Task batch triggering methods, devices, storage media and computer equipment

Abstract

The task batch triggering method, apparatus, storage medium, and computer equipment provided in this application receive and automatically parse user messages from an instant messaging platform, identify and extract business work order lists and environmental parameters, and can replace a series of tedious operations such as manual login, manual task search, and manual parameter filling, effectively reducing the risk of operational errors caused by manual intervention. For each work order's business type and environmental parameters, the corresponding task group identifier is accurately located, and at least one invocation task within that group is executed sequentially, ensuring that independent task groups in different environments are correctly scheduled, thereby avoiding environmental confusion and incorrect task execution caused by manual verification. Finally, the corresponding invocation task is automatically executed, and the execution results are summarized and fed back to the instant messaging platform in natural language, thus achieving fully automated processing of multiple work order task triggering.

Description

Technical Field

[0001] This application relates to the field of operation and maintenance testing technology, and in particular to a method, apparatus, storage medium and computer equipment for batch triggering of tasks. Background Technology

[0002] In the daily operation and testing processes of enterprises, distributed task scheduling platforms have become the core support tool for key tasks such as batch data processing and business process advancement. In actual operation, the execution of platform tasks is usually deeply bound to business work orders, requiring operators to trigger related sub-tasks in a specified order in testing, pre-release and other environments.

[0003] Current mainstream solutions rely heavily on manual operation. Operation and maintenance personnel need to manually log in to the platform to find tasks, fill in parameters, and click to execute one by one. Task groups and task identifiers in different environments are independent of each other, which can easily lead to environment confusion and incorrect task execution due to manual verification errors. Summary of the Invention

[0004] The purpose of this application is to at least solve one of the above-mentioned technical defects, especially the technical defect in the prior art where maintenance personnel need to manually log in to the platform to find tasks, fill in parameters and click to execute one by one. The task groups and task identifiers in different environments are independent of each other, which is very easy to cause environment confusion and task misrun due to manual verification errors.

[0005] Firstly, this application provides a method for batch triggering tasks, the method comprising:

[0006] When a user message is received from an instant messaging platform, the user message is parsed. If a business work order identifier is identified during parsing, the business work order list and environmental parameters are extracted.

[0007] Determine the business type corresponding to each work order in the business work order list, and query the task group identifier corresponding to each work order based on the environment parameters and the business type of each work order.

[0008] For each work order, determine at least one calling task corresponding to the task group identifier of the work order, and execute each calling task for the work order to obtain the work order execution result;

[0009] The execution results of each work order are summarized to obtain the overall execution result. Based on the overall execution result, a natural language response is generated and then returned to the instant messaging platform.

[0010] In one embodiment, parsing the user message, if a business work order identifier is identified, then extracting the business work order list and environmental parameters, includes:

[0011] According to a preset regular expression, the service work order identifier containing a preset prefix is ​​matched and identified in the user message;

[0012] If a business work order identifier containing a preset prefix is ​​matched and identified in the user message, a business work order list is generated based on the identified business work order identifier, and the environmental parameter field value is extracted from the user message to obtain the environmental parameters.

[0013] In one embodiment, determining the business type corresponding to each work order in the business work order list includes:

[0014] For each work order in the aforementioned work order, the corresponding business type is queried in the preset business type mapping library;

[0015] If the query is successful, the business type found will be used as the business type of the work order.

[0016] If the query fails, the semantic description of the work order is obtained, and the similarity between the semantic description and each business type in the business type mapping library is calculated. The business type with the highest similarity to the semantic description is taken as the business type of the work order.

[0017] In one embodiment, querying the task group identifier corresponding to each work order based on the environmental parameters and the business type of each work order includes:

[0018] Obtain a preset configuration table, and read at least one target task group identifier corresponding to the environment parameters from the preset configuration table;

[0019] For each work order, query the target task group identifier that matches the business type of the work order from each target task group identifier, and determine the queried target task group identifier as the task group identifier of the work order.

[0020] In one embodiment, determining at least one calling task corresponding to the task group identifier of the work order, and executing each calling task for the work order to obtain the work order execution result includes:

[0021] Obtain the preset configuration table;

[0022] The task to be invoked and the task invocation order under the task group identifier belonging to this work order are determined in the preset configuration table;

[0023] Based on the task invocation order, each invocation task is executed sequentially for the work order, and the execution result of each invocation task is recorded to generate the work order execution result.

