A context-aware interactive silencing method, system, and computer-readable storage medium

Abstract

In various scenarios such as industrial control, IoT, smart homes, and automotive systems, systems need to determine whether to respond to input information based on context. For example, when industrial sensors periodically report non-urgent data, the control system can remain silent to save computing power; a smart light bulb silently responds to a "turn on the light" command when it senses sufficient light during the day. However, existing technologies suffer from fixed response strategies, lack of learning ability, insufficient scene awareness, and resource waste. This application provides a context-aware interactive silent method, system, and computer-readable storage medium, aiming to solve the problems of fixed response strategies, lack of learning ability, and insufficient scene awareness in existing systems. It enables the system to dynamically decide whether to silently process input information based on its importance, processing capacity, and operating scenario, and to continuously optimize through feedback learning.

Description

Technical Field

[0001] This application relates to the field of human-computer interaction and intelligent system control technology, and more specifically, to a context-aware interactive silencing method, system, and computer-readable storage medium. Background Technology

[0002] In intelligent interaction scenarios such as industrial control, IoT, smart homes, and automotive systems, systems continuously receive input information from sensors, user commands, or external devices. For example, when industrial sensors periodically report non-critical status data, a complete response from the control system would consume additional computing power; smart lighting devices receiving a light-on command under sufficient ambient light conditions would waste energy if executed mechanically; and automotive systems processing non-urgent notifications while the driver is focused on driving could distract attention and create safety hazards. Ideally, the system should be able to intelligently discern the intrinsic value of input information and implement silent processing of unnecessary responses to optimize resource allocation. However, current technologies generally suffer from rigid response strategies, relying solely on preset static rules, either forcing responses to all inputs or completely ignoring specific types of inputs, failing to dynamically adjust decision logic based on real-time context. Furthermore, existing mechanisms lack effective feedback learning capabilities, failing to extract patterns from historical silent events to iteratively optimize decision rules, resulting in the system operating in a suboptimal state for extended periods. Significant deficiencies also exist at the scene perception level; the system struggles to accurately identify diverse operating environments such as low-load energy-saving scenarios, high-priority emergency scenarios, or scenarios with deferred tasks, making it unable to implement differentiated silent strategies. This technological limitation directly leads to the ineffective use of system resources. For example, processing redundant inputs consumes computing units, memory bandwidth, or network resources, thereby interfering with the smoothness of core task execution and the overall stability of the system, and severely restricting the adaptability and reliability of smart devices in complex environments.

[0003] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0004] The purpose of this application is to provide a context-aware interactive silencing method, system, and computer-readable storage medium that can dynamically adjust response strategies according to real-time context, avoid unnecessary resource consumption, and improve system efficiency and reliability.

[0005] This application provides a context-aware interactive silencing method, the technical solution of which is as follows: Includes the following steps: Receive input information; The evaluation results are obtained by assessing the necessity of responding to input information, the processing capability of the current task, and identifying at least one of the current operating scenarios. Based on the evaluation results, a decision is made according to preset rules as to whether to silently process the input information. Record silent events and subsequent feedback to form feedback samples, which are used to optimize silent decision-making rules.

[0006] Furthermore, this application also proposes that the evaluation includes at least one of the following: Assess the necessity of responding to the input information and obtain the necessity assessment results; Assess the ability to handle the current task and obtain the capability assessment results; Identify the current operating scenario.

[0007] Furthermore, this application also proposes that assessing the necessity of responding to input information includes: Analyze the importance or urgency of the input information; Calculate the overall necessity score by considering the priority of the relationship with the information source.

[0008] Furthermore, this application also proposes that the assessment of processing capability for the current task includes: Based on the frequency and complexity of related tasks in historical processing records, the ability to process the current task can be determined.

[0009] Furthermore, this application also proposes that identifying the current operating scenario includes, but is not limited to: The system detects whether it is in a low-load state and identifies it as an energy-saving scenario. Detect whether the task is non-urgent and can be delayed, and identify it as a delayable scenario; The system detects whether the source of the information has a high priority and identifies it as a high-priority scenario.

[0010] Furthermore, this application also proposes that the preset rules include multiple configurable parameters, which can be adjusted by the user or the system through the configuration interface.

[0011] Furthermore, this application also proposes to include: if a decision is made to handle the situation silently, recording the silent event without performing the corresponding response action.

[0012] Furthermore, this application proposes that feedback samples include subsequent behavior of the information source after silencing, changes in system performance, or user feedback, which can be used to adjust the silencing decision parameters.

[0013] Furthermore, this application also proposes that the method be applied to intelligent agents, robots, industrial control systems, Internet of Things devices, smart home systems, vehicle systems, or communication networks.

[0014] Furthermore, this application also proposes a context-aware interactive silence system, comprising: The input receiving module is used to receive input information; The evaluation module is used to evaluate the input information and obtain the evaluation results; A silent decision engine is used to determine whether to silently process input information based on evaluation results and according to preset rules. The feedback learning module is used to record silent events and subsequent feedback to form feedback samples and optimize silent decision-making rules.

[0015] Furthermore, this application also proposes that the evaluation module be configured to perform at least one of the following evaluations: Necessity assessment is used to analyze the necessity of responding to input information; Capability assessment is used to evaluate the system's ability to handle the current task; Scene recognition is used to identify the current running scene.

[0016] Furthermore, this application also proposes that the necessity assessment module is configured to connect with the priority management module to obtain priority information of the information source.

[0017] Furthermore, this application also proposes that the capability assessment module is configured to connect with the history module to obtain historical processing data of relevant tasks.

[0018] Furthermore, this application also proposes to include an event logging module for recording a silence event when a decision is made to handle silence.

[0019] Furthermore, this application also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0020] As can be seen from the above, the context-aware interactive silencing method, system, and computer-readable storage medium provided in this application dynamically determine the silencing process by receiving input information, evaluating the necessity of the response, processing capacity, and scenario, and recording feedback optimization rules. This solves the problem of resource waste and system instability caused by rigid response strategies in the background technology. It has the advantages of being able to dynamically adjust the response strategy according to the real-time context, avoiding unnecessary resource consumption, and improving system efficiency and reliability. Attached Figure Description

[0021] Several embodiments of this application are described below with reference to the accompanying drawings. It should be noted that the specific structures, modules, steps, parameters, and connections shown in the drawings are preferred embodiments of this application and not limitations on the scope of protection of this application. Those skilled in the art can make various modifications, substitutions, or combinations to the specific details shown in the drawings based on the teachings of this application, and these modified embodiments should still be considered to fall within the scope of protection of this application.

[0022] Figure 1This application provides an overall architecture diagram of a silent interactive system, which serves as an exemplary architecture for the silent interactive system. The evaluation module can be configured with one or more sub-modules according to actual needs, and the silence decision can be based on the output of any sub-module. Each module can be adjusted according to actual applications without limiting the scope of protection.

[0023] Figure 2 This application provides a flowchart of a silent decision-making process, illustrating the complete flow from receiving input to deciding whether to remain silent. Silent decision-making can be based on a single factor or a fusion of multiple factors, with feedback learning used to continuously optimize decision parameters. This diagram is merely an example and does not constitute a limitation of the claims.

[0024] Figure 3 The illustrations provided in this application represent silent prompts in different scenarios. The specific implementation can be designed according to application requirements and does not constitute a limitation on the claims. Detailed Implementation

[0025] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application. Other technologies that may be mentioned in the embodiments can be implemented using existing technology or other patent applications filed by the applicant on the same day, and will not be repeated here. It should be particularly noted that the specific module divisions, process steps, data flow directions, status names, time values, etc., shown in the accompanying drawings are merely illustrative examples and should not constitute a limitation on the scope of protection of the claims of this application. The scope of protection of the claims is determined solely by their wording and should be interpreted in accordance with the overall content of the specification.

[0026] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] In traditional industrial control systems, IoT devices, smart home devices, and vehicle systems, the system's response strategy to input information is fixed and cannot be dynamically adjusted according to context. Furthermore, the necessity of responding to input information is not dynamically assessed, and the system's current processing capacity and operating scenario are not incorporated into the decision-making process. Consequently, unnecessary inputs are processed, system resources are consumed, the efficiency of core tasks is affected, and overall system performance is reduced.

[0028] For example, in a smart home environment, when a user command to "turn on the living room lights" is received, the system senses that the ambient light is sufficient, and the command should be deemed unnecessary. However, due to a lack of scene recognition capabilities, the system fails to recognize that it is currently daytime and well-lit, and instead executes the action of turning on the lights, wasting electricity and consuming computing resources. Furthermore, historical silent events are not learned, and decision-making rules are not optimized, causing the system to be unable to adapt to recurring similar scenarios, resulting in continuous and ineffective resource consumption.

[0029] If these issues are not addressed, system resources will continue to be consumed by unnecessary inputs, critical task responses will be delayed, and system stability will be reduced. In industrial control scenarios, security risks will be triggered; in IoT environments, device battery life will be shortened, and network bandwidth utilization will be reduced. Furthermore, the system will not adapt to dynamic changes in operating scenarios, leading to deterioration in system efficiency and a compromised user experience.

[0030] To address this, this application proposes a context-aware interactive silencing method, comprising the following steps: The input information was received; The necessity of responding to input information, the processing capability of the current task, and at least one of the current operating scenario are evaluated, and the evaluation results are obtained. Based on the evaluation results, a decision is made as to whether to silently process the input information according to preset rules; Silent events and subsequent feedback are recorded, and feedback samples are formed for the optimization of silent decision-making rules.

[0031] For ease of understanding, the following explains some key terms in this embodiment: "Input information" refers to various types of data, instructions, or events received by the system from the external environment or internal components. This information can originate from various channels such as sensors, user interfaces, other system modules, and network communications, and forms the basis for subsequent system processing.

[0032] Context awareness refers to a system's ability to acquire and understand its current operating environment, its own state, and the intentions of relevant entities (such as users and other devices). Through context awareness, a system can obtain more comprehensive information, thereby making decisions that are more in line with the current situation.

[0033] "Interactive silence" refers to a system's deliberate choice not to immediately execute a corresponding response action or to delay the execution of the response action after receiving input information, based on specific evaluation results and preset rules. This approach aims to optimize system resource utilization, reduce unnecessary interference, or improve user experience.

[0034] "Evaluation results" refer to the comprehensive judgment or data generated after assessing the necessity of the system's response to input information, its processing capability for the current task, and the current operating scenario. These results provide quantitative or qualitative basis for subsequent silent decision-making.

[0035] "Preset rules" refer to a set of logical conditions or parameters pre-configured within the system to guide the system in determining whether to silently process the input information after receiving the evaluation results. These rules can be adjusted according to application requirements.

[0036] A "silent event" refers to a record of a successful interactive silent process executed by the system. This record contains key data such as the time the silence occurred, the corresponding input information, the evaluation result, and the silent decision, which forms the basis for the system's learning and optimization.

[0037] "Feedback samples" refer to a set of data formed by recording silent events and the changes in the system or environment after silent treatment (i.e., subsequent feedback). These samples are used to train or adjust silent decision-making models, enabling the system to learn from historical experience and improve its silent strategy.

[0038] A "silent decision rule" refers to the algorithm, model, or logic used by a system to decide whether to perform silent processing. This rule is dynamically optimizable, and its decision-making ability can be continuously improved through learning from feedback samples.

[0039] This embodiment provides a context-aware interactive silencing method, which aims to solve the problems of fixed response strategies, lack of learning ability and insufficient scene awareness in existing systems, thereby achieving dynamic silencing processing and optimizing decision-making.

[0040] First, the system is configured to "receive input information." This step is the starting point of the entire silent processing flow, ensuring that the system can acquire the various types of data that need to be processed. For example, the system can continuously listen to a specific communication port or periodically read data from connected sensors. As one implementation method, the system can use a polling mechanism to periodically check for new data arrivals; alternatively, the system can be configured in an event-driven mode, where the information source actively triggers the system to receive new information. Through these methods, the system can acquire input from different sources, providing a foundation for subsequent evaluation.

