Distributed smart home equipment management system based on Internet of Things

Abstract

The invention discloses a distributed smart home equipment management system based on the Internet of Things, and relates to the technical field of smart home. The central controller is in communication connection with an equipment access module, a data acquisition module, an equipment anomaly classification module, an equipment anomaly judgment module, a resource allocation evaluation module, a strategy optimization module and a log recording module, and all the modules are in electric signal connection. According to the invention, by monitoring the operation state of the smart home equipment, the equipment abnormity can be found and responded, and by using the cooperative work of the equipment abnormity judgment module and the resource allocation evaluation module, when the performance reduction or security vulnerability of the smart home equipment is detected, the resource reallocation strategy is triggered, and the key equipment is ensured to obtain priority processing. The response speed of the system is improved, the equipment fault can be repaired in the shortest time, and the service interruption time caused by the equipment fault is shortened.

Description

Technical Field

[0001] The present invention relates to the field of smart home technology, and in particular to a distributed smart home equipment management system based on the Internet of Things. Background Art

[0002] With the continuous advancement of science and technology and the improvement of people's living standards, smart home systems, as an advanced home management system that integrates information technology, communication technology and home appliances, have gradually emerged in recent years. Smart home systems use sensors, controllers and other technologies to monitor, control and manage home appliances. They have the characteristics of intelligent control, automation and networking. By connecting various devices in the home through the Internet of Things technology, the interconnection between devices is achieved, which can provide users with a more convenient, intelligent and efficient life experience.

[0003] In the management of distributed smart home devices, due to the large number of devices and complex working environment, when a device is in an abnormal state, the system resources will be unevenly allocated, resulting in some devices being unable to obtain sufficient resources to respond quickly. Therefore, how to evaluate the abnormal state and optimize the system resource allocation strategy to quickly start the corresponding processing flow is the problem we need to solve. To this end, a distributed smart home device management system based on the Internet of Things is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a distributed smart home device management system based on the Internet of Things to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A distributed smart home device management system based on the Internet of Things, comprising a central controller, wherein the central controller is communicatively connected with a device access module, a data acquisition module, a device abnormality classification module, a device abnormality judgment module, a resource allocation evaluation module, a strategy optimization module and a log recording module, wherein electrical signals are connected between the modules;

[0007] The device access module is used to access and identify smart home devices, including device registration, identity authentication and connection management, to ensure that all smart home devices can access the system safely and stably, providing a basis for subsequent management;

[0008] The data acquisition module is used to collect the operation data of smart home devices, including status information and performance parameters, and pre-process the collected data to obtain the device operation data table, providing real-time and accurate data support;

[0009] The device anomaly classification module collects abnormal data related to the operation of smart home devices in advance, and classifies the abnormality levels according to the system resource allocation results of historical abnormal situations;

[0010] The device abnormality judgment module determines the evaluation indicators for device abnormality judgment based on the device operation data table, which are performance stability indicators and device safety indicators, and evaluates the performance stability indicators and device safety indicators of smart home devices one by one to identify the existing abnormal conditions;

[0011] The resource allocation evaluation module evaluates the current allocation of system resources based on the evaluation results of the performance stability index and the equipment safety index, and determines the abnormality level of the current abnormal situation;

[0012] The strategy optimization module dynamically adjusts the system resource allocation strategy according to the results of equipment abnormality judgment and resource allocation evaluation, and initiates the corresponding processing flow, including alarm, notification, repair, and optimization of resource allocation to ensure that key equipment can obtain sufficient resources to respond quickly in abnormal conditions;

[0013] The log recording module records the equipment operating status, equipment anomaly judgment, resource allocation evaluation and strategy optimization information, provides historical data support for system operation and maintenance and troubleshooting, improves the maintainability of the system, and provides a user interface for users to view equipment status, receive alarm information, and configure system parameters.

[0014] A further improvement of the technical solution of the present invention is that in the device access module, the process of accessing and identifying smart home devices is as follows:

[0015] After the device access module is started, it uses the UPnP (Universal Plug and Play) network protocol to scan the network to discover smart home devices on the network. Users can complete the connection between smart home devices and the system by scanning the QR code on the smart home device or pairing through Wi-Fi / Bluetooth. Through UPnP, the device access module can identify and list all smart home devices on the network;

[0016] The smart home device sends an authentication request to the central controller of the smart home device management system, which includes the device identification and authentication information. After receiving the authentication request, the system authenticates the smart home device according to the preset authentication mechanism and protocol. If the authentication is successful, the system will send a successful authentication response to the smart home device and assign corresponding access rights.

[0017] The device access module requests the device to send its current status to ensure that the system has the latest device information. The system updates the device status information and synchronizes it to the central database to monitor the connection status of smart home devices to ensure that the device always stays online.

[0018] When a smart home device is disconnected, try to reconnect or notify the system administrator, and record the access logs of all devices, including access time, device information, and error or warning messages.

