Electrical equipment management system based on Internet of Things

Abstract

The invention relates to the technical field of Internet of Things data management. The electrical equipment management system based on the Internet of Things comprises a hierarchical edge compression module used for performing hierarchical edge compression processing on unstructured data to generate a lightweight data packet; the hierarchical transmission scheduling module is used for performing priority marking and network slice mapping processing on the lightweight data packets to obtain hierarchical transmission data streams; the edge cloud collaboration module is used for carrying out edge-cloud collaboration processing based on the hierarchical transmission data stream to generate structured metadata and a depth analysis result; and the state linkage control module is used for carrying out state linkage management on the electrical equipment according to the structured metadata and the deep analysis result and generating an equipment control instruction so as to solve the problems that the compression efficiency and fidelity of a fixed coding algorithm are unbalanced, network channel priority distribution is rigid and equipment control and data flow dynamic linkage is missing.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things data management, and in particular to an electrical equipment management system based on the Internet of Things. Background Art

[0002] With the rapid adoption of IoT technology, electrical equipment management systems have become a core enabler for intelligent upgrades in industrial production, commercial buildings, smart cities, and other scenarios. Efficient equipment management requires the real-time transmission, accurate analysis, and dynamic control of massive amounts of unstructured data (such as equipment status images and vibration waveforms).

[0003] However, related technologies have the following problems in unstructured data processing: fixed coding algorithms make it difficult to balance compression rate and fidelity, resulting in high latency in high-frequency data transmission; a single network channel cannot distinguish data priorities, and critical businesses face the risk of transmission delays; there is a lack of dynamic linkage mechanism between control instruction generation and data analysis results, resulting in delayed device response or malfunction. Summary of the Invention

[0004] Based on this, it is necessary to provide an electrical equipment management system based on the Internet of Things to address the above technical problems, so as to solve the problems of imbalance between compression efficiency and fidelity of fixed coding algorithms, rigid network channel priority allocation, and lack of dynamic linkage between equipment control and data flow.

[0005] In a first aspect, the present application provides an electrical equipment management system based on the Internet of Things, the system comprising:

[0006] The layered edge compression module is used to perform layered edge compression processing on unstructured data to generate lightweight data packets;

[0007] A hierarchical transmission scheduling module is used to perform priority marking and network slice mapping on lightweight data packets to obtain hierarchical transmission data flows;

[0008] The edge-cloud collaboration module is used to perform edge-cloud collaborative processing based on hierarchical transmission data streams to generate structured metadata and in-depth analysis results;

[0009] The status linkage control module is used to manage the status linkage of electrical equipment based on structured metadata and in-depth analysis results and generate equipment control instructions.

[0010] Furthermore, priority marking and network slice mapping are performed on the lightweight data packets to obtain a hierarchical transmission data flow, including:

[0011] Perform multi-dimensional semantic analysis on lightweight data packets to generate dynamic priority tags and data content type identifiers;

[0012] Based on dynamic priority labels and data content type identifiers, lightweight data packets are mapped to adapted network slice channels to generate hierarchical transmission data streams.

[0013] Furthermore, based on the dynamic priority label and data content type identifier, lightweight data packets are mapped to the adapted network slice channel to generate hierarchical transmission data flows, including:

[0014] Generate network slice channel selection strategy based on dynamic priority label and data content type identification;

[0015] Perform real-time network status evaluation on lightweight data packets to generate channel load and transmission delay parameters;

[0016] According to the network slicing channel selection strategy and channel load and transmission delay parameters, resource reservation and channel mapping are performed on lightweight data packets to generate hierarchical transmission data streams.

[0017] Furthermore, the lightweight data packets are evaluated in real time for network status, generating channel load and transmission delay parameters, including:

[0018] Perform dynamic channel quality monitoring on the target network slice channel to generate channel interference intensity and available bandwidth parameters;

[0019] Perform node load prediction based on the historical transmission records of the target network slice channel to generate a channel load prediction value;

[0020] Channel load and transmission delay parameters are generated based on channel interference intensity, available bandwidth parameters and channel load prediction values.

[0021] Furthermore, based on the network slice channel selection strategy and channel load and transmission delay parameters, resource reservation and channel mapping are performed on the lightweight data packets to generate hierarchical transmission data flows, including:

[0022] Generate a dynamic resource allocation plan based on the network slice channel selection strategy and channel load parameters. The dynamic resource allocation plan includes the time slot reservation ratio and frequency band occupancy priority.

