Self-adaptive energy management method, device and equipment for full life cycle of safety measure tool

Abstract

The invention provides a safety measure tool full-life-cycle adaptive energy management method, device and equipment, and relates to the technical field of equipment energy management. The method comprises the following steps: acquiring historical working mode data of a target safety measure tool and lasting time of a current working mode; based on the historical working mode data, the duration of the current working mode and a preset weight, determining a state transition urgency index of the target safety measure tool; wherein the state transition urgency index represents the degree of future state change possibility of the target safety measure tool; and determining a next wake-up detection time interval of the target safety measure tool in a wake-up time interval range allowed by the system based on the state transition urgency index. According to the invention, dynamic decision-making of the wake-up time interval of the safety measure tool can be realized, and intelligent switching of the safety measure tool among different power consumption modes is facilitated while energy is saved.

Description

Technical Field

[0001] This invention relates to the field of equipment energy management technology, and in particular to an adaptive energy management method, device and equipment for the entire life cycle of safety tools. Background Technology

[0002] In high-risk work environments such as the maintenance of secondary equipment in power systems, the adoption of secondary safety measures is a crucial link in ensuring the safety of personnel and equipment. To achieve accurate and automated monitoring of the on-site status of safety tools, intelligent monitoring devices have emerged. These devices typically have built-in batteries and integrated wireless communication modules to achieve real-time reporting of status data and remote management, thereby effectively compensating for the shortcomings of traditional manual inspections.

[0003] However, current intelligent safety devices generally employ a fixed, periodic wake-up detection mechanism for energy management. The device's main control chip periodically wakes from deep sleep mode at fixed, short intervals to perform status checks and data processing, before returning to sleep mode. This power consumption strategy has fundamental flaws. During the static monitoring period of safety tools, which can last for months or even years, the high-frequency periodic wake-ups result in significant energy waste. Furthermore, the short cycles designed to ensure immediate response during operations solidify a high-power operating mode, leading to a severe mismatch between the device's battery life and actual application requirements. Limited battery life has become a core bottleneck restricting the large-scale application and reliability improvement of such devices. Replacing and maintaining the battery after it is depleted is not only costly and difficult to implement in special environments such as substations, but may also introduce new human error risks. Summary of the Invention

[0004] This invention provides an adaptive energy management method, apparatus, and device for the entire lifecycle of safety tools to address the problem of poor energy management performance of safety tools.

[0005] In a first aspect, embodiments of the present invention provide an adaptive energy management method for the entire lifecycle of safety devices, including: Obtain historical operating mode data and the duration of the current operating mode of the target safety measures tool; Based on historical working mode data, the duration of the current working mode, and preset weights, a state transition urgency index for the target safety measure tool is determined; the state transition urgency index represents the degree of likelihood of future state changes for the target safety measure tool. Based on the state transition urgency index, the next wake-up detection interval for the target safety tool is determined within the system's allowed wake-up interval range.

[0006] In one possible implementation, based on historical operating mode data, the duration of the current operating mode, and preset weights, a state transition urgency index for the target safety measure tool is determined, including: Calculate the historical factors of the status changes of the target safety measures tools based on historical working mode data; Calculate the stable duration suppression factor based on the duration of the current working mode; Based on the state change history factor, the stability duration inhibition factor, and the preset weights, the state transition urgency index of the target safety measure tool is calculated.

[0007] In one possible implementation, the formula for calculating the state history change factor is:

[0008] in, The current state is a historical change factor. As the attenuation factor, For indicator functions, This is the current working mode. This refers to the work mode of the previous moment. This is the state history change factor from the previous moment.

[0009] In one possible implementation, the formula for calculating the stability duration suppression factor is:

[0010] in, As a stable duration suppressor, This represents the duration of the current work pattern. This is the base duration.

[0011] In one possible implementation, the formula for calculating the state transition urgency index is:

[0012] in, This is a state transition urgency index. For the state history change factor, Weighting for historical changes As a preset context mode factor, For context weights, As a stable duration suppressor, To stabilize the duration weight, For interaction weights.

[0013] In one possible implementation, the formula for calculating the next wake-up detection time interval is:

[0014] in, This is the interval for the next wake-up detection. The longest time interval allowed by the system. The shortest time interval allowed by the system. This is the slope adjustment coefficient. This is a state transition urgency index. The threshold for urgency-based decision-making.

