Event information processing method, system, device, electronic device and storage medium

Abstract

This application discloses an event information processing method, system, apparatus, electronic device, and storage medium. The method includes: acquiring target event data about a target event; analyzing the target event data to generate at least one query task; wherein the query task instructs the querying of target event data on an external data platform; performing data queries on the external data platform corresponding to each query task to obtain target query data for each query task; and obtaining event information about the target event based on the target query data. Through the above methods, this application can automatically initiate relevant data queries about a target event on an external data platform.

Description

[0001] This application claims priority to Chinese Patent Application No. 2025119982645, filed on December 28, 2025, entitled “Event Information Processing Method, System, Apparatus, Electronic Device and Storage Medium”, the entirety of which is incorporated herein by reference. Technical Field

[0002] This application relates to the field of computer technology, and in particular to an event information processing method, system, apparatus, electronic device, and storage medium. Background Technology

[0003] Traditional event query methods require event handlers to manually perform tedious operations on different external data platforms: submitting query requests, waiting for query results, and organizing different results. This results in a large workload, low efficiency, and a high risk of missing queries. In addition, traditional event query methods rely on frequent manual switching between multiple external data platforms, which not only increases the manpower burden but also leads to poor information flow and untimely information correlation analysis. Summary of the Invention

[0004] This application provides at least one event information processing method, system, apparatus, electronic device, and storage medium.

[0005] To address the aforementioned technical problems, the first aspect of this application provides an event information processing method, comprising: acquiring target event data about a target event; analyzing the target event data to generate at least one query task; wherein the query task is used to instruct querying data of the target event on an external data platform; performing data query on the external data platform corresponding to each query task to obtain target query data for each query task; and obtaining event information of the target event based on each target query data.

[0006] Therefore, by performing data queries on the external data platform corresponding to each query task, the target query data for each query task can be obtained. This enables the automatic initiation of relevant data queries about the target event on the external data platform, improving the efficiency of querying relevant data about the target event, and also improving the completeness and accuracy of the target query data obtained, reducing the problem of missing queries.

[0007] The steps of acquiring target event data and organizing target query data are performed using the first event processing module; the step of analyzing target event data to generate at least one query task is performed using the second event processing module; and the step of querying data on the external data platform corresponding to each query task to obtain the target query data for each query task is performed using the third event processing module.

[0008] Therefore, it can be flexibly configured to run on different physical entities.

[0009] The second event processing module is an intelligent agent.

[0010] Therefore, when the second event processing module is an intelligent agent, it can analyze the target event data to accurately generate different query tasks for data querying on different external data platforms, thereby realizing the automatic and accurate generation of different query tasks for data querying on different external data platforms.

[0011] The process of analyzing target event data to generate at least one query task includes: extracting at least one first entity from the target event data; selecting at least one second entity from each first entity; wherein the second entity is the first entity that needs to be queried on an external data platform; and generating corresponding query tasks for each second entity.

[0012] Therefore, the second entity is the first entity that needs to be queried on the external data platform, indicating that the second entity is a key entity involved in the target event. Thus, generating a corresponding query task for the second entity—that is, generating a corresponding query task for the key entity in the target event data—enables subsequent data queries about the key entity on the external data platform, improving the targeting and efficiency of these queries. Furthermore, it reduces irrelevant or redundant data queries on the external data platform, thereby saving network bandwidth and computing resources and lowering data query costs. Moreover, since subsequent data queries are based on key entities, the relevant data obtained from the external data platform is related to the target event, improving the relevance and quality of the retrieved data and providing stronger support for subsequent analysis and decision-making.

[0013] The process of selecting at least one second entity from each first entity includes: determining the entity category of each first entity; and obtaining a preset query strategy library; determining the query strategy of each first entity based on the entity category of each first entity and the preset query strategy library; wherein the query strategy is used to characterize whether data query is performed on an external data platform; and the first entity whose query strategy characterizes data query on an external data platform is designated as the second entity.

[0014] Therefore, the preset query strategy library provides a unified and professional standard for determining the query strategy for each first entity. That is, it provides a unified and professional standard for determining whether each first entity needs to perform data queries on an external data platform. With the help of the preset query strategy library, the first entity that needs to perform further data queries on an external data platform can be quickly identified, which improves decision-making efficiency and accuracy and saves decision-making time.

[0015] Specifically, generating corresponding query tasks for each second entity includes: determining the external data platform corresponding to each second entity; and for each second entity, using the external data platform corresponding to the second entity and the second entity as task parameters to form the query task corresponding to the second entity.

[0016] Therefore, by using the external data platform and the second entity as the task parameters for the query task corresponding to the second entity, subsequent data queries about the second entity can be initiated on the corresponding external data platform, thereby improving the targeting and efficiency of data queries about the second entity.

[0017] The task parameters also include at least one of the following: query priority and task type.

[0018] Therefore, query priority is used as a task parameter for the query task corresponding to the second entity. Query tasks with higher query priority will be executed first, avoiding delays in obtaining relevant data for important second entities. Furthermore, higher query priority allows the system to allocate computing power and bandwidth resources to higher-priority query tasks, reducing the resource consumption of higher-priority query tasks by lower-priority ones. The task type is also used as a task parameter for the query task corresponding to the second entity to facilitate differentiation between different query operations later.

[0019] Each query task corresponds to a second entity, which is the entity extracted from the target event data that needs to be queried on an external data platform. The task parameters of the query task include the external data platform corresponding to the second entity and the second entity itself. The process of querying data on the external data platform corresponding to each query task to obtain the target query data includes: submitting a data query request on the external data platform corresponding to each query task; wherein the data query request includes the second entity corresponding to the query task; determining whether each query task has received a valid response; wherein a valid response is a valid query result received from the external data platform within a preset time; and for each query task, in response to receiving a valid response, parsing the valid query result to obtain the target query data of the query task.

[0020] Therefore, data query requests are submitted to the external data platform corresponding to the query task so that data queries can be performed on the external data platform corresponding to the query task; by determining whether each query task receives a valid response within a preset time, a time constraint is imposed on the query process, which helps to understand the query progress in a timely manner and avoid long waits for queries without results; after receiving the valid query results corresponding to the query task, the valid query results are automatically parsed to obtain the target query data of the query task, thus realizing the automation of valid query result parsing.

[0021] The invalid response to a query task is an erroneous query result received from an external data platform within a preset time. The event information processing method further includes: in response to receiving an invalid response to a query task, evaluating whether to re-execute the query task to obtain a first evaluation result; in response to the first evaluation result being to re-execute the query task, treating the query task as a new query task, and re-executing the data query request submitted to the external data platform corresponding to each query task and its subsequent steps until a valid query result is obtained, or until the number of retries reaches a first preset number and a valid query result is not obtained, and recording the task execution failure log corresponding to the query task.

[0022] Therefore, when a query task receives an invalid response, re-executing the query task provides multiple opportunities to obtain valid query results. Sometimes, invalid responses may be due to temporary issues such as momentary system failures or network fluctuations on external data platforms. Re-executing the query task may bypass these problems, thereby obtaining valid query results and improving the quality and reliability of the final data obtained. Furthermore, complete and accurate data is crucial for data queries. This retry mechanism helps avoid abandoning data retrieval due to a single failed query, ensuring that as much relevant and valid information as possible is collected, allowing subsequent analysis and decision-making to be based on a more complete data foundation.

[0023] The evaluation process, which assesses whether to re-execute the query task and obtains a first evaluation result, includes: determining the cause of the exception corresponding to the query task; and, in response to the exception being a second entity error corresponding to the query task, determining that the query task should not be re-executed.

[0024] Therefore, if the second entity parameter of the query task is incorrect, no valid query result can be obtained no matter how many times it is retried, so it is not retried.

[0025] The process involves several steps: First, a query task that meets the review requirements is designated as a review task. These requirements include that the query task successfully submits a data query request and does not receive a response from an external data platform within a preset time. Second, a review task that meets the result retrieval requirements is designated as a first review task, and the remaining review tasks are designated as second review tasks. The result retrieval requirements include that the review tasks have execution results and are in a completed state. For each first review task, usable execution results are retrieved from the corresponding execution results of the first review task as valid query results. These valid query results are then parsed to obtain the target query data for the first review task. Finally, for each second review task, the second review task is designated as a new review task, and the process of re-executing at least one review task that meets the result retrieval requirements as a first review task and its subsequent steps continues until the target query data for the second review task is obtained.

[0026] Therefore, it will poll all query tasks that have been successfully submitted and have not received a response from the external data platform within a preset time, in order to obtain the target query data of all query tasks as much as possible.

[0027] The step of parsing the valid query results to obtain the target query data includes: using a parsing strategy that matches the result type of the valid query results to parse the valid query results and obtain the corresponding target query data.

[0028] Therefore, a differentiated parsing strategy is implemented for valid query results of different result types. The parsing strategy is matched with the result type to which the valid query results belong, and the valid query results are parsed in a more targeted and accurate manner, so as to achieve more targeted and accurate parsing of valid query results automatically.

[0029] Specifically, the parsing strategy, which matches the result type of the valid query result, is used to parse the valid query result to obtain the corresponding target query data. This includes at least one of the following steps: If the valid query result is a task-related page, perform page element analysis on the task-related page to obtain the page analysis result, which is used as the target query data; if the valid query result is an interface, extract the response body corresponding to the interface and parse the response body to obtain the response body parsing result, which is used as the target query data; if the valid query result is a file, read the file content of the file and parse the file content to obtain the file parsing result, which is used as the target query data.

