Intelligent agent-based service resource scheduling processing method and system, and electronic device

Abstract

Embodiments of the present application provide a kind of service resource scheduling processing method, system and electronic equipment based on agent. It is related to artificial intelligence technical field, the method comprises: the service data of service area is input into agent and is handled, and the service state identification result of service area is output;First prompt information is sent to client based on service state identification result, and first prompt information is used to guide client to send report request to server, and report work order is used for client to apply for the service resource scheduling service area to audit end;Report request sent by client is received, and report work order is generated based on service data using agent;Report work order is sent to client. The report work order of the present application is automatically generated by real-time analysis of service data using agent, which improves the processing efficiency of data reporting, and gains time window for resource scheduling, and solves the technical problem that the reporting efficiency of abnormal data in related technology is not ideal.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a service resource scheduling and processing method, system and electronic device based on intelligent agents. Background Technology

[0002] When workload changes in a service area, it's necessary to report the pressure on that area to receive adjustments to regional resource strategies, thereby ensuring the quality and efficiency of service task execution. However, due to a lack of proactive detection and real-time early warning capabilities for abnormal states in the relevant technologies, when sudden situations cause a surge in regional tasks or resource bottlenecks, automatic identification and proactive alerts are not possible. This causes technical personnel to miss the window for resource intervention, leading to worsening problems. Furthermore, the manual reporting process is lengthy and cumbersome. From anomaly detection to completion, multiple steps are required across various operating platforms, relying on manual data collection and verification, and repeated filling and communication between different platforms. Frequent interruptions not only result in a poor user experience but also lead to omissions or errors due to fatigue or negligence, resulting in unsatisfactory reporting efficiency. Summary of the Invention

[0003] This application provides a service resource scheduling and processing method, system, and electronic device based on intelligent agents to alleviate or solve the technical problem of unsatisfactory reporting efficiency of abnormal data in related technologies.

[0004] In a first aspect, embodiments of this application provide a service resource scheduling and processing method based on intelligent agents, applied to a server, including: The service data of the service area is input into the intelligent agent for processing, and the service status identification result of the service area is output. The intelligent agent is trained based on historical service data and historical service status identification results. The service status identification result represents the status identification result of the service resource that changes to meet the predetermined conditions so that the service task executed in the service area can meet the predetermined conditions. Based on the service status identification result, a first prompt message is sent to the client. The first prompt message is used to guide the client to send a reporting request to the server. The reporting request is used to request the server to generate a reporting work order corresponding to the service data. The reporting work order is used by the client to apply to the review end for scheduling service resources for the service area. Upon receiving the reporting request sent by the client, the intelligent agent generates a reporting work order based on the service data, and the reporting work order includes the service status identification result; Send the work order to the client.

[0005] In the embodiments provided in this application, before inputting the service data of the service area into the intelligent agent for processing, the method further includes: Receive target data sent by the client; the target data is selected by the client from a page of a predetermined type. Based on the fact that the target data is abnormal, a second prompt message is sent to the client. The second prompt message is used to prompt the client to send a submission instruction to the server to submit the target data. The system receives the reporting instruction from the client and uses the target data as the service data.

[0006] In the embodiments provided in this application, the method further includes: Based on the parameter type of the target data and the predetermined type of the page, the semantic features of the target data are identified; Based on the semantic features, the target data is determined to be abnormal data.

[0007] In the embodiments provided in this application, the step of inputting service data of the service area into an intelligent agent for processing and outputting the service status identification result of the service area includes: Based on at least one of the following data: page identifier, region identifier, and timestamp of the service data, output the intent recognition result corresponding to the reporting request. The page identifier is the identifier of the page displaying the service data in the client, and the region identifier represents the identifier of the service region that generated the service data. Based on the intent recognition result being a predetermined intent type, the service data is input into the intelligent agent for processing, and the service status recognition result is output. The predetermined intent type is used to represent the intent to report the pressure represented by the service data to the review end.

[0008] In the embodiments provided in this application, sending a first prompt message to the client based on the service status identification result includes: Based on a predefined corpus template, a first prompt message is generated, which includes the service status identification result and a region identifier, wherein the region identifier represents the identifier of the service region that generated the service data. Send the first prompt message to the client.

[0009] In the embodiments provided in this application, before receiving the target data sent by the client, the method further includes: Obtain the duration of the client's stay on the page of the predetermined type; Based on the fact that the dwell time exceeds a predetermined time threshold, a third prompt message is sent to the client, which prompts the client to select the target data on the current page.

[0010] In the embodiments provided in this application, the service status identification result is obtained by the intelligent agent based on abnormal service data in the local data of the server; the step of sending a first prompt message to the client based on the service status identification result includes: Based on the service status identification result conforming to the predetermined status, the first prompt information is sent to the client, whereby the predetermined status indicates that the demand for the service resources has reached the predetermined level.

[0011] In the embodiments provided in this application, the work order includes a scheduling type, which represents the type of service resource scheduled in the service area. The step of generating a work order based on the service data using the intelligent agent includes: Using the intelligent agent, the state type of the service area is determined based on the service data, and the state type represents the demand type of service resources in the service area on different service indicators; Based on the state type, the scheduling type of the service area scheduling service resources is determined; The scheduling type is pre-filled into the work order.

[0012] Secondly, embodiments of this application provide a service resource scheduling method based on intelligent agents, applied to a client, including: The client receives a first prompt message from the server. The first prompt message is used to guide the client to send a submission request to the server. The submission request is used to request the server to generate a submission work order corresponding to the service data. The submission work order is used by the client to apply to the review end for service resources to be scheduled for the service area. The system sends the reporting request to the server and receives the reporting work order sent by the server. The reporting work order includes a service status identification result. The service status identification result is obtained by inputting service data of the service area into an intelligent agent for processing. The intelligent agent is trained based on historical service data and historical service status identification results. The service status identification result represents the status identification result of the service resource that changes so that the service task executed in the service area can meet predetermined conditions. Upon receiving the second confirmation information, the submitted work order is submitted to the review end after confirmation. The second confirmation information is used to indicate the information pre-filled in the submitted work order.