[0019] A further improvement of the technical solution of the present invention is that in the data acquisition module, the process of acquiring the equipment operation data table is as follows:

[0020] Collect data from smart home devices at preset time intervals, including status information and performance parameters of smart home devices, status information such as trouble-free operation time, disconnection rate, response speed, network signal strength and device load, and device performance parameters such as data encryption strength, data transmission rate, task processing time, power factor, device power consumption and network delay;

[0021] The collected data is transmitted to the central controller of the smart home system via wireless communication technology (Wi-Fi), and the data is pre-processed, wherein the pre-processing operation includes the steps of data cleaning and data format conversion;

[0022] Integrate data from different time periods to form an equipment operation data table, including aggregating data from the same equipment at different time points and classifying data from different equipment, and store the relevant data from the equipment operation data table in the central database.

[0023] A further improvement of the technical solution of the present invention is that in the device abnormality classification module, the abnormality level classification process is as follows:

[0024] Collect historical data related to the operation of smart home devices in advance, including device status data and event data, and analyze them to extract abnormal data related to device safety and performance stability. Abnormal data is manifested as device performance degradation, frequent failures or abnormal energy consumption;

[0025] According to the historical abnormal data and system resource allocation results, and combined with the urgency of the abnormal data, formulate the threshold for distinguishing the abnormal level;

[0026] Based on the abnormality level distinction threshold, the abnormal conditions of smart home devices are classified into a slight abnormality level, a general abnormality level, and a severe abnormality level;

[0027] According to the classified abnormality levels, the allocation standards of system resources are set and recorded in the central database.

[0028] A further improvement of the technical solution of the present invention is that in the device abnormality judgment module, the abnormal situation identification process is:

[0029] Extract data related to the operation of smart home devices from the device operation data table, and determine the evaluation indicators for device abnormality judgment in combination with historical abnormal data, which are performance stability index and device safety index;

[0030] Analyze the performance stability index and determine its sub-indicators, including response speed, data transmission rate, and task processing time; and analyze the equipment safety index and determine its sub-indicators, including power factor, equipment power consumption, equipment load, and network delay;

[0031] Analyze the data of each sub-indicator in the performance stability index and equipment safety index, and set the abnormal threshold of each sub-indicator data in combination with historical abnormal data to distinguish normal data from abnormal data;

[0032] Based on the results of differentiating each sub-indicator of the performance stability index, the performance trend index is calculated to evaluate the operating efficiency and stability of the equipment, so as to analyze the development trend of the equipment performance;

[0033] Comprehensively distinguish the results of each sub-indicator of the equipment safety index, calculate the equipment safety trend index, evaluate the electrical safety and network security of the equipment, and analyze the potential risks of equipment safety;

[0034] According to the calculated performance trend index and device safety trend index, the abnormal situations existing in the performance stability index and device safety index of the smart home device are respectively identified, analyzed, and the sub-indicators with abnormalities are marked.

[0035] A further improvement of the technical solution of the present invention is that the calculation expression of the performance trend index is:

[0036] ;

[0037] in, is the performance trend index, is the response speed, which indicates the time it takes for the device to respond to user instructions. is the reference value of response speed, which is the preset target response speed. is the standard deviation of the response speed, indicating the fluctuation range of the response speed. is the task processing time, which indicates the time required for the device to complete a specific task. It is the benchmark value of task processing time, which is the preset standard task processing time. It is the maximum value of the task processing time, indicating the time required for the device to complete the task under the most unfavorable conditions. It is the root index, which is used to adjust the impact of task processing time on the performance trend index. It is the data transmission rate, which indicates the speed and efficiency of the device in the data transmission process. It is the maximum value of the data transmission rate, indicating the data transmission rate of the device under the most ideal conditions. The value range is between 0 and 1. The closer the value is to 1, the closer the performance of the device is to the ideal state. The closer the value is to 0, the worse the performance is.

[0038] The calculation expression of the equipment safety trend index is:

[0039] ;

[0040] in, is the equipment safety trend index, is the power factor, which is used to evaluate the energy efficiency and safety of equipment. It is the reference value of the power factor, indicating the power factor within the safe range. is the standard deviation of the power factor, indicating the normal fluctuation range of the power factor. is the device power consumption, which is used to evaluate the energy consumption of the device. It is the baseline value of the device power consumption, indicating the power consumption of the device in normal working state. is the equipment load, which is used to evaluate the current load of the equipment. It is the reference value of the equipment load, indicating the load of the equipment in normal working condition. It is the network delay, which is used to evaluate the delay of the device in network communication. It is the maximum allowable value of network delay, indicating the maximum delay threshold of network communication. The value range is between 0 and 1. The closer the value is to 1, the better the security status of the device is. The closer the value is to 0, the higher the security risk is.