[0041] Furthermore, the system is configured to "evaluate the necessity of responding to input information, the processing capability of the current task, and identify at least one of the current operating scenario to obtain an evaluation result." This evaluation step is the core of achieving context awareness, providing multi-dimensional basis for silent decision-making.

[0042] Specifically, when assessing the necessity of responding to input information, the system can assign a preset static priority to the input information based on its type or source. For example, information from the security alarm module may be assigned high necessity, while information from the non-critical status reporting module may be assigned low necessity.

[0043] When assessing its capacity to handle the current task, the system can monitor its critical resource usage. For example, it can determine if the system is under high load by checking the average CPU load or the amount of free memory. When resource utilization exceeds a certain fixed threshold, the system is considered to have low processing capacity.

[0044] When identifying the current operating scenario, the system can determine the current scenario based on a preset time period. For example, during nighttime, the system can be identified as a "low-activity scenario"; during weekday daytime, it is identified as a "normal working scenario".

[0045] Through the above evaluation, the system can generate a comprehensive evaluation result, which can be a simple Boolean value (e.g., whether silence is appropriate) or a numerical score that reflects the appropriateness of the silence process.

[0046] Based on this, the system "decides whether to silently process the input information according to preset rules based on the evaluation results." This step is a crucial one in making decisions based on contextual information. For example, the system can have a built-in simple decision table that maps different combinations of evaluation results to decisions of "silent processing" or "normal response." When the evaluation results indicate low necessity for the response and the system's processing capacity is strained, the decision table may indicate silent processing. As another implementation, the system can use a series of hard-coded conditional statements, such as "if the evaluation results show necessity is lower than X and scenario is Y, then execute silence." Through these preset rules, the system can dynamically adjust its response strategy to input information based on the current contextual information.

[0047] Finally, the system is configured to "record silent events and subsequent feedback to form feedback samples for optimizing silent decision rules." This step is core to enabling the system's learning and adaptive capabilities. When the system decides to silence a piece of input information, it records detailed information about the silencing action (e.g., the content of the input information, evaluation results, decision time, etc.) to form a silent event record. Subsequently, the system observes whether, within a certain period after the silencing, the relevant information source generates repeated input, whether system performance changes, or whether there are other indirect indications of the effectiveness of the silencing. For example, if, after silencing, the information source does not send the same or similar information again within a certain period, this may be considered positive feedback. These silent events and subsequent feedback data are integrated to form feedback samples. These feedback samples are then used to adjust or update the silent decision rules. For example, through statistical analysis, the system can discover that the success rate of silencing is higher under certain combinations of evaluation results, thereby strengthening the corresponding decision rules; conversely, if silencing frequently leads to negative feedback, the corresponding rules will be weakened or modified. Through this continuous feedback learning mechanism, silent decision-making rules can constantly adapt to new environments and needs, thereby improving the intelligence and efficiency of the system.

[0048] The following example will provide a more detailed explanation of the above technical solution: Imagine a smart home system with multiple sensors and controllers deployed to provide users with a convenient living experience. Late at night, say 3 a.m., a light sensor in the bedroom detects a faint change in light and sends an "indicating a change in light" message to the main control system.

[0049] First, the main control system "receives input information," that is, it acquires "light change" data from the bedroom light sensor.

[0050] The system then begins its evaluation. When assessing the necessity of responding to the input information, the system, based on preset rules, determines that at 3 AM, faint changes in light typically do not indicate an emergency requiring immediate response; therefore, the necessity of responding to this input information is assessed as "low." When assessing the processing capacity for the current task, the system checks its CPU load and memory usage, finding that the system is currently under low load and has sufficient processing capacity. When identifying the current operating scenario, the system determines, based on time information, that it is currently in a "sleep mode" scenario. Therefore, by combining the above evaluation results, the system arrives at an assessment result indicating "suitable for silent processing."

[0051] Next, the main control system "determines whether to silently process the input information based on the evaluation results and according to preset rules." The system's internal decision-making logic is triggered, which may include a rule: "If the necessity of responding to the light change information is low and the current scene is in sleep mode, then silent processing is performed." Based on this rule and the previously obtained evaluation results, the system decides to silently process the "light change" information, that is, not to immediately trigger any lighting adjustments or notifications.

[0052] Finally, the system "records the silent event and subsequent feedback to form a feedback sample for optimizing the silent decision-making rules." The system records detailed information about this silent process (including input information, evaluation results, decision time, etc.) as a silent event. For a period after the silent process, the system continuously monitors the bedroom environment and user behavior. For example, if the user does not manually turn on the bedroom light or interact with the system in any other way after the silent process, this is considered positive follow-up feedback. This silent event and positive feedback together constitute a feedback sample. This sample is then used to adjust or strengthen the silent decision-making rules, making the system more inclined to execute silent processes in similar late-night lighting scenarios in the future.

[0053] Based on the examples above, the technical concept of this application demonstrates a significant technological contribution. Traditional smart home systems, when faced with such inputs, may employ fixed response strategies; for example, attempting to adjust the lights whenever a change in light is detected, or completely ignoring all non-urgent light information. This fixed strategy leads to unnecessary resource consumption or a degraded user experience.

[0054] In contrast, this application achieves deep context awareness by introducing a mechanism that "assesses the necessity of responding to input information, the processing capability of the current task, and identifies at least one of the current operating scenario." In the example above, the system can dynamically determine the low necessity of changes in weak light at night and the current sleep mode scenario, thereby avoiding invalid or interfering responses that may occur in traditional systems.

[0055] Furthermore, this application endows the system with learning and adaptive capabilities through the step of "recording silent events and subsequent feedback to form feedback samples for optimizing silent decision-making rules." In traditional systems, once the decision logic is set, its behavior is difficult to adjust based on actual operating results. However, the feedback learning mechanism of this application allows the system to learn from the success or failure of each silent process and continuously optimize its decision-making rules. For example, if multiple silent processes involving changes in nighttime lighting receive positive feedback, the system will strengthen that decision-making pattern; conversely, if silent processes lead to frequent manual intervention by the user, the system will adjust the rules to reduce future silences. This continuous optimization capability enables the system to become more intelligent and efficient over time, thereby solving the problems of fixed response strategies, lack of learning ability, and insufficient scene awareness in traditional systems, and improving the overall performance of the system and user satisfaction.

[0056] In some of the embodiments described above in this application, an evaluation is proposed to obtain evaluation results to determine whether to perform silent processing. However, in its implementation, the evaluation may lack a specific assessment of the necessity of response, processing capacity and operating scenario, resulting in incomplete or inaccurate evaluation results, thereby affecting the accuracy and reliability of silent decision-making.

[0057] In response, this application further proposes that the assessment includes: assessing the necessity of responding to the input information and obtaining a necessity assessment result; assessing the processing capability of the current task and obtaining a capability assessment result; and identifying the current operating scenario.

[0058] Specifically, assessing the necessity of responding to input information aims to determine the urgency or importance of the system's response to received input. This can be achieved in various ways. For example, the system can analyze the type, source, keywords, and criticality of the input information within pre-defined business processes. For instance, alarm information from security systems is typically of higher necessity, while periodic status reports from non-critical sensors are less necessary. Furthermore, time factors can be considered, such as the timeliness of the information and whether it needs to be processed within a specific time window. For example, real-time control commands are generally more necessary than historical data archiving requests. Through this assessment, the system can ensure that critical information is prioritized and resources are not wasted on unimportant input.

[0059] Assessing the system's capacity to handle current tasks aims to measure its ability to process new tasks or respond to input. This can be achieved by monitoring system resource usage, such as CPU utilization, memory consumption, network bandwidth, and I / O throughput. When these resources are nearing saturation, the assessment result will be low. Another approach is to analyze the length of the currently executing task queue, task priorities, and estimated completion times. A large number of high-priority or time-consuming tasks will also result in a low assessment result. This capacity assessment mechanism effectively prevents system overload, ensures the stable operation of core tasks, and avoids system performance degradation or crashes caused by processing unnecessary input.

[0060] Identifying the current operating scenario aims to determine the system's current external environment or internal state, enabling adjustments to decisions based on scenario characteristics. This can be achieved through various means, such as using sensor data, system configuration, or external scheduling commands. For example, it can identify a daytime or nighttime scenario based on ambient light intensity, an energy-saving mode scenario based on user settings, or a low-bandwidth scenario based on network conditions. Furthermore, it can be inferred by combining contextual information such as time, geographical location, and user behavior patterns. For instance, it can identify off-peak hours as outside of working hours or security-sensitive scenarios in specific areas. By identifying the current operating scenario, the system can adopt differentiated silent strategies based on different operating environments, thereby improving the system's adaptability and intelligence.

[0061] The proposed solution refines the evaluation process into three aspects: the necessity of responding to input information, the processing capability of the current task, and the identification of the current operating scenario. This enables the system to perform a multi-dimensional and more comprehensive analysis after receiving input information. First, the system receives the input information. Then, instead of performing a single or fuzzy evaluation, it conducts specific evaluations of the three aspects in parallel or sequentially. The necessity evaluation ensures that the importance of the input information is fully considered, avoiding blind responses to non-critical information. The capability evaluation ensures that the system can wisely choose whether to process new tasks when its resources are limited or the load is high, preventing system performance degradation. Scenario identification provides important contextual information for decision-making, enabling the system to make the most appropriate silent processing judgment based on the characteristics of the environment. The results of these three evaluations constitute a comprehensive evaluation result, which is then used to determine whether to silently process the input information based on preset rules. This meticulous evaluation mechanism significantly improves the accuracy and reliability of silent decision-making, solving the problems of one-sided evaluation and inaccurate decision-making in traditional methods. It enables the system to operate more intelligently and efficiently, and provides a more accurate data foundation for subsequent silent event recording and feedback learning, thereby continuously optimizing the silent decision-making rules.

[0062] The following example illustrates this. In a smart home system, a smart speaker receives a voice command from a user: "Play music." The system first evaluates this input. When assessing the necessity of responding, the system analyzes the command as an entertainment command, with a medium importance level and low urgency. Considering the source is a direct user command, the necessity assessment result is moderately low. Simultaneously, when assessing the processing capacity for the current task, the system detects that video surveillance data from the home security system is currently being uploaded, with CPU usage reaching 80% and network bandwidth nearing its limit; therefore, the capacity assessment result is low processing capacity. Furthermore, the system identifies the current time as late at night based on the internal clock and the user's schedule, and that all family members are asleep, thus identifying the current operating scenario as a "silent rest scenario." Based on these three assessment results, the system comprehensively judges that it is not suitable to immediately respond to the "Play music" command and decides to silence it, for example, by not playing music immediately, playing it at a very low volume, or displaying a message on the screen such as "Currently in silent mode, please try again later."

[0063] Through the above technical solutions, the system can clearly assess the necessity of responding to input information, thereby distinguishing between important and unimportant information, avoiding the processing of unnecessary information, and effectively saving system resources. Simultaneously, by assessing the system's processing capacity for the current task, the system can avoid accepting new tasks when its own load is too high, preventing system overload and ensuring the stable operation of core tasks and the overall performance of the system. Furthermore, by identifying the current operating scenario, the system can adjust its behavior according to different environmental contexts (such as energy saving, high priority, rest, etc.), making silent decision-making more adaptive and intelligent. The combination of these three assessments makes the assessment results more comprehensive, accurate, and multi-dimensional, significantly improving the accuracy and reliability of silent decision-making. It solves the problems of one-sided assessment and inaccurate decision-making in traditional methods, enabling the system to no longer simply respond or ignore, but to make intelligent judgments based on multiple factors, thereby achieving more efficient and stable operation.

[0064] In some of the solutions described above in this application, an assessment of the necessity of responding to input information is proposed to determine whether to perform silent processing. However, in its implementation, the assessment may not be accurate enough and may not fully consider the importance and source priority of the input information, leading to decision bias.

