Equipment load monitoring system and method based on dynamic threshold adjustment

Abstract

The invention relates to the technical field of intelligent equipment adaptive control, in particular to an equipment load monitoring system and method based on dynamic threshold adjustment, and the method comprises the steps: firstly obtaining a use record of target equipment to generate a load-electric quantity-time curved surface, and then obtaining a real-time optimal load at the current time based on the load-electric quantity-time curved surface; and then distributing the real-time optimal load to a plurality of functional modules of the target equipment, and finally, based on the available load, obtaining an operation parameter dynamic threshold value of each functional module and monitoring the target equipment. According to the method, the load-electric quantity-time three-dimensional curved surface model reflecting a real use scene is constructed, so that available load evaluation is more fit with an actual working condition, a system can accurately match a dynamic optimal load upper limit, optimal allocation of resources is realized, and the service life of the system is prolonged. The problem that in the prior art, the load management technology based on fixed threshold value control cannot achieve the optimal endurance is solved.

Description

Technical Field

[0001] This invention relates to the field of adaptive control technology for intelligent devices, and in particular to a device load monitoring system and method based on dynamic threshold adjustment. Background Technology

[0002] Currently, various smart devices integrate multiple functional modules to meet diverse user needs. While these modules enhance interaction convenience, strengthen security, and expand application scenarios, they also bring significant power consumption pressure. The continuous operation of multi-module collaboration mechanisms leads to faster device power consumption, especially in battery-powered scenarios, where battery life becomes a key bottleneck restricting user experience.

[0003] To extend device battery life, existing technologies generally employ load management strategies based on fixed thresholds: by pre-setting a hard operating condition (such as when the battery level is below 20%), unnecessary functional modules are directly shut down. While this method can reduce power consumption to some extent, its one-size-fits-all control logic has significant drawbacks: on the one hand, directly shutting down unnecessary modules directly impacts user experience; on the other hand, fixed thresholds cannot dynamically optimize resource allocation based on real-time load, potentially over-restricting non-critical modules in some scenarios, while failing to effectively reduce redundant load on core modules in others.

[0004] Therefore, a new load management technology, distinct from traditional fixed threshold control, is needed to achieve more refined dynamic load regulation and extend device battery life. Summary of the Invention

[0005] Therefore, the present invention provides a device load monitoring system and method based on dynamic threshold adjustment to solve the problem that load management technology based on fixed threshold control in the prior art cannot achieve optimal battery life.

[0006] This invention provides a device load monitoring system based on dynamic threshold adjustment, comprising: The personalized adaptation module is used to obtain the load-power curve of the target device and the usage records of the target device at different times, and adjust the load-power curve according to the usage records to obtain the load-power-time surface. The load-power curve is a preset curve that represents the optimal load of the target device under different power levels. The status analysis module is used to obtain the remaining power of the target device at the current time, and to obtain the real-time optimal load at the current time based on the load-power-time surface; The load distribution module is used to distribute the optimal real-time load to multiple functional modules of the target device, thereby obtaining the available load for each functional module. The dynamic monitoring module is used to obtain the dynamic threshold of the operating parameters of each functional module based on the available load, and to monitor the target device based on the dynamic threshold of the operating parameters.

[0007] In a preferred embodiment: the load-power curve of the target device and the usage records of the target device at different times are obtained, and the load-power curve is adjusted according to the usage records to obtain a load-power-time surface, including: Obtain the load-power curve of the target device and the usage records of the target device at different times; Based on usage records, the average number of tasks performed by the target device over multiple time periods is obtained; The time correction factor for different time periods is obtained based on the average number of tasks in multiple time periods. The load-power curves were adjusted according to the time correction factor at different time intervals, and the adjusted load-power curves at different time intervals were fitted to obtain the load-power-time surface.

[0008] In a preferred embodiment: the real-time optimal load is allocated to multiple functional modules of the target device, resulting in the available load for each functional module, including: Based on the load-power-time surface, the gradient of the real-time optimal load on the power dimension is obtained, which serves as the power sensitivity. Adjust the real-time optimal load based on power sensitivity; The adjusted real-time optimal load is allocated to multiple functional modules of the target device to obtain the available load for each functional module.

