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 the Internet of Things, the system comprises a cloud management center, and the cloud management center is in communication connection with the following modules: an equipment resource sensing module, which is used for monitoring an equipment resource state in real time to form an equipment resource state diagram, ant behaviors are simulated to release pheromones, and the pheromone concentration is dynamically adjusted to guide task allocation; and the task scheduling distribution engine is used for analyzing pheromone concentration and global load based on an ant colony algorithm. According to the method, a pheromone mechanism is simulated through the ant colony algorithm, the equipment resource state is monitored in real time, task allocation is dynamically adjusted, cloud global monitoring is combined with volatilization rate regulation and control, tasks are forced to migrate to light-load equipment, uniform load distribution is ensured, hardware loss caused by long-term high load of the equipment is effectively reduced, the service life of the equipment is prolonged, and the service life of the equipment is prolonged. Meanwhile, task failures caused by resource competition are reduced, and the overall reliability of the system is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and more specifically to a distributed smart home device management system based on IoT. Background Technology

[0002] Modern families are increasingly reliant on smart home devices, such as smart lights, air conditioners, security systems, and smart door locks. These devices provide users with convenient remote control and automation functions. As the smart home ecosystem matures, the number of connected smart devices in a single household has surged from a few to dozens. Traditional centralized management systems struggle to cope with the concurrent connections, data processing, and real-time response needs of a massive number of devices. Therefore, how to efficiently manage and coordinate these devices has become a challenge.

[0003] In existing technologies, tasks in distributed smart home device systems are typically handled by fixed devices, leading to overload on some devices while others remain idle. This shortens device lifespan and reduces system reliability. Therefore, the problem this invention aims to solve is how to use an ant colony algorithm to treat devices as ants, releasing pheromones based on their remaining resources and task priorities. High-resource devices attract more tasks, while low-resource devices receive fewer tasks. Furthermore, by monitoring the global load in the cloud and adjusting the pheromone evaporation rate, tasks can be forced to migrate to lightly loaded devices, avoiding single-point overload and extending device lifespan. To achieve load-balanced task allocation, this invention proposes a distributed smart home device management system based on the Internet of Things. Summary of the Invention

[0004] The purpose of this invention is to provide a distributed smart home device management system based on the Internet of Things (IoT) to solve the problems mentioned in the background.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0006] A distributed smart home device management system based on the Internet of Things (IoT) includes a cloud management center, which is communicatively connected to the following modules, wherein:

[0007] The equipment resource sensing module is used to monitor the equipment resource status in real time, generate an equipment resource status map, simulate ant behavior to release pheromones, and dynamically adjust the pheromone concentration to guide task allocation.

[0008] The task scheduling and allocation engine analyzes pheromone concentration and global load based on the ant colony algorithm to perform optimal matching between tasks to be assigned and smart home devices.

[0009] A global load analysis module is used for aggregating resource data of all smart home devices in a cloud management center, identifying overload devices and light load devices, generating a load heat map, providing a decision basis for pheromone evaporation rate adjustment, and preventing single point failure;

[0010] A pheromone evaporation control module is used for dynamically adjusting the pheromone evaporation rate (overload devices accelerate evaporation, and light load devices slow down evaporation) according to the global load analysis result, outputting a task allocation instruction, forcing task migration to low load devices, breaking local congestion through forced intervention, and achieving global load balancing.

[0011] A communication protocol adaptation module is used for compatible with Wi-Fi, Zigbee, Bluetooth and other different smart home device communication protocols, and optimizing the transmission path of the task allocation instruction to reduce communication delay.

[0012] The technical scheme of the present application is further improved in that the device resource perception module includes a device resource monitoring unit and a pheromone dynamic release unit.

[0013] The device resource monitoring unit is used for collecting resource data of smart home devices in real time through sensors and edge computing nodes, and uploading the data to the cloud management center to form a device resource state diagram, thereby avoiding task overload caused by resource blind area.

[0014] The pheromone dynamic release unit generates pheromones according to the remaining resources of smart home devices (high resource devices release strong pheromones), and controls task migration through evaporation rate to dynamically adjust pheromone concentration to guide task allocation.

[0015] The technical scheme of the present application is further improved in that the device resource monitoring unit forms a device resource state diagram in the following process:

[0016] The embedded sensors and edge computing nodes deployed on each smart home device are connected to the cloud management center in real time, and resource data including CPU usage, memory remaining amount, bandwidth occupation and power remaining value of each smart home device are collected at a set period.

[0017] The edge computing node pre-processes the collected raw resource data, filters invalid and error data, classifies and aggregates valid data, reduces data transmission volume, improves data quality, marks time stamp and device ID, and detects the online state of the device.

[0018] The preprocessed resource data is uploaded to the cloud management center after being encrypted, the cloud management center stores the resource data in the device resource database after receiving, forms a device resource state diagram, and analyzes the device online state, if the device is disconnected or the data is not updated due to timeout, the cloud management center marks the state as unavailable, avoids subsequent task allocation to the device, and prevents the overload risk caused by the resource blind area.

[0019] Further improvement of the technical scheme of the application is that in the pheromone dynamic release unit, the process of dynamically adjusting the pheromone concentration to guide task allocation is:

[0020] According to the real-time collected resource data of the smart home device, the device resource consumption rate is analyzed based on the remaining resource amount, and the current load state of the device is calculated, the high resource device generates high concentration pheromone, and the low resource device generates low concentration pheromone, forming an initial pheromone distribution map;

[0021] A preset volatilization rate function is combined with the device resource consumption rate to dynamically attenuate the pheromone concentration: the high-load device accelerates the volatilization of pheromone to reduce the task attraction, and the low-load device slows down the volatilization to maintain the task acceptance ability, and the real-time matching of pheromone concentration and resource state is carried out;

[0022] According to the pheromone concentration gradient, the task is allocated to the high concentration area (resource sufficient device), and the task migration is triggered by the concentration change, when the device resource decreases and the pheromone concentration is lower than the concentration threshold, the task is automatically transferred to other high concentration devices, and the system load balancing is ensured.