[0024] In one embodiment, the step of summarizing the execution results of each work order to obtain the overall execution result includes:

[0025] Extract the execution status of each work order from the execution results of each work order, and count the number of work orders corresponding to different execution statuses.

[0026] Failed work orders with a failed execution status are filtered out from each work order to form a list of failed work orders;

[0027] The overall execution result is generated based on the execution results of each work order, the number of work orders corresponding to different execution states, and the list of failed work orders.

[0028] In one embodiment, generating a natural language response based on the overall execution result includes:

[0029] The contents of the overall execution result are converted into structured information, and the display format of the structured information is adjusted.

[0030] The adjusted structured information is returned to the instant messaging platform so that the platform can visualize the adjusted structured information.

[0031] Secondly, this application provides a task batch triggering device, the device comprising:

[0032] The message parsing module is used to parse user messages sent by the instant messaging platform when it receives such messages. If the parsing identifies a business work order identifier, it extracts the business work order list and environmental parameters.

[0033] The task group determination module is used to determine the business type corresponding to each work order in the business work order list, and to query the task group identifier corresponding to each work order based on the environmental parameters and the business type of each work order.

[0034] The work order execution module is used to determine at least one calling task corresponding to the task group identifier of each work order, and execute each calling task for the work order to obtain the work order execution result.

[0035] The result feedback module is used to summarize the execution results of each work order to obtain the overall execution result, and based on the overall execution result, generate a natural language response and return the natural language response to the instant messaging platform.

[0036] Thirdly, this application provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the task batch triggering method as described in any of the above embodiments.

[0037] Fourthly, this application provides a computer device, including: one or more processors, and a memory;

[0038] The memory stores computer-readable instructions, and when the one or more processors execute the computer-readable instructions, they perform the steps of the task batch triggering method as described in any of the above embodiments.

[0039] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0040] The task batch triggering method, apparatus, storage medium, and computer equipment provided in this application receive and automatically parse user messages from an instant messaging platform, identify and extract business work order lists and environmental parameters, and can replace a series of tedious operations such as manual login, manual task search, and manual parameter filling, effectively reducing the risk of operational errors caused by manual intervention. For each work order's business type and environmental parameters, the corresponding task group identifier is accurately located, and at least one invocation task within that group is executed sequentially, ensuring that independent task groups in different environments are correctly scheduled, thereby avoiding environmental confusion and incorrect task execution caused by manual verification. Finally, the corresponding invocation task is automatically executed, and the execution results are summarized and fed back to the instant messaging platform in natural language, thus achieving fully automated processing of multiple work order task triggering. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 A flowchart illustrating a method for batch triggering tasks provided in an embodiment of this application;

[0043] Figure 2 Example diagram of a task batch triggering method provided in an embodiment of this application;

[0044] Figure 3 This is a schematic diagram of the structure of a task batch triggering device provided in an embodiment of this application;

[0045] Figure 4 This is an internal structural diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0046] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] In one embodiment, this application provides a method for batch triggering tasks. The following embodiments illustrate the application of this method to a server. It is understood that the batch triggering method can be executed by a single server or by a server cluster consisting of multiple servers, and this application does not impose any specific limitations on this.

[0048] like Figure 1 As shown, this application provides a method for batch triggering tasks, the method comprising:

[0049] S101: When a user message is received from the instant messaging platform, the user message is parsed. If a business work order identifier is identified during parsing, the business work order list and environmental parameters are extracted.

[0050] Here, the business work order identifier refers to the number of a specific business process. The business work order list is an ordered set of one or more business work order identifiers parsed from user messages. Environment parameters are identification information used to specify the task execution environment.

[0051] In this step, the server continuously listens for message events pushed by the instant messaging platform while running. When it receives a message sent by a user, it first performs basic validity and integrity checks on the message, then performs structured parsing on the text content. It checks whether the message contains business work order identifiers by means of keyword matching or regular expression matching. If the identifiers are successfully identified, all valid business work order identifiers are extracted from the text and integrated into a business work order list. At the same time, the server identifies keywords in the user message that indicate the execution environment and converts them into environment parameters, thereby obtaining the business work order list and environment parameters.