[0013] In the embodiments provided in this application, before receiving the first prompt information sent by the receiving server, the method further includes: On the page of the predefined type, select the target data and send the target data to the server; Receive and display the second prompt information sent by the server, the second prompt information being used to prompt the client to send a submission instruction to the server to submit the target data; A submission instruction is sent to the server, which uses the target data as the service data based on the instruction.

[0014] In the embodiments provided in this application, the method further includes: Send the entry time of the client to the server, and the entry time is used by the server to determine the duration of the client's stay on the page of the predetermined type; The client receives a third prompt message sent by the server, which prompts the client to select target data on the current page.

[0015] Thirdly, embodiments of this application provide a service resource scheduling and processing system based on intelligent agents, including: a server and a client. The server is used to input service data of the service area into an intelligent agent for processing and output the service status identification result of the service area. The intelligent agent is trained based on historical service data and historical service status identification results. The service status identification result represents the status identification result of service resources that change to meet predetermined conditions so that the service tasks executed in the service area can meet predetermined conditions. Based on the service status identification result, a first prompt message is sent to the client. The first prompt message is used to guide the client to send a reporting request to the server. The reporting request is used to request the server to generate a reporting work order corresponding to the service data. The reporting work order is used by the client to apply to the review end for scheduling service resources for the service area. The client is used to send the submission request to the server; The server is configured to use the intelligent agent to generate a work order based on the service data, the work order including the service status identification result; and send the work order to the client. The client is used to receive the second confirmation information and submit the confirmed work order to the review end. The second confirmation information is used to indicate the information pre-filled in the work order.

[0016] Fourthly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor implements any of the methods of embodiments of this application when executing the computer program.

[0017] Fifthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method of any one of the embodiments of this application.

[0018] Sixthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements any of the methods described in the embodiments of this application.

[0019] Based on the agent-based service resource scheduling and processing method described in the first aspect above, this application has at least the following beneficial effects or advantages: This application's embodiments introduce an intelligent agent trained based on historical service data and historical service status identification results, enabling quantitative analysis of service status identification results. This improves the accuracy and response efficiency of stress identification, and saves time in the reporting process by automating work order submissions, providing ample time for resource scheduling to alleviate delivery pressure. Simultaneously, it avoids subjective judgment by the client regarding whether service data needs stress reporting, ensuring the objectivity and reliability of submitted work orders. By improving the efficiency of work order generation, it enhances the accuracy and response efficiency of delivery stress identification, thereby effectively guaranteeing the quality of service task fulfillment.

[0020] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application, it can be implemented according to the contents of the specification. In order to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0021] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments according to this application and should not be construed as limiting the scope of this application.

[0022] Figure 1 A flowchart of a service resource scheduling method based on an agent according to an embodiment of this application is shown; Figure 2 A schematic diagram of a service resource scheduling processing method based on intelligent agents according to an embodiment of this application is shown; Figure 3 A flowchart of another agent-based service resource scheduling method according to an embodiment of this application is shown; Figure 4 A schematic diagram of the agent-based service resource scheduling and processing system according to an embodiment of this application is shown. Figure 5A flowchart of an agent-based service resource scheduling and processing apparatus according to an embodiment of this application is shown; Figure 6 A flowchart of yet another agent-based service resource scheduling and processing device according to an embodiment of this application is shown; Figure 7 A block diagram of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0023] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the concept or scope of this application. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0024] To facilitate understanding of the technical solutions of the embodiments of this application, the relevant technologies of the embodiments of this application are described below. The following relevant technologies are optional solutions and can be combined with the technical solutions of the embodiments of this application in any way, and all of them fall within the protection scope of the embodiments of this application.

[0025] It should be noted that the application scenarios or examples provided in the embodiments of this application are for ease of understanding, and the embodiments of this application do not specifically limit the application of the technical solutions. In addition, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0026] Service data reflects service levels, but service resource scheduling does not immediately alleviate pressure; there is a certain delay. Therefore, resource scheduling requires a time window to allow time for easing service pressure. However, the lengthy reporting time in related technologies consumes this time window, preventing timely relief of service pressure and further exacerbating the problem.

[0027] The technical solution of this application and how it solves the aforementioned technical problems are described in detail below with specific embodiments. The listed specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0028] Figure 1 A flowchart of the agent-based service resource scheduling method according to an embodiment of this application is shown, such as... Figure 1 As shown, the optional execution entity is the server, and the method may include steps S101 to S104.

[0029] Step S101: Input the service data of the service area into the intelligent agent for processing, and output the service status identification result of the service area; the intelligent agent is trained based on historical service data and historical service status identification results, and the service status identification result represents the status identification result of the service resources that change so that the service tasks executed in the service area can meet the predetermined conditions. Step S102: Based on the service status identification result, send the first prompt information to the client. The first prompt information is used to guide the client to send a report request to the server. The report request is used to request the server to generate a report work order corresponding to the service data. The report work order is used by the client to apply to the review end for scheduling service resources for the service area. Step S103: Upon receiving a reporting request from the client, the intelligent agent generates a reporting work order based on the service data. The reporting work order includes the service status identification result. Step S104: Send a work order to the client.

[0030] In this embodiment, a training dataset is pre-constructed using historical service data and historical service status identification results of the service area. This dataset is then used to train the agent, enabling it to output corresponding service status identification results based on real-time service data. The aforementioned service data can be of various service types, including delivery data and data processing volume at different edge nodes in a cloud computing service. Service resources and service tasks correspond to service types; delivery data corresponds to delivery resources and delivery tasks. For the data processing volume of edge nodes, there are corresponding computing resources and computing tasks, which can be specifically set according to service requirements.

