Method and system for resource full-link lightweight synchronization and real-time presentation

Abstract

The application discloses a resource full-link lightweight synchronization and real-time presentation method and system, the method comprises the following steps: uniformly abstracting resources of a full-network heterogeneous resource, and abstracting the resources into digital objects; creating a lightweight transmission mechanism, deploying a resource lightweight collection agent on the resource supply side distributed in the full network, preprocessing resource data, and adopting the lightweight transmission mechanism to send the resource global fusion data to the resource demand side in a demand mode through multi-level lightweight aggregation of edge nodes, regional centers and cloud service centers, and performing resource lightweight presentation; the system comprises a resource uniform abstraction module, a lightweight reliable transmission module, a resource lightweight collection module, a resource lightweight aggregation module and a resource lightweight presentation module. The application realizes source lightweight collection and adaptation of resource data in a high-mobility and weak-connection environment, ensures low delay and high reliability of full-link data synchronization, and realizes real-time, accurate and low-load visualization of resource states.

Description

Technical Field

[0001] This invention relates to the field of resource management technology, and in particular to a lightweight method and system for end-to-end synchronization and real-time presentation of resources. Background Technology

[0002] In emergency response and industrial IoT scenarios, the demand for real-time monitoring of equipment resource status, fault early warning, and rapid response is increasing. However, the types of terminal devices in industrial and emergency situations are diverse, with significant differences in hardware computing power, storage capacity, and communication capabilities, as well as varying software platforms and data interfaces, making resource description and synchronization difficult. Furthermore, the complex on-site environment and frequent interference, bandwidth fluctuations, and network interruptions make it difficult to maintain stable and reliable long-term connections, posing a severe challenge to end-to-end resource synchronization and real-time status presentation.

[0003] Currently, most common resource synchronization solutions rely on centralized architectures or continuous high-bandwidth communication, which are difficult to adapt to the real-time requirements of high mobility and weak connectivity at the edge. In situations where device resources are heterogeneous and network conditions are unstable, existing technologies often cannot achieve lightweight end-to-end resource synchronization, nor can they support real-time visualization and dynamic updates of resources. This leads to delays in operation and maintenance response and lag in decision-making information, failing to meet the urgent needs of emergency command and industrial IoT systems for real-time knowledge and control of resource status.

[0004] Therefore, there is an urgent need for a technical solution that can achieve lightweight synchronization and real-time visualization of resources across the entire chain in scenarios such as emergency response and industrial IoT, in order to improve the reliability, real-time performance and operation and maintenance efficiency of the system in complex environments. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to provide a method and system for lightweight synchronization and real-time presentation of resources across the entire chain.

[0006] Technical solution: The resource end-to-end lightweight synchronization and real-time presentation method of the present invention includes the following steps:

[0007] S1: Perform unified resource abstraction on heterogeneous resources across the entire network. By defining resource metadata standards and interface specifications, resources are abstracted into digital objects with standard attributes and behaviors.

[0008] S2: Build a lightweight transmission mechanism, including delivery strategy determination based on communication status and address book, packet transmission and retransmission based on UDP protocol, and multi-path parallel transmission, for transmitting data during resource collection, aggregation and presentation.

[0009] S3: On the resource supply side distributed across the entire network, deploy a lightweight resource collection agent to collect resource ontology information and real-time resource status data, as well as perform data filtering, difference extraction, binary encoding, data compression, and data caching preprocessing on the resource data.

[0010] S4: The pre-processed resource data is aggregated in a lightweight manner through multiple levels of edge nodes, regional centers, and cloud service centers, including data transmission at each level and multi-level data aggregation, to form global fused resource data.

[0011] S5: Employs a lightweight transmission mechanism to deliver globally integrated resource data to the resource demand side on demand, and displays resource status through a grid viewport to achieve lightweight resource presentation.

[0012] Furthermore, step S1, which performs unified resource abstraction on heterogeneous resources across the entire network, includes:

[0013] S11: Assign a resource a unique identifier across the entire network, a resource name, a resource type, a resource status, a resource mobility attribute, and a resource capability attribute;

[0014] S12: Use a text-based structured data format to standardize the definition of resource metadata;

[0015] S13: Standardize resource interfaces by adopting standardized network service interfaces and unified data exchange formats. The unified data exchange format means that resource interfaces use a structured data format for data serialization when facing upper-layer applications.

[0016] Furthermore, step S2, which establishes a lightweight transmission mechanism, includes:

[0017] S21: Resource data delivery strategy is determined based on lightweight communication status awareness and precise addressing in the address book, dynamically selecting whether to wait, deliver directly to the destination, or deliver resource data through relay nodes; among them, lightweight communication status awareness monitors network conditions in real time through a combined identification mode, obtaining indicators such as connectivity, latency, bandwidth, and packet loss rate; precise addressing in the address book maintains the resource ownership address mapping, and the resource ownership department should plan and implement multiple communication addresses to support proximity addressing based on geographical location or network topology; the delivery strategy is dynamically selected based on real-time communication status and communication address availability;

[0018] S22: The resource data transmission protocol is lightweight and uses UDP connectionless mode to implement resource data packet transmission, acknowledgment, and timeout retransmission. The data packetization adopts a fixed-size or dynamically adjusted packetization strategy, which adapts to network conditions. The acknowledgment mechanism requires the receiver to reply with an ACK or NACK response at the sender's request to confirm the data packet reception status. The timeout retransmission mechanism sets a dynamic timeout threshold and a dynamic maximum number of retransmissions, calculated based on round-trip time and real-time network conditions. Retransmission is triggered when no acknowledgment is received, until the maximum number of retransmissions is reached.

[0019] S23: Resource data is transmitted in parallel via multiple paths, integrating dynamic path selection, load balancing algorithms, and failover mechanisms. Dynamic path selection calculates multiple available transmission paths based on communication status and address book information. The load balancing algorithm allocates data streams equally to each selected path based on path quality. The failover mechanism monitors the health status of the paths and automatically switches to a backup path when a path fails.

[0020] Furthermore, step S21 adopts a lightweight sensing of the communication status by using passive listening as the main method and active detection as the auxiliary method. The passive listening captures and parses network broadcast and locally transmitted and received data packets to determine the connected nodes. The active detection sends a lightweight query request to obtain the connection status when the node's communication status has not been updated for a long time.