[0030] Therefore, the parsing strategy corresponding to the valid query results of different result types can be flexibly set.

[0031] Specifically, at least one review task that meets the result capture requirements is designated as the first review task, and the remaining review tasks are designated as the second review tasks. This includes: for each review task, checking the execution status of the review task and whether there is a task execution result based on the target task page of the review task; in response to the existence of a task execution result and the review task being in the execution completed state, designating the review task as the first review task; in response to the absence of a task execution result or the review task being in the execution incomplete state, designating the review task as the second review task.

[0032] Therefore, the review tasks that have execution results and are in the completed state are designated as the first review tasks, and the review tasks that have no execution results or are in the incomplete state are designated as the second review tasks.

[0033] Before determining whether each query task has received a valid response, the event information processing method further includes: designating at least one query task that requires access verification from the corresponding external data platform as a task requiring verification; performing access verification on the external data platform corresponding to each task requiring verification, and designating the tasks requiring verification that pass the access verification as verified tasks; determining whether the data query requests corresponding to verified tasks and tasks that do not require verification have been successfully submitted, wherein tasks that do not require verification are query tasks other than those requiring verification; and determining whether each query task has received a valid response, including: in response to the successful submission of the data query requests corresponding to verified tasks and tasks that do not require verification, determining whether the verified tasks and tasks that do not require verification have received a valid response.

[0034] Therefore, query tasks that require external platform permission verification are marked as verification-required tasks. Through the verification process of the external data platform, it is ensured that only authorized tasks can reach the external data source, and unauthorized or malicious requests can be blocked to prevent security risks such as data leakage and unauthorized access. Tasks that do not require external verification are directly classified as verification-free tasks to avoid unnecessary verification overhead and optimize resource allocation.

[0035] The access verification includes permission verification. The steps for performing access verification on the external data platform corresponding to each task to be verified include: for each task to be verified, obtaining the query notification corresponding to the task to be verified, and uploading the corresponding query notification to the external data platform corresponding to the task to be verified; in response to the successful upload of the query notification corresponding to the task to be verified, determining that the permission verification of the task to be verified has passed.

[0036] Therefore, external data platforms verify the legitimacy of the task to be verified by querying the notification letter, thus avoiding the risk of unauthorized access.

[0037] The access verification includes identity verification. The steps for performing access verification on the external data platform corresponding to each verification task include: for each verification task, obtaining a verification code image on the external data platform corresponding to the verification task; identifying the verification code text from the verification code image; filling in the verification code text on the external data platform; and confirming that the identity verification of the verification task is successful in response to the correct verification code text being filled in.

[0038] Therefore, the design goal of CAPTCHA is to distinguish between human users and automated programs. By requiring users to identify and submit the CAPTCHA text, external data platforms can intercept automated attacks such as mass web crawling, brute-force attacks, and malicious registrations.

[0039] The event information processing method further includes: in response to an incorrect verification code text, re-execute the steps of obtaining the verification code image and subsequent steps on the external data platform corresponding to the task to be verified, until the verification code text entered on the external data platform is correct, or until the number of incorrect verification code text entries reaches a second preset number.

[0040] Therefore, during the verification code recognition or submission process, failures may occur due to temporary factors such as network latency, abnormal image loading, and OCR recognition errors. The automatic retry mechanism can avoid these accidental problems and prevent legitimate users from being blocked due to non-subjective errors.

[0041] The process of obtaining event information for a target event based on the query data for each target includes at least one of the following steps: updating the event view of the target event using the query data for each target; and generating an event query map using the query data for each target.

[0042] Therefore, after obtaining the target query data corresponding to each query task, the event view of the target event can be automatically updated.

[0043] After obtaining the target query data corresponding to each query task, an event query map can be automatically generated.

[0044] The target query data is obtained by querying the external data platform corresponding to the second entity. The second entity is the entity extracted from the target event data that needs to be queried on the external data platform. Before obtaining the event information of the target event based on each target query data, the event information processing method further includes: performing entity mining based on each target query data to obtain at least one third entity, generating query tasks for each third entity, and re-executing the data query on the external data platform corresponding to each query task to obtain the target query data of each query task and its subsequent steps. The third entity is at least one of the following: a second entity that does not exist in the target event data, or a second entity whose entity information is added.

[0045] Therefore, entity mining is performed on each target query data. If a third entity that does not exist in the target event data is discovered, a query task is generated for the third entity, and the data query is re-executed on the external data platform corresponding to each query task to obtain the target query data and subsequent steps of each query task, so as to query relevant data about the third entity on the corresponding external data platform. If the acquisition of the target query data results in the increase or enrichment of the entity information of some second entities, the second entities with increased entity information can also be treated as third entities, a query task is generated for the third entity, and the data query is re-executed on the external data platform corresponding to each query task to obtain the target query data and subsequent steps of each query task, so as to query relevant data about the third entity on the corresponding external data platform, thereby further enriching the relevant data about the third entity.

[0046] To address the aforementioned technical problems, a second aspect of this application provides an event information processing system. This system includes a first event processing module, a second event processing module, and a third event processing module. The first event processing module is used in the aforementioned method for the steps of acquiring target event data about a target event and obtaining event information of the target event based on each target query data. The second event processing module is used to execute the aforementioned method for the step of analyzing the target event data to generate at least one query task. The third event processing module is used to execute the aforementioned method for the step of querying data on an external data platform corresponding to each query task to obtain the target query data for each query task.

[0047] To address the aforementioned technical problems, a third aspect of this application provides an event information processing apparatus. The apparatus includes an acquisition module for acquiring target event data about a target event; a task generation module for analyzing the target event data to generate at least one query task; wherein the query task instructs the querying of target event data on an external data platform; a query module for performing data queries on the external data platform corresponding to each query task to obtain target query data for each query task; and an organization module for obtaining event information about the target event based on the target query data.

[0048] To address the aforementioned technical problems, a fourth aspect of this application provides an electronic device comprising a memory and a processor. The memory stores program instructions, and the processor executes the program instructions to implement the steps of acquiring target event data about a target event and obtaining event information of the target event based on each target query data in the aforementioned method, and / or the step of analyzing target event data to generate at least one query task in the aforementioned method, and / or the step of performing data query on an external data platform corresponding to each query task to obtain target query data for each query task in the aforementioned method.

[0049] To address the aforementioned technical problems, a fifth aspect of this application provides a computer-readable storage medium for storing program instructions that can be executed to implement the aforementioned event information processing method.

[0050] The above technical solution performs data queries on the external data platform corresponding to each query task to obtain the target query data for each query task. It realizes the automatic initiation of relevant data queries about the target event on the external data platform, which improves the efficiency of querying relevant data about the target event, and can also improve the completeness and accuracy of the target query data obtained, reducing the problem of missing queries. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating an embodiment of the event information processing method provided in this application; Figure 2 This is a timing diagram of an embodiment of the event information processing method provided in this application; Figure 3 yes Figure 1 The flowchart of step S12 shown is a schematic diagram of one embodiment; Figure 4 yes Figure 1 The flowchart of step S13 shown is a schematic diagram of one embodiment. Figure 5 yes Figure 1 A flowchart illustrating another embodiment of step S13 is shown. Figure 6 This is a schematic diagram of the framework of an embodiment of the event information processing system provided in this application; Figure 7 This is a schematic diagram of the framework of an embodiment of the event information processing device provided in this application; Figure 8 This is a schematic diagram of the framework of an embodiment of the electronic device provided in this application; Figure 9 This is a schematic diagram of an embodiment of the computer-readable storage medium provided in this application. Detailed Implementation

[0052] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0053] In the following description, specific details such as particular system architectures, interfaces, and technologies are presented for illustrative purposes rather than for limiting purposes, in order to provide a thorough understanding of this application.

[0054] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship. Furthermore, "many" in this document means two or more. Moreover, the term "at least one" in this document means any combination of at least two of any one or more of a plurality of objects. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0055] Please see Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the event information processing method provided in this application. It should be noted that if substantially the same result is achieved, this embodiment does not necessarily replace it with a similar method. Figure 1 The illustrated process sequence is limited. For example... Figure 1 As shown, this embodiment includes: Step S11: Obtain target event data about the target event.

[0056] In this embodiment, target event data is acquired regarding the target event. The target event is not limited. For example, the target event could be an abnormal financial transaction event in the financial sector. Or, for example, the target event could be a cybersecurity event in the public safety sector.

[0057] In one embodiment, the target event data may include basic information about the target event and entity information about the target event. Of course, in other embodiments, the target event data may only include basic information about the target event, and this is not limited here.

[0058] For example, taking an abnormal financial transaction event in the financial field as an example, the target event data includes the basic information of the target event, which may include personnel information of relevant personnel, transaction records, communication records, and event descriptions of the target event.

[0059] In one implementation, the target event data can be obtained from one or more data sources, and no limitation is made here.

[0060] In one embodiment, obtaining target event data about the target event specifically involves: obtaining initial event data about the target event, and performing normalization processing on the initial event data about the target event to obtain target event data about the target event.

[0061] In one embodiment, the step of obtaining target event data about the target event is performed using a first event processing module.

[0062] In one implementation, such as Figure 2 As shown, Figure 2 This is a timing diagram of an embodiment of the event information processing method provided in this application. The target event data regarding the target event can be user (…). Figure 2 The data is uploaded and submitted by the event handlers (in the event processing team). Of course, in other implementations, the target event data can also be automatically obtained from one or more data sources, which is not limited here.

