Dynamic resource scheduling method, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a dynamic resource scheduling method, electronic equipment and a storage medium, and relates to the field of resource scheduling. The method comprises the following steps: an upper computer obtains a source event triggering condition, and determines a triggering source and a source event priority in combination with a preset event priority table; when the trigger source is a lower computer, a starting signal is received, and whether the source event is executed or not is judged according to current task state information, the priority of the source event and a preset table; if execution is determined, sending a starting feedback signal to obtain a lower computer state message, converting the lower computer state message, and inputting the converted lower computer state message into the large language model agent to obtain a first instruction and an auditing result; if the verification is passed, the instruction is converted into a message to be issued, and after the lower computer permission decision is passed and executed, a first completion signal is received. The method is used for improving real-time performance, adaptability and reliability of resource scheduling, and avoiding insufficient system stability caused by the fact that static scheduling cannot respond to sudden demands or resource distribution is unbalanced.

Description

Technical Field

[0001] This application relates to the field of resource scheduling, and more particularly to a dynamic resource scheduling method, electronic device, and storage medium. Background Technology

[0002] In multi-tasking scenarios, the control core needs to interface with multiple task requirements and undertake the core tasks of task information exchange and operational control. As the number and complexity of tasks increase, the resource allocation of the control core is related to the continuity of task execution and the reliability of the results.

[0003] Currently, resource scheduling in the control core mainly adopts two schemes: one is based on static priority and interrupt polling mechanism, which allocates resources involved in various tasks through preset rules and executes tasks in a fixed priority order. However, this static method cannot perceive the global resource status in real time, and it is easy for high-priority tasks to occupy resources for a long time, while low-priority tasks will suffer from resource scarcity due to long-term lack of resources. Moreover, the preset priority is difficult to adapt to the needs of sudden tasks. The second is to upgrade resources when conditions permit, and adopt a control core with more abundant resources to directly avoid the problem of resource constraints from the resources themselves. However, this scheme cannot be implemented during task execution and lacks feasibility in the face of sudden situations.

[0004] Existing technologies cannot achieve real-time dynamic resource adaptation to sudden surges in tasks, such as multi-task concurrency and data volume caused by sudden events, under the condition of limited core resources. When encountering sudden data surges or equipment failures, resources will be limited, which will make the system prone to crashing. Summary of the Invention

[0005] This application provides a dynamic resource scheduling method, electronic device, and storage medium to solve the problem of poor resource scheduling stability in the prior art.

[0006] In a first aspect, embodiments of this application provide a dynamic resource scheduling method applied to a host computer, wherein a large language model intelligent agent is deployed in the host computer, and the method includes:

[0007] Obtain the source event triggering conditions, and determine the source event triggering source and source event priority based on the source event triggering conditions and the preset event priority table;

[0008] When the source of the source event is detected to be a lower-level device, the start signal sent by the lower-level device is received.

[0009] Obtain the current task status information, and determine whether to execute the source event that is triggered by the source event based on the current task status information, the source event priority, and the preset event priority table;

[0010] When the source event is detected to be executed, a startup feedback signal is generated based on the startup signal, and the startup feedback signal is sent to the lower-level machine so that the lower-level machine can obtain the current status message of the lower-level machine based on the startup feedback signal.

[0011] Receive the current status message of the lower-level machine sent by the lower-level machine and perform conversion processing to obtain the data to be input;

[0012] The input data is input into the large language model agent to obtain the first instruction review result and the first instruction. When the first instruction review result is detected as passed, the first instruction is format converted to obtain the first instruction message.

[0013] The first instruction message is sent to the lower-level machine so that the lower-level machine makes an execution permission decision on the first instruction message. When the permission decision is passed, the first instruction is executed and a first completion signal is generated.

[0014] Receive the first completion signal sent by the lower-level machine.

[0015] In one possible implementation, the source event triggering conditions include threshold triggering conditions, interactive response triggering conditions, change triggering conditions, and timer triggering conditions, and the preset events include the correspondence between source events, priorities, triggering conditions, and triggering sources;

[0016] The step of determining the source event triggering source and the source event priority based on the source event triggering conditions and the preset event priority table includes:

[0017] The target triggering condition is determined from the threshold triggering condition, interactive response triggering condition, change triggering condition and timer triggering condition in the preset event priority table based on the source event triggering condition.

[0018] Based on the correspondence between the target triggering conditions and the source event, priority, triggering conditions, and triggering source, determine the source event triggering source and source event priority.

[0019] In one possible implementation, determining whether to execute the source event that triggers the source event based on the current task status information, the source event priority, and the preset event priority table includes:

[0020] Determine whether the host computer is idle based on the current task status information;

[0021] When the host computer is detected to be in an idle state, the current triggered event of the slave computer is obtained;

[0022] The priority of the current triggering event is determined based on the current triggering event of the lower-level machine and the preset event priority table;

[0023] The priority of the source event is compared with the priority of the current triggering event to determine whether to execute the source event that is triggered by the source event.

[0024] In one possible implementation, after determining whether the host computer is idle based on the current task status information, the method further includes:

[0025] When the host computer is detected to be in a non-idle state, the current task being executed by the host computer is obtained, and the priority of the current task being executed by the host computer is determined according to the preset time priority table.

[0026] The priority of the currently executing task on the host computer is compared with the priority of the source event to determine whether to execute the source event triggered by the source event.

[0027] In one possible implementation, the step of receiving and converting the current status message sent by the lower-level machine to obtain the input data includes:

[0028] Receive the current status message of the lower-level machine sent by the lower-level machine;

[0029] The current status message of the lower-level machine is converted into a new format to obtain the data to be judged.

[0030] The instruction control judgment is performed based on the data to be judged, so that when the data to be judged is detected to require instruction control, the data to be judged is used as the input data.

[0031] In one possible implementation, the step of inputting the data to be input into the large language model agent to obtain a first instruction review result and a first instruction includes:

[0032] The large language model agent translates the input data to obtain the first demand translation data.

[0033] Based on the first requirement translation data and the pre-stored lower-level machine historical status data, a decision analysis and processing is performed to obtain the first instruction;

[0034] A triple intelligent review mechanism is executed on the first instruction to obtain the review result of the first instruction.

[0035] In one possible implementation, it also includes:

[0036] When the source of the event is detected to be a host computer, user interaction data is acquired.

[0037] Obtain the current task status information, and determine whether to execute the source event that is triggered by the source event based on the current task status information, the source event priority, and the preset event priority table;

[0038] The user interaction data is input into the large language model agent to obtain the second instruction review result and the second instruction. When the second instruction review result is detected as passed, the second instruction is processed for format conversion to obtain the second instruction message and the lower-level machine start signal.