[0023] According to the dynamic resource allocation scheme, lightweight data packets are processed for multi-level priority conflict resolution and channel mapping rules are generated;

[0024] Based on channel mapping rules and real-time transmission delay parameters, dynamic channel switching is performed on lightweight data packets to generate hierarchical transmission data streams.

[0025] Furthermore, according to the dynamic resource allocation scheme, multi-level priority conflict resolution is performed on lightweight data packets to generate channel mapping rules, including:

[0026] Based on the time slot reservation ratio and frequency band occupancy priority in the dynamic resource allocation scheme, the lightweight data packet is divided into transmission time windows to generate a conflict detection time window;

[0027] Within the conflict detection time window, arbitration rules are generated for high-priority data packets competing for the same network slice channel to generate priority arbitration rules;

[0028] The transmission order of high-priority data packets is dynamically adjusted according to the priority arbitration rules to generate channel mapping rules.

[0029] Furthermore, the state of electrical equipment is linked and managed based on structured metadata and in-depth analysis results, generating equipment control instructions, including:

[0030] Analyze the equipment operation status of structured metadata and generate real-time abnormality marks;

[0031] Generate dynamic control strategies based on predictive maintenance strategies and real-time anomaly flagging from deep analysis results;

[0032] According to the dynamic control strategy, the control instructions of the target electrical equipment are adaptively adjusted to generate equipment control instructions.

[0033] In a second aspect, the present application further provides an electrical equipment management method based on the Internet of Things, the method comprising:

[0034] Perform layered edge compression on unstructured data to generate lightweight data packets;

[0035] Priority marking and network slicing mapping are performed on lightweight data packets to obtain hierarchical transmission data streams;

[0036] Perform edge-cloud collaborative processing based on hierarchical transmission data streams to generate structured metadata and in-depth analysis results;

[0037] Conduct status linkage management of electrical equipment based on structured metadata and in-depth analysis results, and generate equipment control instructions.

[0038] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, any step in the first aspect of the present application is implemented.

[0039] In a fourth aspect, the present application further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, any step in the first aspect of the present application is implemented.

[0040] The technical solution provided in this application includes the following technical effects: by providing an electrical equipment management system based on the Internet of Things, the system includes: a hierarchical edge compression module, which is used to perform hierarchical edge compression processing on unstructured data to generate lightweight data packets; a hierarchical transmission scheduling module, which is used to perform priority marking and network slice mapping processing on lightweight data packets to obtain hierarchical transmission data streams; an edge-cloud collaboration module, which is used to perform edge-cloud collaborative processing based on hierarchical transmission data streams to generate structured metadata and deep analysis results; a state linkage control module, which is used to perform state linkage management of electrical equipment based on structured metadata and deep analysis results, and generate device control instructions, so as to solve the problems of imbalance between compression efficiency and fidelity of fixed coding algorithms, rigid network channel priority allocation, and lack of dynamic linkage between device control and data flow. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. 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 paying any creative work.

[0042] Figure 1 This is a structural diagram of an electrical equipment management system based on the Internet of Things in one embodiment of the present invention;

[0043] Figure 2 A flowchart of performing multi-level priority conflict resolution processing on lightweight data packets and generating channel mapping rules according to a dynamic resource allocation scheme in one embodiment of the present invention;

[0044] Figure 3 The present invention is a flowchart of an electrical equipment management method based on the Internet of Things in one embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the above-mentioned purposes, features and advantages of the present application more clearly understood, the specific implementation methods of the present application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to fully understand the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar improvements without violating the connotation of the application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0046] like Figure 1 As shown, the present application provides an electrical equipment management system 100 based on the Internet of Things, and the system 100 includes:

[0047] The layered edge compression module 101 is used to perform layered edge compression processing on unstructured data to generate lightweight data packets.