[0065] In this regard, this application further proposes that assessing the necessity of responding to the input information includes: analyzing the importance level or urgency of the input information; and calculating a comprehensive necessity score by combining the priority of the relationship with the information source.

[0066] Analyzing the importance or urgency of input information refers to evaluating the inherent attributes of the received input information to determine its impact on system operation or user experience. Specifically, this can be achieved in several ways: One approach is for the system to pre-define a set of rules to categorize input information into different importance levels, such as "high," "medium," and "low," based on its type (e.g., system alarms, user commands, sensor data, log information), keywords (e.g., words containing "urgent," "fault," or "warning"), or the criticality of the business processes it triggers. Another approach is for the system to use machine learning models trained on historical data to automatically identify the urgency of input information. For example, by analyzing the information's time sensitivity, expected response time, or potential risks, it can determine whether the information requires immediate processing.

[0067] Calculating a comprehensive necessity score by combining the priority of information sources with their relationships means that when assessing the necessity of responding to input information, not only the attributes of the information itself but also the importance or credibility of the information sender are considered, and this is quantified into a comprehensive evaluation result. Specifically, a priority list of information sources can be maintained. This list can assign different priorities based on the identity of the information source (e.g., system administrator, ordinary user, specific sensor, external cooperative system, etc.), its role in the system, or the credibility of its historical behavior. For example, instructions from the core module of the system may be given high priority, while requests from unauthenticated users may have lower priority. When calculating the comprehensive necessity score, the importance level or urgency score of the input information obtained from the analysis can be weighted and fused with the priority of the information source. For example, weighted average, product, or other multi-factor fusion algorithms can be used to quantify both into a unified value that more comprehensively reflects the degree to which the input information needs to be responded to.

[0068] This application's solution addresses the potential accuracy limitations of traditional assessment methods by conducting a more detailed necessity evaluation of input information. Specifically, upon receiving input information, the system first performs in-depth analysis of its content to determine its inherent importance or urgency. This step ensures an objective assessment of the information's value and timeliness. Based on this, the system further considers the source of the input information and combines preset or dynamically adjusted information source priority relationships. For example, instructions from critical system components or authorized users naturally have higher priority than information from ordinary logs or non-critical sensors. By organically combining the importance or urgency of the information itself with the priority of its source, the system can calculate a comprehensive necessity score. This score not only reflects the attributes of the information itself but also considers the authority or credibility of its sender, thus providing a more comprehensive and accurate assessment of response necessity. This comprehensive evaluation mechanism enables the system to make more accurate and intelligent decisions when deciding whether to silently process input information, effectively avoiding decision biases caused by incomplete evaluation, thereby improving the reliability and adaptability of the entire interactive silencing method.

[0069] The following is a concrete example to illustrate this. In a smart home system, the system receives input information from various sources. For instance, when the system receives a voice command from family member A (an authenticated user) saying, "Please adjust the indoor temperature to 25 degrees Celsius," the system first analyzes the importance level of this command. Since this is a clear temperature adjustment command, its importance level might be rated as "medium." Simultaneously, the system recognizes that the source of this command is family member A, whose relationship priority within the system might be set to "high." The system combines the "medium" importance level with the "high" relationship priority to calculate a higher overall necessity score, thus deciding not to remain silent and immediately executing the temperature adjustment operation. In contrast, if the system receives a voice command from an unauthenticated visitor saying, "Please turn on the TV," the system analyzes the importance level of this command, which might be rated as "medium." However, since the source is an unauthenticated visitor, its relationship priority might be set to "low." The system combines the "medium" importance level with the "low" relationship priority to calculate a lower overall necessity score. At this point, if the system is currently executing other high-priority tasks or is in energy-saving mode, it may decide to silence the instruction and not respond immediately. For another example, in an industrial control system, the system receives data from sensors on the production line. If the sensor reports routine temperature or pressure data, its importance level might be rated as "low," and the sensor's priority might be "medium," resulting in a low overall necessity score. However, if the sensor reports an "equipment overheat warning," its urgency is rated as "high," and even if the sensor's priority is still "medium," the overall calculation will yield a very high necessity score. The system will respond immediately and will not remain silent. In this way, the system can dynamically adjust its response strategy based on the specific content of the input information and the reliability of its source.

[0070] Through the above technical solution, this application effectively addresses the issues of insufficient accuracy and decision-making bias in traditional evaluation methods when determining the necessity of input information responses. By meticulously analyzing the importance level or urgency of input information and comprehensively considering the priority of information sources, the system can obtain a more comprehensive, accurate, and quantifiable necessity assessment result. This enables the silent decision engine to make more intelligent and reliable judgments when deciding whether to silently process input information, avoiding erroneous silent processing of important or urgent information and over-responding to unimportant or non-urgent information. Ultimately, this not only improves system resource utilization efficiency but also enhances system robustness and user experience, enabling it to better adapt to complex and ever-changing operating environments.

[0071] When traditional assessments are used to assist silent decision-making regarding the current task's processing capabilities, the assessments may rely solely on the current instantaneous state or subjective judgment, lacking objective evidence based on historical data. This leads to inaccurate capability assessment results, which in turn affects the accuracy of silent decision-making, resulting in wasted resources or delays in core tasks.

[0072] In this regard, this application further proposes to assess the processing capability for the current task by: judging the processing capability for the current task based on the frequency and complexity of related tasks in the historical processing records.

[0073] The frequency of related tasks in historical processing records refers to the number of times tasks of the same or similar type as the task to be evaluated have been received and processed by the system in logs, database entries, or other forms of data collections of various tasks processed by the system over a past period, or its proportion in the total task volume. For example, the system can maintain a task counter to record the number of times each task type appears within a specific time window; or it can analyze system logs to statistically analyze the frequency of occurrence of specific task patterns. This frequency data provides the system with an objective basis for predicting future task load trends, avoiding the one-sidedness of judging solely based on the current instantaneous state. Task complexity refers to the amount of resources required to complete a specific task (such as CPU time, memory, I / O operations, network bandwidth, etc.) or its inherent logical complexity. Task complexity can be quantified or evaluated in various ways. For example, complexity levels (such as low, medium, and high) can be preset for different types of tasks; or the complexity score can be dynamically calculated based on the amount of data involved, calculation steps, dependencies, etc.; or the complexity of the current task can be predicted based on the actual resource consumption of historical tasks using machine learning models. Assessing task complexity allows the system to more precisely understand the input required to process tasks, thus more accurately measuring its own capabilities. Judging the processing capability for the current task refers to the system comprehensively considering factors such as the frequency and complexity of related tasks in the historical processing records to arrive at a conclusion about its ability to efficiently and promptly process the current task. This judgment can be based on a pre-defined rule engine; for example, if a certain type of task has a high historical processing frequency and low complexity, its processing capability is judged to be strong; if it has a low historical processing frequency but high complexity, its processing capability is judged to be weak. Alternatively, more complex algorithms, such as weighted averages, fuzzy logic reasoning, or machine learning classifiers, can be used, taking frequency and complexity as input features and outputting a capability score or capability level to guide subsequent silent decision-making.

[0074] In some of the above embodiments, the system needs to assess its processing capacity for the current task to aid silent decision-making. To overcome the limitations of relying solely on instantaneous states or subjective judgments, this application constructs a more comprehensive and objective capability assessment mechanism by introducing consideration of the frequency and complexity of related tasks in historical processing records. Specifically, when the system receives input information and needs to assess its processing capacity, it no longer focuses solely on instantaneous indicators such as current CPU utilization or memory usage. Instead, the system first queries its historical processing records to statistically analyze the frequency of tasks similar to or related to the current task over a past period. High frequency may indicate that the system's processing flow for such tasks is relatively mature, or that such tasks are part of the system's routine processing. Simultaneously, the system also assesses the complexity of the current task itself, which may involve analyzing the task's instruction set, data volume, or required computing resources. By combining historical frequency data with the complexity of the task itself, the system can form a more accurate and dynamic judgment of processing capacity. For example, even if the current system load is not high, if historical data shows that a certain type of highly complex task is inefficient in processing during specific periods, or if the complexity of the current task far exceeds the system's normal processing range, the system can more prudently assess its processing capacity. This comprehensive judgment based on historical data and the inherent attributes of the task makes the capacity assessment results more predictable and reliable, thus providing a solid foundation for subsequent silent decision-making. In the overall interactive silent approach, this refined capacity assessment works in conjunction with other assessments (such as response necessity and operational scenario identification) to ensure that when deciding whether to process silently, the system considers not only external demands and the environment but also its own internal capacity, thereby making a more intelligent and optimized decision.

[0075] The following is a concrete example. In a smart factory's production line control system, the core controller needs to receive and process instructions from multiple sensors and operating terminals. Suppose the controller receives an instruction to "adjust production parameters." To determine whether to process the instruction immediately, the system initiates a capability assessment. At this point, the controller queries its internally stored "historical processing records," which detail the reception time, processing time, resource consumption, and instruction type of all instructions received over the past few hours or days. The system calculates the "frequency of occurrence" of instructions related to "adjusting production parameters" (e.g., adjustments to similar parameters, or instructions involving the same production unit) in the historical records. For example, if such instructions appear only once in the past hour and have a long processing time, this may indicate that the processing flow is complex or uncommon. Simultaneously, the system analyzes the specific content of the current "adjust production parameters" instruction. For instance, if the instruction requires adjusting 100 parameters and involves multiple linked devices, its "task complexity" is assessed as high. Conversely, if only one parameter is adjusted, the complexity is low. The controller considers this information comprehensively: if the historical frequency is low and the current task complexity is high, even if the current CPU utilization is not high, the system may determine that its processing power is insufficient to complete the task efficiently, or that immediate processing may affect other more urgent production tasks. Based on this judgment, the system may decide to silently process the "adjust production parameters" instruction (e.g., delay processing or prompt the operator to try again later), rather than responding blindly.

[0076] Through the above technical solution, when assessing the processing capacity for the current task, the system no longer relies solely on the instantaneous state, but can comprehensively judge by combining the frequency of occurrence and complexity of related tasks in historical processing records. This significantly improves the objectivity and accuracy of capacity assessment, avoiding silent decision errors caused by inaccurate assessments. The system can more accurately identify its true carrying capacity under specific task types and complexities, thereby making more reasonable silent or response decisions. This helps to effectively avoid consuming system resources by processing unnecessary input information, ensure the smooth execution of core tasks, and prevent resource waste or delays in critical tasks caused by misjudgment of processing capacity, thereby improving the intelligence level and resource utilization efficiency of the entire interactive silent method.

[0077] In some of the solutions mentioned above in this application, the current operating scenario is identified to evaluate the operating scenario and assist in silent decision-making. However, in the implementation process, the identification method may not be specific enough, resulting in inaccurate or incomplete scenario identification, which cannot effectively distinguish different scenario types, thereby affecting the accuracy and adaptability of silent decision-making.

[0078] In response, this application further proposes specific methods for identifying the current operating scenario, including but not limited to: detecting whether the system is in a low-load state and identifying it as an energy-saving scenario; detecting whether the task is non-urgent and can be delayed and identifying it as a delayable scenario; and detecting whether the information source has a high priority and identifying it as a high-priority scenario.

[0079] The detection of whether the system is in a low-load state refers to a situation where the utilization rate of the system's current resources (such as the central processing unit (CPU), memory, network bandwidth, input / output (I / O), etc.) is lower than a preset threshold. This detection is used to determine whether the system has sufficient idle resources to handle non-urgent or deferred tasks, thus providing a basis for entering power-saving mode or performing silent processing. This can be achieved by obtaining real-time data such as CPU utilization, memory usage, and network traffic through the operating system or hardware monitoring interface and comparing it with the preset low-load threshold; or by indirectly judging the system load based on indicators such as system queue length and task response time. When the queue length is short and the response time is fast, the system is considered to be in a low-load state. When the system is in a low-load state, it is identified as a power-saving scenario, meaning that the system is currently in a state where energy conservation can be prioritized. This can trigger corresponding silent strategies, such as reducing unnecessary responses, lowering processing frequency, or entering a low-power mode to achieve energy saving.

