Cross-scene device resource management and control method, system and device based on Internet of Things

Abstract

The invention is applicable to the technical field of multimedia Internet of Things, and provides an Internet of Things-based cross-scene equipment resource management and control method, which comprises the following steps of: managing and scheduling equipment resources of different scenes by constructing a distributed network service framework; based on the distributed network service framework, enabling different equipment resources to communicate and control on a physical level and a logic level through a unified control center; based on the unified control center and the distributed network service framework, carrying out information synchronization and content distribution on equipment resources in different scenes through a multicast technology; based on different scene characteristics, a visual interface is established for real-time interaction of different scenes, and cross-scene equipment resources can be efficiently managed and controlled.

Description

Technical Field

[0001] The present application belongs to the field of multimedia Internet of Things technology, and in particular relates to a method, system and device for cross-scenario device resource management based on the Internet of Things. Background Art

[0002] Multimedia device control and resource scheduling are important research areas in modern intelligent IoT systems. They are crucial to improving efficiency and user experience in scenarios such as education, conferences, and public events. With the popularization of multi-scenario applications, the demand for cross-scenario device integration and intelligent scheduling is becoming increasingly urgent, especially in diverse environments such as multimedia classrooms, computer rooms, conference rooms, and auditoriums with thousands of people. The stability and adaptability of the system directly affect the operating results.

[0003] However, existing technologies struggle with the fragmented nature of device resources across scenarios, making unified scheduling and sharing across them difficult. This results in low resource utilization. Furthermore, multimedia devices often come from different manufacturers and adhere to different protocols, resulting in heterogeneity and difficulty communicating and controlling them on a unified platform. Furthermore, information synchronization and content distribution across multiple scenarios and devices are difficult, impacting applications such as teaching demonstrations, remote conferencing, and live events. Furthermore, there is a lack of efficient means for managing and controlling multimedia devices, making operations complex and error-prone. Existing multimedia control systems struggle to meet diverse needs. Therefore, a comprehensive management and control approach is needed to address these challenges. Summary of the Invention

[0004] The embodiments of the present application provide a method, system, and device for cross-scenario device resource management and control based on the Internet of Things, which can solve one of the above-mentioned problems in the prior art.

[0005] In a first aspect, an embodiment of the present application provides a cross-scenario device resource management method based on the Internet of Things, including: By building a distributed network service framework, we can manage and schedule device resources in different scenarios. Based on the distributed network service framework, different device resources can communicate and be controlled at the physical and logical levels through a unified control center; Based on the unified control center and the distributed network service framework, information synchronization and content distribution are performed on device resources in different scenarios through multicast technology; Based on the characteristics of different scenarios, a visual interface is established for real-time interaction in different scenarios.

[0006] Furthermore, the construction of a distributed network service framework includes: Based on the preset node communication protocol, service nodes are deployed for each scenario to generate a distributed network; Acquire device resources of each service node, generate a distributed resource pool, and generate a device identifier for each device resource in the distributed resource pool; Based on the distributed resource pool, resource registration data is recorded for each service node through a resource registration mechanism to generate a resource registration table. The resource registration data includes resource type, resource capacity, resource location and resource status.

[0007] Furthermore, acquiring the device resources of each service node, generating a distributed resource pool, and generating a device identifier for each resource in the distributed resource pool includes: Obtaining device information of device resources in each service node and generating a preliminary resource list, wherein the device resources include computing devices, storage devices, and multimedia devices; According to the preliminary resource list, assigning a device identifier to each device resource using a unified coding rule; Mapping the preliminary resource list with the service node to generate a distributed resource pool; Establish a resource list update mechanism to update the corresponding preliminary resource list when the service node collects new device information or the device status changes.

[0008] Furthermore, the management and scheduling of device resources in different scenarios includes: For device resources in different scenarios, the usage status data of each device resource is recorded through distributed ledger technology to generate a device status record set, which includes the device identification, recording timestamp and occupancy status of each device resource; The polling interval for each device resource is obtained by calculating the basic score, anomaly score, and device level for each device resource. The basic score is measured by the device resource type and system load, the anomaly score is measured by anomalies within a preset time period, and the device level is measured by the key indicators, cost indicators, and security indicators of the device resource. For a device status record set of the same device resource, if the time interval between the record timestamps of two adjacent status records is greater than or less than the polling interval, the device status record set is polled by the real-time tracking module to generate resource usage data and device usage status; Based on the resource usage data and the device usage status, analyzing the occupancy status in the device status record set using an anomaly detection algorithm to determine the risk trend of the device resources; Based on the risk trend, an optimization scheduling instruction is generated for the equipment resources.

[0009] Furthermore, based on the distributed network service framework, different device resources are enabled to communicate and control at the physical and logical levels through a unified control center, including: Obtain the physical layer connection configuration between the edge computing node and the unified control center; Based on the physical layer connection configuration, obtaining the protocol type of each device resource, decomposing the protocol type through a protocol parsing algorithm, generating a protocol standardization description, and determining a protocol mapping rule; parsing and reorganizing the communication data of each of the device resources based on the protocol mapping rules to generate standardized data for the communication of each of the device resources; Based on the standardized data, extracting attribute parameters of each of the device resources from a preset physical device model, matching the attribute parameters with a preset logical model, and generating a mapping technology conversion result; Based on the mapping technology conversion result, a function mapping control instruction is obtained from the unified control center.

[0010] Furthermore, based on the unified control center and the distributed network service framework, information synchronization and content distribution are performed on device resources in different scenarios through multicast technology, including: By analyzing the network topology and the device resources of each service node, the connection paths between nodes and the processing capacity of each device resource are obtained to generate a multicast networking solution; Based on the multicast networking solution, obtaining communication data of each target device resource, and based on the communication data, obtaining a function mapping control instruction from the unified control center; Extracting a data packet from the function mapping control instruction, and compressing the data packet to obtain a compressed data packet; The compressed data packet is sent to the target device resource via a multicast protocol.