[0048] Specifically, unstructured data is analyzed and classified, categorizing it into different levels based on its characteristics and importance. For equipment status images, image recognition and processing algorithms are applied to extract key information, such as key contours, feature points, and texture, while removing redundant pixels and background noise. For vibration waveforms, time and frequency domain analysis methods are used to identify key periodicity, amplitude, and frequency components, eliminating high-frequency noise and minor fluctuations. Next, appropriate compression algorithms are selected based on the data's level and characteristics. For example, for image data, the JPEG compression algorithm based on discrete cosine transform (DCT) or the JPEG2000 compression algorithm based on wavelet transform are used, while for vibration waveforms, linear predictive coding (LPC) or wavelet compression algorithms are used. Targeted compression is performed on each data level. During the compression process, different compression parameters are set to balance compression rate and fidelity, ensuring the preservation of key information. The compressed data is then integrated and packaged, generating lightweight data packets according to specific data formats and protocols for easy transmission and processing.

[0049] The hierarchical transmission scheduling module 102 is used to perform priority marking and network slice mapping on lightweight data packets to obtain a hierarchical transmission data stream.

[0050] Specifically, semantic parsing and content recognition are performed on lightweight data packets to extract data service types (such as equipment fault alarms, routine status monitoring), data timeliness (such as real-time alarms, historical records) and equipment relevance (such as critical motor control, auxiliary sensor monitoring), and generate dynamic priority tags and data content type identifiers. Based on the dynamic priority tags and data content type identifiers, combined with the preset transmission policy rule base, a network slice channel selection strategy is generated to clarify the target transmission channels for data of different priorities. Real-time channel quality monitoring and historical load data analysis are performed on the target network slice channel to predict short-term channel load trends and generate channel load and transmission delay quantitative parameters. According to the channel selection strategy and real-time network status parameters, dedicated time slots and frequency band resources are reserved for high-priority data packets to avoid multi-service competition; based on the dynamic priority conflict resolution mechanism, data packets are mapped to adaptive slice channels in order of priority to form a hierarchical transmission data stream. The above steps achieve low-latency and reliable transmission of critical business data through semantically driven dynamic labeling, network status-aware slicing strategies and resource reservation mechanisms, solving the problems of insufficient transmission efficiency and reliability caused by the rigid allocation of traditional network channels.

[0051] The edge-cloud collaboration module 103 is used to perform edge-cloud collaborative processing based on hierarchical transmission data streams to generate structured metadata and in-depth analysis results.

[0052] Specifically, real-time semantic parsing of hierarchical transmission data streams is performed at the edge node to extract key parameters of device status, filter redundant information, and generate structured metadata. The structured metadata and associated lightweight data packets are synchronously uploaded to the cloud, and distributed computing tasks are triggered based on business needs, calling pre-trained models for multi-dimensional analysis. The cloud allocates computing resources based on the complexity of the analysis - lightweight tasks are transmitted back to the edge node for rapid execution; complex tasks are completed by the cloud's high-performance cluster to generate in-depth analysis results. The in-depth analysis results generated in the cloud are aligned and logically associated with the structured metadata parsed at the edge in time and space to generate an executable control policy library, which is synchronized to the edge node through the reverse channel to update the local control rule library. The above steps achieve the coordinated optimization of low-latency response and high-precision analysis through the dynamic division of labor between real-time analysis on the edge and deep computing on the cloud, combined with a two-way synchronization mechanism of data and results, solving the problems of response lag and resource waste caused by traditional centralized processing.

[0053] The state linkage control module 104 is used to perform state linkage management on the electrical equipment according to the structured metadata and the in-depth analysis results, and generate equipment control instructions.

[0054] Specifically, feature extraction and standardized analysis are performed on structured metadata to generate real-time anomaly markers, and time series analysis is combined to identify equipment status trends. Based on the predictive maintenance strategy and real-time anomaly markers in the deep analysis results, a dynamic control strategy is generated, and the triggering conditions of the equipment control instructions are associated. According to the dynamic control strategy, the control interface protocol of the target electrical equipment is matched to generate executable instructions, and the instruction execution status is verified based on the real-time feedback signal of the equipment. If the execution fails, the instruction regeneration mechanism is triggered. The equipment control instructions, execution feedback signals and associated structured metadata are synchronously stored in the edge-cloud shared log library for optimizing subsequent control strategy generation rules. The above steps realize the dynamic linkage between equipment control instructions and data analysis results through a closed-loop link of state analysis, strategy generation, and instruction verification, solving the problem of instruction lag and state disconnection in traditional control systems.

