Intelligent optimization scheduling method for household energy use

Abstract

The invention discloses an intelligent optimization scheduling method for household energy use, and relates to the technical field of household energy intelligent optimization, and the method comprises the following steps: building a communication network through an Internet of Things device, collecting the operation state information of household appliances, an energy storage device and an energy supply device, and when the device communication abnormality is detected, sending the operation state information to a server; and recording the state data of the fault equipment and sending detailed fault reminding information to the user terminal. According to the invention, through the multi-protocol standby communication path and the dynamic switching mechanism, the household energy management system rapidly switches the path when the communication fault occurs, the communication stability is ensured, and the system robustness and the resource efficiency are improved. And in combination with priority scheduling and an intelligent algorithm, the system preferentially guarantees operation of key equipment, optimizes energy distribution, reduces the cost in the peak period, improves the utilization efficiency and reduces waste. According to the scheme, core function continuity is ensured, user experience is improved, and remarkable economic and environment-friendly benefits are brought.

Description

Technical Field

[0001] The present invention relates to the technical field of household energy intelligent optimization, and in particular to an intelligent optimization scheduling method for household energy use. Background Art

[0002] Intelligent optimization and scheduling of household energy use refers to the dynamic management and optimization of energy use in the home through the introduction of intelligent technology to improve energy efficiency, reduce energy costs and reduce environmental impact. Specifically, the process uses Internet of Things (IoT) devices to monitor the operating status of household appliances and energy facilities in real time, combined with artificial intelligence (AI) algorithms and optimization models, to intelligently schedule energy distribution and equipment operation time according to electricity price fluctuations, household users' energy needs and equipment usage priorities. For example, high-energy consumption equipment is automatically started during power troughs, and local energy is used preferentially when photovoltaic power generation or energy storage equipment has sufficient energy, thereby minimizing external energy demand. This approach not only saves energy expenses for users, but also eases the burden on the power grid and supports the efficient use of renewable energy.

[0003] The prior art has the following deficiencies:

[0004] In the process of intelligent optimization and scheduling of household energy use in existing technologies, communication failures between devices may lead to incorrect energy scheduling, which in turn leads to serious consequences. Smart home systems rely on real-time communication and coordination between IoT devices. When key node communication fails due to signal interference, network interruption, or incompatible device protocols, the system may not be able to accurately obtain device status or execute scheduling commands. For example, failure to update the status information of energy storage devices in a timely manner may lead to erroneous acquisition of high-cost electricity from the power grid during peak hours, rather than giving priority to local energy storage resources; in more serious cases, inaccurate scheduling instructions may cause multiple high-energy-consuming devices to start at the same time in the same time period, causing excessive load on the power grid, and even causing safety problems such as short circuits in household circuits. Although the probability of this problem occurring is low, once it occurs, it will have a significant impact on users' energy costs, equipment safety, and system stability.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent optimization scheduling method for household energy use. By constructing a multi-protocol backup communication path and a dynamic switching mechanism, the home energy management system can quickly switch to the backup path when a communication failure occurs, maintain the stability of communication between devices, and significantly improve the system robustness and resource allocation efficiency. Combining the priority scheduling strategy with the intelligent scheduling algorithm, the system gives priority to the operation of high-priority devices, and optimizes energy allocation after scheduling is restored, reducing peak energy costs, while improving energy utilization efficiency and reducing waste. This solution not only ensures the continuity of the core functions of the family, but also significantly improves the user experience, bringing economic and environmental benefits, so as to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a method for intelligent optimization and scheduling of household energy use, comprising the following steps:

[0008] Build a communication network through IoT devices to collect operating status information of household appliances, energy storage equipment, and energy supply equipment. When abnormal device communication is detected, record the status data of the faulty device and send detailed fault reminder information to the user terminal;

[0009] In addition to the main communication path, design backup communication paths for multiple communication protocols. Through the switching mechanism, when the main communication path fails, the backup path is immediately enabled to restore communication between devices to ensure stable transmission of scheduling instructions and device data.

[0010] According to the importance of device functions, a fixed priority strategy is assigned to devices. When communication paths fail and data updates are delayed, high-priority devices are scheduled first to ensure the normal operation of core devices.

[0011] Utilizes anomaly detection algorithms based on support vector machines to analyze the stability of communication data and the rationality of device status, and automatically performs recovery operations once an abnormal pattern is detected;

[0012] The dynamic optimization scheduling algorithm based on mixed integer programming inputs the equipment priority, real-time status and energy use strategy into the model. Under the premise of ensuring equipment operation safety and user comfort, the optimal scheduling plan is calculated and output to ensure energy efficiency and cost optimization.

[0013] Preferably, the specific steps of building a communication network through IoT devices, collecting the operating status information of household appliances, energy storage devices and energy supply devices, and recording the status data of the faulty device and sending detailed fault reminder information to the user terminal when abnormal device communication is detected are as follows:

[0014] Building a communication network in home energy management system through IoT technology;

[0015] After the communication network is established, the operating status information of each device will be collected in real time;

[0016] Continuously analyze the communication data flow of the device through preset monitoring rules;

[0017] When a device communication anomaly is detected, a fault reminder message will be immediately generated and sent to the user terminal.

[0018] Preferably, in addition to the main communication path, backup communication paths of multiple communication protocols are designed. Through the switching mechanism, when the main communication path fails, the backup path is immediately enabled to restore communication between devices. The specific steps to ensure that the scheduling instructions and device data can be stably transmitted are as follows:

[0019] On the basis of building the IoT communication network, design backup paths for multiple communication protocols for household appliances, energy storage equipment, and energy supply equipment;

[0020] When the main communication path is working normally, various operating status information is collected in real time to detect potential communication problems in advance;

[0021] When the main communication path fails, the path switching mechanism is automatically triggered;

[0022] After the backup path is successfully enabled, information related to the communication failure is recorded.