[0011] Furthermore, the information synchronization and content distribution of device resources in different scenarios also includes: Obtaining task requests for each target device resource, parsing the task requests, and generating a task queue set; Using a distributed scheduling algorithm to allocate computing resources to each task queue in the task queue set, and generating a task dependency graph; If the task queue involves multiple scenarios, the task queue is decomposed based on the coordination protocol to generate a subtask set and a coordination protocol configuration.

[0012] Furthermore, the visual interface is established based on the characteristics of different scenarios for real-time interaction in different scenarios, including: By analyzing the device resources, number of devices and interaction requirements of different scenarios, the scenario interface type is determined, and a visual interface is generated based on the scenario interface type; Obtaining a user interaction request from the visual interface, calling a collaborative protocol, and generating a multi-device collaborative control sequence; Extracting a collaborative protocol configuration and a function mapping control instruction from the multi-device collaborative control sequence, and transmitting the collaborative protocol configuration and the function mapping control instruction to a target device resource; Monitor the device status, system resource usage, and network traffic data of each device resource in real time, and generate dynamic visual charts.

[0013] In a second aspect, an embodiment of the present application provides a cross-scenario device resource management and control system based on the Internet of Things, including: The first processing module is used to manage and schedule device resources in different scenarios by building a distributed network service framework; The second processing module is used to enable different device resources to communicate and control at the physical and logical levels through a unified control center based on the distributed network service framework; A third processing module is configured to synchronize information and distribute content to device resources in different scenarios through multicast technology based on the unified control center and the distributed network service framework; The fourth processing module is used to establish a visual interface based on the characteristics of different scenarios for real-time interaction in different scenarios.

[0014] In a third aspect, an embodiment of the present application provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned cross-scenario device resource management method based on the Internet of Things is implemented.

[0015] Compared with the prior art, the embodiments of the present application have at least one of the following beneficial effects: 1. Build a distributed network service architecture to centrally manage and schedule computing resources, storage resources, and multimedia resources scattered across different scenarios, enabling dynamic discovery, registration, and invocation of resources. This enables resource scheduling and sharing between devices across different scenarios, allowing devices in each scenario to obtain and use system resources on demand. 2. By establishing physical network connections and logical protocol adaptation and data exchange, the unified control center and edge computing nodes can work together to ensure that equipment resources from different manufacturers and types can communicate and be controlled in the unified control center. 3. Use multicast technology to send the function mapping control instructions corresponding to the task request to multiple target device resources, realizing information synchronization and content distribution among multiple scenarios and multiple devices, ensuring that information can be delivered to each target device reliably and efficiently; 4. Based on the characteristics of different scenarios, a visual interface and interactive method are established to support the collaborative work between complex equipment resources. At the same time, the system operation status, resource usage and equipment working status are monitored and displayed in real time to help workers discover and solve problems in a timely manner, thereby improving the practicality, flexibility and adaptability of cross-scenario equipment resource management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0017] Figure 1 This is a flow chart of a method for cross-scenario device resource management and control based on the Internet of Things provided by one embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a cross-scenario device resource management and control system based on the Internet of Things provided by one embodiment of the present invention; Figure 3 It is a structural diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0019] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0020] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0021] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0022] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0023] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0024] See also Figure 1 As shown, the present invention is a cross-scenario device resource management method based on the Internet of Things, comprising the following steps: S100: Manage and schedule device resources in different scenarios by building a distributed network service framework; In this application, by building a distributed network service architecture, computing resources, storage resources and multimedia resources scattered in different scenarios are uniformly managed and scheduled, and dynamic discovery, registration and calling of resources are realized, so that resource scheduling and sharing can be carried out between devices across scenarios, so that devices in each scenario can obtain and use system resources on demand.

[0025] In some embodiments, the building of a distributed network service framework includes: Based on the preset node communication protocol, service nodes are deployed for each scenario to generate a distributed network; Acquire device resources of each service node, generate a distributed resource pool, and generate a device identifier for each device resource in the distributed resource pool; Based on the distributed resource pool, resource registration data is recorded for each service node through a resource registration mechanism to generate a resource registration table. The resource registration data includes resource type, resource capacity, resource location and resource status.

[0026] In this embodiment, service nodes are deployed in different scenarios to generate a distributed network. Specifically, the scenarios include classrooms, computer rooms, or conference rooms. Each service node includes device resources with network communication functions. At the same time, a unique identifier is assigned to each service node, such as an IP address, MAC address, or a custom node ID, and the deployment location information of the service node is recorded.

[0027] In this embodiment, the pre-built node communication protocol code is deployed on each service node, specifically including configuring the network parameters of the service node and setting the initial state parameters of the service node. The network parameters include the IP address, subnet mask and gateway, which are used to ensure that the service node can access the network normally, and the initial state parameters include the working mode of the service node and the collection frequency, etc. In one possible embodiment, the working mode of the service node includes active collection or passive response.

[0028] In one embodiment, after a service node is activated, it discovers itself through a predefined protocol. For example, a service node can broadcast a discovery message, and other nodes respond with their own identifier and service node information. The service node information includes the identifier, deployment location, and corresponding scenario. By registering each service node's service node information with a central node, other service nodes can obtain information about all service nodes in the distributed network.

[0029] For each service node, the corresponding device resources are obtained, and a unique device identifier is set for each device resource to facilitate the subsequent classification and identification of the device resources. Then, the corresponding device resources are summarized and organized to generate a distributed resource pool to facilitate the subsequent call and management of each device resource.

[0030] To be more specific, for the resource information of each device in the distributed resource pool, a resource registration mechanism is adopted to register the resource information of each device to the corresponding service node information, that is, the resource type, resource capacity, resource location and resource availability status of each device resource are recorded on each service node for subsequent calls to the device resources, and matched with the above service node information to finally generate a resource registration table.

[0031] In some embodiments, obtaining device resources of each service node, generating a distributed resource pool, and generating a device identifier for each resource in the distributed resource pool includes: Obtaining device information of device resources in each service node and generating a preliminary resource list, wherein the device resources include computing devices, storage devices, and multimedia devices; According to the preliminary resource list, assigning a device identifier to each device resource using a unified coding rule; Mapping the preliminary resource list with the service node to generate a distributed resource pool; Establish a resource list update mechanism to update the corresponding preliminary resource list when the service node collects new device information or the device status changes.

