Device operation method and apparatus, electronic device, and storage medium

Abstract

The present disclosure relates to a device operation method and device, an electronic device, and a storage medium. The method includes obtaining current time-related information of at least one target device in operation; collecting, based on a plurality of preset data collection modules, a plurality of target data of a space in which the at least one target device is located in real time; inputting the current time-related information and the plurality of target data into a preset fusion module, performing data fusion on the plurality of target data based on an analysis of respective corresponding weights of the plurality of target data based on the current time-related information, and obtaining fused operation feature information, environment feature information, and object feature information of a target object; inputting the operation feature information, the environment feature information, and the object feature information into a preset operation analysis module, analyzing an operation mode of the at least one target device, and outputting a device operation mode to control operation of the at least one target device. The embodiments of the present disclosure can improve the reliability, economy, and efficiency of device operation.

Description

Technical Field

[0001] This disclosure relates to the field of smart home appliance technology, and in particular to a device operation method, apparatus, electronic device and storage medium. Background Technology

[0002] As a significant energy-consuming unit in homes and commercial spaces, the energy efficiency management level of kitchen appliances directly impacts overall energy consumption and operating costs. Traditional energy-saving methods primarily rely on manual intervention, such as using time-of-use pricing mechanisms to guide users to perform high-power cooking tasks during off-peak hours, or manually selecting preset function levels based on cooking needs. However, current technological systems have significant limitations: at the sensing level, systems often employ single-dimensional sensors, making it difficult to capture dynamic variables in the kitchen environment in real time; at the control logic level, devices often operate based on static programs, lacking the ability to dynamically adjust power distribution according to real-time operating conditions. This results in a disconnect between energy allocation and actual demand, a lack of dynamic response capabilities, and difficulties in achieving refined and adaptive energy saving. Summary of the Invention

[0003] This disclosure provides a device operation method, apparatus, electronic device, and storage medium to at least solve the problems in the related art, such as the disconnect between energy distribution and actual demand, lack of dynamic response capability, and difficulty in achieving refined and adaptive energy saving.

[0004] According to a first aspect of the present disclosure, a device operation method is provided, comprising: Obtain at least one type of target device's current time-related information during runtime; Based on multiple preset data acquisition modules, various target data in the space where the at least one target device is located are collected in real time. The various target data are used to indicate the current operating status of the at least one target device, the current environmental status of the space where the at least one target device is located, and the current object status of the target object using the at least one target device. The current time association information and the multiple target data are input into a preset fusion module. Based on the analysis of the weights corresponding to each of the multiple target data according to the current time association information, the multiple target data are fused to obtain the fused operating characteristic information of the at least one target device, the environmental characteristic information of the space where the at least one target device is located, and the object characteristic information of the target object using the at least one target device. The operation feature information, the environmental feature information, and the object feature information are input into a preset operation analysis module to analyze the operation mode of the at least one target device and output the device operation mode. Based on the device operating mode, control the operation of the at least one target device.

[0005] According to a second aspect of the present disclosure, a device operating apparatus is provided, comprising: The current time-related information acquisition module is used to acquire at least one type of current time-related information of the target device during operation; The target data acquisition module is used to collect multiple target data in real time from the space where the at least one target device is located based on multiple preset data acquisition modules. The multiple target data are used to indicate the current operating status of the at least one target device, the current environmental status of the space where the at least one target device is located, and the current object status of the target object using the at least one target device. The feature information acquisition module is used to input the current time association information and the multiple target data into a preset fusion module. Based on the analysis of the weights corresponding to the multiple target data according to the current time association information, the multiple target data are fused to obtain the fused operation feature information of the at least one target device, the environmental feature information of the space where the at least one target device is located, and the object feature information of the target object using the at least one target device. The device operation mode acquisition module is used to input the operation feature information, the environmental feature information and the object feature information into a preset operation analysis module, analyze the operation mode of the at least one target device, and output the device operation mode. The equipment control module is used to control the operation of the at least one target device based on the equipment operating mode.

[0006] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the method as described in any one of the first aspects above.

[0007] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided such that, when instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method described in any of the first aspects of the present disclosure. According to a fifth aspect of the present disclosure, a computer program product including instructions is provided that, when run on a computer, causes the computer to perform the method described in any of the first aspects of the present disclosure.

[0008] The technical solutions provided by the embodiments of this disclosure have at least the following beneficial effects: By acquiring the current time-related information of the target device during operation, and using multiple preset data acquisition modules to collect in real time various target data indicating the current operating status of at least one target device, the current environmental conditions of the space where at least one target device is located, and the current object status of the target object using at least one target device, a preset fusion module analyzes the weight of each target data based on the current time-related information and performs data fusion to obtain device operating characteristic information, spatial environment characteristic information, and object characteristic information. The focus of attention on multiple target data is dynamically adjusted, improving the accuracy and pertinence of data analysis. This data is then input into a preset operation analysis module to output the device operation mode. Finally, the operation of the target device is controlled based on this mode, achieving precise regulation of the target device operation, enhancing the device's adaptability to different operating scenarios and object requirements, improving the intelligence level, energy efficiency, and economy of the target device operation, ensuring the safety and comfort of the target object in using the target device, and realizing refined and dynamic device operation management. It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0009] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0010] Figure 1 This is a flowchart illustrating a device operation method according to an exemplary embodiment; Figure 2 This is a schematic diagram illustrating a process for real-time acquisition of multiple target data based on multiple preset data acquisition modules and target acquisition modes, according to an exemplary embodiment. Figure 3 This is a flowchart illustrating an operating mode of an output device according to an exemplary embodiment; Figure 4 This is a flowchart illustrating another output device operating mode according to an exemplary embodiment; Figure 5 This is a flowchart illustrating another output device operating mode according to an exemplary embodiment; Figure 6 This is a flowchart illustrating a predetermined device operating mode according to an exemplary embodiment; Figure 7 This is a schematic diagram illustrating a process for outputting pre-run information according to an exemplary embodiment; Figure 8 This is a block diagram illustrating a device operation apparatus according to an exemplary embodiment; Figure 9 This is a block diagram illustrating an electronic device for device operation according to an exemplary embodiment. Detailed Implementation

[0011] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0012] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar different contents and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0013] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0014] Figure 1 This is a flowchart illustrating a device operation method according to an exemplary embodiment, such as... Figure 1 As shown, the device operation method is used in electronic devices such as servers and includes the following steps.