[0052] For example, a user can @ the bot in Lark chat and send "TB1260409000001TB1260409000002 Test Environment Triggered". After the server captures the user's message, it uses regular expression matching to extract the two ticket numbers starting with TB to form a ticket list, and identifies "Test Environment" as an environment parameter. In addition, this process is also compatible with common input formats such as multiple tickets separated by spaces and commas, and can also adapt to the message structure and event format of different platforms, thereby improving the compatibility and stability of message parsing.

[0053] S102: Determine the business type corresponding to each work order in the business work order list, and query the task group identifier corresponding to each work order based on the environment parameters and the business type of each work order.

[0054] The business type is used to distinguish the task execution logic under different business scenarios. The task group identifier is a number used in the distributed task scheduling platform to classify and manage tasks of the same type.

[0055] In this step, the server first performs feature parsing and rule matching on each work order identifier in the work order list to determine its business type. Then, it combines the determined business type with the previously extracted environment parameters and retrieves the corresponding task group identifier for each work order in the specified environment by querying a pre-configured environment parameter-business type-task group mapping table.

[0056] For example, when a business work order with the prefix TB is parsed, it can be determined as a business type of issuing a logistics order based on its numbering rules. Combined with the environment parameter of the test environment, the task group identifier corresponding to the task of issuing a logistics order in the test environment can be queried from the configuration table.

[0057] S103: For each work order, determine at least one calling task corresponding to the task group identifier of the work order, and execute each calling task for the work order to obtain the work order execution result.

[0058] Here, a task refers to a specific scheduling unit belonging to a corresponding task group that needs to be executed according to business logic. The work order execution result refers to the execution result formed by combining the task response status after all its subordinate tasks have been completed in sequence, which includes execution success and execution failure.

[0059] In this step, the server can process work orders one by one in the order of the business work order list. For the current work order, it first reads one or more calling tasks bound to the task group from the preset configuration or scheduling platform interface based on the determined task group identifier, and clarifies the order of task execution and dependencies. Then, it sends task calling requests to the scheduling platform in the predetermined sequence, carrying necessary parameters such as the current work order number in the request, and receives the return information of task execution. After all calling tasks have been executed, the return information of each calling task is combined to form the final execution result of the work order, i.e., the work order execution result.

[0060] For example, a picking work order typically includes two calling tasks under the task group: an inventory locking task and a picking and shipping execution task. In this case, the inventory locking task can be triggered first and the return is confirmed to be normal. Then, the picking and shipping task can be triggered at a specified interval. If both calls pass the verification, the picking work order is determined to be executed successfully. If any call fails or the verification fails, it is marked as a failure and the exception information is recorded.

[0061] S104: Summarize the execution results of each work order to obtain the overall execution result, and based on the overall execution result, generate a natural language response and return the natural language response to the instant messaging platform.

[0062] The overall execution result refers to the global execution conclusion formed by uniformly statistically integrating and encapsulating the execution status, number of successes, number of failures, failure work order numbers, and reasons for exceptions of all work orders in the business work order list.

[0063] In this step, the execution results of all work orders are iterated one by one, and the total number of successfully executed work orders, the total number of failed work orders, and all successful work order numbers and failed work order numbers are counted. The reasons for the failure of abnormal work orders are also categorized and organized to form the overall execution result. Then, according to the preset message template or based on the preset large language model, the structured overall execution result is converted into fluent and easy-to-understand natural language text, and then the generated natural language response is sent to the original session that initiated the user's request through the instant messaging platform's message push interface.

[0064] In the above embodiments, by receiving and automatically parsing user messages from the instant messaging platform, identifying and extracting the business work order list and environmental parameters, a series of tedious operations such as manual login, manual task search, and manual parameter filling can be replaced, effectively reducing the risk of operational errors caused by manual intervention. For each work order's business type and environmental parameters, the corresponding task group identifier is accurately located, and at least one invocation task within that group is executed sequentially, ensuring that independent task groups in different environments are correctly scheduled, thereby avoiding environmental confusion and incorrect task execution caused by manual verification. Finally, the corresponding invocation task is automatically executed, and the execution results are summarized and fed back to the instant messaging platform in natural language, thus achieving fully automated processing of multiple work order tasks.