[0055] An embodiment of the present application provides an electrical equipment management system based on the Internet of Things, and the system includes: a hierarchical edge compression module, which is used to perform hierarchical edge compression processing on unstructured data to generate lightweight data packets; a hierarchical transmission scheduling module, which is used to perform priority marking and network slice mapping processing on lightweight data packets to obtain hierarchical transmission data streams; an edge-cloud collaboration module, which is used to perform edge-cloud collaborative processing based on the hierarchical transmission data stream to generate structured metadata and deep analysis results; a state linkage control module, which is used to perform state linkage management of electrical equipment based on the structured metadata and deep analysis results, and generate device control instructions, so as to solve the problems of imbalance between compression efficiency and fidelity of fixed coding algorithms, rigid network channel priority allocation, and lack of dynamic linkage between device control and data flow.

[0056] Furthermore, priority marking and network slice mapping are performed on the lightweight data packets to obtain a hierarchical transmission data flow, including:

[0057] Perform multi-dimensional semantic analysis on lightweight data packets to generate dynamic priority tags and data content type identifiers;

[0058] Based on dynamic priority labels and data content type identifiers, lightweight data packets are mapped to adapted network slice channels to generate hierarchical transmission data streams.

[0059] Specifically, multi-dimensional semantic analysis is performed on lightweight data packets. First, content features are extracted from the packets. For example, for packets containing images of equipment operating status, semantic information such as the status of key components and fault indications within the images is analyzed. For vibration waveform packets, the frequency and amplitude characteristics of the waveforms are extracted to reflect the semantics of the equipment operating status. Second, the semantic importance of the packets is comprehensively determined based on the packet context. Based on the results of multi-dimensional semantic analysis, dynamic priority tags and data content type identifiers are generated for the packets. Dynamic priority tags are determined based on the urgency, importance, and timeliness of the data. For example, equipment fault alarm data has a higher priority than regular operating data. The data content type identifier distinguishes data types such as images and waveforms, facilitating subsequent processing and transmission. Based on the dynamic priority tags and data content type identifiers, lightweight data packets are mapped to appropriate network slice channels. High-priority, real-time packets are assigned to low-latency, high-reliability network slice channels, while low-priority, high-volume data is assigned to channels with higher bandwidth but slightly higher latency. This generates hierarchical data flows for efficient and reliable data transmission.

[0060] Furthermore, based on the dynamic priority label and data content type identifier, lightweight data packets are mapped to the adapted network slice channel to generate hierarchical transmission data flows, including:

[0061] Generate network slice channel selection strategy based on dynamic priority label and data content type identification;

[0062] Perform real-time network status evaluation on lightweight data packets to generate channel load and transmission delay parameters;

[0063] According to the network slicing channel selection strategy and channel load and transmission delay parameters, resource reservation and channel mapping are performed on lightweight data packets to generate hierarchical transmission data streams.

[0064] Specifically, a network slice channel selection strategy is developed based on the dynamic priority tag and data content type identifier of lightweight data packets. Based on the packet priority and type, the network slice channel characteristics that determine the transmission characteristics of packets of different types and priorities are determined. For example, high-priority control instruction packets with strict real-time requirements are selected for low-latency, high-reliability network slice channels; whereas low-priority, large-capacity historical data backup packets are selected for network slice channels with higher bandwidth but slightly higher latency. Real-time network status assessment is performed on the target network slice channel to generate channel load and transmission delay parameters. Dynamic channel quality monitoring is performed on the target network slice channel to obtain channel interference strength and available bandwidth parameters. Node load is predicted based on the target network slice channel's historical transmission records to generate a channel load prediction value. By integrating channel interference strength, available bandwidth parameters, and channel load prediction values, channel load and transmission delay parameters are derived, providing a comprehensive understanding of the current transmission performance and potential transmission delay of the network slice channel. Based on the network slice channel selection strategy and channel load and transmission delay parameters, resource reservation and channel mapping are performed for lightweight data packets. Based on the network slice channel selection strategy, the network slice channel that the data packet should use is determined. Based on channel load and transmission delay parameters, resources are allocated appropriately, such as reserving sufficient time slots and frequency band resources for high-priority data packets to ensure their fast and reliable transmission. Subsequently, lightweight data packets are mapped to the appropriate network slice channel, generating hierarchical transmission data flows for efficient and reliable data transmission.

[0065] Furthermore, the lightweight data packets are evaluated in real time for network status, generating channel load and transmission delay parameters, including:

[0066] Perform dynamic channel quality monitoring on the target network slice channel to generate channel interference intensity and available bandwidth parameters;

[0067] Perform node load prediction based on the historical transmission records of the target network slice channel to generate a channel load prediction value;

[0068] Channel load and transmission delay parameters are generated based on channel interference intensity, available bandwidth parameters and channel load prediction values.

