and the user screening step is initiated.

[0054] In one alternative implementation, the targeted release mode includes a manual specification mode and an intelligent recommendation mode;

[0055] User filtering based on manual specification includes:

[0056] Based on the target SMS service area of ​​the SMS task, the corresponding target user grid is matched among several user grids generated by the user grid management module of the business logic layer.

[0057] Based on the target user grid and target user group, user data is queried in the population heat map and profile query module to obtain target user data of several target users.

[0058] Based on the target user data, users are filtered within the target user group to obtain a list of target user IDs.

[0059] User filtering based on intelligent recommendation models includes:

[0060] Based on the target SMS service area, extract target user data of the target user group from the population heat map and profile query module;

[0061] Based on the target SMS service area, target weather information, target SMS template, and target user data in the SMS task, a response and feedback prediction model is used to predict the response and feedback, and the response and feedback prediction results are obtained. The response and feedback prediction results include the expected response rate and the negative feedback rate.

[0062] Based on the target SMS service area, target weather information, target SMS template, target user data, and response and feedback prediction results, a user screening decision model is used to screen users and obtain a list of target user IDs.

[0063] In one optional implementation, SMS content is generated based on the target weather information and target SMS template of the SMS task, and the SMS content is then sent to all users in the target user ID list, including:

[0064] Based on the target meteorological information of the SMS task, the target standard meteorological data is obtained by retrieving it from the in-memory database and / or cache database.

[0065] Write the target standard meteorological data into the target SMS template to generate SMS content;

[0066] Send the SMS content, the list of target user IDs, and the targeted delivery parameters to the operator's server;

[0067] The carrier's server, based on the targeted delivery parameters, sends the SMS content to all users in the target user ID list.

[0068] In one optional implementation, feedback information on SMS content delivery is collected, and the user filtering decision model in the SMS task management module is updated based on this feedback information to obtain an updated user filtering decision model, including:

[0069] Periodically collect feedback information on SMS content releases and associate the feedback information with the corresponding SMS task and target user ID list to obtain several task-effect records;

[0070] The fitness function of the BO algorithm is set with the goal of maximizing the total reward of all SMS tasks.

[0071] The weight vector of the user selection decision model is encoded into the individual vector of the bowerbird algorithm, and the bowerbird population parameters and the maximum number of iterations are set.

[0072] Based on the bowerbird population parameters, the initial bowerbird population is obtained by initializing using the Tent chaotic mapping sequence; each bowerbird in the bowerbird population corresponds to a candidate weight vector.

[0073] Based on several task-effect records, the fitness function is used to obtain the fitness value of each initial bowerbird individual, and the initial bowerbird individual with the best fitness value is taken as the optimal solution.

[0074] Explore the initial bowerbird population by building or decorating a gazebo, and obtain an updated first bowerbird population.

[0075] Using the theft probability, several first bowerbird individuals were randomly selected from the updated first bowerbird population to simulate theft behavior, resulting in several updated second bowerbird individuals.

[0076] Based on several task-effect records, the fitness function is used to obtain the fitness values ​​of the first and second updated bowerbird individuals, and the updated bowerbird individual with the best fitness value is updated as the optimal solution.

[0077] When the number of iterations reaches the maximum number of iterations or the fitness value of the optimal solution meets the requirements, the iterative update of the bowerbird population is terminated, and the optimal solution of the current iteration is output.

[0078] Decode the individual vector of the bowerbird corresponding to the optimal solution to obtain the optimal weight vector in the user selection decision model;

[0079] Based on the optimal weight vector, the user selection decision model in the SMS task management module is updated to obtain the updated user selection decision model.

[0080] A third aspect of this invention provides an electronic device, which includes:

[0081] At least one processor; and a memory communicatively connected to the at least one processor; wherein,

[0082] The memory stores instructions that can be executed by at least one processor, such that the at least one processor can perform the method proposed in the first aspect of the present invention.

[0083] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in the first aspect of the present invention.

[0084] The technical solution provided in this application has at least the following beneficial effects:

[0085] By integrating operator base station, user distribution heatmap, and user profile data, and constructing a refined user grid, precise spatial matching of meteorological information and users is achieved. Unified data scales allow for precise dissemination from "administrative regions" to "geographical grids," and even specific risk points, completely transforming the traditional mass messaging model. This ensures information is delivered only to those most in need, significantly improving the effectiveness and relevance of information dissemination. Optimized SMS task management and efficient collaboration mechanisms with operators reduce SMS channel congestion, lower costs, and ensure timely delivery of weather warnings and other information. For example, during severe weather, it can quickly reach affected populations, enhancing emergency response capabilities. The system also incorporates response and... The system employs a feedback prediction model and a user screening decision model. It can automatically predict the responses of different user groups to specific weather information and intelligently select the optimal user group based on a multi-objective reward function (comprehensively considering click-through rate, positive feedback, cost, and complaint rate). This replaces traditional manual experience-based decision-making, making the release strategy more scientific and efficient, adaptable to changing weather scenarios, and improving the level of intelligence. Furthermore, an intelligent profiling analysis model is introduced to automatically identify the salient features of people in specific areas and automatically generate easy-to-understand text summaries. This provides meteorological service personnel with intuitive and in-depth data insights, enabling them to formulate more personalized service strategies and content that fit the actual scenario. Attached Figure Description

[0086] Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of the present invention;

[0087] Figure 2 This is a schematic diagram of the functional units of a targeted meteorological information dissemination system provided in an embodiment of the present invention.

[0088] Figure 3 This is a flowchart illustrating the steps of a targeted meteorological information dissemination method provided in an embodiment of the present invention; Detailed Implementation

[0089] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0090] The present invention will be further described below with reference to the accompanying drawings.

[0091] Reference Figure 1 , Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of the present invention.

[0092] like Figure 1 As shown, the electronic device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0093] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0094] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and an electronic program for a meteorological information targeted release system.

[0095] exist Figure 1 In the electronic device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the electronic device of the present invention can be set in the electronic device. The electronic device calls the electronic program of the meteorological information targeted release system stored in the memory 1005 through the processor 1001 and executes the meteorological information targeted release method provided in the embodiment of the present invention.