[0015] In one possible implementation, after determining the next wake-up detection interval for the target safety measure based on the system's allowed wake-up interval range and the state transition urgency index, the following is also included: The total energy consumption and average event latency of all safety measures tools in the target area are calculated within the current period; the target area is the area where the target safety measures tool is located. With the maximum latency requirement as a constraint and the minimum total energy consumption as the objective, the preset weights are optimized to obtain updated weights. The wake-up interval of the target safety tool is then calculated based on the updated weights in the next cycle.

[0016] Secondly, embodiments of the present invention provide an adaptive energy management device for the entire lifecycle of safety measures tools, comprising: The acquisition module is used to acquire historical operating mode data and the duration of the current operating mode of the target safety measure tool. The calculation module is used to determine the state transition urgency index of the target safety measure tool based on historical working mode data, the duration of the current working mode, and preset weights; wherein, the state transition urgency index represents the degree of probability of future state changes of the target safety measure tool; The mapping module is used to determine the next wake-up detection interval of the target safety tool based on the state transition urgency index, within the range of wake-up intervals allowed by the system.

[0017] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0018] The adaptive energy management method, apparatus, and device for the entire lifecycle of safety devices provided in this invention assesses the likelihood of changes in the operating mode of the safety device based on historical operating mode data and the duration of the current operating mode. It then combines these two types of data with preset weights to form a state transition urgency index, quantifying the likelihood of changes in the operating mode. Finally, it uses the state transition urgency index to determine the most suitable wake-up detection interval within the system's allowed wake-up time interval range. This enables dynamic decision-making regarding the wake-up time interval of the safety device, saving energy while facilitating intelligent switching between different power consumption modes. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the implementation of the adaptive energy management method for the entire lifecycle of safety measures tools provided in this embodiment of the invention. Figure 2 This is a schematic diagram of the adaptive energy management device for the entire lifecycle of safety measures provided in this embodiment of the invention; Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0020] To address the aforementioned problems, this invention proposes an adaptive energy management method applicable to the entire lifecycle of secondary safety devices. This method achieves dynamic optimization of the operating mode and wake-up cycle of the safety device by constructing an intelligent power consumption decision core based on a predictive model.

[0021] The purpose of this invention is to overcome the problems of high power consumption and short battery life caused by the fixed-period polling mechanism in existing intelligent safety devices, and to provide an adaptive energy management method that can intelligently adjust according to the device's life cycle stage, historical state changes and external environmental commands.

[0022] The system architecture upon which this method is based is implemented collaboratively by an intelligent safety measure tool unit, a signal acquisition gateway, and a back-end management platform. The intelligent safety measure tool unit serves as the execution terminal for energy management strategies, responsible for status awareness and adaptive sleep / wake-up. The signal acquisition gateway aggregates status data from each tool unit and relays management commands. The back-end management platform is responsible for running a global optimization algorithm, continuously learning and configuring key parameters of the energy management model. The embodiments of this invention will be described in detail below with reference to the accompanying drawings.

[0023] See Figure 1 The document illustrates a flowchart of the adaptive energy management method for the entire lifecycle of safety measures provided in this embodiment of the invention, detailed below: Step 101: Obtain the historical working mode data and the duration of the current working mode of the target safety measure tool.

[0024] In this embodiment, the system pre-defines multiple working modes based on the different stages of the safety measure tool's lifecycle, including deployment and debugging mode, stable monitoring mode, dynamic monitoring mode, and emergency low-power mode. The intelligent safety measure tool unit can automatically identify and switch to the corresponding working mode based on its own status history or received instructions from the background management platform. The suitable wake-up cycle length varies under different working modes. A detailed description of each mode is as follows: (1) Deployment and Debugging Mode: This mode is entered when the device is first powered on and registered or activated locally by the administrator. The device uses a short and fixed wake-up cycle. Status polling is performed to provide real-time status feedback during the installation and debugging process, ensuring that the tools are installed correctly.

[0025] (2) Stable duty mode: When the device does not detect any state change for a preset period of time, it will automatically switch to this mode. This is the device's most important normal working mode, and its wake-up cycle is... The energy savings are calculated dynamically by the enhanced predictive model described below, typically over longer time intervals, to achieve maximum energy savings.