[0021] Delay is estimated by calculating the difference between the arrival time and the timestamp of the data packet;

[0022] Bandwidth is estimated by monitoring the total amount of data packets successfully transmitted per unit time.

[0023] Packet loss rate is estimated by comparing the sequence numbers of the actual received data packets with those of the expected received data packets.

[0024] The predictions of latency, bandwidth, and packet loss rate are all achieved by removing noise using a lightweight filtering algorithm.

[0025] Delivery strategies include waiting, direct delivery to the destination, and relaying to the nearest accessible node via uplink, downlink, or parallel transmission.

[0026] The formula for dynamically selecting the delivery strategy is as follows:

[0027]

[0028] in, This indicates the connectivity state with the destination, where Represents connectivity, This indicates a disconnection; Indicates the direction of transmission to the destination. Represents uplink transmission, Represents downlink transmission, Represents parallel non-friendly transmission, Represents parallel neighbor transmission; This indicates the delivery strategy selection value, where 0 represents direct delivery to the destination, 1 represents uplink transmission selecting the nearest accessible node for relay delivery, 2 represents downlink transmission selecting the nearest accessible node for relay delivery, 3 represents parallel transmission selecting the nearest accessible node for relay delivery, and 4 represents waiting for connection to the destination.

[0029] The adaptive change formula for the subcontracting strategy is as follows:

[0030]

[0031] in, Indicates the base package size; Indicates the current network bandwidth; Indicates the reference bandwidth; This indicates the current network packet loss rate; Indicates the current round-trip time; This indicates the estimated round-trip time, based on historical data or environmental presets. Indicates the packet size after adaptive optimization; function Indicates the preset adjustment coefficient;

[0032] The formula for calculating the dynamic timeout threshold is as follows:

[0033]

[0034] in, This indicates the round-trip time currently being measured. Indicates the variance of round-trip time. and This indicates the preset weighting coefficients. Indicates the dynamic timeout threshold;

[0035] The formula for calculating the dynamic maximum number of retransmissions is as follows:

[0036]

[0037] in, Indicates the basic retransmission count. This indicates the current network packet loss rate. Indicates the scaling factor. Indicates the maximum number of dynamic retransmissions;

[0038] Dynamic path selection is based on communication status and address book information, determined by delivery strategies. For uplink, downlink, and parallel transmission, multiple nearby accessible nodes exist. Specifically, when the department responsible for the nearest accessible resource to the destination has two or more candidate nodes, the quality of each candidate path is calculated, and a primary or backup path is selected based on the overall quality of the candidate paths. The candidate path quality scoring formula is as follows:

[0039]

[0040] in, Indicates the candidate path index; Indicates the first Available bandwidth for each candidate path; Indicates the first End-to-end latency of each candidate path; Indicates the first Packet loss rate of each candidate path; Indicates the first The current load of each candidate path; Indicates the first Maximum load capacity of each candidate path; Achieve bandwidth metric normalization. For the first The maximum bandwidth threshold for each candidate path; Achieve normalization of latency metrics, For the first The maximum allowed delay for each candidate path; Achieve normalization of packet loss rate; arrive For the weighting coefficients, satisfying Dynamically adjusted based on the actual scenario; Indicates the first The quality score of each candidate path is a factor; the higher the score, the better the path quality.

[0041] Furthermore, arrive The dynamic adjustment of weighting coefficients includes the following principles:

[0042] Prioritize network conditions: Increase the weight of packet loss rate in weak connection environments. Increasing bandwidth weight in high-bandwidth environments and delay weight ;

[0043] Resource type adaptation: Increase latency weight when sensing resource transmission. Increase the weight of packet loss rate when handling resource transmission. Increase bandwidth weight when ensuring resource transmission ;

[0044] Dynamic environment adaptation: Fixed weights are used in stable environments, while the weight values ​​are optimized through lightweight machine learning algorithms in highly mobile environments. The reward function is calculated based on the increase in transmission success rate.

[0045] Furthermore, step S3, lightweight resource acquisition, includes:

[0046] S31: Resource ontology information collection, which is obtained from the resource's own metadata storage, central registry, configuration files or resource description documents, including the resource's unique identifier, resource name, resource type, resource capability attributes and resource interface definition static information; the resource ontology information collection frequency adopts one-time collection or low-frequency update, which is only triggered when the resource is registered or the configuration is changed;

[0047] S32: Real-time resource status collection, acquired from the resource's own real-time output interface, central monitoring service, event stream, and local collection agent, including resource health status, resource load rate, resource geographical location, and dynamic status of resources due to interface changes; the frequency of real-time resource status collection is adaptively and dynamically adjusted based on resource priority and network load, and the formula for dynamically adjusting the collection frequency is as follows:

[0048]

[0049] in, Indicates the fundamental frequency; Indicates priority weight; Indicates the variance of network load variation; and These are the weighting coefficients; This indicates that the sampling frequency will be dynamically adjusted.

[0050] S33: Resource raw data preprocessing, including: data filtering, using a lightweight filtering algorithm to filter redundant, invalid, or abnormal data in the raw data; difference extraction, by comparing the current state with historical states, extracting the differences in changes from the filtered data; binary encoding, using a preset structured data conversion binary template to convert the extracted variable data into binary format; data compression, applying a lightweight compression algorithm to perform lossless compression on the binary-encoded data to reduce data volume; and data caching, temporarily storing the compressed data in a local lightweight cache database, supporting batch transmission, with the batch size dynamically adjusted based on network conditions. The formula for dynamic batch size adjustment is as follows:

[0051]

[0052] in, Indicates the base batch size. This represents the network load factor (0-1). Indicates the weighting coefficient. Indicates the dynamic batch size.

[0053] Furthermore, step S4, lightweight resource aggregation, includes:

[0054] S41: Edge Resource Aggregation. At edge nodes, preprocessed resource data from multiple collection points is aggregated, deduplicated, and compressed to generate an edge resource status summary, including a summary of resource status, health indicators, and geographic location. The aggregation frequency is dynamically adjusted based on the data change rate, using the following formula:

[0055]

[0056] in, Indicates the fundamental frequency; Indicates the variance of the data; These are the weighting coefficients; This indicates that the aggregation frequency is dynamically adjusted.