[0063] Step S12: Analyze the target event data to generate at least one query task.

[0064] In this embodiment, target event data is analyzed to generate at least one query task; wherein, the query task is used to instruct the querying of target event data on an external data platform. The target event data is analyzed to generate different query tasks for data querying on different external data platforms, thereby achieving automatic generation of different query tasks for data querying on different external data platforms. The number of generated query tasks is not limited.

[0065] The external data platform can be an event information query platform, an event data query platform, a video and image retrieval platform, a communication record verification platform, etc., and is not limited here. It should be noted that the corresponding external data platform may be different depending on the domain to which the target event belongs.

[0066] In one implementation, the step of analyzing target event data to generate at least one query task is performed using a second event processing module.

[0067] In one specific implementation, the second event processing module is an intelligent agent. In the context of large models, an intelligent agent refers to a workflow or program that can invoke tools (such as calculators, APIs, databases, etc.) and utilize a large language model for analysis to complete complex tasks. A large language model is a deep learning model trained on massive amounts of data, capable of understanding, generating, and processing natural language, and can perform a wide range of tasks such as question answering, translation, summarization, and code generation. Therefore, when the second event processing module is an intelligent agent, it can analyze target event data to accurately generate different query tasks for data queries on different external data platforms, achieving automatic and accurate generation of different query tasks for data queries on different external data platforms.

[0068] In one specific implementation, acquiring target event data about the target event is performed using a first event processing module; analyzing the target event data to generate at least one query task is performed using a second event processing module. The first event processing module sends the target event data about the target event to the second event processing module. Upon receiving the target event data, the second event processing module analyzes the target event data to generate at least one query task.

[0069] In one specific implementation, after the second event processing module analyzes the target event data to generate at least one query task, it can send the task to the user for confirmation. Once the user confirms, the task is then sent to the third event processing module. If the user performs a modification operation, the modified query task is sent to the third event processing module.

[0070] Step S13: Perform data queries on the external data platform corresponding to each query task to obtain the target query data for each query task.

[0071] In this embodiment, data queries are performed on the external data platforms corresponding to each query task to obtain the target query data for each task. For each query task, data queries are performed on the corresponding external data platforms, automatically initiating relevant data queries about the target event on these platforms. This improves the efficiency of querying relevant data about the target event and enhances the completeness and accuracy of the retrieved target query data, reducing the risk of missed queries. Furthermore, when there are at least two external data platforms corresponding to each query task, automatic initiation of relevant data queries about the target event on different external data platforms further improves the efficiency of querying relevant data about the target event and reduces the time cost of repetitive operations.

[0072] For example, when querying account transaction records on a bank's transaction platform, or when checking account behavior patterns on a social media platform, all data query operations are completed automatically according to the query task, without the need for manual operation.

[0073] In one implementation, data queries are performed on the external data platform corresponding to each query task to obtain the target query data for each query task, which is executed using a third event processing module.

[0074] In one specific implementation, the step of analyzing target event data to generate at least one query task is performed using a second event processing module; the step of querying data on the external data platform corresponding to each query task to obtain the target query data for each query task is performed using a third event processing module. The second event processing module sends the generated at least one query task to the third event processing module, and the third event processing module performs data queries on the external data platform corresponding to each query task to obtain the target query data for each query task.

[0075] In one specific implementation, the step of analyzing target event data to generate at least one query task is performed by the second event processing module; the step of querying data on the external data platform corresponding to each query task to obtain the target query data for each query task is performed by the third event processing module; the second event processing module calls the Playwright MCP service for automated querying to send the generated at least one query task to the third event processing module, that is, the second event processing module sends the generated at least one query task to the third event processing module in conjunction with the MCP interface, and encapsulates different query tasks into the MCP (Model Control Protocol) interface. MCP is used to encapsulate different query tasks into standardized callable interfaces, so that the second event processing module can dynamically combine and send query tasks through task orchestration logic, which significantly improves the scalability and task reusability of the system.

[0076] MCP is a standardized communication interface between the second event processing module and external tools / frameworks. It allows the second event processing module (e.g., an agent) to invoke Playwright automation programs and receive their execution results. In other words, it acts as a bridge between the second event processing module and Playwright automation programs, defining a unified request / response format so that the second event processing module doesn't need to concern itself with the underlying implementation of Playwright automation programs; it only needs to send instructions and receive results according to the protocol.

[0077] Each query task corresponds to a second entity, which is an entity extracted from the target event data that needs to be queried on an external data platform. The task parameters of the query task include the external data platform corresponding to the second entity and the second entity. In one specific implementation, the second event processing module sends the query tasks corresponding to each second entity to the third event processing module. The third event processing module performs data queries on the external data platform corresponding to each query task to obtain the target query data of each query task.

[0078] The task parameters for the query tasks corresponding to the second entity include the external data platform. The query tasks corresponding to each second entity can be aggregated to obtain a list of query tasks for each external data platform. That is, query tasks located in the same query task list correspond to the same external data platform. In one specific implementation, the second event processing module can package the query tasks in the query task list and send them together to the third event processing module.

[0079] The task parameters for the query tasks corresponding to the second entity include the task type. Alternatively, the query tasks corresponding to each second entity can be aggregated to obtain a list of query tasks for each task type; that is, query tasks within the same query task list have the same task type. In one specific implementation, the second event processing module can package the query tasks in the query task list and send them together to the third event processing module.

[0080] Step S14: Based on the query data of each target, obtain the event information of the target event.

[0081] In this embodiment, event information of the target event is obtained based on the query data of each target.

[0082] In one implementation, such as Figure 2 As shown, data queries are performed on the external data platform corresponding to each query task to obtain the target query data for each query task, which is executed using the third event processing model; based on each target query data, the event information of the target event is obtained, which is executed using the first event processing module. The third event processing module feeds back each target query data to the first event processing module.

[0083] In one specific implementation, the third event processing module can directly feed back to the first event processing module after obtaining the target query data corresponding to a query task. Of course, in other implementations, the third event processing module can also obtain and summarize the target query data corresponding to all query tasks before feeding back to the first event processing module.

[0084] In one implementation, obtaining event information of a target event based on each target query data can specifically involve: integrating the target query data to obtain the event information of the target event. By integrating the target query data, automatic integration of the target query data is achieved.

[0085] In one specific implementation, the target query data is integrated to obtain the event information of the target event. Specifically, this can be achieved by updating the event view of the target event using the target query data. In other words, after obtaining the target query data corresponding to each query task, the event view of the target event can be automatically updated.

[0086] In one specific implementation, the target query data is obtained by querying an external data platform corresponding to the second entity. The second entity is the entity extracted from the target event data that needs to be queried on the external data platform. The event view of the target event is updated using each target query data, specifically by establishing a mapping relationship between each target query data and the corresponding second entity, and updating the event view of the target event.

[0087] In one specific implementation, the target query data is integrated to obtain event information for the target event. Specifically, this can be achieved by generating an event query map using the target query data. In other words, after obtaining the target query data corresponding to each query task, an event query map can be automatically generated.

[0088] In one specific implementation, the target query data is obtained from an external data platform corresponding to the second entity. An event query map is generated using this target query data. Specifically, the relationships between the second entity, the query task, and the target query data are summarized to generate the event query map. The event query map not only displays the logical path of the query process but can also be used for subsequent reasoning and strategy optimization, enabling self-learning and path optimization based on historical event experience, and continuously improving the accuracy and efficiency of the event query strategy. Furthermore, the event investigation map provides a clear view of the relationships between event clues, the target query data from various external data platforms, and the event data chain, allowing for rapid determination of the event's development direction.

[0089] In one embodiment, the target query data is obtained by querying an external data platform corresponding to a second entity. The second entity is an entity extracted from the target event data that needs to be queried on the external data platform. Before obtaining the event information of the target event based on each target query data, entity mining is performed based on each target query data to obtain at least one third entity. Query tasks are generated for each third entity, and data queries are re-executed on the external data platform corresponding to each query task to obtain the target query data of each query task and its subsequent steps. The third entity is at least one of the following: a second entity that does not exist in the target event data, or a second entity whose entity information is added.

[0090] In other words, entity mining is performed on each target query data. If a third entity that does not exist in the target event data is discovered, a query task is generated for the third entity, and the data query is re-executed on the external data platform corresponding to each query task to obtain the target query data and subsequent steps of each query task, so as to query relevant data about the third entity on the corresponding external data platform. If the acquisition of the target query data results in the increase or enrichment of the entity information of some second entities, the second entities with increased entity information can also be treated as third entities, a query task is generated for the third entity, and the data query is re-executed on the external data platform corresponding to each query task to obtain the target query data and subsequent steps of each query task, so as to query relevant data about the third entity on the corresponding external data platform, thereby further enriching the relevant data about the third entity.

[0091] It should be noted that the event information of the target event is obtained based on the query data of each target until no third entity is found based on the query data of each target, that is, until it is decided not to continue the data query.

[0092] In one specific implementation, the step of performing entity mining based on each target query data to obtain at least one third entity is executed using the first event processing module.