[0031] The pre-defined conditions are determined based on the service type of the service task. For delivery-type service tasks, pre-defined conditions may include meeting timeliness and delivery requirements within the service area. For cloud computing-type service tasks, pre-defined conditions may include computation time and edge node computing resource consumption. Delivery requirements indicate that current service resources can ensure the service task is accurately delivered to the designated location according to pre-defined requirements. Service resources are limited within a certain timeframe, while demand for service resources fluctuates. Changes in service resources represent the changes made to the service resources to execute the service task and meet the pre-defined conditions. In practical applications, service data for the service area is input into the trained agent in real time. After analysis and calculation, the agent outputs the service status identification result for the service area. Based on the service status identification result output by the agent, the server sends a first prompt message to the client, guiding the client to initiate a reporting request to the server. The first prompt message can be included in the page data, which is rendered on the client and may be displayed as a pop-up. The purpose of the reporting request is to prompt the server to generate a reporting work order matching the current service data, providing a basis for the client to apply for service resource scheduling from the review end. After receiving the submission request from the client, the server invokes the intelligent agent to automatically generate a submission work order based on the service data of the service area. The generated standard submission work order is then sent to the client for subsequent submission to the review end for resource scheduling application.

[0032] Through the above processing, the intelligent agent trained on historical data avoids the limitations of subjective experience-based judgment, providing objective data support for resource scheduling requests. This prevents resource redundancy or capacity gaps caused by subjective misjudgments, improving the accuracy of resource scheduling. Clients no longer need to manually organize data or fill it into work orders according to rules; they only need to initiate a submission request based on the initial prompt, reducing the time spent on manual data entry and shortening the response cycle from pressure detection to submission. Furthermore, for the review end, the reliability of the submitted work orders is improved because the pressure level is determined by the intelligent agent's model. The automatically generated work orders contain quantitative indicators of service status identification results, helping the review end determine the required resource scheduling range and response sequence for the service area, reducing communication costs in the review process, and further accelerating the implementation of service resource scheduling to ensure the quality of service task fulfillment.

[0033] In the embodiments provided in this application, before inputting service data of the service area into the intelligent agent for processing, the method further includes: Receive target data sent by the client; the target data is selected by the client from a page of a predefined type. Based on the fact that the target data is abnormal, a second prompt message is sent to the client. The second prompt message is used to prompt the client to send a submission instruction to the server to submit the target data. Receive the client's submission instruction and use the target data as service data.

[0034] In this embodiment, the server receives target data manually selected by the client from a predetermined type of page. The target data represents data that the user is interested in or data that they believe may pose a service resource burden. The predetermined type of page is a page used to display real-time service data, and can be a list-style page or a chart-style page. The method of selecting the target data can be various, such as selection by highlighting or box selection. The target data can include one or a set of parameter values, and a set of parameters can include multiple parameters of the same or different types. The server performs anomaly identification on the received target data. After determining that it belongs to abnormal delivery data, it sends a second prompt message to the client, prompting the client to send a reporting instruction to the server. This reporting instruction is used to report the abnormal target data to the server. The server, upon receiving the reporting instruction from the client, treats the target data as service data, inputs it into the intelligent agent, and the intelligent agent analyzes and outputs the corresponding service status identification result, and automatically generates and issues a work order.

[0035] By adding a trigger path for clients to actively select target data, and providing an interface for clients to proactively initiate specific data reporting, even if the system determines that the service status represented by the data has not reached the predetermined state, users can still automatically report it by focusing on the target data. Based on the user's selection of target data on the page, combined with the server's determination that the target data is abnormal, invalid data interference can be filtered out, and normal data will not be reported, ensuring that the service data input to the intelligent agent is indeed abnormal. This can meet users' personalized reporting needs for abnormal data and achieve comprehensive coverage of abnormal service data reporting triggers.

[0036] like Figure 2 As shown, after the target data is selected through a predefined selection process, the selected area can be marked with highlight or shadow. After the server confirms that the target data is abnormal, it sends a second prompt message to the client. The client renders the page data including the second prompt message, and can optionally display the second prompt message as a tab or pop-up. The client receives the user's selection operation, determines that a reporting instruction is generated for the target data, and sends the reporting instruction to the server. Based on the reporting instruction, the server can perceive the user's reporting request for the service data, input the service data into the intelligent agent, and start the automatic work order reporting process.

[0037] In some embodiments provided in this application, the method may further include: Based on the parameter types of the target data and the predefined types of the page, identify the semantic features of the target data; Based on semantic features, the target data was identified as anomalous data.

[0038] In this embodiment, the server receives target data selected by the client from a page of a predetermined type. The predetermined page type represents the scenario information of the page displayed by the client, and can be used to provide contextual information about the tasks associated with the target data. The page type can provide scenario information for the semantic analysis of the target data, avoiding data semantic judgments that are divorced from the actual scenario, and helping to improve the accuracy of identifying whether the target data is abnormal data. The parameter type represents the indicator category or attribute characteristics of the target data, and can be a classification label for the target data, used to clarify the parameter representation meaning of the target data. Through the parameter type, it can be known that the target data represents the performance dimension of the service task in the service area.

[0039] By generating semantic features from target data, qualitative analysis of the target data can be achieved, identifying the data as anomalous. Compared to quantitative analysis based on predetermined data, qualitative semantic analysis is more aligned with the service scenario. By combining scenario constraints of page type and attribute constraints of parameter type, the original numerical and textual information of the target data is transformed into semantic features with qualitative meaning in the service scenario, reducing misjudgment of anomalous data in the scenario and avoiding invalid reports.

[0040] In this embodiment, natural language processing (NLP) recognition technology can be optionally employed. Algorithms perform semantic analysis on the text information, parameter types, and page types of the target data. Combined with the contextual information of the page type and parameter types, semantic understanding of the target data within the service scenario is achieved. For example, in a delivery scenario, if a user selects "order density > 150 orders / km²" as the target data on a real-time heatmap page, and considering that the page type is a real-time heatmap and the parameter type is order density, the semantic features are determined to be a surge in order volume or insufficient delivery resources. The server then determines that this target data is abnormal.

