User activeness-driven electromechanical equipment self-adaptive dormancy method and energy-saving system

Abstract

The invention relates to the technical field of electromechanical equipment, and provides a user activeness-driven electromechanical equipment self-adaptive dormancy method and an energy-saving system, an organic cooperative energy-saving system is constructed by integrating a perception acquisition module, an edge processing module and a dormancy decision module, energy-saving management of electromechanical equipment is realized, and the user activeness-driven electromechanical equipment self-adaptive dormancy method and energy-saving system are provided. According to the invention, real-time information interaction and cooperative work among the modules are realized, and the operation state, user behavior and environment change of the electromechanical equipment can be accurately perceived, so that whether a projection interaction mode and a sleep mode of the electromechanical equipment are triggered or not is judged, the intelligent level, energy utilization efficiency and user experience of the electromechanical equipment are improved, and energy conservation of the electromechanical equipment is realized.

Description

Technical Field

[0001] The present application relates to the technical field of electromechanical equipment, and in particular to an adaptive sleep method and energy-saving system for electromechanical equipment driven by user activity. Background Art

[0002] In the actual application of electromechanical equipment, the sleep strategies of existing electromechanical equipment mostly fail to fully consider the user activity factor. They usually adopt a preset fixed sleep time or a simple operation interval judgment method, which cannot accurately match the user's actual usage status. Moreover, the current means of evaluating user activity are single and inaccurate, mainly relying on simple device operation counts, such as the number of clicks and key presses, etc., ignoring the importance of multi-dimensional information such as environmental data and network data for judging user activity.

[0003] For example, a patent application with authorization announcement number CN214278671U discloses an electromechanical equipment installation energy-saving control system, including an electromechanical equipment installation end and a device housing. The electromechanical equipment installation end performs installation processing of the electromechanical equipment, and at the same time, transmits and collects signals during installation. The technical solution includes a lighting system and a signal converter, wherein the signal of the lighting system is directly transmitted to the wireless signal transmitter for transmission, and the signal feedback module of the system can transmit the energy-saving signal in a timely manner.

[0004] The above technical solution has the problems raised by this background technology: it does not fully consider the user activity factor and cannot accurately match the user's actual usage status; and the current evaluation method of user activity is single and inaccurate. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present application provides a user activity-driven adaptive sleep method and energy-saving system for electromechanical equipment.

[0006] In a first aspect, the present application provides a user activity-driven adaptive energy-saving system for electromechanical equipment, the system comprising: a perception acquisition module, an edge processing module, and a sleep decision module; The sensing acquisition module is used to acquire sensing data and complete the automatic networking of sensor nodes and the transmission of sensing data through a self-organizing mechanism. When an abnormality occurs in a sensor node, the network topology reconstruction is triggered; The edge processing module is used to extract the perceptual features of the perceptual data to dynamically evaluate the user's activity, and comprehensively determine whether to trigger the projection interaction mode based on the activity interval, activity trend and duration; The sleep decision module is used to continuously monitor the duration of the electromechanical device in the projection interaction mode when the electromechanical device is in the projection interaction mode, determine whether it is necessary to switch the sleep mode according to the duration and task priority, and when it is necessary to switch the sleep mode, comprehensively weigh the power consumption and performance of the electromechanical device and the user's historical preferences, formulate and optimize the intelligent sleep strategy through a genetic algorithm, determine the sleep depth and sleep time of the electromechanical device, and display the operation simulation status of the electromechanical device under different intelligent sleep strategies through holographic projection, including the power consumption changes and performance fluctuations of the electromechanical device.

[0007] As an optional implementation, the perception data includes device operation data, environment data and network data, and the logic of the self-organizing mechanism includes: Generate a node identifier for each sensor node based on the geographical location of the sensor node, and perform neighbor discovery based on the sensor node to generate a neighbor table for each sensor node; Based on the link quality index and energy reserve in the neighbor table, the initial network topology is constructed, and the parent node is selected according to the node degree, signal strength and energy reserve to form a hierarchical network topology; According to the hierarchical network topology, the routing table is constructed and the single-hop or multi-hop transmission mode is automatically selected according to the data priority; Each sensor node determines the data transmission path according to the routing table and starts the hybrid retransmission mechanism; During the data transmission process, each sensor node periodically performs network topology scanning, monitors the status information of each sensor node in real time, and determines whether each sensor node is abnormal, so as to send an abnormal signal to trigger network topology reconstruction.

[0008] As an optional implementation, the logic of the network topology reconstruction includes: Receive abnormal signals, transmit the abnormal data of the abnormal sensor nodes through the network to the sink node and integrate them to form an abnormal data set; Determine the abnormal type of sensor nodes based on the abnormal data set, which includes local node failure, local network failure and global network failure; A multi-level reconstruction mechanism is triggered according to the abnormal type of the sensor node, and the reconstruction effect is verified to optimize the network topology reconstruction.

[0009] As an optional implementation, the user activity evaluation logic includes: Extracting perceptual features of the perceptual data, the perceptual features include device operation frequency, light change rate, and network request frequency; Calculate activity scores based on perception features to divide activity intervals; Calculate the rate of change of the activity score to determine the activity trend.

[0010] As an optional implementation, the triggering logic of the projection interaction mode includes: Determine whether the user's activity is in a low activity range and determine the user's activity trend to determine whether the user is in a low activity state; Record the duration of the user's low activity state to determine whether the projection interaction mode is triggered; If the projection interaction mode is triggered, the user's historical preferences are obtained based on the device operation data to generate personalized projection content.

[0011] As an optional implementation manner, the sleep mode conversion judgment logic includes: Obtain the operation tasks of electromechanical equipment and evaluate the task priorities; Monitor the duration of time that the electromechanical device is in projection interaction mode; Whether it is necessary to switch to a sleep mode is determined according to the priority of the running task and the duration of the electromechanical device being in the projection interaction mode. If it is necessary to switch to a sleep mode, a sleep signal is generated.

[0012] As an optional implementation, the intelligent sleep strategy includes: receiving a sleep signal, encoding the sleep depth, sleep time and wake-up condition to form an individual, and randomly generating a plurality of individuals to form an initial population, wherein each individual in the initial population represents an intelligent sleep strategy; Determine a fitness function according to the power consumption and performance of the electromechanical device and the user's historical preferences, and calculate the fitness value of each individual; Update the initial population according to the fitness value of each individual to generate a dormant population; The individual with the highest fitness value is selected from the dormant population and decoded into an intelligent dormancy strategy, including dormancy depth, dormancy time, and wake-up conditions.