[0009] In a preferred embodiment: the real-time optimal load is allocated to multiple functional modules of the target device, resulting in the available load for each functional module, including: Based on the load-power-time surface, the gradient of the real-time optimal load in the time dimension is obtained, which serves as the time sensitivity. Based on time sensitivity, the optimal real-time load is allocated to multiple functional modules of the target device to obtain the available load for each functional module.

[0010] In a preferred embodiment: based on time sensitivity, the optimal real-time load is allocated to multiple functional modules of the target device, resulting in the available load for each functional module, including: Obtain the priority score for each functional module; Adjust priority scores based on time sensitivity; Based on the adjusted priority scores, the real-time optimal load is allocated to multiple functional modules of the target device to obtain the available load for each functional module.

[0011] In a preferred approach: based on time sensitivity, priority scores are adjusted, including: Priority scores are adjusted based on the following formula: ; in, and These are all serial numbers of the functional modules. For functional modules Priority score before adjustment For functional modules Adjusted priority scores For functional modules Priority score before adjustment For time sensitivity, It is a natural exponential function. This is a function that takes the minimum value.

[0012] In a preferred approach: obtain the priority score for each functional module, including: Obtain the importance index, utility index, and historical usage probability of each functional module at the current moment; A priority score is obtained based on the importance index, utility index, and historical usage probability of each functional module at the current moment.

[0013] The present invention also provides a device load monitoring method based on dynamic threshold adjustment, comprising: The load-power curve of the target device and the usage records of the target device at different times are obtained, and the load-power curve is adjusted according to the usage records to obtain the load-power-time surface. The load-power curve is a preset curve that represents the optimal load of the target device under different power levels. Obtain the remaining battery power of the target device at the current time, and obtain the real-time optimal load based on the load-battery-time surface; The real-time optimal load is allocated to multiple functional modules of the target device to obtain the available load for each functional module; Based on the available load, the dynamic threshold of the operating parameters of each functional module is obtained, and the target device is monitored based on the dynamic threshold of the operating parameters.

[0014] The present invention also provides an electronic device, comprising: Memory and processor; The memory is used to store the program, and the processor is used to execute the steps of any of the above-described device load monitoring methods based on dynamic threshold adjustment when the program is executed.

[0015] The present invention also provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps of any of the above-described device load monitoring methods based on dynamic threshold adjustment.

[0016] The beneficial effects of adopting the above scheme are: This invention provides a device load monitoring system based on dynamic threshold adjustment. It acquires the load-power curve and usage records of the target device at different times through a personalized adaptation module, adjusts the load-power curve based on the usage records to obtain a load-power-time surface, then obtains the remaining power of the target device at the current time through a status analysis module, and obtains the real-time optimal load based on the load-power-time surface. Next, a load allocation module distributes the real-time optimal load to multiple functional modules of the target device to obtain the available load for each functional module. Finally, a dynamic monitoring module obtains the dynamic threshold of the operating parameters for each functional module based on the available load, and monitors the target device based on these dynamic thresholds. This invention significantly improves the balance between device battery life and functional experience. Specifically, it constructs a three-dimensional surface model of "load-power-time" that reflects real-world usage scenarios, making the available load assessment more closely match actual working conditions. This allows the system to accurately match the dynamic optimal load limit. Then, by intelligently allocating the globally optimal load to each functional module and generating differentiated dynamic thresholds for operating parameters (such as fingerprint sampling rate and communication frequency), it achieves optimal resource allocation. While ensuring the continuous and reliable operation of core functions, it significantly extends device battery life and maintains the user's critical functional experience without being affected. This avoids the defects of "over-restriction" or "insufficient reservation" in traditional solutions and solves the problem that existing load management technologies based on fixed threshold control cannot achieve optimal battery life. Attached Figure Description