[0041] In some embodiments provided in this application, service data of the service area is input into an intelligent agent for processing, and the service status identification result of the service area is output, including: Based on at least one of the following data: page identifier, region identifier, and timestamp of service data, output the intent recognition result corresponding to the submission request. The page identifier is the identifier of the page displaying the service data in the client, and the region identifier is the identifier of the service region that generated the service data. Based on the intent recognition result being a predetermined intent type, service data is input into the intelligent agent for processing, and service status recognition result is output. The predetermined intent type is used to represent the intent to report the pressure represented by the service data to the review end.

[0042] In the embodiments provided in this application, the client displays a unique page identifier for the service data. Within the service scenario to which the associated data belongs, the region identifier is the service area identifier that generated the service data, which can be used to locate the geographic and service range of the data. The timestamp is used to associate the service data with a specific time scenario and can be the time the service data was collected. The server analyzes the intent corresponding to the submission request based on at least one of the collected page identifier, region identifier, and timestamp, and outputs an intent recognition result. The intent recognition result characterizes the type of service data the client expects to submit. The predetermined intent type is an intent to report service resource pressure to the review end and request service resource scheduling, which can be optionally set as a pressure reporting type to trigger the service status recognition result identification process of the intelligent agent. The service status recognition result can be divided into multiple levels according to a predetermined grading threshold to qualitatively characterize the current service resource pressure.

[0043] Through the above processing, relying on the contextual constraints of at least one of the following data: page identifier, region identifier, and timestamp, the server can identify the intent type corresponding to the reporting request and distinguish different reporting intents. The agent is trained based on historical service status recognition results and historical service data. Its function is to automatically identify the pressure level, filter invalid reporting requests, avoid redundant analysis of irrelevant data by the agent, reduce computing power consumption, and improve the accuracy and response efficiency of the agent's service status recognition.

[0044] In some embodiments provided in this application, sending a first prompt message to the client based on the service status identification result may include the following steps: Based on a predefined corpus template, a first prompt message is generated, which includes the service status identification result and the region identifier. The region identifier indicates the identifier of the service region that generated the service data. Send the first notification message to the client.

[0045] In this embodiment, the server pre-configures a corpus template matching the delivery pressure reporting scenario. The template includes placeholders for core information such as pressure level and region identifier. Based on the service status identification result output by the intelligent agent and the region identifier of the corresponding service area, the server fills the placeholders in the pre-configured corpus template with data to generate a first prompt message. The first prompt message includes the region identifier and the service status identification result, clearly indicating the specific location and severity of the service pressure, facilitating the review end to quickly locate the problem area, clarify the necessity of resource scheduling and the response order, and reduce the cost of secondary confirmation of the service status.

[0046] Through the above processing, prompt information is generated based on a pre-defined corpus template, avoiding problems such as chaotic expression and missing key information caused by manual subjective form filling. This ensures that the prompt information received by the client has a unified structure and includes key information of the work order, improving the efficiency of the client and the review end in understanding the pressure report content of the service status.

[0047] For example, the corpus template and the result of generating the first prompt message are as follows: the predetermined corpus template is "The current service status identification result of xxx is level x. Do you want to generate a work order for you?", and the first prompt message after filling in the information is "The current service status identification result of location A is level 3. Do you want to generate a work order for you?". In the embodiments provided in this application, before receiving the target data sent by the client, the method further includes: Get the duration of the client's stay on the page of the pre-defined type; If the dwell time exceeds the predetermined time threshold, a third prompt message is sent to the client. The third prompt message is used to prompt the client to select target data on the current page.

[0048] This embodiment uses a user behavior perception mechanism based on page dwell time. The server monitors in real time the duration the client spends on a predetermined type of page. This dwell time represents the duration the user browses the data on the current page, and to a certain extent, it characterizes the user's level of attention to the data displayed on the page. The server compares the collected dwell time with a preset time threshold. If the dwell time exceeds the preset time threshold, it is determined that the user is deeply interested in the data on the current page. The longer the dwell time, the more likely the user is interested in a particular service data and the more likely there is a pressure to report service status. The server actively guides the user to select data, prompting the user that there is a function to automatically generate a report work order by selecting data. The server sends a third prompt message to the client, prompting the user to select target data on the current page, providing a data source for the reporting process.

[0049] Through the above processing, based on behavioral perception of page dwell time, the system identifies user attention behavior towards predetermined types of page data. By proactively sending third-party prompts, it guides users to select target data, uncovers potential user reporting needs, and improves the coverage of client-triggered automated reporting. For users unfamiliar with the reporting process, the system proactively provides instructions on selecting target data, reducing the user's cognitive cost and preventing them from switching to manual reporting due to not being able to find the automatic reporting entry point, thus improving the convenience of client-side automated stress reporting.

[0050] In some embodiments provided in this application, the service status identification result is obtained by the intelligent agent based on abnormal service data in the local data of the server; sending a first prompt message to the client based on the service status identification result may include the following steps: Based on the service status identification result meeting the predetermined status, the first prompt message is sent to the client, indicating that the demand for service resources has reached the predetermined level.

[0051] In this embodiment, the data source for the intelligent agent's analysis originates from abnormal service data stored locally on the server. This locally stored data includes service data monitored by the server or data reported by other endpoints associated with the server. This service data is detected as abnormal data by the server in the background, without requiring the client to determine it through a pre-defined selection method. A predetermined pressure level is set as the warning trigger condition. When the service status identification result output by the intelligent agent matches the predetermined state, it indicates that the demand for service resources has reached the predetermined state. The demand level represents the pressure level of service resources, and the server sends a first prompt message to the client, guiding the user to initiate a reporting request. If the service status identification result does not match the predetermined state, the warning process is not triggered.