[0039] The lower-level machine start signal is sent to the lower-level machine so that the lower-level machine stops the current task according to the lower-level machine start signal and returns a lower-level machine start feedback signal;

[0040] The system receives the lower-level machine start feedback signal sent by the lower-level machine, sends the second instruction message to the lower-level machine, so that the lower-level machine makes an execution permission decision on the second instruction message, executes the second instruction when the permission decision is passed, and generates a second completion signal.

[0041] Receive the second completion signal sent by the lower-level machine.

[0042] In one possible implementation, after receiving the first completion signal sent by the lower-level machine, the method further includes:

[0043] A first completion feedback signal is generated based on the first completion signal;

[0044] The first completion feedback signal is sent to the lower-level machine so that the lower-level machine restarts the timer and continuously sends heartbeat packets to the upper-level machine through the timer.

[0045] Secondly, embodiments of this application provide a dynamic resource scheduling device, comprising:

[0046] The acquisition module is used to acquire the source event triggering conditions and determine the source event triggering source and the source event priority based on the source event triggering conditions and the preset event priority table.

[0047] The detection module is used to receive a start signal sent by the lower-level machine when the source of the source event is detected to be a lower-level machine.

[0048] The execution confirmation module is used to obtain the current task status information and, based on the current task status information, the source event priority, and the preset event priority table, determine whether to execute the source event that is triggered by the source event.

[0049] The feedback module is used to generate a startup feedback signal based on the startup signal when the source event is detected to be executed, and send the startup feedback signal to the lower-level machine so that the lower-level machine can obtain the current status message of the lower-level machine based on the startup feedback signal.

[0050] The conversion module is used to receive the current status message of the lower-level machine sent by the lower-level machine and perform conversion processing to obtain the data to be input;

[0051] The input module is used to input the data to be input into the large language model agent, obtain the first instruction review result and the first instruction, and when the first instruction review result is detected as passed, the first instruction is processed by format conversion to obtain the first instruction message;

[0052] The sending module is used to send the first instruction message to the lower-level machine, so that the lower-level machine performs an execution permission decision on the first instruction message, executes the first instruction when the permission decision is passed, and generates a first completion signal;

[0053] The receiving module is used to receive the first completion signal sent by the lower-level machine.

[0054] In one possible implementation, the source event triggering conditions acquired by the acquisition module include threshold triggering conditions, interactive response triggering conditions, change triggering conditions, and timer triggering conditions, and the preset events include the correspondence between source events, priorities, triggering conditions, and triggering sources;

[0055] The step of determining the source event triggering source and the source event priority based on the source event triggering conditions and the preset event priority table includes:

[0056] The target triggering condition is determined from the threshold triggering condition, interactive response triggering condition, change triggering condition and timer triggering condition in the preset event priority table based on the source event triggering condition.

[0057] Based on the correspondence between the target triggering conditions and the source event, priority, triggering conditions, and triggering source, determine the source event triggering source and source event priority.

[0058] In one possible implementation, the execution confirmation module is specifically used for:

[0059] Determine whether the host computer is idle based on the current task status information;

[0060] When the host computer is detected to be in an idle state, the current triggered event of the slave computer is obtained;

[0061] The priority of the current triggering event is determined based on the current triggering event of the lower-level machine and the preset event priority table;

[0062] The priority of the source event is compared with the priority of the current triggering event to determine whether to execute the source event that is triggered by the source event.

[0063] In one possible implementation, the execution confirmation module is further configured to:

[0064] When the host computer is detected to be in a non-idle state, the current task being executed by the host computer is obtained, and the priority of the current task being executed by the host computer is determined according to the preset time priority table.

[0065] The priority of the currently executing task on the host computer is compared with the priority of the source event to determine whether to execute the source event triggered by the source event.

[0066] In one possible implementation, the conversion module is specifically used for:

[0067] Receive the current status message of the lower-level machine sent by the lower-level machine;

[0068] The current status message of the lower-level machine is converted into a new format to obtain the data to be judged.

[0069] The instruction control judgment is performed based on the data to be judged, so that when the data to be judged is detected to require instruction control, the data to be judged is used as the input data.

[0070] In one possible implementation, the input module is specifically used for:

[0071] The large language model agent translates the input data to obtain the first demand translation data.

[0072] Based on the first requirement translation data and the pre-stored lower-level machine historical status data, a decision analysis and processing is performed to obtain the first instruction;

[0073] A triple intelligent review mechanism is executed on the first instruction to obtain the review result of the first instruction.

[0074] In one possible implementation, the detection module is further configured to:

[0075] When the source of the event is detected to be a host computer, user interaction data is acquired.

[0076] Obtain the current task status information, and determine whether to execute the source event that is triggered by the source event based on the current task status information, the source event priority, and the preset event priority table;

[0077] The user interaction data is input into the large language model agent to obtain the second instruction review result and the second instruction. When the second instruction review result is detected as passed, the second instruction is processed for format conversion to obtain the second instruction message and the lower-level machine start signal.

[0078] The lower-level machine start signal is sent to the lower-level machine so that the lower-level machine stops the current task according to the lower-level machine start signal and returns a lower-level machine start feedback signal;

[0079] The system receives the lower-level machine start feedback signal sent by the lower-level machine, sends the second instruction message to the lower-level machine, so that the lower-level machine makes an execution permission decision on the second instruction message, executes the second instruction when the permission decision is passed, and generates a second completion signal.

[0080] Receive the second completion signal sent by the lower-level machine.

[0081] In one possible implementation, the detection module is further configured to:

[0082] A first completion feedback signal is generated based on the first completion signal;

[0083] The first completion feedback signal is sent to the lower-level machine so that the lower-level machine restarts the timer and continuously sends heartbeat packets to the upper-level machine through the timer.

[0084] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0085] The memory stores computer-executed instructions;

[0086] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0087] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0088] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0089] The dynamic resource scheduling method, electronic device, and storage medium provided in this application embodiment first obtain the source event triggering conditions through the host computer, determine the source event triggering source and source event priority by combining a preset event priority table, and receive the start signal sent by the lower-level machine when the triggering source is detected as the lower-level machine. Then, based on the current task status information, the source event priority, and the preset event priority table, it is determined whether to execute the corresponding source event. If execution is determined, a start feedback signal is generated and sent to the lower-level machine to obtain its current status message. The message is converted and processed to obtain the input data, which is then input into the large language model intelligent agent. The first instruction that has passed the review is obtained and converted into a first instruction message and sent to the lower-level machine. After the lower-level machine's permission decision is approved and the instruction is executed, the first completion signal sent by the lower-level machine is received. This realizes dynamic decision-making based on trigger source identification, real-time task status, and priority, as well as the instruction generation and review mechanism of the large language model intelligent agent, thereby improving the real-time performance, adaptability, and reliability of resource scheduling and avoiding the effect of insufficient system stability caused by static scheduling being unable to respond to sudden demands or resource allocation imbalance. Attached Figure Description

[0090] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0091] Figure 1 A flowchart illustrating the dynamic resource scheduling method provided in this application;

[0092] Figure 2 A schematic diagram of the dynamic resource scheduling device provided in this application;

[0093] Figure 3 A schematic diagram of the structure of the electronic device provided in this application.