[0013] As an optional implementation, the generation logic of the dormant population includes: Determine the probability of each individual being selected according to the fitness value of each individual and arrange them in descending order to select the parent individuals; Perform crossover operation on the selected parent individuals to generate offspring individuals; Perform mutation operation on offspring individuals, merge the mutated offspring individuals with the parent individuals to obtain a new population; Repeatedly calculate the fitness value of each individual in the new population and update the new population until the number of iterations is met to obtain a dormant population.

[0014] As an optional implementation, the logic of the holographic projection display includes: Simulate and record the power consumption changes and performance fluctuations of electromechanical equipment under different intelligent sleep strategies, and generate operation simulation data of electromechanical equipment; A holographic projection model of the electromechanical equipment is constructed through 3D modeling, and the operation simulation data of the electromechanical equipment is mapped to the holographic projection model of the electromechanical equipment to display the operation simulation status of the electromechanical equipment under different intelligent sleep strategies; Add interactive features to holographic projection models of electromechanical devices and monitor user interactions to update the holographic projection display.

[0015] In a second aspect, the present application provides a method for adaptive dormancy of electromechanical devices driven by user activity, the method comprising: acquiring sensor data, and completing automatic networking of sensor nodes and transmission of sensor data through a self-organizing mechanism; When a sensor node is abnormal, it triggers network topology reconstruction; Extract perceptual features of perceptual data to dynamically evaluate user activity; Comprehensively determine whether to trigger the projection interaction mode based on the activity interval, activity trend, and duration; When the electromechanical device is in the projection interaction mode, continuously monitoring the duration of the electromechanical device being in the projection interaction mode; Determine whether to switch to sleep mode based on duration and task priority; When the sleep mode needs to be switched, the power consumption and performance of the electromechanical device and the user's historical preferences are comprehensively weighed, and an intelligent sleep strategy is formulated and optimized through a genetic algorithm to determine the sleep depth and sleep time of the electromechanical device; The operation simulation status of electromechanical equipment under different intelligent sleep strategies is displayed through holographic projection, including the power consumption change and performance fluctuation of electromechanical equipment; Generate wake-up signals for electromechanical devices and perform safety protection for electromechanical devices, and collaboratively manage the power supply of electromechanical devices through distributed power supply and energy recovery.

[0016] Compared with the prior art, the beneficial effects of the present application are: by integrating the perception acquisition module, the edge processing module and the sleep decision module, an organically coordinated energy-saving system is constructed, the energy-saving management of electromechanical equipment is realized, the information between the modules interacts and works together in real time, and the operating status, user behavior and environmental changes of the electromechanical equipment can be accurately perceived, so as to determine whether to trigger the projection interaction mode and sleep mode of the electromechanical equipment, so as to improve the intelligence level, energy utilization efficiency and user experience of the electromechanical equipment, and realize energy saving of the electromechanical equipment.

[0017] The perception acquisition module builds a stable and reliable sensor network through geographic location node identification and self-organizing mechanism. It not only realizes the efficient acquisition and transmission of perception data, but also can quickly trigger network topology reconstruction when sensor nodes have abnormalities, ensuring the continuity and accuracy of data transmission, and providing comprehensive and real-time data support for the entire energy-saving system.

[0018] The edge processing module can accurately extract the features of perception data, dynamically evaluate user activity, accurately determine whether to trigger the projection interaction mode based on the activity range, activity trend and duration, and generate personalized projection content based on the user's historical preferences, thereby improving the user's interaction experience with electromechanical equipment and enabling users to obtain more convenient and personalized services when operating electromechanical equipment.

[0019] When the electromechanical equipment is in the projection interaction mode, the sleep decision module continuously monitors the user activity, determines whether to switch to the sleep mode based on the task priority, and uses a genetic algorithm to comprehensively weigh the power consumption, performance and user historical preferences of the electromechanical equipment, formulates and optimizes the intelligent sleep strategy, and determines the optimal sleep depth and sleep time, thereby effectively realizing the intelligent sleep of the electromechanical equipment, reducing energy consumption and extending the service life of the electromechanical equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them: Figure 1 A system flow chart of the user activity-driven adaptive energy-saving system for electromechanical equipment provided in an embodiment of the present application; Figure 2 A flow chart of the self-organizing mechanism of the electromechanical equipment adaptive energy-saving system driven by user activity provided in an embodiment of the present application; Figure 3 A sleep mode conversion judgment logic diagram of a user activity-driven electromechanical equipment adaptive energy-saving system provided in an embodiment of the present application; Figure 4 A flow chart of an intelligent sleep strategy for an adaptive energy-saving system for electromechanical equipment driven by user activity provided in an embodiment of the present application; Figure 5 A method flow chart of the user activity-driven adaptive sleep method for electromechanical devices provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the objectives, technical solutions and advantages of the embodiments of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application are clearly and completely described below in conjunction with the drawings in the specification. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0022] Example 1

[0023] like Figure 1 As shown, a system flow chart of an electromechanical equipment adaptive energy-saving system driven by user activity is provided for an embodiment of the present application. The electromechanical equipment adaptive energy-saving system driven by user activity includes a perception acquisition module, an edge processing module, a sleep decision module and an energy-saving management module.

[0024] The perception acquisition module is used to acquire perception data and complete the automatic networking of sensor nodes and the transmission of perception data through a self-organizing mechanism. When an abnormality occurs in a sensor node, the network topology reconstruction is triggered.

[0025] Perception data includes device operation data, environmental data, and network data.

[0026] Specifically, in a smart park, sensors are used to obtain perception data of users and electromechanical equipment. Equipment operation data refers to the recorded user operation frequency, operation type (such as clicking, sliding and inputting) and operation duration; environmental data refers to the changes in ambient light and sound intensity around the electromechanical equipment. The changes in ambient light are used to infer whether the user is in the use area of ​​the electromechanical equipment, and the sound intensity is used to determine whether there are signs of user activity; network data refers to monitoring the network request frequency and request type of electromechanical equipment, such as whether large amounts of data are transmitted, in order to infer whether the user is performing active operations.

[0027] like Figure 2 As shown, the logic of the self-organizing mechanism includes: Generate a node identifier for each sensor node based on the geographical location of the sensor node, and perform neighbor discovery based on the sensor node to generate a neighbor table for each sensor node; Based on the link quality index and energy reserve in the neighbor table, the initial network topology is constructed, and the parent node is selected according to the node degree, signal strength and energy reserve to form a hierarchical network topology; Build routing tables based on hierarchical network topology and automatically select single-hop or multi-hop transmission mode based on data priority; Each sensor node determines the data transmission path according to the routing table and starts the hybrid retransmission mechanism; During the data transmission process, each sensor node periodically performs network topology scanning, monitors the status information of each sensor node in real time, and determines whether each sensor node is abnormal, so as to send an abnormal signal to trigger network topology reconstruction.