[0015] In step S101, the current time-related information of at least one target device during operation is obtained.

[0016] In one specific embodiment, the aforementioned current time-related information is data related to the operating time of the target device and capable of reflecting time attributes. For example, the current time-related information may include at least one type of data, such as the current time period and periodic characteristics. Specifically, the periodic characteristics may include at least one type of data, such as the temporal regularity of the target device's location (e.g., weekday morning rush hour), the preset period to which the current time period belongs (e.g., the 3rd day within the device maintenance cycle), or the periodic fluctuation characteristics of environmental parameters (e.g., peak afternoon temperature in summer). The periodic characteristics can be preset by the user in the mobile application software. If the user does not preset them, the server can customize the periodic characteristics based on the current time period.

[0017] In step S103, multiple target data of at least one target device in the space is collected in real time based on multiple preset data acquisition modules.

[0018] In one specific embodiment, the aforementioned multiple target data are used to indicate the current operating status of at least one target device, the current environmental status of the space where at least one target device is located, and the current object status of the target object using at least one target device.

[0019] In one specific embodiment, the aforementioned multiple target data can be categorized based on multiple preset data acquisition modules. For example, when the multiple preset data acquisition modules include a visual sensor, a temperature and humidity sensor, a current sensor, and a user behavior recognition module, the multiple target data may include visual data acquired by the visual sensor, temperature and humidity data acquired by the temperature and humidity sensor, current data acquired by the current sensor, and object data acquired by the user behavior recognition module.

[0020] In a specific embodiment, the aforementioned multiple target data can be divided based on the dimension of the object to which the data belongs. Specifically, the object to which the data belongs can be at least one object such as the target device, the space where the target device is located, and the target object using the target device. For example, when multiple preset data acquisition modules include a visual sensor, a temperature and humidity sensor, a current sensor, and a user behavior recognition module, by integrating the data collected by the visual sensor, temperature and humidity sensor, current sensor, and user behavior recognition module, all the collected data can be classified into at least one type of data, such as device-related data of the target device (including the device's mechanical state identified by the visual sensor (e.g., air conditioner blade angle) and device power consumption mode monitored by the current sensor (e.g., oven current ripple), which directly characterizes the operating status of the target device), environmental-related data of the space where the target device is located (including the spatial temperature and humidity distribution collected by the temperature and humidity sensor (e.g., regional temperature gradient) and light changes captured by the visual sensor (e.g., curtain light transmittance), which together reflect the environmental conditions of the space where the target device is located), and object-related data of the target object using the target device (including user operating habits obtained by the user behavior recognition module and user orientation captured by the visual sensor, which accurately describes the object status of the target object using the target device).

[0021] In a specific embodiment, such as Figure 2 As shown, the above-mentioned data collected in real time from the space where at least one target device is located, based on multiple preset data acquisition modules, includes: In step S201, based on the current time association information and the preset acquisition strategy, the target acquisition mode corresponding to each of the various preset data acquisition modules is determined.

[0022] For example, the various preset data acquisition modules may include at least two data acquisition modules such as vision sensors, temperature and humidity sensors, and current sensors. The vision sensor can acquire at least one type of data, such as device operation visual data (e.g., the opening angle of the air outlet blades of the target device (e.g., an air conditioner) and display fault codes), environmental visual data (e.g., the distribution of light intensity in the space, personnel movement trajectories, and the opening and closing status of doors and windows), and object visual data (e.g., gesture commands from the target object (e.g., pointing at the device), distance from the device, and orientation). The temperature and humidity sensor can acquire at least one type of data, such as device temperature and humidity data (e.g., the temperature and humidity difference between the air inlet and outlet of the target device (e.g., a dehumidifier)) and environmental temperature and humidity data (e.g., real-time temperature gradient and humidity distribution in different areas of the space). The current sensor can acquire at least one type of data, such as the device operating current status (e.g., the real-time operating current of the target device (e.g., an oven), standby power consumption and abnormal current ripple, fluctuations in the total circuit load of the space, and voltage drops caused by interference from other electrical appliances).

[0023] In a specific embodiment, the above-mentioned preset acquisition strategy represents the correspondence between the current time-related information and the preset acquisition modes corresponding to the various preset data acquisition modules.

[0024] For example, for vision sensors, the preset acquisition modes can be any of the following: global sleep (activated only by external abnormal events), area-triggered capture (activated by moving objects within a specified space), active high-definition recording (continuous recording of a preset area + face recognition), motion detection alarm (real-time alarm only for abnormal behavior), real-time thermal analysis (dynamically optimizes device control), and infrared supplementary light monitoring (detects intrusion only in low-light environments). For temperature and humidity sensors, the preset acquisition modes can be any of the following: low-frequency sampling (30 minutes / time, only storing trends), regular sampling (10 minutes / time), and high-frequency sampling (5 minutes / time, real-time device adjustment). For current sensors, the preset acquisition modes can be any of the following: low-frequency monitoring (standby current, alarm for abnormal fluctuations), real-time monitoring (specifying high-power devices), continuous whole-house monitoring (marking abnormally power-consuming devices), continuous device-focused monitoring (air conditioners / refrigerators, etc.), peak tracking (predicting overload by starting and stopping kitchen appliances), and dynamic monitoring (entertainment devices and charging status).