[0065] In one embodiment, the user message is parsed. If a service order identifier is identified during parsing, a list of service orders and environmental parameters are extracted, including:

[0066] S1: Based on a preset regular expression, match and identify the business work order identifier containing a preset prefix in the user message.

[0067] S2: If a business work order identifier containing a preset prefix is ​​matched and identified in the user message, a business work order list is generated based on the identified business work order identifier, and the environmental parameter field value is extracted from the user message to obtain the environmental parameters.

[0068] Regular expressions are used to quickly search, locate, and extract strings that conform to fixed format characteristics in unstructured text.

[0069] In this embodiment, the server can first load a pre-configured regular expression. Using this expression as the matching rule, it scans and matches the text content of the user message segment by segment, identifying business order identifiers containing a specified prefix. When a business order identifier that meets the format requirements is matched, it is extracted and temporarily stored. If there are multiple business order identifiers in the user message, they are collected in the order of appearance to generate a business order list. Simultaneously, the server can continue to scan the message text for environment-related keywords, extract the corresponding environment parameter field values, and determine them as environment parameters.

[0070] For example, for supply chain distribution TB work orders, a regular expression prefixed with "TB" can be used to accurately match two business work order identifiers starting with "TB" from the user-sent message "@robot TB240501001 TB240501002 Test environment trigger" and form a list, while extracting "test environment" as an environment parameter.

[0071] Specifically, regular expressions are used to automatically match and identify business work order identifiers, and business work order list generation and environmental parameter extraction are completed simultaneously. This quickly transforms unstructured natural language messages into recognizable structured data, providing a reliable data foundation for subsequent task triggering.

[0072] In one embodiment, determining the business type corresponding to each work order in the work order list includes:

[0073] S1: For each work order in the business work order, query the business type corresponding to the work order in the preset business type mapping library.

[0074] S2: If the query is successful, the business type found will be used as the business type of the work order.

[0075] S3: If the query fails, obtain the semantic description of the work order, and calculate the similarity between the semantic description and each business type in the business type mapping library. The business type with the highest similarity to the semantic description is taken as the business type of the work order.

[0076] Among them, the business type mapping library refers to a pre-built and stored structured configuration library, which internally stores the correspondence between business work order identifiers and business types in the form of key-value pairs. Semantic description refers to the textual description of the meaning, purpose, business scenario and processing content of the business work order itself.

[0077] In this embodiment, the server first iterates through each work order in the work order list, using the work order identifier as a query condition, and matches it against a preset business type mapping library. If a corresponding entry is found directly, the query is considered successful, and the matched business type is assigned to the work order. If no corresponding record is found in the business type mapping library, the query is considered unsuccessful. In this case, the semantic description of the work order can be obtained, and then the semantic description is sequentially compared with the standard descriptions of all business types in the business type mapping library to calculate semantic similarity. By comparing the scores, the business type with the highest similarity is determined as the business type of the work order. In this process, fast and accurate matching of known work orders is ensured, and intelligent type inference can be achieved for new work orders with unknown formats and no mapping relationship, effectively solving the problem that a single matching method cannot cover all business scenarios.

[0078] Furthermore, information such as the coding rules and business purpose of work orders can be extracted using a pre-defined large language model to obtain a semantic description of the work order. In addition, when calculating similarity, the semantic description of the work order and the text of various business types in the mapping library are first decomposed into feature words and semantic vectors. The distance difference between the two vector spaces is calculated based on the text comparison algorithm, and the similarity value is obtained. The higher the value, the stronger the business fit.

[0079] For example, for order picking starting with TB, you can first directly match the types such as "store picking" and "warehouse replenishment" in the mapping library. When you encounter a new format or an order identifier without mapping, you can compare the semantic similarity with various business types in the library based on semantic descriptions such as "warehouse preparation and delivery" to finally infer the most suitable business type.

[0080] In one embodiment, based on environmental parameters and the business type of each work order, the task group identifier corresponding to each work order is queried, including:

[0081] S1: Obtain the preset configuration table and read at least one target task group identifier corresponding to the environment parameters from the preset configuration table.

[0082] S2: For each work order, query the target task group identifier that matches the business type of the work order in each target task group identifier, and determine the queried target task group identifier as the task group identifier of the work order.