[0093] In one embodiment, the steps of acquiring target event data and obtaining event information of the target event based on each target query data are performed using a first event processing module; the step of analyzing the target event data to generate at least one query task is performed using a second event processing module; and the step of querying data on the external data platform corresponding to each query task to obtain the target query data for each query task is performed using a third event processing module. By encapsulating the first, second, and third event processing modules as independent containers and using Kubernetes for unified orchestration and scheduling, high availability, horizontal scalability, and task isolation capabilities are achieved. This not only improves system reliability and resource utilization but also facilitates rapid reuse and dynamic expansion across different event types and query tasks.

[0094] Please see Figure 3 , Figure 3 yes Figure 1 The flowchart shown is a schematic diagram of one embodiment of step S12. It should be noted that if substantially the same result is achieved, this embodiment does not necessarily follow the same pattern. Figure 3 The illustrated process sequence is limited. For example... Figure 3 As shown, this embodiment includes: Step S31: Extract at least one first entity from the target event data.

[0095] In this embodiment, at least one first entity is extracted from the target event data. Specifically, the target event data is analyzed in depth, and entity extraction is performed on the parsed target event data to extract at least one first entity.

[0096] For example, the first entities extracted from the target event data include personnel entities, account entities, transaction entities, and platform entities. Among these, personnel refer to the tasks involved in the target event data; accounts refer to virtual identity credentials used to identify users in the target event data, such as social media accounts (e.g., WeChat accounts, Weibo accounts, QQ accounts), financial accounts (e.g., bank accounts, Alipay accounts, WeChat Pay accounts), and other platform accounts (e.g., e-commerce platform accounts, game accounts); transactions refer to economic activities or behaviors involving the exchange of funds, goods, or information in the target event data, such as fund transactions (e.g., bank transfers, payment platform transactions, cash transactions), goods transactions (e.g., buying and selling goods), and information transactions; platforms refer to the network or real-world environment that provides support and services for the activities in the target event data, such as network platforms (e.g., social media platforms, e-commerce platforms, financial platforms), and physical location platforms (e.g., shopping malls, hotels, stations).

[0097] Step S32: Select at least one second entity from each of the first entities.

[0098] In this embodiment, at least one second entity is selected from each first entity; wherein, the second entity is the first entity that needs to be queried on an external data platform. The fact that the second entity is a first entity that needs to be queried on an external data platform indicates that the second entity is a key or valuable entity involved in the target event. Therefore, selecting at least one second entity from each first entity further filters out the key entities that need to be queried on the external data platform, enabling subsequent data queries about key entities to be initiated on the external data platform, thus improving the targeting and efficiency of data queries about key entities. Furthermore, it reduces irrelevant or redundant data queries on the external data platform, thereby saving network bandwidth, computing resources, etc., and reducing data query costs. Moreover, since the subsequent data queries are based on key entities, the relevant data obtained from the external data platform is related to the target event, thereby improving the relevance and quality of the retrieved data and providing more favorable support for subsequent analysis and decision-making.

[0099] In one embodiment, selecting at least one second entity from each first entity specifically involves: determining the entity category of each first entity; and obtaining a preset query strategy library; determining a query strategy for each first entity based on the entity category of each first entity and the preset query strategy library, wherein the query strategy is used to characterize whether data query is performed on an external data platform; and designating the first entity whose query strategy characterizes data query on an external data platform as the second entity. Specifically, as shown... Figure 2 As shown, based on the entity category of each first entity, the query strategy for each first entity is retrieved from the preset query strategy library. The preset query strategy library returns the query strategy for each first entity to determine the first entity that needs to be queried on the external data platform. The preset query strategy library provides a unified and professional standard for determining the query strategy for each first entity; that is, it provides a unified and professional standard for determining whether each first entity needs to be queried on the external data platform. It can quickly identify the first entity that needs to be further queried on the external data platform by means of the preset query strategy library, improving decision-making efficiency and accuracy, and saving decision-making time.

[0100] For example, the first entities extracted from the target event data include personnel entities and account entities. The categories of personnel entities can be victims, suspects, etc., and the categories of account entities can be transfers, traffic redirection, etc.

[0101] In one specific implementation, the query priority corresponding to each second entity will also be determined by combining a preset query strategy library.

[0102] Step S33: Generate corresponding query tasks for each second entity.

[0103] In this embodiment, corresponding query tasks are generated for each second entity. The second entity is the first entity that needs to be queried on the external data platform, meaning it is a key entity involved in the target event. Therefore, generating corresponding query tasks for the second entities—that is, generating corresponding query tasks for key entities in the target event data—enables subsequent data queries about key entities on the external data platform, improving the targeting and efficiency of these queries. Furthermore, it reduces irrelevant or redundant data queries on the external data platform, saving network bandwidth and computing resources, and lowering data query costs. Moreover, since subsequent queries are based on key entities, the relevant data obtained from the external data platform is related to the target event, thus improving the relevance and quality of the retrieved data and providing stronger support for subsequent analysis and decision-making.

[0104] In one implementation, a corresponding query task is generated for each second entity. Specifically, this involves: determining the external data platform corresponding to each second entity; and for each second entity, using the external data platform and the second entity itself as task parameters to construct a query task for that entity. In other words, a query task for the second entity is constructed based on its external data platform and the second entity itself. By using the external data platform and the second entity as task parameters for the query task, subsequent data queries related to the second entity can be initiated on the corresponding external data platform, improving the targeting and efficiency of data queries related to the second entity.

[0105] In one specific implementation, the task parameters for the query task corresponding to the second entity further include at least one of the following: query priority and task type. Using query priority as a task parameter for the query task corresponding to the second entity ensures that query tasks with higher query priority are executed first, avoiding delays in obtaining relevant data for important second entities. Furthermore, higher query priority allows the system to allocate computing power and bandwidth resources to higher-priority query tasks, reducing the resource consumption of higher-priority query tasks by lower-priority ones. Using task type as a task parameter for the query task corresponding to the second entity facilitates the differentiation of different query operations subsequently.

[0106] In one specific implementation, such as Figure 2 As shown, the task parameters for the query task corresponding to the second entity include the external data platform ( Figure 2 Platforms and task types (in the text) Figure 2 (type) and execution parameter set ( Figure 2 The set of execution parameters (params) can include a second entity, etc., without being specifically limited here.

[0107] Since the task parameters of the query task corresponding to the second entity include the external data platform, in one specific implementation, the query tasks corresponding to each second entity can be summarized to obtain a list of query tasks for each external data platform. That is, the external data platforms corresponding to the query tasks in the same list of query tasks are the same.

[0108] In one specific implementation, the task parameters of the query task corresponding to the second entity include the task type. Alternatively, the query tasks corresponding to each second entity can be summarized to obtain a list of query tasks corresponding to each task type. That is, the query tasks located in the same list of query tasks have the same task type.

[0109] In one implementation, the query strategy represents the first entity that does not perform data queries on an external data platform. It can be marked with tags, and the content of the tags can be manually processed and / or supplemented data collection.

[0110] Please see Figure 4 , Figure 4 yes Figure 1 The diagram shows a flowchart of one embodiment of step S13. It should be noted that if substantially the same result is achieved, this embodiment does not necessarily follow that approach. Figure 4 The illustrated process sequence is limited. For example... Figure 4 As shown, each query task corresponds to a second entity. The second entity is the entity extracted from the target event data that needs to be queried on an external data platform. The task parameters of the query task include the external data platform corresponding to the second entity and the second entity itself. This embodiment includes: Step S41: Submit a data query request to the external data platform corresponding to each query task.

[0111] In this embodiment, a data query request is submitted to the external data platform corresponding to each query task; wherein, the data query request includes a second entity corresponding to the query task. Specifically, for each query task, a data query request is submitted to the external data platform corresponding to the query task so that data can be queried on the external data platform corresponding to the query task.

[0112] In one implementation, such as Figure 2 As shown, Playwright automation programs are used to submit data query requests to the external data platforms corresponding to each query task. Playwright automation programs have stable multi-browser automation capabilities, which can accurately simulate the real operational behavior of event handlers on different external data platforms, realizing the automated execution of complex, multi-stage business processes. Playwright is an automated testing / operation framework that simulates user operations in a browser across browsers and platforms, such as clicking, typing, scrolling, taking screenshots, and crawling page data.

[0113] In one specific implementation, the step of analyzing target event data to generate at least one query task is executed by a second event processing module, which is an intelligent agent. Submitting data query requests to the external data platforms corresponding to each query task is executed by a third event processing module calling the Playwright automation program. The intelligent agent, driven by a large language model, can undertake intelligent decision-making tasks such as task understanding, process orchestration, data extraction, and semantic analysis. It can generate automated operation plans and dynamically adjust strategies through natural language descriptions, thereby achieving intelligent orchestration and automatic closed-loop management of the data query process. By combining the browser operation capabilities of the Playwright automation program with the cognitive reasoning and semantic understanding capabilities of the intelligent agent, a perceptible, decision-making, and executable intelligent query workflow can be constructed. For example, the system can automatically log in to various external data platforms, initiate relevant data queries, extract query results in real time, and have the intelligent agent perform structured processing and semantic comparison on the returned query results, ultimately generating an entity summary and intelligent analysis report.

[0114] In one specific implementation, the submission of data query requests to the external data platform corresponding to each query task is executed by the third event processing module calling the Playwright automation program; the second event processing module, in conjunction with the mcp interface, sends at least one generated query task to the third event processing module. Through the Playwright-mcp framework, data query behavior is extracted into programmable automated task components, achieving full automation of the data query process from query to result parsing. This allows the system to complete a large number of repetitive operations, significantly reducing manual workload and shortening the event processing cycle.

