A distributed collaborative processing and management platform for multi-source surveying and mapping data

Abstract

This invention discloses a distributed collaborative processing and management platform for multi-source mapping data, specifically relating to the field of distributed data processing technology. It generates a unified data sequence by collecting node spatial coordinates, time, and signal strength parameters; calculates data correlation by combining spatial and temporal offset differences; performs synchronization scheduling based on bandwidth and delay parameters; tracks error propagation trends through residual analysis and compensates for delays in nodes exceeding limits, achieving efficient collaboration and dynamic synchronization management of multi-source data. This invention achieves accurate alignment and efficient integration of heterogeneous data through unified modeling of spatial, temporal, and signal multi-dimensional information; significantly improves synchronization accuracy and bandwidth utilization through offset residual calculation and adaptive timing scheduling; and effectively suppresses error propagation and drift accumulation through error tracking and delay compensation mechanisms, enhancing the stability, real-time performance, and data consistency of the distributed mapping system in a multi-node environment.

Description

Technical Field

[0001] This invention relates to the field of distributed data processing technology, and in particular to a distributed collaborative processing and management platform for multi-source surveying and mapping data. Background Technology

[0002] Distributed data processing technology encompasses techniques that utilize multiple computing units to work collaboratively to process large-scale data. The core of this field is the distributed storage, processing, and management of data, addressing data processing needs that traditional single computing devices cannot meet. Distributed data processing technologies typically rely on multiple computing nodes that collaborate through a network to complete data processing tasks, and are widely used, especially in scenarios with large data volumes and complex processing requirements. This technology includes data partitioning, parallel processing, synchronization, and load balancing, and is widely applied in cloud computing, big data analytics, geographic information systems, and internet services.

[0003] A distributed collaborative processing and management platform for multi-source surveying and mapping data refers to a system specifically designed for processing and managing multi-source surveying and mapping data. It primarily addresses how to achieve collaborative processing and efficient management of data during the acquisition, storage, and processing of multi-source surveying and mapping data through a distributed computing architecture. This platform utilizes distributed data processing technology to uniformly manage and process surveying and mapping data from different sources, achieving efficient data integration and optimization. In this process, the platform employs distributed computing nodes for data allocation and parallel processing, ensuring efficient collaboration and resource utilization during data processing.

[0004] In practical operation, existing technologies often divide and parallelize tasks as the smallest unit. There is a lack of fine-grained alignment mechanisms between nodes based on joint spatial and temporal semantics. When cross-source data enters the computation stage, alignment deviations and duplicate comparisons occur, and network bandwidth is occupied by invalid traffic. Synchronization often relies on unified barriers or coarse-grained windows, which lead to timing jitter and periodic congestion when facing heterogeneous sampling frequencies and drift. Task queuing and preemption are exacerbated, and link idleness and congestion coexist. Load balancing focuses more on computation and storage dimensions, paying insufficient attention to the burstiness of link latency and bandwidth occupancy structure. Short-term congestion extends to multi-hop paths, resulting in a decrease in overall throughput and an increase in tail latency. Error handling mainly focuses on result correction specifications, lacking incremental tracing along node paths. Errors accumulate in multiple rounds of convergence, forming splicing gaps and local distortions. For example, in mobile mapping scenarios, timestamp drift between different nodes causes boundary misalignment between blocks, resulting in repeated recalculation and backhaul occupying links, affecting continuous service delivery and resource utilization. Summary of the Invention

[0005] The main objective of this invention is to provide a distributed collaborative processing and management platform for multi-source surveying and mapping data. By constructing a full-process collaborative mechanism that integrates multi-source acquisition input, content correlation identification, distributed synchronous scheduling, error propagation tracking, and conflict correction management, this platform addresses the problem of inaccurate data fusion caused by spatial offset, temporal asynchrony, unstable signal correlation, and cumulative error propagation in multi-source surveying and mapping data.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A distributed collaborative processing and management platform for multi-source mapping data, the platform comprising:

[0008] The multi-source acquisition input module acquires node spatial coordinates, acquisition time and signal strength parameters, performs coordinate transformation to obtain spatial position parameters, sorts time to obtain time offset parameters, compares signals to obtain signal association identifiers, synchronously records node numbers and transmission order to generate node transmission path parameters, and integrates them to generate a unified data sequence value for mapping nodes.