[0023] Further improvement of the technical scheme of the application is that the task scheduling and distribution engine includes a task priority evaluation unit and an ant colony optimization distribution unit;

[0024] The task priority evaluation unit is used to divide the priority according to the task urgency and resource demand, and determine the allocation relationship between the to-be-allocated task and the device in combination with the device resource state diagram, so as to ensure that the high-priority task is preferentially allocated to the high-resource device;

[0025] The ant colony optimization distribution unit is used to simulate the ant foraging behavior, the task selects the smart home device according to the pheromone concentration (the high concentration path is preferentially selected), and adjusts the distribution strategy in combination with the feedback of the cloud management center, so as to realize load balancing and avoid single point overload.

[0026] Further improvement of the technical scheme of the application is that in the task priority evaluation unit, the process of determining the allocation relationship between the to-be-allocated task and the device in combination with the device resource state diagram is:

[0027] Receiving a task to be allocated, analyzing its metadata, extracting the task urgency and resource requirement, combining the preset rules of the task urgency to divide the task to be allocated into high, medium and low three levels, and forming the priority order of the task queue to be allocated;

[0028] According to the task type and resource requirement, the resource consumption is estimated, the current device resource state diagram is scanned, the candidate device set meeting the task resource requirement is screened out, and whether the current load state of the device meets the minimum execution requirement of the task is verified, and the device not meeting the condition is filtered out;

[0029] Combined with the priority order result and the candidate device set, the high priority task is bound to the device with sufficient resources and high stability, and the device resource state diagram and the task queue state are updated.

[0030] The further improvement of the technical scheme of the application is that, in the ant colony optimization allocation unit, the process of adjusting the allocation strategy combined with the feedback of the cloud management center is:

[0031] The pheromone concentration data of each smart home device is acquired in real time, a virtual path network with smart home devices as nodes and pheromone concentration as edge weight is constructed, the target device is selected according to the current path network state and the pheromone concentration probability, the initial allocation tendency is formed, and the historical allocation times of each device are recorded to avoid local path over-concentration;

[0032] The cloud management center continuously monitors the device resource state, compares the matching degree of pheromone concentration and resource remaining amount, if it is found that the pheromone concentration is too high but the device is actually overloaded, the volatilization rate acceleration mechanism is triggered to forcibly reduce the attraction of the path, otherwise, for the low-load high-resource device, the pheromone volatilization is slowed down, and the task capture ability is enhanced, so that the dynamic balance of the path weight is realized;

[0033] Based on the periodic global load analysis result, the ant colony algorithm adjusts the pheromone update rule, that is, after the task is successfully executed, the path pheromone is weighted and enhanced according to the resource consumption efficiency, if the task is migrated due to device overload, the original path pheromone is punished and weakened, through positive and negative feedback cycles, gradually converging to the load balancing state, and continuously adapting to the dynamic change of device resources.

[0034] The further improvement of the technical scheme of the application is that, in the global load analysis module, the process of identifying the overloaded device and the light load device and generating the load thermal map is:

[0035] The cloud management center calls the resource data of all smart home devices, constructs a global resource state matrix, and divides the device load state combined with the preset load threshold, identifies the overloaded device and the light load device;

[0036] The load heat map is generated in combination with the spatial topological relationship, the heat map directly displays the global load distribution by using a color gradient, marks potential overload risk points and idle resource clusters, forms an abnormal area mapping table, and is updated to the pheromone volatilization control module.

[0037] The further improvement of the technical scheme of the present application is that in the pheromone volatilization control module, the process of outputting the task allocation instruction and forcing the task to migrate to the low-load device is as follows:

[0038] The cloud management center receives the global load analysis result, combines the load heat map and the abnormal area mapping table, analyzes the device load state, and dynamically calculates the volatilization rate according to the preset rule, wherein the overload device accelerates volatilization by a factor of two, the light-load device slows down volatilization by a factor of 0.5, and the normal device maintains a baseline value.

[0039] Each smart home device updates the local pheromone concentration according to the adjusted volatilization rate, wherein the overload device accelerates volatilization, the pheromone concentration decreases rapidly, and the task attraction decreases; the light-load device slows down volatilization, maintains a high concentration to attract tasks, and synchronizes the local pheromone concentration data to the cloud management center to form the latest path weight distribution.

[0040] When the pheromone concentration of a certain smart home device is lower than a set threshold, the cloud management center triggers a task migration instruction to reassign its pending tasks to a high-concentration (i.e., low-load) device, and after the migration is completed, the pheromone concentration of the original device is reset to 50% of the baseline value, thereby breaking the local congestion through forced intervention, ensuring that the task flows to a resource-rich node, and ultimately achieving global load balancing.

[0041] The further improvement of the technical scheme of the present application is that in the communication protocol adaptation module, the process of optimizing the transmission path of the task allocation instruction is as follows:

[0042] The communication protocol adaptation module identifies the communication protocol type (Wi-Fi / Zigbee / Bluetooth, etc.) of the access smart home device through data analysis of the device registration information, and unifies the data format into a standardized message based on the communication protocol specification, ensuring the compatibility of the cloud instruction and the device data structure.