[0026] (3) Dynamic Monitoring Mode: This mode is triggered when a job start command is received from the local main gateway, or when the device itself detects a state change that conforms to specific rules within a short period of time. In this mode, the wake-up cycle... It is dynamically calculated and limited to a relatively short numerical range to ensure a rapid response to changes in the status of safety equipment during high-risk operations.

[0027] (4) Emergency Low Power Mode: When the power monitoring circuit inside the device detects that the battery voltage is lower than the preset critical alarm threshold, it will be forced to enter this mode. The device will stop complex model calculations and instead send the most basic online heartbeat signal at a very long fixed period to maximize its effective existence time as a network node.

[0028] Step 102: Based on historical working mode data, the duration of the current working mode, and preset weights, determine the state transition urgency index of the target safety measure tool; wherein, the state transition urgency index represents the degree of possibility of future state changes of the target safety measure tool.

[0029] In this embodiment, under any operating mode, the intelligent safety measure tool unit invokes the built-in predictive evaluation model. This model accurately integrates data from three dimensions: the device's state change history, the current context mode, and the stable operating time, to calculate a state transition urgency index that quantifies the likelihood of future state changes.

[0030] Step 103: Based on the state transition urgency index, determine the next wake-up detection interval for the target safety tool within the system's allowed wake-up interval range.

[0031] In this embodiment, a nonlinear mapping function can be used to map the state transition urgency index calculated in the previous step to the range of wake-up time intervals allowed by the system, which serves as the optimal wake-up detection time interval for the next wake-up. This ensures that the greater the probability of future state changes for the target safety tool, the shorter the next wake-up detection time interval will be, thereby meeting the requirement for intelligent switching of the safety tool between different power consumption modes.

[0032] The intelligent safety measure tool unit will calculate the next wake-up detection time interval. An internal sleep / wake-up timer completes one adaptive cycle. Simultaneously, core data related to power consumption and status are reported to the backend management platform. The backend management platform aggregates data from across the network for long-term analysis, iteratively optimizing the weight coefficients of the predictive evaluation model using a predetermined objective function, and then distributing the optimized parameters to the tool units. This enables the entire system's energy management strategy to continuously learn and improve.

[0033] This invention assesses the likelihood of changes in the working mode of a safety device based on its historical working mode data and the duration of its current working mode. It then combines these two types of data with preset weights to form a state transition urgency index, quantifying the probability of a change in the working mode. Finally, the state transition urgency index is used to determine the most suitable wake-up detection interval within the system's allowed wake-up time interval range. This enables dynamic decision-making regarding the wake-up time interval of the safety device, saving energy while facilitating intelligent switching between different power consumption modes.

[0034] In one possible implementation, based on historical operating mode data, the duration of the current operating mode, and preset weights, a state transition urgency index for the target safety measure tool is determined, including: Calculate the historical factors of the status changes of the target safety measures tools based on historical working mode data; Calculate the stable duration suppression factor based on the duration of the current working mode; Based on the state change history factor, the stability duration inhibition factor, and the preset weights, the state transition urgency index of the target safety measure tool is calculated.

[0035] In this embodiment, the state change history factor is used to characterize the frequency of recent state changes of the safety measure tool, while the stability duration suppression factor reflects the suppressive effect of long-term state invariance on urgency. By assigning different importance proportions to the two factors through preset weights, a state transition urgency index is obtained through fusion. The state transition urgency index is a comprehensive evaluation value; the higher the index, the greater the likelihood of state change and the more frequent the detection.

[0036] In one possible implementation, the formula for calculating the state history change factor is:

[0037] in, The current state is a historical change factor. As the attenuation factor, For indicator functions, This is the current working mode. This refers to the work mode of the previous moment. This is the state history change factor from the previous moment.

[0038] In this embodiment, the state change history factor is calculated using the exponentially weighted moving average method. The attenuation factor is defined within the interval (0, 1). The indicator function is a standard characteristic function that outputs a specific binary value based on the truth value of the logical condition within parentheses. Its values ​​and the conditions for taking those values ​​are defined as follows: (1) When the logical condition inside the parentheses is true, the value of the indicator function 𝕀(•) is 1.