[0052] Through the above processing, the server is provided with the function of proactively prompting the initiation of anomaly reporting in the background, avoiding the client from missing the reporting of abnormal data and reducing the workload of manual anomaly investigation. Based on the automatic collection of local abnormal data on the server and the identification of intelligent agent service status, and using the predetermined status as the condition for triggering reporting reminders, the server can identify service areas with high service status pressure in real time in the background and proactively push warnings. This eliminates the limitation of requiring manual discovery of anomalies before reporting, effectively avoiding the omission of abnormal data due to human negligence, and helping to ensure the stable operation of service tasks.

[0053] For example, if the predetermined pressure level is set to level 2, the server will only push the first prompt information to the client when the agent outputs a pressure level of level 3 or higher.

[0054] In some embodiments provided in this application, the work order includes a scheduling type, which represents the type of service resource scheduled in the service area. An intelligent agent generates the work order based on service data, including: Using intelligent agents, the state type of a service area is determined based on service data. The state type represents the type of demand for service resources in the service area on different service indicators. Based on the status type, the scheduling type of the service area scheduling service resources is determined; The scheduling type is pre-filled into the work order. In this embodiment, different service indicators represent the quantitative values ​​of service resources in different performance dimensions. Service indicators can be the number of delivery orders or delivery time, etc. Different scheduling types are required for different status types. Taking delivery service as an example, for scheduling when there is pressure on the number of orders in the service area, in order to ensure that the order data volume meets the demand, the scheduling type is to extend the delivery time. For scheduling when there is pressure on the delivery time in the service area, the number of orders is reduced by reducing the gain resources in the service area, thereby ensuring that the delivery time in the area meets the predetermined conditions.

[0055] Through the above processing, the intelligent agent integrates the determined state type, the matched scheduling type, and information such as the area identifier and pressure level, providing the client with a complete reporting work order. This reduces the subjective judgment required by the client and facilitates the client to submit the information to the review end for execution of resource scheduling decisions.

[0056] Figure 3 A flowchart of the agent-based service resource scheduling method according to an embodiment of this application is shown, such as... Figure 3 As shown, the optional execution subject is the client, and the method may include steps S301, S302 and S303.

[0057] Step S301: Receive the first prompt information sent by the server. The first prompt information is used to guide the client to send a submission request to the server. The submission request is used to request the server to generate a submission work order corresponding to the service data. The submission work order is used by the client to apply to the review end for service resources to be scheduled for the service area. Step S302: Send a reporting request to the server and receive a reporting work order sent by the server. The reporting work order includes a service status identification result. The service status identification result is obtained by inputting the service data of the service area into the intelligent agent for processing. The intelligent agent is trained based on historical service data and historical service status identification results. The service status identification result represents the status identification result of the service resources that change so that the service tasks executed in the service area can meet the predetermined conditions. Step S303: Receive the second confirmation information and submit the confirmed submission work order to the review end. The second confirmation information is used to indicate and confirm the information pre-filled in the submission work order.

[0058] In this embodiment, the client receives a first prompt message pushed by the server, which guides the client to initiate a reporting request. Following the guidance of the first prompt message, the client sends a reporting request to the server. The purpose of the request is to prompt the server to generate a reporting work order matching the current service data, providing a basis for subsequent application to the review end for service resource scheduling. After responding to the request, the server generates a reporting work order based on agent analysis and feeds it back to the client. The service status identification result included in the work order is calculated by the server inputting service data of the service area into the agent. The client receives and verifies the pre-filled information in the reporting work order. After obtaining a second confirmation message from the user, the client submits the confirmed reporting work order to the review end to apply for service resource scheduling.

[0059] Through the above processing, the client only needs to perform simple operations such as initiating a submission request, confirming the pre-filled content in the submission form, and submitting for review. This eliminates the need for subjective data analysis and manual form filling, avoiding delays caused by incorrect information, improving the first-time pass rate of the submission process, and lowering the operational threshold for the client. The reduced time from receiving service data pressure warnings to submitting the work order to the review end helps accelerate the response speed of service resource scheduling, greatly simplifying the submission process and improving its timeliness.

[0060] In some embodiments provided in this application, before receiving the first prompt information sent by the server, the method further includes: On the page for the pre-order type, select the target data and send the target data to the server; Receive and display the second prompt message sent by the server. The second prompt message is used to prompt the client to send a submission instruction to the server to submit the target data. A submission command is sent to the server, which uses the target data as service data based on the instructions in the submission command.

[0061] In this embodiment, the client selects target data of interest on a predetermined type page and sends the target data to the server. The server performs anomaly detection on the target data sent by the client, determining it to be abnormal to avoid redundant reporting of irrelevant data. The server pushes a second prompt to the client, indicating that the client can initiate a reporting command. The client receives and responds to the second prompt, sending a reporting command to the server, instructing the server to input service data into the intelligent agent for analysis, thereby initiating an automated reporting work order generation process. The target data selected by the client can be directly transmitted to the server, and the reporting command can directly specify this data as service data.

[0062] Through the above processing, the target data flows automatically between the client and server without the need for secondary data processing or format conversion on the client side. There is no need to navigate to a dedicated reporting interface or manually fill in redundant information such as data source and anomaly description. This reduces manual intervention, lowers the probability of operational errors, and aligns the operation path with users' daily data browsing habits, reducing cognitive costs and facilitating efficient response to pressure reporting.

[0063] In the embodiments provided in this application, the method further includes: Send the entry time of the page for the reservation type to the server. The entry time is used by the server to determine the duration of the client's stay on the page for the reservation type. Receive the third prompt message sent by the server. The third prompt message is used to prompt the client to select target data on the current page.

[0064] In the embodiments provided in this application, when a client enters a page of a predetermined type, the entry time is automatically sent to the server. This entry time is used by the server to calculate the client's dwell time on the page. Based on the entry time reported by the client and the current time, the server calculates the client's dwell time on the predetermined type of page. If the dwell time exceeds a predetermined threshold, it is determined that the user is interested in the page data and there may be a potential need to generate a work order. The server then sends a third prompt to the client, suggesting that the target data can be manually selected on the current page.