[0115] Step S42: Determine whether each query task has received a valid response.

[0116] In this embodiment, it is determined whether each query task has received a valid response; a valid response is defined as receiving a valid query result from an external data platform within a preset time. Specifically, after the external data platform corresponding to the query task submits a data query request, it waits for a response and determines whether a valid query result has been received from the external data platform within the preset time. By determining whether each query task receives a valid response within the preset time, a time constraint is imposed on the query process, which helps to understand the query progress in a timely manner and avoids long waits for queries without results.

[0117] In one specific implementation, the valid feedback results corresponding to the query task are retrieved from the external data platform and executed by the third event processing module calling the Playwright automation program.

[0118] Step S43: For each query task, in response to receiving a valid response, the valid query results are parsed to obtain the target query data of the query task.

[0119] In this implementation, for each query task, in response to receiving a valid response, the valid query results are parsed to obtain the target query data for the query task. Upon receiving a valid query result corresponding to a query task, the valid query result is automatically parsed to obtain the target query data for the query task, thus automating the parsing of valid query results. Parsing the valid query results of query tasks that have received valid responses to obtain the target query data ensures that the final obtained target query data is valid information verified by an external data platform and returned within a preset time, improving data quality and reliability.

[0120] In one implementation, such as Figure 2 As shown, for each query task, in response to receiving a valid response, the valid query results are parsed to obtain the target query data for the query task. This process is executed using the third event processing module. The third event processing module then feeds back the target query data for each query task to the first event processing module.

[0121] In one implementation, an invalid response to a query task is receiving an erroneous query result from an external data platform within a preset time. In response to receiving an invalid response, an evaluation is conducted to determine whether the query task should be re-executed, resulting in a first evaluation result. If the first evaluation result indicates re-execution of the query task, the query task is treated as a new query task, and the data query request submitted to the external data platform corresponding to each query task and its subsequent steps are re-executed until a valid query result is obtained, or until the number of retries reaches a first preset number and a valid query result is not obtained. A task execution failure log corresponding to the query task is recorded. When a query task receives an invalid response, re-execution of the query task through evaluation provides multiple opportunities to obtain a valid query result. Sometimes, invalid responses may be due to temporary issues such as momentary system failures or network fluctuations on the external data platform. Re-execution of the query task may bypass these issues, thereby obtaining a valid query result and improving the quality and reliability of the final data obtained. Furthermore, complete and accurate data is crucial for data queries. This retry mechanism helps avoid abandoning data acquisition due to a single data query failure, ensuring that as much relevant and valid information as possible is collected, allowing subsequent analysis and decision-making to be based on a more complete data foundation.

[0122] The number of preset times is not limited and can be set according to actual usage needs.

[0123] In one specific implementation, an evaluation is conducted to determine whether the query task should be re-executed, resulting in a first evaluation result. Specifically, this involves: identifying the cause of the exception for the query task; and, in response to the exception being a second entity error corresponding to the query task, determining that the query task should not be re-executed. If the second entity error is a task parameter of the query task, no valid query result can be obtained regardless of how many times it is retried, therefore, it is not retried.

[0124] In one specific implementation, such as Figure 2 As shown, the task execution failure log corresponding to the query task is recorded using the third event handling module. The third event handling module then feeds back the task execution failure log corresponding to the query task to the first event handling module.

[0125] Furthermore, the first event handling module will feed back the query task and the corresponding reason for the task execution failure to the second event handling module, which will further evaluate whether to retry. If the evaluation determines that retry is not necessary, manual intervention will be marked.

[0126] In one embodiment, at least one query task that meets the review requirements is designated as a review task, wherein the review requirements include that the query task has successfully submitted a data query request and has not received a response from an external data platform within a preset time; at least one review task that meets the result retrieval requirements is designated as a first review task, and the remaining review tasks are designated as second review tasks, wherein the result retrieval requirements include that the review task has a task execution result and is in an execution completed state; for each first review task, usable execution results are retrieved from the task execution results corresponding to the first review task as valid query results of the first review task, and the valid query results of the first review task are parsed to obtain the target query data of the first review task; and for each second review task, the second review task is designated as a new review task, and at least one review task that meets the result retrieval requirements is re-executed as a first review task and its subsequent steps, until the target query data of the second review task is obtained.

[0127] In other words, it will poll all query tasks that have been successfully submitted and have not received a response from the external data platform within a preset time, in order to obtain the target query data of all query tasks as much as possible.

[0128] Specifically, if a query task has been successfully submitted but has not received a response from the external data platform within a preset time, a query task is retrieved from the queue as a review task and reviewed. If the review task has an execution result and is in a completed state, it is designated as the first review task, and usable execution results are extracted from its corresponding execution results as valid query results. If the review task has no execution result or is in an incomplete state (or in progress), it is designated as the second review task and added back to the queue as a new review task for later retry. This process is repeated until no query task has been successfully submitted and has not received a response from the external data platform within the preset time.

[0129] Valid query results can include task-related pages, interfaces, files, etc., without any restrictions.

[0130] In one specific implementation, at least one review task that meets the result retrieval requirements is designated as the first review task, and the remaining review tasks are designated as the second review tasks. Specifically, for each review task, based on the target task page of the review task, the execution status of the review task and whether there is a task execution result are checked; in response to the existence of a task execution result and the review task being in the execution completed state, the review task is designated as the first review task; in response to the absence of a task execution result or the review task being in the execution incomplete state, the review task is designated as the second review task.

[0131] In one specific implementation, a parsing strategy matching the result type of the valid query results is used to parse the valid query results and obtain the corresponding target query data. Differentiated parsing strategies are executed for valid query results of different result types, using a parsing strategy matching the result type of the valid query results to parse the valid query results more specifically and accurately, thereby achieving automatic, more targeted, and accurate parsing of valid query results.

[0132] In one specific implementation, a unified result parsing engine, combined with Playwright's DOM access capabilities and file parsing module, automatically identifies the result type of valid query results and adopts the corresponding parsing strategy.

[0133] In one specific implementation, in response to a valid query result being a task-related page, page element analysis is performed on the task-related page to obtain page analysis results, which are then used as target query data. After obtaining a valid query result corresponding to the query task, and if it is determined that the valid query result is a task-related page, page element analysis is performed on the task-related page.

[0134] Page element analysis can include locating specific web page elements on task-related pages, extracting text content and attribute values ​​from element HTML tags on task-related pages, taking screenshots of specific located web page elements, or taking screenshots of the entire task-related page.

[0135] In one specific implementation, if the page analysis results include text content and / or attribute values ​​in HTML tags of task-related pages, the text content and / or attribute values ​​in HTML tags of task-related pages will also be structured to form a standard data model.

[0136] In one specific implementation, in response to a valid query result being an interface, the response body corresponding to the interface is extracted and parsed to obtain the parsed response body result, which is used as the target query data. After obtaining a valid query result corresponding to the query task, if it is determined that the valid query result is an interface, the response body corresponding to the interface is parsed.

[0137] The response body of the interface can be in the format of JSON, XML, text, etc., and there is no restriction here.

[0138] In one specific implementation, the parsed response body will also undergo structured processing, using a structured data model as the standard. This structured processing can include field validation, type conversion, etc., and is not limited here.

[0139] In one specific implementation, in response to a valid query result being a file, the file content is read and parsed to obtain a file parsing result, which is then used as the target query data. When a valid query result corresponding to the query task is obtained, and if the valid query result is determined to be a file, the file content is parsed.

[0140] Specifically, if the valid query result is a file, download and export the file, where the file format can be CSV, PDF, XLSX, etc.; load and read the file content, which can be done by splitting the file into tables, OCR recognition, etc., to obtain the file content; parse the file content to obtain the file parsing result.

[0141] In one specific implementation, the file parsing results will also be structured, using structured data as the standard data model.

[0142] This application's data query covers various data formats, including task-related pages, interfaces, and files, enabling structured, comprehensive, and semantically matched data, thereby ensuring the completeness of data acquisition and the accuracy of analysis results.

[0143] In one implementation, after parsing the valid query results corresponding to the query task to obtain the corresponding target query data, the target query data corresponding to the query task is also tagged. The tag content may include the external data platform corresponding to the target query data, the query task ID, and the acquisition time of the valid query results corresponding to the target query data, etc., which are not limited here. In other words, the external data platform and acquisition time of the valid query results can be automatically identified, providing a traceable data foundation for subsequent event analysis.

[0144] In one specific implementation, after parsing the valid query results corresponding to the query tasks to obtain the corresponding target query data, the target query data corresponding to each query task is summarized. Summarizing the target query data corresponding to each query task may involve merging, deduplicating, or supplementing metadata, etc., and is not limited here.

[0145] In one specific implementation, after summarizing the target query data corresponding to each query task, a result message and / or result event are generated.

[0146] Please see Figure 5 , Figure 5 yes Figure 1 The flowchart of another embodiment of step S13 is shown. It should be noted that if substantially the same result is achieved, this embodiment does not necessarily follow the same pattern. Figure 5 The illustrated process sequence is limited. For example... Figure 5 As shown, each query task corresponds to a second entity. The second entity is the entity extracted from the target event data that needs to be queried on an external data platform. The task parameters of the query task include the external data platform corresponding to the second entity and the second entity itself. This embodiment includes: Step S51: Submit a data query request to the external data platform corresponding to each query task.