[0080] Detecting whether a task is non-urgent and can be delayed involves analyzing the attributes of tasks corresponding to the input information received by the system to determine whether they lack immediate urgency and can be processed at a later time without causing serious consequences. This detection distinguishes task priority and timeliness, ensuring that the system prioritizes urgent tasks when resources are limited or when quiescence is required, while quiescing to non-urgent and delayable tasks. This can be achieved by attaching metadata tags indicating urgency (e.g., high, medium, low) and delayability (e.g., yes, no) to tasks during creation or receipt, allowing the system to make judgments based on these tags; or by analyzing task type, source, content keywords, etc., combined with a pre-defined task priority rule base to dynamically assess the urgency and delayability of tasks. When a non-urgent and delayable task is detected, it is identified as a delayable scenario, meaning the system is currently in a state where certain tasks can be delayed. The system can then decide to quiesce to these tasks based on this scenario information, thereby optimizing resource allocation or avoiding unnecessary immediate responses.

[0081] Detecting whether an information source has high priority refers to evaluating the entity that generates the input information (such as a user, sensor, or other system module) to determine whether its importance or authority is higher than other sources. This detection ensures that information from critical or important sources is processed promptly and with priority, avoiding the omission of important instructions or data due to a silencing strategy. This can be implemented by the system maintaining a priority list of information sources, containing unique identifiers for different sources and their corresponding priority values. When input information is received, the system queries the priority of its source; alternatively, the information source may include its priority identifier in the message header or metadata when sending the information, and the system directly parses this identifier for judgment. When a high-priority information source is detected, it is identified as a high-priority scenario, meaning the system is currently in a state where it needs to prioritize responses to information from specific high-priority sources. The system can adjust its silencing decisions based on this scenario information to ensure that high-priority information receives a timely response even if other conditions might trigger silencing.

[0082] This solution achieves accurate judgment of the current operating scenario through multi-dimensional and refined detection and identification of system operating status, task attributes, and information sources. Specifically, the system monitors the utilization of resources such as CPU, memory, and network in real time to determine whether the system is in a low-load state. Once the low-load condition is met, it is explicitly identified as an energy-saving scenario. This allows the system to prioritize energy-saving strategies and silently process non-critical inputs when resources are abundant. Simultaneously, the system also performs in-depth analysis of received tasks, such as determining whether they belong to the category of non-urgent and deferred tasks based on task type, preset priority, or timeliness tags. When such tasks are identified, the system enters a deferred scenario, providing a basis for subsequent silencing decisions and allowing the system to postpone or ignore the immediate response of some tasks without affecting core functions. Furthermore, the system can detect whether the source of input information has high priority based on a preset priority list or information metadata. Once a high-priority information source is identified, the system enters a high-priority scenario, ensuring that information from important sources receives a timely response even when other silencing conditions are met, avoiding misjudgment and silencing of critical information. Through the aforementioned specific scenario identification methods, this solution provides more accurate and comprehensive contextual information for the underlying silent decision-making process. When the evaluation module performs its assessment, it no longer relies solely on a vague "identify the current operating scenario," but instead obtains explicit scenario labels such as "energy-saving scenario," "delayable scenario," or "high-priority scenario." This specific scenario information, combined with the assessment results of the necessity of responding to input information and the assessment results of the processing capacity of the current task, is input into the silent decision engine. The silent decision engine can then make more intelligent and adaptive silent decisions based on this refined scenario information and pre-defined silent rules. For example, in an energy-saving scenario, even if the necessity of the response is moderate, the system may tend to remain silent; while in a high-priority scenario, even if the system's processing capacity is limited, it may force a response. This multi-dimensional and refined scenario identification significantly improves the accuracy and reliability of silent decision-making, enabling the system to better balance resource utilization, task priority, and user experience.

[0083] The following is a concrete example. In a smart home system, the system is configured with an input receiving module, an evaluation module, a silent decision engine, and a feedback learning module. As a specific implementation, when the system receives the user's voice command "play background music," the scene recognition function in the evaluation module is activated. First, the system detects the CPU utilization and memory usage of its processor (e.g., an embedded ARM processor). If the current CPU utilization is below 10% and the memory idle rate is above 80%, the system identifies it as being in a low-load state and marks the current operating scene as an "energy-saving scene." Simultaneously, the system analyzes the attributes of the "play background music" task. According to preset task classification rules, playing background music is categorized as an "entertainment task," with an urgency level of "low" and a "delayable" attribute. Therefore, the system identifies the current task as non-urgent and delayable and marks the current operating scene as a "delayable scene." Furthermore, the system also detects the information source of the voice command. If the instruction comes from a registered family member (e.g., confirmed via voiceprint recognition), and that family member's priority in the system is set to "regular user," the system will not identify it as a high-priority scenario. Based on the scenario identification results (energy-saving scenario, deferred scenario), the silent decision engine combines the necessity of responding to the input information (e.g., the necessity of playing music is low) and the processing capacity of the current task (e.g., the system is under low load and processing capacity is sufficient) to make a decision according to preset rules. For example, if the preset rules stipulate that non-urgent entertainment instructions can be silently processed under "energy-saving scenario" and "deferred scenario," then the system will decide to silently process the "play background music" instruction, meaning it will not play the music immediately, but may process it later when the system is idle, or inform the user through other means (such as on-screen prompts).

[0084] Through the above technical solutions, this application effectively solves the problems of inaccurate or incomplete scene recognition in existing technologies, which fails to effectively distinguish different scene types and thus affects the accuracy and adaptability of silent decision-making. Specifically, by detecting whether the system is in a low-load state and identifying it as an energy-saving scenario, the system can more accurately determine when to perform silent processing to save energy and avoid unnecessary resource consumption when resources are sufficient. By detecting whether a task is non-urgent and can be delayed and identifying it as a delayable scenario, the system can flexibly delay or silence non-critical tasks according to their actual attributes, thereby optimizing task scheduling and system response strategies. By detecting whether an information source has high priority and identifying it as a high-priority scenario, the system can ensure that information from critical or important sources receives priority response, avoiding the omission of important instructions or data due to silent strategies, and significantly improving the reliability and security of silent decision-making. These specific scene recognition methods work together to enable the silent decision-making engine to obtain richer and more accurate contextual information, thereby making more intelligent and adaptable silent decisions, improving the overall efficiency of the system and the user experience.

[0085] Traditional existing systems have fixed preset rules when responding to input information, which cannot be dynamically adjusted by users or the system according to actual needs. This results in a lack of personalized or adaptive decision-making, which may lead to inaccurate or inefficient decisions in certain scenarios.

[0086] To address this, this application further proposes that the aforementioned preset rules include multiple configurable parameters, which users or the system can adjust through a configuration interface. These configurable parameters refer to variables or thresholds used to define or adjust the silent decision-making logic. These parameters allow the system to flexibly adjust its silent behavior based on different application scenarios, user preferences, or system states without modifying the core algorithm. These parameters can be stored in the system's configuration file, for example, in XML or JSON format, or stored in a database for dynamic reading and updating during system runtime. Parameters may include, but are not limited to: priority weights for silent decisions (e.g., assessment of the necessity of responding to input information, assessment of the processing capacity of the current task, and the weight of each element in the decision-making process based on the current operating scenario), importance thresholds for various types of input information, priority lists for specific information sources, judgment thresholds for system load, and default behaviors for silent processing in different scenarios (e.g., delayed response or direct discard). The configuration interface is an interactive tool provided to users or system administrators for viewing, modifying, and managing system parameters. It simplifies the parameter adjustment process without requiring in-depth understanding of the underlying code or complex command-line operations. For users, the configuration interface can be a graphical user interface (GUI), such as a web console, a desktop application interface, or a mobile application interface, allowing users to intuitively modify parameters through controls such as drop-down menus, sliders, and text input boxes. For the system, the configuration interface can be an application programming interface (API), allowing other automation systems or services to dynamically adjust quiescent decision parameters by programmatically calling the interface. For example, an operations and maintenance platform can automatically adjust the quiescent strategy based on system operating status.

[0087] This application's solution parameterizes the preset rules for silent decision-making and provides an adjustable configuration interface, enabling the system to decide whether to silently process input information based on evaluation results and dynamically adjusted parameters. Specifically, after receiving input information and evaluating the necessity of the response, processing capacity, and operating scenario, the silent decision engine makes a judgment based on preset rules. At this point, these preset rules are no longer fixed logic but reference configurable parameters. For example, a rule might be "If the necessity score is below a certain threshold A and the system load is above threshold B, then perform silent processing." Thresholds A and B are configurable parameters. Users or the system can modify the values ​​of these parameters through the configuration interface, thereby changing the sensitivity and strategy of silent decision-making in real time. This mechanism allows silent decision-making to flexibly adapt to different operating environments and user needs. For example, when system resources are scarce, the sensitivity of silent processing can be increased through the configuration interface to more actively conserve resources; while when users have high requirements for real-time response, the sensitivity can be decreased. This dynamic adjustment capability, combined with the basic evaluation and decision-making process, significantly improves the system's adaptability and intelligence.

[0088] The following example illustrates this. In a smart home system, a smart speaker receives voice commands from the user. The speaker assesses the necessity of responding to the command (e.g., the importance or urgency of the command), its own ability to handle the current task (e.g., whether it is playing music or performing other complex tasks), and the current operating scenario (e.g., whether it is in "Do Not Disturb" mode). To make the silence decision more flexible, the system parameterizes the preset rules for silence decisions. For example, the rules include a configurable parameter called "Non-urgent Command Silence Threshold," which sets the minimum necessity score for silencing non-urgent commands; and a configurable parameter called "Do Not Disturb Mode Silence Level," which defines the strictness of silence processing in "Do Not Disturb" mode (e.g., "strict silence" or "partial silence"). Users can adjust these parameters through the smart speaker's accompanying mobile application (which serves as the configuration interface). For example, if a user finds that the speaker silences the "turn on the living room lights" command too frequently during daylight when there is sufficient light, they can use the mobile application to lower the "Non-urgent Command Silence Threshold" from the default 50 to 30. In this way, even in bright light, the speaker will not silently process a command if its necessity score is higher than 30. Conversely, if users want the speaker to silently process all non-urgent commands in "Do Not Disturb" mode at night, they can set the "Do Not Disturb Mode Silence Level" to "Strict Silence." In this way, users can personalize the smart speaker's silent behavior according to their own lifestyle habits and preferences.

[0089] Through the above technical solution, the system can dynamically adjust the preset rules for silent decision-making according to the actual needs of users or the system, thereby solving the problems of fixed response strategies, lack of personalization and adaptability in traditional systems. This makes silent processing more accurate and efficient, avoids unnecessary responses, optimizes system resource utilization, and significantly improves user experience and the system's intelligence level.

[0090] Traditional systems cannot generate feedback samples when performing silent processing if they do not record silent events, thus failing to achieve learning optimization and preventing the system from continuously improving its decision rules.

[0091] In this regard, this application further proposes that the context-aware interactive silencing method also includes: if it is decided to handle silence, recording the silence event without executing the corresponding response action.

[0092] "If a silent processing decision is made" refers to the system's strategy of determining whether to respond to the current input information immediately or with a delayed response based on preset rules and evaluation results. This is usually based on a comprehensive consideration of the necessity of responding to the input information, the processing capability of the current task, and the identification of the current operating scenario. For example, the system's internal silent decision engine can decide whether to process silently based on the decision results output by the algorithm; or, the system can determine whether the current situation meets the silent conditions by consulting a preset silent policy table or rule set.