[0094] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0095] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0096] First, let me explain the terms used in this application:

[0097] Host computer: refers to a device that is equipped with a large language model intelligent agent, which can acquire user interaction data, receive information from lower-level machines, generate and process instructions through the large language model intelligent agent, and interact with lower-level machines. In dynamic resource scheduling, it undertakes core tasks such as instruction generation, decision-making and coordination.

[0098] Lower-level machine: refers to a device that can periodically collect detailed task information and current running status and send it to the upper-level machine, receive instructions from the upper-level machine and execute corresponding tasks, update status and provide feedback results. It is mainly responsible for the execution of specific tasks and the collection of basic information.

[0099] Large Language Model Agent: Deployed in the host computer, the large language model agent comprises multiple lightweight models and a core decision-making model. The lightweight models include instruction understanding, instruction review, and dialogue interaction models, which are selected from the large language model agent based on different needs, thus better balancing real-time performance and resources. It possesses capabilities such as semantic understanding, dynamic analysis, and causal analysis, and generates decision instructions by combining relevant information from the lower-level machine.

[0100] Existing technologies for resource scheduling, such as static priority and interrupt polling mechanisms, suffer from limitations due to the inability to perceive the global resource status in real time. This can lead to situations where high-priority tasks occupy resources for extended periods while low-priority tasks experience resource scarcity. Furthermore, preset priorities are ill-suited to handling sudden task demands. While hardware upgrades can mitigate resource constraints to some extent, they are not feasible during task execution and lack practicality in the face of unforeseen circumstances. Additionally, existing overload protection mechanisms are lagging and fail to guarantee data integrity. They cannot achieve intelligent resource scheduling and efficient collaboration through dynamic instruction generation, as is the approach described in this application, and are ill-equipped to handle resource scheduling needs in complex scenarios.

[0101] The dynamic resource scheduling method provided in this application first determines the source and priority of a source event based on the source event triggering conditions and a preset event priority table. When the triggering source is a lower-level machine, it receives its start signal. Then, it combines the current task status information, the source event priority, and the preset event priority table to determine whether to execute the corresponding source event. After determining to execute, it sends a start feedback signal to obtain the current status message of the lower-level machine. After conversion, it obtains the data to be input and inputs it into the first instruction generated and approved by the large language model intelligent agent. After converting the instruction format, it sends it to the lower-level machine. After the lower-level machine's permission decision is approved and the instruction is executed, it receives the first completion signal. This method solves the technical problems of existing scheduling schemes based on static priority and interrupt polling, which cannot perceive the global resource status in real time, are prone to resource allocation imbalance, and are difficult to adapt to sudden task demands. It also solves the technical problems of resource upgrade schemes being unable to be implemented during task execution and lacking feasibility in the face of sudden situations, resulting in difficulty in coping with sudden surges in tasks and easy system crashes under the condition of limited control core resources.

[0102] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These 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 now be described with reference to the accompanying drawings.

[0103] Figure 1 A flowchart illustrating the dynamic resource scheduling method provided in this application is shown below. Figure 1 As shown, this embodiment provides a dynamic resource scheduling method applied to a host computer, where a large language model agent is deployed. The process includes the following steps:

[0104] Step S101: Obtain the source event triggering conditions, and determine the source event triggering source and source event priority based on the source event triggering conditions and the preset event priority table.

[0105] Specifically, based on the matching of preset event triggering conditions and preset rules, identification is achieved by querying the correspondence between triggering conditions, triggering sources, and priorities in the preset event priority table. Its purpose is to accurately locate the initiator and importance of the event, providing a basis for subsequent scheduling decisions. The effect is to ensure that the system's initial judgment of events is clear and consistent, avoiding scheduling chaos caused by confusion between triggering sources and priorities.

[0106] Step S102: When the source of the detected event is a lower-level device, the start signal sent by the lower-level device is received.

[0107] Specifically, through a pre-defined signal interaction protocol between the host computer and the slave computer, when the slave computer is identified as the trigger source, a specific communication port is activated to receive signals. Its function is to establish a formal interaction link for the slave computer's trigger event and confirm the slave computer's event initiation intent. The effect is to achieve initial synchronization between the trigger source and the processing end, laying the communication foundation for subsequent status acquisition and command issuance.

[0108] Step S103: Obtain the current task status information, and determine whether to execute the source event that is triggered by the source event based on the current task status information, the source event priority, and the preset event priority table.

[0109] Specifically, based on the current system load and event priorities, the necessity of execution is determined by comparing the priority relationship between the current task and the source event. Its function is to decide whether to respond to the currently triggered event based on real-time resource usage and event importance. The effect is to avoid resource conflicts caused by blindly executing events, ensuring that high-priority events receive resources first, and improving the rationality of resource utilization.

[0110] Step S104: When a source event is detected to be executed, a start feedback signal is generated based on the start signal, and the start feedback signal is sent to the lower-level machine so that the lower-level machine can obtain the current status message of the lower-level machine based on the start feedback signal.

[0111] Specifically, a feedback signal is generated based on the confirmation execution result and transmitted to the lower-level machine via the communication link, triggering the lower-level machine's status acquisition process. Its function is to pass execution permission to the lower-level machine and simultaneously trigger the lower-level machine's status reporting mechanism. The effect is to achieve execution consensus between the upper and lower-level machines, ensuring that the lower-level machine's status obtained by the upper-level machine matches the current event processing requirements.

[0112] Step S105: Receive the current status message of the lower-level machine sent by the lower-level machine and perform conversion processing to obtain the input data.

[0113] Specifically, the current status message of the lower-level machine refers to the message encapsulation and uploading after the lower-level machine collects status information, and the message is stored in the lower-level machine's storage module. Through message format conversion and information filtering, the specific format message of the lower-level machine is converted into a unified data format, and it is determined whether instruction control is required. Its function is to transform the raw status information of the lower-level machine into data that can be processed by the large language model agent, and to filter out the valid information that needs intervention. The effect is to eliminate processing obstacles caused by data format differences, ensuring that the data input to the agent is valid and consistent.

[0114] Step S106: Input the data to be input into the large language model agent to obtain the first instruction review result and the first instruction. When the first instruction review result is detected as passed, the first instruction is format converted to obtain the first instruction message.

[0115] Specifically, through natural language processing and logical analysis using a large language model, the generated instructions are verified by an audit, and then converted into a format. The function is to utilize an intelligent agent to generate instructions adapted to the scenario, ensure the correctness and feasibility of the instructions through auditing, and then convert them into a format recognizable by the lower-level machine. The effect is to improve the intelligence and accuracy of instruction generation, avoid issuing erroneous instructions, and ensure the executability of instructions.