[0096] Embodiments of the present invention provide a targeted meteorological information dissemination system, referring to... Figure 2 The system includes: a data acquisition layer 201, a data processing layer 202, a business logic layer 203, and an application layer 204.

[0097] The data acquisition layer 201 is communicatively connected to the operator server 205 and the meteorological server 206, respectively.

[0098] The data processing layer 202 includes a data cleaning and denoising module 2021, a data association and integration module 2022, and a data caching and updating module 2023, and the data processing layer is connected to the in-memory database 2024;

[0099] The business logic layer 203 includes a user grid management module 2031, an access control module 2032, an SMS service area creation module 2033, an SMS template management module 2034, an SMS task management module 2035, and a population heat map and profile query module 2036.

[0100] In one optional implementation, the data acquisition layer 201 is used to collect raw geographic data and raw user data from the operator server and raw meteorological data from the meteorological server. After performing preliminary processing and labeling on the collected raw data, the preliminary processed data is transmitted to the data processing layer.

[0101] The data processing layer 202 is used to process the data after preliminary processing transmitted from the data acquisition layer to obtain preprocessed geographic data, preprocessed user data and preprocessed meteorological data. The preprocessed user data and preprocessed meteorological data are converted into standard user data and standard meteorological data and stored in the memory database and / or cache database.

[0102] The business logic layer 203 is used to construct several user grids based on the preprocessed geographic data and preprocessed user data output from the data processing layer; perform business authentication on staff login information and permission verification on SMS tasks collected by the application layer; create several SMS service areas based on the preprocessed geographic data; create several SMS templates to be reviewed and review them to obtain several SMS templates; filter users and generate a target user ID list based on the target SMS service area and targeted release mode of the SMS task; query target standard user data in the cache database and / or memory database based on the target SMS service area and generate the target summary corresponding to the target user data; generate SMS content based on the target meteorological information and target SMS template of the SMS task, and send the SMS content, target user ID list and targeted release parameters to the operator server.

[0103] Application layer 204 is used to collect staff login information and SMS tasks.

[0104] In one optional implementation, the data cleaning and denoising module 2021 is used to clean and denoise the data after the initial processing of the data transmitted from the data acquisition layer, so as to obtain preprocessed geographic data, preprocessed user data and preprocessed meteorological data.

[0105] The Data Association and Integration Module 2022 is used to associate and integrate preprocessed user data and meteorological data into a user grid built based on the user grid management module of the business logic layer, so as to obtain standard user data and standard meteorological data with unified spatial scale, and store the standard user data and standard meteorological data in the memory database.

[0106] The data caching and update module 2023 is used to store standard user data and standard meteorological data in a cache database according to a preset data caching mechanism, and to update the cache database periodically.

[0107] User grid management module 2031 is used to construct several user grids based on the preprocessed geographic data and preprocessed user data output by the data processing layer.

[0108] The permission management module 2032 is used to perform business authentication on login information collected by the application layer and to perform permission verification on SMS tasks collected by the application layer.

[0109] The SMS service area creation module 2033 is used to create several SMS service areas based on preprocessed geographic data.

[0110] The SMS template management module 2034 is used to create several SMS templates to be approved and to approve the SMS templates to be approved, thereby obtaining several SMS templates.

[0111] The SMS task management module 2035 is equipped with a response and feedback prediction model and a user screening decision model. It is used to screen users and generate a target user ID list based on the target SMS service area and targeted release mode of the SMS task; it generates SMS content based on the target weather information and target SMS template of the SMS task, and sends the SMS content, the target user ID list and targeted release parameters to the operator server.

[0112] The Population Heat Mapping and Profile Query Module 2036 is equipped with an intelligent profile analysis model. It is used to query target standard user data in the cache database and / or memory database based on the target SMS service area, and use the intelligent profile analysis model to generate target summaries corresponding to the target user data.

[0113] This invention also provides a method for targeted dissemination of meteorological information, referring to... Figure 3 Based on a targeted meteorological information dissemination system, the methods include:

[0114] S301: Initialize the meteorological information targeted dissemination system;

[0115] S302: Using the application layer, collect the login information of the staff, and call the permission management module of the business logic layer to perform business authentication on the login information. After the business authentication is successful, receive the SMS task entered by the staff.

[0116] SMS tasks include target SMS service areas, target weather information, target user groups, target SMS templates, targeted delivery parameters, and targeted delivery modes;

[0117] S303: Based on the SMS task, call the permission management module of the business logic layer to verify the permissions of the staff. After the permission verification is successful, proceed to the next step.

[0118] S304: Based on the target SMS service area, target weather information, and targeted delivery mode of the SMS task, call the SMS task management module of the business logic layer to filter users and generate a list of target user IDs;

[0119] S305: Based on the target weather information and target SMS template of the SMS task, generate SMS content and publish the SMS content to all users in the target user ID list according to the target publishing parameters;

[0120] S306: Collect feedback information on SMS content release, and update the user screening decision model in the SMS task management module based on the feedback information to obtain the updated user screening decision model.

[0121] In one optional implementation, the meteorological information targeted dissemination system is initialized, including:

[0122] S3011: Using the data acquisition layer, raw geographic data and raw user data are collected from the operator server and raw meteorological data are collected from the meteorological server. After preliminary processing and labeling of the collected raw data, the preliminary processed data is transmitted to the data processing layer.

[0123] S3012: Use the data processing layer to process the initially sorted data to obtain preprocessed geographic data, preprocessed user data and preprocessed meteorological data. Convert the preprocessed user data and preprocessed meteorological data into standard user data and standard meteorological data and store them in the memory database and / or cache database.

[0124] User data includes user distribution heat map information, user profile information, and user location data;

[0125] S3013: Using the user grid management module of the business logic layer, several user grids are constructed based on the preprocessed geographic data and preprocessed user data output by the data processing layer, and several SMS service areas are created based on the preprocessed geographic data using the SMS service area creation module of the business logic layer.