[0032] In this embodiment, various device resources are identified in scenarios such as classrooms, computer rooms, and conference rooms. The device resources include computers, servers, projectors, audio equipment, storage arrays, etc. Furthermore, each device resource is classified according to the device type, specifically computing devices, storage devices, and multimedia devices. At the same time, the information items that need to be collected are determined according to the functions of different device resources. For example, for computer equipment, resource utilization information such as CPU usage, memory usage, and disk space needs to be collected; for projector equipment, resource collection information such as brightness, resolution, and working status may need to be collected.

[0033] In this embodiment, different information collection methods are used for different device resources to obtain data of different device resources in each service node. The information collection methods include direct collection methods and indirect collection methods. Specifically, the direct collection method is applicable to devices that support network communication. For such devices, the service node can directly communicate with the device through network protocols such as SNMP and SSH to obtain the device status information and resource usage. The indirect collection method is applicable to devices that do not support direct network communication. Specifically, information is collected through sensors or intermediate devices. For example, temperature sensors, humidity sensors, etc. are installed in the computer room, and the collected environmental data is sent to the corresponding service node.

[0034] In this embodiment, each service node summarizes and organizes the collected device information, classifies and stores it according to the device type and deployment location, and generates a preliminary resource list. It can be understood that the preliminary resource list includes the basic information of the device, resource usage and status information, wherein the basic information includes the device name, model and location, the resource usage includes CPU performance, computing resource usage, storage space occupancy, etc., and the status information specifically includes information such as whether the device is operating normally. At the same time, through a unified coding rule, a unique device identifier is assigned to each device resource in the preliminary resource list, wherein the unified coding rule can include information such as device type, deployment location, node ID, serial number, etc. In a possible embodiment, the unified coding rule can be device type code-deployment location code-node ID-serial number.

[0035] In this embodiment, the preliminary resource lists collected by each service node are mapped and merged to eventually generate a distributed resource pool. The distributed resource pool can be stored in a table or database format. At the same time, each preliminary resource list is provided with a unique identifier that matches the service node, and records information such as the generation time and data source node for subsequent query and management.

[0036] In this embodiment, a resource list update mechanism is also established. When the service node collects new device information or the device status changes, the corresponding preliminary resource list is updated in a timely manner to ensure the real-time and validity of the resource list.

[0037] In some embodiments, the management and scheduling of device resources in different scenarios includes: For device resources in different scenarios, the usage status data of each device resource is recorded through distributed ledger technology to generate a device status record set, which includes the device identification, recording timestamp and occupancy status of each device resource; The polling interval for each device resource is obtained by calculating the basic score, anomaly score, and device level for each device resource. The basic score is measured by the device resource type and system load, the anomaly score is measured by anomalies within a preset time period, and the device level is measured by the key indicators, cost indicators, and security indicators of the device resource. For a device status record set of the same device resource, if the time interval between the record timestamps of two adjacent status records is greater than or less than the polling interval, the device status record set is polled by the real-time tracking module to generate resource usage data and device usage status; Based on the resource usage data and the device usage status, analyzing the occupancy status in the device status record set using an anomaly detection algorithm to determine the risk trend of the device resources; Based on the risk trend, an optimization scheduling instruction is generated for the equipment resources.

[0038] In this embodiment, distributed ledger technology is used to record the information related to the device status collected by each service node. Specifically, the real-time status information of each device resource in different scenarios is collected through IoT sensors. For example, the device switch status, the number of users, and the resource occupancy rate are detected by sensors to generate raw status data. Based on the collected raw status data and combined with the device identification information, the data is standardized and converted into usage status data with a unified format. Each usage status data contains an accurate timestamp, a unique device identification code, and the current occupancy status. The usage status data is then written into the distributed ledger to form a device status record set containing a timestamp, a device identification, and an occupancy status.

[0039] In some embodiments, a hash algorithm is used to encrypt each piece of usage status data and calculate a unique hash value to ensure the immutability and integrity of the data in the distributed ledger. At the same time, the encrypted usage status data is stored in the blockchain network through distributed ledger technology, and a complete distributed ledger copy is saved for each service node in the blockchain network to achieve distributed storage and synchronization of data.

[0040] In this embodiment, for the device status record set in the distributed ledger, a timestamp interval checking method is used to obtain the current resource usage data and fault status of each device resource. Specifically, the timestamp interval of the adjacent status records of each device resource in the device status record set is checked within a preset time, and the polling interval is set to preliminarily judge the resource usage status.

[0041] In this embodiment, the polling interval of the corresponding device resources is calculated through the basic score, anomaly score and device classification of the device resources, thereby improving the anomaly detection capability of the device resources. Specifically, the polling interval indicates that each data in the device status record set needs to be collected once every adjacent polling interval. If the timestamp interval exceeds the polling interval, it means that there is a delay or anomaly in the data collection. At this time, the overall resource utilization of the system may fail, thereby triggering the real-time tracking module to poll each status record in the device status record set to obtain resource usage data, such as CPU usage, memory occupancy, etc., as well as device usage status, such as whether each device resource is operating normally and whether there are any error prompts.

[0042] In one embodiment, the calculation formula for the polling interval is specifically: interval = baseScore * (1.0 + math.Log1p (anomalyScore)) / math.Pow (1.2, float64 (priority)), where baseScore represents the base score, which is calculated based on the device resource type and system load; anomalyScore represents the anomaly score, which is calculated based on the anomaly within a preset time; priority represents the device classification, which is calculated based on the key indicators, cost indicators, and security indicators of the device resources; More specifically, a basic score is calculated based on the device resource type and the system load condition. Specifically, a basic polling value is preset for each of the device resource types of the above-mentioned computing devices, storage devices, and multimedia devices. In a preferred embodiment, the basic polling value of the computing device is 500ms, the basic polling value of the storage device is 5s, and the basic polling value of the multimedia device is 50ms. Furthermore, based on the system load condition, the basic polling value is dynamically adjusted to determine the basic score of the corresponding device resource polling interval. For example, in one embodiment, when the system load exceeds 80%, the basic polling value is multiplied by 1.5 to increase the polling interval, thereby reducing the system load and ensuring the normal operation of the system while performing abnormality detection.