[0043] Real-time monitoring of network topology and device state, combined with communication protocol characteristics to build a multi-dimensional path evaluation model, taking transmission delay and energy consumption as indicators, calculating path scores for all available transmission paths, dynamically selecting the optimal transmission path to minimize the transmission delay of the task allocation instruction.

[0044] For the selected path, the transmission parameters are adjusted according to the protocol characteristics of the communication protocol, the data packet retransmission strategy and the flow control are optimized, the instruction receiving state is confirmed through the bidirectional handshake, the path selection algorithm is dynamically calibrated in combination with the real-time delay feedback, and a closed-loop optimization mechanism is formed.

[0045] Due to the adoption of the above technical solutions, the present application has the following technical progress compared with the prior art:

[0046] 1. The present application provides a distributed intelligent home device management system based on the Internet of Things, which simulates the pheromone mechanism through the ant colony algorithm, monitors the device resource state in real time and dynamically adjusts task allocation, and uses cloud global monitoring combined with evaporation rate regulation to force task migration to lightly loaded devices, ensuring uniform load distribution, effectively reducing hardware wear and tear caused by long-term high load, prolonging device service life, and reducing task failures caused by resource competition, significantly improving the overall reliability of the system.

[0047] 2. The present application provides a distributed intelligent home device management system based on the Internet of Things, which divides priorities according to task urgency and resource demand, and allocates high-priority tasks to high-resource devices first, dynamically filters candidate devices and binds the optimal node to ensure immediate response to critical tasks, and introduces a task degradation mechanism to avoid high-priority tasks being blocked due to insufficient resources, optimizing resource utilization and meeting real-time requirements of diversified scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0049] Figure 1 The working flowchart of the present application is a distributed intelligent home device management system based on the Internet of Things.

[0050] Figure 2 The system function module schematic diagram of the present application is a distributed intelligent home device management system based on the Internet of Things. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0052] Embodiment 1, as shown in Figure 1 、 Figure 2 The application provides a distributed smart home device management system based on Internet of Things, comprising a cloud management center, the cloud management center being communicatively connected with the following modules, wherein:

[0053] The cloud management center serves as a global coordination core, aggregates device resource data, task scheduling strategies and load analysis results, and issues control instructions to devices;

[0054] A device resource perception module is configured to monitor the device resource state in real time to form a device resource state diagram, simulate ant behavior to release pheromones, and dynamically adjust the pheromone concentration to guide task allocation. The device resource perception module comprises a device resource monitoring unit and a pheromone dynamic release unit.

[0055] The device resource monitoring unit is configured to collect resource data of smart home devices in real time through sensors and edge computing nodes, and upload the resource data to the cloud management center to form a device resource state diagram, so as to avoid task overload caused by resource blind areas. The embedded sensors and edge computing nodes deployed on each smart home device are connected with the cloud management center in real time, and resource data including CPU usage, memory remaining amount, bandwidth occupation and power remaining value of each smart home device are collected at a set period. The edge computing node pre-processes the collected original resource data, filters invalid and error data, classifies and aggregates the valid data, reduces data transmission volume, improves data quality, marks time stamp and device ID, and detects the online state of the device. The pre-processed resource data is uploaded to the cloud management center after encryption. The cloud management center stores the resource data in a device resource database after receiving the resource data, forms a device resource state diagram, and analyzes the device online state. If the device is disconnected or the data is overdue and not updated, the cloud management center marks the state as unavailable, avoids subsequent task allocation to the device, and prevents overload risk caused by resource blind areas.

[0056] The device resource monitoring unit specifically works as follows: the device resource monitoring unit collects dynamic resource data of the device in real time through the embedded sensor and edge computing node deployed on the smart home device, including CPU usage, memory remaining amount, bandwidth occupation, and power remaining value. The sensor samples at a fixed period to ensure the timeliness of the data, and the edge computing node receives the raw data and performs preliminary verification to eliminate outliers. The collected data is classified by device type and resource category, and is attached with accurate timestamps and device IDs (unique identifiers) for precise matching and management by the cloud. The edge computing node pre-processes the collected resource data locally to reduce the computing pressure and communication overhead of the cloud. The pre-processing process includes data cleaning, data aggregation, and data compression. The processed data is packaged in a standardized format and encrypted using a lightweight encryption algorithm (AES-128) to ensure secure transmission. At the same time, the edge computing node detects the online status of the device and triggers local alarms and records abnormal logs if data reporting is overdue or communication is interrupted. The pre-processed resource data is uploaded to the cloud management center through a secure channel, where it is stored persistently and managed. The cloud management center forms a device resource state diagram and verifies the integrity and timeliness of the data. If a device does not report data within a set time window, the cloud management center marks it as "unavailable" and temporarily removes it from the current list of schedulable devices to avoid assigning tasks to offline or faulty devices.

[0057] The pheromone dynamic release unit generates pheromones based on the remaining resources of the smart home device (high-resource devices release strong pheromones) and controls task migration through evaporation rate to dynamically adjust the pheromone concentration and guide task allocation (high-resource devices release high-concentration pheromones, and low-resource devices reduce the release amount). Based on the real-time collected resource data of the smart home device, the device resource consumption rate is analyzed based on the remaining resource amount, and the current load state of the device is calculated. High-resource devices generate high-concentration pheromones, and low-resource devices generate low-concentration pheromones to form an initial pheromone distribution map. A pre-set evaporation rate function is combined with the device resource consumption rate to dynamically attenuate the pheromone concentration: high-load devices accelerate pheromone evaporation to reduce task attractiveness, and low-load devices slow down evaporation to maintain task handling capacity. Real-time matching of pheromone concentration and resource state is performed, and tasks are allocated to high-concentration areas (resource-rich devices) based on the pheromone concentration gradient. Task migration is triggered by changes in concentration, and when the device resource decreases and the pheromone concentration falls below the concentration threshold, the task is automatically transferred to other high-concentration devices to ensure system load balancing.