[0126] In this embodiment, after receiving the preprocessed user data (including user distribution heat information, user profile information and user location data) and preprocessed geographic data from the data acquisition layer, the latitude and longitude backfill rasterization algorithm is used to process these data.

[0127] Specifically, the geographical area of ​​Xiong'an New Area is divided into 500*500m grids, and each grid is assigned a unique identifier (ID). For each user's location data, the grid to which the user belongs is determined by calculating the spatial relationship between the user's latitude and longitude and each grid. The user identifier is then associated with the corresponding grid ID and stored in the user-grid association table in the database. For example, for the location coordinates (x, y) of user A, all grids are traversed, and the grid (x, y) is determined based on the grid boundary coordinates. If the user falls into the grid with grid ID "001", the correspondence between user A and "001" is recorded in the association table.

[0128] Meanwhile, this module records the distribution information of operator base stations corresponding to each grid. This information also comes from the data acquisition layer. By matching the base station location data with the grid, it determines the number of base stations and base station IDs in each grid and stores them in the grid-base station association table, providing data support for subsequent information dissemination based on base stations and user grids.

[0129] Upon receiving the preprocessed geographic data from the data acquisition layer, first check if the data format is valid latitude and longitude, remove data with incorrect format, arrange the coordinate points of the preprocessed geographic data in order, and use a graphical algorithm (such as the ray method) to determine whether the connection of the points can form a closed figure. If not, an error is reported.

[0130] Calculate the boundary range of the closed graphic and compare it one by one with the raster boundary data pre-stored in the memory database. Based on the inclusion relationship of the coordinate range, determine which rasters are completely or partially within the graphic. Store these raster IDs into a temporary set. Remove duplicates from the temporary set to obtain the final set of covered raster IDs, and store them in the region-raster association table.

[0131] Receive the list of raster IDs selected by the user, check whether the IDs are within the range of valid raster IDs in the database, filter out invalid IDs, and store the remaining valid raster IDs in the region-raster association table.

[0132] After receiving the administrative region code, the corresponding set of raster IDs is searched in the administrative region-raster mapping table. The searched set of raster IDs is deduplicated and its validity is checked, and then stored in the region-raster association table.

[0133] S3015: Use the business logic layer to initialize legitimate identities and build a legitimate identity database that includes dynamic business authentication factors and user permission policies for several legitimate personnel.

[0134] In this embodiment, it also includes using the SMS template management module of the business logic layer to create several SMS templates to be reviewed, and reviewing the SMS templates to be reviewed to obtain several SMS templates.

[0135] Specifically, the system receives template content, name, attachments, and variable arrays. It performs character encoding conversion on the template content to ensure the database can correctly store special characters. It checks whether the template name conforms to naming rules (such as length limits and character types). If it does not conform, it prompts for modification. It checks the file format and size of attachments. If they do not meet the requirements, it rejects the upload. It verifies whether the variable format in the variable array is the preset format (such as {variable name}) and checks whether the variable has a corresponding placeholder in the template content. It stores the verified data in the template information table and stores the variable information in the template-variable association table to establish the association between the two.

[0136] Extract key information (content, name, etc.) of the template to be reviewed from the template information table, encapsulate the data in the format required by the operator, send the data to the operator's review platform through the interface, receive the review results, update the review status field in the template information table according to the review results, and if it fails, store the review comments in the corresponding field of the table.

[0137] In one optional implementation, based on the target SMS service area, the population heat map and profile query module of the business logic layer is invoked to query the target standard user data in the cache database and / or memory database, and the intelligent profile analysis model is used to generate the target summary corresponding to the target user data.

[0138] Specifically, the data query and processing flow of the population heat map and profile query module is as follows:

[0139] Receive query requests: Receive region ID query requests initiated by the application layer;

[0140] Cache query: First, search the Redis cache for pre-calculated population heat map data and profile analysis report for the region; if they exist and have not expired, return the cached results directly.

[0141] Database query (when cache miss): If there is no data in the cache or the data has expired, query from the in-memory database;

[0142] 1) Population heat map data: From the raster population statistics table, the population of all the rasters covered by the area is summarized to obtain basic indicators such as total population and population density;

[0143] 2) Original profile data: Extract the original profile fields (such as age, occupation, place of residence, place of origin, gender, etc.) of users in this region from the user information table.

[0144] Generate a target summary corresponding to the target user data, including:

[0145] C-1: Based on the target SMS service area, call the population heat map and profile query module of the business logic layer to query the target standard user data in the cache database and / or memory database, and input the target standard user data into the intelligent profile analysis model;

[0146] The intelligent profile analysis model is built based on the Significant Deviation Score (SDS) algorithm;

[0147] C-2: Use intelligent profiling analysis models to generate regional feature saliency values ​​for target user data;

[0148] The formula is:

[0149]

[0150] In the formula, The user profile features of the target users in the target user data are obtained through multi-dimensional feature statistics and quantification: the distribution of each profile field (such as age percentage, occupation percentage, gender ratio, etc.) is calculated to form a preliminary statistical vector, i.e., user profile features). Target SMS service area; The significance value of the regional characteristics; The probability of user profile features falling within the target SMS service area; The average probability across the entire region; The standard deviation is the global standard deviation, which measures the volatility of the feature itself. If a feature itself is highly volatile ( If the fluctuation is large, then a small deviation in a certain area is not considered abnormal; if the fluctuation is small... Even very small deviations appear significant. Number of users in the region; Meaning: The more users a region has, the more reliable its statistical results are, and the more confident we are in the significance of its calculation. Therefore, we can amplify its score by taking the logarithm. For regions with very few users, this factor will be close to 0, thereby reducing its significance score and avoiding statistical noise caused by small data volume.

[0151] C-3: Based on the saliency value of regional features, significant features selected from user profile features are automatically generated using Natural Language Generation (NLG) technology, such as: "The population composition of this area is mainly construction workers, with a proportion far exceeding the average level of the new district; the proportion of elderly people is significantly lower than the average level of the new district; meteorological service recommendations: pay close attention to weather conditions that affect outdoor construction, such as strong winds and high temperatures."