[0041] A further improvement of the technical solution of the present invention is that: in the resource allocation evaluation module, the process of evaluating the current system resource allocation situation is:

[0042] Collect and summarize the evaluation results of performance stability index and equipment safety index, and then obtain the calculation results of performance trend index and equipment safety trend index, and analyze the overall performance and safety trend of the equipment;

[0043] Based on the needs of smart home device management, corresponding weights are set for the calculated performance trend index and device security trend index. The weight setting comprehensively considers factors such as the purpose and importance of the device and the user's security needs. The performance trend index and the device security trend index are weighted and integrated to obtain the abnormal evaluation coefficient. According to the size of the abnormal evaluation coefficient, the overall performance and security trend of the device are analyzed;

[0044] Combine the data of the abnormality assessment coefficient and the preset abnormality level classification standard to determine the abnormality level of the current abnormal situation, and combine the abnormality assessment coefficient and the determined abnormality level to evaluate the current allocation of system resources.

[0045] A further improvement of the technical solution of the present invention is that the calculation expression of the abnormal evaluation coefficient is:

[0046] ;

[0047] in, is the anomaly assessment coefficient, It is a performance trend index that reflects the development trend of equipment performance and measures the efficiency and stability of equipment performance. It is an equipment safety trend index, reflecting the potential risk of equipment safety and measuring the electrical safety and network security of the equipment. is the weight of the performance trend index, which is set according to the performance importance of the device. is the weight of the equipment safety trend index, which is set according to the safety importance of the equipment. The value range is between 0 and 1. The closer the value is to 1, the closer the performance and security of the device are to the ideal state. The closer the value is to 0, the worse the performance and security are.

[0048] The multiple abnormal levels are matched one by one with multiple distinction thresholds, specifically:

[0049] The threshold for distinguishing the slight abnormality level is: ;

[0050] The general abnormal level distinction threshold is: ;

[0051] The threshold for distinguishing the serious abnormality level is: ;

[0052] in, is the abnormal assessment coefficient, is the lower threshold of the slight abnormality level and the upper threshold of the general abnormality level, is the lower threshold of the general abnormality level and the upper threshold of the severe abnormality level, , .

[0053] A further improvement of the technical solution of the present invention is that: in the strategy optimization module, the process of dynamically adjusting the system resource allocation strategy is:

[0054] The evaluation results of the performance stability index and the equipment safety index of the equipment are obtained from the equipment abnormality judgment module, and combined with the abnormality evaluation coefficient and abnormality level obtained from the resource allocation evaluation module, it is analyzed whether the resource allocation strategy needs to be adjusted. If the current resource allocation is insufficient to cope with the abnormal situation, the resource allocation strategy is adjusted;

[0055] Based on the abnormality level and resource allocation assessment, the action plan is decided, resources are allocated to key equipment with severe abnormality levels first, and different processing processes are initiated according to the urgency of the abnormality, including alarm, notification of relevant personnel, automatic repair or manual intervention;

[0056] For serious anomalies, an alarm mechanism is triggered immediately to attract the attention of relevant personnel. The alarm is carried out through sound and light signals, SMS or application notifications, and provides details of the anomaly, recommended action steps and expected impact;

[0057] For abnormal situations that can be repaired automatically, execute the preset automatic repair process. For abnormal situations that require manual intervention, guide relevant personnel to repair them, monitor the execution and effect of the exception handling process, and collect feedback information to evaluate the effectiveness of the handling process.

[0058] A further improvement of the technical solution of the present invention is that the log recording module specifically includes:

[0059] Set the events and data to be recorded, including device operation status, abnormal judgment results, resource allocation evaluation and strategy optimization actions, and capture various events and operation data in the smart home device management system, and collect relevant information from the device abnormal judgment module, resource allocation evaluation module and strategy optimization module;

[0060] Record the captured data into log files in a predefined format, including timestamp, event type, device identification, anomaly level, resource allocation and policy adjustment details, and then store the log data in a central database to ensure the security and integrity of the log data and prevent unauthorized access;

[0061] Check log files regularly, archive and clean them to save storage space, and monitor the growth of log files to ensure that system performance is not affected by log records. Integrate log viewing function in the user interface to allow users to query and view device status and historical logs, and provide filtering and search functions to facilitate users to find logs for specific events or time periods.

[0062] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0063] 1. The present invention provides a distributed smart home device management system based on the Internet of Things. By monitoring the operating status of smart home devices, it can discover and respond to device anomalies. By utilizing the collaborative work of a device anomaly judgment module and a resource allocation evaluation module, when performance degradation or security vulnerabilities of smart home devices are detected, a resource reallocation strategy is triggered to ensure that key devices receive priority processing, thereby improving the response speed of the system, enabling device failures to be repaired in the shortest time, and reducing the service interruption time caused by device failures.