[0058] The pheromone dynamic release unit specifically works as follows: based on the real-time collected smart home device resource data, the device resource consumption rate is analyzed taking the resource remaining amount as the core index, and the current load state of the device is calculated, and the smart home device is divided into two categories of high resource and low resource, wherein the high resource device generates high concentration pheromone, indicating its strong task undertaking capability, and the low resource device generates low concentration pheromone, reflecting its resource shortage state, through the mapping relationship between the quantitative resource remaining amount and the pheromone concentration, an initial pheromone distribution map covering all devices is formed, reflecting the real-time resource state of the device; a preset volatilization rate function is combined with the device resource consumption rate to real-time decay the pheromone concentration, for the high load device, because its resource is consumed rapidly, leading to the decline of the task undertaking capability, the system accelerates the pheromone volatilization (such as increasing the volatilization coefficient), and reduces the attraction to the task; for the low load device, because its resource is idle or consumed slowly, the system slows down the pheromone volatilization, maintains its high concentration state to undertake more tasks, during the adjustment process, the device resource change is continuously monitored, and the volatilization rate parameter is dynamically corrected, so that the pheromone concentration is strictly matched with the actual resource state; according to the device resource consumption rate, the pheromone concentration gradient is divided, and the target device is selected based on the pheromone concentration gradient, the task is preferentially distributed to the device node with the highest concentration, when the device resource is continuously consumed, leading to the pheromone concentration being lower than the set concentration threshold, a task migration mechanism is triggered, the cloud management center generates a migration instruction, and the to-be-processed task of the device is re-distributed to the current high concentration device, so that the overall load balance of the system is ensured, wherein the dynamic change of the pheromone concentration forms a positive feedback loop: the device with sufficient resources continuously attracts tasks through high concentration, and the device with insufficient resources gradually exits the task competition due to concentration decay, and finally the global load is adaptively balanced;

[0059] The calculation expression of the resource remaining amount is as follows:

[0060] ;

[0061] In the formula: is the comprehensive resource remaining rate (normalized to [0, 1]) of the device at time , is the number of resource types, ; is the resource type index, wherein CPU=1, memory=2, bandwidth=3, and electric quantity=4; is the weight of the resource , is the remaining amount of the resource of the device at time , is the total amount of the resource of the device ;

[0062] The calculation expression of the device resource consumption rate is as follows:

[0063] ;

[0064] In the formula: is the resource consumption rate of the device at time (1 / s), is the sampling time interval (default 5s);

[0065] The calculation expression of the current load state of the device is as follows:

[0066] ;

[0067] In the formula: is the load state of the device at time , is the high resource threshold value (default 0.6, i.e., remaining 60%), is the low resource threshold value (default 0.3), is the low consumption rate threshold value (default 0.002 / s, i.e., 0.2% / s), is the high consumption rate threshold value (default 0.005 / s);

[0068] The calculation expression of the pheromone concentration is as follows:

[0069] ;

[0070] In the formula: is the pheromone concentration of the device at time , is the basic concentration value (default 50), is the gain coefficient of the resource remaining amount (default 1.2), is the penalty coefficient of the consumption rate (default 10, unit: s);

[0071] The calculation expression of the volatilization rate function is as follows:

[0072] ;

[0073] In the formula: is the pheromone volatilization rate of the device at time (1 / s), is the basic volatilization rate (default 0.1 / s), is the volatilization sensitivity coefficient (default 50, controlling the intensity of the exponential change of the rate with the consumption rate), For reference consumption rate (default 0.003 / s), the evaporation rate is the base value;

[0074] The task scheduling distribution engine analyzes pheromone concentration and global load based on an ant colony algorithm to perform optimal matching of the to-be-distributed tasks and the smart home devices, and includes a task priority evaluation unit and an ant colony optimization distribution unit.

[0075] The task priority evaluation unit is configured to divide priorities according to task urgency and resource demand, determine the allocation relationship between the to-be-distributed tasks and the devices in combination with a device resource state diagram, ensure that high-priority tasks are preferentially allocated to high-resource devices, receive the to-be-distributed tasks, analyze metadata thereof, extract task urgency and resource demand, divide the to-be-distributed tasks into three levels of high, medium and low in combination with preset rules of the task urgency, form a priority order of the to-be-distributed task queue, estimate resource consumption of the tasks according to the task type and the resource demand, scan the current device resource state diagram, filter out candidate device sets that meet the task resource demand, filter out devices that do not meet the conditions, and bind high-priority tasks to devices that are rich in resources and high in stability in combination with the priority order result and the candidate device sets, and update the device resource state diagram and the task queue state.