[0152] The basic population heat map data, raw statistical results, and automatically generated NLG descriptive summaries are combined to form the final query result. This result is stored in a Redis cache with a reasonable expiration time, and then returned to the requester.

[0153] In one optional implementation, a data acquisition layer is used to collect raw geographic data and raw user data from the operator's server and raw meteorological data from the meteorological server. After preliminary processing and labeling of the collected raw data, the preliminarily processed data is transmitted to the data processing layer, including:

[0154] S30111: Use the data acquisition layer to collect raw geographic data and raw user data from the operator server, and raw meteorological data from the meteorological server.

[0155] In this embodiment, the interface provided by the operator's server is used to collect user distribution heat map information and user profile information in real time; with the help of the operator's big data platform, raw mobile phone signaling data and network basic data are collected to obtain user location data, behavioral preferences and other feature information; at the same time, meteorological data and related instructions output by the meteorological brain are received; during the collection process, in order to ensure data accuracy and consistency, different types of data are standardized using specific collection protocols and formats. For example, when collecting base station information, data such as base station ID, latitude and longitude coordinates, and signal coverage range are recorded in a unified format; when collecting user profile information, the format of fields such as age, gender, and place of residence is standardized.

[0156] S30112: After preliminary processing and labeling of the collected raw data, the preliminary processed data is transmitted to the data processing layer.

[0157] In this embodiment, the collected data is initially organized at this layer, and a source identifier and collection timestamp are added to each data. For example, for user location data, it is marked which operator base station it comes from and the specific collection time, which facilitates subsequent data processing and traceability.

[0158] In one optional implementation, a data processing layer is used to process the initially organized data, obtaining preprocessed geographic data, preprocessed user data, and preprocessed meteorological data. The preprocessed user data and preprocessed meteorological data are then converted into standard user data and standard meteorological data, and stored in an in-memory database and / or a cache database, including:

[0159] S30121: The data cleaning and denoising module of the data processing layer uses rule-based and statistical data cleaning algorithms to standardize the initially processed data, remove duplicate, erroneous and incomplete data, and obtain preprocessed geographic data, preprocessed user data and preprocessed meteorological data.

[0160] For user data: By setting geofence thresholds (such as latitude and longitude range) and movement speed thresholds, abnormal location data can be identified and removed. For example, if the user's location coordinates are outside the geographical range of Xiong'an New Area, or the movement speed of two consecutive points exceeds the physical possibility value (such as 500km / h), it is determined to be abnormal data and removed.

[0161] For meteorological data: A new meteorological data cleaning process has been added, and a set of dynamically configurable business threshold tables has been maintained (such as temperature range: -30℃~50℃, non-negative precipitation, etc.). During cleaning, the configuration table is read and the data is verified according to the rules. For data that exceeds the reasonable range, the data is tagged and replaced with the valid value of the previous time period or directly removed to ensure the validity and business rationality of the data used in subsequent analysis.

[0162] For geographic data:

[0163] S30122: Using the data association and integration module of the data processing layer, based on the user grid management module of the business logic layer, the user grid is constructed to perform data association and integration on the preprocessed user data and meteorological data to obtain standard user data and standard meteorological data with unified spatial scale, and store the standard user data and standard meteorological data in the memory database.

[0164] In this embodiment, spatial association and data fusion technology is used to construct a unified data view with user grid as the basic unit. Interpolation calculation details are added. Since the scale and resolution of meteorological data and user grid may be inconsistent, data remapping is required. A bilinear interpolation algorithm is used to interpolate the original 100*100 grid data of the meteorological model to a 500m*500m user grid to form standard user data and standard meteorological data with unified spatial scale.

[0165] Furthermore, to further explore the value of the data, principal component analysis and other data dimensionality reduction algorithms can be applied to the standardized user data and standard meteorological datasets with unified spatial scales to reduce the complexity of subsequent calculations and highlight core features. Lightweight deep learning models (such as Long Short-Term Memory (LSTM) networks) can also be introduced to predict meteorological trends within the grid, with historical standard meteorological data as input and short-term forecasts of standard meteorological data as output.

[0166] S30123: The data caching and update module of the data processing layer stores standard user data and standard meteorological data into the cache database according to the preset data caching mechanism, and updates the cache database periodically.

[0167] In this embodiment, a multi-level data caching mechanism is established using high-performance caching technologies such as Redis, which significantly improves the system response speed. Frequently accessed data (such as population heat data of popular areas, commonly used meteorological data, and approved SMS templates) are stored in the cache. A cache data update monitoring mechanism is established, which prioritizes cache updates when there are corresponding new data updates in the underlying database to ensure data timeliness. The Least Recently Used (LRU) algorithm is used as the cache eviction strategy. When the cache space is insufficient, the data object that has not been accessed for the longest time is automatically removed to maintain a balance between cache hit rate and memory usage efficiency. The cache database is updated regularly.

[0168] In one optional implementation, the application layer collects staff login information and calls the permission management module of the business logic layer to perform business authentication on the login information. After successful business authentication, the system receives SMS tasks input by the staff, including:

[0169] S3021: Using the application layer, collect the login information of staff and extract the user identifier UID, timestamp T, and geographic area information GEO_CODE corresponding to the login information (which can roughly locate the prefecture-level city).

[0170] S3022: Based on user ID, timestamp, and geographic region information, call the permission management module of the business logic layer to generate dynamic business authentication factors for staff.

[0171] The formula is:

[0172]

[0173] In the formula, For dynamic business authentication factors; It uses the SM3 hash function; this formula binds three factors: user identity, time validity, and login location validity, so even if the credentials are leaked, attackers cannot use it if they are not in a commonly used area.

[0174] S3023: Based on the SM2 public key, encrypt the dynamic business authentication factor of the staff to obtain the encrypted dynamic business authentication factor, and transmit it to the legitimate identity database;

[0175] S3024: In the legitimate identity database, the encrypted dynamic business authentication factor is decrypted based on the SM2 private key to obtain the decrypted dynamic business authentication factor;

[0176] S3025: Use the legitimate identity database to perform business authentication on the decrypted dynamic business authentication factor. If there is a legitimate person's dynamic business authentication factor that matches the decrypted dynamic business authentication factor, the business authentication is successful.