[0025] For example, the preset data collection strategy can be as follows: During the period from 22:00 to 5:00, corresponding to the nighttime quiet period, the visual sensor is in sleep mode, only activating in response to abnormal events; the temperature and humidity sensor samples at a low frequency (30 minutes / time), only storing basic environmental trends; the current sensor monitors standby current at a low frequency, triggering alarms when abnormal fluctuations occur; and the user behavior recognition module is in sleep mode, not actively tracking user activity. During the period from 5:00 to 8:00, corresponding to the family's morning start-up period, the visual sensor only triggers capture when movement occurs in the kitchen area; the temperature and humidity sensor samples at a high frequency (e.g., 5 minutes / time), optimizing the operation of breakfast equipment in real time; the current sensor monitors high-power devices (e.g., rice cookers / ovens) in real time; and the user behavior recognition module is activated, locating the user's kitchen activity status. During the 8:00-12:00 time period, corresponding to the active daytime work period, the visual sensor actively records high-definition video (living room / entrance) and supports facial recognition; the temperature and humidity sensor performs regular sampling (e.g., every 10 minutes); the current sensor continuously monitors all appliances in the house and marks non-standard power-consuming devices; the user behavior recognition module tracks activity trajectories at high frequency. During the 12:00-16:00 time period, corresponding to the stable midday operation period, the visual sensor operates in low-power motion detection mode, only alerting in real time to abnormal behavior; the temperature and humidity sensor performs regular sampling; the current sensor focuses on monitoring continuously operating devices such as air conditioners and refrigerators; the user behavior recognition module detects locations at low frequency. During the 16:00-19:00 time period, corresponding to the family dinner preparation period, the visual sensor analyzes the number of people and activity hotspots in real time to optimize lighting and air conditioning; the temperature and humidity sensor performs high-frequency sampling (e.g., every 5 minutes) to respond to changes in population density; the current sensor tracks the peak startup time of kitchen appliances to predict overload risks; the user behavior recognition module actively analyzes behavior in the kitchen and living room. During the period from 19:00 to 22:00, which corresponds to the family leisure and entertainment period, the visual sensor is in infrared supplementary light mode, and only abnormal intrusions are detected in low light environment; the temperature and humidity sensor is in regular sampling mode; the current sensor is used to monitor entertainment equipment (such as TV, stereo, etc.) and charging status; the user behavior recognition module is down-frequency to behavioral trend analysis (such as sitting still, moving, etc.).

[0026] In step S203, multiple target data are collected in real time based on multiple preset data acquisition modules and target acquisition modes.

[0027] In the above embodiments, by dynamically determining the target acquisition mode corresponding to each of the various preset data acquisition modules based on the current time association information and preset acquisition strategies, the acquisition needs can be accurately adapted in real time, realizing the intelligent and on-demand configuration of acquisition modes. By driving the various preset data acquisition modules to operate according to the target acquisition mode, the real-time acquisition of various target data can be completed accurately and efficiently while ensuring the timeliness and integrity of the data. This effectively avoids resource waste and ensures that the acquisition behavior closely matches the actual needs of a specific time period, thereby improving the overall data acquisition efficiency and accuracy.

[0028] In step S105, the current time-related information and multiple target data are input into the preset fusion module. Based on the analysis of the weights corresponding to each of the multiple target data according to the current time-related information, the multiple target data are fused to obtain the fused operating characteristic information of at least one target device, the environmental characteristic information of the space where at least one target device is located, and the object characteristic information of the target object using at least one target device.

[0029] In a specific embodiment, the aforementioned preset fusion module can be a deep learning network that performs data fusion on multiple target data based on the analysis of the weights corresponding to each of the multiple target data, such as convolutional neural networks and recurrent neural networks.

[0030] In an optional embodiment, the aforementioned preset fusion module can be trained based on multiple sample time-related information during the target device's operation, multiple sample data in the target device's spatial location, target operational feature information of the target device, target environmental feature information in at least one target device's spatial location, and target object feature information of a target object using at least one target device. In a specific embodiment, the aforementioned multiple sample data can be the data to be fused.

[0031] In step S107, the running feature information, environmental feature information, and object feature information are input into the preset running analysis module to analyze the running mode of at least one target device and output the device running mode.

[0032] In a specific embodiment, the aforementioned device operation mode may include at least one type of data, such as the target device's basic operating mode, safety control parameters, and environmental linkage constraints. Specifically, the basic operating mode may be a preset basic operating framework for the target device to achieve its core functional objectives, determining the control logic for the target device's core operating parameters (such as power / speed). The safety control parameters may be rigid thresholds that the target device's operating parameters cannot exceed, used to prevent the target device from exceeding its limits. The environmental linkage constraints may be automatic corrections of the target device's control rules triggered based on environmental characteristic information. For example, the basic operating mode may be any of the modes such as constant temperature mode, variable speed mode, and standby mode. The safety control parameters may be at least one of the following: current ≤ preset current threshold and temperature threshold ≤ preset temperature threshold. The environmental linkage constraints may be a command to disable the heating module when humidity > preset humidity threshold.

[0033] In a specific embodiment, such as Figure 3 As shown, the above method also includes: In step S301, first operational correlation data of at least one target device within a first preset time period is acquired.