[0064] 2. The present invention provides a distributed smart home device management system based on the Internet of Things. By monitoring and analyzing the performance stability index and device safety index of the device, the operating efficiency and stability of the device can be improved, and the device abnormality judgment module can be used to perform real-time evaluation on the performance indicators such as device response speed and data transmission rate, so as to timely discover performance bottlenecks and abnormal situations, thereby taking corresponding optimization measures, and dynamically adjusting the resource allocation strategy through the resource allocation evaluation module to ensure that key devices can obtain sufficient resources to respond quickly under abnormal conditions, effectively avoiding the degradation of system performance due to insufficient resources, so that smart home devices can operate more efficiently and stably, improving user experience, and also reducing the failure rate and maintenance cost of the equipment.

[0065] 3. The present invention provides a distributed smart home device management system based on the Internet of Things. The performance stability index and the device safety index are weighted and integrated through the abnormal evaluation coefficient to obtain a comprehensive abnormal evaluation coefficient, so as to more accurately judge the abnormal level of the smart home device, so as to ensure that different processing processes are started according to the urgency of the abnormal situation, including automatic repair or manual intervention, to restore the normal operation of the device as quickly as possible, thereby maximizing the user's safety and the reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] 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.

[0067] Figure 1 It is a module diagram of the present invention;

[0068] Figure 2 is a flow chart for identifying abnormal situations of the present invention;

[0069] Figure 3 The present invention is a flow chart for evaluating the current system resource allocation situation. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] Embodiment 1, as Figure 1 , Figure 2 As shown, the present invention provides a distributed smart home device management system based on the Internet of Things, including a central controller, the central controller is communicatively connected with a device access module, a data acquisition module, a device abnormality classification module, a device abnormality judgment module, a resource allocation evaluation module, a strategy optimization module and a log recording module, wherein the modules are connected by electrical signals;

[0072] The device access module is used to access and identify smart home devices, including device registration, identity authentication and connection management, to ensure that all smart home devices can access the system safely and stably, and provide a basis for subsequent management. After the device access module is started, it uses the UPnP (Universal Plug and Play) network protocol to scan the network to discover smart home devices on the network. Users can complete the connection between smart home devices and the system by scanning the QR code on the smart home device or through Wi-Fi / Bluetooth pairing. Through UPnP, the device access module can identify and list all smart home devices on the network. Smart home devices send authentication requests to the central controller of the smart home device management system, including Contains device identification and authentication information. After receiving the authentication request, the system authenticates the smart home device according to the preset authentication mechanism and protocol. If the authentication is successful, the system will send a successful authentication response to the smart home device and assign corresponding access rights. The device access module requests the device to send its current status to ensure that the system has the latest device information. The system updates the device status information and synchronizes it to the central database. It monitors the connection status of the smart home device to ensure that the device is always online. When the smart home device is disconnected, it attempts to reconnect or notify the system administrator, and records the access logs of all devices, including access time, device information, and error or warning information;

[0073] The data acquisition module is used to collect the operation data of smart home devices, including status information and performance parameters, and pre-process the collected data to obtain the device operation data table, provide real-time and accurate data support, and collect data from smart home devices at preset time intervals, including the status information and performance parameters of smart home devices, collect the status information of the device's trouble-free operation time, disconnection rate, response speed, network signal strength and device load, and collect device performance parameters including data encryption strength, data transmission rate, task processing time, power factor, device power consumption and network delay, and transmit the collected data through wireless communication technology (Wi-Fi). Fi) to the central controller of the smart home system, and pre-process the data, wherein the pre-processing operation includes the steps of data cleaning and data format conversion, wherein the data cleaning removes duplicate data, invalid data and abnormal data, fills or interpolates missing data, converts the data into a unified format through data format conversion for subsequent processing and analysis, converts the timestamp into a standard time format, integrates data from different time periods to form a device operation data table, including aggregating data from the same device at different time points, and classifying data from different devices, and stores relevant data from the device operation data table in the central database;

[0074] The device abnormality classification module collects abnormal data related to the operation of smart home devices in advance, and classifies abnormalities according to the system resource allocation results of historical abnormal situations. It collects historical data related to the operation of smart home devices in advance, including device status data and event data, and analyzes them to extract abnormal data related to device safety and stable performance. The abnormal data is manifested as device performance degradation, frequent failures or abnormal energy consumption. According to the historical abnormal data and system resource allocation results, and combined with the urgency of the abnormal data, a threshold for distinguishing abnormal levels is formulated. Based on the threshold for distinguishing abnormal levels, the abnormal situations of smart home devices are graded into slight abnormality levels, general abnormality levels and severe abnormality levels. According to the divided abnormality levels, the allocation standards of system resources are set and recorded in the central database, which helps to track the process and results of abnormality handling and provide a reference for subsequent equipment maintenance and management;