[0065] Through the above processing, the client no longer needs to manually search for a submission entry point or determine whether a submission is possible. The server, sensing a potential submission need based on the duration of client dwell time, proactively pushes a third-party prompt to guide the selection of target data, reducing the client's cognitive burden regarding the submission process. The prompt is triggered on the client's currently viewed page of the designated type, eliminating the need to navigate to other dedicated submission interfaces. Target data selection, submission order initiation, and confirmation can all be completed on the current page, significantly improving the convenience of proactive submission.

[0066] Figure 4 A schematic diagram of the agent-based service resource scheduling and processing system according to an embodiment of this application is shown, as follows: Figure 4 As shown, it includes: server-side 401 and client-side 402. Server 401 is used to input service data of the service area into the intelligent agent for processing and output the service status identification result of the service area. The intelligent agent is trained based on historical service data and historical service status identification results. The service status identification result represents the status identification result of service resources that change to meet predetermined conditions so that the service tasks executed in the service area can meet predetermined conditions. Based on the service status identification result, a first prompt message is sent to client 402. The first prompt message is used to guide client 402 to send a reporting request to server 401. The reporting request is used to request server 401 to generate a reporting work order corresponding to the service data. The reporting work order is used by client 402 to apply to the review end for scheduling service resources for the service area. Client 402 is used to send a submission request to server 401; Server 401 is used to generate a work order based on service data using an intelligent agent. The work order includes the service status identification result. The work order is then sent to client 402. Client 402 is used to receive the second confirmation information and submit the confirmed submission work order to the review end. The second confirmation information is used to indicate and confirm the information pre-filled in the submission work order.

[0067] In this embodiment, the server acquires service data for a service area and inputs it into a pre-trained agent. This agent is trained based on historical service data and historical service status identification results. The agent analyzes and calculates the service data, outputting the service status identification result for the service area. This result represents the status of service resources changed to ensure the service task meets predetermined conditions. Based on the output service status identification result, the server sends a first prompt to the client, guiding the client to initiate a reporting request. This request asks the server to generate a corresponding reporting work order, which is used to apply for service resource scheduling from the review end. After receiving the first prompt, the client sends a reporting request to the server. The server responds to the client's request by calling the agent to generate a reporting work order based on the service data. The work order includes the service status identification result information and is pushed to the client. After receiving the work order, the client obtains a second confirmation, which verifies the correctness of the pre-filled information. After confirming that the content of the work order is correct, the client formally submits the work order to the review end to schedule service resources for the corresponding service area.

[0068] Through the above processing, the server-side intelligent agent replaces human to complete the stress level determination and work order writing. The client only needs to perform lightweight operations of initiating requests and confirming submissions, which shortens the process time from stress detection to work order completion and improves the response speed of service resource scheduling.

[0069] Based on the above embodiments and optional embodiments, this application also provides an optional implementation method. Taking delivery service as the application scenario, for the display page of real-time delivery method, a dual-path reporting generation method is adopted. In the dual-path method, one method is that the server actively discovers abnormal data and initiates a reporting prompt, and the other method is that the client selects data and chooses whether to generate a reporting work order. The combination of the above two reporting methods can avoid missing the reporting of abnormal delivery data.

[0070] For the server-side proactive detection of abnormal data and initiation of reporting prompts, the server detects abnormal delivery data in the service area in the background. An intelligent agent determines the service status of this delivery data. When the service status matches a predetermined state, the server sends a prompt to the client, indicating that the service area has detected a high pressure level and requesting a report. After the client confirms the report, the server's intelligent agent generates a reporting work order. After the client confirms the pre-filled content in the work order, the client submits the work order to the review panel.

[0071] like Figure 2 As shown, for the client-triggered data selection method, when a user browses a page of a predetermined type, if the dwell time reaches the predetermined duration, the server will send a prompt message to the client to select data to generate a report work order for that data. Following the prompt message, the client selects the target data and sends it to the server. If the server determines through semantic recognition that the data is abnormal, it allows the reporting process to be triggered, prompting the client in a pop-up window to click "Report" to send the reporting command. After the client sends the reporting command, the agent analyzes the context information of the service data through intent recognition, performing context matching on metadata such as the current page identifier, service area identifier, and timestamp, determining that the reporting intent is a stress reporting task. The server guides the client in a dialogue manner, asking, "The current service status of location A is detected to be Level 3 (high-pressure warning). Do you want to generate a report work order for you?" Based on the above guidance, the user sends a reporting request to the server and receives the generated report work order from the server. Users confirm the information in the work order submitted through the client. If the information is correct, the work order is sent to the review end. If there are errors in the pre-filled content, users can correct the content through the client.

[0072] After a work order is submitted to the review end, the work order status is synchronized on the client side, providing progress tracking, rejection reminders and other prompts, so that users can keep track of the submission status in real time. This avoids problems such as no feedback after work order submission or being unaware of rejection after review, improves the controllability of the submission process, and further ensures the timeliness of submission.

[0073] The aforementioned dual-path triggering mechanism proactively monitors for anomalies on the server side while simultaneously triggering proactive reporting requests via keyword highlighting on the client side. This addresses the coverage blind spots of the single reporting mode, facilitating the rapid identification of various delivery pressure anomalies. It simplifies the lengthy reporting process; automatically populated reporting work orders, driven by intelligent agents to execute cross-system tasks, reduce manual operation steps, improve response time by over 80%, and are expected to reduce average processing time by approximately 25 minutes.