[0009] The content relevance identification module calls the spatial location and timestamp in the unified data sequence value of the surveying and mapping nodes, calculates the offset difference and performs similarity comparison, and generates the content relevance ratio of the data stream based on the threshold.

[0010] The distributed synchronization scheduling module calls the spatial offset ratio, time offset ratio, and bandwidth occupancy ratio in the data stream content correlation ratio, performs time difference and spatial difference operations, records the results as offset residual parameters, determines the synchronization priority based on the differences, adjusts the timing according to the bandwidth parameters, and generates multi-source data synchronization timing coefficients.

[0011] The error propagation tracking module calls the offset residual parameter in the multi-source data synchronization time series coefficient and the node transmission path parameter and node number parameter in the unified data sequence value of the mapping node to calculate the residual difference between adjacent nodes and determine the propagation trend, and generate the error propagation intensity value.

[0012] The conflict correction management module calls the time offset parameter based on the error propagation intensity value, compares the ratio with the threshold, performs delay compensation on nodes that exceed the limit, and generates a conflict correction balance rate.

[0013] Preferably, the unified data sequence value of the mapping nodes includes a spatial location set, a time offset set, a signal association set, and a node path set; the data stream content correlation ratio specifically includes the spatial offset ratio, the time offset ratio, and the bandwidth occupancy ratio; the multi-source data synchronization timing coefficient includes the synchronization priority sequence, the bandwidth scheduling coefficient, and the residual record set; the error propagation intensity value specifically refers to the node residual increment, the propagation direction identifier, and the trend matching index; the conflict correction balance rate includes the delay compensation coefficient, the time adjustment amount, and the node correction ratio.

[0014] Preferably, the multi-source acquisition input module includes:

[0015] Spatial coordinate transformation submodule: obtains node spatial coordinate parameters and geographic reference coordinates, performs coordinate system transformation on latitude and longitude values ​​and calculates position offset, compares with reference benchmark positioning points to determine spatially consistent intervals, and generates spatial position parameter values;

[0016] Time series calculation submodule: Performs time difference calculation based on the collected time parameters and a unified time base, sorts the node timestamps according to the collection order and calculates the offset interval, pairs the time offset of each node with the sequence position, and generates time offset parameter values;

[0017] Signal comparison and identification submodule: Based on the node signal strength parameters, call the device identification code, compare the signal strength value with the threshold signal benchmark, filter signal data within the intensity fluctuation range, extract the matching relationship between the identification code and the intensity change trend, and generate signal association identification value;

[0018] The path integration and generation submodule calls the spatial location parameter value, time offset parameter value and signal association identifier value, performs mapping matching on the node number and transmission order and generates node path parameters, integrates all parameter sets into a unified structured sequence, and generates unified data sequence values ​​for surveying nodes.

[0019] Preferably, the content relevance identification module includes:

[0020] Spatial offset recognition submodule: calls the spatial position parameters in the unified data sequence value of the surveying nodes, detects the coordinate differences of each node based on the reference coordinate benchmark, performs distance difference calculation on adjacent coordinate points, judges the consistency of offset direction and counts the offset distribution interval, and generates spatial offset difference coefficient;

[0021] Time correlation determination submodule: Calls the timestamps collected in the time series calculation submodule, calculates the adjacent difference of the timestamp sequence and compares it with a unified time benchmark, judges the time correlation of nodes based on the time interval change trend, and generates a time synchronization matching degree value;

[0022] Similarity filtering submodule: Based on the spatial offset difference coefficient and the time synchronization matching degree value, the module performs a weighted comparison between the two, calculates the comprehensive similarity parameter, and performs filtering actions according to the set similarity threshold. It extracts the relevance intervals that meet the threshold conditions and generates the relevance ratio of the data stream content.

[0023] Preferably, the distributed synchronization scheduling module includes:

[0024] Offset Calculation Submodule: Calls the spatial offset ratio, time offset ratio and bandwidth occupancy ratio in the data stream content correlation ratio, calculates the time offset difference and performs differential on the spatial position sequence, compares it with the reference coordinate set according to the unified time base to obtain the residual sequence, and generates the offset residual coefficient;

[0025] Priority determination submodule: Based on the offset residual coefficient, it calls the time offset difference sequence and the spatial difference sequence, compares the consistency of residual amplitude and direction according to the synchronization priority benchmark value, counts the sorting position of each data stream and normalizes it to obtain the synchronization priority index;

[0026] Timing adjustment submodule: Based on the synchronization priority index, it calls the bandwidth occupancy ratio and network latency measurement value, determines the queue slice length according to the preset bandwidth threshold and latency threshold, adjusts the synchronization start and end time and sending interval, records the execution sequence, and generates multi-source data synchronization timing coefficients.