[0076] The task priority evaluation unit specifically performs the following operations: receiving metadata of a task to be allocated, and parsing key attributes in the metadata, including task urgency and resource requirement type. The task urgency is divided into three types of high, medium and low urgency tasks according to a preset rule. The high urgency task corresponds to a first priority, the medium urgency task corresponds to a second priority, and the low urgency task corresponds to a third priority. A preliminary order of a task queue to be allocated is formed. The high urgency task has a first priority. The determination condition is that any one of time sensitivity, safety influence and system criticality is met. The time sensitivity is that the response delay is required to be less than or equal to 100 ms. The safety influence is related to personal safety or equipment safety. The system criticality is core service interruption. The medium urgency task has a second priority. The determination condition is that the delay is allowed to be 500 ms to 2 s, a non-core function but affecting user experience, and a single device needs to occupy more than 30% of CPU or memory. The low urgency task has a third priority. The determination condition is that the time sensitivity delay tolerance is greater than or equal to 5 s, the resource requirement is low resource occupation (CPU < 10%), and the task can be suspended or retried. After the priority sorting is completed, the computing amount, memory occupation and network bandwidth index required for execution of the task are estimated according to the resource requirement of the task. At the same time, a real-time updated device resource state diagram is scanned, and a candidate device set satisfying the minimum requirement of the task in a current resource remaining amount is selected. In the selection process, the load state of the device is checked, and the device with insufficient resources or poor stability is excluded. A device pool with high availability and meeting the task resource constraint is established. The task is allocated by combining the task priority sorting result and the candidate device set, and by using the strategy of matching high-priority tasks with high-availability devices first. The task with the highest priority is selected from the head of the task queue to be allocated, and the device with the most sufficient resource remaining amount and the highest stability score is selected for the task. After the binding is successful, the device resource state diagram is immediately updated, the resource amount occupied by the device is deducted, and the load state of the device is marked as execution. At the same time, the task is moved from the task queue to be allocated to an execution queue, and the state information of the start time and the estimated completion time of the task is recorded. If there is no suitable device for the high-priority task, task degradation processing is triggered to ensure that the key task is not blocked.

[0077] The ant colony optimization distribution unit is used for simulating the ant pathfinding behavior, the task selects the smart home device according to the pheromone concentration (the high concentration path is preferentially selected), the distribution strategy is adjusted in combination with the feedback of the cloud management center, load balancing is realized, single point overload is avoided, the pheromone concentration data of each smart home device is obtained in real time, the virtual path network taking the smart home device as a node and the pheromone concentration as an edge weight is constructed, the target device is selected according to the current path network state according to the pheromone concentration proportional probability, the initial distribution tendency is formed, at the same time, the system records the historical distribution times of each device, the local path is avoided from being excessively concentrated, the cloud management center continuously monitors the device resource state, the matching degree of the pheromone concentration and the resource remaining amount is compared, if it is found that the pheromone concentration is excessively high but the device is actually overloaded, the volatilization rate acceleration mechanism is triggered, the attraction of the path is forced to be reduced, on the contrary, for the low load high resource device, the pheromone volatilization is slowed down, the task capture ability is enhanced, the dynamic balance of the path weight is realized, based on the periodic global load analysis result, the ant colony algorithm adjusts the pheromone update rule, namely after the task is successfully executed, the path pheromone is weighted and enhanced according to the resource consumption efficiency, if the task is migrated due to device overload, the original path pheromone is punished and weakened, through the positive and negative feedback cycle, the load balancing state is gradually converged, and the dynamic change of the device resource is continuously adapted;

[0078] The specific working content of the ant colony optimization distribution unit is: taking smart home devices as nodes, through real-time collection of pheromone concentration data of each device, a dynamic virtual path network is constructed, in which the pheromone concentration between devices is used as an edge weight to reflect the potential attractiveness of task allocation. When the to-be-allocated task arrives, the system selects the target device based on the current path network state and the pheromone concentration proportional probability to form an initial allocation tendency. In order to avoid excessive concentration of local paths (i.e. some devices are frequently selected due to pheromone accumulation), a historical allocation frequency recording mechanism is introduced to dynamically attenuate the pheromone concentration of high-frequency allocation devices, suppress short-sighted selection behavior, and ensure that task allocation remains dispersed in the global range. The cloud management center continuously monitors the device resource state and performs real-time matching degree analysis on the pheromone concentration in the path network. If it is found that the pheromone concentration of a device is too high (i.e. the path attractiveness is strong but the actual resources are overloaded), the evaporation rate acceleration mechanism is triggered to forcibly reduce the weight of the path by increasing the pheromone evaporation coefficient, thereby weakening its task capture ability and preventing task failure due to resource exhaustion. Conversely, for devices with low load and sufficient resources, the pheromone evaporation rate is slowed down to enhance the path attractiveness by additional deposition, guiding the task to migrate to high-availability devices, ensuring that the pheromone concentration is strongly related to the actual resource state of the device, avoiding the disconnection between path weight and device capability, and improving the rationality of task allocation. Based on the periodic global load analysis results, the ant colony algorithm dynamically adjusts the pheromone update rule to form a positive and negative feedback loop. After the successful execution of a task, the system performs weighted enhancement on the path pheromone according to the resource consumption efficiency. If the task migrates due to device overload, the original path pheromone is punished and weakened to reduce the probability of being selected in the future, so that the path weight gradually converges to a load-balanced state, i.e. high-resource devices obtain more task allocation and overloaded devices automatically exit the competition, continuously adapting to the dynamic changes of device resources, optimizing the pheromone distribution through iteration, ensuring that task allocation always matches the real-time state of the system, and achieving long-term stable load balancing and maximum resource utilization.

[0079] A global load analysis module is configured to aggregate resource data of all smart home devices at the cloud management center, identify overloaded devices and lightly loaded devices, generate a load heat map, and provide a decision basis for pheromone evaporation rate adjustment to prevent single-point failures.

[0080] A pheromone evaporation control module is configured to dynamically adjust the pheromone evaporation rate (overloaded devices accelerate evaporation and lightly loaded devices slow down evaporation) based on the global load analysis results, output task allocation instructions, and forcibly migrate tasks to low-load devices to break local congestion and achieve global load balancing.

[0081] A communication protocol adaptation module is configured to be compatible with different smart home device communication protocols such as Wi-Fi, Zigbee, Bluetooth, etc., and to optimize the transmission path of task allocation instructions to reduce communication delay.