[0017] Figure 1 The flowchart of the device load monitoring method based on dynamic threshold adjustment provided by the present invention is as follows: Figure 2 for Figure 1 A detailed step diagram of step S103 is shown below; Figure 3 for Figure 2 A detailed step diagram of step S202 is shown below; Figure 4 The system architecture diagram of the device load monitoring system based on dynamic threshold adjustment provided for the invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Combination Figure 1 As shown, a specific embodiment of the present invention discloses a device load monitoring method based on dynamic threshold adjustment, comprising: S101. Obtain the load-power curve of the target device and the usage records of the target device at different times, and adjust the load-power curve according to the usage records to obtain the load-power-time surface. The load-power curve is a preset curve that represents the optimal load of the target device under different power levels. S102. Obtain the remaining power of the target device at the current time, and obtain the real-time optimal load at the current time based on the load-power-time surface; S103. Distribute the real-time optimal load to multiple functional modules of the target device to obtain the available load of each functional module; S104. Based on the available load, obtain the dynamic threshold of the operating parameters of each functional module, and monitor the target device based on the dynamic threshold of the operating parameters.

[0020] It is understood that the steps in the above process do not necessarily need to be performed on the target device. When the target device itself has limited resources, monitoring and analysis can be performed remotely via cloud services or other means. The load-power curve is a preset curve obtained at the factory through testing and calibration, representing the optimal load of the target device under different power levels. Similarly, the load-power-time surface is a surface that incorporates a time dimension, representing the optimal load of the target device under different power levels and different times. In this embodiment, the load-power-time surface is optimized based on the load-power curve according to usage records to conform to the user's actual usage habits and achieve personalization.

[0021] It should be noted that the target device in this embodiment can be any device, and the functional module is an artificially defined module that can perform any function in the target device and has adjustable power consumption, including but not limited to the hardware or software in the target device. For example, taking a smart door lock as an example, the functional modules include fingerprint recognition, face recognition, remote communication, environmental sensing, etc. More specifically, communication with other devices in the smart home and communication with devices such as the user's mobile phone in the remote communication function can also be divided into two different functional modules. For another example, the software and hardware module units that use different communication protocols (such as Bluetooth and Wi-Fi communication respectively) or different hardware resources (such as two different chip processors respectively) in the remote communication function can also be divided into different modules.

[0022] Operating parameters are adjustable parameters that affect power consumption in each functional module. For example, the operating parameter for fingerprint recognition could be the sampling rate, the operating parameter for remote communication could be the communication frequency, and the operating parameter for face recognition could be the sampling rate or resolution. This embodiment monitors these operating parameters by setting dynamic thresholds, maintaining the operating parameters of each functional module below the dynamic thresholds to extend battery life.

[0023] The steps in the above process are described in detail below. In one embodiment, step S101, which involves obtaining the load-power curve of the target device and the usage records of the target device at different times, and adjusting the load-power curve according to the usage records to obtain a load-power-time surface, specifically includes: Obtain the load-power curve of the target device and the usage records of the target device at different times; Based on usage records, the average number of tasks performed by the target device over multiple time periods is obtained; The time correction factor for different time periods is obtained based on the average number of tasks in multiple time periods. The load-power curves were adjusted according to the time correction factor at different time intervals, and the adjusted load-power curves at different time intervals were fitted to obtain the load-power-time surface.

[0024] The average number of tasks is calculated by collecting historical user data and statistically analyzing the average load demand intensity across various time intervals (e.g., 24 intervals divided by hours). It is defined as the average number of tasks per unit time during that period (e.g., the sum of fingerprint recognition counts, remote request counts, etc.). The time correction factor is a manually defined factor representing load intensity, used to adjust the curve.

[0025] This embodiment transforms the complex, continuous modeling problem across all time periods into a discrete processing of multiple independent time periods through a time-segmentation strategy. This significantly reduces the complexity of real-time computation and effectively solves the computational and storage bottlenecks of devices with limited hardware resources (such as smart locks where high-power, high-performance MCUs cannot be installed). Furthermore, this embodiment accurately captures the temporal regularity of user habits (such as fewer operations at night and more frequent operations after get off work) through a time correction factor, making the generated surface model closer to actual usage scenarios. This avoids the resource waste or inadequacy of traditional fixed-threshold schemes that treat all time periods "equally," while maintaining the lightweight nature of the model through discretization. It achieves an ideal balance between computational accuracy and resource consumption, providing a feasible technical path for resource-constrained devices to achieve refined dynamic load management.