[0028] In order to realize the automatic networking of sensor nodes, a unique identifier is assigned to each sensor node and a neighbor table is established to lay the foundation for the subsequent network topology construction. Combined with the geographical location of the sensor node, a unique node identifier is generated for each sensor node. The node identifier not only includes the location code of the sensor node, but also integrates the timestamp information to ensure the uniqueness and traceability in a large-scale dynamic network environment. After each sensor node completes the node identifier generation, each sensor node periodically (for example, every 1 minute) broadcasts a beacon frame containing its own node identifier, geographical location, energy reserve (remaining energy value), signal strength and working status (including idle, busy and abnormal). After receiving the beacon frame, other sensor nodes check the beacon. The signal strength in the frame, if the signal strength is greater than the preset strength threshold, the sensor node is considered to be its neighbor node. Each sensor node records the information of the neighbor node (node ​​identification, geographic location, energy reserve, signal strength and working status) in its own neighbor table, and sorts the sensor nodes in the neighbor table according to the signal strength. In this way, each sensor node can quickly discover the surrounding neighbor nodes, receive the information of the neighbor nodes, and thus generate a neighbor table for each sensor node. The sensor node can grasp the information of the surrounding neighbor nodes, which provides a basis for subsequent network organization and data transmission, thereby realizing mutual discovery and preliminary communication capabilities between sensor nodes, and providing data support for network topology construction.

[0029] In order to optimize the data transmission path, it is necessary to build a hierarchical network topology and elect a parent node to ensure the efficient operation of the sensor network. After obtaining the neighbor table, the link quality index of each neighbor node is calculated. The link quality index can be obtained through signal strength and signal-to-noise ratio. According to the link quality index and energy reserve of the neighbor node, the initial network topology is constructed through the minimum spanning tree algorithm. During the execution of the minimum spanning tree algorithm, neighbor nodes with high link quality index and sufficient energy reserve are preferentially selected for connection. The initial network topology constructed in this way can ensure the stability and sustainability of the network, thereby determining the connection relationship and data transmission path between sensor nodes, and providing a framework for subsequent data transmission.

[0030] The election of parent nodes can build a hierarchical network structure and improve the efficiency of sensor network management and data transmission. The parent node is responsible for coordinating and forwarding data. Reasonable parent node selection can improve the overall performance of the sensor network. Therefore, after obtaining the initial network topology, each sensor node calculates its own node degree, signal strength and energy reserve. The node degree refers to the number of neighbor nodes directly connected to the sensor node. Here, a parent node election function can be defined, such as ,in represents the parent node score, represents the node degree of the sensor node, represents the signal strength of the sensor node, Indicates the energy reserve percentage of the sensor node, , and They represent the influence of the node degree of the sensor node on the parent node election, the influence of the signal strength of the sensor node on the parent node election, and the influence of the energy reserve percentage of the sensor node on the parent node election. , and The value range of is between 0 and 1, and .

[0031] Each sensor node calculates its own and its neighboring nodes' parent node scores according to the parent node election function, where the sensor node with the highest parent node score is elected as the parent node, thereby determining the parent node of each subnet. The parent node broadcasts its own information to its neighboring nodes, thus forming a hierarchical network topology and building a hierarchical network structure, which facilitates the network management and data routing of sensors and improves the reliability and scalability of the sensor network.

[0032] Each sensor node calculates the shortest path to other sensor nodes through the distance vector routing algorithm according to the hierarchical network topology. Each sensor node records the shortest path information in the routing table, including the next hop node, transmission delay, number of hops and energy consumption, which provides path planning for data transmission and ensures the orderly transmission of data in the sensor network.

[0033] Data with different data priorities have different requirements for transmission timeliness and reliability. Selecting the transmission mode according to the data priority can optimize the utilization of network resources and ensure the transmission of key data. Defining the data priority of different types of data, such as abnormal data of sensor nodes has high priority, and related monitoring data of electromechanical equipment has low priority. When the sensor node has data to be transmitted, it is judged according to the data priority. If it is high-priority data, single-hop transmission is preferred to send the data directly to the target sensor node. If it is low-priority data, multi-hop transmission is selected to forward the data through the intermediate sensor node. When selecting multi-hop transmission, the shortest data transmission path is selected according to the routing table, which improves the utilization efficiency of sensor network resources and ensures the rapid transmission of high-priority data.

[0034] To ensure that data can be accurately transmitted along the planned path, the hybrid retransmission mechanism can improve the reliability of data transmission. The sensor node determines the next hop node for data transmission based on the routing table. During data transmission, the hybrid retransmission mechanism is adopted, combined with forward error correction coding and selective automatic repeat request protocol. Before sending data, forward error correction coding is performed on the data to add redundant information. After receiving the data, the receiving node first tries to use forward error correction coding for error correction. If the error cannot be corrected, the sending node is requested to retransmit the data through the selective automatic repeat request protocol, which ensures the reliable transmission of data in complex network environments and reduces data loss and errors.

[0035] The node status in the sensor network may change at any time, such as energy exhaustion and signal weakening. Each sensor node periodically (for example, every 5 minutes) sends a detection message to the neighbor node. The detection message contains the node identification and timestamp of the sensor node. After receiving the detection message, the neighbor node immediately replies with a response message. The sending sensor node determines the status information of the neighbor node based on the reception status and signal strength of the response message. Each sensor node records the energy reserve, signal strength, link quality, working status and transmission success rate of the monitored neighbor node, and grasps the operating status of the sensor nodes in the sensor network in real time, providing data support for determining whether the sensor node is abnormal.

[0036] A threshold is set for each monitoring indicator (energy reserve, signal strength, link quality, working status and transmission success rate). The set threshold is determined according to the actual situation. The sensor node compares the monitoring indicator with the set threshold. If a monitoring indicator is greater than the set threshold, the sensor node is judged to be abnormal. When the sensor node judges that itself or a neighboring node is abnormal, it sends an abnormal signal to the sensor network, triggering the reconstruction of the network topology, and responding to abnormal situations in the sensor network in a timely manner to ensure the continuous and stable operation of the sensor network.

[0037] The logic of network topology reconstruction includes: Receive abnormal signals, transmit the abnormal data of the abnormal sensor nodes through the network to the sink node and integrate them to form an abnormal data set; Determine the abnormal type of sensor nodes based on the abnormal data set, which includes local node failure, local network failure and global network failure; A multi-level reconstruction mechanism is triggered according to the abnormal type of the sensor node, and the reconstruction effect is verified to optimize the network topology reconstruction.