[0023] Preferably, a fixed priority strategy is assigned to the device according to the importance of the device function. When the communication path fails and the data update is delayed, the scheduling tasks of the high-priority devices are completed first to ensure the normal operation of the core devices. The specific steps are as follows:

[0024] Classify devices into high priority, medium priority, and low priority based on the importance of their functions;

[0025] Monitor the status of devices and the health of communication paths in real time, and load priority strategies;

[0026] After the communication path is switched to the backup path, the scheduling task of the high-priority device is executed first;

[0027] After completing the scheduling task of the high-priority device, a notification is sent to the user through the user terminal to explain the execution status of the current priority strategy and the scheduling result.

[0028] Preferably, the stability of communication data and the rationality of device status are analyzed by using an abnormality detection algorithm based on a support vector machine. Once an abnormal pattern is detected, the specific steps of automatically performing a recovery operation are as follows:

[0029] First, a variety of feature vectors are extracted from the communication data to characterize the stability of the data and the rationality of the device status. The acquired data features include packet loss rate L, signal strength fluctuation amplitude S, and transmission delay mean T. m and the variance of the delay T v , the feature extraction formula is as follows:

[0030] ,

[0031] , where L is the packet loss rate, P lost is the number of packets lost, P total is the total number of packets, S is the signal strength fluctuation amplitude, S t is the signal strength value at the tth sampling time, max(S t ) is the maximum signal strength value at the tth sampling time, min(S t ) is the signal strength value at the tth sampling time, max(S t ) is the minimum signal strength value at the tth sampling time, T m is the mean transmission delay, T j is the delay time of the jth data transmission, N is the total number of sampling times, T v is the variance of the delay;

[0032] Generate feature vectors based on the extracted features, and finally generate feature vector X, where: X =

[0033] [L,S,T m ,T v ]

[0034] The feature vector X is input into the support vector machine model to classify the state of the communication data. The decision boundary of the support vector machine model is defined as follows:

[0035] f(X)=w T X+b, where f(X) is the output value of the optimized classification model, b is the bias term, w is the weight vector, and w T is the transpose of the weight vector w;

[0036] The model is optimized by maximizing the classification interval, and the optimization expression is as follows:

[0037] , where y j is the sample label;

[0038] In real-time operation, the currently collected feature vector X is input into the trained support vector machine model. When f(X)<τ, τ is the set abnormal threshold, the current communication data is judged to be abnormal, and the abnormal degree index Δ is extracted:

[0039] Δ=|τ-f(X)|, where Δ is the severity of the current anomaly;

[0040] According to the detected abnormal feature Δ and the abnormal cause, appropriate recovery operations are performed. The specific optimization goal is to minimize the recovery delay and improve the communication quality index. The calculation expression is as follows:

[0041] , where is to minimize the recovery delay, Is to maximize the communication quality index, D restore is the amount of data to be restored, v switch is the switching speed of the backup path, R d is the recovery delay Q c is the communication quality indicator after recovery, and ∈ is a small constant to prevent the denominator from being zero.

[0042] Preferably, a dynamic optimization scheduling algorithm based on mixed integer programming inputs the priority, real-time status and energy use strategy of the equipment into the model, calculates and outputs the optimal scheduling plan under the premise of ensuring equipment operation safety and user comfort, and ensures that the energy efficiency and cost optimization calculation expression is as follows:

[0043] The first step of the dynamic optimization scheduling algorithm is to initialize the scheduling system and input key parameters to define the following parameters and variables: i is the priority weight of the ith device, S i (t) is the state variable of the ith device at time t, E i (t) is the power consumption of the ith device at time t, C t is the unit electricity price at time t, R(t) is the total energy available at time t, including energy storage capacity and available grid capacity, U i is the user comfort requirement of the i-th device, and the objective function formula is defined as follows:

[0044] , where T is the total number of time points, M is the total number of devices, λ is the weight coefficient used to balance the goals of energy utilization and cost optimization, and MinimizeZ is the value of the objective function Z that needs to be minimized through optimization calculation;

[0045] Establish constraints based on system requirements to ensure that the scheduling plan meets the needs of equipment operation safety and energy constraints:

[0046] 1. Total energy consumption must not exceed currently available energy:

[0047]

[0048] It means that at any time t, the total power consumption of all devices must not exceed the total available energy R(t);

[0049] 2. High-priority devices must meet operational requirements:

[0050] , where θ is the priority threshold;

[0051] 3. User comfort constraints:

[0052]

[0053] This constraint ensures that the device marked as non-compromise by the user must always run the scheduling policy generated by the constraint, so that the state variable S of the device i (t) meet the energy supply and demand balance;

[0054] Based on the objective function and constraints, mixed integer programming is used to solve the optimal scheduling solution.

[0055] 1. Input dynamic variables: In each iteration cycle t, input the dynamically updated parameters R(t), C t 、E i (t),

[0056] 2. Iterative update state: Use branch and bound method to solve the integer variable S i (t) and the continuous variable E i (t) combinatorial optimization problem, output equipment scheduling status,

[0057] 3. Modify the objective function: recalculate the priority weight P based on the scheduling results of the previous cycle i , increase the weight value of the equipment that fails to meet the comfort level, and update the objective function to:

[0058] , where μ is the penalty coefficient of user comfort, Z new is the objective function of optimal scheduling;

[0059] Finally, the scheduling plan is output according to the optimization results, including the operation schedule T of each device. i And power consumption allocation E i (t):

[0060] 1. Scheduling result table: The output format is as follows:

[0061] Device Status Table = {S i (t)|i=1,…,N; t=1,…,T}.

[0062] The scheduling result table shows the operating status of each device during the scheduling period, making it easier for users to view the scheduling plan;

[0063] 2. Energy allocation plan: Output total energy usage and energy storage priority strategy:

[0064] , where R used is the total energy use;

[0065] Indicates the total amount of energy used and records the priority use of energy storage equipment by energy dispatch;

[0066] User comfort feedback: Feedback unmet user needs to the optimization model to increase the priority of the next round of scheduling.

[0067] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0068] By constructing a multi-protocol backup communication path and adopting a dynamic switching mechanism, the home energy management system can quickly switch to the backup path when the main communication path fails, ensuring the stability of communication between devices. Whether it is the interruption of the Wi-Fi signal or the data delay caused by network congestion, the backup communication path can intervene with protocols such as Zigbee or Bluetooth to maintain the normal operation of key devices. The existence of the backup path not only improves the system's ability to respond to communication problems, but also optimizes resource allocation through dynamic path selection, making data transmission more efficient.