[0075] The device abnormality judgment module determines the evaluation indicators of device abnormality judgment based on the device operation data table, which are performance stability indicators and device safety indicators, and evaluates the performance stability indicators and device safety indicators of smart home devices one by one to identify existing abnormal situations, extract data related to the operation of smart home devices from the device operation data table, and determine the evaluation indicators of device abnormality judgment in combination with historical abnormal data, which are performance stability indicators and device safety indicators, respectively. The performance stability indicator is analyzed to determine the sub-indicators of the performance stability indicator, including response speed, data transmission rate and task processing time, and the device safety indicator is analyzed to determine the sub-indicators of the device safety indicator, including power factor, device power consumption, device load and network delay. The response speed is used to evaluate the response time of the device to user instructions or external stimuli, the data transmission rate is used to evaluate the speed and efficiency of the device during data transmission, and the task processing time is used to evaluate the time required for the device to complete a specific task; the power factor is used to evaluate whether the power factor of the device is within a reasonable range to judge whether the electrical equipment Energy efficiency and safety, equipment power consumption is used to evaluate the energy consumption of the equipment to determine whether there is abnormal power consumption, equipment load is used to evaluate the current load of the equipment to determine whether the equipment is in an overloaded state, network delay is used to evaluate the delay of the equipment in network communication to determine the stability and security of the network connection, analyze the data of each sub-indicator in the performance stability index and the equipment safety index, and set the abnormal threshold of each sub-indicator data in combination with historical abnormal data to distinguish normal data from abnormal data, calculate the performance trend index based on the distinction results of each sub-indicator of the performance stability index, evaluate the operating efficiency and stability of the equipment, and analyze the development trend of the equipment performance, calculate the equipment safety trend index based on the distinction results of each sub-indicator of the equipment safety index, evaluate the electrical safety and network security of the equipment, and analyze the potential risks of equipment safety, and identify the abnormal conditions in the performance stability index and equipment safety index of smart home devices according to the calculated performance trend index and equipment safety trend index, analyze them, and mark the sub-indicators with abnormalities;

[0076] Furthermore, the calculation expression of the performance trend index is:

[0077] ;

[0078] in, is the performance trend index, is the response speed, which indicates the time it takes for the device to respond to user instructions. is the reference value of response speed, which is the preset target response speed. is the standard deviation of the response speed, indicating the fluctuation range of the response speed. is the task processing time, which indicates the time required for the device to complete a specific task. It is the benchmark value of task processing time, which is the preset standard task processing time. It is the maximum value of the task processing time, indicating the time required for the device to complete the task under the most unfavorable conditions. It is the root index, which is used to adjust the impact of task processing time on the performance trend index. It is the data transmission rate, which indicates the speed and efficiency of the device in the data transmission process. It is the maximum value of the data transmission rate, indicating the data transmission rate of the device under the most ideal conditions. The value range is between 0 and 1. The closer the value is to 1, the closer the performance of the device is to the ideal state. The closer the value is to 0, the worse the performance is. is a Sigmoid function used to convert the response speed into a value between 0 and 1. near When , the value is close to 0.5, Much greater than When Much smaller than When , the value is close to 0, Convert the task processing time into a weighted exponential form, The larger the value of , the greater the impact of task processing time on the performance trend index. Indicates that the data transmission rate is converted to a value between 0 and 1 using a logarithmic function. near When , the value is close to 1; when When it decreases, the value decreases;

[0079] The calculation expression of equipment safety trend index is:

[0080] ;

[0081] in, is the equipment safety trend index, is the power factor, which is used to evaluate the energy efficiency and safety of equipment. It is the reference value of the power factor, indicating the power factor within the safe range. is the standard deviation of the power factor, indicating the normal fluctuation range of the power factor. is the device power consumption, which is used to evaluate the energy consumption of the device. It is the baseline value of the device power consumption, indicating the power consumption of the device in normal working state. is the equipment load, which is used to evaluate the current load of the equipment. It is the reference value of the equipment load, indicating the load of the equipment in normal working condition. It is the network delay, which is used to evaluate the delay of the device in network communication. It is the maximum allowable value of network delay, indicating the maximum delay threshold of network communication. The value range is between 0 and 1. The closer the value is to 1, the better the security status of the device is. The closer the value is to 0, the higher the security risk is. is a Sigmoid function used to convert the power factor to a value between 0 and 1. near When , the value is close to 0.5, Much greater than When Much smaller than When , the value is close to 0, The part converts the equipment load into a value in radical form. near When Much greater than When , the value decreases, The logarithmic function is used to convert the network delay to a value between 0 and 1. near When When it decreases, the value increases;

[0082] The resource allocation assessment module evaluates the current allocation of system resources based on the assessment results of performance stability indicators and equipment safety indicators, and determines the abnormality level of the current abnormal situation;

[0083] The strategy optimization module dynamically adjusts the system resource allocation strategy based on the results of equipment anomaly judgment and resource allocation evaluation, and initiates the corresponding processing flow, including alarm, notification, repair, and optimization of resource allocation to ensure that key equipment can obtain sufficient resources to respond quickly in abnormal conditions;

[0084] The log recording module records the equipment operation status, equipment abnormality judgment, resource allocation evaluation and strategy optimization information, provides historical data support for system operation and maintenance and troubleshooting, improves the maintainability of the system, and provides a user interface for users to view equipment status, receive alarm information, and configure system parameters.