[0093] "Recording silent events" refers to storing the fact that the system decides to perform a silent operation, along with related information. Its purpose is to provide a data foundation for subsequent feedback and learning, enabling the system to analyze the effects and impacts of the silent operation. Specifically, the system can write the silent event's timestamp, input information identifier, decision basis, and silent type to a log file or database; alternatively, it can send the silent event to a dedicated event logging service for persistent storage via a message queue.

[0094] "Not executing the corresponding response action" means that after deciding to handle the input silently, the system does not perform the standard response operation that would normally be performed on the input information. Its purpose is to avoid unnecessary resource consumption, prevent potential conflicts or errors caused by response actions, and ensure the effectiveness of silent handling. For example, the system can directly discard the input information without triggering any processing flow; or, it can mark the input information as "silent" and place it in a low-priority queue or a delayed processing queue, but not execute it immediately.

[0095] When the system receives input information and undergoes necessity assessment, capability assessment, or scenario identification by the evaluation module, the silent decision engine determines whether to silently process the input information based on preset rules. Once the silent decision engine decides to silent process the input information, this solution immediately triggers a recording mechanism to record the detailed information of this silent event. Simultaneously, the system explicitly blocks or cancels the execution of the originally planned response action for this input information. This mechanism ensures the integrity of the silent processing; not only is there no response, but the act of not responding itself is recorded as a traceable event. In this way, the recording of silent events provides crucial raw data for the subsequent feedback learning module, enabling the system to analyze the effectiveness of silent processing, user or external system reactions, and optimize the silent decision rules accordingly, thus forming a closed-loop adaptive learning process. This combination of recording and non-response saves resources and lays the foundation for continuous system improvement.

[0096] As a specific implementation method in a smart home system, when a user issues a voice command to "turn on the living room light," the system first receives this input. Then, the system evaluates the necessity of responding to the command, such as checking if the ambient light is sufficient. If the evaluation indicates sufficient light and the necessity score is low, the silent decision engine will decide to silence the command based on preset rules. At this point, the system immediately stores the event "the user issued the 'turn on the living room light' command at a specific time, and the system decided to remain silent due to sufficient light," along with the timestamp, command content, evaluation criteria, and other information, in a local log file or cloud database as a record of the silent event. Simultaneously, the system will not send a "turn on light" command to the living room light; the light remains off, meaning no corresponding response action is executed.

[0097] By explicitly recording the silencing event when deciding on silencing, this approach provides the necessary data foundation for subsequent feedback learning, thus solving the problem of traditional systems lacking learning capabilities and being unable to optimize decision rules from historical silencing events. Simultaneously, by not executing corresponding response actions, unnecessary resource consumption and potential system conflicts are effectively avoided, ensuring the efficiency and accuracy of silencing. This mechanism enables the system to continuously learn and self-optimize based on actual silencing behavior and its subsequent effects, significantly improving the system's adaptability and intelligence, allowing it to make more accurate optimal interaction decisions in different contexts.

[0098] In some of the solutions described above in this application, a feedback sample is formed by recording silent events and subsequent feedback to optimize silent decision-making rules. However, in this process, the unclear content of the feedback sample may lead to inaccurate or invalid optimization. For example, the decision effect may not be accurately quantified or there may be a lack of targeted data, which may affect the reliability of rule adjustment.

[0099] In response, this application further proposes feedback samples including subsequent behavior of the information source after silencing, changes in system performance, or user feedback, to adjust the silencing decision parameters.

[0100] Feedback samples refer to the dataset used to optimize silent decision-making rules. They can be structured data records containing contextual information about the silent decision-making process (such as input type, evaluation results, and operating scenarios) and various feedback data after the silent processing. Alternatively, feedback samples can exist as unstructured log files, from which the system can extract effective feedback information using data mining and natural language processing techniques to support subsequent rule optimization. Subsequent behavior of the information source after silencing refers to the actions or state changes exhibited by the information source after the system performs silent processing on the input information. Its purpose is to provide direct evidence of the rationality of the silent decision. For example, the system can record whether the information source retried operations, sent new instructions, or adjusted its own sending strategy after silencing. Another implementation method is to continuously monitor the communication status, behavior patterns, or data flow changes of the information source to infer its subsequent behavior, such as determining whether it changed the interaction frequency or content due to silencing. System performance changes refer to the changes in various system operating indicators before and after the system performs silent processing on the input information. Its purpose is to quantify the objective impact of silent decisions on system resource utilization and operating efficiency. Specifically, the changes in key performance indicators such as CPU utilization, memory usage, network bandwidth, response time, and task queue length before and after silent processing can be monitored. Alternatively, the actual effect of silent decision-making can be evaluated by comparing the differences in key system performance indicators between silent and non-silent processing. User feedback refers to users' subjective evaluation or reaction to the system's silent processing behavior. Its role is to supplement objective behavioral data, making the optimization of silent decisions more comprehensive and humanized. One implementation method is to provide explicit feedback options (such as "satisfied," "dissatisfied," and "suggestion") through the user interface, allowing users to directly express their feelings. Another implementation method is to indirectly obtain user feedback by analyzing subsequent user behavior, such as whether the user manually performed the silent operation, or by analyzing user evaluations of system behavior on social media, forums, or customer service channels through natural language processing technology. Adjusting silent decision parameters refers to modifying configurable items in the preset rules based on feedback samples to optimize the accuracy and adaptability of silent decisions. Its role is to ensure that silent decision rules can be dynamically optimized based on specific feedback information. For example, machine learning algorithms (such as reinforcement learning and supervised learning) can be used to adjust the weights, thresholds, or model parameters in preset rules based on feedback samples. Alternatively, feedback samples can be manually reviewed, and configurable parameters in preset rules can be adjusted periodically to adapt to constantly changing operating environments and user needs.

[0101] This solution addresses the issue of data ambiguity in the optimization of silent decision-making rules by clearly defining the specific composition of the feedback sample. After the system silently processes input information, it collects and records multi-dimensional feedback information. Specifically, the subsequent behavior of the information source after silencing provides direct evidence of the immediate impact of the silent decision, such as whether the information source retried or adjusted its strategy due to silencing. This helps assess the impact of silent processing on the interacting parties. Simultaneously, changes in system performance provide objective quantitative data on the impact of silent decisions on system resources and efficiency, such as whether resources like CPU, memory, and network bandwidth are effectively saved, thus evaluating the system benefits of silent processing. Furthermore, user feedback supplements subjective evaluations, reflecting users' acceptance and satisfaction with silent processing, ensuring that the optimization process considers user experience. These three types of feedback complement each other, forming a comprehensive and multi-dimensional feedback sample. By analyzing this rich and specific data, the system can more accurately evaluate the actual effect of each silent decision, identify successful silent processes and decisions requiring improvement, thus providing a solid data foundation for adjusting silent decision parameters. This mechanism enables the optimization of silent decision-making rules to no longer rely on fuzzy or single-dimensional information, but rather on iterative learning based on real feedback from multiple perspectives, significantly improving the accuracy, reliability, and adaptability of decision-making.

[0102] The following example illustrates this. In a smart home system, when a user says "turn on the living room light" via voice command, the system, based on an assessment that the ambient light is sufficient, decides to silence the command, i.e., not turn on the light. To optimize future silence decisions, the system records this silence event and collects feedback samples. Specifically, the system monitors subsequent behaviors of the information source after the silence, such as whether the user issues the "turn on the living room light" command again within a short period of time, or whether they manually operate the physical switch. Simultaneously, the system records system performance changes, such as whether the CPU load and memory usage of the smart home controller remain at a low level after the silence, and whether the power consumption of the living room light remains at the baseline value of the off state. Furthermore, the system can collect user feedback through user interface or smart speaker interaction logs, such as whether the user expressed dissatisfaction with the silence or raised questions. This specific feedback data, including the user not issuing the command again (positive subsequent behavior), no increase in system resources (positive performance change), and no user expression of dissatisfaction (positive user feedback), will be integrated to form a feedback sample. The feedback learning module will use these samples to adjust the silence decision parameters. For example, if the user frequently repeats the command to turn on the light when the light is bright, the system may increase the necessity threshold for silence in the "bright light" scenario, or provide clearer voice prompts when silent to avoid confusing the user.

[0103] Through the above technical solution, this application effectively solves the problem of inaccurate or ineffective optimization caused by the ambiguity of traditional feedback samples. By explicitly including subsequent behaviors of the information source after silence, system performance changes, or user feedback in the feedback samples, the system can obtain multi-dimensional and more comprehensive data for evaluating the decision-making effect. Subsequent behaviors of the information source provide direct evidence of the impact of silent processing on the interacting parties, system performance changes quantify the objective benefits of silent processing on resource utilization, and user feedback supplements the evaluation of subjective experience. This comprehensive feedback mechanism makes the adjustment of silent decision-making rules more targeted and reliable, avoiding the drawbacks of optimization based on incomplete or one-sided information. Therefore, the system can learn and adapt to different contextual environments more accurately, continuously optimize the accuracy of silent decisions and user satisfaction, thereby improving the overall intelligent interaction experience while ensuring efficient system operation.

[0104] In some of the solutions mentioned above in this application, a context-aware interactive silencing method is proposed to dynamically determine whether to respond to input information. However, in this process, the application scope of the method is not clearly extended to multiple systems, which may lead to a lack of universality and adaptability in actual deployment, and cannot ensure the effective execution of the method in different scenarios such as intelligent agents, robots, and industrial control systems.

[0105] In this regard, this application further proposes that the method be applied to intelligent agents, robots, industrial control systems, IoT devices, smart home systems, vehicle systems, or communication networks. Specifically, the "intelligent agent" refers to an autonomous entity capable of sensing the environment, making decisions, and performing actions. Its implementation can include software agents, AI assistants, virtual robots, etc., and it can handle dynamic and complex interactive scenarios. The "robot" refers to a physical device capable of performing pre-programmed tasks or autonomous operations. Its implementation can include industrial robots, service robots, drones, etc., and it needs to flexibly adjust its response strategy according to environmental changes when performing tasks. The "industrial control system" refers to a system used to monitor and control industrial processes. Its implementation can include programmable logic controllers (PLCs), distributed control systems (DCS), monitoring and data acquisition systems (SCADA), etc., and it has high requirements for real-time performance, reliability, and resource efficiency. The "IoT device" refers to a physical device connected to the Internet and capable of collecting and exchanging data. Its implementation can include smart sensors, smart home appliances, wearable devices, etc., and it is usually resource-constrained and faces a large amount of data interaction. The "smart home system" refers to a system that improves living comfort and efficiency through automation and interconnectivity technologies. Its implementation can include smart lighting systems, smart security systems, and environmental control systems, requiring intelligent responses based on user habits and environmental changes. The "vehicle system" refers to a system integrated into a vehicle that provides infotainment, navigation, and driver assistance functions. Its implementation can include in-vehicle infotainment systems, autonomous driving assistance systems, and vehicle diagnostic systems, and is crucial for driving safety and user experience. The "communication network" refers to the infrastructure used for data transmission and information exchange. Its implementation can include cellular networks, Wi-Fi networks, local area networks (LANs), and wide area networks (WANs), requiring efficient management of network traffic and device connections.

[0106] This application's solution applies the aforementioned context-aware interaction silencing method to various systems, enabling it to fully leverage its adaptability and effectiveness across diverse application scenarios. Specifically, whether handling dynamic interactions in intelligent agents, optimizing resource allocation in industrial control systems, adapting to network changes in IoT devices, responding to environmental demands in smart home systems, handling driving scenarios in vehicle systems, or coordinating data transmission in communication networks, this method can evaluate each system's unique input information, task processing capabilities, and operating scenarios, and decide whether to implement silencing based on the evaluation results and preset rules. For example, in resource-constrained IoT devices, this method can avoid unnecessary responses to save power and computing resources; in industrial control systems with high real-time requirements, it can prioritize critical tasks while silencing non-urgent requests. Furthermore, by recording silencing events and subsequent feedback to form feedback samples, this method can continuously optimize silencing decision rules, thereby achieving smarter and more efficient interaction management in these diverse application environments. This broad applicability means that this method is no longer limited to a single or limited scenario, but can serve as a general intelligent interaction management strategy, playing a role in various complex and dynamic systems, significantly improving the system's versatility and adaptability.

