Integrated resource scheduling platform and integrated resource scheduling method

Abstract

An integrated resource scheduling platform includes an aggregation system and an agent hub system. The aggregation system receives a plurality of sensed data from a plurality of data sources. The agent hub system is coupled to the aggregation system. The agent hub system includes a plurality of intelligent agent models. The agent hub system performs the following operations: generating service scenario scores and service scenario indicators corresponding to the plurality of sensed data; determining service scenario procedures based on the service scenario scores and service scenario indicators; selecting a plurality of target intelligent agent models from the plurality of intelligent agent models based on the service scenario procedures; and executing the service scenario procedures through the plurality of target intelligent agent models.

Claims

1. An integrated resource scheduling platform, comprising: an aggregation system for receiving a plurality of sensed data from a plurality of data sources respectively; and an agent central system coupled to the aggregation system, wherein the agent central system includes a plurality of intelligent agent models, a scheduling server, a decision server, a resource connector, and an event prediction model, the decision server being coupled to the scheduling server, the resource connector being coupled to the intelligent agent models, and the agent central system performing the following operations: the decision server analyzing one of the service context contents of the sensed data, and generating a service context score and a service context indicator corresponding to the service context content based on the service context content; and the decision server determining a service context process based on the service context score and the service context indicator. The scheduling server selects a plurality of target intelligent agent models from the intelligent agent models based on the service context process; and executes the service context process through the target intelligent agent models, wherein the resource connector is used to record the execution of the service context process by the target intelligent agent models, and the decision server is further used to perform the following operations: train the event prediction model based on the perceived data; and analyze the perceived data through the trained event prediction model to generate a prediction result; and determine the service context process based on the prediction result, the service context score, and the service context index.

2. The integrated resource scheduling platform as described in claim 1, wherein the aggregation system is further configured to perform the following operations: classify the sensed data according to a data level, wherein the data level is associated with an importance level and a sensitivity level of the sensed data.

3. The integrated resource scheduling platform as described in claim 1 further includes: a resource integration system coupled to the agent hub system, a plurality of ground systems and a plurality of cloud systems, and used to manage a communication mode between the smart agent models and the ground systems and the cloud systems based on a standardized data protocol.

4. The integrated resource scheduling platform as described in claim 3, wherein when one of the ground systems and the cloud systems is abnormal, the resource integration system is further configured to perform the following operation: switch from one of the ground systems and the cloud systems to the other of the ground systems and the cloud systems.

5. The integrated resource scheduling platform as described in claim 4 further includes: a management authority system coupled to the agent central system and the resource integration system, and used to process a first access control authority for each of the perceived data sources.

6. The integrated resource scheduling platform as described in claim 5, wherein the management authority system is further configured to perform the following operations: manage a second access control authority for the intelligent agent models.

7. An integrated resource scheduling method applicable to an integrated resource scheduling platform, wherein the integrated resource scheduling platform includes an aggregation system and an agent central system, the aggregation system being coupled to the agent central system, the agent central system including a plurality of intelligent agent models, a scheduling server, a decision server, a resource connector, and an event prediction model, the decision server being coupled to the scheduling server, and the resource connector being coupled to the intelligent agent models, wherein the integrated resource scheduling method includes the following steps: the aggregation system receiving a plurality of sensing data from a plurality of data sources respectively; the decision server of the agent central system analyzing a service context content of the sensing data, and generating a service context score and a service context index corresponding to the service context content based on the service context content of the sensing data; and the decision server of the agent central system determining a service context process based on the service context score and the service context index. The scheduling server of the agent central system selects a plurality of target intelligent agent models from the intelligent agent models based on the service scenario process; and the agent central system executes the service scenario process through the target intelligent agent models, wherein the resource connector is used to record the execution of the service scenario process by the target intelligent agent models, and the decision server is further used to perform the following operations: training the event prediction model based on the perceived data; and analyzing the perceived data through the trained event prediction model to generate a prediction result; and determining the service scenario process based on the prediction result, the service scenario score, and the service scenario index.

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