[0043] Furthermore, an anomaly score is calculated based on abnormal situations within a preset time. Specifically, it is measured by the occupancy deviation of each resource in the device resources, including the occupancy deviation of the CPU, the occupancy deviation of the memory, and the occupancy deviation of the network. Then, the total occupancy deviation is calculated through a weighted average algorithm, where the calculation formula for the occupancy deviation is: deviation = |current value-baseline| / baseline. In a preferred embodiment, the weight values ​​of the CPU, memory, and network are 40%, 30%, and 30%, respectively.

[0044] Furthermore, the equipment classification is calculated based on the key indicators, cost indicators and security indicators of the equipment resources, where the key indicators are the business levels of the equipment resources in the business implementation process. For example, in a possible embodiment, the business level of the computing device is 10, the business level of the storage device is 6, and the business level of the multimedia device is 3. The cost indicator is measured by the hourly power consumption cost of the equipment resources. The security indicator is measured by the preset security level. Specifically, based on security requirements, a security level is assigned to each equipment resource. In a preferred embodiment, the security level is level three, where the safety factor of security level three is 1.5, indicating that its security requirements are relatively high, the safety factor of security level two is 1.2, indicating that its security requirements are medium, and the safety factor of security level one is 1.0, indicating that its security requirements are relatively low. Therefore, the specific calculation formula for equipment classification is priority=min(10,max(1,round(business*0.5+cost*0.3+security*0.2))), where business represents the key indicator, cost represents the cost indicator, and security represents the safety indicator. Furthermore, max(1,...) is used to ensure that the result is at least 1, thereby preventing the calculation result from being 0 or a negative number, and min(10,...) is used to ensure that the result does not exceed 10, thereby preventing the calculation result from being too large and exceeding the reasonable range.

[0045] Specifically, in the polling interval calculation formula, math.Log1p(anomalyScore) performs a logarithmic transformation on the anomaly score to avoid the logarithm of zero and amplify the impact of small scores. math.Pow(1.2, float64(priority)) is used to quantify the impact of the device resource's device classification on the polling interval. For devices with higher priority, their priority value is larger, so the power of 1.2 is also larger, and the resulting value is also larger. Devices with higher device classifications are polled more frequently because they are more important to the system and require more timely monitoring. Devices with lower device classifications are polled less frequently because they have less impact on the system, which can reduce monitoring overhead to a certain extent.

[0046] In this embodiment, the preliminary resource list of device resources in each service node is divided into data subsets according to computing devices, storage devices, and multimedia devices, and the resource usage data and device usage status of each device in the data subset are obtained. The mean resource occupancy rate and the mean failure rate of the devices in each data subset are calculated to generate subset-level statistical indicators. For each data subset, if its mean resource occupancy rate exceeds a preset threshold, an anomaly detection algorithm is used to determine whether there is a potential performance bottleneck for each device in the data subset, and a performance bottleneck judgment result is obtained. In addition, a subset with a mean failure rate higher than the preset threshold is extracted from the data subset, and the relationship between the failure rate and the device operating environment is analyzed to obtain a fault association rule. Based on the fault association rule and the performance bottleneck judgment result, a comprehensive analysis data set containing insights into resource occupancy rate and failure rate is generated.

[0047] More specifically, the above-mentioned anomaly detection algorithm is used to determine whether there is a potential performance bottleneck in each device in the data subset, and the performance bottleneck judgment result is obtained, which specifically includes: obtaining devices in the data subset whose resource occupancy rate is higher than a preset threshold, generating a list of over-threshold devices, obtaining resource usage data of each device in the over-threshold device list from the device status record set, using data cleaning technology to remove noise data, and obtaining a cleaned monitoring data set, performing anomaly detection on the cleaned monitoring data set through the isolation forest algorithm, analyzing whether the data points deviate from the normal pattern, and determining the set of abnormal data points; based on the set of abnormal data points, using a time series analysis method to calculate the frequency and duration of the abnormal data points, and determine whether there is a performance bottleneck; if a performance bottleneck exists, the abnormal data point set is grouped through a cluster analysis method to obtain a bottleneck-related data feature set, and based on the data feature set, a regression analysis method is used to predict the future trend of device utilization rate and obtain a utilization rate prediction result; by comparing the utilization rate prediction result with the historical resource utilization data, it is determined whether the bottleneck is likely to continue and the final performance bottleneck status is determined.

[0048] For devices with performance bottlenecks, obtain the device's operation log, analyze the abnormal patterns in the operation log through anomaly detection algorithms, and determine potential security risks. Specifically, clean the data in the operation log, such as removing noise data in the log and processing missing values, and extract feature data for anomaly detection from the processed operation log. Use the isolation forest algorithm to build an anomaly detection model, input the feature data into the anomaly detection model, output an anomaly score for each feature data, and determine whether the feature data is abnormal based on the preset threshold. For feature data with abnormal detection results, analyze the context information of the corresponding operation log, such as the time when the anomaly occurred, the device status, related operations, etc., and organize the analysis results into a security risk report. It can be understood that the security risk report includes device information, abnormal pattern description and potential security risks. Device information includes the device identification and device type of the device with performance bottlenecks. The abnormal pattern description includes the time and frequency of the abnormality and related log messages. Potential security risks are potential security risks derived from the abnormal pattern analysis, as well as possible impacts and consequences.

[0049] In one embodiment, historical status data of relevant equipment is obtained from the equipment status record set. It can be understood that the historical status data can provide the usage and fault information of the equipment in different time periods. Combined with big data analysis technology, the historical status data is input into a preset risk trend prediction model. It is worth noting that the risk trend prediction model is a model generated after training and testing based on the historical status data of different equipment resources, and can output risk assessment results for predicting the risk trend of equipment in the future.