[0057] S42: Regional resource aggregation. This aggregates edge resource status summaries from multiple edge nodes at the regional center, performing deduplication, difference synchronization, multi-source association, and conflict resolution to generate a regional resource aggregation view. This view includes a summary of the region's resource status, health indicators, geographic location, load rate statistics, and conflict resolution results. A sliding window mechanism is used to aggregate data streams in real time, dynamically adjusting the size of the sliding window based on the data inflow rate and network latency. The formula for dynamically adjusting the sliding window size is as follows:

[0058]

[0059] in, Indicates the base window size; Indicates the data inflow rate; Indicates network latency; and These are the weighting coefficients; Indicates the size of the sliding window;

[0060] S43: Global Resource Fusion. This involves fusing data from all regions in the cloud service center. A data fusion algorithm is used to integrate multi-regional data, generating a global resource status, including overall resource distribution, real-time health status, and dynamic trends. The global fusion frequency is based on event-driven or timed polling, and the adjustment formula is as follows:

[0061]

[0062] in, Indicates the minimum fusion frequency; Indicates the number of regions; Indicates a region index; Indicates the first The rate of change of data in each region; This is the scaling factor; Indicates the global fusion frequency.

[0063] Furthermore, step S5, lightweight resource presentation, includes:

[0064] S51: Grid division, which divides the display area into uniform grid cells, each grid cell is associated with a specific geographic range or logical region, and the grid size is dynamically adjusted based on the zoom level, supporting spatial indexing and fast query; each grid cell is assigned a unique coded ID, and the coded structure contains hierarchical information, enabling multi-resolution navigation;

[0065] S52: Viewport management, only renders the mesh cells within the current user viewport on the resource demand side, filters invisible resources through the viewport clipping algorithm, and dynamically loads or unloads mesh data when the viewport changes;

[0066] S53: Incremental update. It compares the changes in resource status between frames before and after by difference detection, identifies only incremental data, and marks the status with version number or timestamp. Local rendering only updates the resource status within the changed mesh cell. Among them, difference detection uses a double-buffered comparison algorithm to calculate the status change; local rendering locates the mesh cell to which the changed resource belongs and only re-renders that mesh.

[0067] S54: Multi-resolution adaptation, dynamically adjusts rendering precision according to the resolution and scaling of the display device, uses simplified models for low resolution, loads high-precision resources for high resolution, adopts a hierarchy of detail to set multi-level detail models for resources, and dynamically switches model details based on viewport distance.

[0068] S55: Interactive operation, binds event listeners to resources to support click, hover, and drag interactions, binds events to grid cells through event delegation, and interactive operations trigger real-time visual feedback such as highlighting selected resources or popping up details panels. The real-time feedback is based on WebSocket or Lightweight HTTP to achieve low-latency transmission; gesture recognition is optimized through inertial scrolling and trajectory analysis algorithms.

[0069] The resource end-to-end lightweight synchronization and real-time presentation system of the present invention includes:

[0070] The resource unification abstraction module is deployed on the cloud service center node and is used to perform unified abstraction of heterogeneous resources across the entire network. By defining the metadata standards and interface specifications of resources, it abstracts resources into digital objects with standard attributes and behaviors.

[0071] Lightweight and reliable transmission modules are deployed on the resource supply side, edge nodes, regional center nodes, cloud service center nodes, and resource demand side. They are used to transmit data during resource collection, aggregation, and presentation, including delivery strategy determination based on communication status and address book, packet transmission and retransmission based on UDP protocol, and multi-path parallel transmission.

[0072] The lightweight resource acquisition module is deployed on various resource devices or nearby edge nodes on the resource supply side. It is used to deploy lightweight resource acquisition agents on the resource supply side distributed throughout the network, collect resource ontology information and real-time resource status data, and perform data filtering, difference extraction, binary encoding, data compression, and data caching preprocessing on the resource data.

[0073] The lightweight resource aggregation module is deployed on edge nodes, regional center nodes, and cloud service center nodes. It is used to aggregate pre-processed resource data through multiple levels of lightweight aggregation, including data transmission at each level and multi-level data aggregation, to form globally integrated resource data.

[0074] The lightweight resource presentation module is deployed on the resource demand side. It is used to deliver globally integrated resource data to the resource demand side on demand and display the resource status through the grid viewport to perform lightweight resource presentation.

[0075] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0076] (1) This invention deploys lightweight acquisition agents at the resource end and edge end to perform unified abstraction and local preprocessing of heterogeneous resources, thereby realizing lightweight acquisition and adaptation of resource data at the source in a highly mobile and weakly connected environment; by adopting a dynamic delivery strategy based on communication status and address book, reliable UDP packet transmission and multi-path parallel transmission mechanism, a highly efficient and robust lightweight aggregation link is constructed, which effectively overcomes the challenges of network discontinuity, bandwidth limitation and high latency, and ensures low latency and high reliability of full-link data synchronization; finally, through incremental update driven by grid viewport and multi-resolution adaptation presentation technology, real-time, accurate and low-load visualization of resource status is realized, which significantly reduces the front-end rendering overhead;

[0077] (2) The present invention integrates the concept of lightweighting into the entire chain of resource abstraction, acquisition, transmission, aggregation and presentation, enabling it to be widely used in fields such as emergency command and industrial Internet of Things that require real-time monitoring and coordination of resources under complex network conditions, thereby greatly improving the system’s resilience in harsh environments, resource synchronization efficiency and timeliness of personnel decision-making. Attached Figure Description

[0078] Figure 1 A schematic diagram illustrating the application scenario of a lightweight synchronization and real-time presentation method for the entire resource chain;

[0079] Figure 2 A schematic diagram illustrating the process of unifying and abstracting resources;

[0080] Figure 3 A schematic diagram illustrating the lightweight resource acquisition process;

[0081] Figure 4 A schematic diagram illustrating the process of lightweight resource aggregation;

[0082] Figure 5 A schematic diagram illustrating the process of presenting resources in a lightweight manner. Detailed Implementation

[0083] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0084] like Figure 1 As shown, the resource end-to-end lightweight synchronization and real-time presentation method of the present invention includes the following steps:

[0085] S1: Unified Resource Abstraction. This involves unifying the abstraction of heterogeneous resources across the entire network. By defining metadata standards and interface specifications for resources, resources are abstracted into digital objects with standard attributes and behaviors.