[0069] Specifically, network monitoring tools or technologies are used to monitor the channel quality of the target network slice in real time. For example, by analyzing factors such as network signal strength, stability, and interference sources, the channel interference level is assessed and the channel interference intensity parameter is determined. Furthermore, based on the network bandwidth allocation and current usage, the available bandwidth parameter for data transmission is calculated. Historical transmission records of the target network slice channel are collected, including information such as data volume, transmission rate, and node load over different time periods. Data analysis and prediction algorithms, such as time series analysis and machine learning, are used to mine and analyze this historical data and establish a node load prediction model. Based on this model, and taking into account factors such as the current time and traffic trends, channel load is predicted for the future, generating a channel load forecast. The available bandwidth parameter determines the maximum data transmission capacity. When available bandwidth is low, the data transmission rate is limited, and transmission delay increases accordingly. The channel load forecast reflects the future network traffic level. Higher load means longer queue times for data packets during transmission, resulting in greater transmission delay. By integrating the three factors of channel interference intensity, available bandwidth parameters, and the channel load forecast, mathematical models and other methods are established to calculate the channel load and transmission delay parameters. For example, the weighted average method can be used to assign different weights to each factor according to its importance. Then, the channel interference intensity, available bandwidth parameters, and channel load prediction values can be substituted into the model to calculate the channel load and transmission delay parameters, providing a basis for subsequent network resource allocation and data transmission scheduling.

[0070] Furthermore, based on the network slice channel selection strategy and channel load and transmission delay parameters, resource reservation and channel mapping are performed on the lightweight data packets to generate hierarchical transmission data flows, including:

[0071] Generate a dynamic resource allocation plan based on the network slice channel selection strategy and channel load parameters. The dynamic resource allocation plan includes the time slot reservation ratio and frequency band occupancy priority.

[0072] According to the dynamic resource allocation scheme, lightweight data packets are processed for multi-level priority conflict resolution and channel mapping rules are generated;

[0073] Based on channel mapping rules and real-time transmission delay parameters, dynamic channel switching is performed on lightweight data packets to generate hierarchical transmission data streams.

[0074] Specifically, a dynamic resource allocation plan is generated based on the network slice channel selection strategy and channel load parameters, including the time slot reservation ratio and frequency band occupancy priority. The time slot reservation ratio determines the transmission time window size reserved for lightweight data packets, and the frequency band occupancy priority determines the order and priority of the data packet's use of different frequency band resources during transmission. Based on the dynamic resource allocation plan, multi-level priority conflict resolution is performed on lightweight data packets, and channel mapping rules are generated. When multiple data packets compete for the same network slice channel resources, arbitration is performed according to preset priority rules, ensuring that high-priority data packets receive priority, effectively resolving resource contention and ensuring orderly and efficient data transmission. Based on the generated channel mapping rules and real-time transmission delay parameters, dynamic channel switching is performed for lightweight data packets, generating hierarchical transmission data streams. During data transmission, network status is monitored in real time. If the transmission delay of the current channel exceeds a preset threshold or other transmission quality issues are detected, the data packet is switched to a more suitable network slice channel for transmission based on the channel mapping rules and real-time transmission delay parameters. This ensures data transmission using the optimal path and resources, improving transmission efficiency and reliability.

[0075] like Figure 2 As shown, according to the dynamic resource allocation scheme, multi-level priority conflict resolution is performed on lightweight data packets to generate channel mapping rules, including:

[0076] S201: Based on the time slot reservation ratio and frequency band occupancy priority in the dynamic resource allocation scheme, the lightweight data packet is divided into transmission time windows to generate a conflict detection time window;

[0077] S202: within the conflict detection time window, performing arbitration rule generation processing on high-priority data packets competing for the same network slice channel to generate a priority arbitration rule;

[0078] S203: Dynamically adjust the transmission order of the high-priority data packets according to the priority arbitration rule to generate a channel mapping rule.