[0050] Furthermore, if the risk assessment result of the device is higher than the preset threshold, the operating parameters of the device are extracted from the device status record set. Specifically, the operating parameters include the performance indicators and load conditions of the device. Based on the operating parameters and business needs, optimization scheduling instructions are generated. Through the optimization scheduling instructions, the resource allocation of each device is adjusted, or tasks are migrated to other devices to ensure that resources can be more reasonably allocated to each device, thereby improving the overall performance and stability of the system.

[0051] S200, based on the distributed network service framework, through a unified control center, enabling different device resources to communicate and control at the physical and logical levels; In this application, by establishing network connections at the physical level and protocol adaptation and data exchange at the logical level, the unified control center and edge computing nodes can work together to ensure that equipment resources from different manufacturers and different types can communicate and be controlled in the unified control center.

[0052] In some embodiments, step S200 includes: Obtain the physical layer connection configuration between the edge computing node and the unified control center; Based on the physical layer connection configuration, obtaining the protocol type of each device resource, decomposing the protocol type through a protocol parsing algorithm, generating a protocol standardization description, and determining a protocol mapping rule; parsing and reorganizing the communication data of each of the device resources based on the protocol mapping rules to generate standardized data for the communication of each of the device resources; Based on the standardized data, extracting attribute parameters of each of the device resources from a preset physical device model, matching the attribute parameters with a preset logical model, and generating a mapping technology conversion result; Based on the mapping technology conversion result, a function mapping control instruction is obtained from the unified control center.

[0053] Specifically, the edge computing node is deployed at the edge of the distributed network and can cooperate with the device resources in each service node to process, analyze and store data, thereby realizing efficient and stable management and control of cross-scenario device resources in this application.

[0054] Specifically, the edge computing node serves as a regional computing resource aggregation point, integrating the computing devices in each service node. In some embodiments, the computing devices may include microprocessors, FPGAs, ASICs, etc., which are distributed in different service nodes based on actual application needs. The edge computing node centralizes its scattered computing resources through unified management and scheduling to form a powerful computing power pool to cope with various complex computing tasks, and thus can process relevant data in real time.

[0055] To be more specific, the edge computing node is responsible for managing and coordinating the storage devices in each service node. In some embodiments, the storage devices may include hard disks, solid-state drives, storage arrays, etc., which are used to store various types of data, such as sensor data, user data, application data, etc. The edge computing node will classify, store and manage this data to ensure the security and availability of the data. In some embodiments, the edge computing node can also store frequently accessed data in an edge storage device close to the data source. When users or applications need to access this data, they can obtain it directly from the edge storage device without having to obtain it from a remote data center, thereby greatly reducing data access delays. In addition, the edge computing node can also back up important data in each service node to prevent data loss or damage.

[0056] To be more specific, the edge computing node works closely with the multimedia devices in each service node, such as cameras, microphones, display screens, etc., to realize real-time processing and transmission of multimedia data. The multimedia devices are responsible for collecting and generating multimedia data, such as video, audio, etc., and the edge computing node encodes, compresses, analyzes and processes these data, and transmits the processed data to the corresponding target device. In some embodiments, the edge computing node is also responsible for managing and controlling the multimedia devices in each service node, such as remotely configuring the parameters of the multimedia devices, such as the angle and focal length of the camera, the volume of the microphone, etc., and can also monitor the status of the multimedia devices and diagnose faults to promptly discover and solve problems.

[0057] In this embodiment, the physical layer connection configuration between the edge computing node and the unified control center is obtained, and the protocol mapping rules are determined based on the complexity of the physical layer connection configuration. That is, the performance indicators of the physical layer connection configuration directly affect the selection and optimization of the logical layer protocol mapping. Specifically, high-bandwidth, low-latency physical connections can support more complex protocol mapping rules, while low-bandwidth, high-latency connections require simpler and more efficient protocol mapping.

[0058] Specifically, the physical layer connection parameters of the edge computing node and the unified control center, specifically bandwidth, delay and packet loss rate, are obtained through network topology scanning to generate an initial connection data set. At the same time, a link quality assessment algorithm is used to process the initial connection data set to generate a reliability index and obtain the physical layer connection configuration. It can be understood that the reliability index is the basis for determining the subsequent protocol mapping rules.

[0059] In this embodiment, based on the physical layer connection configuration and the logical layer adaptation requirements, the protocol type and data format of each device resource are obtained. In some preferred embodiments, the protocol types include HTTP, MQTT and Modbus, and the data formats include JSON, XML and binary. The protocol fields are then decomposed by a protocol parsing algorithm to generate a standardized description of the protocol metadata, where the standardized description includes the field name, type and meaning. In one possible embodiment, the protocol parsing algorithm is a regular expression-based parsing algorithm. Thus, field correspondences and data type conversion rules are established based on the standardized description to determine the protocol mapping rules.

[0060] Furthermore, by loading the protocol mapping rules through the middleware adapter, the communication data of each device resource is parsed and reorganized in real time, and each communication data is converted into standardized data. Based on the standardized data, the attribute parameters of each device resource are obtained from the preset physical device model. It is worth noting that the physical device model is an abstract representation of each device resource at the digital level, specifically including device resource attributes, behaviors and functional characteristics, which are used to reflect the status of the device, operation mode and interaction relationship with other devices or systems, and provide a basis for subsequent logical mapping, control and management. In one possible embodiment, the physical device model includes static attributes, dynamic attributes, functional description and interface information of the device, wherein the static attributes include device identification, device type, device model, manufacturer and hardware configuration, the dynamic attributes include the operating status and real-time data of the device, the functional description is the operations and tasks that the device can perform, and the interface information is the way the device communicates and interacts with other systems or devices.

[0061] In this embodiment, the attribute parameters of the device resources are mapped to the logical model. For example, the "device model" attribute of the physical device is directly mapped to the "device model" attribute in the logical model. Through the mapping relationship between the attribute parameters and the logical model, the operations that the device can perform are mapped to the behaviors defined in the logical model. For example, a button operation on the physical device is mapped to the "start" or "stop" behavior in the logical model. Similarly, if there is a clear correspondence between the functional description of the physical device and the functional module in the logical model, module mapping can also be performed directly. The motion control function in the physical device can be mapped to the "motion control module" in the logical model. Based on the above matching process, the mapping technology conversion result is generated.