[0034] In one specific embodiment, the first preset time period is the historical operating time period of at least one target device, which can be set according to the actual application. For example, the first preset time period can be one year of operation of at least one target device before the current operating time. The first operation-related data may include at least one type of data such as historical operating data and historical maintenance information.

[0035] In step S303, the preset component association information of at least one target device, the current operating data among multiple target data, the current environmental data among multiple target data, and the first operating association data are input into the preset device analysis module to analyze the remaining usage time of at least one target device and output the usage time analysis information of at least one target device.

[0036] In one specific embodiment, the aforementioned preset component association information may include at least one type of data, such as the material properties of the target device itself and historical fault records of similar devices. Specifically, the material properties may be the inherent characteristics of the substances constituting the device components, directly determining their mechanical or chemical behavior boundaries. The historical fault records may include the fault time, corresponding fault type, and cause of the fault in similar devices. For example, the material properties may be the coefficient of thermal expansion (e.g., 23 × 10⁻⁶ for aluminum). -6 The data includes any one of the following: / K, yield strength (e.g., 355MPa for steel), insulation class (e.g., H class with resistance to 180℃), and corrosion resistance index (e.g., 316L stainless steel = grade 5).

[0037] In a specific embodiment, the aforementioned usage time analysis information can be used to indicate the remaining effective working time of the target equipment. For example, the usage time analysis information may include the remaining service life of the target equipment, and may also include at least one type of data such as component wear correlation data, equipment failure judgment thresholds, and feasible measures to extend the usage time of the target equipment. Specifically, component wear correlation data is used to characterize the performance degradation or wear-related influencing factors of key components in the target equipment. For example, component wear correlation data may include at least one type of data such as component identification data, wear type characteristic data, wear intensity quantification data, causal correlation parameter data, correlation impact diffusion data, and key wear countermeasure data. Specifically, component identification data characterizes the specific physical location where wear occurs, and may include at least one type of data such as component code and installation location coordinates. Wear type characteristic data characterizes the physical mechanism type of performance degradation, for example, it can be any type such as wear, fatigue cracks, and corrosion. Wear intensity quantification data characterizes the real-time degree of performance degradation, for example, any data such as bearing clearance value, tooth surface pitting area, and seal leakage rate. Causal correlation parameter data characterizes the key influencing factors leading to wear, for example, any factor such as environmental erosion. Correlation impact diffusion data characterizes the impact of component wear on the overall performance of the machine. In practical applications, usage time analysis information can be fed back to the target object, enabling the target object to maintain, optimize, and update the target equipment based on the usage time analysis information. Key loss response data characterizes the correspondence between different specific loss modes and different loss response measures. For example, when the loss is of the wear type, a lubrication program can be initiated (increasing the supply pressure of the lubricating medium, or increasing the lubrication frequency, or automatically triggering execution when the wear factor exceeds the limit).

[0038] In one specific embodiment, the aforementioned preset device analysis module can be a deep learning network, such as a convolutional neural network or a recurrent neural network, that analyzes the remaining usage time of the target device.

[0039] In an optional embodiment, the aforementioned preset operation analysis module can be trained based on the sample component association information of the target device, the sample operation data in multiple sample target data, the sample environment data in multiple sample target data, the first sample operation association data, and the target usage time analysis information.

[0040] In a specific embodiment, the above-mentioned input of operational feature information, environmental feature information, and object feature information into a preset operational analysis module analyzes the operational mode of at least one target device and outputs the device operational mode, including: In step S305, the time analysis information, operation characteristic information, environmental characteristic information and object characteristic information are input into the preset operation analysis module to analyze the operation mode of at least one target device and output the device operation mode.

[0041] In the above embodiments, by integrating the first operational data within a first preset time period, as well as the current operational data, current environmental data, and preset component association information, the preset equipment analysis module accurately analyzes the component wear trend and performance degradation status of at least one target device, outputting highly reliable usage time analysis information. Subsequently, the usage time analysis information, operational characteristic information, environmental characteristic information, and object characteristic information are input into the preset operational analysis module to output a device operation mode that is highly adapted to real-time conditions, i.e., outputting a set of operational parameters that balances safety and functionality. This enhances the predictability and adaptability of operational control, ensuring that the safety threshold of device operation is dynamically controlled, optimizing the timeliness of resource allocation strategies, and improving the precision of device lifecycle management.

[0042] In a specific embodiment, such as Figure 4 As shown, the above method also includes: In step S401, current energy-related information, current remaining energy budget, and target operation plan for at least one target device are obtained.

[0043] In a specific embodiment, the aforementioned current energy-related information can be a set of dynamic energy supply environment states directly related to equipment operation. For example, the current energy-related information may include at least one data point such as real-time time-of-use electricity price and regional grid instantaneous load rate. The aforementioned current remaining energy budget represents the upper limit of allocable economic resources for equipment energy consumption within a preset period (e.g., the current month). For example, the current remaining energy budget may be the remaining allocable electricity budget for the current month. The aforementioned target operation plan includes at least one specific functional objective that the target equipment needs to achieve (e.g., cooling to 25°C) and a time limit requirement (i.e., the deadline for completing the task, e.g., finishing washing before 19:00). Specifically, the current energy-related information can be directly obtained from the power grid company's official website via the network, and the current remaining energy budget and the target operation plan of at least one target equipment can be set by the target object through an interactive interface (e.g., mobile application software / target equipment control panel).

[0044] In a specific embodiment, the above-mentioned time analysis information, operational characteristic information, environmental characteristic information, and object characteristic information are input into a preset operation analysis module to analyze the operation mode of at least one target device, and the output device operation mode includes: In step S403, the preset energy consumption priority, the preset performance benchmark of at least one target device, the current energy association information, the current remaining energy budget, the target operation plan, the usage time analysis information, the operation characteristic information, the environmental characteristic information, and the object characteristic information are input into the preset operation analysis module to analyze the operation mode of at least one target device and output the device operation mode.