[0038] The aggregation node is responsible for collecting and managing the information of the entire sensor network, transmitting abnormal data to the aggregation node for unified analysis and processing, and fully understanding the abnormal situation of the sensor network. The abnormal sensor node encapsulates its own abnormal data (including its own node identification, geographical location and abnormal monitoring indicators) into an abnormal data packet, and sends the abnormal data packet to the aggregation node according to the routing table. During the transmission process, if the path in the routing table is unavailable, the abnormal sensor node can find a new transmission path by broadcasting. When the aggregation node receives the abnormal data packet, it stores the abnormal data packet in the database and classifies and organizes it according to the node identification and abnormal monitoring indicators to form an abnormal data set, which provides a data basis for subsequent abnormal type judgment.

[0039] Different types of anomalies require different network topology reconstructions. Accurately judging the type of anomaly can enable targeted reconstruction measures to be taken to improve reconstruction efficiency and network recovery capabilities. The aggregation node analyzes the abnormal data set and classifies the anomaly type according to the distribution of the sensor nodes with the anomaly. If anomalies occur in one or a few isolated sensor nodes, and the anomalies of these sensor nodes do not have a significant impact on the normal communication and data transmission of their neighboring nodes, and at the same time, through checking the routing table, it is found that only the paths related to these sensor nodes are abnormal, while the transmission paths of most other subnets are normal, it is determined that the local node has failed; if it appears within a certain area When multiple adjacent sensor nodes are abnormal at the same time, and the link quality index of the subnet where these sensor nodes are located generally decreases, a large area of ​​the routing within the subnet becomes unreachable, and the communication between the subnet and other subnets is also seriously affected, but some subnets still maintain normal operation. At this time, it is determined to be a local network failure. When a large number of sensor nodes are abnormal at the same time, the link quality index of the entire network is seriously reduced, the routing table is completely invalid, the sensor nodes cannot communicate normally, and the overall network function is paralyzed, it is determined to be a global network failure. The severity and scope of the abnormality of the sensor nodes are clarified, providing a basis for selecting an appropriate reconstruction mechanism.

[0040] Different types of anomalies require reconstruction mechanisms of different strengths and methods. When a local node is determined to be failed, the sink node notifies the neighbor nodes of the abnormal sensor node, selects sensor nodes with sufficient energy reserves and high link quality index from the neighbor nodes, replaces the abnormal sensor node, and updates the neighbor table and routing table of the sensor node; when a local network failure is determined, the sink node re-plans the topology of the local network, based on the boundary nodes of the failed subnet, allocates more spare nodes from the spare node pool, and supplements them to the failed subnet to form a new subnet structure. At the same time, the routing path between the failed subnet and other normal subnets is re-planned to ensure the overall connectivity of the network; when a global network failure is determined, the sink node initiates network topology initialization, including re-assigning node identifiers to all sensor nodes, performing neighbor discovery and building a hierarchical network topology, and giving priority to restoring the core data transmission link, thereby restoring the normal function of the sensor network and improving network performance and stability.

[0041] Verifying the reconstruction effect can ensure that the network returns to normal and performance is optimized. After the reconstruction is completed, the aggregation node verifies the reconstruction effect by monitoring the performance indicators of the entire network (such as transmission delay, packet loss rate and throughput). If the performance indicators do not meet expectations, the aggregation node can adjust the network topology reconstruction and reconstruct it again.

[0042] The edge processing module is used to extract the perceptual features of the perception data to dynamically evaluate the user activity, and comprehensively determine whether to trigger the projection interaction mode based on the activity interval, activity trend and duration.

[0043] The evaluation logic of user activity includes: Extracting perceptual features of the perceptual data, the perceptual features include device operation frequency, light change rate, and network request frequency; Calculate activity scores based on perception features to divide activity intervals; Calculate the rate of change of the activity score to determine the activity trend.

[0044] Extracting the device operation frequency from the device operation data, such as the number of user operations per unit time (such as 10 minutes), can directly reflect the degree of interaction between the user and the electromechanical equipment; extracting the light change rate from the environmental data, such as the standard deviation of the ambient light intensity within a time window of 30 seconds, can assist in determining whether the user is in the electromechanical equipment use area; extracting the network request frequency from the network data, such as monitoring the number of network requests initiated by the electromechanical equipment per second, can reflect the usage activity of the electromechanical equipment, and mapping the corresponding value of each perception feature to between 0 and 1, thereby converting the original perception data into quantifiable perception features, which is convenient for subsequent calculation and analysis.

[0045] Different perceptual features have different degrees of influence on user activity. The importance of different perceptual features is reflected through weight allocation, so as to more accurately calculate the activity score. A weight is assigned to each perceptual feature, and the activity score is calculated by weighted summation, where the sum of the weights of all perceptual features is 1; the activity interval is divided by a preset activity threshold, for example, the activity threshold is set to 0.4 and 0.7, and the activity interval includes a low activity interval (for example, the activity score is less than 0.4), a medium activity interval (for example, the activity score is greater than or equal to 0.4 and less than 0.7) and a high activity interval (for example, the activity score is greater than or equal to 0.7). The calculated activity score is matched with the activity interval to determine the activity interval to which the current user activity belongs, providing key information for determining whether to trigger the projection interaction mode.

[0046] At the same time, it is necessary to predict the trend changes of user activity to avoid false triggering of the projection interaction mode due to short-term fluctuations and calculate the change rate of the activity score, such as ,in represents the rate of change of activity score, express Time user activity score, express Time user activity score, express Time and Time intervals (e.g. 5 minutes) can reflect the user's activity trend, which is an upward trend (e.g. ), downward trend (e.g. ) and stable trends (e.g. ), which provides a basis for judging the trend of user activity, improves the accuracy of active status determination, and reduces the probability of false triggering of projection interaction mode.

[0047] The triggering logic of the projection interaction mode includes: Determine whether the user's activity is in a low activity range and determine the user's activity trend to determine whether the user is in a low activity state; Record the duration of the user's low activity state to determine whether the projection interaction mode is triggered; If the projection interaction mode is triggered, the user's historical preferences are obtained based on the device operation data to generate personalized projection content.

[0048] When the user activity is in the low activity range and the activity trend shows a downward or stable trend, the user is judged to be in a low activity state, thereby accurately judging whether the user is in a low activity state, avoiding false triggers caused by short-term low activity, improving the reliability of judgment, and providing a basis for subsequent trigger decisions.