[0177] In this embodiment, after decryption using the SM2 private key, not only is the validity of the timestamp verified, but the geographical area information GEO_CODE of this login is also compared with the commonly used login locations or authorized operation areas of legitimate personnel recorded in the legitimate identity database. If an abnormal area login is found (such as a user from Hebei suddenly logging in from Hainan), even if the account and password are correct, secondary verification or an alarm can be triggered, which greatly improves security.

[0178] S3026: After business authentication is successful, receive SMS tasks entered by staff.

[0179] In one optional implementation, based on the SMS task, the permission management module of the business logic layer is invoked to verify the staff's permissions using the Weather-Attribute Based Encryption (W-ABE) scheme. After successful permission verification, the process proceeds to the next step, which includes:

[0180] S3031: Extract the user permission policies corresponding to staff members from the legitimate identity database;

[0181] In this embodiment, meteorological permission attributes are defined: user permissions are no longer considered as a simple list, but rather as a collection of business attributes. For example, a user permission can be represented as: { subject: "SEND_SMS", object: "WEATHER_ALERT", constraint: "AREA_CODE=133100 && ALERT_LEVEL>=RED &&TARGET_GROUP=ALL"} (Subject: Send SMS; Object: Weather warning; Constraint: Region is Xiong'an New Area and the warning level is ≥ Red and the target population is all).

[0182] A custom lightweight attribute-based encryption algorithm is adopted. During encryption, the aforementioned user permission policy is embedded into the encryption process. The core custom formula for encrypting a symmetric key K is as follows:

[0183]

[0184] In the formula, The encrypted symmetric key; For W-ABE encryption function; The policy public key is used; (A_region, A_level, A_group) is an attribute vector representing the region, alert level, and population type. This algorithm ensures that only a key with the attribute combination that satisfies the policy can decrypt the symmetric key K; the specific permission parameters are encrypted using the symmetric key K, and then... The encrypted authorization data is stored together in the legitimate identity database;

[0185] S3032: Generate the corresponding business attribute vector based on the SMS task;

[0186] For example, when staff try to send a red rainstorm warning text message targeting the Xiong'an New Area:

[0187] Extracting Operation Attributes: The system extracts the business attribute vector of this operation from the operation request: Op_Vector = (region: 133100, level: RED, group: ALL);

[0188] S3033: Based on the business attribute vector, use the staff's private key to perform attribute matching and decryption. If the business attribute vector meets the user permission policy, decryption generates a preset symmetric key, permission verification is passed, and the user screening step is initiated.

[0189] In this embodiment, the staff member's private key SK_USER (generated or obtained upon login) is used to attempt decryption. The encryption and decryption process is essentially a matching of attribute logic. Only when the following equation is true can the symmetric key K be successfully decrypted, thereby gaining plaintext access:

[0190] The formula is:

[0191]

[0192] In the formula, The function is the W-ABE decryption function; Op_Vector is the business attribute vector; Policy is the user permission policy.

[0193] Successful decryption proves that the staff member has the authority to perform this specific business operation, and execution is allowed; otherwise, it is rejected. The verification process does not require querying the permission list and performing logical comparisons. The cryptographic proof itself is the credential for authorization, which is secure and efficient.

[0194] In one alternative implementation, the targeted release mode includes a manual specification mode and an intelligent recommendation mode;

[0195] User filtering based on manual specification includes:

[0196] A-1: Based on the target SMS service area of ​​the SMS task, match the corresponding target user grid among several user grids generated by the user grid management module of the business logic layer;

[0197] A-2: Based on the target user grid and target user group, perform user data query in the population heat map and profile query module to obtain target user data for several target users;

[0198] A-3: Based on the target user data, filter users within the target user group to obtain a list of target user IDs.

[0199] User filtering based on intelligent recommendation models includes:

[0200] B-1: Based on the target SMS service area, extract target user data of the target user group from the population heat map and profile query module;

[0201] In this embodiment, some of the filtering conditions manually entered by the user (or used as basic conditions) will be ignored and processed by the response and feedback prediction model and the user filtering decision model.

[0202] B-2: Based on the target SMS service area, target weather information, target SMS template, and target user data in the SMS task, use the response and feedback prediction model to predict the response and feedback, and obtain the response and feedback prediction results; the response and feedback prediction results include the expected response rate and the negative feedback rate;

[0203] The response and feedback prediction model is built based on the LSTM algorithm;

[0204] B-3: Based on the target SMS service area, target weather information, target SMS template, target user data, and response and feedback prediction results, use the user screening decision model to screen users and obtain a list of target user IDs;

[0205] The user selection decision model is built on the Targeting Association Reinforcement Learning (TARL) algorithm;

[0206] The state space of the user selection decision model is based on the target SMS service area, target weather information, target SMS template, target user data, and response and feedback prediction results.

[0207] Action space: Sending text messages to users in the XX characteristic group;

[0208] The formula for the reward function is:

[0209]

[0210] In the formula, This is the reward value; For click-through rate; Positive feedback rate; Cost per capita; for; This is the coefficient of the reward function, used to balance multiple objectives such as click-through rate, positive feedback rate, cost per user, and complaint rate, with the ultimate goal of maximizing long-term cumulative rewards.

[0211] For a single user, the expected long-term value of taking an action (i.e., including them in this sending task) under a given state is represented by the Q-value. Since directly calculating the Q-value for each user is extremely costly, it is simplified to a user association score, which represents the degree of matching between the user and the current SMS task. The formula is:

[0212] The formula is:

[0213]

[0214] In the formula, For user u in state User-related ratings below; This is the user-state joint feature vector; Filter the weight vector of the decision model for the user; For time indication; Use the Sigmoid activation function;

[0215] In this embodiment, the association score of users corresponding to all target user data within the target SMS service area is calculated, the top-N users with the highest scores are selected, and a target user ID list is generated.