[0062] In this embodiment, according to the conversion result of the mapping technology, the control instruction set of the unified control center is loaded, and the mapping relationship between the control instruction set and the logical model is bound by the function mapping implementation algorithm to generate the control logic of the unified control platform and obtain the function mapping control instruction. Specifically, a predefined control instruction set is obtained from the database or configuration file of the unified control center. It is worth noting that the control instruction set includes various instructions for controlling the device, such as start instructions, stop instructions, parameter setting instructions, etc., and then the instructions in the control instruction set are parsed to obtain the format, parameters and functions of each instruction. For example, a start instruction includes parameters such as device identification and start mode. Further, according to the mapping technology conversion The relationship between the attribute parameter mapping, operation mapping and functional module mapping established in the conversion result is used to bind the instructions in the control instruction set with the behaviors and functional modules in the logical model, such as mapping the startup instruction to the "start" behavior in the logical model, and associating the instruction with other devices. At the same time, it is also necessary to clarify how the instructions in the control instruction set are executed according to the status and conditions in the logical model. For example, the startup instruction can only be executed when the device is in the ready state. Specifically, according to the mapping relationship between the control instruction set and the logical model, a control logic script for the unified control platform is generated, and a script compilation tool is used for syntax checking to obtain an executable control logic script, and finally a functional mapping control instruction is generated.

[0063] S300, based on the unified control center and the distributed network service framework, using multicast technology to synchronize information and distribute content to device resources in different scenarios; In this application, the function mapping control instructions corresponding to the task request are sent to multiple target device resources through multicast technology to achieve information synchronization and content distribution between multiple scenarios and multiple devices, ensuring that the information can be delivered to each target device reliably and efficiently.

[0064] In some embodiments, step S300 includes: By analyzing the network topology and the device resources of each service node, the connection paths between nodes and the processing capacity of each device resource are obtained to generate a multicast networking solution; Based on the multicast networking solution, obtaining communication data of each target device resource, and based on the communication data, obtaining a function mapping control instruction from the unified control center; Extracting a data packet from the function mapping control instruction, and compressing the data packet to obtain a compressed data packet; The compressed data packet is sent to the target device resource via a multicast protocol.

[0065] In this embodiment, the network topology is analyzed to obtain inter-node connection paths and network data flow characteristics, and an initial set of paths is determined to ensure data can be transmitted between nodes at the lowest cost. Specifically, a graph theory algorithm is used to calculate the shortest paths between nodes, combined with network data flow characteristics to obtain an initial set of paths. Furthermore, node information is extracted from the initial set of paths, and the node processing capability level is quantified based on the resource usage of the receiving device resources. In some possible embodiments, a weighted scoring method is used to calculate the node processing capability level. Furthermore, if the node processing capability is insufficient, path optimization is triggered. The paths in the initial set of paths are recalculated using a path optimization algorithm to bypass low-performance nodes, resulting in an optimized path set. Based on the optimized path set and network data flow characteristics, a multicast networking solution is generated and a multicast distribution priority is determined. In some possible embodiments, the multicast distribution priority is determined through a weighted calculation based on path quality and node processing capability, ultimately generating a multicast networking solution. Specifically, path quality is measured by latency and bandwidth, while network data flow characteristics are typically measured by traffic distribution and real-time requirements.

[0066] In some embodiments, the maximum flow algorithm is used to calculate the rate of each node to ensure that it does not exceed the link capacity and device processing capability. If the rate of some nodes is too low, the priority is readjusted and the calculation is iteratively performed to dynamically adapt the network status to avoid congestion or waste of resources, thereby continuously optimizing the multicast networking solution.

[0067] In this embodiment, based on the multicast networking scheme generated above, data transmission between various device resources is realized. In actual applications, the communication data of each device resource in the current application scenario is obtained, and the communication data is parsed and reorganized by the method shown in the above step S200 to generate standardized data, thereby obtaining attribute parameters, and thus obtaining function mapping control instructions. Furthermore, data packets related to function implementation and data transmission are extracted from the function mapping control instructions, and the data packets are compressed, so that the information can be reliably and efficiently delivered to each target device resource.

[0068] In some embodiments, the information synchronization and content distribution for device resources in different scenarios further includes: Obtaining task requests for each target device resource, parsing the task requests, and generating a task queue set; Using a distributed scheduling algorithm to allocate computing resources to each task queue in the task queue set, and generating a task dependency graph; If the task queue involves multiple scenarios, the task queue is decomposed based on the coordination protocol to generate a subtask set and a coordination protocol configuration.

[0069] In actual applications, an application scenario may require the coordinated cooperation of equipment resources in one or more scenarios, that is, a device may be scheduled across scenarios. For example, a university may carry out an "interdisciplinary teaching project". The development of this project involves three scenarios: classrooms, computer rooms, and conference rooms. Specifically, the classroom displays teaching PPTs and video materials to provide students with a basic teaching audio-visual environment, while the computer room is responsible for server storage and management of teaching data and other resources, and the conference room uses large display screens, video conferencing systems, recording and broadcasting equipment, etc. to display results and record the teaching process.

[0070] Therefore, for the execution tasks extended from an application scenario, the mutual cooperation between the device resources of multiple scenarios is required. Specifically, the task requests received by each target device resource are obtained through a batch operation protocol. Specifically, the task requests are usually distributed through a multicast protocol, and then a protocol parser is used to decompose the compressed data packets carried in the task request to obtain structured data containing device identification and configuration parameters. Based on the parsed request, one or more task queues are generated, where each task queue represents a group of tasks that can be executed in parallel within a specific time period. Then, the priority and dependency relationship of each task queue are obtained, and a task dependency graph is generated. At the same time, a distributed scheduling algorithm is used to calculate the computing resources required for each task queue and perform resource allocation. Specifically, based on the resource requirements of the task queue, such as CPU, memory, and storage, combined with the currently available resources, computing resources are allocated to each task queue.