[0069] This mechanism also significantly improves the robustness of the system. Under normal communication conditions, the backup path can share data traffic and reduce the pressure on the main path; under fault conditions, the backup path can quickly take over the main path to reduce the risk of data loss or scheduling interruption. For example, energy storage devices can still send power information through the backup path during communication failures to ensure the continuity of scheduling logic, thereby avoiding unnecessary energy waste or operational risks. This multi-path redundancy design provides higher reliability and user experience in home intelligent energy management.

[0070] By introducing priority scheduling strategies and intelligent scheduling algorithms, the system can prioritize the operation of high-priority devices (such as energy storage devices and security alarm systems) when resources are limited or communication is abnormal. The status data of high-priority devices is obtained through backup paths, while the tasks of low-priority devices are delayed or suspended. This strategy not only reduces resource conflicts, but also ensures the continuity of the core functions of the home.

[0071] In addition, the intelligent scheduling algorithm combines equipment priority, energy price and current status during the scheduling recovery process to reallocate energy usage plans and minimize energy costs. For example, after the energy storage device is charged first, the system will adjust the operating time of equipment such as air conditioners according to the peak and valley of electricity consumption to avoid running high-energy-consuming equipment during high electricity prices. In this way, the system improves energy utilization efficiency while ensuring user comfort, further reducing overall energy consumption and costs, and bringing significant economic and environmental benefits to users. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0073] Figure 1 This is a method flow chart of the intelligent optimization scheduling method for household energy use of the present invention. DETAILED DESCRIPTION

[0074] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0075] The present invention provides Figure 1 The household energy use intelligent optimization scheduling method shown includes the following steps:

[0076] Build a communication network through IoT devices to collect operating status information of household appliances, energy storage equipment, and energy supply equipment. When abnormal device communication is detected, record the status data of the faulty device and send detailed fault reminder information to the user terminal;

[0077] The specific steps of building a communication network through IoT devices to collect the operating status information of household appliances, energy storage equipment and energy supply equipment, and recording the status data of the faulty equipment and sending detailed fault reminder information to the user terminal when abnormal device communication is detected are as follows:

[0078] Building a communication network in home energy management system through IoT technology;

[0079] Using communication protocols such as Wi-Fi, Bluetooth, and Zigbee, a real-time communication infrastructure is created for home appliances, energy storage devices, and energy supply devices. Each device is equipped with a communication module for uploading operating status data. These communication modules select the appropriate protocol based on the characteristics of the device. For example, low-power devices can use Zigbee, while devices with large data volumes can choose Wi-Fi.

[0080] At the same time, the system connects various devices through a centralized management platform or edge computing nodes to form a unified data transmission network. To ensure the stability of the network, the system can also deploy signal repeaters or boosters to expand the communication coverage and reduce the number of devices unable to connect due to weak signals. This process not only achieves seamless connection between devices, but also lays the foundation for real-time collection of status data.

[0081] After the communication network is established, the operating status information of each device will be collected in real time;

[0082] This information includes parameters such as power consumption, battery level, device operating mode, and connection status. The frequency of data collection can be optimized based on the importance of the device, for example, high-priority devices update their status once a minute, while low-priority devices update their status once an hour.

[0083] The collected data is transmitted to the central control unit or cloud server through the communication network for processing. In order to improve the reliability of data, the system will use redundant data collection technology to obtain status information from multiple sensors or paths at the same time to reduce the possibility of data loss or error. The system also uses a local storage module to save temporary data for subsequent recovery and processing when the communication network is interrupted.

[0084] Continuously analyze the communication data flow of the device through preset monitoring rules;

[0085] If communication interruption, data loss or abnormal status update (such as status not being updated for a long time) is detected, the system immediately marks the relevant device as "faulty state". For example, if a device does not respond to a data request within a set time or the returned data is out of a reasonable range, the system will determine that the device has abnormal communication.

[0086] After detecting an anomaly, the system will not only record the latest status of the device, but also save the specific parameters of the anomaly (such as the time of occurrence, fault type, and data characteristics). These records will be stored in log files for subsequent debugging or analysis. At the same time, the system can generate different recovery suggestions based on the fault type, such as restarting the device or manual inspection, and predict the possible impact range in advance.

[0087] When device communication anomalies are detected, a fault reminder message will be immediately generated and sent to the user terminal;

[0088] This reminder message usually includes the device name, the time when the abnormality occurred, a specific description of the fault, and a recommended solution. For example, the reminder message may show "Energy storage device A has communication abnormality, last updated at 14:05, it is recommended to check the network connection or restart the device."

[0089] To ensure that users receive notifications in a timely manner, the system supports multiple reminder methods, including push messages, SMS, emails, or voice calls. In addition, the system allows users to view the device status history and system-generated recovery suggestions in real time through the mobile app. If the user chooses to perform remote operations (such as trying to restart the device), the system will immediately respond and feedback the results of the operation. The design of this step not only improves the user experience, but also provides direct help for the rapid resolution of the problem.

[0090] In addition to the main communication path, design backup communication paths for multiple communication protocols. Through the switching mechanism, when the main communication path fails, the backup path is immediately enabled to restore communication between devices to ensure stable transmission of scheduling instructions and device data.

[0091] In addition to the main communication path, backup communication paths of multiple communication protocols are designed. Through the switching mechanism, when the main communication path fails, the backup path is immediately enabled to restore communication between devices. The specific steps to ensure that scheduling instructions and device data can be stably transmitted are as follows:

[0092] On the basis of building the IoT communication network, design backup paths for multiple communication protocols for household appliances, energy storage equipment, and energy supply equipment;

[0093] The selection of backup communication paths is flexible and diverse, including low-power Zigbee, high-speed and stable Wi-Fi, and short-range and reliable Bluetooth. When designing backup paths, the characteristics of each communication protocol are matched with the device requirements. For example, Zigbee is configured preferentially for sensors with lower energy consumption, and Wi-Fi is configured for devices with large amounts of data. During the path design phase, the system uses multi-protocol adaptation technology (such as multi-mode communication modules) to ensure that the device can switch seamlessly between different protocols. At the same time, the system monitors the status of each communication path in real time through dynamic signal detection technology to ensure that the backup path is available at any time when needed. In addition, the design of backup paths also needs to consider redundant configurations, such as a device supporting more than two protocols at the same time, increasing the flexibility and fault tolerance of communication, and providing a basic guarantee for subsequent path switching.