[0216] In one optional implementation, SMS content is generated based on the target weather information and target SMS template of the SMS task, and the SMS content is then sent to all users in the target user ID list, including:

[0217] S3051: Based on the target meteorological information of the SMS task, retrieve the target standard meteorological data from the memory database and / or cache database;

[0218] S3052: Write the target standard meteorological data into the target SMS template and generate the SMS content;

[0219] S3053: Send the SMS content, target user ID list, and targeted delivery parameters to the operator's server;

[0220] S3054: The operator's server, based on the targeted delivery parameters, delivers the SMS content to all users in the target user ID list.

[0221] In one optional implementation, feedback information on SMS content delivery is collected, and the user filtering decision model in the SMS task management module is updated based on this feedback information to obtain an updated user filtering decision model, including:

[0222] S3061: Periodically collect feedback information on SMS content release, and associate the feedback information with the corresponding SMS task and target user ID list to obtain several task-effect records;

[0223] Explicit feedback: Obtain SMS delivery rate through operator gateway receipts; count the number of positive feedback (such as "received", "1") and negative feedback (such as "unsubscribe", "TD") through user replies received by the SMS platform.

[0224] Implicit feedback: If the SMS contains a short link, click-through rate is calculated through link click logs; the number of user complaints recorded through customer service channels is used to associate this feedback information with the corresponding SMS task ID and target user ID list to form a complete task-effect record;

[0225] S3062: Set the fitness function of the Bowerbird Optimizer (BO) algorithm with the optimization objective of maximizing the total reward of all SMS tasks;

[0226] The formula is:

[0227]

[0228] In the formula, Let X be the fitness value of the user selection decision model trained with an alternative weight vector, and let N be the average of the total rewards on a selected batch of N historical SMS tasks. For the first The reward value of the user selection decision model corresponding to each SMS task; N is the number of historical SMS tasks; For SMS task instruction quantity;

[0229] S3063: Encode the weight vector of the user selection decision model into the individual vector of the bowerbird algorithm, and set the bowerbird population parameters and the maximum number of iterations;

[0230] S3064: Based on the bowerbird population parameters, the initial bowerbird population is obtained by initializing using the Tent chaotic mapping sequence; each bowerbird in the bowerbird population corresponds to a candidate weight vector.

[0231] The formula is:

[0232]

[0233] In the formula, The i-th initial bowerbird individual in the initial bowerbird population; Let i be the i-th chaotic variable; Let represent the upper and lower bounds of the search space; i is the individual bowerbird indicator.

[0234]

[0235] In the formula, Let i be the (i-1)th chaotic variable; compared with random initialization, chaotic initialization can ensure that the population is evenly distributed in the solution space, thus enhancing diversity.

[0236] S3065: Based on several task-effect records, use the fitness function to obtain the fitness value of each initial bowerbird individual, and take the initial bowerbird individual with the best fitness value as the optimal solution;

[0237] S3066: Explore the initial bowerbird population by building a gazebo or decorating a gazebo, and obtain an updated first bowerbird population.

[0238] The formula is:

[0239]

[0240] In the formula, To explore probability; To explore the minimum and maximum probabilities; t represents the maximum number of iterations; t represents the current number of iterations.

[0241] To explore random numbers (Select behavior pattern within [0, 1]):

[0242] if > To perform exploratory behavior, the formula is:

[0243]

[0244] In the formula, The first bowerbird individual updated for the i-th iteration of the pavilion building exploration at iteration number t+1; Step size factor; is a Levy distribution random number; b is the Levy step size, and b∈[1,2]; Let i be the i-th bowerbird individual in iteration t, and let t be the initial bowerbird individual in the initial iteration. The optimal solution for iteration number t; It is a random integer with a value of 1 or 2; This is an element-wise multiplication method; it enables individual bowerbirds to perform Lévy flight searches around the current optimal solution, which has both randomness and directionality, greatly enhancing the global exploration capability;

[0245] if To execute the attack, the formula is:

[0246]

[0247] In the formula, The first bowerbird individual updated for the i-th iteration of the decorative gazebo at iteration number t+1; The convergence factor; A random number between [0, 1];

[0248]

[0249] In the formula, These are the maximum and minimum values ​​of the convergence factor; , To adjust the parameters; It is the hyperbolic tangent function; in the early stages of the algorithm (when t is small), The attack step size is relatively large, which is beneficial for exploring a larger area around the optimal solution, especially in the later stages of the algorithm (when t is relatively large). Smaller attack step size allows for more refined localized development;

[0250] S3067: Randomly select several first bowerbird individuals from the updated first bowerbird population with the probability of theft to simulate theft behavior, and obtain several updated second bowerbird individuals;

[0251] Generate a random number between [0, 1]. ,if Theft probability If so, then the theft simulation will be executed;

[0252] The formula is:

[0253]

[0254] In the formula, The second bowerbird individual updated for the theft behavior at iteration number t+1; is a random number that is uniformly distributed in the interval [−1,1]. The first updated bowerbird individual is randomly selected for the i, k, j-th iteration number t+1; k, j are the bowerbird individual indicators.

[0255] S3069: Based on several task-effect records, use the fitness function to obtain the fitness values ​​of the first and second updated bowerbird individuals, and update the updated bowerbird individual with the best fitness value as the optimal solution;

[0256] S30610: When the number of iterations reaches the maximum number of iterations or the fitness value of the optimal solution meets the requirements, terminate the iterative update of the bowerbird population and output the optimal solution of the current iteration;

[0257] S30611: Decode the individual vector of the bowerbird corresponding to the optimal solution to obtain the optimal weight vector in the user selection decision model;

[0258] S30612: Based on the optimal weight vector, update the user selection decision model in the SMS task management module to obtain the updated user selection decision model.