[0049] At the same time, it is necessary to decide whether to trigger the projection interaction mode based on the duration of the low-activity state. When the user is in a low-activity state, start the timing mechanism to record the duration of the user's low-activity state, set the basic projection threshold (for example, 15 minutes), and then adjust it based on historical behavior data to obtain the projection duration threshold, such as ,in Indicates the projection duration threshold, represents the basic projection threshold, Indicates the number of times in low activity state. Indicates the total number of times the user operates the electromechanical equipment. When the duration of the user's low activity state is greater than or equal to the projection duration threshold, the projection interaction mode is triggered. Otherwise, the projection interaction mode is not triggered, and the user activity is continuously monitored to decide whether to enter the projection interaction mode, so as to achieve energy saving of electromechanical equipment and optimization of user experience.

[0050] Extract user historical operation records from device operation data, such as commonly used functions and click frequencies, and analyze the data of user historical operation records through machine learning algorithms to explore user historical preferences. Based on user historical preferences, select relevant content from the content library to produce personalized projection content, thereby providing interactive content that meets user needs and enhancing user acceptance and satisfaction with the projection interaction mode.

[0051] The sleep decision module is used to continuously monitor the duration of the electromechanical device in the projection interaction mode when the electromechanical device is in the projection interaction mode, and determine whether it is necessary to switch to the sleep mode based on the duration and task priority. When the sleep mode needs to be switched, the power consumption and performance of the electromechanical device and the user's historical preferences are comprehensively weighed, and the intelligent sleep strategy is formulated and optimized through genetic algorithms to determine the sleep depth and sleep time of the electromechanical device. The operating simulation status of the electromechanical device under different intelligent sleep strategies is displayed through holographic projection, including the power consumption changes and performance fluctuations of the electromechanical device.

[0052] like Figure 3 As shown, the sleep mode conversion judgment logic includes: Obtain the operation tasks of electromechanical equipment and evaluate the task priorities; Monitor the duration of time that the electromechanical device is in projection interaction mode; Whether it is necessary to switch to a sleep mode is determined according to the priority of the running task and the duration of the electromechanical device being in the projection interaction mode. If it is necessary to switch to a sleep mode, a sleep signal is generated.

[0053] Different running tasks have different operating requirements and impacts on electromechanical equipment. Evaluating task priorities helps to comprehensively consider the operating status of electromechanical equipment and the importance of running tasks when determining whether to enter sleep mode, so as to avoid affecting the execution of important tasks due to entering sleep mode. A list of all currently running running tasks is obtained from the operating system of the electromechanical equipment, and task priorities are assigned to each running task according to the type, importance and urgency of the running task. For example, equipment critical tasks (such as calibration and data transmission of electromechanical equipment) can be set to high priority, equipment backup tasks (such as file backup and log recording) can be set to medium priority, and background non-essential tasks (such as standby and idle tasks) can be set to low priority. A task priority table is generated and the minimum running time of each running task is marked to ensure that equipment critical tasks are not affected by sleep mode and maintain the normal operation of electromechanical equipment.

[0054] The duration of the user in the projection interaction mode is one of the important factors to determine whether to switch to the sleep mode. When the electromechanical device is in the projection interaction mode, the timer starts to count, continuously monitors and records the duration of the electromechanical device in the projection interaction mode, sets a basic sleep threshold (for example, 10 minutes), and adjusts it according to the user's historical behavior to obtain the sleep duration threshold, such as ,in Indicates the sleep duration threshold. Indicates the basic sleep threshold, It indicates the number of times the user has used the electromechanical device recently, causing the electromechanical device to be in the projection interaction mode. When the duration of the electromechanical device in the projection interaction mode is greater than or equal to the sleep duration threshold and there is no high-priority task in the current running task of the electromechanical device, it is determined that the sleep mode needs to be switched to ensure the energy saving of the electromechanical device, and a sleep signal is generated. Otherwise, there is no need to switch the sleep mode, and the duration of the electromechanical device in the projection interaction mode is continuously monitored. According to the actual operation of the electromechanical device and the duration of the projection interaction mode, it is accurately determined whether the sleep mode needs to be switched, and a sleep signal is generated in time to provide trigger conditions for the subsequent formulation of an intelligent sleep strategy.

[0055] like Figure 4 As shown, the intelligent sleep strategy includes: receiving a sleep signal, encoding the sleep depth, sleep time and wake-up conditions to form an individual, and randomly generating a number of individuals to form an initial population, wherein each individual in the initial population represents an intelligent sleep strategy; Determine a fitness function according to the power consumption and performance of the electromechanical device and the user's historical preferences, and calculate the fitness value of each individual; Update the initial population according to the fitness value of each individual to generate a dormant population; The individual with the highest fitness value is selected from the dormant population and decoded into an intelligent dormancy strategy, including dormancy depth, dormancy time, and wake-up conditions.

[0056] The genetic algorithm requires an initial population as the basis for optimization. By encoding and randomly generating individuals, it can generate a variety of different intelligent sleep strategies to provide diversity for subsequent optimization. When receiving the sleep signal, the sleep depth, sleep time and wake-up condition are encoded. For example, the sleep depth can be divided into shallow sleep, moderate sleep and deep sleep, represented by the numbers 1, 2 and 3 respectively. The sleep time is in minutes and is represented by an integer. The wake-up condition can be encoded according to different events (such as user operation and timed wake-up). 1 represents user operation and 2 represents timed wake-up. Multiple individuals containing the encoded sleep depth, sleep time and wake-up condition are randomly generated. Each individual represents an intelligent sleep strategy. These individuals are combined together to form an initial population. For example, an initial population containing 100 individuals is generated. The encoding example of each individual is as follows: [2,60,1], where the encoding example means that the current intelligent sleep strategy is moderate sleep, the sleep time is 60 minutes, and the wake-up condition is user operation. A diverse initial population is created, which provides rich candidate strategies for the optimization of the intelligent sleep strategy and avoids falling into local optimality.

[0057] The fitness function is calculated to quantify the pros and cons of the intelligent sleep strategy represented by each individual. The power consumption and performance of the electromechanical equipment and the user's historical preferences are comprehensively considered to find the optimal sleep strategy that is both energy-saving and meets user needs. The fitness function comprehensively considers the power consumption and performance of the electromechanical equipment and the user's historical preferences. For example, the expression of the fitness function is as follows: ; In the formula, represents the fitness value, represents the power consumption weight coefficient, Indicates the power consumption value of electromechanical equipment. represents the performance weight coefficient, Indicates the performance loss rate of electromechanical equipment, represents the preference weight coefficient, A rating that represents a user's historical preferences.