[0045] In one specific embodiment, the aforementioned preset energy consumption priority can be a pre-set indicator used to measure the importance of energy consumption of at least one target device during operation. Specifically, the preset energy consumption priority is preset by the target device. The aforementioned preset performance benchmark can include at least one data such as the core function effectiveness threshold of the target device and the preset service life of the target device. The aforementioned device operation mode can also include an energy efficiency adjustment coefficient. Specifically, the energy efficiency adjustment coefficient can be an adaptation value based on the target operation plan and the preset energy consumption priority output parameters to achieve a balance between energy consumption and performance. For example, the energy efficiency adjustment coefficient can be any one of the following: cooling intensity coefficient as coefficient A (activated when energy efficiency is prioritized), power derating coefficient as coefficient B (effective when budget remaining < preset budget threshold), and standby power consumption optimization ratio as ratio A.

[0046] In the above embodiments, by integrating preset energy consumption priorities, preset performance benchmarks, current energy-related information, current remaining energy budget, and target operating plans, and simultaneously incorporating usage time analysis information, operating characteristic information, environmental characteristic information, and object characteristic information, multi-dimensional collaborative optimization is achieved for lifespan safety boundaries, real-time performance requirements, environmental adaptation conditions, user behavior intentions, energy economic goals, and long-term plan requirements. This results in a globally balanced equipment operating mode that precisely meets the controllability of the target operating plan's progress, dynamically ensures the strict controllability of energy budget thresholds, deeply optimizes resource protection throughout the equipment's entire lifecycle, continuously improves the smoothness of the experience in both environmental and user adaptation, and significantly enhances proactive prevention of sudden risks and long-term degradation.

[0047] In a specific embodiment, such as Figure 5 As shown, the preset energy consumption priority, preset performance benchmark of at least one target device, current energy correlation information, current remaining energy budget, target operation plan, usage time analysis information, operation characteristic information, environmental characteristic information, and object characteristic information are input into the preset operation analysis module to analyze the operation mode of at least one target device, and the output device operation mode includes: In step S501, the target operation plan, operation characteristic information, environmental characteristic information and object characteristic information are input into the first analysis module in the preset operation analysis module to analyze the operation mode of at least one target device and output multiple candidate operation modes.

[0048] In a specific embodiment, the first analysis module described above can be a deep learning network, such as a convolutional neural network or a recurrent neural network, for analyzing the operating mode of the target device.

[0049] In an optional embodiment, the first analysis module described above can be trained based on the sample running plan, sample running feature information, sample environment feature information, sample object feature information, and multiple target candidate running modes.

[0050] In step S503, the preset performance benchmark, usage time analysis information, operation characteristic information and multiple candidate operation modes are input into the second analysis module of the preset operation analysis module. The multiple candidate operation modes are analyzed from the dimensions of multiple preset indicators, and the indicator scores of the multiple candidate operation modes in each of the multiple preset indicators are output.

[0051] In a specific embodiment, the second analysis module described above can be a deep learning network, such as a convolutional neural network and a recurrent neural network, that analyzes multiple candidate operating modes from multiple preset index dimensions.

[0052] In an optional embodiment, the second analysis module described above can be trained based on sample performance benchmarks, sample usage time analysis information, sample running characteristic information, multiple sample candidate running modes, and target indicator scores corresponding to multiple preset indicators.

[0053] For example, the aforementioned preset indicators may include at least two indicators such as safety indicators, energy consumption indicators, and efficiency indicators. Specifically, the safety score corresponding to the safety indicator, the energy consumption score corresponding to the energy consumption indicator, and the efficiency score corresponding to the efficiency indicator can be values ​​between 0 and 1, with higher scores indicating better performance of the corresponding indicator.

[0054] In step S505, the preset energy consumption priority, current energy-related information, and current remaining energy budget are input into the third analysis module of the preset operation analysis module. The weights corresponding to the various preset indicators are analyzed, and the indicator weights corresponding to the various preset indicators are output.

[0055] In a specific embodiment, the third analysis module described above can be a deep learning network that analyzes the weights corresponding to various preset indicators, such as convolutional neural networks and recurrent neural networks.

[0056] In an optional embodiment, the third analysis module described above can be trained based on sample energy consumption priority, sample energy correlation information, sample energy remaining budget, and the target indicator weights corresponding to various preset indicators.

[0057] In step S507, the indicator score and indicator weight are input into the fourth analysis module in the preset operation analysis module, and the equipment operation mode is output.

[0058] In one specific embodiment, the fourth analysis module described above can be a deep learning network for determining the device's operating mode, such as a convolutional neural network or a recurrent neural network.

[0059] In an optional embodiment, the fourth analysis module described above can be trained based on sample index scores, sample index weights, and target device operating modes.

[0060] In the above embodiments, the first analysis module integrates the target operation plan, operation characteristic information, environmental characteristic information, and object characteristic information to generate multiple candidate operation modes with multi-dimensional adaptation; the second analysis module performs multi-indicator quantitative scoring on the candidate modes based on preset performance benchmarks, usage time analysis information, and operation characteristic information; the third analysis module dynamically analyzes the indicator weight allocation based on preset energy consumption priorities, current energy correlation information, and current remaining energy budget; and the fourth analysis module outputs the globally optimal equipment operation mode through the fusion calculation of indicator scores and weights, thereby improving the comprehensiveness and executability of candidate strategies, strengthening the standardization and comparability of indicator scores, ensuring the timeliness and economy of weight allocation, and the scientific, adaptive, and resource-optimal nature of the final decision.