[0086] like Figure 2 As shown, the detailed steps of unified resource abstraction are as follows:

[0087] S11: Resource Digital Objectification. Assign a unique identifier, name, type, status, mobility, and capability attributes to each resource.

[0088] The resource types are classified according to dimensions such as function and ownership. They can be classified according to resource function into sensing resources, disposal resources, and support resources, etc., and classified according to resource ownership to identify the organization (unit, department, address, etc.) to which the resource belongs. Other types and dimensions can be added through metadata configuration to adapt to different application scenarios.

[0089] The resource status is used to describe resource availability and performance in real time, including resource health status, resource load status, and resource geographical location. Other status dimensions can be added through metadata configuration to adapt to real-time monitoring needs.

[0090] The resource mobility is divided into mobile and fixed;

[0091] The resource capability attributes are used to define the behavior and constraints of resources, including the functional description and performance indicators of resources;

[0092] S12: Resource metadata definition. Resource metadata is defined using the text-based structured data format JSON.

[0093] S13: Resource Interface Standardization. Resource interfaces are standardized using RESTful APIs and a unified data exchange format.

[0094] The unified data exchange format refers to the use of the structured data format JSON for data serialization by the resource interface for upper-layer applications.

[0095] S2: Lightweight and Reliable Transmission. A lightweight transmission mechanism is implemented, including delivery strategy determination based on connection status and address book, packet-based transmission and retransmission using the UDP protocol, and multi-path parallel transmission for data transmission during resource acquisition, aggregation, and presentation. This includes:

[0096] S21: Resource data delivery strategy determination. Based on lightweight communication status awareness and precise addressing in the address book, the system dynamically selects whether to wait, deliver directly to the destination, or deliver resource data via relay nodes. Specifically, lightweight communication status awareness monitors network conditions in real time through a combined identification mode, obtaining indicators such as connectivity, latency, bandwidth, and packet loss rate; precise addressing in the address book maintains the resource ownership address mapping, and the resource ownership department should plan and implement multiple communication (node) addresses to support proximity addressing based on geographical location or network topology; the delivery strategy is dynamically selected based on real-time communication status and communication address availability.

[0097] The aforementioned identification mode uses a combination of passive listening as the primary method and active detection as a secondary method to lightly perceive the communication status. The passive listening method captures and analyzes network broadcasts (including wireless transmissions) and locally transmitted and received data packets to determine which nodes are connected. The active detection method sends a lightweight query request to obtain and determine the connectivity status when the node's communication status has not been updated for a long time.

[0098] The delay is estimated by calculating the difference between the arrival time of the data packet and the timestamp of the data packet;

[0099] The bandwidth is estimated by monitoring the total amount of data packets successfully transmitted per unit time.

[0100] The packet loss rate is estimated by comparing the actual received data packet sequence numbers with the expected received data packet sequence numbers.

[0101] The predictions of the latency, bandwidth, and packet loss rate indicators are all performed by removing noise using the lightweight filtering algorithm, Kalman filtering.

[0102] The delivery strategies include waiting, direct delivery to the destination, or relay delivery through the nearest accessible node via uplink, downlink, or parallel transmission.

[0103] The dynamic selection formula for the delivery strategy is as follows:

[0104]

[0105] in, This indicates the connectivity state with the destination, where Represents connectivity, This indicates a disconnection; Indicates the transmission direction to the destination, where Represents uplink transmission, Represents downlink transmission, Represents parallel non-friendly transmission, Represents parallel neighbor transmission; This indicates the delivery strategy selection value, where 0 represents direct delivery to the destination, 1 represents uplink transmission selecting the nearest accessible node for relay delivery, 2 represents downlink transmission selecting the nearest accessible node for relay delivery, 3 represents parallel transmission selecting the nearest accessible node for relay delivery, and 4 represents waiting for connection to the destination.

[0106] S22: Lightweight resource data transmission protocol. It employs a connectionless UDP approach to implement resource data packet transmission, acknowledgment, and timeout retransmission. Data packets can be segmented into fixed-size or dynamically adjusted strategies, with the strategy adapting to network conditions. The acknowledgment mechanism requires the receiver to respond with an ACK or NACK response to confirm packet reception. The timeout retransmission mechanism sets a dynamic timeout threshold and a dynamic maximum number of retransmissions, calculated based on round-trip time (RTT) and real-time network conditions (such as packet loss rate and latency). Retransmission is triggered if no acknowledgment is received, until the maximum number of retransmissions is reached.

[0107] The adaptive change formula for the sub-packet strategy is as follows:

[0108]

[0109] in, Indicates the base package size; Indicates the current network bandwidth; Indicates the reference bandwidth; This indicates the current network packet loss rate; Indicates the current round-trip time; This indicates the estimated round-trip time, based on historical data or environmental presets. Indicates the packet size after adaptive optimization; function Indicates the preset adjustment coefficient;

[0110] The formula for calculating the dynamic timeout threshold is as follows:

[0111]

[0112] in, This indicates the round-trip time currently being measured. Indicates the variance of round-trip time. and This indicates the preset weighting coefficients. Indicates the dynamic timeout threshold;

[0113] The formula for calculating the dynamic maximum number of retransmissions is as follows:

[0114]

[0115] in, Indicates the basic retransmission count. This indicates the current network packet loss rate. Represents the scaling factor (positive integer value). Indicates the maximum number of retransmissions dynamically.

[0116] S23: Multi-path parallel transmission of resource data. This integrates dynamic path selection, load balancing algorithms, and failover mechanisms to ensure more reliable transmission of resource data. Dynamic path selection calculates multiple available transmission paths based on communication status and contact information; the load balancing algorithm allocates data streams equally to each selected path based on path quality, avoiding single-path congestion; and the failover mechanism monitors path health and automatically switches to a backup path when a path fails.