[0026] The above process is illustrated below with a more specific example: At the time of manufacture, the target device is tested and calibrated to determine the optimal total load it can support under different power levels. The nonlinear relationship between these parameters can be fitted as a function: ;

[0027] in, Indicates the amount of electricity in the load-power curve Under optimal load, and All are fitting coefficients, calibrated through laboratory testing.

[0028] The established time correction factor is: ; ;

[0029] in, Indicates a unit of time period Time correction factor, Indicates a unit of time period Average number of tasks This represents the average number of tasks. This is a preset adjustment coefficient used to control the correction range.

[0030] By integrating the base curve and time correction, the three-dimensional surfaces for different time periods are obtained as follows: ;

[0031] Finally, the three-dimensional surfaces from multiple different time periods are merged. By performing smoothing interpolation and other processing, the load-power-time surface can be obtained.

[0032] After obtaining the load-power-time surface, the real-time optimal load can be determined based on the current time and remaining power. Distributing this real-time optimal load to multiple functional modules maximizes the device's battery life while ensuring the best user experience. The specific allocation strategy can be flexibly adjusted according to specific circumstances, and this embodiment provides a more optimized solution.

[0033] In one embodiment, step S103, allocating the real-time optimal load to multiple functional modules of the target device to obtain the available load for each functional module, specifically includes: Based on the load-power-time surface, the gradient of the real-time optimal load on the power dimension is obtained, which serves as the power sensitivity. Adjust the real-time optimal load based on power sensitivity; The adjusted real-time optimal load is allocated to multiple functional modules of the target device to obtain the available load for each functional module.

[0034] This embodiment considers power consumption trends. The gradient characteristics of the power dimension in the power-time-available load surface reflect the rate of change of available load with power consumption, i.e., "how much available load is lost due to a unit decrease in power consumption." This is used to assess the "urgency" of current power consumption and to adjust the actual available load based on this trend, thereby predicting future power consumption risks. Specifically, power sensitivity, i.e., the partial derivative of power consumption, is always negative. When the power sensitivity value is too small, it indicates that the available load will decrease sharply in the future, and there may be a risk of insufficient power in the future. Therefore, it is necessary to reduce the total amount of available load allocated.

[0035] This embodiment has unparalleled advantages in specific fields. For example, in the application scenario of smart door locks, when a user goes on vacation and is away from home for a long time, the smart door lock cannot know when the user will return to use the door lock. This embodiment minimizes the total amount of allocable load (always allocating a load lower than the real-time optimal load while waiting for the user to return), thus maximizing the extension of battery life.

[0036] In practice, the specific method for adjusting the real-time optimal load can be set according to the actual situation. A simple example is as follows: ;

[0037] in, This is the adjusted real-time optimal load. This is the optimal load in real time before adjustment. This refers to power sensitivity, which is the gradient of the load-power-time surface along the power dimension. These are preset coefficients.

[0038] Combination Figure 2As shown, in another embodiment, step S103 above, allocating the real-time optimal load to multiple functional modules of the target device to obtain the available load of each functional module, specifically includes: S201. Based on the load-power-time surface, obtain the gradient of the real-time optimal load in the time dimension, which serves as the time sensitivity. S202. Based on time sensitivity, the real-time optimal load is allocated to multiple functional modules of the target device to obtain the available load for each functional module.

[0039] This embodiment considers the trend of time change and uses the gradient characteristics of the time dimension in the power-time-available load surface as the time sensitivity to reflect the rate of change of available load over time, i.e., "how much will available load increase / decrease after entering the next period", which is used to predict future load demand pressure. If the time partial derivative is too high, it indicates that the user's high-frequency usage period is about to begin, and non-core loads need to be reduced in advance, loads are taken back from low-priority modules, power is reserved, and sudden power outages or degradation of critical functions are avoided.