[0094] When the main communication path is working normally, various operating status information is collected in real time to detect potential communication problems in advance;

[0095] Such as status information such as transmission efficiency, signal strength and device response time. By setting thresholds to monitor key indicators, such as signal strength below a specific value, increased packet loss rate, delayed response time, etc., an early warning mechanism is triggered.

[0096] When the status of the main path is detected to be close to abnormal, the system will evaluate the risk of failure through an algorithm and activate the standby mode of the backup path in advance. This step ensures that the backup path can quickly intervene in communication and shorten the interruption time of the switching process. At the same time, the system uploads all monitoring data to the cloud, allowing users to check the operating status of the communication network at any time and take maintenance measures in advance to avoid sudden failures.

[0097] When the main communication path fails, the path switching mechanism is automatically triggered;

[0098] The switching mechanism gives priority to the backup path with the most stable signal strength. For example, Bluetooth is more reliable between close-range devices, while Zigbee is suitable for communication between multiple devices. The execution of the switch includes interrupting the current task of the main path, switching the device's status data and scheduling instructions to the backup path, and updating the device's communication configuration in the system to adapt to the new protocol.

[0099] The system uses a cache mechanism to save the intermediate state of data transmission during the switching period to prevent important instructions or data from being lost during the switching. For example, unfinished energy scheduling instructions will continue to be transmitted after the backup path is restored, ensuring the continuity of communication between devices and the integrity of the scheduling logic. After the switching is completed, the system will automatically test the stability of the backup path and record the switching log to provide a reference for subsequent network optimization.

[0100] After the backup path is successfully enabled, record information related to the communication failure;

[0101] Including the fault type of the primary path, the time of occurrence, the delay of the switching process, and the current status of the backup path. These records are stored in the system log and serve as data support for optimizing future communication configuration and maintenance plans.

[0102] At the same time, the system notifies the user of the fault details and current communication status through the user terminal (such as mobile applications or web pages). For example, the user may receive the following reminder: "The main path Wi-Fi communication is interrupted, and the switch to the Zigbee path is completed. The current communication is stable. It is recommended to check the Wi-Fi signal source." The user can also view detailed logs through the terminal and adjust the main path according to the system's suggestions to restore it to its normal state. This process not only allows users to understand the fault situation, but also improves the transparency of the system and the user's trust in the intelligent scheduling system.

[0103] According to the importance of device functions, a fixed priority strategy is assigned to devices. When communication paths fail and data updates are delayed, high-priority devices are scheduled first to ensure the normal operation of core devices.

[0104] According to the importance of device functions, a fixed priority strategy is assigned to the device. When the communication path fails and the data update is delayed, the scheduling tasks of high-priority devices are completed first to ensure the normal operation of the core devices. The specific steps are as follows:

[0105] Classify devices into high priority, medium priority, and low priority based on the importance of their functions;

[0106] For example, energy storage equipment and security alarm systems are set as high-priority devices because they directly affect the stability of energy scheduling and family safety; basic life support equipment such as air conditioners and refrigerators are set as medium-priority devices; and entertainment equipment such as televisions, stereos, etc. are classified as low-priority devices.

[0107] Priority setting ensures that critical function equipment can continue to operate when communication failures or data delays occur, while the operation of non-critical equipment can be temporarily delayed or stopped. This classification is deeply integrated with the scheduling system through algorithms, and the priority rules can be dynamically adjusted according to user needs. For example, users can change the priority configuration of certain devices through the application, thereby increasing the flexibility and personalization of the system.

[0108] Monitor the status of devices and the health of communication paths in real time, and load priority strategies;

[0109] When the main communication path is interrupted or data update is delayed, the system determines the execution order of scheduled tasks based on the priority strategy.

[0110] Specifically, the system preferentially obtains the operating data and status information of high-priority devices through alternative communication paths, such as the power level of energy storage devices and the real-time status of alarm systems. For low-priority devices, the system marks their tasks as "pending" or "paused" to ensure that resources are focused on the scheduling of key devices. In this way, the system is able to keep the core functions of home energy management running normally even when communication is limited.

[0111] After the communication path is switched to the backup path, the scheduling task of the high-priority device is executed first;

[0112] When the power of the energy storage device approaches a critical level, the system immediately adjusts the energy allocation strategy to ensure that the energy storage device is charged first. At the same time, for safety devices such as alarm systems, the system ensures that their real-time operating status information can be quickly transmitted through backup paths and updates its status to user terminals first.

[0113] During the scheduling process, the system will dynamically adjust the equipment operation time and energy allocation plan to prevent multiple high-priority devices from consuming too many resources at the same time. For example, during peak hours, the system will give priority to charging energy storage devices and postpone the start-up of medium-priority devices (such as air conditioners) to ensure the rationality of energy allocation and the effectiveness of scheduling strategies. This process not only improves scheduling efficiency, but also enhances the system's robustness in dealing with communication failures.

[0114] After completing the scheduling task of the high-priority device, a notification is sent to the user through the user terminal to explain the execution status of the current priority strategy and the scheduling result;

[0115] The notification content may be: "The current communication path is faulty and has been switched to the backup path. High-priority equipment (energy storage equipment and alarm system) is operating normally, and the scheduling of medium and low priority equipment has been delayed."

[0116] In addition, the system will optimize the priority strategy based on the completion of the scheduled tasks and the real-time status of the equipment. For example, if the tasks of some medium-priority equipment (such as air conditioners) are delayed for too long, the system will dynamically adjust their priorities to prevent affecting the user experience. Users can also view the scheduling log or manually modify the device priority configuration through the terminal, thereby enhancing the flexibility of the system and the user's control over the system.