[0117] The dynamic path selection is based on communication status and address book information, determined by a delivery strategy. For uplink, downlink, and parallel transmission, multiple nearby accessible nodes exist. Specifically, when the department responsible for the nearest accessible resource to the destination has two or more candidate nodes, the quality of each candidate path is calculated, and a primary and backup path is selected based on the overall quality of the candidate paths. The candidate path quality scoring formula is as follows:

[0118]

[0119] in, Indicates the candidate path index; Indicates the first Available bandwidth for each candidate path; Indicates the first End-to-end latency of each candidate path; Indicates the first Packet loss rate of each candidate path; Indicates the first The current load of each candidate path; Indicates the first Maximum load capacity of each candidate path; Achieve bandwidth metric normalization, among which For the first The maximum bandwidth threshold for each candidate path; To achieve normalization of latency metrics, among which For the first The maximum allowed delay for each candidate path; Achieve normalization of packet loss rate; arrive For the weighting coefficients, satisfying Dynamically adjusted based on the actual scenario; Indicates the first The quality score of each candidate path is a factor; the higher the score, the better the path quality.

[0120] The dynamic adjustment of the weighting coefficients includes the following principles:

[0121] Prioritize network conditions: Increase the weight of packet loss rate in weak connection environments. Increasing bandwidth weight in high-bandwidth environments and delay weight ;

[0122] Resource type adaptation: Increase latency weight when sensing resource transmission. Increase the weight of packet loss rate when handling resource transmission. Increase bandwidth weight when ensuring resource transmission ;

[0123] Dynamic environment adaptation: Fixed weights are used in stable environments, while the weight values ​​are optimized through a lightweight machine learning algorithm in highly mobile environments. The reward function is calculated based on the increase in transmission success rate.

[0124] S3: Lightweight Resource Acquisition. Deploy lightweight resource acquisition agents on the resource supply side distributed across the entire network to collect resource metadata such as resource ontology information and real-time resource status, and perform preprocessing on the raw resource data, including data filtering, difference extraction, binary encoding, data compression, and data caching.

[0125] like Figure 3 As shown, the detailed steps for lightweight resource acquisition are as follows:

[0126] S31: Resource ontology information collection, which is obtained from the resource's metadata storage, central registry, configuration files, or resource description documents. This includes static resource information such as the resource's unique identifier, resource name, resource type, resource capability attributes, and resource interface definitions.

[0127] The resource ontology information is collected either once or updated in a low frequency, and is only triggered when the resource is registered or its configuration is changed.

[0128] S32: Real-time resource status collection, which is collected from the real-time output interface of the resource itself, the central monitoring service, the event stream and the local collection agent, including the resource health status, resource load rate, resource geographical location and resource interface changes (such as API endpoint updates) and other dynamic resource statuses.

[0129] The real-time resource status collection frequency is dynamically adjusted based on resource priority and network load. The formula for dynamically adjusting the collection frequency is as follows:

[0130]

[0131] in, Indicates the fundamental frequency; Indicates priority weight; Indicates the variance of network load variation; and These are the weighting coefficients; This indicates that the sampling frequency is dynamically adjusted.

[0132] S33: Resource source data preprocessing, including:

[0133] Data filtering employs a lightweight Bloom filter algorithm to filter out redundant, invalid, or anomalous data from the original data.

[0134] Difference extraction: By comparing the current state with historical states, the differences in changes before and after are extracted from the filtered data; Binary encoding: Using a preset structured data to convert binary templates, the extracted variable data is converted into binary format;

[0135] Data compression is performed using the lightweight compression algorithm LZ4 to losslessly compress the binary encoded data, further reducing the data size.

[0136] Data caching temporarily stores compressed data in a lightweight local cache database, SQLite. It supports batch transfers, and the batch size is dynamically adjusted based on network conditions. The formula for dynamically adjusting the batch size is as follows:

[0137]

[0138] in, Indicates the base batch size. This represents the network load factor (0-1). Indicates the weighting coefficient. Indicates the dynamic batch size.

[0139] S4: Lightweight Resource Aggregation. Preprocessed resource data is aggregated in a lightweight manner through multiple levels, including edge nodes, regional centers, and cloud service centers. This includes step-by-step data transmission (using a lightweight and reliable transmission mechanism) and multi-level data aggregation to form globally fused resource data.

[0140] like Figure 4 As shown, the detailed steps for lightweight resource aggregation are as follows:

[0141] S41: Edge Resource Aggregation. At the edge node, preprocessed resource data from multiple collection points is aggregated, deduplicated, and compressed to generate an edge resource status summary, including a summary of the edge's resource status, health indicators, and geographic location. The aggregation frequency is dynamically adjusted based on the data change rate, using the following formula:

[0142]

[0143] in, Indicates the fundamental frequency; Indicates the variance of the data; These are the weighting coefficients; This indicates that the aggregation frequency is dynamically adjusted.

[0144] S42: Regional Resource Aggregation. At the regional center, edge resource status summaries from multiple edge nodes are aggregated, performing deduplication, difference synchronization, multi-source association, and conflict resolution to generate a regional resource aggregation view. This view includes a summary of the region's resource status, health indicators, geographic location, load rate statistics, and conflict resolution results. A sliding window mechanism is used to aggregate data streams in real time, dynamically adjusting the size of the sliding window based on the data inflow rate and network latency. The formula for dynamically adjusting the sliding window size is as follows:

[0145]

[0146] in, Indicates the base window size; Indicates the data inflow rate; Indicates network latency; and These are the weighting coefficients; Indicates the size of the sliding window;

[0147] S43: Global Resource Fusion. Data from all regions is merged in the cloud service center. Multi-region data fusion is performed using data fusion algorithms (such as Kalman filters) to generate a global resource status, including overall resource distribution, real-time health status, and dynamic trends. The global fusion frequency is based on event-driven or timed polling, and the formula for adjusting the global fusion frequency is as follows:

[0148]

[0149] in, Indicates the minimum fusion frequency; Indicates the number of regions; Indicates a region index; Indicates the first The rate of change of data in each region; This is the scaling factor; Indicates the global fusion frequency.

[0150] S5: Lightweight resource presentation. Employing a lightweight and reliable transmission mechanism, it delivers globally fused resource data to the resource demand side on demand, and displays resource status through a grid viewport, thus presenting resources in a lightweight manner.

[0151] like Figure 5 As shown, the detailed steps for lightweight resource presentation are as follows:

[0152] S51: Grid Partitioning. The display area is divided into uniform grid cells, each associated with a specific geographic range or logical region. The grid size is dynamically adjusted based on the zoom level, supporting spatial indexing and fast querying. Each grid cell is assigned a unique coded ID, and the coded structure contains hierarchical information (multi-level spatial indexes and detailed data), supporting multi-resolution navigation (automatically selecting the appropriate level of grid data for rendering).