[0040] This embodiment further optimizes battery life while ensuring device functionality stability. For example, in smart door lock applications, a high time sensitivity value indicates high-frequency user usage (such as during rush hour), causing the device to face greater instantaneous load pressure. The system will correspondingly increase the available load allocation during this period to ensure agile response from critical functions such as fingerprint recognition and remote notifications. Conversely, when the time sensitivity value is low (such as during low-frequency usage periods like late night or early morning), the system will proactively reduce the available load allocation to minimize unnecessary power consumption. This design cleverly utilizes the temporal regularity of user behavior, achieving intelligent regulation of "ensuring performance during peak hours and saving energy during off-peak hours" through gradient prediction in the time dimension. Without sacrificing the core user experience, it effectively avoids the resource waste or shortage problems caused by the "uniform allocation all day" in traditional solutions. The introduction of time sensitivity enables the system to adaptively adjust the available resources of each functional module at different times, satisfying the performance needs of users during habitually high-load periods while extending the overall battery life through resource convergence during low-load periods, achieving a dual improvement in user experience and energy efficiency.

[0041] Specifically, similar to the aforementioned power sensitivity, in practice, the total amount of the real-time optimal load can be adjusted based on time sensitivity. However, this method weakens the optimization effect of power sensitivity and cannot achieve the optimal result. Therefore, this invention also provides a more preferred method for adjustment based on time sensitivity. Combined with... Figure 3 As shown, in a more preferred embodiment, step S202 above, which involves allocating the real-time optimal load to multiple functional modules of the target device based on time sensitivity to obtain the available load for each functional module, specifically includes: S301. Obtain the priority score for each functional module; S302. Adjust priority scores based on time sensitivity; S303. Based on the adjusted priority score, the real-time optimal load is allocated to multiple functional modules of the target device to obtain the available load for each functional module.

[0042] In the above process, the priority score is artificially defined and represents the importance of each functional module. In a specific embodiment, the priority score is the result of a combination of multiple evaluation indicators. For example, for smart door locks, the priority score can be obtained by any method, such as a weighted sum of the importance index, utility index, and historical usage probability of each functional module at the current moment. That is, in one embodiment, step S301, obtaining the priority score of each functional module, specifically includes: Obtain the importance index, utility index, and historical usage probability of each functional module at the current moment; A priority score is obtained based on the importance index, utility index, and historical usage probability of each functional module at the current moment.

[0043] The importance index reflects the importance of the module; for example, fingerprint recognition is 0.4, communication functions are 0.3, face recognition is 0.2, and environmental sensing is 0.1. The utility index indicates the degree of experience improvement corresponding to the available workload of the functional module. For example, increasing the sampling rate of the fingerprint module can improve response speed and recognition success rate, while increasing the brightness of the screen backlight may have little impact on the user experience. Historical usage probability can be obtained from usage records.

[0044] Next, let's look at steps S302 and S303 above. In this embodiment, these priority scores are intelligently adjusted based on time sensitivity. When the time sensitivity is high (such as during periods of high user frequency), the priority score of key functional modules (such as the security verification module) can be increased to ensure that they receive more available load. At the same time, the priority score of non-core functions (such as environmental monitoring) can be appropriately reduced to achieve a rational allocation of resources in order to cope with the sudden situation where users may return to use the service at any time.

[0045] The key advantage of this design lies in its use of a priority score as an intermediate layer to regulate load distribution. This preserves the time sensitivity to changes in load demand across different time periods while avoiding the potential for offsetting the optimization effects of power sensitivity caused by directly adjusting the real-time optimal load. This allows the system to simultaneously consider both "time-of-day characteristics" and "power status." More importantly, through flexible configuration of module priorities, this solution ensures that safety-critical functions receive basic resource guarantees at all times. Simultaneously, it dynamically optimizes the resource allocation for non-core functions based on time-of-day characteristics. While ensuring the stable operation of core device functions and maintaining a consistent user experience, it achieves optimal load distribution. This provides a more refined and intelligent power management strategy for devices like smart locks that require 24-hour response and diverse functions, significantly improving overall system resource utilization efficiency and user experience consistency.