[0116] Step S107: Send the first instruction message to the lower-level machine so that the lower-level machine can make an execution permission decision on the first instruction message. When the permission decision is passed, the first instruction is executed and a first completion signal is generated.

[0117] Specifically, through command issuance and lower-level machine permission verification, the lower-level machine determines whether to execute the command based on its own security rules and permission scope. Its function is to transmit approved commands to the lower-level machine, and to further ensure the security of command execution through the lower-level machine's permission decisions. The effect is to form a double verification of command execution, reducing the harm to the system caused by unauthorized or insecure commands.

[0118] Step S108: Receive the first completion signal sent by the lower-level machine.

[0119] Specifically, based on the feedback of the event execution result, the final state of instruction execution is confirmed by receiving a completion signal. Its function is to obtain the execution result of the instruction from the lower-level machine, marking the end of a single event processing flow. The effect is to achieve closed-loop management of event processing, providing a status basis for subsequent resource release or new event scheduling.

[0120] The dynamic resource scheduling method provided in this invention achieves real-time and adaptability of resource scheduling through a collaborative mechanism of trigger source identification, dynamic priority decision-making, intelligent instruction generation and review, and closed-loop interaction between upper and lower level machines, thereby improving the system's ability to cope with sudden tasks and its operational stability.

[0121] In some optional implementations, the methods provided in the above embodiments of this application can also be applied to servers other than host computers and slave computers, as long as the server establishes a data connection relationship with the host computer and slave computer and the server has the large language model intelligent agent described in the above embodiments deployed in its internal structure.

[0122] In some optional embodiments, the source event triggering conditions include threshold triggering conditions, interactive response triggering conditions, change triggering conditions, and timer triggering conditions. The preset events include the correspondence between source events, priorities, triggering conditions, and triggering sources. Determining the source event triggering source and source event priority based on the source event triggering conditions and the preset event priority table includes: determining the target triggering condition based on the threshold triggering conditions, interactive response triggering conditions, change triggering conditions, and timer triggering conditions in the preset event priority table; and determining the source event triggering source and source event priority based on the correspondence between the target triggering condition and the source event, priorities, triggering conditions, and triggering sources.

[0123] Specifically, based on the predefined threshold trigger conditions, interaction response trigger conditions, change trigger conditions, and timer trigger conditions in the preset event priority table, feature matching is used to compare the acquired source event trigger conditions with the four types of trigger conditions in the table to locate the unique corresponding trigger condition type. This completes the classification and attribution of source event trigger conditions, transforming abstract trigger information into specific types within the preset classification system, providing a clear query index for subsequent association of trigger sources and priorities. This avoids subsequent decision-making biases caused by ambiguous trigger condition types, ensures the accuracy of trigger condition classification, and lays a unified judgment foundation for subsequent steps. As shown in Table 1:

[0124] Table 1 Event Priority Table

[0125]

[0126] By leveraging the established association mappings in the pre-defined event priority table, and using the identified target trigger condition as the retrieval key, the system retrieves the trigger source information and priority parameters bound to that trigger condition. By establishing a direct association between the trigger condition and the triggering entity and the importance of the event, the classification results of the trigger conditions are transformed into the core parameters (trigger source, priority) required for scheduling. This enables rapid conversion of trigger information into decision parameters, ensuring the accuracy and efficiency of trigger source and priority acquisition, and avoiding scheduling logic chaos caused by missing or mismatched parameters.

[0127] This invention achieves standardization and precision in trigger information parsing by first classifying the source event triggering conditions into one of four preset triggering conditions, and then obtaining the triggering source and priority based on the classification results. This provides reliable parameter support for subsequent dynamic scheduling decisions and improves the consistency and efficiency of the system in processing initial event information.

[0128] This embodiment provides a detailed explanation of the process described in the above embodiment for determining whether to execute the source event that triggers the source event based on the current task status information, the source event priority, and the preset event priority table. The specific implementation of this process includes the following steps:

[0129] Step a1: Determine whether the host computer is in an idle state based on the current task status information.

[0130] Specifically, by collecting current task queue and resource utilization data from the host computer, such as processor and memory usage, and task execution progress, and comparing it with a preset idle state judgment threshold, a state determination is achieved if there are no unexecuted tasks and resource utilization is below the threshold. This serves as a preliminary judgment node for subsequent scheduling decisions, clarifying the current load of the host computer and providing a basis for distinguishing different processing logics in idle and non-idle scenarios. This avoids blindly executing new events due to a lack of host computer status assessment, ensuring that scheduling decisions align with the actual system load and reducing resource conflicts.

[0131] Step a2: When the host computer is detected to be in an idle state, the current triggered event of the slave computer is obtained.

[0132] Specifically, based on the determination of the idle state of the host computer, communication interaction with the slave computer is triggered. The event identifier and related parameters currently initiated by the slave computer are read through the preset communication protocol. In the idle scenario, the subject of the slave computer event to be processed is identified, providing a specific event object for subsequent priority comparison and avoiding a goalless decision-making process. This ensures that the host computer can accurately locate the events triggered by the slave computer when it is idle, laying the foundation for subsequent priority judgment and improving the targeting of event processing.

[0133] Step a3: Determine the priority of the current triggering event based on the current triggering event and the preset event priority table of the lower-level machine.

[0134] Specifically, by using the mapping relationship between events and priorities in the preset event priority table, the current triggered event of the lower-level machine is obtained as the search item, and the corresponding priority parameter in the table is retrieved to complete the priority matching.

[0135] The current triggering event of the lower-level machine is transformed into a quantifiable and comparable priority indicator, providing a unified standard for subsequent comparison with the priority of the source event, avoiding comparison confusion caused by the lack of clear basis for event priority, ensuring the consistency and accuracy of priority determination, and supporting subsequent decision-making logic.

[0136] Step a4: Compare the priority of the source event with the priority of the currently triggered event to determine whether to execute the source event that matches the triggering conditions of the source event.

[0137] Specifically, based on preset priority comparison rules, such as numerical value or level sorting, the priority of the source event is compared with the priority of the currently triggered event by numerical value or level. The decision result is output based on the comparison result. If the source event has a higher priority, it is executed; if the priority is lower, it is not executed.

[0138] By comparing the priorities of two events in an idle scenario, it is determined whether to execute the source event, avoiding disordered processing when multiple events are concurrent, realizing orderly decision-making on the execution order of events in an idle state, ensuring that high-priority events get processing resources first, and improving the rationality of resource allocation.

[0139] In some alternative implementations, after step a1 above, the method further includes:

[0140] Step a11: When the host computer is detected to be in a non-idle state, the current task being executed by the host computer is obtained, and the priority of the current task being executed by the host computer is determined according to the preset time priority table.