[0147] Step S51 is similar to step S41, and will not be described again here.

[0148] Step S52: Designate at least one query task that requires access verification from the corresponding external data platform as the task to be verified.

[0149] In this embodiment, at least one query task that requires access verification from the corresponding external data platform is designated as a task requiring verification. That is, if an external data platform corresponding to a query task requires access verification before data querying, then it is designated as a task requiring verification; if an external data platform corresponding to a query task can directly perform data querying without access verification, then it is designated as a task requiring no verification.

[0150] Step S53: Perform access verification on the external data platform corresponding to each task that needs to be verified, and designate the tasks that pass the access verification as verified tasks.

[0151] In this embodiment, access verification is performed on the external data platform corresponding to each task that needs to be verified, and the task that passes the access verification is regarded as the verified task.

[0152] In one implementation, based on the remote control browser feature of Chrome DevTools Protocol, different remote control ports are configured for different external data platforms. On the query terminal, the corresponding Chrome remote control browser is opened for different external data platforms. The third event processing module acts as the server to connect to the remote browsers of different external data platforms on the query terminal, and the server performs access verification for different external data platforms.

[0153] Specifically, the query terminal configures an independent CDP remote control port for each external data platform, and starts the corresponding chrome / Chromium-remote-debugging-port for each external data platform. The third event handling module reads the external data platform and port mapping configuration and establishes a CDP session with the remote control browser as the third processing module. When there are pending query tasks, a query task is retrieved from the queue of pending query tasks. It is determined whether a connection has been established with the corresponding remote control browser. If not, an attempt is made to establish or reconnect to the remote control browser's CDP session. If a connection is established, interaction occurs on the corresponding external data platform through the CDP session. If the external data platform corresponding to the query task requires access verification, the query task is designated as a verification-required task and access verification is performed on the corresponding external data platform. If the external data platform does not require access verification, the query task is designated as a non-verification-required task and interaction occurs on the corresponding external data platform. This process is repeated until there are no more pending query tasks.

[0154] This application enables query tasks to be executed simultaneously on multiple external data platforms. It operates on multiple external data platforms through a remote CDP browser instance and achieves unified scheduling of query tasks by combining the MCP interface.

[0155] In one specific implementation, Playwright automation is used to perform access verification on the external data platforms corresponding to each verification task. Playwright, as the browser control core, establishes a remote control interface with the help of CDP to achieve parallel control of browser instances on multiple external data platforms.

[0156] In one embodiment, access verification includes permission verification. The step of performing access verification on the external data platform corresponding to each task requiring verification specifically involves: for each task requiring verification, obtaining the query notification corresponding to the task and uploading the corresponding query notification to the external data platform; in response to the successful upload of the query notification corresponding to the task requiring verification, determining that the permission verification of the task requiring verification has passed. For tasks requiring verification that require permission verification, obtaining and uploading the corresponding query notification to the corresponding external data platform is performed to conduct permission verification.

[0157] In one specific implementation, for a task requiring authorization verification, if a query notification needs to be uploaded to the corresponding external data platform, the third event processing module sends a template request to the query terminal. The template request includes the task parameter - second entity. The query terminal receives the template request sent by the third event module, provides a user-specified query notification template file, and renders and generates the query notification corresponding to the task requiring verification based on the task parameter - second entity. The query notification can be in PDF or Word format. The query terminal sends the query notification corresponding to the task requiring verification back to the third event processing module. The third event processing module receives the query notification corresponding to the task requiring verification and uploads it to the external data platform corresponding to the task requiring verification to perform authorization verification for the task requiring verification.

[0158] In other words, the third event processing module acts as a remote browser connecting the server to different external data platforms on the query terminal. The server performs access verification for different external data platforms. When an operation that requires uploading a query notification is encountered, the server downloads the template file given by the user through the query terminal, and then, in combination with the task parameter of the task to be verified—the second entity, renders the corresponding query notification and uploads the query notification to perform permission verification for the task to be verified.

[0159] In one specific implementation, the Playwright automation program is used to perform access verification on the external data platform corresponding to each verification task requiring authorization, thereby automatically downloading and uploading template files. The program dynamically downloads specified template files, generates corresponding query notifications by combining them with second entity rendering, and automatically executes the upload action, achieving fully unattended operation.

[0160] In one embodiment, access verification includes identity verification. The steps of performing access verification on the external data platform corresponding to each task requiring verification specifically include: for each task requiring verification, obtaining a verification code image on the corresponding external data platform; identifying the verification code text from the verification code image; filling in the verification code text on the external data platform; and determining that the identity verification of the task requiring verification is successful if the filled-in verification code text is correct. For tasks requiring verification that require authentication, the verification code text is identified from the obtained verification code image and filled in on the external data platform to perform identity verification.

[0161] In one specific implementation, for a verification task requiring identity verification, if a verification code needs to be entered on the corresponding external data platform, the third event processing module captures the verification code image on the external data platform corresponding to the verification task and saves it as a temporary file or binary data; the third event processing module sends the verification code image to the second event processing module—the intelligent agent; the second event processing module receives the verification code image sent by the third event processing module and uses multimodal recognition capabilities to recognize the verification code image, obtaining the verification code text and the corresponding confidence level; the second event processing module feeds back the verification code text and the corresponding confidence level to the third event processing module; the third event processing module receives the verification code text and the corresponding confidence level, enters the verification code text on the external data platform corresponding to the verification task, and submits it to perform identity verification for the verification task; if the external data platform does not indicate an error in the verification code, it indicates that the entered verification code text is correct, the identity verification of the verification task is successful, and the subsequent process continues.

[0162] In other words, the third event processing module acts as a remote browser connecting the server to different external data platforms on the query terminal. The server performs access verification for different external data platforms. When a verification code is required, it will capture the verification code image from the corresponding external data platform and provide it to the agent. The agent will then use its multimodal recognition capabilities to identify the corresponding verification code text and fill in the verification code text on the external data platform to verify the identity of the task.

[0163] In one specific implementation, the Playwright automation program is used to perform access verification on the external data platform corresponding to each verification task that requires identity verification. By combining the multimodal recognition capability of the intelligent agent with the control of the Playwright browser, automatic recognition of verification code images and error retry are achieved.

[0164] In one specific implementation, in response to an incorrectly entered verification code text, the steps of obtaining the verification code image and subsequent steps on the external data platform corresponding to the task requiring verification are re-executed until the verification code text entered on the external data platform is correct, or until the number of incorrectly entered verification code texts reaches a second preset number. That is, if the verification code text entered on the external data platform is incorrect, the process automatically retryes until the entered verification code text is correct, or until the number of incorrectly entered verification code texts reaches the second preset number, at which point retrying stops.

[0165] The number of preset times is not limited and can be set according to actual usage needs.

[0166] Step S54: Determine whether the data query requests for the verified tasks and the tasks that do not require verification have been successfully submitted.

[0167] In this implementation, it is determined whether the data query requests corresponding to the verified tasks and the tasks that do not require verification have been successfully submitted.

[0168] Step S55: In response to the successful submission of the data query requests corresponding to the verified task and the task that does not require verification, determine whether the verified task and the task that does not require verification have received a valid response.

[0169] Step S55 is similar to step S42, and will not be described again here.

[0170] Step S56: For tasks that pass verification and tasks that do not require verification, in response to receiving a valid response, parse the valid query results to obtain the target query data.

[0171] Step S56 is similar to step S43, and will not be described again here.

[0172] In one embodiment, in response to the failure of data query request submission for a verified task and / or a task that does not require verification, a decision is made based on the type of failure reason to determine whether to retry. If a retry is determined, the verified task and / or the task that does not require verification are treated as new query tasks, and the data query request and subsequent steps are re-executed on the external data platform corresponding to each query task until the corresponding data query request is successfully submitted, or until the number of retries reaches a third preset number, at which point retries stop. The size of the third preset number is not limited and can be set according to actual usage needs.

[0173] In one specific implementation, when the number of retries reaches a third preset number, retries are stopped, and a failure is reported and logged and / or marked as requiring manual intervention.

[0174] Please see Figure 6 , Figure 6This is a schematic diagram of the framework of an embodiment of the event information processing system provided in this application. The event information processing system 60 includes a first event processing module 61, a second event processing module 62, and a third event processing module 63. The first event processing module 61 is used to execute any embodiment and any non-conflicting combination of the steps of obtaining target event data about a target event and obtaining event information of the target event based on each target query data in the above method. The second event processing module 62 is used to execute any embodiment and any non-conflicting combination of the steps of analyzing target event data to generate at least one query task in the above method. The third event processing module 63 is used to execute any embodiment and any non-conflicting combination of the steps of performing data queries on the external data platform corresponding to each query task in the above method to obtain the target query data of each query task.

[0175] Please see Figure 7 , Figure 7 This is a schematic diagram of the framework of an embodiment of the event information processing apparatus provided in this application. The event information processing apparatus 70 includes an acquisition module 71, a task generation module 72, a query module 73, and a processing module 74. The acquisition module 71 is used to acquire target event data about a target event; the task generation module 72 is used to analyze the target event data to generate at least one query task; wherein, the query task is used to instruct the querying of data of the target event on an external data platform; the query module 73 is used to perform data querying on the external data platform corresponding to each query task to obtain the target query data of each query task; the processing module 74 is used to obtain event information of the target event based on each target query data.