[0046] It is understandable that the method of adjusting priority scores can be designed specifically according to the actual situation. For example, priority scores can be divided according to a threshold to reduce the load that can be allocated to low-priority modules and increase the load that can be allocated to high-priority functional modules. However, this invention provides a more preferred method. In a preferred embodiment, the above-mentioned S302, adjusting priority scores based on time sensitivity, specifically involves the following process: Priority scores are adjusted based on the following formula: ; in, and These are all serial numbers of the functional modules. For functional modules Priority score before adjustment For functional modules Adjusted priority scores For functional modules Priority score before adjustment This refers to time sensitivity (i.e., gradient, partial derivative). It is a natural exponential function. This is a function that takes the minimum value.

[0047] The principle behind the above formula is to perform two normalization processes on the original priority scores: one is ordinary normalization calculation, and the other is time-sensitivity-based normalization. In the latter, the greater the time sensitivity, the greater the difference between the various priority scores after normalization; conversely, the greater the time sensitivity, the smaller the difference between the various priority scores after normalization. Finally, the minimum value of the two normalizations is taken as the adjusted priority score, so that the priority score is always maintained at a low level. For example, when the time sensitivity is high, it means that the probability of the user using the device in the future is higher. In this case, in the priority scores adjusted by the above formula, the higher priority scores remain basically unchanged, while the lower priority scores become smaller, thus achieving the goal of "reclaiming load from low-priority modules".

[0048] For devices in specific fields, such as smart door locks, the above method can increase the load allocated to the core module when peak usage is approaching, which best suits user habits. For example, elderly users are accustomed to facial recognition, find it inconvenient to use fingerprints while wearing gloves, and may forget their passwords, and generally do not carry physical keys when going out. In this case, the adjustment in this embodiment can allocate the most load resources to the facial recognition module under the same total load, ensuring a good user experience, while other modules maintain a minimum level of operation to cope with unexpected situations.

[0049] It's worth noting that the two adjustment methods mentioned above—power-sensitive adjustment and time-sensitive adjustment—can be used in combination. Furthermore, they possess unique and unexpected effects in specific fields such as smart locks. For example, for smart locks, on the one hand, smart lock software lacks a "background" concept—all functions are "foreground essential": such as remote command processing, power monitoring, and anti-brute-force algorithms, all need to run in real time. Therefore, software downgrading can only be "adjusting the function intensity" (e.g., reducing the intensity of remote communication from "high" to "medium," rather than directly disabling it). On the other hand, users of devices like smartphones can accept "temporarily disabling certain functions" (e.g., not being able to use the camera when the battery is low), but smart lock users cannot accept "disabling core functions." For example, users may return to unlock the lock at any time, requiring a "shallow standby" state. Therefore, the software needs to maintain a minimum wake-up mechanism (e.g., checking fingerprint sensor input every second), further compressing the downgrading space. Another example is that smart locks cannot disable "abnormal unlocking detection"—for instance, if someone attempts to pry the lock, even if the battery is only 5%, an alarm must be triggered; otherwise, the meaning of "secure entry" is lost.

[0050] This embodiment, targeting the unique software architecture of smart locks—characterized by "no backend, fully foreground, and strong security"—innovatively designs a load control mechanism of "gradient function intensity adjustment + critical function protection," perfectly solving the applicability problem of traditional device degradation strategies in door lock scenarios. First, this embodiment innovatively adopts a "dynamic function intensity adjustment" strategy, optimizing power consumption while ensuring the continuous operation of core functions. Second, addressing users' stringent requirement for "absolute availability of core functions," this solution uses a priority score system to ensure that security-critical modules (abnormal unlocking detection, anti-brute-force algorithms) always receive basic resource guarantees. Even in extremely low battery scenarios (e.g., 5%), a lock-picking alarm can be triggered, maintaining the lock's fundamental value as a "security entry point" and balancing response speed and power consumption control. The monitoring strategy of this embodiment overcomes the limitation of smart locks not being able to directly disable functions as in other fields. Through precise function priority division and a historical data-driven decision-making mechanism, it maximizes battery life while ensuring user safety and experience, providing an industry-leading load management solution for special devices like door locks that are "functionally essential, experience-critical, and security-first."