[0153] S52: Viewport Management. Only renders mesh cells within the current user viewport on the resource demand side, filters out invisible resources using the Cohen-Sutherland line segment clipping algorithm, and dynamically loads or unloads mesh data when the viewport changes;

[0154] S53: Incremental Update. This feature compares resource state changes between consecutive frames using difference detection, identifying only incremental data and marking the state with a version number or timestamp. Local rendering only updates the resource state within the changed mesh cell, rather than performing a full-screen redraw. Difference detection uses a double-buffered comparison algorithm to calculate state changes; local rendering locates the mesh cell to which the changed resource belongs and only re-renders that mesh.

[0155] S54: Multi-resolution adaptation. Dynamically adjusts rendering precision based on the display device's resolution and scaling ratio. Uses simplified models for low resolutions and loads high-precision resources for high resolutions. Employs Level of Detail (LOD) to set multi-level detail models for resources and dynamically switches model details based on viewport distance.

[0156] S55: Interactive Operations. Event listeners are bound to resources to support click, hover, and drag interactions. Events are bound to grid cells via event delegation. Interactive operations trigger real-time visual feedback, such as highlighting selected resources or popping up details panels. Gestures and multi-touch, such as zooming and panning, are supported. Real-time feedback is achieved with low-latency transmission using WebSocket and Lightweight HTTP; gesture recognition is optimized for smoothness through inertial scrolling and trajectory analysis algorithms.

[0157] The resource end-to-end lightweight synchronization and real-time presentation system of the present invention includes:

[0158] The resource unification abstraction module is deployed on the cloud service center node and is used to perform unified abstraction of heterogeneous resources across the entire network. By defining the metadata standards and interface specifications of resources, it abstracts resources into digital objects with standard attributes and behaviors.

[0159] Lightweight and reliable transmission modules are deployed on the resource supply side, edge nodes, regional center nodes, cloud service center nodes, and resource demand side. They are used to transmit data during resource collection, aggregation, and presentation, including delivery strategy determination based on communication status and address book, packet transmission and retransmission based on UDP protocol, and multi-path parallel transmission.

[0160] The lightweight resource acquisition module is deployed on various resource devices or nearby edge nodes on the resource supply side. It is used to deploy lightweight resource acquisition agents on the resource supply side distributed throughout the network, collect resource source data such as resource ontology information and real-time resource status, and perform preprocessing on the resource source data such as data filtering, difference extraction, binary encoding, data compression, and data caching.

[0161] The lightweight resource aggregation module is deployed on edge nodes, regional center nodes, and cloud service center nodes. It is used to aggregate pre-processed resource data through multiple levels of lightweight aggregation, including data transmission at each level and multi-level data aggregation, to form globally integrated resource data.

[0162] The lightweight resource presentation module is deployed on the resource demand side. It is used to deliver globally integrated resource data to the resource demand side on demand and display the resource status through the grid viewport to perform lightweight resource presentation.

[0163] The specific implementation, deployment, and operation methods for each role in the system can be found in the descriptions of the aforementioned methods and procedures, and will not be repeated here.

Claims

1. A lightweight, end-to-end resource synchronization and real-time presentation method, characterized in that, Includes the following steps: S1: Perform unified resource abstraction on heterogeneous resources across the entire network. By defining resource metadata standards and interface specifications, resources are abstracted into digital objects with standard attributes and behaviors. S2: Build a lightweight transmission mechanism, including delivery strategy determination based on communication status and address book, packet transmission and retransmission based on UDP protocol, and multi-path parallel transmission, for transmitting data during resource collection, aggregation and presentation. S3: On the resource supply side distributed across the entire network, deploy a lightweight resource collection agent to collect resource ontology information and real-time resource status data, as well as perform data filtering, difference extraction, binary encoding, data compression, and data caching preprocessing on the resource data. S4: The pre-processed resource data is aggregated in a lightweight manner through multiple levels of edge nodes, regional centers, and cloud service centers, including data transmission at each level and multi-level data aggregation, to form global fused resource data; S5: Employs a lightweight transmission mechanism to deliver globally integrated resource data to the resource demand side on demand, and displays resource status through a grid viewport to present resources in a lightweight manner; Step S2, which establishes a lightweight transmission mechanism, includes: S21: Resource data delivery strategy is determined based on lightweight communication status awareness and precise addressing in the address book, dynamically selecting whether to wait, deliver directly to the destination, or deliver resource data through relay nodes; among them, lightweight communication status awareness monitors network conditions in real time through a combined identification mode, obtaining indicators such as connectivity, latency, bandwidth, and packet loss rate; precise addressing in the address book maintains the resource ownership address mapping, and the resource ownership department should plan and implement multiple communication addresses to support proximity addressing based on geographical location or network topology; the delivery strategy is dynamically selected based on real-time communication status and communication address availability. S22: The resource data transmission protocol is lightweight and uses UDP connectionless mode to implement resource data packet transmission, acknowledgment, and timeout retransmission. The data packetization adopts a fixed-size or dynamically adjusted packetization strategy, which adapts to network conditions. The acknowledgment mechanism requires the receiver to reply with an ACK or NACK response at the sender's request to confirm the data packet reception status. The timeout retransmission mechanism sets a dynamic timeout threshold and a dynamic maximum number of retransmissions, calculated based on round-trip time and real-time network conditions. Retransmission is triggered when no acknowledgment is received, until the maximum number of retransmissions is reached. S23: Resource data is transmitted in parallel via multiple paths, integrating dynamic path selection, load balancing algorithms, and failover mechanisms. Dynamic path selection calculates multiple available transmission paths based on communication status and address book information. The load balancing algorithm allocates data streams equally to each selected path based on path quality. The failover mechanism monitors the health status of the paths and automatically switches to a backup path when a path fails.