[0141] Specifically, by reading the task identifier currently in execution from the host computer's task management module, and combining it with the correspondence between tasks and priorities in the preset event priority table, the priority of the currently executing task is extracted and determined.

[0142] In non-idle scenarios, clearly defining the importance of the tasks currently occupying system resources provides key parameters for determining whether the current task needs to be interrupted to execute the source event. This avoids blindly interrupting tasks due to a lack of understanding of their priority, ensuring that subsequent decisions can balance the resource requirements of existing tasks and new events, and reducing the arbitrariness of task execution interruption.

[0143] Step a12: Compare the current task priority and the source event priority on the host computer to determine whether to execute the source event triggered by the source event.

[0144] Specifically, the priority of the currently executing task is compared with the priority of the source event using a preset priority comparison rule. A final decision is generated based on the comparison result. If the source event has a higher priority, the current task will be interrupted to execute the source event; otherwise, the current task will continue to execute.

[0145] By comparing the priorities of existing tasks and new events in non-idle scenarios, it can determine whether to interrupt existing tasks to respond to the source event, thus achieving dynamic resource scheduling. This ensures that the system can prioritize higher-priority events in non-idle states, while preventing low-priority events from arbitrarily interrupting high-priority tasks, thereby improving the flexibility and rationality of resource scheduling and ensuring the continuity of high-priority task execution.

[0146] This invention implements a hierarchical processing mechanism that first determines the idle state of the host computer, then obtains the corresponding event / task priorities for idle and non-idle scenarios and compares and makes decisions. This mechanism achieves accurate scheduling based on system load and event priority, ensuring the rationality of event execution order under different load scenarios and improving the dynamic adaptability and decision accuracy of resource scheduling.

[0147] This embodiment provides a detailed description of the process described in the above embodiment of receiving and converting the current status message sent by the lower-level machine to obtain the input data. The specific implementation of this process includes the following steps:

[0148] Step b1: Receive the current status message of the lower-level machine sent by the lower-level machine.

[0149] Specifically, based on the preset communication protocols between the host computer and the slave computer, such as serial port and network port protocols, a designated communication port is enabled to monitor the message transmission requests of the slave computer, and the integrity of the received data is ensured through data frame verification to complete message reception.

[0150] By acquiring raw data on the current operating status of the lower-level machine, such as hardware parameters, task execution progress, and resource usage, underlying data support is provided for subsequent data processing and decision-making. A transmission channel for status information between the upper and lower-level machines is established to ensure that the upper-level machine can obtain the status of the lower-level machine in real time and accurately, avoiding decision-making biases caused by missing information.

[0151] Step b2: Convert the format of the current status message of the lower-level machine to obtain the data to be judged.

[0152] Specifically, based on preset data format conversion rules, the specific format of the lower-level machine message is converted into a standard format that is uniformly recognized by the upper-level machine. Specific formats include binary, custom protocol formats, and standard formats include JSON and XML formats. At the same time, data parsing and field mapping are completed, and key status parameters are extracted.

[0153] It effectively eliminates compatibility issues caused by differences in data formats between upper and lower level machines, transforms raw messages into structured and processable intermediate data, provides a standardized data carrier for subsequent command control and judgment, realizes unified processing of data of different formats, improves data processing efficiency, avoids data parsing errors caused by format incompatibility, and ensures the smoothness of subsequent processes.

[0154] Step b3: Perform instruction control judgment based on the data to be judged, so that when it is detected that the data to be judged needs to be controlled by instruction, the data to be judged is used as the input data.

[0155] Specifically, based on preset command control triggering rules, such as status parameters exceeding thresholds or task execution anomalies, key indicators in the data to be judged are verified to determine whether the conditions for generating control commands are met. If the conditions are met, the data is marked as input data.

[0156] By filtering out valid state data that requires intervention and excluding normal state data that does not require control, invalid data is prevented from entering the subsequent intelligent decision-making process, reducing resource waste and achieving effective filtering of state data. This ensures that the subsequent instruction generation process is only initiated for scenarios that require control, improving the efficiency of system resource utilization, while also ensuring the relevance and necessity of instruction generation.

[0157] In this embodiment, after the lower-level machine ends sending the current status message, the method further includes: storing the current status message of the lower-level machine in the relational database of the upper-level machine to complete the upper-level machine log recording, and displaying the current status message of the lower-level machine.

[0158] Specifically, displaying the current status message of the lower-level machine can be achieved by sending the current status message and related data to a front-end display connected to the upper-level machine or by sending it to the user terminal as a log.

[0159] This invention achieves a standardized flow of lower-level machine status information from acquisition to effective utilization through a step-by-step processing procedure: first receiving the original status message from the lower-level machine, then performing a unified format conversion, and finally filtering the data to be controlled. This solves the data format compatibility problem, ensures the effectiveness of subsequent intelligent decision-making data, and improves the accuracy of status data processing and the efficiency of system resource utilization.

[0160] This embodiment provides a detailed description of the process in the above embodiment of inputting the data to be input into a large language model agent to obtain the first instruction review result and the first instruction. The specific implementation of this process includes the following steps:

[0161] Step c1: Translate the input data into the large language model agent to obtain the first requirement translation data.

[0162] Specifically, based on the natural language understanding and structured data parsing capabilities of the large language model intelligent agent, the lower-level machine status information and potential control requirements contained in the input data are transformed into standardized and logically clear requirement description text, eliminating ambiguity and redundant information in the data.

[0163] By transforming unstructured or semi-structured input data into structured requirement data that can be deeply analyzed by a large language model, clear requirement guidance is provided for subsequent decision analysis, avoiding instruction generation deviations caused by unclear requirement expressions. This ensures that the large language model agent accurately understands the control requirements behind the input data, improves the accuracy of requirement interpretation, and lays a unified and clear requirement foundation for subsequent instruction generation.

[0164] Step c2 involves performing decision analysis and processing based on the translated data of the first requirement and the pre-stored historical status data of the lower-level machine to obtain the first instruction.

[0165] Specifically, the large language model agent combines the current requirements in the translation data of the first requirement, retrieves the pre-stored historical state data of the lower-level machine, such as historical task execution parameters, fault handling records, and resource adaptation rules, and generates executable control instructions that conform to the operating characteristics of the lower-level machine and meet the current requirements through multi-dimensional data correlation analysis and logical analysis.

[0166] By integrating current needs with historical data, we avoid the short-sightedness of generating instructions based solely on the current state. This ensures that instructions are both suitable for the current scenario and conform to the long-term operating patterns of the lower-level machine, improving the scientific nature and adaptability of the instructions. The generated first instruction is both targeted and reasonable, meeting current control requirements while avoiding the mismatch between instructions and the actual operating characteristics of the lower-level machine caused by deviating from historical operating data, thus guaranteeing the feasibility of instruction execution.