[0051] Finally, after determining the available load (the specific load allocation method can be any existing method according to the actual situation, such as weighted allocation, greedy algorithm, etc., which will not be explained in detail in this invention), step S104 can be performed: based on the available load, obtain the dynamic threshold of the operating parameters of each functional module, and monitor the target device based on the dynamic threshold of the operating parameters. It is understood that how to map the available load to the specific operating parameters of the functional module varies depending on the actual functional module, and all of them are existing technologies that can be understood by those skilled in the art, so they will not be explained in detail.

[0052] Combination Figure 4 As shown, the present invention also provides a device load monitoring system based on dynamic threshold adjustment, comprising: The personalized adaptation module 410 is used to obtain the load-power curve of the target device and the usage records of the target device at different times, and adjust the load-power curve according to the usage records to obtain the load-power-time surface. The load-power curve is a preset curve that represents the optimal load of the target device under different power levels. The status analysis module 420 is used to obtain the remaining power of the target device at the current time and obtain the real-time optimal load at the current time based on the load-power-time surface; The load distribution module 430 is used to distribute the real-time optimal load to multiple functional modules of the target device to obtain the available load of each functional module. The dynamic monitoring module 440 is used to obtain the dynamic threshold of the operating parameters of each functional module based on the available load, and to monitor the target device based on the dynamic threshold of the operating parameters.

[0053] It should be noted that the corresponding systems provided in the above embodiments are computer program products that can implement the technical solutions described in the above method embodiments. The specific implementation principles of the above modules or units can be found in the corresponding content in the above method embodiments, and will not be repeated here.

[0054] The present invention also provides an electronic device, comprising: Memory and processor; The memory is used to store the program, and the processor is used to execute the steps of any of the above-described device load monitoring methods based on dynamic threshold adjustment when the program is executed.

[0055] The present invention also provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps of any of the above-described device load monitoring methods based on dynamic threshold adjustment.

[0056] This invention provides a device load monitoring system based on dynamic threshold adjustment. It acquires the load-power curve and usage records of the target device at different times through a personalized adaptation module, adjusts the load-power curve based on the usage records to obtain a load-power-time surface, then obtains the remaining power of the target device at the current time through a status analysis module, and obtains the real-time optimal load based on the load-power-time surface. Next, a load allocation module distributes the real-time optimal load to multiple functional modules of the target device to obtain the available load for each functional module. Finally, a dynamic monitoring module obtains the dynamic threshold of the operating parameters for each functional module based on the available load, and monitors the target device based on these dynamic thresholds. This invention significantly improves the balance between device battery life and functional experience. Specifically, it constructs a three-dimensional surface model of "load-power-time" that reflects real-world usage scenarios, making the available load assessment more closely match actual working conditions. This allows the system to accurately match the dynamic optimal load limit. Then, by intelligently allocating the globally optimal load to each functional module and generating differentiated dynamic thresholds for operating parameters (such as fingerprint sampling rate and communication frequency), it achieves optimal resource allocation. While ensuring the continuous and reliable operation of core functions, it significantly extends device battery life and maintains the user's critical functional experience without being affected. This avoids the defects of "over-restriction" or "insufficient reservation" in traditional solutions and solves the problem that existing load management technologies based on fixed threshold control cannot achieve optimal battery life.

[0057] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0058] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A device load monitoring system based on dynamic threshold adjustment, characterized in that, include: The personalized adaptation module is used to obtain the load-power curve of the target device and the usage records of the target device at different times, and adjust the load-power curve according to the usage records to obtain the load-power-time surface. The load-power curve is a preset curve that represents the optimal load of the target device under different power levels. The status analysis module is used to obtain the remaining power of the target device at the current time, and to obtain the real-time optimal load at the current time based on the load-power-time surface; The load distribution module is used to distribute the optimal real-time load to multiple functional modules of the target device, thereby obtaining the available load for each functional module. The dynamic monitoring module is used to obtain the dynamic threshold of the operating parameters of each functional module based on the available load, and to monitor the target device based on the dynamic threshold of the operating parameters.