[0079] Specifically, based on the time slot reservation ratio and frequency band occupancy priority in the dynamic resource allocation scheme, lightweight data packets are segmented into transmission time windows to generate collision detection time windows. The time slot reservation ratio is used to determine the size of the transmission time window available for each data packet, and frequency band resources are allocated based on frequency band occupancy priority, paving the way for the subsequent arbitration rule generation within the collision detection time window. Within the collision detection time window, arbitration rules are generated for high-priority data packets competing for the same network slice channel, generating priority arbitration rules. Priority-based arbitration strategies, such as priority sorting, are used to prioritize high-priority data packets competing for the same channel, clarifying the principle of prioritizing high-priority data packets. Furthermore, arbitration rules are further refined by comprehensively considering multiple factors, such as data packet urgency and latency sensitivity, to ensure a reasonable transmission order in complex contention scenarios. The transmission order of high-priority data packets is dynamically adjusted based on the priority arbitration rules, generating channel mapping rules. The transmission position of high-priority data packets is adjusted in real time according to the transmission order determined by the arbitration rules. If a new high-priority data packet is inserted into the transmission queue, the transmission order will be re-evaluated and adjusted according to the arbitration rules to ensure that the high-priority data packets can be transmitted in a timely and preferential manner. At the same time, the adjustment results will be recorded in the channel mapping rules to provide guidance for subsequent data transmission.

[0080] Furthermore, the state of electrical equipment is linked and managed based on structured metadata and in-depth analysis results, generating equipment control instructions, including:

[0081] Analyze the equipment operation status of structured metadata and generate real-time abnormality marks;

[0082] Generate dynamic control strategies based on predictive maintenance strategies and real-time anomaly flagging from deep analysis results;

[0083] According to the dynamic control strategy, the control instructions of the target electrical equipment are adaptively adjusted to generate equipment control instructions.

[0084] Specifically, the system analyzes the device's operating status using structured metadata. By monitoring key device parameters in real time, it applies techniques such as threshold judgment and pattern recognition to identify abnormal operating conditions and generate real-time anomaly markers, providing immediate device status information for subsequent control decisions. Secondly, the system combines the predictive maintenance strategy derived from the deep analysis results with the real-time anomaly markers to formulate a dynamic control strategy. Based on big data analytics and machine learning models, the deep analysis results can predict potential equipment failures and their development trends. Combined with the real-time anomaly markers, the dynamic control strategy determines the optimal control measures based on the device's current operating status and predicted maintenance needs, such as adjusting device operating parameters, optimizing operating modes, or performing preventive maintenance operations. Subsequently, the dynamic control strategy adaptively adjusts the control instructions for the target electrical equipment. Based on the device's real-time feedback and the requirements of the dynamic control strategy, control instructions are modified in real time, such as adjusting the device's operating mode, output power, or operating time. Furthermore, control instructions are continuously optimized based on the device's actual response and new real-time data, ensuring optimal equipment operation. This improves equipment efficiency and reliability, extends its service life, and reduces maintenance costs and downtime.

[0085] In one embodiment, if Figure 3 As shown, the present application also provides an electrical equipment management method based on the Internet of Things, the method comprising:

[0086] S301: performing layered edge compression processing on unstructured data to generate lightweight data packets;

[0087] S302: Priority marking and network slice mapping are performed on the lightweight data packet to obtain a hierarchical transmission data stream;

[0088] S303: Perform edge-cloud collaborative processing based on hierarchical transmission data streams to generate structured metadata and in-depth analysis results;

[0089] S304: Perform status linkage management on electrical equipment based on structured metadata and in-depth analysis results, and generate equipment control instructions.

[0090] Specifically, layered edge compression processing is performed on the collected unstructured data to generate lightweight data packets. Unstructured data encompasses various complex data types generated by electrical equipment during operation, such as equipment status images and vibration waveforms. By employing layered compression technology at the edge (i.e., on the device side close to the data source), key data features are extracted layer by layer and redundant information is removed, achieving efficient data compression. This reduces data volume and improves the efficiency of subsequent transmission and processing. The generated lightweight data packets are then prioritized and mapped to network slices to produce hierarchical transmission data streams. Based on factors such as the urgency and importance of the data, as well as the type of service, the lightweight data packets are assigned a corresponding priority and mapped to the appropriate network slice channel. Network slicing technology divides the physical network into multiple virtual network slices, each with unique characteristics to meet the transmission requirements of different data types. This ensures the priority transmission of critical business data and improves the reliability and timeliness of data transmission.