[0082] As shown in Embodiment 2, based on Embodiment 1, the application provides a technical solution: preferably, in the global load analysis module, the process of identifying overloaded devices and lightly loaded devices and generating a load heat map is as follows: Figure 1 、 Figure 2 As shown in Embodiment 2, based on Embodiment 1, the application provides a technical solution: preferably, in the global load analysis module, the process of identifying overloaded devices and lightly loaded devices and generating a load heat map is as follows:

[0083] The cloud management center calls the resource data of all smart home devices, constructs a global resource state matrix, and combines the preset load threshold to divide the device load state, identify overloaded devices and lightly loaded devices, generate a load heat map in combination with the spatial topological relationship, and intuitively display the global load distribution with a color gradient, mark potential overload risk points and idle resource clusters, form an abnormal area mapping table, and update to the pheromone evaporation control module.

[0084] The specific working content of the global load analysis module is as follows: the cloud management center aggregates the resource data of all smart home devices in real time, constructs a global resource state matrix, the matrix takes devices as rows and resource indicators as columns, and each element represents the current resource utilization rate of a specific device. Based on the preset load threshold, the system automatically classifies the device load state, divides the devices into three categories of overload (resource shortage), normal (resource balance), and light load (resource idle), and generates a binary label vector. At the same time, the system records the historical load trend. Based on the device load classification result and the spatial topological relationship, the system generates a load heat map, which takes geographic information system (GIS) as the base map and intuitively displays the load distribution through a color gradient (red-yellow-green). Among them, the red area represents an overloaded device, the yellow area represents a lightly loaded device, and the green area represents a normal device. Through cluster analysis, the system automatically identifies potential overload risk points and idle resource clusters and marks abnormal areas. The marking result is stored in the form of a mapping table of device ID and state label, forming an abnormal area mapping table, which is updated to the pheromone evaporation control module in real time.

[0085] In the pheromone evaporation control module, the process of outputting task allocation instructions to force task migration to low-load devices is as follows:

[0086] The cloud management center receives the global load analysis result, combines the load heat map and the abnormal area mapping table, analyzes the device load state, and dynamically calculates the evaporation rate according to the preset rule, wherein the overload device accelerates evaporation, which is twice the rate coefficient; the light load device slows down evaporation, which is 0.5 times the rate coefficient x 0.5, the normal device maintains the baseline value, and each smart home device updates the local pheromone concentration according to the adjusted evaporation rate, wherein the overload device accelerates evaporation, the pheromone decreases rapidly, and the task attraction decreases; the light load device slows down evaporation, maintains a higher concentration to attract tasks, and synchronizes the local pheromone concentration data to the cloud management center to form the latest path weight distribution; when the pheromone concentration of a certain smart home device is lower than the set threshold, the cloud management center triggers the task migration instruction, and reassigns the pending task to the high concentration (i.e. low load) device, after the migration is completed, the pheromone concentration of the original device is reset to 50% of the baseline value, the local congestion is broken through forced intervention, the task flow is ensured to the resource sufficient node, and finally the global load balancing is realized.

[0087] The pheromone volatilization regulation module specifically works as follows: the cloud management center receives the abnormal area mapping table and the load heat map data generated by the global load analysis module in real time, dynamically calculates the pheromone volatilization rate adjustment coefficient of each device based on the preset load grading rules, wherein the volatilization rate of the overloaded device is increased to twice the benchmark value, and the task attraction is reduced by accelerating the pheromone decay; the volatilization rate of the light load device is reduced to half of the benchmark value, and the concentration decay is slowed down to maintain high task capture capability; the volatilization rate of the normal load device remains unchanged, and during the calculation process, the abnormal area devices marked in the load heat map are preferentially processed, ensuring that the volatilization rate adjustment priority of the high load cluster is the highest, and all adjustment strategies are issued to the device nodes in the form of instructions to ensure the consistency of the global strategy; each smart home device updates the local pheromone concentration in real time according to the volatilization rate issued by the cloud management center, wherein the concentration of the overloaded device decreases exponentially due to the doubled volatilization rate, and the task allocation probability is significantly reduced; the concentration of the light load device decays slowly due to the halved volatilization rate, and gradually accumulates higher attraction; after each concentration update, the device synchronizes the current pheromone value to the cloud management center through an encrypted channel, the cloud management center reconstructs the edge weight distribution of the virtual path network, and then forms a closed loop control, the load state drives the volatilization rate adjustment, the rate change affects the concentration distribution, and the concentration distribution further feeds back to the task allocation decision; at the same time, the time stamp is checked to ensure the time sequence consistency of the concentration data, and the state conflict caused by network delay is avoided; when the pheromone concentration of a device is lower than the preset safety threshold, the cloud management center determines that it is in a sustained overload state, and immediately triggers the task migration mechanism, the migration instruction includes a task list to be transferred and a target device screening rule, and the light load device with the highest concentration in the same area is preferentially selected to undertake the task; after the migration is completed, the pheromone concentration of the original device is reset to 50% of the benchmark value to avoid immediately re-entering the allocation pool; at the same time, the cloud continues to monitor the migration effect, and if the load of the target device exceeds the safety threshold due to task injection, a secondary migration is started, the local resource competition deadlock is broken through forced intervention, the task flow is guided to the globally optimal node, and load balancing is achieved.