[0071] In this embodiment, for each task queue, the scenario to which the device resources required in the task queue belong is obtained. Specifically, it can be determined by the device identification of each device resource. As shown in the above step S100, in one embodiment, the coding rule of the device identification is specifically device type code-deployment location code-node ID-serial number. The service node to which the device resource belongs, that is, the corresponding scenario, can be determined through the node ID. Furthermore, for task queues involving multiple scenarios, they are further decomposed. Specifically, based on the scenario type, the device resources involved in the task queue are decomposed into a set of subtasks. Thus, the task queue is split into multiple subtasks, each subtask only depends on the resources of a single scenario. If there is a dependency relationship between the subtasks, a subtask chain is generated in the dependency order. Furthermore, after the task queue is decomposed, communication between the subtasks is realized through the collaborative protocol configuration, while ensuring that each subsystem is executed in the correct order to ensure the ultimate consistency of the task.

[0072] S400: Based on the characteristics of different scenarios, a visual interface is established for real-time interaction in different scenarios.

[0073] In this application, a visual interface and interactive method are established based on the characteristics of different scenarios, thereby supporting the collaborative work between complex equipment resources. At the same time, the system operation status, resource usage and equipment working status are monitored and displayed in real time to help workers discover and solve problems in a timely manner, thereby improving the practicality, flexibility and adaptability of cross-scenario equipment resource management.

[0074] In some embodiments, step S400 includes: By analyzing the device resources, number of devices and interaction requirements of different scenarios, the scenario interface type is determined, and a visual interface is generated based on the scenario interface type; Obtaining a user interaction request from the visual interface, calling a collaborative protocol, and generating a multi-device collaborative control sequence; Extracting a collaborative protocol configuration and a function mapping control instruction from the multi-device collaborative control sequence, and transmitting the collaborative protocol configuration and the function mapping control instruction to a target device resource; Monitor the device status, system resource usage, and network traffic data of each device resource in real time, and generate dynamic visual charts.

[0075] In this embodiment, the device types for different scenarios are determined, and the number of device resources of different device types is counted to clarify the device scale and device deployment location. In addition, the user roles of the usage scenarios need to be determined, such as meeting participants, teachers, operators, etc. At the same time, the interaction methods between users and devices are analyzed, such as touch operation, voice control, gesture recognition, keyboard and mouse input, etc. By evaluating the frequency and complexity of user interaction with the interface, the response speed and functional layout of the interface are determined. For example, interfaces with high-frequency interactions require faster response speeds and simpler operation processes. Then, according to the device type, user role and interaction needs, the appropriate interface style is selected, such as flat design, skeuomorphic design, dark mode, etc.

[0076] In some embodiments, based on the device scale, deployment location, user role and interface interaction data of the device resources in the scene, interface configuration parameters are generated to determine the scene interface type, and based on the scene interface type, the corresponding interface is called from the interface template library to generate a visual interface. Specifically, the configuration parameter dimensions are defined, including layout parameters, functional parameters, visual parameters and data parameters, wherein the layout parameters include interface partitioning and multi-device adaptation rules, the functional parameters include functional modules corresponding to user roles and interaction mode support, the visual parameters include theme style and visual element configurations such as fonts, colors, and icons, and the data parameters include real-time data source configuration. The device scale, deployment location, user role and interface interaction data of the device resources of each scene are mapped to the corresponding configuration parameter dimensions, and finally the interface configuration parameters are generated. By analyzing the interface configuration parameters, the scene interface type is determined based on the preset interface type classification rules. A possible embodiment of the preset interface type classification rules is as follows: when the number of devices exceeds the preset threshold, it corresponds to a complex interface type; when the number of user roles is greater than 2, it is determined to be a multi-role interface type, etc.

[0077] In this embodiment, the task requests of different target device resources are determined based on the user interaction requests generated by the user in the initialization interface. Furthermore, the collaborative protocol configuration is generated by calling the corresponding collaborative protocol. At the same time, based on the communication data of the target device resources, the corresponding function mapping control instructions are obtained from the unified control center. Based on the collaborative protocol configuration and the function mapping control instructions, each target device resource can achieve corresponding function control.

[0078] Furthermore, the visual interface also includes dynamic visualization charts for displaying the resource usage status of each device resource, thereby helping staff to discover and solve problems in a timely manner.

[0079] See also Figure 2 As shown, the present invention also provides a cross-scenario device resource management and control system based on the Internet of Things, the system comprising: The first processing module 201 is used to manage and schedule device resources in different scenarios by building a distributed network service framework; The second processing module 202 is configured to enable different device resources to communicate and be controlled at the physical and logical levels through a unified control center based on the distributed network service framework; The third processing module 203 is configured to synchronize information and distribute content to device resources in different scenarios through multicast technology based on the unified control center and the distributed network service framework; The fourth processing module 204 is used to establish a visual interface based on the characteristics of different scenarios for real-time interaction in different scenarios.

[0080] It is understandable that if Figure 1 The contents of the embodiment of the cross-scenario device resource control method based on the Internet of Things shown in the embodiment are applicable to the embodiment of the cross-scenario device resource control system based on the Internet of Things. The functions specifically implemented by the embodiment of the cross-scenario device resource control system based on the Internet of Things are the same as those in the embodiment of the cross-scenario device resource control method based on the Internet of Things. Figure 1 The embodiment of the cross-scenario device resource management method based on the Internet of Things is the same as that shown in FIG. Figure 1 The beneficial effects achieved by the embodiment of the cross-scenario device resource management method based on the Internet of Things are also the same.

[0081] It should be noted that the information interaction, execution process and other contents between the above-mentioned systems are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0082] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0083] See also Figure 3 As shown, an embodiment of the present invention also provides a computer device 3, including: a memory 302 and a processor 301 and a computer program 303 stored on the memory 302. When the computer program 303 is executed on the processor 301, it implements the cross-scenario device resource management method based on the Internet of Things as described in any one of the above methods.

[0084] The computer device 3 may be a desktop computer, a notebook computer, a PDA, a cloud server or other computing devices. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will understand that Figure 3This is merely an example of the computer device 3 and does not constitute a limitation on the computer device 3 . The computer device 3 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device 3 may also include input and output devices, network access devices, etc.