[0039] (2) When the logical condition within the parentheses is false, the value of the indicator function 𝕀(•) is 0. In this embodiment, the condition for the indicator function to take the value is "the current working mode is not equal to the previous working mode, that is..." Therefore, when the working mode of the safety device changes, that is... hour, The value is 1. When the working mode of the safety device remains unchanged, that is... hour, The value is 0. By setting the decay factor, the indicator function, and combining it with the state history change factor from the previous moment, it can be ensured that the state history change factor only changes when the operating mode changes. Only when the calculation is triggered by a pulse with a value of 1 can the frequency of recent state changes be accurately quantified and memorized.

[0040] In one possible implementation, the formula for calculating the stability duration suppression factor is:

[0041] in, As a stable duration suppressor, This represents the duration of the current work pattern. This is the base duration.

[0042] In this embodiment, the stability duration suppression factor is calculated using a logarithmic function, and the baseline duration can be set according to the actual situation.

[0043] In one possible implementation, the formula for calculating the state transition urgency index is:

[0044] in, This is a state transition urgency index. For the state history change factor, Weighting for historical changes As a preset context mode factor, For context weights, As a stable duration suppressor, To stabilize the duration weight, For interaction weights.

[0045] In this embodiment, the context mode factor is a quantified value issued by the local main gateway based on the job plan, directly reflecting the requirements of the current operating environment. The core meaning of the context mode factor is to provide an external, global intervention command for the autonomous energy-saving decisions of the safety measures tool. By adjusting... The system administrator or main gateway can force an increase or decrease in the monitoring sensitivity of the safety measures tool to keep its behavior synchronized with the actual on-site work plan. In short, it represents an external perspective's assessment of the current environmental risk level.

[0046] The specific process by which the local master gateway determines the context mode factor is as follows: High-urgency scenario (work commencement): Before scheduled maintenance work on a relay protection room, the system administrator or the upper-level work order system will issue a "work commencement" command to the main gateway of that area. Upon receiving the command, the main gateway will broadcast a message to all relevant safety devices within its jurisdiction, instructing them to... Set to a preset high positive value (e.g., =10). After the safety tool receives this value, its The calculation results will be significantly improved, thus immediately entering or remaining in a "dynamic monitoring mode" with extremely short wake-up intervals to cope with upcoming intensive operations.

[0047] Low-urgency scenarios (job completion / routine monitoring): Similarly, when a job ends, or during routine monitoring without any planned jobs, the main gateway will broadcast new instructions. Restore to zero ( = 0). When When it is zero, The calculation will mainly rely on the device's own historical status and stable duration, so that once the safety measures tool confirms that its status is stable, it can quickly return to the "stable duty mode" with energy saving as the main goal.

[0048] To more accurately predict the urgency of state transitions, this embodiment also introduces interaction terms. This describes the nonlinear interactions of key features, capturing deep connections that simple linear combinations cannot reveal. Its significance lies in the fact that only when the state actually changes ( >0) and the system is already in a high-attention "dynamic monitoring" mode ( When the value is a relatively large positive value, the interaction term will produce a significant amplification effect that far exceeds linear superposition, thereby drastically increasing the urgency index and ensuring the fastest response to critical changes at critical moments.

[0049] Each weight can be updated by the local main gateway according to the optimization algorithm.

[0050] In one possible implementation, the formula for calculating the next wake-up detection time interval is:

[0051] in, This is the interval for the next wake-up detection. The longest time interval allowed by the system. The shortest time interval allowed by the system. This is the slope adjustment coefficient. This is a state transition urgency index. The threshold for urgency-based decision-making.

[0052] In this embodiment, the state transition urgency index can be mapped to the time interval allowed by the system through a nonlinear function. The slope adjustment coefficient and urgency decision threshold can be set according to the actual situation.

[0053] Next wake-up detection interval The calculation and application of this mechanism begin each time the safety measure tool is awakened from its dormant state, forming the core of this work cycle. Its complete timing logic includes: 1. Calculation Timing: When the internal timer of the safety device finishes its countdown and the main control unit (MCU) is awakened from low-power sleep mode, the MCU will immediately begin executing the method described in this invention. It will first obtain the current operating mode ( ), and combine it with historical information stored in memory (such as , According to the formula defined in this invention, within the brief working window period after the current wake-up, the next wake-up detection time interval determined in this cycle is calculated. .