[0088] In the communication protocol adaptation module, the process of optimizing the transmission path of the task allocation instruction is as follows:

[0089] The communication protocol adaptation module identifies the communication protocol type (Wi-Fi / Zigbee / Bluetooth, etc.) of the accessed smart home device through data analysis of device registration information, and unifies the data format based on the communication protocol specification to encapsulate it as a standardized message, ensuring the compatibility of cloud instructions and device data structures, real-time monitoring of network topology and device status, and the construction of a multi-dimensional path evaluation model based on communication protocol characteristics, with transmission delay and energy consumption as indicators, calculating path scores for all available transmission paths, dynamically selecting the optimal transmission path to minimize the transmission delay of task allocation instructions, adjusting transmission parameters according to the protocol characteristics of the selected path, optimizing data packet retransmission strategies and flow control, and simultaneously verifying the instruction reception status through a two-way handshake, dynamically calibrating the path selection algorithm based on real-time delay feedback, forming a closed-loop optimization mechanism;

[0090] The specific work of the communication protocol adaptation module is as follows: when a smart home device is connected to the system, the protocol characteristic fields (protocol version number, manufacturer identification, data frame structure, etc.) in its registration information are analyzed, and the type of communication protocol used by the device is quickly determined by comparing with a predefined protocol characteristic library (covering mainstream protocols such as Wi-Fi, Zigbee, and Bluetooth). Based on the protocol specification, the original data of the device is analyzed and re-encapsulated into a standardized message according to the unified requirements of the cloud management center, ensuring that the original data of different protocol devices is converted into a common format recognizable by the cloud management center, eliminating compatibility issues caused by protocol heterogeneity. Real-time awareness of network topology and device state changes is achieved by continuously monitoring device online status, signal strength, link quality, and current network load, and by considering the characteristics of different communication protocols (Wi-Fi has high bandwidth but is easily disturbed, Zigbee has low power consumption but limited transmission distance, and Bluetooth has short-range high stability). A multi-dimensional path evaluation model is constructed, with transmission delay and energy consumption as indicators, to quantitatively score all available transmission paths, calculate path scores, and select the path with the highest score as the optimal transmission channel, ensuring that instructions can bypass congested or highly disturbed areas and preferentially pass through stable, low-delay links to reach the target device, thereby improving system response speed. After selecting the transmission path, the underlying transmission parameters are adjusted according to the protocol characteristics to optimize performance. For Wi-Fi protocols, the retransmission threshold is increased to cope with occasional signal fluctuations. For Zigbee protocols, the data packet fragmentation size is reduced to reduce the risk of transmission failure. At the same time, a two-way handshake mechanism is introduced to verify the instruction reception status in real time. If the device does not respond, retransmission or path switching is triggered. The weight parameters of the path evaluation model are dynamically calibrated based on real-time monitoring of transmission delay data, forming a closed-loop control process of evaluation-selection-optimization-feedback, adapting to changes in the network environment, continuously optimizing transmission efficiency, and ensuring the reliability and real-time performance of data interaction between the cloud and the device.

[0091] The calculation expression of path score is as follows:

[0092] ;

[0093] wherein: is the comprehensive score of the path , the larger the value, the better the path, is the weight coefficient of the delay index, is the measured end-to-end transmission delay of the path , is the minimum delay (reference value) among all optional paths, is the maximum allowed delay threshold (beyond which the path is directly eliminated), is the single instruction transmission energy consumption of the path , is the minimum energy consumption among all optional paths, is the maximum allowed energy consumption threshold (beyond which the path is directly eliminated).