[0117] Utilizes anomaly detection algorithms based on support vector machines to analyze the stability of communication data and the rationality of device status, and automatically performs recovery operations once an abnormal pattern is detected;

[0118] Using the support vector machine-based anomaly detection algorithm, the stability of communication data and the rationality of device status are analyzed. Once an abnormal pattern is detected, the specific steps for automatically performing recovery operations are as follows:

[0119] First, a variety of feature vectors are extracted from the communication data to characterize the stability of the data and the rationality of the device status. The acquired data features include packet loss rate L, signal strength fluctuation amplitude S, and transmission delay mean T. m and the variance of the delay T v , the feature extraction formula is as follows:

[0120] ,

[0121] , where L is the packet loss rate, P lost is the number of packets lost, P total is the total number of packets, S is the signal strength fluctuation amplitude, S t is the signal strength value at the tth sampling time, max(S t ) is the maximum signal strength value at the tth sampling time, min(S t) is the signal strength value at the tth sampling time, max(S t ) is the minimum signal strength value at the tth sampling time, T m is the mean transmission delay, T j is the delay time of the jth data transmission, N is the total number of sampling times, T v is the variance of the delay;

[0122] Generate feature vectors based on the extracted features, and finally generate feature vector X, where: X =

[0123] [L,S,T m ,T v ]

[0124] This feature vector will be used as input to the classification model in the subsequent steps.

[0125] The feature vector X is input into the support vector machine (SVM) model to classify the state of the communication data. The decision boundary of the support vector machine (SVM) model is defined as follows:

[0126] f(X)=w T ·X+b, where f(X) is the output value of the optimized classification model, which is used to determine the category of the input feature vector X. When f(X)≥0, the classification result is a positive category (such as normal communication data). When f(X)<0, the classification result is a negative category (such as abnormal communication data). b is a bias term used to adjust the position of the decision boundary. w is a weight vector, w=[w 1 ,w 2 ,w 3 ,w 4 ], corresponding to the features [L,S,T m ,T v The importance of T It is the transpose of the weight vector w, which represents the linear combination coefficient of w, and is summed after multiplying each feature vector X item by item;

[0127] The model is optimized by maximizing the classification interval (margin), and the optimization expression is as follows:

[0128] , where y j is the sample label, +1 indicates normal, -1 indicates abnormal;

[0129] After training, an optimized classification model output value f(X) is generated for subsequent real-time anomaly detection.

[0130] During real-time operation, the currently collected feature vector X is input into the trained support vector machine (SVM) model. When f(X)<τ, τ is the set abnormal threshold, the current communication data is judged to be abnormal, and the abnormal degree index Δ is extracted:

[0131] Δ=|τ-f(X)|, where Δ is the severity of the current anomaly. The larger the value, the more serious the anomaly.

[0132] According to the detected abnormal characteristics Δ and the abnormal causes, appropriate recovery operations are performed. For example, when the communication path is interrupted, the system will switch to the backup path and reconfigure the transmission parameters according to the characteristics of the abnormal data. The specific optimization goal is to minimize the recovery delay and improve the communication quality index. The calculation expression is as follows:

[0133] , where is to minimize the recovery delay, Is to maximize the communication quality index, D restore is the amount of data to be restored, v switch is the switching speed of the backup path, R d is the recovery delay, Q c is the communication quality indicator after recovery, and ∈ is a small constant to prevent the denominator from being zero.

[0134] After the recovery is complete, the system will c The abnormal recovery effect is evaluated based on the recovery time and the data is stored in the log for subsequent system optimization.

[0135] The dynamic optimization scheduling algorithm based on mixed integer programming inputs the equipment priority, real-time status and energy use strategy into the model, calculates and outputs the optimal scheduling plan under the premise of ensuring equipment operation safety and user comfort, and ensures energy efficiency and cost optimization;

[0136] The dynamic optimization scheduling algorithm based on mixed integer programming inputs the equipment priority, real-time status and energy use strategy into the model. Under the premise of ensuring equipment operation safety and user comfort, the optimal scheduling plan is calculated and output to ensure energy efficiency and cost optimization. The calculation expression is as follows:

[0137] The first step of the dynamic optimization scheduling algorithm is to initialize the scheduling system and input key parameters to define the following parameters and variables: i is the priority weight of the ith device (value range [0,1], higher priority devices have higher weights), S i (t) is the state variable of the ith device at time t, with a value of 1 for running and 0 for stopping. i (t) is the power consumption of the ith device at time t (unit: kWh), C t is the unit electricity price at time t (unit: yuan / kWh), R(t) is the total energy available at time t (unit: kWh), including energy storage power and available grid power, U iis the user comfort requirement of the i-th device. A value of 1 indicates that it must be met, and a value of 0 indicates that it can be adjusted. The objective function formula is defined as follows:

[0138] , where T is the total number of time points, M is the total number of devices, λ is the weight coefficient used to balance the goals of energy utilization and cost optimization, and MinimizeZ is the value of the objective function Z that needs to be minimized through optimization calculation;

[0139] In this step, the device priority P is calculated i and initialization state S i (0), providing basic input data for subsequent scheduling.

[0140] Establish constraints based on system requirements to ensure that the scheduling plan meets the needs of equipment operation safety and energy constraints:

[0141] 1. Total energy consumption must not exceed currently available energy:

[0142]

[0143] It means that at any time t, the total power consumption of all devices must not exceed the total available energy R(t);

[0144] 2. High-priority devices must meet operational requirements:

[0145] , where θ is the priority threshold; for example, θ = 0.8, devices above this value are forced to run.

[0146] 3. User comfort constraints:

[0147]

[0148] This constraint ensures that devices marked as non-compromiseable by the user (such as security devices) must always run the scheduling policy generated by the constraint, so that the state variable S of the device i (t) meet the energy supply and demand balance;

[0149] Based on the objective function and constraints, mixed integer programming is used to solve the optimal scheduling solution.