[0061] In step S109, the operation of at least one target device is controlled based on the device operating mode.

[0062] In a specific embodiment, such as Figure 6 As shown, the above method also includes: In step S601, at least one target device is acquired, including second operational data of the target device within a second preset time period, historical trigger events within the second preset time period, and target trigger events within a third preset time period.

[0063] In one specific embodiment, the second preset time period is the historical operating time period of the target device, and the second preset time period is shorter than the first preset time period. For example, the first preset time period can be one year of operation of at least one target device before the current operating time, and the second preset time period can be six months of operation of at least one target device before the current operating time. The third preset time period is a future time period. For example, the third preset time period can be six months of future operation of at least one target device after the current operating time.

[0064] In one specific embodiment, the aforementioned historical triggering events can be a set of special date markers, which may include objective external events occurring within a second preset time period and their times of occurrence. For example, historical triggering events may include at least one event information such as holidays, birthdays, anniversaries, and specific weather conditions. Further details regarding the aforementioned target triggering events can be found in the section on historical triggering events, and will not be repeated here.

[0065] In step S603, the second running associated data, historical trigger events and target trigger events are input into the preset template analysis module to analyze the device usage of at least one target device in the third preset time period, output the pre-running information of at least one target device in the third preset time period, and feed the pre-running information back to the target object.

[0066] In one specific embodiment, the aforementioned pre-running information may be a set of device pre-scheduling instructions generated based on matching future events with a running template, targeting a third preset time period. For example, the pre-running information may include the preset usage time of the target device within the third preset time period, or it may include... At least one of the following information: trigger event, target device list, preset operating mode, and rules for automatic device shutdown.

[0067] In step S605, upon receiving a confirmation instruction for pre-running information, within a fourth preset time period before the preset usage time, the process jumps to step S101 above to obtain the current time-related information of at least one target device during operation, so as to pre-determine the device operation mode.

[0068] In the above embodiments, by integrating the second operational data, historical triggering events, and target triggering events within the second preset time period, and leveraging the preset template analysis module, the potential usage patterns of the device during the third preset time period are accurately predicted, and high-confidence pre-operation information is output. This pre-operation information is fed back to the target object in real time, providing a basis for proactive decision-making. By accurately predicting device usage periods and proactively pushing pre-operation information, manual setting operations are significantly reduced. Pre-start optimization based on user confirmation commands enhances the user's sense of smoothness, control, and ritualistic experience. After the user confirmation command is triggered, the pre-start process is activated in advance during the fourth preset time period, automatically jumping to the current time-related information acquisition step, realizing the pre-loading and pre-optimization of the device operation mode. This enhances the accuracy and scenario adaptability of device usage prediction, strengthens the user's right to know and control the operation plan, and ensures the proactive nature of resource scheduling and operation optimization. In a specific embodiment, such as Figure 7 As shown, the above-mentioned second operation-related data, historical trigger events, and target trigger events are input into the preset template analysis module to analyze the device usage of at least one target device within a third preset time period, output pre-operation information of at least one target device within the third preset time period, and feed back the pre-operation information to the target object, including: In step S701, the second running association data and historical trigger events are input into the template analysis module of the preset template analysis module to analyze the historical device usage of at least one target device and output the running template information of at least one target device in at least one target scenario.

[0069] In a specific embodiment, the aforementioned operation template information can be the standardized operating rules of the target device in the target scenario. For example, the operation template information may include data such as scenario type, device to be started, device start-up advance, duration, peak period, power level, and functional mode set. Specifically, the device start-up advance can be the time offset by which the target device needs to start in advance to execute tasks on time in the target scenario. The duration can be the necessary time length for the target device to continuously run in the target scenario to complete its core functions. The peak period can be the start and end time window within the operating cycle of the target scenario when the power consumption of the target device reaches a preset peak. The power level can be a predefined division of the output power level of the target device in the target scenario. The functional mode set can be a combination of preset operating logic required by the target device in the target scenario.

[0070] In one specific embodiment, the template analysis module described above can be a deep learning network, such as a convolutional neural network or a recurrent neural network, that analyzes the historical device usage of the target device.

[0071] In an optional embodiment, the template analysis module described above can be trained based on the second sample running association data of the target device, sample historical trigger events, and target running template information.

[0072] In step S703, the running template information and target trigger event are input into the pre-run analysis module in the preset template analysis module to analyze the device usage of at least one target device within a third preset time period and output pre-run information.

[0073] In one specific embodiment, the aforementioned pre-run analysis module can be a deep learning network, such as a convolutional neural network or a recurrent neural network, that analyzes the device usage of the target device within a third preset time period.

[0074] In an optional embodiment, the pre-run analysis module described above can be obtained based on sample run template information, sample target trigger events, and target pre-run information.

[0075] In the above embodiments, the template analysis module mines the correlation between the second operation-related data and historical triggering events to generate standardized operation template information of the target device in typical historical scenarios; the pre-operation analysis module generates pre-operation information such as device startup sequence and mode selection within a third preset time period based on the accurate matching of this operation template information and the target triggering event, and feeds it back to the target object, thereby realizing a high degree of scenario-based and reusability of historical pattern analysis.

[0076] Figure 8 This is a block diagram illustrating a device operation apparatus according to an exemplary embodiment. (Refer to...) Figure 8 The device includes: The current time association information acquisition module 810 is used to acquire at least one current time association information of the target device during operation; The target data acquisition module 820 is used to acquire multiple target data in real time based on multiple preset data acquisition modules in the space where at least one target device is located; The feature information acquisition module 830 is used to input current time-related information and multiple target data into a preset fusion module. Based on the analysis of the weights corresponding to each of the multiple target data according to the current time-related information, the multiple target data are fused to obtain the fused operation feature information of at least one target device, the environmental feature information of the space where at least one target device is located, and the object feature information of the target object using at least one target device. The device operation mode acquisition module 840 is used to input operation characteristic information, environmental characteristic information and object characteristic information into a preset operation analysis module, analyze the operation mode of at least one target device, and output the device operation mode; The equipment control module 850 is used to control the operation of at least one target device based on the device operating mode.