[0058] It should be noted that: power consumption weight coefficient Refers to the degree of influence of the power consumption of electromechanical equipment on the fitness value, performance weight coefficient Refers to the degree of influence of the performance of electromechanical equipment on the fitness value, preference weight coefficient It refers to the influence of user historical preference on fitness value. , and The value range of is between 0 and 1, and ; Power consumption of electromechanical equipment , performance loss rate of electromechanical equipment and the user's historical preference ratings The operation of the electromechanical equipment under the intelligent sleep strategy is simulated by simulating the sleep depth, sleep time and wake-up conditions of each individual according to its encoded sleep depth, sleep time and wake-up conditions, so as to obtain the power consumption value and performance loss rate of the electromechanical equipment and the score of the user's historical preference.

[0059] The fitness value of each individual is obtained according to the fitness function, thereby measuring the pros and cons of the individual as an intelligent sleep strategy, providing a basis for subsequent population selection and optimization.

[0060] Through iterative optimization of the genetic algorithm, the intelligent sleep strategy is continuously screened and improved, so that the individuals in the initial population gradually tend to the optimal solution, thereby finding the most suitable intelligent sleep strategy for electromechanical equipment. According to the fitness value of each individual, the initial population is updated by operations such as selection, crossover and mutation. Individuals with higher fitness values ​​are selected as parent individuals, and child individuals are generated through crossover and mutation operations. The child individuals are merged with the parent individuals to form a new population. The above process is repeated until the preset number of iterations (such as 100 times) is met to obtain a dormant population. Through iterative optimization, the intelligent sleep strategy in the initial population has been continuously improved, so that it contains better intelligent sleep strategies.

[0061] After optimization by the genetic algorithm, the intelligent sleep strategy represented by the individual with the highest fitness value has achieved the relative optimality in terms of power consumption, performance and user preference. Taking it as the final intelligent sleep strategy can achieve a balance between energy saving, performance and meeting user needs. The individual with the highest fitness value is found in the sleep population, and the encoding of the individual is decoded to obtain the corresponding sleep depth, sleep time and wake-up conditions to form the final intelligent sleep strategy. The final intelligent sleep strategy is determined, which provides specific parameters and conditions for the sleep operation of electromechanical equipment.

[0062] The generation logic of dormant population includes: Determine the probability of each individual being selected according to the fitness value of each individual and arrange them in descending order to select the parent individuals; Perform crossover operation on the selected parent individuals to generate offspring individuals; Perform mutation operation on offspring individuals, merge the mutated offspring individuals with the parent individuals to obtain a new population; Repeatedly calculate the fitness value of each individual in the new population and update the new population until the number of iterations is met to obtain a dormant population.

[0063] Individuals with high fitness values ​​will produce excellent offspring individuals. Selecting parent individuals according to probability can ensure that the population is optimized in a better direction. The fitness value of each individual is calculated as the ratio of the total fitness value of the initial population. As the probability of the individual being selected, the individuals are sorted from high to low according to the probability of being selected. According to the preset selection ratio, the individuals with the highest ranking are selected as parent individuals, thereby screening out relatively excellent individuals as parent individuals (for example, 50), which provides a good foundation for subsequent crossover and mutation operations.

[0064] The crossover operation can combine the good genes of parent individuals to generate new individuals, increase the diversity and adaptability of the population, randomly select two parent individuals, randomly select a crossover point in the code of each parent individual, exchange the parts after the crossover point of the two parent individuals, generate two offspring individuals, repeat the above process until a sufficient number of offspring individuals (for example, 50) are generated. For example, select parent individuals [2,60,1] and [3,90,2], and the randomly selected crossover point is in the second position. After crossover, offspring individuals [2,90,2] and [3,60,1] are generated. Through the crossover operation, new individuals are generated, enriching the initial population, and providing more possibilities for finding a better intelligent hibernation strategy.

[0065] The mutation operation can introduce new genes, prevent the initial population from falling into the local optimal solution, and ensure the evolutionary ability of the initial population. For each offspring individual, a coding position is randomly selected for mutation with a certain mutation probability, and the mutated offspring individual is merged with the parent individual to generate a new population. For example, the offspring individual [2,90,2] mutates with a mutation probability of 0.01, and the dormancy depth changes from 2 to 3, and the mutated offspring individual [3,90,2] is obtained. The mutated offspring individual is merged with the parent individual to obtain a new population containing 100 individuals. Through the mutation operation, the diversity of the population is increased, and the possibility of finding the global optimal solution is improved.

[0066] By updating the population through multiple iterations, the intelligent sleep strategy can be continuously optimized so that the individuals in the population gradually tend to the optimal solution. For each individual in the new population, the fitness value of each individual is recalculated. According to the new fitness value, the above selection, crossover and mutation operations are repeated, and the population is updated. This process is repeated until the preset number of iterations (such as 100 times) is reached to obtain the final dormant population. Through iterative optimization, the relatively optimal combination of intelligent sleep strategies is found, which provides a wealth of candidate solutions for the final selection of the best intelligent sleep strategy.

[0067] The logic of holographic projection display includes: Simulate and record the power consumption changes and performance fluctuations of electromechanical equipment under different intelligent sleep strategies, and generate operation simulation data of electromechanical equipment; A holographic projection model of the electromechanical equipment is constructed through 3D modeling, and the operation simulation data of the electromechanical equipment is mapped to the holographic projection model of the electromechanical equipment to display the operation simulation status of the electromechanical equipment under different intelligent sleep strategies; Add interactive features to holographic projection models of electromechanical devices and monitor user interactions to update the holographic projection display.

[0068] By simulating the operating status under different intelligent sleep strategies, we can intuitively understand the impact of each intelligent sleep strategy on the power consumption and performance of electromechanical equipment, and provide data support for users to choose appropriate intelligent sleep strategies. For the intelligent sleep strategy represented by each individual in the sleep population, the operating status of electromechanical equipment under different intelligent sleep strategies is simulated, and the power consumption changes and performance fluctuations of electromechanical equipment during sleep and wake-up are recorded. These data are organized into operation simulation data, thereby obtaining the operating information of the equipment under different intelligent sleep strategies, providing a data basis for holographic projection display.

[0069] The holographic projection display can let users understand the impact of different intelligent sleep strategies on electromechanical equipment in an intuitive way, help users better understand and choose appropriate intelligent sleep strategies, use 3D modeling software to build a holographic projection model according to the actual structure and appearance of the electromechanical equipment, and associate the power consumption changes and performance fluctuations in the running simulation data with the holographic projection model. For example, different colors are used to represent different power consumption levels, and the length of the line is used to represent performance fluctuations. The holographic projection model that maps the running simulation data is displayed through the holographic projection device, presenting the running simulation status of the electromechanical equipment under different intelligent sleep strategies, and visually displaying the running status of the equipment under different intelligent sleep strategies, which improves the user's understanding and selection efficiency of intelligent sleep strategies.