[0085] Embodiment 2, as Figure 3 As shown, based on Example 1, the present invention provides a technical solution: Preferably, in the resource allocation evaluation module, the process of evaluating the current system resource allocation situation is:

[0086] Collect and summarize the evaluation results of performance stability index and equipment safety index, and then obtain the calculation results of performance trend index and equipment safety trend index, analyze the overall performance and safety trend of the equipment, and set corresponding weights for the calculated performance trend index and equipment safety trend index based on the needs of smart home equipment management. The weight setting comprehensively considers factors such as the purpose and importance of the equipment and the safety needs of users, and performs weighted synthesis of the performance trend index and equipment safety trend index to obtain the abnormal evaluation coefficient. According to the size of the abnormal evaluation coefficient, analyze the overall performance and safety trend of the equipment, combine the data of the abnormal evaluation coefficient and the preset abnormal level classification standard, determine the abnormal level of the current abnormal situation, and combine the abnormal evaluation coefficient and the determined abnormal level to evaluate the current allocation of system resources. If the abnormal evaluation coefficient is high, it means that the equipment may have more serious performance or safety problems, which require more system resources to handle;

[0087] Furthermore, the calculation expression of the abnormal evaluation coefficient is:

[0088] ;

[0089] in, is the anomaly assessment coefficient, It is a performance trend index that reflects the development trend of equipment performance and measures the efficiency and stability of equipment performance. It is an equipment safety trend index, reflecting the potential risk of equipment safety and measuring the electrical safety and network security of the equipment. is the weight of the performance trend index, which is set according to the performance importance of the device. is the weight of the equipment safety trend index, which is set according to the safety importance of the equipment. The value range is between 0 and 1. The closer the value is to 1, the closer the performance and security of the device are to the ideal state. The closer the value is to 0, the worse the performance and security are. and When they are all very high, Close to 1, it means the overall condition of the device is good. or When any one of the will decrease, indicating that there are performance or safety issues with the device;

[0090] There are multiple thresholds for distinguishing multiple abnormal levels, which are:

[0091] The threshold for distinguishing the slight abnormality level is: ; No immediate increase in resources is required, but abnormal conditions need to be monitored to prevent them from deteriorating;

[0092] The general abnormal level distinction threshold is: ;Some resources need to be reallocated to address potential performance or security issues;

[0093] The thresholds for distinguishing severe abnormality levels are: ; Immediate action and significant resources are required to resolve the problem and prevent potentially serious consequences;

[0094] in, is the abnormal assessment coefficient, is the lower threshold of the slight abnormality level and the upper threshold of the general abnormality level, is the lower threshold of the general abnormality level and the upper threshold of the severe abnormality level, , ;

[0095] In the policy optimization module, the process of dynamically adjusting the system resource allocation strategy is as follows:

[0096] The evaluation results of the equipment's performance stability index and equipment safety index are obtained from the equipment anomaly judgment module, and combined with the anomaly evaluation coefficient and anomaly level obtained from the resource allocation evaluation module, it is analyzed whether the resource allocation strategy needs to be adjusted. If the current resource allocation is insufficient to cope with the abnormal situation, the resource allocation strategy is adjusted. According to the abnormal level and resource allocation evaluation, the action plan is decided, and resources are allocated to key equipment at the serious abnormal level first. According to the urgency of the abnormal situation, different processing procedures are initiated, including alarm, notification of relevant personnel, automatic repair or manual intervention. For serious abnormalities, the alarm mechanism is triggered immediately to attract the attention of relevant personnel. The alarm is carried out through sound and light signals, SMS or application notification, and the details of the abnormal situation, recommended action steps and expected impact are provided. For abnormal situations that can be automatically repaired, the preset automatic repair process is executed. For abnormalities that require manual intervention, the relevant personnel are guided to repair, and the execution and effect of the abnormal handling process are monitored, and feedback information is collected to evaluate the effectiveness of the processing process.

[0097] The logging module specifically includes:

[0098] Set the events and data to be recorded, including device operation status, abnormal judgment results, resource allocation evaluation and policy optimization actions, and capture various events and operation data in the smart home device management system, collect relevant information from the device abnormal judgment module, resource allocation evaluation module and policy optimization module, and record the captured data in the log file in a predefined format, including timestamp, event type, device identification, abnormal level, resource allocation status and policy adjustment details, and then store the log data in the central database to ensure the security and integrity of the log data and prevent unauthorized access. Check the log files regularly, archive and clean them to save storage space, monitor the growth of log files, ensure that system performance is not affected by log records, integrate the log viewing function in the user interface, allow users to query and view device status and historical logs, and provide filtering and search functions to facilitate users to find logs for specific events or time periods.