[0077] In an optional embodiment, the above-described apparatus further includes: The data acquisition module is used to acquire first operational related data of at least one target device within a first preset time period. The time analysis information acquisition module is used to input the preset component association information of at least one target device, the current running data from multiple target data, the current environmental data from multiple target data, and the first running association data into the preset device analysis module to analyze the remaining usage time of at least one target device and output the usage time analysis information of at least one target device. The above-mentioned device operation mode acquisition module 840 includes: The equipment operation mode acquisition unit is used to input usage time analysis information, operation characteristic information, environmental characteristic information and object characteristic information into a preset operation analysis module, analyze the operation mode of at least one target device, and output the equipment operation mode.

[0078] In an optional embodiment, the above-described apparatus further includes: The first information acquisition module is used to acquire current energy-related information, current remaining energy budget, and target operation plan for at least one target device; The aforementioned device operation mode acquisition unit includes: The first sub-unit for obtaining the operating mode is used to input the preset energy consumption priority, the preset performance benchmark of at least one target device, the current energy association information, the current remaining energy budget, the target operating plan, the usage time analysis information, the operating characteristic information, the environmental characteristic information, and the object characteristic information into the preset operating analysis module to analyze the operating mode of at least one target device and output the device operating mode.

[0079] In an optional embodiment, the first acquisition subunit of the above-described operating mode includes: The candidate operation mode acquisition subunit is used to input the target operation plan, operation characteristic information, environmental characteristic information and object characteristic information into the first analysis module in the preset operation analysis module, analyze the operation mode of at least one target device, and output multiple candidate operation modes; The indicator scoring acquisition subunit is used to input the preset performance benchmark, usage time analysis information, operation characteristic information and multiple candidate operation modes into the second analysis module in the preset operation analysis module, analyze multiple candidate operation modes from the dimensions of multiple preset indicators, and output the indicator scores corresponding to each preset indicator under multiple candidate operation modes. The indicator weight acquisition sub-unit is used to input the preset energy consumption priority, current energy-related information and current energy remaining budget into the third analysis module in the preset operation analysis module, analyze the weights corresponding to various preset indicators, and output the indicator weights corresponding to various preset indicators. The second sub-unit of the operation mode is used to input the indicator score and indicator weight into the fourth analysis module in the preset operation analysis module, and output the device operation mode.

[0080] In an optional embodiment, the target data acquisition module 820 includes: The target acquisition mode acquisition unit is used to determine the target acquisition mode corresponding to each of the various preset data acquisition modules based on the current time association information and the preset acquisition strategy. The target data acquisition unit is used to acquire various target data in real time based on multiple preset data acquisition modules and target acquisition modes.

[0081] In an optional embodiment, the above-described apparatus further includes: The second information acquisition module is used to acquire at least one target device's second operation-related data within a second preset time period, historical trigger events within the second preset time period, and target trigger events within a third preset time period. The pre-run information acquisition module is used to input the second running related data, historical trigger events and target trigger events into the preset template analysis module, analyze the equipment usage of at least one target device in the third preset time period, output the pre-run information of at least one target device in the third preset time period, and feed back the pre-run information to the target object; The jump module is used to, upon receiving a confirmation instruction for pre-running information, jump to the step of obtaining the current time-related information of at least one target device during operation within a fourth preset time period before the preset usage time, so as to pre-determine the device operation mode.

[0082] In an optional embodiment, the pre-run information acquisition module includes: The template information acquisition unit is used to input the second running associated data and historical trigger events into the template analysis module in the preset template analysis module, analyze the historical device usage of at least one target device, and output the running template information of at least one target device in at least one target scenario; The pre-run information acquisition unit is used to input the running template information and target trigger events into the pre-run analysis module in the preset template analysis module, analyze the equipment usage of at least one target device within a third preset time period, and output pre-run information.

[0083] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0084] Figure 9 This is a block diagram illustrating an electronic device for device operation according to an exemplary embodiment. The electronic device may be a server, and its internal structure diagram may be as follows: Figure 9As shown, the electronic device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a device operation method. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0085] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present disclosure and does not constitute a limitation on the electronic device to which the present disclosure is applied. A specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements. In an exemplary embodiment, an electronic device is also provided, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement a device operation method as described in the embodiments of this disclosure.

[0086] In an exemplary embodiment, a computer-readable storage medium is also provided, wherein when the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the device operation method of the present disclosure embodiments.

[0087] In an exemplary embodiment, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to perform the device operation method of the present disclosure embodiments.

[0088] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0089] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0090] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for operating equipment, characterized in that, The method includes: Obtain at least one type of target device's current time-related information during runtime; Based on multiple preset data acquisition modules, various target data in the space where the at least one target device is located are collected in real time. The various target data are used to indicate the current operating status of the at least one target device, the current environmental status of the space where the at least one target device is located, and the current object status of the target object using the at least one target device. The current time association information and the multiple target data are input into a preset fusion module. Based on the analysis of the weights corresponding to each of the multiple target data according to the current time association information, the multiple target data are fused to obtain the fused operating characteristic information of the at least one target device, the environmental characteristic information of the space where the at least one target device is located, and the object characteristic information of the target object using the at least one target device. The operation feature information, the environmental feature information, and the object feature information are input into a preset operation analysis module to analyze the operation mode of the at least one target device and output the device operation mode. Based on the device operating mode, control the operation of the at least one target device.