[0259] The technical solution provided in this application has at least the following beneficial effects:

[0260] By integrating operator base station, user distribution heatmap, and user profile data, and constructing a refined user grid, precise spatial matching of meteorological information and users is achieved. Unified data scales allow for precise dissemination from "administrative regions" to "geographical grids," and even specific risk points, completely transforming the traditional mass messaging model. This ensures information is delivered only to those most in need, significantly improving the effectiveness and relevance of information dissemination. Optimized SMS task management and efficient collaboration mechanisms with operators reduce SMS channel congestion, lower costs, and ensure timely delivery of weather warnings and other information. For example, during severe weather, it can quickly reach affected populations, enhancing emergency response capabilities. The system also incorporates response and... The system employs a feedback prediction model and a user screening decision model. It can automatically predict the responses of different user groups to specific weather information and intelligently select the optimal user group based on a multi-objective reward function (comprehensively considering click-through rate, positive feedback, cost, and complaint rate). This replaces traditional manual experience-based decision-making, making the release strategy more scientific and efficient, adaptable to changing weather scenarios, and improving the level of intelligence. Furthermore, an intelligent profiling analysis model is introduced to automatically identify the salient features of people in specific areas and automatically generate easy-to-understand text summaries. This provides meteorological service personnel with intuitive and in-depth data insights, enabling them to formulate more personalized service strategies and content that fit the actual scenario.

[0261] Based on the same inventive concept, another embodiment of the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus.

[0262] Memory, used to store computer programs;

[0263] The processor, when executing the program stored in the memory, implements the meteorological information targeted dissemination method of the present invention.

[0264] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus. The communication interface is used for communication between the aforementioned terminal and other devices. The memory can include Random Access Memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory can also be at least one storage device located remotely from the aforementioned processor.

[0265] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0266] In addition, to achieve the above objectives, embodiments of the present invention also propose a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the meteorological information targeted dissemination method of the present invention.

[0267] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable hardware devices (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0268] The embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (apparatus), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0269] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0270] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0271] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. "And / or" indicates that either one or both can be chosen. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes the element.

[0272] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A targeted meteorological information dissemination system, characterized in that, The system includes: a data acquisition layer, a data processing layer, a business logic layer, and an application layer; The data acquisition layer is communicatively connected to both the operator server and the meteorological server. The data processing layer includes a data cleaning and noise reduction module, a data association and integration module, and a data caching and update module, and the data processing layer is connected to an in-memory database. The business logic layer includes a user grid management module, an access control module, an SMS service area creation module, an SMS template management module, an SMS task management module, and a population heat map and profile query module. The SMS task management module is connected to the operator's server.

2. The meteorological information targeted dissemination system according to claim 1, characterized in that, The data acquisition layer is used to collect raw geographic data and raw user data from the operator server and raw meteorological data from the meteorological server. After preliminary processing and labeling of the collected raw data, the preliminary processed data is transmitted to the data processing layer. The data processing layer is used to process the data after initial processing transmitted by the data acquisition layer to obtain preprocessed geographic data, preprocessed user data, and preprocessed meteorological data. The preprocessed user data and preprocessed meteorological data are then converted into standard user data and standard meteorological data and stored in an in-memory database and / or a cache database. The business logic layer is used to construct several user grids based on the preprocessed geographic data and preprocessed user data output by the data processing layer. Perform business authentication on staff login information and permission verification on SMS tasks collected at the application layer; create several SMS service areas based on preprocessed geographic data; Create several SMS templates to be reviewed and review them to obtain several SMS templates; based on the target SMS service area and targeted delivery mode of the SMS task, filter users and generate a list of target user IDs; Based on the target SMS service area, query the target standard user data in the cache database and / or memory database, and generate the target summary corresponding to the target user data; based on the target weather information and target SMS template of the SMS task, generate the SMS content, and send the SMS content, target user ID list and targeted release parameters to the operator server; The application layer is used to collect staff login information and SMS tasks.

3. The meteorological information targeted dissemination system according to claim 2, characterized in that, The data cleaning and denoising module is used to clean and denoise the data after the initial processing transmitted from the data acquisition layer, so as to obtain preprocessed geographic data, preprocessed user data and preprocessed meteorological data. The data association and integration module is used to associate and integrate preprocessed user data and meteorological data based on the user grid management module of the business logic layer to obtain standard user data and standard meteorological data with unified spatial scale, and store the standard user data and standard meteorological data in the memory database. The data caching and updating module is used to store standard user data and standard meteorological data into a cache database according to a preset data caching mechanism, and to update the cache database periodically. The user grid management module is used to construct several user grids based on the preprocessed geographic data and preprocessed user data output by the data processing layer. The permission management module is used to perform business authentication on login information collected by the application layer, and to perform permission verification on SMS tasks collected by the application layer. The SMS service area creation module is used to create several SMS service areas based on the preprocessed geographic data. The SMS template management module is used to create several SMS templates to be reviewed and to review the SMS templates to be reviewed, thereby obtaining several SMS templates. The SMS task management module is equipped with a response and feedback prediction model and a user screening decision model. It is used to screen users and generate a target user ID list based on the target SMS service area and targeted release mode of the SMS task; generate SMS content based on the target weather information and target SMS template of the SMS task; and send the SMS content, the target user ID list and targeted release parameters to the operator server. The population heat map and profile query module is equipped with an intelligent profile analysis model, which is used to query target standard user data in the cache database and / or memory database according to the target SMS service area, and use the intelligent profile analysis model to generate target summaries corresponding to the target user data.

4. A method for targeted meteorological information dissemination, based on the targeted meteorological information dissemination system as described in any one of claims 1-3, characterized in that, The method includes: Initialize the targeted meteorological information dissemination system; Using the application layer, collect the login information of the staff, and call the permission management module of the business logic layer to perform business authentication on the login information. After the business authentication is successful, receive the SMS task entered by the staff. The SMS task includes the target SMS service area, target weather information, target user group, target SMS template, targeted release parameters, and targeted release mode; Based on the SMS task, the permission management module of the business logic layer is called to verify the permissions of the staff. After the permission verification is successful, the process proceeds to the next step. Based on the target SMS service area, target weather information, and targeted delivery mode of the SMS task, the SMS task management module of the business logic layer is invoked to filter users and generate a list of target user IDs. Based on the target weather information and target SMS template of the SMS task, generate SMS content and, according to the targeted release parameters, release the SMS content to all users in the target user ID list; The system collects feedback information on SMS content releases and updates the user screening decision model in the SMS task management module based on this feedback information, resulting in an updated user screening decision model.