[0167] Step c3: Perform a triple intelligent review mechanism on the first instruction to obtain the review result of the first instruction.

[0168] Specifically, the triple intelligent review mechanism sequentially performs format review (verifies whether the instruction syntax and parameter format conform to the lower-level machine's recognition standards), semantic review (verifies whether the instruction logic is self-consistent and without contradictions), and resource feasibility review (determines whether the lower-level machine's current resources support instruction execution), forming the final review result through multi-dimensional verification.

[0169] The first instruction is fully validated from three core dimensions: format, logic, and resources. Invalid instructions with format errors, logical contradictions, or unusable resources are filtered out to prevent erroneous instructions from being sent to the lower-level machine and causing system anomalies. This significantly reduces the probability of erroneous instructions being sent, improves the security and executability of instructions, provides reliable quality assurance for subsequent instructions sent to the lower-level machine, and reduces system operation errors caused by instruction problems.

[0170] This invention achieves intelligent and precise processing of the entire process of instructions from requirement understanding to generation to verification through a progressive processing mechanism that translates the input data into requirements, generates instructions by combining historical data, and verifies instructions through triple review. This ensures that the instructions meet the current requirements and the characteristics of the lower-level machine, while also guaranteeing the security and executability of the instructions, effectively improving the reliability and scientific nature of instruction generation in dynamic resource scheduling.

[0171] In some optional implementations, the following steps are also included:

[0172] Step d1: When the source of the event is detected to be the host computer, user interaction data is acquired.

[0173] Specifically, based on the identification results of the source event trigger, the host computer user interaction interface is enabled, and the control request information input by the user through natural language or other interaction methods is received and parsed according to the preset data acquisition protocol to form structured user interaction data.

[0174] By capturing the user's control intent initiated through the host computer, the user's needs are transformed into data that the system can process, providing input for subsequent task scheduling and instruction generation, realizing effective interaction between the user and the system, ensuring that the host computer can accurately obtain the user's control needs, and laying the data foundation for subsequent responses to user instructions.

[0175] Step d2: Obtain the current task status information, and determine whether to execute the source event that is triggered by the source event based on the current task status information, the source event priority, and the preset event priority table.

[0176] Specifically, by collecting status data such as the current task queue, resource utilization, and task execution progress of the host computer and associated slave computers, and combining the source event priority with the rules in the preset event priority table, the system determines whether to execute the source event based on priority comparison and resource adaptability analysis.

[0177] Before responding to user interaction events, analyze the matching degree between the current system load and the event priority to avoid resource conflicts or interruptions of high-priority tasks caused by blind execution. Ensure that the execution decision of user interaction events conforms to the overall system resource scheduling strategy, balance user needs and system operation stability, and improve the rationality of the decision.

[0178] Step d3: Input the user interaction data into the large language model agent to obtain the second instruction review result and the second instruction. When the second instruction review result is detected as passed, the second instruction is format converted to obtain the second instruction message and the lower-level machine start signal.

[0179] Specifically, the large language model intelligent agent performs requirement parsing and logical analysis on user interaction data to generate an adapted second instruction; it verifies the validity of the instruction through a triple intelligent review mechanism; after the review is passed, it converts the instruction into a message format that the lower-level machine can recognize according to the communication protocol, and generates a start signal to trigger the lower-level machine's state switching.

[0180] By transforming user interaction data into standardized, executable control commands, and ensuring the correctness of the commands through an auditing mechanism, while generating signals to trigger responses from lower-level machines, the system realizes the transformation of user needs into system commands. This ensures that user needs are accurately transformed into legitimate and valid control commands, providing precise basis for lower-level machine execution, and also ensures command compatibility through format conversion.

[0181] Step d4: Send a lower-level machine start signal to the lower-level machine so that the lower-level machine stops the current task according to the lower-level machine start signal and returns a lower-level machine start feedback signal.

[0182] Specifically, a start signal is sent through the communication link between the upper and lower computers. After receiving the signal, the lower computer terminates the current low-priority task, suspends the relevant process, and generates a start feedback signal to send back to the upper computer to complete the state synchronization.

[0183] The lower-level machine is forced to terminate low-priority tasks that conflict with the current user command, freeing up resources for the execution of new commands. At the same time, feedback signals confirm that the lower-level machine has entered the ready state, ensuring that the lower-level machine can respond to user commands initiated by the upper-level machine in a timely manner. The task termination mechanism ensures the priority execution of high-priority user requests and realizes dynamic adjustment of resources.

[0184] Step d5: Receive the lower-level machine start feedback signal sent by the lower-level machine, and send the second instruction message to the lower-level machine so that the lower-level machine can make an execution permission decision on the second instruction message. When the permission decision is passed, the second instruction is executed and a second completion signal is generated.

[0185] Specifically, the second instruction message is sent based on the start feedback signal received from the host computer. The slave computer verifies the legality and security of the instruction according to its own permission management rules. If the verification is successful, the instruction is executed and a completion signal is generated. After confirming that the slave computer is ready, the instruction is delivered. A second verification is performed through the slave computer's permission decision to ensure that the instruction execution complies with security specifications. This achieves timing control and security verification of instruction issuance, preventing the instruction from being executed in a non-ready state or from being executed without authorization, thus improving the security and reliability of instruction execution.

[0186] Step d6: Receive the second completion signal sent by the lower-level machine.

[0187] Specifically, the system receives the instruction execution final status signal returned by the lower-level machine through the communication link. The instruction execution final status signal contains an execution result identifier, which is used to confirm that the instruction corresponding to the user interaction event has been processed.

[0188] The second completion signal marks the end of the user interaction event processing flow, providing a basis for the host computer to update the task status, release resources, or trigger subsequent operations. This achieves closed-loop management of user interaction events from initiation to completion, ensuring that the system's execution status of user commands is traceable and improving the integrity and controllability of the process.

[0189] This invention achieves accurate response and secure execution of user interaction events by identifying the host computer trigger source and acquiring user interaction data, combining system status and priority to make execution decisions, generating and verifying instructions through intelligent processing, and controlling the lower-level machine to execute and confirm the results through signal interaction. This ensures dynamic adaptation between user needs and system resource scheduling, and improves the system's efficiency and reliability in handling host computer trigger events.

[0190] In some optional implementations, the process of acquiring user interaction data is described in detail, and the specific implementation of this process includes the following steps:

[0191] Step d11: Respond to the user's multimodal interaction to obtain natural language.

[0192] Specifically, the system integrates a multimodal interaction interface, which can recognize and parse operation commands triggered by users through heterogeneous interaction methods such as voice, text, and gestures. It also uses a semantic mapping algorithm to convert various interaction commands into natural language text. For example, voice signals are converted into text through speech-to-text processing, and gesture commands are converted into descriptive text through preset mapping rules.