2. The resource end-to-end lightweight synchronization and real-time presentation method according to claim 1, characterized in that, Step S1, which performs unified resource abstraction on heterogeneous resources across the entire network, includes: S11: Assign a resource a unique identifier across the entire network, a resource name, a resource type, a resource status, a resource mobility attribute, and a resource capability attribute; S12: Use a text-based structured data format to standardize the definition of resource metadata; S13: Standardize resource interfaces by adopting standardized network service interfaces and unified data exchange formats. The unified data exchange format means that resource interfaces use a structured data format for data serialization when facing upper-layer applications.

3. The resource end-to-end lightweight synchronization and real-time presentation method according to claim 2, characterized in that, The S21 identification mode adopts a lightweight perception of the communication status by mainly passive listening and supplementing it with active detection. The passive listening captures and parses network broadcast and locally transmitted and received data packets to determine the connected nodes. The active detection sends a lightweight query request to obtain the connection status when the node's communication status has not been updated for a long time. Delay is estimated by calculating the difference between the arrival time and the timestamp of the data packet; Bandwidth is estimated by monitoring the total amount of data packets successfully transmitted per unit time. Packet loss rate is estimated by comparing the sequence numbers of the actual received data packets with those of the expected received data packets. The predictions of latency, bandwidth, and packet loss rate are all achieved by removing noise using a lightweight filtering algorithm. Delivery strategies include waiting, direct delivery to the destination, and relaying to the nearest accessible node via uplink, downlink, or parallel transmission. The formula for dynamically selecting the delivery strategy is as follows: ， in, This indicates the connectivity state with the destination, where Represents connectivity, This indicates a disconnection; Indicates the direction of transmission to the destination. Represents uplink transmission, Represents downlink transmission, Represents parallel non-friendly transmission, Represents parallel neighbor transmission; This indicates the delivery strategy selection value, where 0 represents direct delivery to the destination, 1 represents uplink transmission selecting the nearest accessible node for relay delivery, 2 represents downlink transmission selecting the nearest accessible node for relay delivery, 3 represents parallel transmission selecting the nearest accessible node for relay delivery, and 4 represents waiting for connection to the destination. The adaptive change formula for the subcontracting strategy is as follows: ， in, Indicates the base package size; Indicates the current network bandwidth; Indicates the reference bandwidth; This indicates the current network packet loss rate; Indicates the current round-trip time; This indicates the estimated round-trip time, based on historical data or environmental presets. Indicates the packet size after adaptive optimization; function Indicates the preset adjustment coefficient; The formula for calculating the dynamic timeout threshold is as follows: ， in, Indicates the round-trip time of the current measurement. Indicates the variance of round-trip time. and This indicates the preset weighting coefficients. Indicates the dynamic timeout threshold; The formula for calculating the maximum number of dynamic retransmissions is as follows: ， in, Indicates the basic retransmission count. This indicates the current network packet loss rate. Indicates the scaling factor. Indicates the maximum number of dynamic retransmissions; Dynamic path selection is based on communication status and address book information, determined by delivery strategies. For uplink, downlink, and parallel transmission, multiple nearby accessible nodes exist. Specifically, when the department responsible for the nearest accessible resource to the destination has two or more candidate nodes, the quality of each candidate path is calculated, and a primary or backup path is selected based on the overall quality of the candidate paths. The candidate path quality scoring formula is as follows: ， in, Indicates the candidate path index; Indicates the first Available bandwidth for each candidate path; Indicates the first End-to-end latency of each candidate path; Indicates the first Packet loss rate of each candidate path; Indicates the first The current load of each candidate path; Indicates the first Maximum load capacity of each candidate path; Achieve bandwidth metric normalization. For the first The maximum bandwidth threshold for each candidate path; Achieve normalization of latency metrics, For the first The maximum allowed delay for each candidate path; Achieve normalization of packet loss rate; arrive For the weighting coefficients, satisfying Dynamically adjusted based on the actual scenario; Indicates the first The quality score of each candidate path is a factor; the higher the score, the better the path quality.

4. The resource end-to-end lightweight synchronization and real-time presentation method according to claim 3, characterized in that, arrive The dynamic adjustment of weighting coefficients includes the following principles: Prioritize network conditions: Increase the weight of packet loss rate in weak connection environments. Increasing bandwidth weight in high-bandwidth environments and delay weight ; Resource type adaptation: Increase latency weight when sensing resource transmission. Increase the weight of packet loss rate when handling resource transmission. Increase bandwidth weight when ensuring resource transmission ; Dynamic environment adaptation: Fixed weights are used in stable environments, while the weight values ​​are optimized through lightweight machine learning algorithms in highly mobile environments. The reward function is calculated based on the increase in transmission success rate.

5. The resource end-to-end lightweight synchronization and real-time presentation method according to claim 1, characterized in that, The lightweight resource acquisition step S3 includes: S31: Resource ontology information collection, which is obtained from the resource's own metadata storage, central registry, configuration files or resource description documents, including the resource's unique identifier, resource name, resource type, resource capability attributes and resource interface definition static information; the resource ontology information collection frequency adopts one-time collection or low-frequency update, which is only triggered when the resource is registered or the configuration is changed; S32: Real-time resource status collection, acquired from the resource's own real-time output interface, central monitoring service, event stream, and local collection agent, including resource health status, resource load rate, resource geographical location, and dynamic status of resources due to interface changes; the frequency of real-time resource status collection is adaptively and dynamically adjusted based on resource priority and network load, and the formula for dynamically adjusting the collection frequency is as follows: ， in, Indicates the fundamental frequency; Indicates priority weight; Indicates the variance of network load variation; and These are the weighting coefficients; This indicates that the sampling frequency will be dynamically adjusted. S33: Resource raw data preprocessing, including: data filtering, using a lightweight filtering algorithm to filter redundant, invalid, or abnormal data in the raw data; difference extraction, by comparing the current state with historical states, extracting the differences in changes from the filtered data; binary encoding, using a preset structured data conversion binary template to convert the extracted variable data into binary format; data compression, applying a lightweight compression algorithm to perform lossless compression on the binary-encoded data to reduce data volume; and data caching, temporarily storing the compressed data in a local lightweight cache database, supporting batch transmission, with the batch size dynamically adjusted based on network conditions. The formula for dynamic batch size adjustment is as follows: ， in, Indicates the base batch size. Indicates the network load factor. Indicates the weighting coefficient. Indicates the dynamic batch size.