[0083] The preset configuration table is used to record the association between environment parameters, business types and task group identifiers.

[0084] In this embodiment, the server can first retrieve the built-in preset configuration table, search the form content based on the extracted environment parameters, filter out all target task group identifiers associated with the operating environment, and then sequentially traverse each business work order, using the business type determined by the work order as the matching condition, compare and filter one by one within the range of the filtered task group identifiers, lock out the matching identifier and determine it as the task group identifier of the work order.

[0085] Specifically, relying on the preset configuration table, the system completes the initial screening of the environment dimension and the accurate matching of the business type, locks the task group identifier corresponding to the work order in layers, realizes the automatic association and binding of work order information and scheduling group, and avoids the mismatch problem caused by manual verification of environment and business type.

[0086] In one embodiment, at least one calling task corresponding to the task group identifier of the work order is determined, and each calling task is executed for the work order to obtain the work order execution result, including:

[0087] S1: Get the preset configuration table.

[0088] S2: Determine the tasks to be called under the task group identifier of this work order and the order in which the tasks are called in the preset configuration table.

[0089] S3: Based on the task invocation order, execute each invocation task of the work order in sequence, and record the execution result of each invocation task to generate the work order execution result.

[0090] In this embodiment, a preset configuration table stored internally is retrieved. Based on the identified work order task group identifier, all tasks bound to that group are retrieved. Simultaneously, the pre-set task invocation order within the group is read, and task invocation requests are initiated sequentially according to the predetermined order, triggering the corresponding scheduling tasks one by one. After each task is completed, its running status, response data, and exception information are immediately recorded. Once all tasks under the work order have been executed, the execution records of all individual tasks are integrated, and the work order execution result is formed. This approach can adapt to business processes of varying complexity, flexibly adding or removing task items and adjusting the execution order, while also reducing the risk of business testing errors caused by human error.

[0091] For example, in the supply chain distribution business scenario, after matching the corresponding task group, related tasks such as inventory verification, document entry, and outbound delivery can be retrieved and the established execution order can be followed. First, the warehouse inventory balance corresponding to the distribution document can be verified, then the store's inventory information can be registered, and finally the outbound dispatch operation can be initiated. The data of each step of the process is retained, and the execution effectiveness of the distribution work order is determined by comprehensively considering the situation of all links.

[0092] In one embodiment, the execution results of each work order are summarized to obtain the overall execution result, including:

[0093] S1: Extract the execution status of each work order from the execution results of each work order, and count the number of work orders corresponding to different execution statuses.

[0094] S2: Filter out the failed work orders from each work order and form a list of failed work orders.

[0095] S3: Generate the overall execution result based on the execution results of each work order, the number of work orders corresponding to different execution states, and the list of failed work orders.

[0096] In this embodiment, the execution results of each work order are retrieved one by one, and the final execution status information of each work order is extracted separately. The total number of corresponding work orders is counted according to the execution status categories such as success and failure. Then, all work order execution results are traversed, and work orders marked as failure are uniformly filtered and collected to form a list of failed work orders. Finally, the execution status of individual work orders, the statistical values ​​of various statuses, and the list of failed work orders are integrated to construct the overall execution result. This not only intuitively displays the overall processing effectiveness of batch work orders, but also accurately identifies problematic documents, making it easier for staff to quickly locate faults and troubleshoot problems.

[0097] In one embodiment, a natural language response is generated based on the overall execution result, including:

[0098] S1: Convert all items in the overall execution result into structured information and adjust the display format of the structured information.

[0099] S2: Return the adjusted structured information to the instant messaging platform so that the platform can visualize the adjusted structured information.

[0100] In this embodiment, upon obtaining the overall execution result, the statistical values, work order status, and anomaly details within the overall execution result can be first broken down and organized into standardized structured data entries. Then, the layout style is adjusted according to the needs of reading, viewing, and office reporting, the information arrangement order is streamlined, content sections are divided, and the data presentation format is optimized. After processing, the organized structured information is pushed to an instant messaging platform, where its own display function completes the visual presentation. This improves the efficiency of information reading and analysis, and also facilitates relevant personnel in quickly grasping the overall execution status of the distribution work order.