[0150] 1. Input dynamic variables: In each iteration cycle t, input the dynamically updated parameters R(t), C t 、E i (t),

[0151] 2. Iterative update state: Use branch and bound method to solve the integer variable S i (t) and the continuous variable E i(t) combinatorial optimization problem, output equipment scheduling status,

[0152] 3. Modify the objective function: recalculate the priority weight P based on the scheduling results of the previous cycle i , increase the weight value of the equipment that fails to meet the comfort level, and update the objective function to:

[0153] , where μ is the penalty coefficient of user comfort, which is used to improve the priority of unsatisfied equipment scheduling, Z new It is the objective function of the optimal scheduling, which means that within a time period, the sum of the energy cost of equipment operation and the penalty cost of not meeting user needs is minimized by dynamically adjusting the scheduling plan;

[0154] Through this process, the system dynamically optimizes the scheduling results so that the scheduling plan gradually approaches the global optimum.

[0155] Finally, the scheduling plan is output according to the optimization results, including the operation schedule T of each device. i And power consumption allocation E i (t):

[0156] 1. Scheduling result table: The output format is as follows:

[0157] Device Status Table = {S i (t)|i=1,…,N; t=1,…,T}.

[0158] The scheduling result table shows the operating status of each device during the scheduling period, making it easier for users to view the scheduling plan;

[0159] 2. Energy allocation plan: Output total energy usage and energy storage priority strategy:

[0160] , where R used is the total energy usage, which indicates the total amount of energy actually used by all devices during the scheduling period;

[0161] Indicates the total amount of energy used and records the priority use of energy storage equipment by energy dispatch;

[0162] 3. User comfort feedback: Feedback unmet user needs to the optimization model to increase the priority of the next round of scheduling.

[0163] Through the above steps, the dynamic optimization scheduling algorithm not only ensures the safety of equipment operation and user comfort, but also greatly improves energy efficiency and achieves cost optimization.

[0164] Implementation 1: In a home energy management system, the communication network between devices is the basis for intelligent scheduling, and the main communication path is usually Wi-Fi because it has high bandwidth and a large coverage area. However, Wi-Fi signals are susceptible to external interference, such as electromagnetic interference generated by other devices in the home or failure of the router itself. When the main communication path is interrupted, energy scheduling and device operation status transmission may come to a standstill. Therefore, the construction of a backup communication path is crucial to system stability.

[0165] The system adopts a design strategy of multi-protocol backup communication paths, mainly including Zigbee and Bluetooth protocols. Zigbee is a low-power, low-bandwidth communication protocol suitable for scenarios that support multi-device networking, such as real-time data synchronization of multiple home appliances and sensors. Bluetooth is suitable for direct communication between devices within a short distance, and can provide short-range support for key devices when Wi-Fi is not available. In the system design, each device is equipped with a multi-mode communication module that can automatically switch protocols according to the communication environment. For example, when the Wi-Fi signal is lost, it switches to the Zigbee network to continue to maintain communication.

[0166] The dynamic switching mechanism of the backup path ensures that the system can quickly respond to failures in the primary path. For example, when the system detects that the Wi-Fi signal strength is below the preset threshold or there is no response for a long time, it will automatically scan the signal strength and availability of the backup path and select the optimal path to activate the backup communication. During the switching process, the system uses cache technology to save the key data being transmitted to prevent command loss or scheduling interruption due to switching. At the same time, the system verifies the stability of the backup path through the path test function and records the specific information of the switch in the log, including the trigger time, the basis for path selection, and the current status of the backup path.

[0167] The application of backup paths is not limited to communication failure recovery, but can also serve as redundant support for the main path to share data transmission in high-load scenarios. For example, when multiple devices upload data at the same time, the backup path can automatically divert part of the communication volume, reduce the pressure on the main path, and improve overall communication efficiency. In addition, the backup path can also provide dedicated support in the communication of mission-critical equipment. For example, when energy storage equipment or security alarm systems need to transmit urgent data, the system can prioritize the allocation of backup path resources to ensure that the data can be quickly transmitted to the central management unit or user terminal.

[0168] This backup communication path design and dynamic switching mechanism significantly improves the robustness and stability of the home energy management system in complex communication environments. Even when the primary path fails, the system can maintain the continuity of energy scheduling and device management through the backup path, while providing users with timely status notifications to reduce possible interference and losses.

[0169] Implementation method 2: Intelligent scheduling of household energy use needs to find a balance between limited resources and complex user needs, and the setting of device priorities is the key to achieving this goal. The system classifies devices into high priority, medium priority, and low priority by evaluating the functional importance of the devices, energy requirements, and impact on family life. For example, energy storage devices and security alarm systems are set to high priority because they are directly related to the reliability of energy scheduling and family safety; basic life support equipment such as air conditioners and refrigerators are set to medium priority; entertainment equipment such as televisions and stereos are classified as low priority.

[0170] This priority strategy is not only effective under normal communication conditions, but also provides guidance during communication path failures or data update delays. When communication is interrupted, the bandwidth and resources of the backup path are limited. The system prioritizes obtaining status information of high-priority devices and executing scheduling tasks based on the priority strategy. For example, the system quickly obtains the power data of energy storage devices through the backup path to ensure that energy scheduling can give priority to charging them. At the same time, the real-time monitoring data of the alarm system is transmitted through the backup path to ensure that home safety functions are not affected. The tasks of low-priority devices will be marked as "pending" and will be executed after communication is restored.

[0171] Dynamic adjustment of priority strategies is also a major feature of this implementation. For example, when electricity prices enter peak hours, the system will temporarily increase the priority of energy storage devices to ensure that they can obtain charging resources first, so that they can power other devices during low hours and reduce energy costs. At the same time, users can adjust the priority configuration of devices at any time through mobile terminals. For example, when a user plans to use a low-priority device, the priority can be temporarily increased, and the system will give priority to the operation of this device in scheduling.

[0172] During the actual execution of scheduling, the system will also optimize the scheduling strategy based on the mutual influence between devices. For example, to avoid resource shortages caused by the simultaneous startup of multiple high-priority devices, the system will set time intervals for these devices to ensure that each device can get enough energy and communication resources. This scheduling logic not only improves the operating efficiency of the system, but also reduces resource waste or operating delays caused by priority conflicts. Through the dynamic execution of priority strategies, the system can always ensure the normal operation of the core functional devices of the home when resources are limited, while providing users with a flexible scheduling experience.