[0107] The following is a concrete example to illustrate this. As a specific implementation method, the above approach can be applied to IoT devices. For example, in a smart agricultural IoT system, a large number of IoT devices, such as soil moisture sensors and ambient temperature sensors, are deployed. These devices periodically report data to a cloud service. When the cloud service (as the system receiving input information) receives a configuration update request from an edge node (IoT device), it initiates a silent decision-making process. First, the system assesses the necessity of responding to this input information. For example, if the edge node is detected to be currently offline and the network quality is poor, the necessity is considered low. Simultaneously, the system identifies the current operating scenario; for example, it determines that the configuration update is a "non-urgent configuration," meaning that not executing it immediately will not cause serious impact on the system. Based on these assessment results, the system decides to silence the configuration update request according to preset rules, i.e., not to issue the update for the time being. The system records this silent event and attempts to issue the update again after the edge node returns to online. If the edge node works normally during the offline period, the cloud records this silence as positive feedback, used to adjust the silent decision parameters, so that in future situations with similar poor network conditions and non-urgent tasks, the system will prioritize the silent strategy.

[0108] Through the aforementioned technical solutions, the context-aware interactive silencing method proposed in this application can be widely applied to various technical fields such as intelligent agents, robots, industrial control systems, IoT devices, smart home systems, vehicle systems, and communication networks. This significantly solves the problems of limited application scope, lack of versatility, and adaptability of traditional methods. By deploying this method in these diverse systems, the systems can dynamically perform context awareness, evaluation, and silent decision-making based on their unique operating environments and needs, thereby ensuring the effective execution of the method in various complex scenarios. For example, in resource-constrained IoT devices, it can effectively save power and computing resources; in industrial control systems with high real-time requirements, it can ensure the priority processing of critical tasks; and in smart home or vehicle systems, it can improve user experience and the intelligence level of system response. This broad applicability not only enhances the practical value of the method but also enables these systems to manage interactions more intelligently and efficiently, optimize resource utilization, and continuously optimize themselves through feedback learning, thereby achieving superior performance and user satisfaction in their respective fields.

[0109] Traditional interactive silent systems suffer from fixed response strategies, lack of learning capabilities, and insufficient context awareness when processing input information, leading to wasted system resources and low response efficiency. For example, the system either responds to everything or ignores everything, unable to dynamically adjust according to the context; it cannot learn to optimize decision rules from historical silent events; and it cannot identify and differentiate between different operating scenarios.

[0110] To address this issue, this application proposes a context-aware interactive silence system, comprising an input receiving module, an evaluation module, a silence decision engine, and a feedback learning module. This system can dynamically determine whether to process input information silently based on the importance of the input, the system's processing capacity, and the operating scenario, and continuously optimize through feedback learning.

[0111] The input receiving module is configured to receive input information from sensors, user commands, system status, etc., providing the system with raw data. The evaluation module is configured to evaluate the input information, including analyzing its necessity, the system's processing capacity for the current task, and identifying the current operating scenario, to obtain evaluation results. The silent decision engine is configured to determine whether to silently process the input information based on the evaluation results and according to preset rules. The feedback learning module is configured to record silent events and subsequent feedback to form feedback samples and optimize the silent decision rules.

[0112] The proposed solution ensures that all inputs are captured and processed through an input receiving module, which is a prerequisite for subsequent evaluation and decision-making. The evaluation module analyzes the contextual attributes of the input information to provide comprehensive evidence for decision-making, addressing the problem of insufficient scene awareness. The silent decision engine dynamically adjusts the response strategy based on the evaluation results, rather than using a fixed pattern, thus solving the problem of fixed response strategies. The feedback learning module improves decision-making accuracy by learning from historical events, addressing the problem of lacking learning capabilities.

[0113] The following is a concrete example. In a smart home system, when a user issues a "turn on the living room lights" command during a daytime with ample natural light, the input receiving module receives the command, the evaluation module assesses its necessity as low (due to sufficient light), and the silent decision engine decides to handle it silently, without executing the action of turning on the lights. If the user does not take any further action, the feedback learning module records this as positive feedback, and the system continues to remain silent in similar future scenarios. If the user subsequently issues another command, the system records negative feedback and adjusts the decision parameters accordingly. Through this technical solution, the system can dynamically adjust its response strategy, saving system resources and improving overall efficiency.

[0114] In some of the solutions mentioned above in this application, an evaluation module is proposed to evaluate the input information. However, in this process, the evaluation module may not have clearly defined the specific dimensions of its evaluation, resulting in an incomplete and inaccurate evaluation result. It is unable to effectively distinguish the importance of the input information, the system processing capacity, and the differences in the operating scenario, thereby affecting the accuracy and adaptability of silent decision-making.

[0115] In response, this application further proposes that the evaluation module be configured to perform at least one of the following evaluations: necessity evaluation, used to analyze the necessity of responding to input information; capability evaluation, used to evaluate the system's ability to process the current task; and scenario recognition, used to identify the current operating scenario.

[0116] Necessity assessment aims to determine the urgency or importance of responding to received input information. This can be achieved by analyzing the attributes of the input information itself, such as its content, type, source, and preset importance or urgency labels. For example, alarms from system security modules are typically assigned high necessity, while routine system status reports may be considered low necessity. Furthermore, the identity or permissions of the information sender can be considered; for instance, instructions from system administrators usually have higher priority than those from ordinary users, thus influencing the necessity assessment result. Capacity assessment measures the system's current ability to handle new tasks or respond to new input. This can be achieved by real-time monitoring of key system resource metrics, such as CPU utilization, RAM usage, network bandwidth consumption, and I / O operation load. When these metrics approach or reach preset thresholds, it indicates that the system's processing capacity is approaching saturation or declining. Another approach is to predict the additional workload the system can handle in its current state based on the number of tasks currently being executed, the complexity of these tasks, and historical task processing records. For example, if a system is processing multiple computationally intensive tasks simultaneously, its ability to handle new tasks will be assessed as low. Scene recognition aims to determine the specific operating environment or context in which the system is currently operating. This can be achieved by detecting and analyzing external environmental parameters, such as current time (day / night), geographical location, ambient light intensity, and temperature, or the system's internal state, such as battery level, network connection quality, and user activity patterns (e.g., sleep mode, work mode). Using these parameters, the system can identify specific operating scenarios; for example, a "power-saving silent scenario" can be identified when the user's device is at night and low on power. Furthermore, a pre-defined scene rule base or a machine learning-based model can be used to comprehensively analyze various sensor data and system events to infer more complex operating scenarios; for example, a "quiet maintenance scenario" can be identified when the system is in a low-load state for an extended period.

[0117] This application's solution refines the evaluation module's functionality into necessity assessment, capability assessment, and scenario recognition, enabling the evaluation module to conduct a comprehensive and in-depth analysis of input information from multiple dimensions. When the input receiving module receives input information, the evaluation module no longer performs a single-dimensional, general assessment but instead performs a necessity assessment to determine the urgency and importance of the response; simultaneously, it performs a capability assessment to consider the system's current ability to process the input information; and it performs scenario recognition to determine the system's specific operating environment. These three assessments can be performed independently or in combination, generating a multi-dimensional evaluation result. This more refined and comprehensive evaluation result is then passed to the silent decision engine. Based on this multi-dimensional evaluation result and combined with preset rules, the silent decision engine can make more accurate and intelligent silent processing decisions. For example, even if the input information has a certain necessity, if the system's processing capacity is insufficient or the current scenario is unsuitable for a response, the silent decision engine can still decide to perform silent processing. This multi-dimensional evaluation mechanism enables the system to fully consider the inherent attributes of the input information, the system's own operating status, and external environmental factors when making silent decisions, thereby avoiding the one-sidedness of traditional evaluation methods and significantly improving the accuracy and adaptability of silent decision-making.

[0118] The following example illustrates this. In a smart home system, the system receives a voice command from the user, "Turn on the living room lights." At this point, the evaluation module activates its internal evaluation mechanism. First, a necessity assessment is performed. The system analyzes that it is currently daytime and the light sensor detects sufficient light in the living room, therefore determining that responding to the "turn on the living room lights" command is of low necessity. Second, a capacity assessment is performed. The system detects that it is currently performing multiple high-load tasks, such as a comprehensive inspection and data backup of the home security system, resulting in high CPU utilization and memory usage, leading to an assessment of insufficient system processing capacity. Third, scene recognition is performed. Based on the user's preset "energy-saving mode" and the fact that all family members are currently at work, the system identifies the current operating scene as an "energy-saving scene." The evaluation module integrates these necessity, capacity, and scene assessment results to form a comprehensive evaluation report. After receiving this report, the silent decision engine, based on the preset silent rules (for example, when there is sufficient light during the day, the system is under high load and in energy-saving mode, the "turn on the light" command is processed silently), decides to process the "turn on the living room light" command silently, that is, not to immediately execute the action of turning on the light.

[0119] Through the aforementioned technical solution, the evaluation module is no longer limited to a single or vague evaluation dimension, but can conduct a comprehensive and detailed evaluation from three key dimensions: the necessity of the input information response, the system's processing capacity for the current task, and the current operating scenario. This solves the problem of insufficient comprehensiveness and accuracy in evaluation results in traditional methods, enabling the system to effectively distinguish the importance of different input information, system resource status, and differences in the operating environment. Therefore, the silent decision engine can make more intelligent and adaptive silent processing decisions based on more accurate and context-aware evaluation results, avoiding invalid responses to unnecessary or inappropriate inputs, thereby optimizing system resource utilization and improving system operating efficiency and stability.

[0120] In some of the solutions described above in this application, a necessity assessment is proposed to analyze the necessity of responding to input information. However, in its implementation, the necessity assessment may not accurately combine the priority information of the information source, resulting in incomplete or incorrect assessment results, which affects the accuracy of silent decision-making.

[0121] In this regard, this application further proposes that the necessity assessment module is also configured to connect with the priority management module to obtain priority information of the information source.

[0122] The necessity assessment module is a component of the evaluation module. Its main function is to analyze the necessity of responding to input information, typically considering factors such as the importance level and urgency of the information, and outputting a necessity score or level. This module can be an independent software component, a service interface, or a hardware unit, and its core role is to provide a basis for evaluation for silent decision-making. The priority management module is responsible for storing, maintaining, and providing priority data for various information sources. These priorities can be preset, dynamically adjusted, or user-configured. For example, they can be defined according to the type of information source (such as security sensors, user commands), the sender's identity (such as administrators, ordinary users), or business criticality. The priority management module can be an independent database service, a configuration management system, a policy engine, or a user interface module used to define and manage the priorities of different information sources. The connection between the necessity assessment module and the priority management module can be implemented through API calls, message queues, shared memory, remote procedure calls (RPC), or direct function calls, aiming to establish a data or control channel to ensure that the necessity assessment module can obtain the information source priority data maintained by the priority management module in real time and accurately. Obtaining priority information of information sources refers to the necessity assessment module retrieving priority data related to the current input information source from the priority management module through the aforementioned connection mechanism. This is a key step in achieving a more comprehensive and accurate necessity assessment, as it incorporates the external attributes of the information source into the assessment consideration.

[0123] The necessity assessment module within the evaluation module determines the necessity of responding to input information upon receipt. To ensure a more comprehensive and accurate assessment, this module is configured to connect with the priority management module. Through this connection, the necessity assessment module can query the priority management module for the priority information corresponding to the source of the current input information. The priority management module stores and maintains priority data for various information sources; this data can be preset, dynamically adjusted, or user-configured. After obtaining the priority information, the necessity assessment module comprehensively considers it against its own analysis of the importance or urgency of the input information to calculate a more refined and accurate comprehensive necessity score. This comprehensive necessity score is then passed to the silent decision engine, enabling it to decide whether to silently process the input information based on a more comprehensive evaluation result and preset rules. This mechanism effectively solves the problem of incomplete or erroneous evaluations caused by the lack of prioritization of information sources in traditional necessity assessments, thereby improving the accuracy and intelligence of silent decision-making.