[0176] The steps of obtaining target event data and organizing target query data are performed using the first event processing module; the step of analyzing target event data to generate at least one query task is performed using the second event processing module; and the step of querying data on the external data platform corresponding to each query task to obtain the target query data for each query task is performed using the third event processing module.

[0177] The second event processing module mentioned above is an intelligent agent.

[0178] The task generation module 72 is used to analyze the target event data to generate at least one query task, including: extracting at least one first entity from the target event data; selecting at least one second entity from each first entity; wherein the second entity is the first entity that needs to be queried on an external data platform; and generating corresponding query tasks for each second entity.

[0179] The task generation module 72 is used to select at least one second entity from each first entity, including: determining the entity category of each first entity; and obtaining a preset query strategy library; determining the query strategy of each first entity based on the entity category of each first entity and the preset query strategy library; wherein the query strategy is used to characterize whether data query is performed on an external data platform; and the first entity whose query strategy characterizes data query on an external data platform is designated as the second entity.

[0180] The task generation module 72 is used to generate corresponding query tasks for each second entity, including: determining the external data platform corresponding to each second entity; and for each second entity, using the external data platform corresponding to the second entity and the second entity as task parameters to form the query task corresponding to the second entity.

[0181] The above task parameters also include at least one of the following: query priority and task type.

[0182] Each query task corresponds to a second entity, which is the entity extracted from the target event data that needs to be queried on an external data platform. The task parameters of the query task include the external data platform corresponding to the second entity and the second entity itself. The query module 73 is used to perform data queries on the external data platform corresponding to each query task to obtain the target query data for each query task. This includes: submitting a data query request on the external data platform corresponding to each query task; wherein the data query request includes the second entity corresponding to the query task; determining whether each query task has received a valid response; wherein a valid response is a valid query result received from the external data platform within a preset time; for each query task, in response to receiving a valid response, parsing the valid query result to obtain the target query data for the query task.

[0183] The invalid response to the above query task is an erroneous query result received from an external data platform within a preset time. The query module 73 is also used to evaluate whether to re-execute the query task in response to receiving an invalid response, and obtain a first evaluation result. In response to the first evaluation result being to re-execute the query task, the query task is treated as a new query task, and the data query request submitted to the external data platform corresponding to each query task and its subsequent steps are re-executed until a valid query result is obtained, or until the number of retries reaches the first preset number and no valid query result is obtained, and the task execution failure log corresponding to the query task is recorded.

[0184] The query module 73 is used to evaluate whether to re-execute the query task and obtain a first evaluation result, including: determining the cause of the exception corresponding to the query task; and in response to the exception cause being a second entity error corresponding to the query task, determining that the query task should not be re-executed.

[0185] The query module 73 is further configured to: 1) designate at least one query task that meets the review requirements as a review task; wherein the review requirements include that the query task has successfully submitted a data query request and has not received a response from an external data platform within a preset time; 2) designate at least one review task that meets the result retrieval requirements as a first review task, and designate the remaining review tasks as second review tasks; wherein the result retrieval requirements include that the review tasks have execution results and are in a completed state; 3) for each first review task, retrieve usable execution results from the corresponding task execution results of the first review task as valid query results of the first review task, and parse the valid query results of the first review task to obtain the target query data of the first review task; and 4) for each second review task, designate the second review task as a new review task, and re-execute at least one review task that meets the result retrieval requirements as a first review task and its subsequent steps, until the target query data of the second review task is obtained.

[0186] The query module 73 is used to parse the valid query results to obtain the target query data, including: using a parsing strategy that matches the result type of the valid query results to parse the valid query results to obtain the corresponding target query data.

[0187] The query module 73 is used to parse the valid query results using a parsing strategy that matches the result type of the valid query results, and obtain the corresponding target query data. This includes at least one of the following steps: if the valid query result is a task-related page, perform page element analysis on the task-related page to obtain page analysis results, which are used as target query data; if the valid query result is an interface, extract the response body corresponding to the interface and parse the response body to obtain response body parsing results, which are used as target query data; if the valid query result is a file, read the file content of the file and parse the file content to obtain file parsing results, which are used as target query data.

[0188] The query module 73 is used to designate at least one review task that meets the result retrieval requirements as the first review task and the remaining review tasks as the second review tasks. This includes: for each review task, checking the execution status of the review task and whether there is a task execution result based on the target task page of the review task; in response to the existence of a task execution result and the review task being in the execution completed state, designating the review task as the first review task; in response to the absence of a task execution result or the review task being in the execution incomplete state, designating the review task as the second review task.

[0189] The query module 73, before determining whether each query task has received a valid response, includes: designating at least one query task that requires access verification from the corresponding external data platform as a task requiring verification; performing access verification on the external data platform corresponding to each task requiring verification, and designating the tasks requiring verification that pass the access verification as verified tasks; determining whether the data query requests corresponding to verified tasks and tasks that do not require verification have been successfully submitted, wherein tasks that do not require verification are query tasks other than those requiring verification; and determining whether each query task has received a valid response in response to the successful submission of the data query requests corresponding to verified tasks and tasks that do not require verification.

[0190] The access verification mentioned above includes permission verification; the query module 73 is used to perform access verification on the external data platform corresponding to each task requiring verification, including: for each task requiring verification, obtaining the query notification corresponding to the task requiring verification, and uploading the corresponding query notification to the external data platform corresponding to the task requiring verification; in response to the successful upload of the query notification corresponding to the task requiring verification, determining that the permission verification of the task requiring verification has passed.

[0191] The aforementioned access verification includes identity verification; the query module 73 is used to perform access verification on the external data platform corresponding to each task requiring verification, including: for each task requiring verification, obtaining a verification code image on the external data platform corresponding to the task requiring verification; identifying the verification code text from the verification code image; filling in the verification code text on the external data platform; and determining that the identity verification of the task requiring verification has passed in response to the correct verification code text being filled in.

[0192] The query module 73 is also used to respond to the error in the entered verification code text by re-executing the steps of obtaining the verification code image and subsequent steps on the external data platform corresponding to the task to be verified, until the verification code text entered on the external data platform is correct, or until the number of times the verification code text is entered incorrectly is the second preset number.

[0193] The sorting module 74 is used to obtain the event information of the target event based on the query data of each target, including at least one of the following steps: updating the event view of the target event using the query data of each target; and generating an event query map using the query data of each target.

[0194] The target query data mentioned above is obtained by querying the external data platform corresponding to the second entity. The second entity is the entity extracted from the target event data that needs to be queried on the external data platform. The query module 73 is also used to perform entity mining based on each target query data before obtaining the event information of the target event. This includes: performing entity mining based on each target query data to obtain at least one third entity, generating query tasks for each third entity, and re-executing the data query on the external data platform corresponding to each query task to obtain the target query data of each query task and its subsequent steps. The third entity is at least one of the following: a second entity that does not exist in the target event data, or a second entity whose entity information has been added.

[0195] Please see Figure 8 , Figure 8 This is a schematic diagram of an embodiment of the electronic device provided in this application. The electronic device 80 includes a memory 81 and a processor 82 coupled to each other. The memory 81 stores program instructions. The processor 82 is used for any embodiment and any non-conflicting combination of the steps in the above method of obtaining target event data about a target event and obtaining event information of the target event based on each target query data, and / or, any embodiment and any non-conflicting combination of the step in the above method of analyzing target event data to generate at least one query task, and / or, any embodiment and any non-conflicting combination of the step in the above method of performing data queries on an external data platform corresponding to each query task to obtain target query data for each query task. Specifically, the electronic device 80 may include, but is not limited to, desktop computers, laptops, servers, mobile phones, tablet computers, etc., and is not limited thereto.

[0196] Specifically, processor 82 controls itself and memory 81 to implement any embodiment and any non-conflicting combination of the steps in the above method of acquiring target event data about the target event and obtaining event information of the target event based on each target query data, and / or any embodiment and any non-conflicting combination of the step in the above method of analyzing target event data to generate at least one query task, and / or any embodiment and any non-conflicting combination of the step in the above method of performing data queries on external data platforms corresponding to each query task to obtain target query data for each query task. Processor 82 can also be called a CPU (Central Processing Unit). Processor 82 may be an integrated circuit chip with signal processing capabilities. Processor 82 can also be a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component. A general-purpose processor can be a microprocessor or any conventional processor, etc. In addition, the processor 82 can be implemented by integrated circuit chips.

[0197] Please see Figure 9 , Figure 9 This is a schematic diagram of a computer-readable storage medium according to an embodiment of the present application. The computer-readable storage medium 90 of this embodiment stores program instructions 91. When executed, these program instructions 91 implement the methods provided by any embodiment of the event information processing method of this application and any non-conflicting combination thereof. The program instructions 91 can form a program file and be stored in the aforementioned computer-readable storage medium 90 in the form of a software product, so that a computer device (which may be a personal computer, server, or network device, etc.) can execute all or part of the steps of the methods of various embodiments of this application. The aforementioned computer-readable storage medium 90 includes various media capable of storing program code, such as a USB flash drive, mobile hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, or terminal devices such as computers, servers, mobile phones, and tablets.

[0198] If the technical solution of this application involves personal information, the product using this technical solution has clearly informed the user of the personal information processing rules and obtained the user's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using this technical solution has obtained the user's separate consent before processing the sensitive personal information, and also meets the requirement of "express consent". For example, at personal information collection devices such as cameras, clear and prominent signs are set up to inform users that they have entered the scope of personal information collection and that personal information will be collected. If an individual voluntarily enters the collection scope, it is deemed that they have agreed to the collection of their personal information; or on the personal information processing device, with clear signs / information informing users of the personal information processing rules, authorization is obtained from the individual through pop-up information or by asking the individual to upload their personal information; wherein, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the types of personal information processed.