[0173] Implementation method 3: The home energy scheduling system relies on the accuracy of the communication network and device data. Once a communication anomaly or data error occurs, the scheduling task may be seriously affected. To this end, the system uses an artificial intelligence-based anomaly detection algorithm to monitor the communication path status and device operation data in real time. For example, an algorithm model built using a support vector machine (SVM) or a recurrent neural network (RNN) is used to analyze the historical trend of device status data and the changing rules of real-time data. Once a communication interruption or data anomaly is detected, such as the device status has not been updated for a long time or the returned data exceeds a reasonable range, the system immediately starts the exception handling mechanism.

[0174] The exception handling mechanism consists of two parts: one is to switch the communication path and reacquire the device status data through the backup path; the other is to use historical data and algorithm models to predict the current status of the device. For example, when the status data of the energy storage device cannot be updated due to communication interruption, the system predicts the remaining power of the energy storage device based on past power usage and current load. Although this kind of prediction data is not as accurate as real-time data, it can provide a temporary reference for scheduling tasks and ensure the continuity of the system scheduling logic.

[0175] After anomaly detection and communication restoration, the system recalculates the scheduling plan through a dynamic optimization scheduling algorithm based on mixed integer programming. The scheduling algorithm takes device priority, real-time status, and energy prices as inputs, and outputs the optimal result including the device operation schedule and energy allocation plan. For example, the system prioritizes the charging resources of energy storage devices, while delaying the operation time of non-essential equipment to reduce energy costs and reduce grid load. For users, this algorithm can provide clear scheduling results, such as prompting users which devices have been running or postponed, and the specific energy-saving effect of each scheduling.

[0176] In addition, the system will record the results of anomaly detection and the processing process in the log and notify the user through the user terminal. For example, when communication is restored after an interruption, the user may receive a notification: "Communication anomaly has been restored, and the scheduling task has been recalculated. The energy storage device has completed charging, and the air conditioner will start in 20 minutes." Users can view the log to understand the cause and processing of the anomaly, and adjust future usage strategies according to the prompts. Through the combination of anomaly detection and intelligent scheduling algorithms, the system not only improves the ability to deal with communication problems, but also optimizes the efficiency and reliability of energy scheduling.

[0177] By constructing a multi-protocol backup communication path and adopting a dynamic switching mechanism, the home energy management system can quickly switch to the backup path when the main communication path fails, ensuring the stability of communication between devices. Whether it is the interruption of the Wi-Fi signal or the data delay caused by network congestion, the backup communication path can intervene with protocols such as Zigbee or Bluetooth to maintain the normal operation of key devices. The existence of the backup path not only improves the system's ability to respond to communication problems, but also optimizes resource allocation through dynamic path selection, making data transmission more efficient.

[0178] This mechanism also significantly improves the robustness of the system. Under normal communication conditions, the backup path can share data traffic and reduce the pressure on the main path; under fault conditions, the backup path can quickly take over the main path to reduce the risk of data loss or scheduling interruption. For example, energy storage devices can still send power information through the backup path during communication failures to ensure the continuity of scheduling logic, thereby avoiding unnecessary energy waste or operational risks. This multi-path redundancy design provides higher reliability and user experience in home intelligent energy management.

[0179] By introducing priority scheduling strategies and intelligent scheduling algorithms, the system can prioritize the operation of high-priority devices (such as energy storage devices and security alarm systems) when resources are limited or communication is abnormal. The status data of high-priority devices is obtained through backup paths, while the tasks of low-priority devices are delayed or suspended. This strategy not only reduces resource conflicts, but also ensures the continuity of the core functions of the home.

[0180] In addition, the intelligent scheduling algorithm combines equipment priority, energy price and current status during the scheduling recovery process to reallocate energy usage plans and minimize energy costs. For example, after the energy storage device is charged first, the system will adjust the operating time of equipment such as air conditioners according to the peak and valley of electricity consumption to avoid running high-energy-consuming equipment during high electricity prices. In this way, the system improves energy utilization efficiency while ensuring user comfort, further reducing overall energy consumption and costs, and bringing significant economic and environmental benefits to users.

[0181] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. An intelligent optimization and scheduling method for household energy use, characterized in that: The following steps are involved: Build a communication network through IoT devices to collect the operating status information of household appliances, energy storage equipment and energy supply equipment. When abnormal device communication is detected, record the status data of the faulty device and send detailed fault reminder information to the user terminal; In addition to the main communication path, design backup communication paths for multiple communication protocols. Through the switching mechanism, when the main communication path fails, the backup path is immediately enabled to restore communication between devices to ensure stable transmission of scheduling instructions and device data. According to the importance of device functions, a fixed priority strategy is assigned to devices. When communication paths fail or data updates are delayed, high-priority devices are scheduled first to ensure the normal operation of core devices. Utilizes anomaly detection algorithms based on support vector machines to analyze the stability of communication data and the rationality of device status, and automatically performs recovery operations once an abnormal pattern is detected; The dynamic optimization scheduling algorithm based on mixed integer programming inputs the equipment priority, real-time status and energy use strategy into the model. Under the premise of ensuring equipment operation safety and user comfort, the optimal scheduling plan is calculated and output to ensure energy efficiency and cost optimization.

2. The intelligent optimization and scheduling method for household energy use according to claim 1 is characterized in that: The specific steps for building a communication network through IoT devices to collect the operating status information of household appliances, energy storage equipment, and energy supply equipment, and recording the status data of the faulty equipment and sending detailed fault reminder information to the user terminal when abnormal device communication is detected are as follows: Building a communication network in home energy management system through IoT technology; After the communication network is established, the operating status information of each device will be collected in real time; Continuously analyze the communication data flow of the device through preset monitoring rules; When a device communication anomaly is detected, a fault reminder message will be immediately generated and sent to the user terminal.