[0074] Corresponding to the application scenarios and methods provided in the embodiments of this application, such as Figure 5 As shown, optionally applicable to the server side, this application embodiment also provides a service resource scheduling and processing device based on intelligent agents, including: The first prediction module 501 is used to input service data of the service area into the intelligent agent for processing and output the service status identification result of the service area. The intelligent agent is trained based on historical service data and historical service status identification results. The service status identification result represents the status identification result of the service resource that changes to meet the predetermined conditions so that the service task executed in the service area can meet the predetermined conditions. The first sending module 502 is used to send a first prompt message to the client based on the service status identification result. The first prompt message is used to guide the client to send a submission request to the server. The submission request is used to request the server to generate a submission work order corresponding to the service data. The submission work order is used by the client to apply to the review end for scheduling service resources for the service area. The first generation module 503 is used to receive the reporting request sent by the client, and use an intelligent agent to generate a reporting work order based on service data. The reporting work order includes the service status identification result. The second sending module 504 is used to send work orders to the client.

[0075] In the embodiments provided in this application, the device further includes: The first receiving module is used to receive target data sent by the client; the target data is selected by the client from a page of a predetermined type. The third message is sent to the client based on the fact that the target data is abnormal. The second message prompts the client to send a submission instruction to the server to submit the target data. The second receiving module is used to receive the client's reporting instructions and use the target data as service data.

[0076] In the embodiments provided in this application, the device includes: The first identification module is used to identify the semantic features of the target data based on the parameter type of the target data and the predefined type of the page; The second identification module is used to determine whether the target data is abnormal based on semantic features.

[0077] In the embodiments provided in this application, the first prediction module includes: The intent recognition module is used to output the intent recognition result corresponding to the submission request based on at least one of the following data: page identifier, region identifier, and timestamp of service data. The page identifier is the identifier of the page displaying service data in the client, and the region identifier is the identifier of the service region that generated the service data. The second prediction module is used to input service data into the intelligent agent for processing based on the intent recognition result as a predetermined intent type, and output the service status recognition result. The predetermined intent type is used to represent the intent to report the pressure represented by the service data to the review end.

[0078] In the embodiments provided in this application, the first sending module includes: The information generation module is used to generate a first prompt message based on a predefined corpus template, including the service status identification result and the region identifier, where the region identifier represents the identifier of the service region that generated the service data. The fourth sending module is used to send the first prompt message to the client.

[0079] In the embodiments provided in this application, the device further includes: The first acquisition module is used to acquire the duration of the client's stay on a page of a predetermined type; The fifth sending module is used to send a third prompt message to the client based on the dwell time exceeding a predetermined time threshold. The third prompt message is used to prompt the client to select target data on the current page.

[0080] In the embodiments provided in this application, the device is configured such that the service status identification result is obtained by an agent based on abnormal service data in the local data of the server; the first sending module includes: The proactive early warning module is used to send a first alert to the client when the service status identification result of the delivery pressure level is greater than the predetermined level and meets the predetermined status. The predetermined status indicates that the demand for service resources has reached the predetermined level.

[0081] In the embodiments provided in this application, the device is configured to submit a work order including a scheduling type, where the scheduling type represents the type of service resource scheduled in the service area, and the first generation module includes: The third prediction module is used to use an intelligent agent to determine the pressure type and status type of the service area based on the delivery data service data. The pressure type and status type indicate the type of demand for delivery resources in the service area under pressure on different delivery indicators. The first determining module is used to determine the scheduling type of service resources for the service area based on the status type; The second generation module is used to pre-fill the scheduling type into the work order.

[0082] Corresponding to the application scenarios and methods provided in the embodiments of this application, such as Figure 6 As shown, optionally applicable to the client, this application embodiment also provides a service resource scheduling and processing device based on an intelligent agent, including: The third receiving module 601 is used to receive the first prompt information sent by the server. The first prompt information is used to guide the client to send a submission request to the server. The submission request is used to request the server to generate a submission work order corresponding to the service data. The submission work order is used by the client to apply to the review end for the scheduling of service resources for the service area. The sixth sending module 602 is used to send a reporting request to the server and receive a reporting work order sent by the server. The reporting work order includes a service status identification result. The service status identification result is obtained by inputting service data of the service area into the intelligent agent for processing. The intelligent agent is trained based on historical service data and historical service status identification results. The service status identification result represents the status identification result of the service resources that change so that the service tasks executed in the service area can meet the predetermined conditions. The second confirmation module 603 is used to receive the second confirmation information and submit the confirmed submission work order to the review end. The second confirmation information is used to indicate the information pre-filled in the submission work order.

[0083] In the embodiments provided in this application, the device further includes: The selection module is used to select target data from a predefined type of page and send the target data to the server. The fourth receiving module is used to receive and display the second prompt information sent by the server. The second prompt information is used to prompt the client to send a reporting instruction to the server. The seventh sending module is used to send a submission instruction to the server, which then uses the target data as service data based on the instruction.

[0084] In the embodiments provided in this application, the device further includes: The eighth sending module is used to send the entry time of the pre-defined type of page to the server. The entry time is used by the server to determine the duration of the client's stay on the pre-defined type of page. The fifth receiving module is used to receive the third prompt information sent by the server. The third prompt information is used to prompt the client to select target data on the current page.

[0085] The functions of each module in each device in the embodiments of this application can be found in the corresponding description in the above method, and they have corresponding beneficial effects, which will not be repeated here.

[0086] Figure 7 This is a block diagram of an electronic device used to implement embodiments of this application. For example... Figure 7 As shown, the electronic device includes a memory 701 and a processor 702. The memory 701 stores a computer program that can run on the processor 702. When the processor 702 executes the computer program, it implements the method described in the above embodiments. The number of memories 701 and processors 702 can be one or more. In a specific implementation, the electronic device may also include a communication interface 703 for communicating with external devices and performing data exchange and transmission.

[0087] In practical implementation, if the memory 701, processor 702, and communication interface 703 are implemented independently, they can be interconnected via a bus to communicate with each other. This bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0088] Optionally, in a specific implementation, if the memory 701, processor 702, and communication interface 703 are integrated on a single chip, the memory 701, processor 702, and communication interface 703 can communicate with each other through an internal interface.

[0089] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method provided in this application.