[0199] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. An event information processing method characterized by comprising: The method includes: Obtain target event data about the target event; The target event data is analyzed to generate at least one query task; wherein the query task is used to instruct a query for data of the target event on an external data platform; Data is queried on the external data platform corresponding to each query task to obtain the target query data for each query task. Based on the target query data, the event information of the target event is obtained.

2. The method according to claim 1, characterized in that, The steps of obtaining target event data about the target event and organizing the target query data are performed using the first event processing module. The step of analyzing the target event data to generate at least one query task is performed using a second event processing module; wherein, the second event processing module is an intelligent agent; The step of querying data on the external data platform corresponding to each query task to obtain the target query data for each query task is executed using the third event processing module.

3. The method according to any one of claims 1-2, characterized in that, The analysis of the target event data to generate at least one query task includes: Extract at least one first entity from the target event data; Select at least one second entity from each of the first entities; wherein the second entity is the first entity that needs to perform data queries on an external data platform; Generate corresponding query tasks for each of the second entities.

4. The method according to claim 3, characterized in that, The step of selecting at least one second entity from each of the first entities includes: Determine the entity category of each of the first entities; And, obtain the preset query strategy library; Based on the entity category of each first entity and the preset query strategy library, a query strategy for each first entity is determined; wherein, the query strategy is used to characterize whether to perform data query on an external data platform; The query strategy represents the first entity that performs data queries on an external data platform, which is then used as the second entity.

5. The method of claim 3, wherein, The step of generating corresponding query tasks for each of the second entities includes: Determine the external data platform corresponding to each of the second entities; For each of the second entities, the external data platform corresponding to the second entity and the second entity are used as task parameters to form the query task corresponding to the second entity.

6. The method according to any one of claims 1 to 5, characterized in that, Each of the query tasks corresponds to a second entity, which is an entity extracted from the target event data that needs to be queried on an external data platform. The task parameters of each query task include the external data platform corresponding to the second entity and the second entity itself. The step of querying data on the external data platform corresponding to each query task to obtain the target query data for each query task includes: Submit a data query request on the external data platform corresponding to each of the query tasks; wherein, the data query request includes the second entity corresponding to the query task; Determine whether each of the query tasks has received a valid response; wherein, a valid response is a valid query result received from the external data platform within a preset time. For each of the query tasks, in response to receiving a valid response, the valid query result is parsed to obtain the target query data of the query task.

7. The method of claim 6, wherein, An invalid response to the query task is an erroneous query result received from the external data platform within the preset time. The method further includes: In response to the query task receiving the invalid response, an evaluation is conducted to determine whether the query task should be re-executed, resulting in a first evaluation result. In response to the first evaluation result being to re-execute the query task, the query task is treated as a new query task, and the steps of submitting the data query request on the external data platform corresponding to each query task and subsequent steps are re-executed until a valid query result of the query task is obtained, or until the number of retries reaches a first preset number and no valid query result of the query task is obtained, and a task execution failure log corresponding to the query task is recorded.

8. The method of claim 7, wherein, The evaluation of whether to re-execute the query task to obtain a first evaluation result includes: Determine the cause of the anomaly corresponding to the query task; In response to the exception being caused by an error in the second entity corresponding to the query task, it is determined that the query task will not be re-executed.

9. The method of claim 6, wherein, The method further includes: At least one query task that meets the review requirements is designated as a review task; wherein, the review requirements include that the query task is successfully submitted for the data query request and no response is received from the external data platform within the preset time. At least one review task that meets the result retrieval requirements is designated as the first review task, and the remaining review tasks are designated as the second review tasks; wherein, the result retrieval requirements include that the review task has a task execution result and is in the execution completed state; For each of the first review tasks, the available execution results are extracted from the task execution results corresponding to the first review task as the valid query results of the first review task, and the valid query results of the first review task are parsed to obtain the target query data of the first review task. Furthermore, for each of the second review tasks, the second review task is treated as a new review task, and at least one review task that meets the result retrieval requirements is re-executed as the first review task and its subsequent steps, until the target query data of the second review task is obtained.

10. The method according to any one of claims 6-9, characterized in that, The steps to parse valid query results and obtain the target query data include: The valid query results are parsed using a parsing strategy that matches the result type of the valid query results to obtain the corresponding target query data.

11. The method according to claim 10, characterized in that, The step of parsing the valid query results using a parsing strategy that matches the result type of the valid query results to obtain the corresponding target query data includes at least one of the following steps: In response to the valid query result being a task-related page, page element analysis is performed on the task-related page to obtain page analysis results, which are used as the target query data. In response to the valid query result being an interface, the response body corresponding to the interface is extracted, and the response body is parsed to obtain the response body parsing result, which is used as the target query data; In response to the valid query result being a file, the file content of the file is read and parsed to obtain the file parsing result, which is used as the target query data.

12. The method according to claim 9, characterized in that, The step of designating at least one review task that meets the result retrieval requirements as the first review task, and designating the remaining review tasks as the second review tasks, includes: For each of the aforementioned review tasks, based on the target task page of the review task, check the execution status of the review task and whether there are any task execution results; In response to the existence of the task execution result and the review task being in the execution completed state, the review task is designated as the first review task; In response to the absence of a result for the task execution or the fact that the review task is in an incomplete state, the review task is designated as the second review task.

13. The method according to claim 6, characterized in that, Before determining whether each of the query tasks has received a valid response, the method further includes: At least one query task that requires access verification from the corresponding external data platform is designated as the task to be verified. Access verification is performed on the external data platform corresponding to each of the aforementioned tasks requiring verification, and the tasks requiring verification that pass the access verification are designated as verified tasks. Determine whether the data query requests corresponding to the verified task and the task that does not require verification have been successfully submitted. The task that does not require verification is the query task other than the task that requires verification. Determining whether each of the query tasks has received a valid response includes: In response to the successful submission of the data query requests corresponding to the verified task and the task that does not require verification, determine whether the verified task and the task that does not require verification have received a valid response.

14. The method according to claim 13, characterized in that, The access verification includes permission verification; the steps of performing access verification on the external data platform corresponding to each of the tasks requiring verification include: For each of the verification tasks, obtain the query notification corresponding to the verification task and upload the corresponding query notification to the external data platform corresponding to the verification task. Upon successful upload of the query notification corresponding to the task requiring verification, it is determined that the permission verification of the task requiring verification has passed. And / or, the access verification includes identity verification; the steps of performing access verification on the external data platform corresponding to each of the tasks requiring verification include: For each of the verification tasks, obtain the verification code image from the external data platform corresponding to the verification task; The verification code text is identified from the verification code image; Enter the verification code text on the external data platform; If the verification code text is correct, the identity verification of the task to be verified is confirmed to be successful. If the verification code text is incorrect, the steps of obtaining the verification code image and subsequent steps on the external data platform corresponding to the task to be verified are re-executed until the verification code text entered on the external data platform is correct, or until the number of times the verification code text is entered incorrectly is a second preset number.

15. The method according to any one of claims 1-14, characterized in that, Obtaining the event information of the target event based on each of the target query data includes at least one of the following steps: Update the event view of the target event using the target query data; Generate an event query map using the target query data.

16. The method according to any one of claims 1-15, characterized in that, The target query data is obtained based on the external data platform corresponding to the second entity, which is the entity extracted from the target event data that needs to be queried on the external data platform; Before obtaining the event information of the target event based on each of the target query data, the method further includes: Entity mining is performed based on the target query data to obtain at least one third entity. Query tasks are generated for each third entity, and the data query is re-executed on the external data platform corresponding to each query task to obtain the target query data and subsequent steps of each query task. The third entity is at least one of the following: a second entity that does not exist in the target event data, or a second entity whose entity information is added.

17. An event information processing system, characterized in that, The event information processing system includes a first event processing module, a second event processing module, and a third event processing module. The first event processing module is used to execute the steps of obtaining target event data about the target event and obtaining event information of the target event based on each of the target query data in the method of any one of claims 1-16. The second event processing module is used to execute the step of analyzing the target event data to generate at least one query task in the method of any one of claims 1-16. The third event processing module is used to execute the step of performing data query on the external data platform corresponding to each of the query tasks to obtain the target query data of each query task in the method of any one of claims 1-16.

18. An event information processing device, characterized in that, The device includes: The acquisition module is used to acquire target event data about the target event; A task generation module is used to analyze the target event data to generate at least one query task; wherein the query task is used to instruct a query for data of the target event on an external data platform; The query module is used to perform data queries on the external data platform corresponding to each query task to obtain the target query data for each query task. The sorting module is used to obtain the event information of the target event based on the query data of each target.

19. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores program instructions, and the processor executes the program instructions to implement the steps of obtaining target event data about a target event and obtaining event information of the target event based on each of the target query data, as described in any one of claims 1-16, and / or the steps of analyzing the target event data to generate at least one query task, as described in any one of claims 1-16, and / or the steps of performing data queries on an external data platform corresponding to each of the query tasks to obtain target query data for each of the query tasks, as described in any one of claims 1-16.

20. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program instructions that can be executed to implement the event information processing method as described in any one of claims 1-16.

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