[0124] In one specific implementation, within a smart home system, the input receiving module receives a "lock tampered with" alarm from a smart door lock. At this point, the necessity assessment module within the evaluation module initiates a necessity assessment of the response to this alarm. To obtain a more comprehensive assessment basis, the necessity assessment module communicates with the priority management module via an internal API call. The priority management module may be a configuration service that stores the priorities of different devices (information sources); for example, the smart door lock's priority might be set to "extremely high," while the ordinary environmental sensor's priority might be "medium." After obtaining the "extremely high" priority information for the "smart door lock," the necessity assessment module comprehensively analyzes it along with the urgency of the alarm information itself (e.g., extremely urgent), calculating a very high overall necessity score. Based on this high score, the silence decision engine will decide not to perform silent processing but to immediately trigger an alarm response (such as sending a notification, activating a camera, etc.). In another example, if the input receiving module receives a "slight temperature increase" alarm from an ordinary environmental sensor, the necessity assessment module queries the priority management module to obtain the "medium" priority of the "ordinary environmental sensor." Simultaneously, the importance of the "slight temperature increase" information itself is analyzed (e.g., low importance). After comprehensive consideration, the necessity assessment module calculates a low overall necessity score. If the system is currently under low load, the silent decision engine may decide to handle the information silently, such as delaying recording or not triggering any response immediately.

[0125] Through the above technical solution, the necessity assessment module can dynamically acquire and combine the priority information of information sources, thereby significantly improving the accuracy and comprehensiveness of the necessity assessment of input information responses. This avoids assessment bias or erroneous decisions caused by ignoring the priority of information sources, enabling the silent decision engine to make more reasonable and intelligent silent processing judgments based on more refined and realistic assessment results. Especially in scenarios with limited system resources and the need to differentiate between information sources of different importance, this solution can more effectively manage resources and schedule tasks, ensuring that high-priority information receives timely responses, while low-priority information can be silently processed according to the context, thereby improving the adaptability, robustness, and user experience of the entire interactive silent system.

[0126] In some of the solutions mentioned above in this application, a capability assessment is proposed to evaluate the system's ability to handle the current task. However, in this process, the capability assessment may lack reliable data sources and cannot be accurately evaluated based on historical performance, thus affecting the optimization of silent decision-making.

[0127] In this regard, this application further proposes that the capability assessment module is also configured to connect with the history module to obtain historical processing data of relevant tasks.

[0128] The capability assessment module monitors and evaluates the system's current task load capacity and resource availability in real time. It continuously tracks key system performance metrics such as CPU utilization, memory usage, disk I / O, and network bandwidth, and combines this with information such as the length of the currently executing task queue, task type, and priority to comprehensively determine whether the system has sufficient processing capacity to respond to new input. This module can be implemented using operating system-provided performance monitoring interfaces (such as the ` / proc` filesystem in Linux or Performance Counters in Windows), or by deploying lightweight agents to periodically collect and summarize system resource data. The history module is specifically designed to persistently store detailed performance data and behavioral records of the system during past task processing. This data can include task start time, completion time, actual resource consumption (such as CPU time, peak memory usage, and network traffic), processing results (success or failure), and response latency under different system load conditions. This module can be implemented using various data storage technologies. For example, it could be a data warehouse based on a relational database (such as PostgreSQL or Oracle) for structured storage and complex queries; a system based on a time-series database (such as InfluxDB or Prometheus) for efficient storage and analysis of time-series performance metrics; or a distributed log system (such as Elasticsearch) for storing and retrieving unstructured task logs. The connection between the capability assessment module and the historical record module refers to establishing a reliable data channel, enabling the capability assessment module to initiate data query requests to the historical record module on demand and receive the returned historical processing data. This connection method can be selected based on system architecture and performance requirements. For example, an internal inter-service communication (IPC) mechanism, such as shared memory or message queues, can be used to achieve low-latency data exchange; a clearly defined API interface (such as RESTful API or gRPC) can be used to allow the capability assessment module to remotely access the historical record module's services via network protocols; or a direct database connection pool can be used to allow the capability assessment module to directly execute SQL queries to obtain the required data. Obtaining historical processing data for relevant tasks refers to the capability assessment module intelligently retrieving past task processing records from the historical record module that highly match the characteristics of the task type, priority, and estimated complexity corresponding to the current input information to be assessed. For example, if the current task is "processing high-priority user requests," the capability assessment module will query the historical records for the average processing time, resource consumption, and success rate of "high-priority user requests" under different system loads.By analyzing this specific, quantifiable historical data, the capability assessment module can more accurately predict the current task's performance in the system, thus providing solid data support for silent decision-making.

[0129] In the embodiments of this application, to improve the accuracy of the system's assessment of the current task processing capability, the capability assessment module is also configured to connect to the history record module to obtain historical processing data of relevant tasks. Specifically, when the input receiving module receives input information, the capability assessment module in the assessment module will initiate an assessment of the system's processing capability. At this time, the capability assessment module no longer relies solely on the real-time system resource status, but actively queries and obtains historical task processing data similar to the current task type, priority, or resource requirements through its connection with the history record module. This historical data may include the average processing time of past tasks, peak resource consumption, success rate or failure rate under different system loads, etc. The capability assessment module comprehensively analyzes and compares this historical data with the current real-time system performance indicators (such as CPU utilization, memory usage, etc.) to form a more comprehensive and accurate capability assessment result. This result is then passed to the silent decision engine as an important basis for its decision on whether to silently process the input information. In this way, the silent decision engine can make more informed decisions based on the system's past actual performance and experience, rather than just the instantaneous state, effectively avoiding resource waste or critical task response delays caused by inaccurate assessments.

[0130] In one specific implementation, within a smart factory's production control system, the capability assessment module can be a microservice deployed on an edge computing node. Upon receiving a new production instruction (input information), the capability assessment module sends a data query request to the historical record module via an internal message queue (e.g., Apache Kafka). The historical record module can be a time-series database (e.g., InfluxDB) deployed on a local server, storing execution logs of all production instructions from the past few months, including the start and end times of each instruction, the involved equipment resources, the actual execution time, and resource usage under different production loads. After receiving historical execution data similar to the current production instruction from the historical record module, the capability assessment module combines this data with real-time monitoring data of the edge computing node's CPU, memory, and network bandwidth, using machine learning models (e.g., regression models based on historical data) to predict the expected processing time and resource consumption of the current production instruction. If the prediction indicates that processing the instruction may lead to system overload or response latency exceeding a preset threshold, the capability assessment module outputs an assessment result of "insufficient processing capacity."

[0131] Through the aforementioned technical solution, the capability assessment module can acquire and utilize valuable experience data accumulated by the system in handling related tasks in the past, thereby significantly improving the accuracy of the assessment of the current task processing capability. This historical data-based assessment method enables the silent decision engine to make more scientific and reasonable judgments when deciding whether to silently process input information, avoiding misjudgments that may arise from relying solely on the instantaneous system state. For example, when system resources appear sufficient but historical experience indicates that handling a certain type of task may cause a potential bottleneck, the system can perform silent processing in advance, thereby effectively preventing system overload and performance degradation. Conversely, when the system's actual processing capability exceeds its instantaneous performance, unnecessary silent processing can be avoided, ensuring timely response to critical information. This not only optimizes the allocation and utilization efficiency of system resources but also enhances the system's robustness and adaptability, enabling the entire interactive silent system to continuously learn and optimize its decision rules as runtime increases, ultimately achieving more efficient and intelligent operation.

[0132] In some of the solutions mentioned above in this application, a feedback learning module is proposed to record silent events and subsequent feedback to form feedback samples. However, in this process, there is a lack of a dedicated event recording module to reliably record silent events, resulting in incomplete or inefficient recording, which affects the optimization effect of feedback learning.

[0133] In this regard, this application further proposes that the above-mentioned context-aware interactive silence system also includes an event logging module, which is used to record silence events when deciding to handle silence.

[0134] The event logging module can be a standalone software service or process, running in the same physical or virtual environment as the silent decision engine, receiving silent event notifications via message queues or application programming interfaces (APIs). Alternatively, it can be a submodule or functional unit embedded within the silent decision engine, directly calling this submodule to record events when the engine makes a silent processing decision. The function of this module is to ensure that silent events are captured promptly and accurately, providing a reliable data foundation for subsequent feedback learning. Specifically, when the silent decision engine outputs a "silent processing" decision, the event logging module can be immediately triggered, packaging the current input information, evaluation results, decision time, and other relevant information into a silent event log. Alternatively, after making a silent processing decision, the engine can send an asynchronous message with silent event details to the event logging module, which then persists the message.

[0135] This application's solution activates an event logging module when the silent decision engine decides to silently process input information. This module is specifically responsible for capturing and recording detailed information about the silent processing, such as the input content, evaluation results, decision basis, and decision time. These reliably recorded silent events are then acquired by the feedback learning module and combined with subsequent feedback (such as subsequent actions of the information source, system performance changes, or user feedback) to form high-quality feedback samples. The feedback learning module uses these samples to optimize the silent decision rules. In this way, the event logging module acts as a bridge between silent decision-making and feedback learning, ensuring the integrity, accuracy, and timeliness of silent event data, thereby significantly improving the efficiency and effectiveness of feedback learning and solving the problem of incomplete or inefficient recording affecting optimization results. It enables the feedback learning module to optimize rules based on more reliable data, thereby improving the overall system's adaptability and decision accuracy.

[0136] The following example illustrates this. In a smart home system, the input receiving module receives a "turn on the living room light" command from a user via a voice assistant. The evaluation module assesses this command, identifying sufficient ambient light (scene recognition result) and determining that the necessity of the "turn on the light" command is low (necessity assessment result). Based on these evaluation results, the silent decision engine decides to silence the "turn on the light" command according to preset rules, i.e., not to execute the action of turning on the light. At this moment, the event recording module is immediately triggered. It records this silent event, including but not limited to: the user's command content ("turn on the living room light"), the command time, the evaluation module's evaluation result (sufficient light, low necessity), the silent decision engine's decision (silent processing), and the current system state. These detailed silent event records are then used by the feedback learning module for analysis. For example, if the user does not issue the "turn on the light" command again after the silence, the feedback learning module will mark this record as a positive feedback sample to reinforce the silent decision-making for the "turn on the light" command in similar well-lit scenarios. Conversely, if the user immediately issues another command, it may be marked as negative feedback, prompting the system to adjust its decision-making rules. The introduction of the event logging module ensures that every silent process is clearly and completely recorded, providing a solid data foundation for feedback learning.

[0137] Through the above technical solution, the system can timely, accurately, and completely capture and record all relevant silent event information when the silent decision engine decides to silently process the input information. This solves the problem of incomplete or inefficient silent event recording in existing solutions, providing high-quality and reliable feedback samples for the feedback learning module. Based on this complete and accurate silent event data, the feedback learning module can more effectively optimize the silent decision rules, thereby significantly improving the adaptive capability and decision accuracy of the entire interactive silent system. Ultimately, this enables the system to more intelligently adjust its response strategy dynamically according to the context, avoid unnecessary resource consumption, and continuously optimize its behavior.

[0138] In some of the solutions mentioned above in this application, a context-aware interactive silencing method is proposed to address the problems of fixed response strategies, lack of learning ability, and insufficient scene awareness. However, when implementing these methods, there is a lack of a specific computer-readable storage medium to store the executable program, which makes it impossible for the method to be directly executed by the computer processor. As a result, it is impossible to deploy, apply, and continuously optimize in actual systems, thus limiting the wide applicability and operability of the method.

[0139] In response, this application proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a context-aware interactive silencing method.