[0070] The curve function of power consumption change is as follows: ; In the formula, Indicates the time at which electromechanical equipment The total power consumption, Indicates the power consumption of electromechanical equipment in non-sleep mode, Indicates the sleep depth coefficient, Represents the power consumption of an electromechanical device in sleep mode.

[0071] It should be noted that the sleep depth coefficient refers to the proportion of power consumption of electromechanical equipment in sleep mode, and The value range is between 0 and 1. It means that the electromechanical equipment is not in sleep mode at all. Indicates that the electromechanical device is completely dormant; the power consumption of the electromechanical device in non-sleep mode and power consumption of electromechanical devices in sleep mode It is obtained through the specification of the electromechanical equipment, where ,The power consumption of electromechanical devices in sleep mode is usually much lower than in non-sleep mode.

[0072] The curve function of performance fluctuation is as follows: ; In the formula, Indicates the time at which electromechanical equipment performance, Indicates the maximum performance of electromechanical equipment in non-sleep mode. Indicates the performance loss rate of electromechanical equipment.

[0073] It should be noted that the performance loss rate of electromechanical equipment It refers to the performance degradation ratio of electromechanical equipment in sleep mode. The value is between 0 and 1. It means that there is no loss in the performance of electromechanical equipment. Indicates that the performance of the electromechanical equipment is completely lost. ,in Indicates the performance of electromechanical equipment in sleep mode. The performance loss rate at different sleep depths is also different.

[0074] Adding interactive functions can enhance the user's sense of participation and experience, allowing users to obtain more detailed information based on their needs and preferences, so as to make more appropriate decisions. By adding interactive functions to the holographic projection model, such as gesture recognition and voice control, users can select different intelligent sleep strategies and view detailed operation simulation data through gestures or voice operations, and monitor user interactive operations in real time, and update the holographic projection display according to the operation content. For example, when the user selects another intelligent sleep strategy, the operation simulation status of the electromechanical equipment under the intelligent sleep strategy is displayed, which improves the interactivity between the user and the holographic projection display, enables users to more actively understand the characteristics of different intelligent sleep strategies, and optimizes the user experience.

[0075] The energy-saving management module is used to generate wake-up signals for electromechanical equipment and perform safety protection for the electromechanical equipment, and to collaboratively manage the power supply of the electromechanical equipment through distributed power supply and energy recovery.

[0076] When the wake-up conditions are met, a wake-up signal for the electromechanical device is generated. After receiving the wake-up signal, the electromechanical device immediately checks the permissions of the current user or operation, and restricts or allows the corresponding operations based on the preset permission strategy. At the same time, during the operation of the electromechanical device, real-time monitoring of activities such as calls and network connections in the electromechanical device is carried out to identify and prevent abnormal user behavior and attacks, thereby ensuring the safety and data integrity of the electromechanical equipment during operation.

[0077] The power supply management logic of electromechanical equipment includes: Monitor the status data of distributed power supply and energy recovery in real time to obtain the power supply status; Generate power supply priority according to the operation tasks and power consumption of electromechanical equipment; Optimize power supply to electromechanical devices based on power supply status and power supply priority.

[0078] Understanding the real-time status of distributed power sources and energy recovery is the basis for the rational management of the power supply of electromechanical equipment. Through sensors and monitoring circuits, the status data of the output voltage, current and remaining power (for batteries) of distributed power sources (such as batteries, solar panels and small generators) and the status data of the energy recovery rate and the current total amount of recovered energy are obtained in real time. Through these status data, the power supply status is evaluated, including whether the distributed power sources are working normally and whether the energy is sufficient, etc., to accurately grasp the real-time situation of the power supply, and provide a basis for subsequent power supply management and optimization.

[0079] Different operating tasks in electromechanical equipment have different power supply requirements and importance. Reasonable allocation of power supply priorities can ensure the normal operation of key tasks and improve energy utilization efficiency. The information of the current operating tasks of the electromechanical equipment, including task type and task priority, is obtained. At the same time, the power consumption requirements of each operating task are determined based on the power consumption monitoring data of the electromechanical equipment. The power supply priority is generated according to the task priority and power consumption requirements. For example, emergency tasks and critical tasks with high power consumption are given priority in power supply. The power supply priority of non-essential background tasks can be lowered when energy is tight. This clarifies the power supply priority of each operating task in the electromechanical equipment and provides a basis for optimizing the power supply management of electromechanical equipment.

[0080] According to the power supply status and power supply priority, the output and energy recovery working mode of the distributed power supply are adjusted. When the distributed power supply is working normally and the energy is sufficient, the power supply needs of high-priority tasks are met first, and power is supplied to low-priority tasks appropriately. When energy is tight, high-priority tasks are given priority, and the power supply of low-priority tasks is reduced or suspended, which optimizes energy distribution, ensures the power supply of critical tasks, and improves energy utilization efficiency.

[0081] Example 2 like Figure 5 As shown, a method flow chart of a method for an electromechanical device adaptive sleep method driven by user activity is provided for an embodiment of the present application. The method for an electromechanical device adaptive sleep method driven by user activity includes: Acquire sensor data and complete automatic networking of sensor nodes and transmission of sensor data through self-organizing mechanism; When a sensor node is abnormal, it triggers network topology reconstruction; Extract perceptual features of perceptual data to dynamically evaluate user activity; Comprehensively determine whether to trigger the projection interaction mode based on the activity interval, activity trend, and duration; When the electromechanical device is in the projection interaction mode, continuously monitoring the duration of the electromechanical device being in the projection interaction mode; Determine whether to switch to sleep mode based on duration and task priority; When the sleep mode needs to be switched, the power consumption and performance of the electromechanical device and the user's historical preferences are comprehensively weighed, and an intelligent sleep strategy is formulated and optimized through a genetic algorithm to determine the sleep depth and sleep time of the electromechanical device; The operation simulation status of electromechanical equipment under different intelligent sleep strategies is displayed through holographic projection, including the power consumption change and performance fluctuation of electromechanical equipment; Generate wake-up signals for electromechanical devices and perform safety protection for electromechanical devices, and collaboratively manage the power supply of electromechanical devices through distributed power supply and energy recovery.

[0082] Since the principle of solving the problem by the method in the embodiment of the present application is similar to that of the system described above in the embodiment of the present application, the implementation of the method refers to the implementation of the system, and the repeated parts will not be repeated.