2. The equipment load monitoring system based on dynamic threshold adjustment according to claim 1, characterized in that, Obtain the load-power curve of the target device and its usage records at different times, and adjust the load-power curve based on the usage records to obtain a load-power-time surface, including: Obtain the load-power curve of the target device and the usage records of the target device at different times; Based on usage records, the average number of tasks performed by the target device over multiple time periods is obtained; The time correction factor for different time periods is obtained based on the average number of tasks in multiple time periods. The load-power curves were adjusted according to the time correction factor at different time intervals, and the adjusted load-power curves at different time intervals were fitted to obtain the load-power-time surface.

3. The equipment load monitoring system based on dynamic threshold adjustment according to claim 1, characterized in that, The optimal real-time load is allocated to multiple functional modules of the target device to obtain the available load for each functional module, including: Based on the load-power-time surface, the gradient of the real-time optimal load on the power dimension is obtained, which serves as the power sensitivity. Adjust the real-time optimal load based on power sensitivity; The adjusted real-time optimal load is allocated to multiple functional modules of the target device to obtain the available load for each functional module.

4. The equipment load monitoring system based on dynamic threshold adjustment according to claim 1, characterized in that, The optimal real-time load is allocated to multiple functional modules of the target device to obtain the available load for each functional module, including: Based on the load-power-time surface, the gradient of the real-time optimal load in the time dimension is obtained, which serves as the time sensitivity. Based on time sensitivity, the optimal real-time load is allocated to multiple functional modules of the target device to obtain the available load for each functional module.

5. The equipment load monitoring system based on dynamic threshold adjustment according to claim 4, characterized in that, Based on time sensitivity, the optimal real-time load is allocated to multiple functional modules of the target device, resulting in the available load for each functional module, including: Obtain the priority score for each functional module; Adjust priority scores based on time sensitivity; Based on the adjusted priority scores, the real-time optimal load is allocated to multiple functional modules of the target device to obtain the available load for each functional module.

6. The equipment load monitoring system based on dynamic threshold adjustment according to claim 5, characterized in that, Based on time sensitivity, priority scores are adjusted, including: Priority scores are adjusted based on the following formula: ; in, and These are all serial numbers of the functional modules. For functional modules Priority score before adjustment For functional modules Adjusted priority scores For functional modules Priority score before adjustment For time sensitivity, It is a natural exponential function. This is a function that takes the minimum value.

7. The equipment load monitoring system based on dynamic threshold adjustment according to claim 5, characterized in that, Obtain the priority score for each functional module, including: Obtain the importance index, utility index, and historical usage probability of each functional module at the current moment; A priority score is obtained based on the importance index, utility index, and historical usage probability of each functional module at the current moment.

8. A method for monitoring equipment load based on dynamic threshold adjustment, characterized in that, include: The load-power curve of the target device and the usage records of the target device at different times are obtained, and the load-power curve is adjusted according to the usage records to obtain the load-power-time surface. The load-power curve is a preset curve that represents the optimal load of the target device under different power levels. Obtain the remaining battery power of the target device at the current time, and obtain the real-time optimal load based on the load-battery-time surface; The real-time optimal load is allocated to multiple functional modules of the target device to obtain the available load for each functional module; Based on the available load, the dynamic threshold of the operating parameters of each functional module is obtained, and the target device is monitored based on the dynamic threshold of the operating parameters.

9. An electronic device, characterized in that, include: Memory and processor; The memory is used to store the program, and the processor is used to execute the steps of the device load monitoring method based on dynamic threshold adjustment in claim 8 when the program is executed.

10. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, enable the implementation of the steps in the device load monitoring method based on dynamic threshold adjustment as described in claim 8.

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