[0101] In one embodiment, the server can be divided into the following modules:

[0102] (1) Interactive Gateway Module:

[0103] 1. Lark Application Adapter: Register your application on the Lark Open Platform, subscribe to message receiving events (such as im.message.receive_v1), and receive text messages from users @bots in group chats or one-on-one chats via HTTP Webhook.

[0104] 2. Message preprocessing: Extract the original message text, sender information, session ID and other context, standardize them and pass them to the intelligent agent scheduling module; at the same time, it is responsible for sending reply messages to the original session according to the Lark API format.

[0105] 3. Security Authentication: Verify the signature and token pushed by Lark server to ensure the legitimacy of the request source and prevent replay.

[0106] (2) Agent scheduling module:

[0107] 1. Intent Recognition: Analyze the text passed in from the gateway. When a work order number starting with "TB" is detected (regular expression matching TB\d+), it is determined that the user's intent is to trigger an XXL-JOB task.

[0108] 2. Skill Invocation Decision: Activate the "tb-xxljob-trigger" skill from the registered skill set, and input the extracted work order number list and the environment (test / pre-release) identified by keywords according to the input format agreed upon by the skill.

[0109] 3. Skill execution monitoring: Convert the return results (success / failure and summary information) after skill execution into natural language responses, send them back to the intelligent interaction gateway module, and finally send them to Lark users.

[0110] 4. Context persistence: Session context can be temporarily stored, supporting multi-turn dialogue (such as when the user modifies environment parameters).

[0111] (3) Skill Execution Module:

[0112] 1. Message parsing submodule: Receives structured parameters (work order number list, environment identifier) ​​from the intelligent agent scheduling module. If the input is a string, it will be parsed again according to the built-in rules (reusing regular expression extraction and environment keyword scanning).

[0113] 2. Environment Configuration Mapping Submodule: Based on the input environment identifier, it reads the scheduling center base address, task group identifier (JobGroup), and first task identifier (…) from the preset configuration table. ), Second task identifier ( (and Referer information, etc.)

[0114] 3. Credential Security Management Submodule: Independently reads the security configuration file deployed locally (such as tb-trigger.config.ps1), obtains the encrypted storage of the scheduling center login identifier (XXL_JOB_LOGIN_IDENTITY in the cookie), uses it only in memory, and releases it after use.

[0115] 4. Task Trigger Execution Submodule: For each work order number, assemble an HTTP POST request, fill in authentication cookies and Referer headers, and use a native command-line tool (such as curl.exe) to send it to the trigger endpoint of the corresponding Job ID in certificate verification mode (--insecure).

[0116] 4.1 First task triggered: Send request to Get a response.

[0117] 4.2 Response Validation: Employs multiple validation strategies (JSON code==200 or text containing success characteristics).

[0118] 4.3 Wait for the preset time (e.g., 1 second).

[0119] 4.4 Second Task Triggered: Send Request to Please verify again.

[0120] 4.5 If both tasks pass the verification, the work order will be successfully triggered.

[0121] 5. Batch Arrangement Submodule: When there are multiple work order numbers, they are sent to the task trigger execution submodule in sequence. The waiting time between work orders is preset (e.g., 2 seconds). The success or failure status of each work order is captured, and finally the total number of successes, failures and a list of failed work orders are summarized and output.

[0122] In one embodiment, such as Figure 2 As shown, Figure 2 This is an example diagram of a task batch triggering method provided in an embodiment of this application.

[0123] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0124] The task batch triggering device provided in the embodiments of this application is described below. The task batch triggering device described below can be referred to in correspondence with the task batch triggering method described above.

[0125] like Figure 3 As shown, this application provides a task batch triggering device 200, the device comprising:

[0126] The message parsing module 201 is used to parse user messages when it receives user messages sent by the instant messaging platform. If the parsing identifies a business work order identifier, it extracts the business work order list and environmental parameters.

[0127] The task group determination module 202 is used to determine the business type corresponding to each work order in the business work order list, and query the task group identifier corresponding to each work order based on the environmental parameters and the business type of each work order.

[0128] The work order execution module 203 is used to determine at least one calling task corresponding to the task group identifier of each work order, and execute each calling task for the work order to obtain the work order execution result.

[0129] The result feedback module 204 is used to summarize the execution results of each work order, obtain the overall execution result, generate a natural language response based on the overall execution result, and return the natural language response to the instant messaging platform.