[0054] 2. Application timing: Completed in the MCU After performing calculations and other necessary tasks (such as data reporting), and before entering its next low-power sleep state, it will send the just-calculated data to the appropriate location. The value is loaded into its internal low-power timer as the new sleep countdown interval.

[0055] 3. Timing Start Point: The countdown of this timer begins the instant the MCU officially enters sleep mode. After [timeout]... After the set duration, the timer will trigger an interrupt again, waking up the MCU and starting the next round of the adaptive cycle of "wake-up → calculation → application → sleep".

[0056] Therefore, it can be summarized as follows: The calculations occur during the brief working window after each wake-up and before entering hibernation; When used as a sleep duration, it covers the entire time period from the start of the current sleep to the next wake-up.

[0057] In one possible implementation, after determining the next wake-up detection interval for the target safety measure based on the system's allowed wake-up interval range and the state transition urgency index, the following is also included: The total energy consumption and average event latency of all safety measures tools in the target area are calculated within the current period; the target area is the area where the target safety measures tool is located. With the maximum latency requirement as a constraint and the minimum total energy consumption as the objective, the preset weights are optimized to obtain updated weights. The wake-up interval of the target safety tool is then calculated based on the updated weights in the next cycle.

[0058] In this embodiment, to achieve continuous optimization of model parameters while strictly adhering to network isolation requirements, a parameter self-optimization closed-loop mechanism that operates entirely within the local area network is proposed. This mechanism centers on the system's main gateway, leveraging its superior computing and storage capabilities compared to security tools to act as a "local optimization engine."

[0059] Its workflow is as follows: Data aggregation: The main gateway continuously collects operational data reported by all intelligent safety measure tool units within its jurisdiction, including precise timestamps of state transitions and wake-up intervals used for each decision. wait.

[0060] Local performance evaluation: The main gateway periodically (e.g., once a day) analyzes the data it collects to calculate the current weighting strategy over the past period. The following two key performance indicators: ① The total energy consumption of the region estimated numerically; ② The average event delay from the occurrence of a state change to its detection by the system.

[0061] Localized weight optimization: Based on the above performance metrics, the main gateway runs a lightweight optimization algorithm, specifically coordinate descent or parametric grid search. The goal of this algorithm is to find a new set of weights. W′ This minimizes the total energy consumption of the region while meeting the maximum latency requirements. This process is entirely offline on the main gateway's processor, without generating any external network communication.

[0062] Configuration update distribution: Once a better weight combination is calculated. W′ The main gateway then sends the new configuration parameters to all security tool units under its jurisdiction via the local communication bus.

[0063] Through this mechanism, this invention constructs a self-learning, adaptive system that operates entirely within a secure, isolated network without relying on an external cloud platform. It can autonomously "evolve" the most suitable energy-saving strategy for a specific work area (such as a relay protection room) based on its actual operating frequency and patterns, achieving truly intelligent energy management throughout its entire lifecycle.

[0064] As can be seen from the above, compared with the prior art, the adaptive energy management method for the entire life cycle of secondary safety devices proposed in this invention has the following significant advantages due to its multi-mode operating strategy, enhanced local prediction model, and local gateway-driven self-optimizing closed-loop mechanism: 1. Ultimate Power Consumption Optimization and Long Lifespan: This invention completely eliminates the inefficient power consumption mode of fixed-period polling. By introducing a multi-mode operating strategy, especially the ability to intelligently and non-linearly extend the sleep interval to its upper limit during stable operation using a predictive model, the average power consumption of the device is reduced by several orders of magnitude. This ultimate energy management efficiency makes it possible to achieve a battery life exceeding the physical service life of the device using ordinary built-in batteries, fundamentally solving the industry's widespread anxiety about battery life and achieving the design goal of "one-time deployment, lifetime maintenance-free".

[0065] 2. Balancing High Reliability and Intelligent Predictive Response: This invention does not simply extend dormancy; rather, it ensures mission reliability through an enhanced local predictive model. The innovative nonlinear interaction term in the model can keenly capture the coupling effect of key factors such as state changes and high-risk operation periods, giving the device intelligent predictive capabilities. It can remain alert to weak signals indicating risk during long periods of dormancy and quickly switch to a high-frequency monitoring mode, achieving a perfect balance between extreme energy saving and high-reliability response.