3. The intelligent optimization and scheduling method for household energy use according to claim 1 is characterized in that: In addition to the main communication path, backup communication paths of multiple communication protocols are designed. Through the switching mechanism, when the main communication path fails, the backup path is immediately enabled to restore communication between devices. The specific steps to ensure stable transmission of scheduling instructions and device data are as follows: On the basis of building the IoT communication network, design backup paths for multiple communication protocols for household appliances, energy storage equipment, and energy supply equipment; When the main communication path is working normally, various operating status information is collected in real time to detect potential communication problems in advance; When the main communication path fails, the path switching mechanism is automatically triggered; After the backup path is successfully enabled, information related to the communication failure is recorded.

4. The intelligent optimization and scheduling method for household energy use according to claim 1 is characterized in that: According to the importance of device functions, a fixed priority strategy is assigned to the device. When the communication path fails and the data update is delayed, the scheduling tasks of high-priority devices are completed first to ensure the normal operation of the core devices. The specific steps are as follows: Classify devices into high priority, medium priority, and low priority based on the importance of their functions; Monitor the status of devices and the health of communication paths in real time, and load priority strategies; After the communication path is switched to the backup path, the scheduling task of the high-priority device is executed first; After completing the scheduling task of the high-priority device, a notification is sent to the user through the user terminal to explain the execution status of the current priority strategy and the scheduling result.

5. The intelligent optimization and scheduling method for household energy use according to claim 1 is characterized in that: Using the support vector machine-based anomaly detection algorithm, the stability of communication data and the rationality of device status are analyzed. Once an abnormal pattern is detected, the specific steps for automatically performing recovery operations are as follows: First, a variety of feature vectors are extracted from the communication data to characterize the stability of the data and the rationality of the device status. The acquired data features include packet loss rate L, signal strength fluctuation amplitude S, and transmission delay mean T. m and the variance of the delay T v , the feature extraction formula is as follows: S=max(S t )-min(S t ), , where L is the packet loss rate, P lost is the number of packets lost, P total is the total number of packets, S is the signal strength fluctuation amplitude, S t is the signal strength value at the tth sampling time, max(S t ) is the maximum signal strength value at the tth sampling time, min(S t ) is the signal strength value at the tth sampling time, max(S t ) is the minimum signal strength value at the tth sampling time, T m is the mean transmission delay, T j is the delay time of the jth data transmission, N is the total number of sampling times, T v is the variance of the delay; Generate feature vectors based on the extracted features, and finally generate feature vector X, where: X = [L, S, T m ,T v ] The feature vector X is input into the support vector machine model to classify the state of the communication data. The decision boundary of the support vector machine model is defined as follows: f(X)=w T X+b, where f(X) is the output value of the optimized classification model, b is the bias term, w is the weight vector, and w T is the transpose of the weight vector w; The model is optimized by maximizing the classification interval, and the optimization expression is as follows: , where y j is the sample label; In real-time operation, the currently collected feature vector X is input into the trained support vector machine model. When f(X)<τ, τ is the set abnormal threshold, the current communication data is judged to be abnormal, and the abnormal degree index Δ is extracted: Δ=|τ-f(X)|, where Δ is the severity of the current anomaly; According to the detected abnormal feature Δ and the abnormal cause, appropriate recovery operations are performed. The specific optimization goal is to minimize the recovery delay and improve the communication quality index. The calculation expression is as follows: , where is to minimize the recovery delay, Is to maximize the communication quality index, D restore is the amount of data to be restored, v switch is the switching speed of the backup path, R d is the recovery delay, Q c is the communication quality indicator after recovery, and ∈ is a small constant to prevent the denominator from being zero.

6. The intelligent optimization and scheduling method for household energy use according to claim 1 is characterized in that: The dynamic optimization scheduling algorithm based on mixed integer programming inputs the equipment priority, real-time status and energy use strategy into the model. Under the premise of ensuring equipment operation safety and user comfort, the optimal scheduling plan is calculated and output to ensure energy efficiency and cost optimization. The calculation expression is as follows: The first step of the dynamic optimization scheduling algorithm is to initialize the scheduling system and input key parameters to define the following parameters and variables: i is the priority weight of the ith device, S i (t) is the state variable of the ith device at time t, E i (t) is the power consumption of the ith device at time t, C t is the unit electricity price at time t, R(t) is the total energy available at time t, including energy storage capacity and available grid capacity, U i is the user comfort requirement of the i-th device, and the objective function formula is defined as follows: , where T is the total number of time points, M is the total number of devices, λ is the weight coefficient used to balance the goals of energy utilization and cost optimization, and MinimizeZ is the value of the objective function Z that needs to be minimized through optimization calculation; Establish constraints based on system requirements to ensure that the scheduling plan meets the needs of equipment operation safety and energy constraints: Total energy consumption must not exceed currently available energy: It means that at any time t, the total power consumption of all devices must not exceed the total available energy R(t); High priority devices must meet operational requirements: , where θ is the priority threshold; User comfort constraints: This constraint ensures that the device marked as non-compromise by the user must always run the scheduling policy generated by the constraint, so that the state variable S of the device i (t) meet the energy supply and demand balance; Based on the objective function and constraints, mixed integer programming is used to solve the optimal scheduling solution. Input dynamic variables: In each iteration cycle t, input the dynamically updated parameters R(t), C t 、E i (t), Iterative update state: Use branch and bound method to solve integer variable S i (t) and the continuous variable E i (t) combinatorial optimization problem, output equipment scheduling status, Modify the objective function: recalculate the priority weight P based on the scheduling result of the previous cycle i , increase the weight value of the equipment that fails to meet the comfort level, and update the objective function to: , where μ is the penalty coefficient of user comfort, Z new is the objective function of optimal scheduling; Finally, the scheduling plan is output according to the optimization results, including the operation schedule T of each device. i And power consumption allocation E i (t): Scheduling result table: The output format is as follows: Device Status Table = {S i (t)|i=1,…,N; t=1,…,T}. The scheduling result table shows the operating status of each device during the scheduling period, making it easier for users to view the scheduling plan; Energy allocation plan: Output total energy usage and energy storage priority strategy: , where R used is the total energy use; Indicates the total amount of energy used and records the priority use of energy storage equipment by energy dispatch; User comfort feedback: Feedback unmet user needs to the optimization model to increase the priority of the next round of scheduling.

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