Claims

1. A user activity-driven adaptive energy-saving system for electromechanical equipment, characterized in that: include: Perception acquisition module, edge processing module and sleep decision module; The sensing acquisition module is used to acquire sensing data and complete the automatic networking of sensor nodes and the transmission of sensing data through a self-organizing mechanism. When an abnormality occurs in a sensor node, the network topology reconstruction is triggered; The edge processing module is used to extract the perceptual features of the perceptual data to dynamically evaluate the user's activity, and comprehensively determine whether to trigger the projection interaction mode based on the activity interval, activity trend and duration; The sleep decision module is used to continuously monitor the duration of the electromechanical device in the projection interaction mode when the electromechanical device is in the projection interaction mode, determine whether it is necessary to switch the sleep mode according to the duration and task priority, and when it is necessary to switch the sleep mode, comprehensively weigh the power consumption and performance of the electromechanical device and the user's historical preferences, formulate and optimize the intelligent sleep strategy through a genetic algorithm, determine the sleep depth and sleep time of the electromechanical device, and display the operation simulation status of the electromechanical device under different intelligent sleep strategies through holographic projection, including the power consumption changes and performance fluctuations of the electromechanical device.

2. The user activity-driven electromechanical equipment adaptive energy-saving system according to claim 1, characterized in that: The perception data includes device operation data, environmental data and network data, and the logic of the self-organizing mechanism includes: Generate a node identifier for each sensor node based on the geographical location of the sensor node, and perform neighbor discovery based on the sensor node to generate a neighbor table for each sensor node; Based on the link quality index and energy reserve in the neighbor table, the initial network topology is constructed, and the parent node is selected according to the node degree, signal strength and energy reserve to form a hierarchical network topology; According to the hierarchical network topology, the routing table is constructed and the single-hop or multi-hop transmission mode is automatically selected according to the data priority; Each sensor node determines the data transmission path according to the routing table and starts the hybrid retransmission mechanism; During the data transmission process, each sensor node periodically performs network topology scanning, monitors the status information of each sensor node in real time, and determines whether each sensor node is abnormal, so as to send an abnormal signal to trigger network topology reconstruction.

3. The user activity-driven electromechanical equipment adaptive energy-saving system according to claim 2, characterized in that: The logic of the network topology reconstruction includes: Receive abnormal signals, transmit the abnormal data of the abnormal sensor nodes through the network to the sink node and integrate them to form an abnormal data set; Determine the abnormal type of sensor nodes based on the abnormal data set, which includes local node failure, local network failure and global network failure; A multi-level reconstruction mechanism is triggered according to the abnormal type of the sensor node, and the reconstruction effect is verified to optimize the network topology reconstruction.

4. The user activity-driven electromechanical equipment adaptive energy-saving system according to claim 3, characterized in that: The evaluation logic of the user activity includes: Extracting perceptual features of the perceptual data, the perceptual features include device operation frequency, light change rate, and network request frequency; Calculate activity scores based on perception features to divide activity intervals; Calculate the rate of change of the activity score to determine the activity trend.

5. The user activity-driven electromechanical equipment adaptive energy-saving system according to claim 4, characterized in that: The triggering logic of the projection interaction mode includes: Determine whether the user's activity is in a low activity range and determine the user's activity trend to determine whether the user is in a low activity state; Record the duration of the user's low activity state to determine whether the projection interaction mode is triggered; If the projection interaction mode is triggered, the user's historical preferences are obtained based on the device operation data to generate personalized projection content.

6. The user activity-driven electromechanical equipment adaptive energy-saving system according to claim 5, characterized in that: The sleep mode conversion judgment logic includes: Obtain the operation tasks of electromechanical equipment and evaluate the task priorities; Monitor the duration of time that the electromechanical device is in projection interaction mode; Whether it is necessary to switch to a sleep mode is determined according to the priority of the running task and the duration of the electromechanical device being in the projection interaction mode. If it is necessary to switch to a sleep mode, a sleep signal is generated.

7. The user activity-driven electromechanical equipment adaptive energy-saving system according to claim 6, characterized in that: The intelligent sleep strategy includes: receiving a sleep signal, encoding the sleep depth, sleep time and wake-up condition to form an individual, and randomly generating a plurality of individuals to form an initial population, wherein each individual in the initial population represents an intelligent sleep strategy; Determine a fitness function according to the power consumption and performance of the electromechanical device and the user's historical preferences, and calculate the fitness value of each individual; Update the initial population according to the fitness value of each individual to generate a dormant population; The individual with the highest fitness value is selected from the dormant population and decoded into an intelligent dormancy strategy, including dormancy depth, dormancy time, and wake-up conditions.

8. The user activity-driven electromechanical equipment adaptive energy-saving system according to claim 7, characterized in that: The generation logic of the dormant population includes: Determine the probability of each individual being selected according to the fitness value of each individual and arrange them in descending order to select the parent individuals; Perform crossover operation on the selected parent individuals to generate offspring individuals; Perform mutation operation on offspring individuals, merge the mutated offspring individuals with the parent individuals to obtain a new population; Repeatedly calculate the fitness value of each individual in the new population and update the new population until the number of iterations is met to obtain a dormant population.

9. The user activity-driven electromechanical equipment adaptive energy-saving system according to claim 8, characterized in that: The logic of the holographic projection display includes: Simulate and record the power consumption changes and performance fluctuations of electromechanical equipment under different intelligent sleep strategies, and generate operation simulation data of electromechanical equipment; A holographic projection model of the electromechanical equipment is constructed through 3D modeling, and the operation simulation data of the electromechanical equipment is mapped to the holographic projection model of the electromechanical equipment to display the operation simulation status of the electromechanical equipment under different intelligent sleep strategies; Add interactive features to holographic projection models of electromechanical devices and monitor user interactions to update the holographic projection display.

10. A method for adaptively hibernating electromechanical devices driven by user activity, implemented based on the adaptive energy-saving system for electromechanical devices driven by user activity according to any one of claims 1 to 9, characterized in that: include: Acquire sensor data and complete automatic networking of sensor nodes and transmission of sensor data through self-organizing mechanism; When a sensor node is abnormal, it triggers network topology reconstruction; Extract perceptual features of perceptual data to dynamically evaluate user activity; Comprehensively determine whether to trigger the projection interaction mode based on the activity interval, activity trend, and duration; When the electromechanical device is in the projection interaction mode, continuously monitoring the duration of the electromechanical device being in the projection interaction mode; Determine whether to switch to sleep mode based on duration and task priority; When the sleep mode needs to be switched, the power consumption and performance of the electromechanical device and the user's historical preferences are comprehensively weighed, and an intelligent sleep strategy is formulated and optimized through a genetic algorithm to determine the sleep depth and sleep time of the electromechanical device; The operation simulation status of electromechanical equipment under different intelligent sleep strategies is displayed through holographic projection, including the power consumption change and performance fluctuation of electromechanical equipment; Generate wake-up signals for electromechanical devices and perform safety protection for electromechanical devices, and collaboratively manage the power supply of electromechanical devices through distributed power supply and energy recovery.

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