[0099] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A distributed smart home device management system based on the Internet of Things, including a central controller, characterized in that: The central controller is communicatively connected with a device access module, a data acquisition module, a device abnormality classification module, a device abnormality judgment module, a resource allocation evaluation module, a strategy optimization module and a log recording module, wherein electrical signals are connected between the modules; The device access module is used to access and identify smart home devices, including device registration, identity authentication and connection management; The data acquisition module is used to collect the operation data of the smart home devices, including status information and performance parameters, and pre-process the collected data to obtain a device operation data table; The device anomaly classification module collects abnormal data related to the operation of smart home devices in advance, and classifies the abnormality levels according to the system resource allocation results of historical abnormal situations; The device abnormality judgment module determines the evaluation indicators for device abnormality judgment based on the device operation data table, which are performance stability indicators and device safety indicators, and evaluates the performance stability indicators and device safety indicators of smart home devices one by one to identify the existing abnormal conditions; The resource allocation evaluation module evaluates the current allocation of system resources based on the evaluation results of the performance stability index and the equipment safety index, and determines the abnormality level of the current abnormal situation; The strategy optimization module dynamically adjusts the system resource allocation strategy according to the results of device abnormality judgment and resource allocation evaluation, and starts the corresponding processing flow; The log recording module records the device operation status, device abnormality judgment, resource allocation evaluation and strategy optimization information, and provides a user interface.

2. According to claim 1, a distributed smart home device management system based on the Internet of Things is characterized by: In the device access module, the process of accessing and identifying smart home devices is as follows: After the device access module is started, the UPnP network protocol is used to scan the network to discover smart home devices on the network. Users can complete the connection between smart home devices and the system by scanning the QR code on the smart home device or pairing through Wi-Fi / Bluetooth. The smart home device sends an authentication request to the central controller of the smart home device management system, which includes the device identification and authentication information. After receiving the authentication request, the system authenticates the smart home device according to the preset authentication mechanism and protocol. If the authentication is successful, the system will send a successful authentication response to the smart home device and assign corresponding access rights. The device access module requests the device to send its current status, and the system updates the device's status information and synchronizes it to the central database to monitor the connection status of smart home devices; When a smart home device is disconnected, try to reconnect or notify the system administrator, and record the access logs of all devices, including access time, device information, and error or warning messages.

3. A distributed smart home device management system based on the Internet of Things according to claim 2, characterized in that: In the data acquisition module, the process of obtaining the equipment operation data table is as follows: Collect data from smart home devices at preset time intervals, including status information and performance parameters of smart home devices, collect status information of devices’ trouble-free operation time, disconnection rate, response speed, network signal strength and device load, and collect device performance parameters including data encryption strength, data transmission rate, task processing time, power factor, device power consumption and network delay; The collected data is transmitted to the central controller of the smart home system through wireless communication technology, and the data is preprocessed, wherein the preprocessing operation includes the steps of data cleaning and data format conversion; Integrate data from different time periods to form an equipment operation data table, including aggregating data from the same equipment at different time points and classifying data from different equipment, and store the relevant data from the equipment operation data table in the central database.

4. The distributed smart home device management system based on the Internet of Things according to claim 3 is characterized by: In the device anomaly classification module, the process of anomaly classification is as follows: Collect historical data related to the operation of smart home devices in advance, including device status data and event data, and analyze them to extract abnormal data related to device safety and performance stability; According to the historical abnormal data and system resource allocation results, and combined with the urgency of the abnormal data, formulate the threshold for distinguishing the abnormal level; Based on the abnormality level distinction threshold, the abnormal conditions of smart home devices are classified into a slight abnormality level, a general abnormality level, and a severe abnormality level; According to the classified abnormality levels, the allocation standards of system resources are set and recorded in the central database.

5. A distributed smart home device management system based on the Internet of Things according to claim 4, characterized in that: In the device abnormality judgment module, the abnormality identification process is as follows: Extract data related to the operation of smart home devices from the device operation data table, and determine the evaluation indicators for device abnormality judgment in combination with historical abnormal data, which are performance stability index and device safety index; Analyze the performance stability index and determine its sub-indicators, including response speed, data transmission rate, and task processing time; and analyze the equipment safety index and determine its sub-indicators, including power factor, equipment power consumption, equipment load, and network delay; Analyze the data of each sub-indicator in the performance stability index and equipment safety index, and set the abnormal threshold of each sub-indicator data in combination with historical abnormal data to distinguish normal data from abnormal data; Based on the results of differentiating each sub-indicator of the performance stability index, the performance trend index is calculated to evaluate the operating efficiency and stability of the equipment, so as to analyze the development trend of the equipment performance; Comprehensively distinguish the results of each sub-indicator of the equipment safety index, calculate the equipment safety trend index, evaluate the electrical safety and network security of the equipment, and analyze the potential risks of equipment safety; According to the calculated performance trend index and device safety trend index, the abnormal situations existing in the performance stability index and device safety index of the smart home device are respectively identified, analyzed, and the sub-indicators with abnormalities are marked.