[0130] In the above embodiments, by receiving and automatically parsing user messages from the instant messaging platform, identifying and extracting the business work order list and environmental parameters, a series of tedious operations such as manual login, manual task search, and manual parameter filling can be replaced, effectively reducing the risk of operational errors caused by manual intervention. For each work order's business type and environmental parameters, the corresponding task group identifier is accurately located, and at least one invocation task within that group is executed sequentially, ensuring that independent task groups in different environments are correctly scheduled, thereby avoiding environmental confusion and incorrect task execution caused by manual verification. Finally, the corresponding invocation task is automatically executed, and the execution results are summarized and fed back to the instant messaging platform in natural language, thus achieving fully automated processing of multiple work order tasks.

[0131] In one embodiment, the message parsing module includes:

[0132] The identifier recognition submodule is used to match and identify business work order identifiers containing preset prefixes in user messages based on preset regular expressions;

[0133] The information extraction submodule is used to generate a list of business work orders based on the identified business work order identifier containing a preset prefix if a business work order identifier containing a preset prefix is ​​matched and identified in the user message, and to extract the environmental parameter field value from the user message to obtain the environmental parameters.

[0134] In one embodiment, the task group determination module includes:

[0135] The type query submodule is used to query the business type corresponding to each work order in the preset business type mapping library for each work order.

[0136] The first determination submodule is used to determine the business type of the work order if the query is successful.

[0137] The second determination submodule is used to obtain the semantic description of the work order if the query fails, and to calculate the similarity between the semantic description and each business type in the business type mapping library, and to take the business type with the highest similarity to the semantic description as the business type of the work order.

[0138] In one embodiment, the task group determination module includes:

[0139] The identifier reading submodule is used to obtain a preset configuration table and read at least one target task group identifier corresponding to the environment parameters from the preset configuration table.

[0140] The identifier query submodule is used to query the target task group identifier that matches the business type of each work order in each target task group identifier, and determine the queried target task group identifier as the task group identifier of the work order.

[0141] In one embodiment, the work order execution module includes:

[0142] The table retrieval submodule is used to retrieve the preset configuration table;

[0143] The task determination submodule is used to determine the tasks to be called under the task group identifier of the work order and the order in which the tasks are called, in the preset configuration table.

[0144] The task execution submodule is used to execute each called task in sequence according to the task invocation order, and record the execution results of each called task to generate the work order execution result.

[0145] In one embodiment, the result feedback module includes:

[0146] The quantity statistics submodule is used to extract the execution status of each work order from the execution results of each work order, and to count the number of work orders corresponding to different execution statuses.

[0147] The work order filtering submodule is used to filter out failed work orders from each work order and form a list of failed work orders.

[0148] The results generation submodule is used to generate the overall execution results based on the execution results of each work order, the number of work orders corresponding to different execution states, and the list of failed work orders.

[0149] In one embodiment, the result feedback module includes:

[0150] The formatting adjustment submodule is used to convert the various contents in the overall execution result into structured information and adjust the display format of the structured information;

[0151] The visualization submodule is used to return the adjusted structured information to the instant messaging platform so that the platform can visualize the adjusted structured information.

[0152] The division of modules in the above-described task batch triggering device is merely illustrative. In other embodiments, the task batch triggering device can be divided into different modules as needed to complete all or part of its functions. Each module in the above-described task batch triggering device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0153] In one embodiment, this application also provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the task batch triggering method as described in any of the above embodiments.

[0154] In one embodiment, this application also provides a computer device storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the task batch triggering method as described in any of the above embodiments.

[0155] Indicatively, such as Figure 4 As shown, Figure 4 This is a schematic diagram of the internal structure of a computer device 300 provided in an embodiment of this application. The computer device 300 can be provided as a server. (Refer to...) Figure 4 The computer device 300 includes a processing component 302, which further includes one or more processors, and memory resources represented by memory 301 for storing instructions, such as application programs, that can be executed by the processing component 302. The application programs stored in memory 301 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 302 is configured to execute instructions to perform the task batch triggering method of any of the above embodiments.

[0156] The computer device 300 may also include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate on an operating system stored in memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or similar.