[0066] 3. Strong Localized Environment Adaptability: The parameter self-optimization closed-loop mechanism driven by the local main gateway, designed in this invention, endows the system with unique environmental adaptability. This allows energy management strategies to move beyond a rigid, one-size-fits-all model, enabling them to autonomously learn and adapt to the actual operating frequency and habits of their specific area (such as a particular relay protection room or terminal box). Safety measures in frequently operated areas will automatically evolve more sensitive response strategies, while tools in long-term idle areas will adopt more aggressive energy-saving strategies, achieving site-specific, precise, and efficient intelligent management.

[0067] 4. Fully Compliant with Network Security Isolation Requirements: The entire intelligent decision-making and optimization system of this invention, from model inference on the edge devices to parameter optimization on the local gateway, is completed within a closed local area network environment physically isolated from the external network. This architecture completely avoids dependence on external cloud platforms or internet connections, fully complying with the stringent network isolation and security regulations for high-security scenarios such as power systems. This not only solves the technical feasibility issue but also makes it a highly secure and compliant invention solution in reality.

[0068] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0069] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0070] Figure 2 A schematic diagram of the adaptive energy management device for the entire lifecycle of safety measures tools provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiments of the present invention are shown, and are described in detail below: like Figure 2 As shown, the adaptive energy management device 2 for the entire lifecycle of Ancuo tools includes: The acquisition module 21 is used to acquire historical working mode data and the duration of the current working mode of the target safety measure tool; The calculation module 22 is used to determine the state transition urgency index of the target safety measure tool based on historical working mode data, the duration of the current working mode, and preset weights; wherein, the state transition urgency index represents the degree of probability of future state changes of the target safety measure tool; The mapping module 23 is used to determine the next wake-up detection interval of the target safety tool based on the state transition urgency index, within the range of wake-up intervals allowed by the system.

[0071] In one possible implementation, the computing module 22 is specifically used for: Calculate the historical factors of the status changes of the target safety measures tools based on historical working mode data; Calculate the stable duration suppression factor based on the duration of the current working mode; Based on the state change history factor, the stability duration inhibition factor, and the preset weights, the state transition urgency index of the target safety measure tool is calculated.

[0072] In one possible implementation, the formula for calculating the state history change factor is:

[0073] in, The current state is a historical change factor. As the attenuation factor, For indicator functions, This is the current working mode. This refers to the work mode of the previous moment. This is the state history change factor from the previous moment.

[0074] In one possible implementation, the formula for calculating the stability duration suppression factor is:

[0075] in, As a stable duration suppressor, This represents the duration of the current work pattern. This is the base duration.

[0076] In one possible implementation, the formula for calculating the state transition urgency index is:

[0077] in, This is a state transition urgency index. For the state history change factor, Weighting for historical changes As a preset context mode factor, For context weights, As a stable duration suppressor, To stabilize the duration weight, For interaction weights.

[0078] In one possible implementation, the formula for calculating the next wake-up detection time interval is:

[0079] in, This is the interval for the next wake-up detection. The longest time interval allowed by the system. The shortest time interval allowed by the system. This is the slope adjustment coefficient. This is a state transition urgency index. The threshold for urgency-based decision-making.

[0080] In one possible implementation, the computing module 22 is also used for: After determining the next wake-up detection interval for the target safety tool based on the system's allowed wake-up interval range and state transition urgency index, the total energy consumption and average event latency of all safety tools in the target area within the current period are calculated; where the target area is the area where the target safety tool is located. With the maximum latency requirement as a constraint and the minimum total energy consumption as the objective, the preset weights are optimized to obtain updated weights. The wake-up interval of the target safety tool is then calculated based on the updated weights in the next cycle.

[0081] This invention assesses the likelihood of changes in the working mode of a safety device based on its historical working mode data and the duration of its current working mode. It then combines these two types of data with preset weights to form a state transition urgency index, quantifying the probability of a change in the working mode. Finally, the state transition urgency index is used to determine the most suitable wake-up detection interval within the system's allowed wake-up time interval range. This enables dynamic decision-making regarding the wake-up time interval of the safety device, saving energy while facilitating intelligent switching between different power consumption modes.