[0193] Step d12: Perform data encapsulation processing based on natural language to obtain user interaction data.

[0194] Specifically, Natural Language Processing (NLP) techniques, such as word segmentation, entity recognition, and intent classification, are used to semantically analyze natural language, extracting key information such as task type, resource object, and priority, and encapsulating it into standardized data formats (such as JSON). For example, the sentence "Start the marine observation equipment at 10:00 AM tomorrow" might be parsed as (Task: "Start equipment", Object: "Marine observation equipment", Time: "10:00 AM tomorrow"). User input can be provided through the host computer's user interaction module via multimodal input of natural language indicating a state change. The host computer will then encapsulate the natural language into JSON data. This transformation of unstructured natural language into computable and storable structured data provides clear input for subsequent resource scheduling decisions, avoids scheduling errors caused by semantic ambiguity, and improves the efficiency and accuracy of data processing.

[0195] In some optional implementations, after receiving the first completion signal sent by the lower-level machine, the following steps are further included:

[0196] Step e1: Generate a first completion feedback signal based on the first completion signal.

[0197] Specifically, based on the instruction execution result information contained in the first completion signal sent by the lower-level machine, a standardized completion feedback signal is generated according to a preset feedback protocol. This signal contains an execution status confirmation identifier and a subsequent operation instruction field.

[0198] By confirming with the host computer that the slave computer has successfully executed the instruction, and simultaneously transmitting the trigger instruction for subsequent operations to the slave computer, a closed-loop confirmation mechanism for instruction execution is formed. This ensures that the host and slave computers reach a consensus on the final state of instruction execution, avoids process interruption or repeated execution due to asymmetric state information, and guarantees the integrity of event processing.

[0199] Step e2: Send the first completion feedback signal to the lower-level machine so that the lower-level machine restarts the timer and continuously sends heartbeat packets to the upper-level machine through the timer.

[0200] Specifically, the first completion feedback signal is transmitted through the communication link between the upper and lower computers. After receiving the signal, the lower computer restarts the preset timer according to the signal instruction. The timer triggers the lower computer to collect its own status at fixed intervals and encapsulate it into a heartbeat packet, which is then continuously uploaded to the upper computer.

[0201] After the instruction is executed, the lower-level machine resumes its regular status monitoring. The heartbeat packet enables the upper-level machine to continuously perceive the real-time status of the lower-level machine, providing basic status data for subsequent event triggering and scheduling. This achieves seamless connection between task execution and regular monitoring, ensuring that the lower-level machine remains within the effective monitoring range of the upper-level machine after completing the instruction. This enhances the system's real-time control over the status of the lower-level machine and reduces the occurrence of missed abnormal states.

[0202] This invention, through the mechanism of generating and sending a first completion feedback signal to drive the lower-level machine to restart the timer and continuously send heartbeat packets, realizes the state monitoring and recovery after a single instruction execution, ensures the synchronization of the upper and lower-level machine states and continuous communication, and improves the system's controllability of the lower-level machine's full-cycle state and the closed-loop integrity of event processing.

[0203] Figure 2 A schematic diagram of the dynamic resource scheduling device provided in this application. Figure 2 As shown, the dynamic resource scheduling device 20 includes:

[0204] The acquisition module 201 is used to acquire the source event triggering conditions and determine the source event triggering source and the source event priority based on the source event triggering conditions and the preset event priority table.

[0205] The detection module 202 is used to receive the start signal sent by the lower-level machine when the source of the detected event is the lower-level machine.

[0206] The execution confirmation module 203 is used to obtain the current task status information and determine whether to execute the source event that is triggered by the source event based on the current task status information, the source event priority and the preset event priority table.

[0207] The feedback module 204 is used to generate a startup feedback signal based on the startup signal when a source event is detected to be executed, and send the startup feedback signal to the lower-level machine so that the lower-level machine can obtain the current status message of the lower-level machine based on the startup feedback signal.

[0208] The conversion module 205 is used to receive the current status message of the lower-level machine sent by the lower-level machine and perform conversion processing to obtain the input data.

[0209] The input module 206 is used to input the data to be input into the large language model agent, obtain the first instruction review result and the first instruction, and when the first instruction review result is detected as passed, the first instruction is processed by format conversion to obtain the first instruction message.

[0210] The sending module 207 is used to send the first instruction message to the lower-level machine so that the lower-level machine can make an execution permission decision on the first instruction message. When the permission decision is passed, the first instruction is executed and a first completion signal is generated.

[0211] The receiving module 208 is used to receive the first completion signal sent by the lower-level machine.

[0212] In one possible implementation, the acquisition module acquires the source event triggering conditions of 201, including threshold triggering conditions, interactive response triggering conditions, change triggering conditions, and timer triggering conditions. The preset events include the correspondence between source events, priorities, triggering conditions, and triggering sources.

[0213] Based on the source event triggering conditions and the preset event priority table, determine the source event triggering source and the source event priority, including:

[0214] The target trigger condition is determined from the threshold trigger condition, interactive response trigger condition, change trigger condition, and timer trigger condition in the preset event priority table based on the source event trigger condition.

[0215] Based on the correspondence between the target triggering conditions and the source event, priority, triggering conditions, and triggering source, determine the source event triggering source and the source event priority.

[0216] In one possible implementation, the confirmation module 203 is specifically used for:

[0217] Determine whether the host computer is in an idle state based on the current task status information.

[0218] When the host computer is detected to be in an idle state, the current triggered event of the slave computer is obtained.

[0219] The priority of the current triggered event is determined based on the current triggered event and the preset event priority table of the lower-level machine.

[0220] The priority of the source event is compared with the priority of the currently triggered event to determine whether to execute the source event that meets the triggering conditions.

[0221] In one possible implementation, the verification module 203 is further configured to:

[0222] When the host computer is detected to be in a non-idle state, the current task being executed by the host computer is obtained, and the priority of the current task being executed by the host computer is determined according to the preset time priority table.

[0223] The system compares the priority of the currently executing task with the priority of the source event to determine whether to execute the source event triggered by the source event.

[0224] In one possible implementation, the conversion module 205 is specifically used for:

[0225] Receive the current status message of the lower-level machine sent by the lower-level machine.

[0226] The current status message from the lower-level machine is formatted and converted to obtain the data to be judged.

[0227] The system performs instruction control judgment based on the data to be judged, so that when it detects that the data to be judged needs to be controlled by instruction, the data to be judged is used as the input data.

[0228] In one possible implementation, the input module 206 is specifically used for:

[0229] The large language model agent translates the input data to obtain the first requirement translation data.

[0230] Based on the translation data of the first requirement and the pre-stored historical status data of the lower-level machine, decision analysis and processing are performed to obtain the first instruction.

[0231] A triple intelligent review mechanism is executed on the first instruction to obtain the review result of the first instruction.