[0090] This application provides a computer program product, including a computer program that, when executed by a processor, implements the method provided in this application.

[0091] This application also provides a chip including a processor for calling and executing instructions stored in a memory, causing a communication device with the chip installed to perform the method provided in this application.

[0092] This application also provides a chip, including: an input interface, an output interface, a processor, and a memory. The input interface, output interface, processor, and memory are connected through an internal connection path. The processor is used to execute code in the memory. When the code is executed, the processor is used to execute the method provided in the application embodiment.

[0093] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting Advanced Reduced Instruction Set Machines (ARM) architecture.

[0094] Further, optionally, the aforementioned memory may include read-only memory and random access memory. The memory may be volatile memory or non-volatile memory, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available. Examples include Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Sync Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0095] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0096] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0097] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0098] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process. Furthermore, the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functionality involved.

[0099] The logic and / or steps described in the flowchart or otherwise herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can fetch and execute instructions from, an instruction execution system, apparatus or device).

[0100] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. All or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware, the program being stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiments.

[0101] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. This storage medium can be a read-only memory, a disk, or an optical disk, etc.

[0102] The above are merely exemplary embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope described in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A service resource scheduling and processing method based on intelligent agents, characterized in that, Applied to the server side, including: The service data of the service area is input into the intelligent agent for processing, and the service status identification result of the service area is output. The intelligent agent is trained based on historical service data and historical service status identification results. The service status identification result represents the status identification result of the service resource that changes to meet the predetermined conditions so that the service task executed in the service area can meet the predetermined conditions. Based on the service status identification result, a first prompt message is sent to the client. The first prompt message is used to guide the client to send a reporting request to the server. The reporting request is used to request the server to generate a reporting work order corresponding to the service data. The reporting work order is used by the client to apply to the review end for scheduling service resources for the service area. Upon receiving the reporting request sent by the client, the intelligent agent generates a reporting work order based on the service data, and the reporting work order includes the service status identification result; Send the work order to the client.

2. The method according to claim 1, characterized in that, Before inputting the service data of the service area into the intelligent agent for processing, the method further includes: Receive target data sent by the client; the target data is selected by the client from a page of a predetermined type. Based on the fact that the target data is abnormal, a second prompt message is sent to the client. The second prompt message is used to prompt the client to send a submission instruction to the server to submit the target data. The system receives the reporting instruction from the client and uses the target data as the service data.

3. The method according to claim 2, characterized in that, The method further includes: Based on the parameter type of the target data and the predetermined type of the page, the semantic features of the target data are identified; Based on the semantic features, the target data is determined to be abnormal data.

4. The method according to claim 2, characterized in that, The step of inputting service data of the service area into the intelligent agent for processing and outputting the service status identification result of the service area includes: Based on at least one of the following data: page identifier, region identifier, and timestamp of the service data, output the intent recognition result corresponding to the reporting request. The page identifier is the identifier of the page displaying the service data in the client, and the region identifier represents the identifier of the service region that generated the service data. Based on the intent recognition result being a predetermined intent type, the service data is input into the intelligent agent for processing, and the service status recognition result is output. The predetermined intent type is used to represent the intent to report the pressure represented by the service data to the review end.

5. The method according to claim 2, characterized in that, Sending a first prompt message to the client based on the service status identification result includes: Based on a predefined corpus template, a first prompt message is generated, which includes the service status identification result and a region identifier, wherein the region identifier represents the identifier of the service region that generated the service data. Send the first prompt message to the client.

6. The method according to claim 2, characterized in that, Before receiving the target data sent by the client, the method further includes: Obtain the duration of the client's stay on the page of the predetermined type; Based on the fact that the dwell time exceeds a predetermined time threshold, a third prompt message is sent to the client, which prompts the client to select the target data on the current page.

7. The method according to claim 1, characterized in that, The service status identification result is obtained by the intelligent agent based on abnormal service data in the local data of the server. Sending a first prompt message to the client based on the service status identification result includes: Based on the service status identification result conforming to the predetermined status, the first prompt information is sent to the client, whereby the predetermined status indicates that the demand for the service resources has reached the predetermined level.

8. A service resource scheduling and processing method based on intelligent agents, characterized in that, Applied to the client side, including: The client receives a first prompt message from the server. The first prompt message is used to guide the client to send a submission request to the server. The submission request is used to request the server to generate a submission work order corresponding to the service data. The submission work order is used by the client to apply to the review end for service resources to be scheduled for the service area. The system sends the reporting request to the server and receives the reporting work order sent by the server. The reporting work order includes a service status identification result. The service status identification result is obtained by inputting service data of the service area into an intelligent agent for processing. The intelligent agent is trained based on historical service data and historical service status identification results. The service status identification result represents the status identification result of the service resource that changes so that the service task executed in the service area can meet predetermined conditions. Upon receiving the second confirmation information, the submitted work order is submitted to the review end after confirmation. The second confirmation information is used to indicate the information pre-filled in the submitted work order.

9. A service resource scheduling and processing system based on intelligent agents, characterized in that, include: Server and client, The server is used to input service data of the service area into the intelligent agent for processing and output the service status identification result of the service area. The intelligent agent is trained based on historical service data and historical service status recognition results. The service status recognition results represent the status recognition results of service resources that change so that the service tasks executed in the service area can meet predetermined conditions. Based on the service status identification result, a first prompt message is sent to the client. The first prompt message is used to guide the client to send a reporting request to the server. The reporting request is used to request the server to generate a reporting work order corresponding to the service data. The reporting work order is used by the client to apply to the review end for scheduling service resources for the service area. The client is used to send the submission request to the server; The server is used to generate a work order based on the service data using the intelligent agent, and the work order includes the service status identification result; Send the work order to the client; The client is used to receive the second confirmation information and submit the confirmed work order to the review end. The second confirmation information is used to indicate the information pre-filled in the work order.

10. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory, wherein the processor, when executing the computer program, implements the method of any one of claims 1 to 8.

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