[0140] Computer-readable storage media refers to non-transient physical media capable of storing computer programs or data. Its function is to provide persistent storage space for computer programs, ensuring that program code and related data are retained even after system power failure and can be loaded and executed by the processor when needed. This media can include, but is not limited to, hard disk drives, solid-state drives, flash memory (such as USB flash drives and SD cards), optical discs (CD-ROM, DVD, Blu-ray disc), etc. It can also be read-only memory or random access memory. A computer program is a set of instructions that, when executed by a computer processor, instructs the computer to perform specific tasks or operations. The program translates abstract algorithms and logic into machine code or assembly instruction sequences that the processor can understand and execute. It can be source code written in high-level programming languages ​​(such as Python, Java, C++), compiled or interpreted to form an executable file or script, or it can be a set of instructions written directly in machine code or assembly language. The processor is the hardware unit that executes computer program instructions and is the core computing component of a computer system. It is responsible for parsing and executing instructions in computer programs, performing data processing, and control operations. The processor can be a central processing unit, such as an Intel Core series or ARM Cortex series, or a microcontroller, graphics processing unit, digital signal processor, or application-specific integrated circuit. Implementing a context-aware interactive silencing method means that a computer program stored on a computer-readable storage medium, when executed by the processor, can completely execute all the steps and logic of the aforementioned context-aware interactive silencing method. This includes receiving input information, evaluating the necessity of responding to the input information, the processing capacity of the current task, and identifying at least one of the current operating scenario to obtain an evaluation result; based on the evaluation result, deciding whether to silence the input information according to preset rules; and recording silencing events and subsequent feedback to form feedback samples for optimizing the silencing decision rules. In this way, the method can run in a real hardware environment, thereby achieving its intended function.

[0141] This application solves the aforementioned technical problems by providing a computer-readable storage medium, enabling the execution and deployment of a context-aware interactive silencing method by a computer system. Specifically, the computer-readable storage medium, as a physical carrier, allows the method to be stored in an executable form, facilitating loading and running on various devices. The computer program stored thereon translates the method into machine-readable instructions, ensuring that the method logic can be accurately parsed and executed by the processor. When the program is executed by the processor, it implements the context-aware interactive silencing method, including receiving input information, evaluating the necessity of the response, processing capacity and operating scenario, deciding on silencing based on the evaluation results, and recording feedback samples to optimize decision rules. This allows for dynamic adjustment of the silencing strategy in practical applications and learning optimization from historical data, improving the system's resource utilization efficiency and scenario adaptability.

[0142] As a specific implementation method, the above-mentioned technical means can be implemented with reference to the following example. In a smart home system, to achieve context-aware interactive silence functionality, an embedded motherboard can be used as the core hardware platform. This motherboard integrates a microcontroller (e.g., an ARM Cortex-M series processor) as the processor and is equipped with a flash memory chip as a computer-readable storage medium. A computer program written and compiled in C language is pre-programmed into this flash memory chip. When the system starts, the microcontroller loads and executes the computer program from the flash memory. The program continuously listens for input information from smart sensors (such as light sensors and human motion sensors) or user voice assistants (such as the "turn on the living room lights" command). During execution, the program evaluates the necessity of responding to these input information (e.g., the necessity of the "turn on the lights" command is low when there is sufficient light during the day), the system's processing capacity for the current task (e.g., whether the system is executing other high-priority tasks), and identifies the current operating scenario (e.g., whether it is in energy-saving mode) according to preset logic. Based on these evaluation results, the program decides whether to silently process the received input information according to internally defined silence decision rules. For example, if the evaluation results indicate that the need for a light-on command is low and the scenario is energy-saving, the program will decide to remain silent and not execute the light-on action. Simultaneously, the program will record this silent event and subsequent user behavior (such as whether the user issues the light-on command again), forming a feedback sample for future optimization of the silent decision rules. In this way, the computer-readable storage medium, computer program, and processor work together to enable the context-aware interactive silent method to be implemented and run on smart home devices.

[0143] Through the above technical solution, this application provides a specific physical carrier, enabling the context-aware interactive silencing method to be carried and executed by a real computer system. This solves the problem that traditional methods are difficult to deploy in diverse hardware environments due to the lack of a specific executable form, greatly improving the practicality and operability of the method. By solidifying the method into an executable program and storing it on the medium, the system can directly utilize the processor's high-efficiency computing power to achieve real-time context awareness, intelligent decision-making, and feedback learning of input information, thereby ensuring the dynamic adjustment and continuous optimization of the silencing strategy. This not only enables the method to be widely applied in various scenarios such as intelligent agents, robots, industrial control systems, IoT devices, smart home systems, vehicle systems, or communication networks, but also further enhances the system's resource optimization, intelligent adaptation, and scenario adaptation capabilities through actual operation and feedback learning, effectively avoiding resource waste and improving the overall efficiency and intelligence level of the system.

[0144] Other application scenarios The following simplified embodiments illustrate the application of the present invention in other fields. Specific implementations of these embodiments can be found in the examples described in the detailed embodiments above, and will not be repeated here.

[0145] Simplified Example 1: Silent Decision Making Based on a Single Factor (Capability Assessment) In industrial control systems, a PLC (Programmable Logic Controller) receives batch parameter configuration requests from a host computer. If the PLC detects that it is currently performing a critical task, has a high CPU load, and a low capability assessment score, it decides to ignore non-urgent configuration requests (not process them temporarily) and process them automatically after the task is completed. The host computer retryes after a timeout, and the PLC responds normally after recovery; the system records this as positive feedback. This example demonstrates a silent decision based solely on capability assessment.

[0146] Simplified Example 2: Silent Decision Making Based on a Single Factor (Scene Recognition) In an IoT environment, sensor nodes periodically report environmental data to a gateway. When a node detects limited network bandwidth, it identifies the current scenario as "low bandwidth" and decides to silently report non-critical data, retaining only the heartbeat. If the gateway does not receive data after bandwidth is restored, it proactively requests a supplementary report, and the node records the silence as positive feedback. This embodiment demonstrates a silent decision based solely on scenario recognition.

[0147] Simplified Example 3: Silent Decision Making Based on Two Factors (Necessity + Scenario) In cloud services, the server receives a large number of heartbeat requests from clients. The server assesses the requests as having low necessity (heartbeats are only for keeping the server alive) and identifies the current scenario as "high load." Based on this comprehensive judgment, the server decides to respond silently to the heartbeat requests (i.e., not returning an acknowledgment packet), allowing the client to time out and retry. After the client times out, the heartbeat frequency is automatically reduced, the server load decreases, and this is recorded as positive feedback. This embodiment demonstrates a multi-factor fusion decision-making process.

[0148] Simplified Example 4: Comprehensive Silent Decision Making Based on Three Factors In a microservice architecture, service A calls service B to retrieve user profiles. Service B assesses its own processing capacity (database connection pool full), the current scenario (peak business period), and the importance of the request (non-core call). Considering these three factors, service B decides to ignore the request, returning an empty response and instructing service A to enable local caching. The system remains stable overall, and service B records the silence as positive feedback. This example demonstrates a multi-factor fusion decision-making process.

[0149] Simplified Example 5: Silent Communication Between Server and Client In a distributed system, clients frequently send log data to the server. The server detects that its response time is too long (low necessity assessment) and that the current scenario is "high-load maintenance" (scenario identification). It decides to silently respond to non-critical log requests—that is, not returning acknowledgment packets, allowing the client to buffer the logs or reduce the frequency after a timeout. Upon receiving a timeout notification, the client automatically adjusts its strategy, and the server load gradually returns to normal. The server records the silent event as positive feedback, prioritizing the use of the silent strategy in similar high-load scenarios in the future.

[0150] Simplified Implementation Example 6: Silent Coordination between Cloud Services and Edge Nodes In IoT systems, cloud services periodically send configuration updates to edge nodes. During one such update, the cloud detects that the edge node is currently offline and has poor network quality (low necessity assessment), and the current scenario is classified as "non-urgent configuration" (scenario recognition). Therefore, it decides to remain silent and not send the update until the node returns to online. The edge node continues to function normally while offline, and the cloud records this silence as positive feedback.

[0151] Simplified Implementation Example 7: Silent Collaboration Between System Modules In a large software system, the logging module frequently writes logs to the storage module. When the storage module's disk utilization reaches a threshold, the storage module assesses its own capabilities (low capability assessment) and decides to silently discard non-critical logs, retaining only critical error logs. The logging module, not receiving write confirmation, automatically reduces its write frequency. The system operates smoothly, and the storage module's silent behavior is considered positive feedback.

[0152] It should be noted that the term "user" in this application is a preferred embodiment of the interactive object, but it is not a limitation on the scope of protection. Those skilled in the art should understand that the interactive object can be any entity capable of generating input information, such as a human user, sensor, other systems, devices, or applications.

[0153] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

[0154] This solution is applicable not only to local devices but can also be deployed on cloud servers. Those skilled in the art should understand that the technical solution of this invention is not limited to a specific deployment environment, and any implementation based on the technical concept of this application should be considered to fall within the protection scope of this invention.

Claims

1. A context-aware based interactive muting method, characterized in that, Includes the following steps: Receive input information; The evaluation results are obtained by assessing at least one of the following: the necessity of responding to the input information, the processing capability of the current task, and the identification of the current operating scenario. Based on the evaluation results, a decision is made according to preset rules as to whether to silence the input information. Record silent events and subsequent feedback to form feedback samples, which are used to optimize silent decision-making rules.

2. The method of claim 1, wherein, The assessment includes at least one of the following: The necessity of responding to the input information is assessed, and a necessity assessment result is obtained; Assess the ability to handle the current task and obtain the capability assessment results; Identify the current operating scenario.

3. The method of claim 2, wherein, The necessity of the assessment in response to the input information includes: Analyze the importance or urgency of the input information; Calculate the overall necessity score by considering the priority of the relationship with the information source.

4. The method of claim 2, wherein, The assessment of the ability to handle the current task includes: Based on the frequency and complexity of related tasks in historical processing records, the ability to process the current task can be determined.

5. The method of claim 2, wherein, The identification of the current operating scenario includes, but is not limited to: The system detects whether it is in a low-load state and identifies it as an energy-saving scenario. Detect whether the task is non-urgent and can be delayed, and identify it as a delayable scenario; The system detects whether the source of the information has a high priority and identifies it as a high-priority scenario.

6. The method of claim 1, wherein, The preset rules include multiple configurable parameters, which can be adjusted by the user or the system through the configuration interface.

7. The method of claim 1, wherein, Also includes: If a decision is made to handle the event silently, the silent event is recorded without executing the corresponding response action.

8. The method of claim 1, wherein, The feedback samples include subsequent behaviors of the information source after the silence, changes in system performance, or user feedback, which are used to adjust the silence decision parameters.

9. The method according to any one of claims 1 to 8, characterized in that, The method can be applied to intelligent agents, robots, industrial control systems, IoT devices, smart home systems, vehicle systems, or communication networks.

10. A context-aware based interactive muting system, characterized in that, include: The input receiving module is used to receive input information; An evaluation module is used to evaluate the input information and obtain an evaluation result; A silent decision engine is used to determine whether to silently process the input information based on the evaluation results and according to preset rules. The feedback learning module is used to record silent events and subsequent feedback to form feedback samples and optimize silent decision-making rules.

11. The system of claim 10, wherein, The evaluation module is configured to perform at least one of the following evaluations: Necessity assessment, used to analyze the necessity of responding to the input information; Capability assessment is used to evaluate the system's ability to handle the current task; Scene recognition is used to identify the current running scene.

12. The system of claim 11, wherein, The necessity assessment module is also configured to connect to the priority management module to obtain priority information from the information source.

13. The system of claim 11, wherein, The capability assessment module is also configured to connect to the history module to obtain historical processing data for relevant tasks.

14. The system of claim 10, wherein, It also includes an event logging module, which records silent events when a decision is made to handle them silently.

15. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method of any one of claims 1 to 9.

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