5. The targeted meteorological information dissemination method according to claim 4, characterized in that, Initialize the targeted meteorological information dissemination system, including: The data acquisition layer collects raw geographic data and raw user data from the operator's server and raw meteorological data from the meteorological server. After preliminary processing and labeling of the collected raw data, the preliminary processed data is transmitted to the data processing layer. The data processing layer is used to process the initially sorted data to obtain preprocessed geographic data, preprocessed user data, and preprocessed meteorological data. The preprocessed user data and preprocessed meteorological data are then converted into standard user data and standard meteorological data and stored in an in-memory database and / or a cache database. The user data includes user distribution heat map information, user profile information, and user location data; Using the business logic layer, several user grids are constructed based on the preprocessed geographic data and preprocessed user data output from the data processing layer, and several SMS service areas are created based on the preprocessed geographic data. Using the business logic layer, we initialize legitimate identities and build a legitimate identity database that includes dynamic business authentication factors and user permission policies for several legitimate personnel.

6. The targeted meteorological information dissemination method according to claim 5, characterized in that, Using the application layer, the system collects staff login information and calls the permission management module in the business logic layer to perform business authentication on the login information. After successful authentication, it receives SMS tasks input by staff, including: Using the application layer, collect staff login information and extract the user ID, timestamp, and geographic region information corresponding to the login information; Based on user ID, timestamp, and geographic region information, the permission management module of the business logic layer is invoked to generate dynamic business authentication factors for staff. Based on the SM2 public key, the dynamic business authentication factor of the staff is encrypted to obtain the encrypted dynamic business authentication factor, which is then transmitted to the legitimate identity database. In the legitimate identity database, the encrypted dynamic business authentication factor is decrypted based on the SM2 private key to obtain the decrypted dynamic business authentication factor. The business authentication is performed on the decrypted dynamic business authentication factor using the legitimate identity database. If there is a legitimate person's dynamic business authentication factor that matches the decrypted dynamic business authentication factor, the business authentication is successful. After business authentication is successful, the system receives SMS tasks input by staff.

7. The targeted meteorological information dissemination method according to claim 6, characterized in that, Based on the SMS task, the permission management module of the business logic layer is invoked to verify the permissions of the staff. After the permission verification is successful, the next step is initiated, including: Extract the user permission policies corresponding to the staff from the legitimate identity database; Generate the corresponding business attribute vector based on the SMS task; Based on the business attribute vector, the staff member's private key is used to perform attribute matching and decryption. If the business attribute vector meets the user permission policy, the preset symmetric key is generated, the permission verification is passed, and the user screening step is initiated.

8. The targeted meteorological information dissemination method according to claim 7, characterized in that, The targeted release mode includes a manual designation mode and an intelligent recommendation mode; User filtering based on the manually specified pattern includes: Based on the target SMS service area of ​​the SMS task, the corresponding target user grid is matched among several user grids generated by the user grid management module of the business logic layer. Based on the target user grid and target user group, user data is queried in the population heat map and profile query module to obtain target user data of several target users. Based on the target user data, users are filtered within the target user group to obtain a list of target user IDs; User filtering based on the aforementioned intelligent recommendation model includes: Based on the target SMS service area, extract target user data of the target user group from the population heat map and profile query module; Based on the target SMS service area, target weather information, target SMS template, and target user data in the SMS task, a response and feedback prediction model is used to predict the response and feedback, and the response and feedback prediction results are obtained; the response and feedback prediction results include the expected response rate and the negative feedback rate; Based on the target SMS service area, target weather information, target SMS template, target user data, and response and feedback prediction results, a user screening decision model is used to screen users and obtain a list of target user IDs.

9. The targeted meteorological information dissemination method according to claim 8, characterized in that, Based on the target weather information and target SMS template for the SMS task, generate SMS content and, according to the targeted delivery parameters, send the SMS content to all users in the target user ID list, including: Based on the target meteorological information of the SMS task, the target standard meteorological data is obtained by retrieving it from the in-memory database and / or cache database. Write the target standard meteorological data into the target SMS template to generate SMS content; Send the SMS content, the list of target user IDs, and the targeted delivery parameters to the operator's server; The carrier's server, based on the targeted delivery parameters, sends the SMS content to all users in the target user ID list.

10. The targeted meteorological information dissemination method according to claim 9, characterized in that, The system collects feedback information on SMS content delivery and updates the user filtering decision model in the SMS task management module based on this feedback, resulting in an updated user filtering decision model, including: Periodically collect feedback information on SMS content releases and associate the feedback information with the corresponding SMS task and target user ID list to obtain several task-effect records; The fitness function of the BO algorithm is set with the goal of maximizing the total reward of all SMS tasks. The weight vector of the user selection decision model is encoded into the individual vector of the bowerbird algorithm, and the bowerbird population parameters and the maximum number of iterations are set. Based on the bowerbird population parameters, the initial bowerbird population is obtained by initialization using the Tent chaotic mapping sequence; each bowerbird in the bowerbird population corresponds to a candidate weight vector. Based on several task-effect records, the fitness function is used to obtain the fitness value of each initial bowerbird individual, and the initial bowerbird individual with the best fitness value is taken as the optimal solution. Explore the initial bowerbird population by building or decorating a gazebo, and obtain an updated first bowerbird population. Using the theft probability, several first bowerbird individuals were randomly selected from the updated first bowerbird population to simulate theft behavior, resulting in several updated second bowerbird individuals. Based on several task-effect records, the fitness function is used to obtain the fitness values ​​of the first and second updated bowerbird individuals, and the updated bowerbird individual with the best fitness value is updated as the optimal solution. When the number of iterations reaches the maximum number of iterations or the fitness value of the optimal solution meets the requirements, the iterative update of the bowerbird population is terminated, and the optimal solution of the current iteration is output. Decode the individual vector of the bowerbird corresponding to the optimal solution to obtain the optimal weight vector in the user selection decision model; Based on the optimal weight vector, the user selection decision model in the SMS task management module is updated to obtain the updated user selection decision model.

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