6. The resource end-to-end lightweight synchronization and real-time presentation method according to claim 1, characterized in that, The step S4, lightweight resource aggregation, includes: S41: Edge resource aggregation. At edge nodes, preprocessed resource data from multiple collection points is aggregated, deduplicated, and compressed to generate an edge resource status summary, including a summary of edge resource status, health indicators, and geographic location. The aggregation frequency is dynamically adjusted based on the data change rate, using the following formula: ， in, Indicates the fundamental frequency; Indicates the variance of the data; These are the weighting coefficients; S42: Regional resource aggregation. This aggregates edge resource status summaries from multiple edge nodes at the regional center, performing deduplication, difference synchronization, multi-source association, and conflict resolution to generate a regional resource aggregation view. This view includes a summary of the region's resource status, health indicators, geographic location, load rate statistics, and conflict resolution results. A sliding window mechanism is used to aggregate data streams in real time, dynamically adjusting the size of the sliding window based on the data inflow rate and network latency. The formula for dynamically adjusting the sliding window size is as follows: ， in, Indicates the base window size; Indicates the data inflow rate; Indicates network latency; and These are the weighting coefficients; Indicates the size of the sliding window; S43: Global Resource Fusion. This involves fusing data from all regions in the cloud service center. A data fusion algorithm is used to integrate multi-regional data, generating a global resource status, including overall resource distribution, real-time health status, and dynamic trends. The global fusion frequency is based on event-driven or timed polling, and the adjustment formula is as follows: ， in, Indicates the minimum fusion frequency; Indicates the number of regions; Indicates a region index; Indicates the first The rate of change of data in each region; This is the scaling factor; Indicates the global fusion frequency.

7. The resource end-to-end lightweight synchronization and real-time presentation method according to claim 1, characterized in that, The lightweight resource presentation step S5 includes: S51: Grid division, which divides the display area into uniform grid cells, each grid cell is associated with a specific geographic range or logical region, and the grid size is dynamically adjusted based on the zoom level, supporting spatial indexing and fast query; each grid cell is assigned a unique coded ID, and the coded structure contains hierarchical information, enabling multi-resolution navigation; S52: Viewport management, only renders the mesh cells within the current user viewport on the resource demand side, filters invisible resources through the viewport clipping algorithm, and dynamically loads or unloads mesh data when the viewport changes; S53: Incremental update. It compares the changes in resource status between frames before and after by difference detection, identifies only incremental data, and marks the status with version number or timestamp. Local rendering only updates the resource status within the changed mesh cell. Among them, difference detection uses a double-buffered comparison algorithm to calculate the status change; local rendering locates the mesh cell to which the changed resource belongs and only re-renders that mesh. S54: Multi-resolution adaptation, dynamically adjusts rendering precision according to the resolution and scaling of the display device, uses simplified models for low resolution, loads high-precision resources for high resolution, adopts a hierarchy of detail to set multi-level detail models for resources, and dynamically switches model details based on viewport distance. S55: Interactive operation, binds event listeners to resources to support click, hover, and drag interactions, binds events to grid cells through event delegation, and interactive operations trigger real-time visual feedback such as highlighting selected resources or popping up details panels. The real-time feedback is based on WebSocket or Lightweight HTTP to achieve low-latency transmission; gesture recognition is optimized through inertial scrolling and trajectory analysis algorithms.

8. A lightweight resource end-to-end synchronization and real-time presentation system, characterized in that, include: The resource unification abstraction module is deployed on the cloud service center node and is used to perform unified abstraction of heterogeneous resources across the entire network. By defining the metadata standards and interface specifications of resources, it abstracts resources into digital objects with standard attributes and behaviors. Lightweight and reliable transmission modules are deployed on the resource supply side, edge nodes, regional center nodes, cloud service center nodes, and resource demand side. They are used to transmit data during resource collection, aggregation, and presentation, including delivery strategy determination based on communication status and address book, packet transmission and retransmission based on UDP protocol, and multi-path parallel transmission. Specifically, it includes: S21: Resource data delivery strategy is determined based on lightweight communication status awareness and precise addressing in the address book, dynamically selecting whether to wait, deliver directly to the destination, or deliver resource data through relay nodes; among them, lightweight communication status awareness monitors network conditions in real time through a combined identification mode, obtaining indicators such as connectivity, latency, bandwidth, and packet loss rate; precise addressing in the address book maintains the resource ownership address mapping, and the resource ownership department should plan and implement multiple communication addresses to support proximity addressing based on geographical location or network topology; the delivery strategy is dynamically selected based on real-time communication status and communication address availability. S22: The resource data transmission protocol is lightweight and uses UDP connectionless mode to implement resource data packet transmission, acknowledgment, and timeout retransmission. The data packetization adopts a fixed-size or dynamically adjusted packetization strategy, which adapts to network conditions. The acknowledgment mechanism requires the receiver to reply with an ACK or NACK response at the sender's request to confirm the data packet reception status. The timeout retransmission mechanism sets a dynamic timeout threshold and a dynamic maximum number of retransmissions, calculated based on round-trip time and real-time network conditions. Retransmission is triggered when no acknowledgment is received, until the maximum number of retransmissions is reached. S23: Resource data is transmitted in parallel via multiple paths, integrating dynamic path selection, load balancing algorithms, and failover mechanisms to transmit resource data; among them, dynamic path selection is based on communication status and address book information to calculate multiple available transmission paths; the load balancing algorithm combines path quality to equally allocate data streams to each selected path; the failover mechanism monitors the health status of the path and automatically switches to the backup path when a path fails. The lightweight resource acquisition module is deployed on various resource devices or nearby edge nodes on the resource supply side. It is used to deploy lightweight resource acquisition agents on the resource supply side distributed throughout the network, collect resource ontology information and real-time resource status data, and perform data filtering, difference extraction, binary encoding, data compression, and data caching preprocessing on the resource data. The lightweight resource aggregation module is deployed on edge nodes, regional center nodes, and cloud service center nodes. It is used to aggregate pre-processed resource data through multiple levels of lightweight aggregation, including data transmission at each level and multi-level data aggregation, to form globally integrated resource data. The lightweight resource presentation module is deployed on the resource demand side. It is used to deliver globally integrated resource data to the resource demand side on demand and display the resource status through the grid viewport to perform lightweight resource presentation.

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