[0232] In one possible implementation, the detection module 202 is further configured to:

[0233] When the source of the detected event is the host computer, user interaction data is obtained.

[0234] Obtain the current task status information, and determine whether to execute the source event that matches the triggering conditions based on the current task status information, the source event priority, and the preset event priority table.

[0235] User interaction data is input into the large language model agent to obtain the second instruction review result and the second instruction. When the second instruction review result is detected as passed, the second instruction is format converted to obtain the second instruction message and the lower-level machine start signal.

[0236] Send a lower-level machine start signal to the lower-level machine so that the lower-level machine stops the current task according to the lower-level machine start signal and returns a lower-level machine start feedback signal.

[0237] The system receives the lower-level machine start feedback signal sent by the lower-level machine, sends the second instruction message to the lower-level machine, so that the lower-level machine can make an execution permission decision on the second instruction message. When the permission decision is passed, the second instruction is executed and a second completion signal is generated.

[0238] Receive the second completion signal sent by the lower-level machine.

[0239] In one possible implementation, the detection module is further configured to:

[0240] A first completion feedback signal is generated based on the first completion signal.

[0241] The first completion feedback signal is sent to the lower-level machine, so that the lower-level machine restarts the timer and continuously sends heartbeat packets to the upper-level machine through the timer.

[0242] The dynamic resource scheduling device provided in this embodiment can be used to execute the dynamic resource scheduling method described above. Its implementation principle and technical effect are similar, and will not be described again in this embodiment.

[0243] Figure 3 A schematic diagram of the hardware structure of the electronic device provided in this application, such as... Figure 3 As shown, the electronic device 30 includes at least one processor 301 and a memory 302. Optionally, the electronic device 30 also includes a communication component 303. The processor 301, memory 302, and communication component 303 are connected via a bus 304.

[0244] In the specific implementation process, at least one processor 301 executes computer execution instructions stored in memory 302, causing at least one processor 301 to perform the above method.

[0245] The specific implementation process of processor 301 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0246] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0247] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0248] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0249] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0250] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0251] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0252] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0253] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0254] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0255] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0256] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0257] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0258] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A dynamic resource scheduling method, characterized in that, The method, applied to a host computer where a large language model agent is deployed, includes: Obtain the source event triggering conditions, and determine the source event triggering source and source event priority based on the source event triggering conditions and the preset event priority table; When the source of the source event is detected to be a lower-level device, the start signal sent by the lower-level device is received. Obtain the current task status information, and determine whether to execute the source event that is triggered by the source event based on the current task status information, the source event priority, and the preset event priority table; When the source event is detected to be executed, a startup feedback signal is generated based on the startup signal, and the startup feedback signal is sent to the lower-level machine so that the lower-level machine can obtain the current status message of the lower-level machine based on the startup feedback signal. Receive the current status message of the lower-level machine sent by the lower-level machine and perform conversion processing to obtain the data to be input; The input data is input into the large language model agent to obtain the first instruction review result and the first instruction. When the first instruction review result is detected as passed, the first instruction is format converted to obtain the first instruction message. The first instruction message is sent to the lower-level machine so that the lower-level machine makes an execution permission decision on the first instruction message. When the permission decision is passed, the first instruction is executed and a first completion signal is generated. Receive the first completion signal sent by the lower-level machine.

2. The method according to claim 1, characterized in that, The source event triggering conditions include threshold triggering conditions, interactive response triggering conditions, change triggering conditions, and timer triggering conditions; the preset events include the correspondence between source events, priorities, triggering conditions, and triggering sources. The step of determining the source event triggering source and the source event priority based on the source event triggering conditions and the preset event priority table includes: The target triggering condition is determined from the threshold triggering condition, interactive response triggering condition, change triggering condition and timer triggering condition in the preset event priority table based on the source event triggering condition. Based on the correspondence between the target triggering conditions and the source event, priority, triggering conditions, and triggering source, determine the source event triggering source and source event priority.

3. The method according to claim 1, characterized in that, The step of determining whether to execute the source event that triggers the source event based on the current task status information, the source event priority, and the preset event priority table includes: Determine whether the host computer is idle based on the current task status information; When the host computer is detected to be in an idle state, the current triggered event of the slave computer is obtained; The priority of the current triggering event is determined based on the current triggering event of the lower-level machine and the preset event priority table; The priority of the source event is compared with the priority of the current triggering event to determine whether to execute the source event that matches the triggering condition of the source event.

4. The method according to claim 3, characterized in that, After determining whether the host computer is idle based on the current task status information, the process further includes: When the host computer is detected to be in a non-idle state, the current task being executed by the host computer is obtained, and the priority of the current task being executed by the host computer is determined according to the preset time priority table. The priority of the currently executing task on the host computer is compared with the priority of the source event to determine whether to execute the source event triggered by the source event.

5. The method according to claim 1, characterized in that, The process of receiving and converting the current status message sent by the lower-level machine to obtain the input data includes: Receive the current status message of the lower-level machine sent by the lower-level machine; The current status message of the lower-level machine is converted into a new format to obtain the data to be judged. The instruction control judgment is performed based on the data to be judged, so that when the data to be judged is detected to require instruction control, the data to be judged is used as the input data.

6. The method according to claim 1, characterized in that, The input data is input into the large language model agent to obtain the first instruction review result and the first instruction, including: The large language model agent translates the input data to obtain the first demand translation data. Based on the first requirement translation data and the pre-stored lower-level machine historical status data, a decision analysis and processing is performed to obtain the first instruction; A triple intelligent review mechanism is executed on the first instruction to obtain the review result of the first instruction.

7. The method according to any one of claims 1 to 6, characterized in that, Also includes: When the source of the event is detected to be a host computer, user interaction data is acquired. Obtain the current task status information, and determine whether to execute the source event that is triggered by the source event based on the current task status information, the source event priority, and the preset event priority table; The user interaction data is input into the large language model agent to obtain the second instruction review result and the second instruction. When the second instruction review result is detected as passed, the second instruction is processed for format conversion to obtain the second instruction message and the lower-level machine start signal. The lower-level machine start signal is sent to the lower-level machine so that the lower-level machine stops the current task according to the lower-level machine start signal and returns a lower-level machine start feedback signal; The system receives the lower-level machine start feedback signal sent by the lower-level machine, sends the second instruction message to the lower-level machine, so that the lower-level machine makes an execution permission decision on the second instruction message, executes the second instruction when the permission decision is passed, and generates a second completion signal. Receive the second completion signal sent by the lower-level machine.

8. The method according to claim 7, characterized in that, After receiving the first completion signal sent by the lower-level machine, the method further includes: A first completion feedback signal is generated based on the first completion signal; The first completion feedback signal is sent to the lower-level machine, so that the lower-level machine restarts the timer and continuously sends heartbeat packets to the upper-level machine through the timer.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-8.

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