[0091] Next, based on the hierarchical data stream, edge-cloud collaborative processing is carried out to generate structured metadata and deep analysis results. The edge performs simple preprocessing on the initially transmitted data, such as data cleaning and preliminary feature extraction. The cloud then uses big data analytics, machine learning, and other technologies to conduct deep data mining and analysis. Structured metadata is the result of organizing and standardizing raw unstructured data into a fixed format with clear semantics, facilitating rapid subsequent query and application. The deep analysis results include trend predictions for equipment operating status, fault diagnosis information, and potential risk assessments. Based on the generated structured metadata and deep analysis results, state-linked management of electrical equipment is implemented, and corresponding control instructions are generated. Real-time analysis of the structured metadata reveals the real-time operating status of the equipment. Combined with the predictive maintenance strategy derived from the deep analysis results, a dynamic control strategy is developed. Based on this strategy, the control instructions for the target electrical equipment are adaptively adjusted to achieve control, ensuring optimal equipment operation, preventing failures, and improving operational efficiency and reliability.

[0092] The hierarchical transmission scheduling module 102 is further configured to:

[0093] Perform multi-dimensional semantic analysis on lightweight data packets to generate dynamic priority tags and data content type identifiers;

[0094] Based on dynamic priority labels and data content type identifiers, lightweight data packets are mapped to adapted network slice channels to generate hierarchical transmission data streams.

[0095] The hierarchical transmission scheduling module 102 is further configured to:

[0096] Generate network slice channel selection strategy based on dynamic priority label and data content type identification;

[0097] Perform real-time network status evaluation on lightweight data packets to generate channel load and transmission delay parameters;

[0098] According to the network slicing channel selection strategy and channel load and transmission delay parameters, resource reservation and channel mapping are performed on lightweight data packets to generate hierarchical transmission data streams.

[0099] The hierarchical transmission scheduling module 102 is further configured to:

[0100] Perform dynamic channel quality monitoring on the target network slice channel to generate channel interference intensity and available bandwidth parameters;

[0101] Perform node load prediction based on the historical transmission records of the target network slice channel to generate a channel load prediction value;

[0102] Channel load and transmission delay parameters are generated based on channel interference intensity, available bandwidth parameters and channel load prediction values.

[0103] The hierarchical transmission scheduling module 102 is further configured to:

[0104] Generate a dynamic resource allocation plan based on the network slice channel selection strategy and channel load parameters. The dynamic resource allocation plan includes the time slot reservation ratio and frequency band occupancy priority.

[0105] According to the dynamic resource allocation scheme, lightweight data packets are processed for multi-level priority conflict resolution and channel mapping rules are generated;

[0106] Based on channel mapping rules and real-time transmission delay parameters, dynamic channel switching is performed on lightweight data packets to generate hierarchical transmission data streams.

[0107] The hierarchical transmission scheduling module 102 is further configured to:

[0108] Based on the time slot reservation ratio and frequency band occupancy priority in the dynamic resource allocation scheme, the lightweight data packet is divided into transmission time windows to generate a conflict detection time window;

[0109] Within the conflict detection time window, arbitration rules are generated for high-priority data packets competing for the same network slice channel to generate priority arbitration rules;

[0110] The transmission order of high-priority data packets is dynamically adjusted according to the priority arbitration rules to generate channel mapping rules.

[0111] The state linkage control module 104 is further configured to:

[0112] Analyze the equipment operation status of structured metadata and generate real-time abnormality marks;

[0113] Generate dynamic control strategies based on predictive maintenance strategies and real-time anomaly flagging from deep analysis results;

[0114] According to the dynamic control strategy, the control instructions of the target electrical equipment are adaptively adjusted to generate equipment control instructions.

[0115] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0116] In one embodiment, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned system embodiments when executing the computer program.

[0117] In one embodiment, the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps in the above-mentioned system embodiments are implemented.

[0118] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0119] The above-described embodiments merely represent several implementation methods of the embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the concept of the embodiments of the present application, and these modifications and improvements fall within the scope of protection of the embodiments of the present application.

Claims

1. An electrical equipment management system based on the Internet of Things, characterized in that: The system comprises: The layered edge compression module is used to perform layered edge compression processing on unstructured data to generate lightweight data packets; A hierarchical transmission scheduling module is used to perform priority marking and network slice mapping processing on the lightweight data packets to obtain a hierarchical transmission data stream; An edge-cloud collaboration module, configured to perform edge-cloud collaborative processing based on the hierarchical transmission data stream to generate structured metadata and in-depth analysis results; A state linkage control module is used to perform state linkage management on the electrical equipment according to the structured metadata and the deep analysis results, and generate equipment control instructions.