6. The distributed smart home device management system based on the Internet of Things according to claim 5 is characterized by: The calculation expression of the performance trend index is: ； in, is the performance trend index, is the response speed, is the reference value of response speed, is the standard deviation of the response speed, indicating the fluctuation range of the response speed. is the task processing time, is the benchmark value of task processing time, is the maximum value of task processing time, It is the root index, which is used to adjust the impact of task processing time on the performance trend index. is the data transfer rate, is the maximum value of the data transmission rate, The value range is between 0 and 1. The closer the value is to 1, the closer the performance of the device is to the ideal state. The calculation expression of the equipment safety trend index is: ； in, is the equipment safety trend index, is the power factor, is the reference value of power factor, is the standard deviation of the power factor, indicating the normal fluctuation range of the power factor. is the device power consumption, is the baseline value of the device power consumption, is the equipment load, is the base value of the equipment load, It's network delay. is the maximum allowed value of network delay, The value range is between 0 and 1. The closer the value is to 1, the better the security status of the device.

7. A distributed smart home device management system based on the Internet of Things according to claim 6, characterized in that: In the resource allocation evaluation module, the process of evaluating the current system resource allocation situation is as follows: Collect and summarize the evaluation results of performance stability index and equipment safety index, and then obtain the calculation results of performance trend index and equipment safety trend index, and analyze the overall performance and safety trend of the equipment; Based on the needs of smart home device management, corresponding weights are set for the calculated performance trend index and device security trend index, and the performance trend index and device security trend index are weighted and integrated to obtain the abnormal evaluation coefficient; Combine the data of the abnormality assessment coefficient and the preset abnormality level classification standard to determine the abnormality level of the current abnormal situation, and combine the abnormality assessment coefficient and the determined abnormality level to evaluate the current allocation of system resources.

8. The distributed smart home device management system based on the Internet of Things according to claim 7 is characterized by: The calculation expression of the abnormal evaluation coefficient is: ； in, is the anomaly assessment coefficient, is the performance trend index, is the equipment safety trend index, is the weight of the performance trend index, is the weight of the equipment safety trend index, The value range is between 0 and 1. The closer the value is to 1, the closer the performance and security of the device are to the ideal state. The multiple abnormal levels are matched one-to-one with multiple distinction thresholds, specifically: The threshold for distinguishing the slight abnormality level is: ; The general abnormal level distinction threshold is: ; The threshold for distinguishing the serious abnormality level is: ; in, is the abnormal assessment coefficient, is the lower threshold of the slight abnormality level and the upper threshold of the general abnormality level, is the lower threshold of the general abnormality level and the upper threshold of the severe abnormality level, , .

9. A distributed smart home device management system based on the Internet of Things according to claim 8, characterized in that: In the policy optimization module, the process of dynamically adjusting the system resource allocation strategy is as follows: The evaluation results of the performance stability index and the equipment safety index of the equipment are obtained from the equipment abnormality judgment module, and combined with the abnormality evaluation coefficient and abnormality level obtained from the resource allocation evaluation module, it is analyzed whether the resource allocation strategy needs to be adjusted. If the current resource allocation is insufficient to cope with the abnormal situation, the resource allocation strategy is adjusted; Based on the abnormality level and resource allocation assessment, the action plan is decided, resources are allocated to key equipment with severe abnormality levels first, and different processing processes are initiated according to the urgency of the abnormality, including alarm, notification of relevant personnel, automatic repair or manual intervention; For serious anomalies, an alarm mechanism is triggered immediately to attract the attention of relevant personnel. The alarm is carried out through sound and light signals, SMS or application notifications, and provides details of the anomaly, recommended action steps and expected impact; For abnormal situations that can be repaired automatically, execute the preset automatic repair process. For abnormal situations that require manual intervention, guide relevant personnel to repair them, monitor the execution and effect of the exception handling process, and collect feedback information to evaluate the effectiveness of the handling process.

10. A distributed smart home device management system based on the Internet of Things according to claim 9, characterized in that: The logging module specifically includes: Set the events and data to be recorded, including device operation status, abnormal judgment results, resource allocation evaluation and strategy optimization actions, and capture various events and operation data in the smart home device management system, and collect relevant information from the device abnormal judgment module, resource allocation evaluation module and strategy optimization module; Record the captured data into log files in a predefined format, including timestamp, event type, device identification, anomaly level, resource allocation and policy adjustment details, and then store the log data in a central database; Check log files regularly, archive and clean them, monitor the growth of log files, and integrate log viewing function in the user interface to allow users to query and view device status and historical logs.

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