[0082] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Figure 3 As shown, the electronic device 3 of this embodiment includes a processor 30 and a memory 31. The memory 31 stores a computer program 32. When the processor 30 executes the computer program 32, it implements the steps in the various method embodiments described above. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the various device embodiments described above.

[0083] For example, computer program 32 may be divided into one or more modules / units, which are stored in memory 31 and executed by processor 30 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 32 in electronic device 3.

[0084] Electronic device 3 may include, but is not limited to, processor 30 and memory 31. Those skilled in the art will understand that... Figure 3 This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 3 may also include input / output devices, network access devices, buses, etc.

[0085] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0086] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0087] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. An adaptive energy management method for the entire lifecycle of safety devices, characterized in that, include: Obtain historical operating mode data and the duration of the current operating mode of the target safety measures tool; Based on the historical working mode data, the duration of the current working mode, and the preset weight, the state transition urgency index of the target safety measure tool is determined; wherein, the state transition urgency index represents the degree of probability of future state changes of the target safety measure tool; Based on the state transition urgency index, the next wake-up detection time interval of the target safety tool is determined within the system's allowed wake-up time interval range.

2. The adaptive energy management method for the entire lifecycle of safety devices according to claim 1, characterized in that, The determination of the state transition urgency index of the target safety measure tool based on the historical working mode data, the duration of the current working mode, and a preset weight includes: Calculate the historical factors of the state changes of the target safety measures tool based on the historical working mode data; Calculate the stable duration suppression factor based on the duration of the current working mode; Based on the state change history factor, the stability duration suppression factor, and the preset weight, the state transition urgency index of the target safety measure tool is calculated.

3. The adaptive energy management method for the entire lifecycle of safety devices according to claim 2, characterized in that, The formula for calculating the state history change factor is: in, The current state is a historical change factor. As the attenuation factor, For indicator functions, This is the current working mode. This refers to the work mode of the previous moment. This is the state history change factor from the previous moment.

4. The adaptive energy management method for the entire lifecycle of safety devices according to claim 2, characterized in that, The formula for calculating the stability duration suppression factor is as follows; in, As a stable duration suppressor, This represents the duration of the current work pattern. This is the base duration.

5. The adaptive energy management method for the entire lifecycle of safety devices according to claim 2, characterized in that, The formula for calculating the state transition urgency index is: in, This is a state transition urgency index. For the state history change factor, Weighting for historical changes, As a preset context mode factor, For context weights, As a stable duration suppressor, To stabilize the duration weight, For interaction weights.

6. The adaptive energy management method for the entire lifecycle of safety devices according to claim 1, characterized in that, The formula for calculating the next wake-up detection time interval is: in, This is the interval for the next wake-up detection. The longest time interval allowed by the system. The shortest time interval allowed by the system. This is the slope adjustment coefficient. This is a state transition urgency index. The threshold for urgency-based decision-making.

7. The adaptive energy management method for the entire lifecycle of safety devices according to claim 1, characterized in that, After determining the next wake-up detection interval of the target safety measure based on the system-allowed wake-up interval range and the state transition urgency index, the method further includes: The total energy consumption and average event latency of all safety devices in the target area are calculated within the current period; wherein, the target area is the area where the target safety device is located; With the maximum latency requirement as a constraint and the minimum total energy consumption as the objective, the preset weights are optimized to obtain updated weights, and the wake-up time interval of the target security tool is calculated based on the updated weights in the next cycle.

8. An adaptive energy management device for the entire lifecycle of safety tools, characterized in that, include: The acquisition module is used to acquire historical operating mode data and the duration of the current operating mode of the target safety measure tool; The calculation module is used to determine the state transition urgency index of the target safety measure tool based on the historical working mode data, the duration of the current working mode, and a preset weight; wherein, the state transition urgency index represents the degree of probability of future state changes of the target safety measure tool; The mapping module is used to determine the next wake-up detection time interval of the target safety tool based on the state transition urgency index, within the range of wake-up time intervals allowed by the system.

9. The adaptive energy management device for the entire life cycle of safety tools according to claim 8, characterized in that, The calculation module is specifically used for: Calculate the historical factors of the state changes of the target safety measures tool based on the historical working mode data; Calculate the stable duration suppression factor based on the duration of the current working mode; Based on the state change history factor, the stability duration suppression factor, and the preset weight, the state transition urgency index of the target safety measure tool is calculated.

10. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 7.

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