[0085] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0086] In some embodiments, the memory 302 may be an internal storage unit of the computer device 3, such as a hard drive or memory of the computer device 3. In other embodiments, the memory 302 may also be an external storage device of the computer device 3, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the computer device 3. Furthermore, the memory 302 may include both an internal storage unit of the computer device 3 and an external storage device. The memory 302 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory 302 may also be used to temporarily store data that has been output or is about to be output.

[0087] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for cross-scenario device resource management based on the Internet of Things as described in any one of the above methods is implemented.

[0088] In this embodiment, if the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a camera / computer device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, removable hard drives, magnetic disks, or optical disks. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0089] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A cross-scenario device resource management and control method based on the Internet of Things, characterized in that: include: By building a distributed network service framework, we can manage and schedule device resources in different scenarios. Based on the distributed network service framework, different device resources can communicate and be controlled at the physical and logical levels through a unified control center; Based on the unified control center and the distributed network service framework, information synchronization and content distribution are performed on device resources in different scenarios through multicast technology; Based on the characteristics of different scenarios, a visual interface is established for real-time interaction in different scenarios.

2. The method according to claim 1, wherein The construction of a distributed network service framework includes: Based on the preset node communication protocol, service nodes are deployed for each scenario to generate a distributed network; Acquire device resources of each service node, generate a distributed resource pool, and generate a device identifier for each device resource in the distributed resource pool; Based on the distributed resource pool, resource registration data is recorded for each service node through a resource registration mechanism to generate a resource registration table. The resource registration data includes resource type, resource capacity, resource location and resource status.

3. The method according to claim 2, wherein The acquiring device resources of each service node, generating a distributed resource pool, and generating a device identifier for each resource in the distributed resource pool includes: Obtaining device information of device resources in each service node and generating a preliminary resource list, wherein the device resources include computing devices, storage devices, and multimedia devices; According to the preliminary resource list, assigning a device identifier to each device resource using a unified coding rule; Mapping the preliminary resource list with the service node to generate a distributed resource pool; Establish a resource list update mechanism to update the corresponding preliminary resource list when the service node collects new device information or the device status changes.

4. The method according to claim 1, wherein The management and scheduling of device resources in different scenarios include: For device resources in different scenarios, the usage status data of each device resource is recorded through distributed ledger technology to generate a device status record set, which includes the device identification, recording timestamp and occupancy status of each device resource; The polling interval for each device resource is obtained by calculating the basic score, anomaly score, and device level for each device resource. The basic score is measured by the device resource type and system load, the anomaly score is measured by anomalies within a preset time period, and the device level is measured by the key indicators, cost indicators, and security indicators of the device resource. For a device status record set of the same device resource, if the time interval between the record timestamps of two adjacent status records is greater than or less than the polling interval, the device status record set is polled by the real-time tracking module to generate resource usage data and device usage status; Based on the resource usage data and the device usage status, analyzing the occupancy status in the device status record set using an anomaly detection algorithm to determine the risk trend of the device resources; Based on the risk trend, an optimization scheduling instruction is generated for the equipment resources.

5. The method according to claim 1, wherein Based on the distributed network service framework, different device resources can communicate and be controlled at the physical and logical levels through a unified control center, including: Obtain the physical layer connection configuration between the edge computing node and the unified control center; Based on the physical layer connection configuration, obtaining the protocol type of each device resource, decomposing the protocol type through a protocol parsing algorithm, generating a protocol standardization description, and determining a protocol mapping rule; parsing and reorganizing the communication data of each of the device resources based on the protocol mapping rules to generate standardized data for the communication of each of the device resources; Based on the standardized data, extracting attribute parameters of each of the device resources from a preset physical device model, matching the attribute parameters with a preset logical model, and generating a mapping technology conversion result; Based on the mapping technology conversion result, a function mapping control instruction is obtained from the unified control center.

6. The method according to claim 1, wherein Based on the unified control center and the distributed network service framework, information synchronization and content distribution are performed on device resources in different scenarios through multicast technology, including: By analyzing the network topology and the device resources of each service node, the connection paths between nodes and the processing capacity of each device resource are obtained to generate a multicast networking solution; Based on the multicast networking solution, obtaining communication data of each target device resource, and based on the communication data, obtaining a function mapping control instruction from the unified control center; Extracting a data packet from the function mapping control instruction, and compressing the data packet to obtain a compressed data packet; The compressed data packet is sent to the target device resource via a multicast protocol.

7. The method according to claim 6, wherein The information synchronization and content distribution of device resources in different scenarios also includes: Obtaining task requests for each target device resource, parsing the task requests, and generating a task queue set; Using a distributed scheduling algorithm to allocate computing resources to each task queue in the task queue set, and generating a task dependency graph; If the task queue involves multiple scenarios, the task queue is decomposed based on the coordination protocol to generate a subtask set and a coordination protocol configuration.

8. The method according to claim 1, wherein The visualization interface is established based on the characteristics of different scenarios for real-time interaction in different scenarios, including: By analyzing the device resources, number of devices and interaction requirements of different scenarios, the scenario interface type is determined, and a visual interface is generated based on the scenario interface type; Obtaining a user interaction request from the visual interface, calling a collaborative protocol, and generating a multi-device collaborative control sequence; Extracting a collaborative protocol configuration and a function mapping control instruction from the multi-device collaborative control sequence, and transmitting the collaborative protocol configuration and the function mapping control instruction to a target device resource; Monitor the device status, system resource usage, and network traffic data of each device resource in real time, and generate dynamic visual charts.

9. A cross-scenario device resource management and control system based on the Internet of Things, characterized by: include: The first processing module is used to manage and schedule device resources in different scenarios by building a distributed network service framework; The second processing module is used to enable different device resources to communicate and control at the physical and logical levels through a unified control center based on the distributed network service framework; A third processing module is configured to synchronize information and distribute content to device resources in different scenarios through multicast technology based on the unified control center and the distributed network service framework; The fourth processing module is used to establish a visual interface based on the characteristics of different scenarios for real-time interaction in different scenarios.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

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