[0157] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0158] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a…" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. In this document, the singular forms "a," "an," and "the" may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising / including” or “having” specify the presence of the stated features, wholes, steps, operations, components, parts or combinations thereof, but do not exclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0159] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0160] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for batch triggering tasks, characterized in that, The method includes: When a user message is received from an instant messaging platform, the user message is parsed. If a business work order identifier is identified during parsing, the business work order list and environmental parameters are extracted. Determine the business type corresponding to each work order in the business work order list, and query the task group identifier corresponding to each work order based on the environment parameters and the business type of each work order. For each work order, determine at least one calling task corresponding to the task group identifier of the work order, and execute each calling task for the work order to obtain the work order execution result; The execution results of each work order are summarized to obtain the overall execution result. Based on the overall execution result, a natural language response is generated and then returned to the instant messaging platform.

2. The task batch triggering method according to claim 1, characterized in that, The process of parsing the user message involves extracting a business work order list and environmental parameters if a business work order identifier is identified. According to a preset regular expression, the service work order identifier containing a preset prefix is ​​matched and identified in the user message; If a business work order identifier containing a preset prefix is ​​matched and identified in the user message, a business work order list is generated based on the identified business work order identifier, and the environmental parameter field value is extracted from the user message to obtain the environmental parameters.

3. The task batch triggering method according to claim 1, characterized in that, Determining the business type corresponding to each work order in the business work order list includes: For each work order in the aforementioned work order, the corresponding business type is queried in the preset business type mapping library; If the query is successful, the business type found will be used as the business type of the work order. If the query fails, the semantic description of the work order is obtained, and the similarity between the semantic description and each business type in the business type mapping library is calculated. The business type with the highest similarity to the semantic description is taken as the business type of the work order.

4. The task batch triggering method according to claim 1, characterized in that, The step of querying the task group identifier corresponding to each work order based on the environmental parameters and the business type of each work order includes: Obtain a preset configuration table, and read at least one target task group identifier corresponding to the environment parameters from the preset configuration table; For each work order, query the target task group identifier that matches the business type of the work order from each target task group identifier, and determine the queried target task group identifier as the task group identifier of the work order.

5. The task batch triggering method according to claim 1, characterized in that, The process of determining at least one calling task corresponding to the task group identifier of the work order, executing each calling task for the work order, and obtaining the work order execution result includes: Get the preset configuration table; The task to be invoked and the task invocation order under the task group identifier belonging to this work order are determined in the preset configuration table; Based on the task invocation order, each invocation task is executed sequentially for the work order, and the execution result of each invocation task is recorded to generate the work order execution result.

6. The task batch triggering method according to claim 1, characterized in that, The overall execution result is obtained by summarizing the execution results of each work order, including: Extract the execution status of each work order from the execution results of each work order, and count the number of work orders corresponding to different execution statuses. Failed work orders with a failed execution status are filtered out from each work order to form a list of failed work orders; The overall execution result is generated based on the execution results of each work order, the number of work orders corresponding to different execution states, and the list of failed work orders.

7. The task batch triggering method according to any one of claims 1 to 6, characterized in that, The generation of a natural language response based on the overall execution result includes: The contents of the overall execution result are converted into structured information, and the display format of the structured information is adjusted. The adjusted structured information is returned to the instant messaging platform so that the platform can visualize the adjusted structured information.

8. A task batch triggering device, characterized in that, The device includes: The message parsing module is used to parse user messages sent by the instant messaging platform when it receives such messages. If the parsing identifies a business work order identifier, it extracts the business work order list and environmental parameters. The task group determination module is used to determine the business type corresponding to each work order in the business work order list, and to query the task group identifier corresponding to each work order based on the environmental parameters and the business type of each work order. The work order execution module is used to determine at least one calling task corresponding to the task group identifier of each work order, and execute each calling task for the work order to obtain the work order execution result. The result feedback module is used to summarize the execution results of each work order to obtain the overall execution result, and based on the overall execution result, generate a natural language response and return the natural language response to the instant messaging platform.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the task batch triggering method as described in any one of claims 1 to 7.

10. A computer device, characterized in that, include: One or more processors, and memory; The memory stores computer-readable instructions, which, when executed by the one or more processors, perform the steps of the task batch triggering method as described in any one of claims 1 to 7.

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