[0094] The above merely provides 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 can easily think of changes or replacements within the technical scope disclosed by the present application, which shall be encompassed within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. An Internet of Things based distributed smart home device management system comprising a cloud management center, characterized in that, The cloud management center is communicatively connected with the following modules, wherein: The device resource perception module is used for monitoring the device resource state in real time to form a device resource state diagram, simulating the behavior of ants to release pheromones, and dynamically adjusting the pheromone concentration to guide task allocation; The task scheduling and allocation engine is used for analyzing the pheromone concentration and global load based on the ant colony algorithm to perform optimal matching of the to-be-allocated task and the smart home device; The global load analysis module is used for aggregating the resource data of all smart home devices at the cloud management center, identifying overloaded devices and lightly loaded devices, and generating a load heat map; The pheromone volatilization control module is used for dynamically adjusting the pheromone volatilization rate according to the global load analysis result, outputting a task allocation instruction, and forcing task migration to a low-load device; The communication protocol adaptation module is used for compatible with the communication protocols of different smart home devices, and optimizing the transmission path of the task allocation instruction. 2.The Internet of Things based distributed smart home device management system according to claim 1, characterized in that: The device resource perception module includes a device resource monitoring unit and a pheromone dynamic release unit; The device resource monitoring unit is used for collecting the resource data of the smart home device in real time through a sensor and an edge computing node, and uploading the resource data to the cloud management center to form a device resource state diagram; The pheromone dynamic release unit generates pheromones according to the remaining resources of the smart home device, controls task migration through the volatilization rate, and dynamically adjusts the pheromone concentration to guide task allocation. 3.The Internet of Things based distributed smart home device management system according to claim 2, characterized in that: In the device resource monitoring unit, the process of forming a device resource state diagram is as follows: The embedded sensor and edge computing node deployed on each smart home device are connected with the cloud management center in real time, and the resource data including CPU usage, memory remaining amount, bandwidth occupation, and power remaining value of each smart home device are collected at a set period; The edge computing node pre-processes the collected raw resource data, filters invalid and error data, classifies and aggregates the valid data, marks the time stamp and device ID, and detects the online state of the device; The pre-processed resource data is uploaded to the cloud management center after encryption, and the cloud management center stores the resource data in the device resource database to form a device resource state diagram, analyzes the device online state, and marks the state as unavailable if the device is disconnected or the data is outdated and not updated. 4.The Internet of Things based distributed smart home device management system according to claim 2, wherein: In the pheromone dynamic release unit, the process of dynamically adjusting the pheromone concentration to guide task allocation is as follows: Based on the real-time collected resource data of the smart home device, the device resource consumption rate is analyzed based on the remaining resource amount, and the current load state of the device is calculated, high resource devices generate high concentration pheromones, and low resource devices generate low concentration pheromones to form an initial pheromone distribution diagram; A preset volatilization rate function is combined with the device resource consumption rate to dynamically attenuate the pheromone concentration, and the real-time matching of the pheromone concentration and the resource state is performed; According to the pheromone concentration gradient, the task is allocated to the high concentration area, and the task migration is triggered by the concentration change. When the device resource decreases and the pheromone concentration is lower than the concentration threshold, the task is automatically transferred to other high concentration devices. 5.The Internet of Things based distributed smart home device management system according to claim 2, wherein: The task scheduling and allocation engine includes a task priority evaluation unit and an ant colony optimization allocation unit; The task priority evaluation unit is configured to divide priorities according to the task urgency and resource demand, and determine the allocation relationship between the to-be-allocated task and the device in combination with a device resource state diagram. The ant colony optimization allocation unit is configured to simulate the path-searching behavior of ants, and the task selects the smart home device according to the pheromone concentration, and adjusts the allocation strategy in combination with the feedback of the cloud management center. 6.The Internet of Things based distributed smart home device management system according to claim 5, characterized in that: In the task priority evaluation unit, the process of determining the allocation relationship between the to-be-allocated task and the device in combination with the device resource state diagram is as follows: The metadata of the to-be-allocated task is parsed, the task urgency and resource demand are extracted, the to-be-allocated task is divided into three levels of high, medium and low according to the preset rule of the task urgency, and the priority order of the to-be-allocated task queue is formed; The resource consumption of the task is estimated according to the task type and resource demand, the current device resource state diagram is scanned, the candidate device set meeting the task resource demand is screened out, and the device whose current load state does not meet the minimum execution requirement of the task is filtered out; In combination with the priority order result and the candidate device set, the high-priority task is bound to the device with sufficient resources and high stability, and the device resource state diagram and the task queue state are updated. 7.The distributed smart home device management system based on the Internet of Things according to claim 5, characterized in that: In the ant colony optimization allocation unit, the process of adjusting the allocation strategy in combination with the feedback of the cloud management center is as follows: The pheromone concentration data of each smart home device is obtained in real time, a virtual path network with the smart home device as a node and the pheromone concentration as an edge weight is constructed, the target device is selected by the to-be-allocated task according to the current path network state according to the pheromone concentration probability, an initial allocation tendency is formed, and the historical allocation times of each device are recorded by the system; The cloud management center continuously monitors the device resource state, compares the matching degree of the pheromone concentration and the resource remaining amount, and if it is found that the pheromone concentration is too high but the device is actually overloaded, a volatilization rate acceleration mechanism is triggered to forcibly reduce the attraction of the path, and vice versa, the pheromone volatilization of the low-load high-resource device is slowed down; Based on the periodic global load analysis result, the ant colony algorithm adjusts the pheromone update rule, that is, after the task is successfully executed, the path pheromone is weighted and enhanced according to the resource consumption efficiency, and if the task is migrated due to device overload, the original path pheromone is punished and weakened, and through the positive and negative feedback cycle, the load balancing state is gradually converged. 8.The distributed smart home device management system based on the Internet of Things according to claim 5, wherein: In the global load analysis module, the process of identifying the overloaded device and the light-load device and generating the load thermal map is as follows: The cloud management center calls the resource data of all smart home devices, constructs a global resource state matrix, divides the device load state in combination with a preset load threshold, and identifies the overloaded device and the light-load device; A load thermal map is generated in combination with the spatial topological relationship, the load thermal map directly displays the global load distribution by color gradient, marks the potential overload risk points and idle resource clusters, forms an abnormal area mapping table, and is updated to the pheromone volatilization regulation module. 9.The Internet of Things based distributed smart home device management system of claim 8, wherein: In the pheromone volatilization regulation module, the process of outputting the task allocation instruction and forcibly migrating the task to the low-load device is as follows: The cloud management center receives the global load analysis result, combines the load heat map and the abnormal area mapping table to analyze the device load state, and dynamically calculates the evaporation rate according to the preset rule, wherein the overload device accelerates evaporation, which is twice the rate coefficient; the light load device slows down evaporation, which is 0.5 times the rate coefficient x 0.5, and the normal device maintains the baseline value; Each smart home device updates the local pheromone concentration according to the adjusted evaporation rate, and synchronizes the local pheromone concentration data to the cloud management center to form the latest path weight distribution; When the pheromone concentration of a smart home device is lower than the set threshold, the cloud management center triggers the task migration instruction to reassign its pending tasks to high-concentration devices. After migration, the pheromone concentration of the original device is reset to 50% of the baseline value, breaking local congestion through forced intervention, and finally achieving global load balancing. 10.The Internet of Things based distributed smart home device management system according to claim 1, wherein: In the communication protocol adaptation module, the process of optimizing the transmission path of the task allocation instruction is: The communication protocol adaptation module identifies the communication protocol type of the access smart home device through data analysis of device registration information, and uniformly encapsulates its data format into standardized messages based on communication protocol specifications; Real-time monitoring of network topology and device status, combined with communication protocol characteristics to build a multi-dimensional path evaluation model, using transmission delay and energy consumption as indicators to calculate path scores for all available transmission paths, dynamically selecting the optimal transmission path to minimize transmission delay of task allocation instructions; For the selected path, adjust the transmission parameters according to the protocol characteristics of the communication protocol, optimize the data packet retransmission strategy and flow control, and at the same time confirm the instruction reception state through bidirectional handshaking, combine real-time delay feedback to dynamically calibrate the path selection algorithm, form a closed-loop optimization mechanism.

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