2. The method according to claim 1, characterized in that, The method further includes: Obtain first operational correlation data of the at least one target device within a first preset time period; The preset component association information of the at least one target device, the current operating data from the multiple target data, the current environmental data from the multiple target data, and the first operating association data are input into the preset device analysis module to analyze the remaining usage time of the at least one target device and output the usage time analysis information of the at least one target device. The step of inputting the operational characteristic information, the environmental characteristic information, and the object characteristic information into a preset operational analysis module to analyze the operational mode of the at least one target device and outputting the device operational mode includes: The usage time analysis information, the operation characteristic information, the environmental characteristic information, and the object characteristic information are input into the preset operation analysis module to analyze the operation mode of the at least one target device and output the device operation mode.

3. The method according to claim 2, characterized in that, The method further includes: Obtain current energy-related information, current remaining energy budget, and target operating plans for at least one of the target devices; The step of inputting the usage time analysis information, the operation characteristic information, the environmental characteristic information, and the object characteristic information into the preset operation analysis module to analyze the operation mode of the at least one target device and output the device operation mode includes: The preset energy consumption priority, the preset performance benchmark of the at least one target device, the current energy association information, the current remaining energy budget, the target operation plan, the usage time analysis information, the operation characteristic information, the environmental characteristic information, and the object characteristic information are input into the preset operation analysis module to analyze the operation mode of the at least one target device and output the device operation mode.

4. The method according to claim 3, characterized in that, The preset energy consumption priority, the preset performance benchmark of the at least one target device, the current energy association information, the current remaining energy budget, the target operation plan, the usage time analysis information, the operation characteristic information, the environmental characteristic information, and the object characteristic information are input into the preset operation analysis module to analyze the operation mode of the at least one target device and output the device operation mode, including: The target operation plan, the operation characteristic information, the environmental characteristic information, and the object characteristic information are input into the first analysis module in the preset operation analysis module to analyze the operation mode of the at least one target device and output multiple candidate operation modes; The preset performance benchmark, the usage time analysis information, the operation characteristic information, and the multiple candidate operation modes are input into the second analysis module in the preset operation analysis module. The multiple candidate operation modes are analyzed from the dimensions of multiple preset indicators, and the indicator scores of the multiple candidate operation modes corresponding to the multiple preset indicators are output. The preset energy consumption priority, the current energy association information, and the current remaining energy budget are input into the third analysis module in the preset operation analysis module to analyze the weights corresponding to each of the multiple preset indicators and output the indicator weights corresponding to each of the multiple preset indicators. The indicator score and the indicator weight are input into the fourth analysis module in the preset operation analysis module, and the equipment operation mode is output.

5. The method according to claim 1, characterized in that, The real-time acquisition of multiple target data in the space where the at least one target device is located, based on multiple preset data acquisition modules, includes: Based on the current time association information and the preset acquisition strategy, the target acquisition mode corresponding to each of the multiple preset data acquisition modules is determined. The preset acquisition strategy represents the correspondence between the current time association information and the preset acquisition mode corresponding to each of the multiple preset data acquisition modules. Based on the various preset data acquisition modules and the target acquisition mode, the various target data are acquired in real time.

6. The method according to claim 1, characterized in that, The method further includes: Acquire the second operational association data of the at least one target device within a second preset time period, the historical triggering events within the second preset time period, and the target triggering events within a third preset time period; The second running association data, the historical triggering event, and the target triggering event are input into the preset template analysis module to analyze the device usage of the at least one target device in the third preset time period, output the pre-running information of the at least one target device in the third preset time period, and feed back the pre-running information to the target object. The pre-running information includes the preset usage time of the at least one target device in the third preset time period. Upon receiving a confirmation instruction for the pre-running information, within a fourth preset time period prior to the preset usage time, the process jumps to the step of obtaining the current time-related information of at least one target device during operation, in order to pre-determine the device operating mode.

7. The method according to claim 6, characterized in that, The step of inputting the second operational association data, the historical triggering events, and the target triggering events into the preset template analysis module, analyzing the device usage of the at least one target device within the third preset time period, outputting the pre-operation information of the at least one target device within the third preset time period, and feeding back the pre-operation information to the target object includes: The second running association data and the historical triggering event are input into the template analysis module of the preset template analysis module to analyze the historical device usage of the at least one target device and output the running template information of the at least one target device in at least one target scenario; The running template information and the target triggering event are input into the pre-running analysis module in the preset template analysis module to analyze the device usage of the at least one target device during the third preset time period and output the pre-running information.

8. A device for operating equipment, characterized in that, include: The current time-related information acquisition module is used to acquire at least one type of current time-related information of the target device during operation; The target data acquisition module is used to collect multiple target data in real time from the space where the at least one target device is located based on multiple preset data acquisition modules. The multiple target data are used to indicate the current operating status of the at least one target device, the current environmental status of the space where the at least one target device is located, and the current object status of the target object using the at least one target device. The feature information acquisition module is used to input the current time association information and the multiple target data into a preset fusion module. Based on the analysis of the weights corresponding to the multiple target data according to the current time association information, the multiple target data are fused to obtain the fused operation feature information of the at least one target device, the environmental feature information of the space where the at least one target device is located, and the object feature information of the target object using the at least one target device. The device operation mode acquisition module is used to input the operation feature information, the environmental feature information and the object feature information into a preset operation analysis module, analyze the operation mode of the at least one target device, and output the device operation mode. The equipment control module is used to control the operation of the at least one target device based on the equipment operating mode.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the device operation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the device operation method as described in any one of claims 1 to 7.

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