2. The electrical equipment management system based on the Internet of Things according to claim 1, characterized in that: The performing priority marking and network slice mapping processing on the lightweight data packet to obtain a hierarchical transmission data stream includes: Performing multi-dimensional semantic analysis on the lightweight data packet to generate a dynamic priority tag and a data content type identifier; Based on the dynamic priority label and data content type identifier, the lightweight data packet is mapped to the adapted network slice channel to generate the hierarchical transmission data stream.

3. The electrical equipment management system based on the Internet of Things according to claim 2, characterized in that: The mapping of the lightweight data packet to the adapted network slice channel based on the dynamic priority label and the data content type identifier to generate the hierarchical transmission data stream includes: Generate a network slice channel selection strategy based on the dynamic priority label and the data content type identifier; Performing real-time network status evaluation processing on the lightweight data packet to generate channel load and transmission delay parameters; According to the network slice channel selection strategy and the channel load and transmission delay parameters, resource reservation and channel mapping are performed on the lightweight data packet to generate the hierarchical transmission data stream.

4. The electrical equipment management system based on the Internet of Things according to claim 3, characterized in that: The performing real-time network status evaluation processing on the lightweight data packet to generate channel load and transmission delay parameters includes: Perform dynamic channel quality monitoring on the target network slice channel to generate channel interference intensity and available bandwidth parameters; Perform node load prediction processing based on the historical transmission records of the target network slice channel to generate a channel load prediction value; The channel load and transmission delay parameters are generated according to the channel interference intensity, the available bandwidth parameter and the channel load prediction value.

5. The electrical equipment management system based on the Internet of Things according to claim 3, characterized in that: The performing resource reservation and channel mapping processing on the lightweight data packet according to the network slice channel selection strategy and the channel load and transmission delay parameters to generate the hierarchical transmission data stream includes: Generate a dynamic resource allocation plan based on the network slice channel selection strategy and channel load parameters, wherein the dynamic resource allocation plan includes a time slot reservation ratio and a frequency band occupancy priority; According to the dynamic resource allocation scheme, performing multi-level priority conflict resolution processing on the lightweight data packet to generate a channel mapping rule; Based on the channel mapping rule and the real-time transmission delay parameter, dynamic channel switching processing is performed on the lightweight data packet to generate the hierarchical transmission data stream.

6. The electrical equipment management system based on the Internet of Things according to claim 5, characterized in that: The step of performing multi-level priority conflict resolution processing on the lightweight data packet according to the dynamic resource allocation scheme to generate a channel mapping rule includes: Based on the time slot reservation ratio and frequency band occupancy priority in the dynamic resource allocation scheme, the lightweight data packet is divided into transmission time windows to generate a conflict detection time window; Within the conflict detection time window, performing arbitration rule generation processing on high-priority data packets competing for the same network slice channel to generate a priority arbitration rule; The transmission order of the high-priority data packets is dynamically adjusted according to the priority arbitration rule to generate the channel mapping rule.

7. The electrical equipment management system based on the Internet of Things according to claim 1, characterized in that: The step of performing linkage management on the electrical equipment based on the structured metadata and the deep analysis results and generating equipment control instructions includes: Performing equipment operation status analysis on the structured metadata to generate real-time abnormality marks; generating a dynamic control strategy based on the predictive maintenance strategy in the in-depth analysis results and the real-time abnormality markers; The control instructions of the target electrical equipment are adaptively adjusted according to the dynamic control strategy to generate equipment control instructions.

8. An electrical equipment management method based on the Internet of Things, characterized in that: The method comprises: Perform layered edge compression on unstructured data to generate lightweight data packets; Priority marking and network slicing mapping are performed on the lightweight data packets to obtain a hierarchical transmission data stream; Performing edge-cloud collaborative processing based on the hierarchical transmission data stream to generate structured metadata and in-depth analysis results; The state linkage management of the electrical equipment is performed according to the structured metadata and the deep analysis result, and the equipment control instructions are generated.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of an electrical equipment management system based on the Internet of Things according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of an electrical equipment management system based on the Internet of Things according to any one of claims 1 to 7 are implemented.

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