[0027] Preferably, the step of adjusting the synchronization start and end times and the transmission interval and recording the execution sequence specifically involves: calling the synchronization start and end time candidates and the transmission interval candidates according to the slice length range, selecting the starting point according to the priority index and performing arithmetic adjustment on the interval, comparing the product constraint with the bandwidth occupancy ratio and the time delay measurement value, determining the time window boundary and step size of each sequence, and generating timing configuration coefficients.

[0028] More preferably, the method for generating multi-source data synchronization timing coefficients is as follows: call the timing configuration coefficients and queue slice length sequence, record the start and end times and sending intervals in index order, merge adjacent splicable segments and remove conflicting segments, calculate the time axis occupancy ratio and synchronization sequence stability of each data stream, summarize them into a unified timing index, and generate multi-source data synchronization timing coefficients.

[0029] Preferably, the error propagation tracking module includes:

[0030] The residual calculation submodule calls the offset residual coefficient in the multi-source data synchronization time series coefficient and the node number parameter in the unified data sequence value of the mapping node. For each node, it calculates the residual difference between adjacent nodes, records the difference direction according to the residual amplitude and sign, statistically analyzes and normalizes the amplitude distribution of the residual sequence, and generates the node residual difference value.

[0031] The propagation path submodule calls the node transmission path parameters in the unified data sequence value of the mapping nodes based on the node residual difference value, matches the difference value and path index in the order of node number, calculates the cumulative residual amount of each path segment and compares the consistency of the difference sign direction, establishes the cumulative residual sequence within the path, and obtains the error path increment value.

[0032] Trend Judgment Submodule: Calculates the incremental difference between adjacent path segments based on the incremental value of the error path, judges the continuity and magnitude of the incremental direction, performs difference smoothing on discontinuous segments and rearranges the path sequence, calculates the propagation direction ratio and intensity quantization value, and generates the error propagation intensity value.

[0033] Preferably, the conflict correction management module includes:

[0034] Delay compensation identification submodule: Obtain the error propagation intensity value, compare it with the time offset parameter, analyze the time offset of each node, determine whether it exceeds the set time offset threshold, if it exceeds the threshold, record the node that exceeds the threshold and generate the delay compensation requirement, and generate the delay compensation requirement value based on the information of the node that exceeds the threshold.

[0035] The over-limit node comparison submodule: Based on the delay compensation requirement value, it calls the error propagation intensity value and time offset parameter to calculate the ratio of the over-limit nodes, determine the deviation magnitude of the over-limit nodes, compare it with the set threshold, determine whether the nodes need additional compensation, and finally generate a list of over-limit nodes and obtain the deviation value of the over-limit nodes.

[0036] The conflict correction calculation submodule calls the deviation value of the out-of-limit node and the delay compensation requirement value to perform correction calculations for each out-of-limit node. It optimizes the synchronization of the nodes by adjusting the time offset, and finally generates the corrected timing information to obtain the conflict correction balance rate.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. This invention achieves a unified serialized representation of multi-source data through multi-dimensional fusion processing of node spatial coordinates, acquisition time, and signal strength. Spatial coordinates are transformed and aligned with a geographic reference benchmark; timestamps are generated as offset parameters based on the acquisition order; and signal strength is filtered by a threshold to form an association identifier. These three types of information are mapped and paired within a unified structure, ensuring spatiotemporal consistency and signal correspondence of multi-source data before entering the computation stage. This process significantly reduces the complexity of decoupling and comparing heterogeneous data, maintaining consistency in spatial distribution, temporal sequence, and signal strength across multiple sources. This improves the overall accuracy and integration efficiency of data input, providing a highly consistent data foundation for subsequent distributed processing.

[0039] 2. This invention establishes a ratio model of spatial offset, temporal offset, and bandwidth usage among data streams to achieve dynamic similarity determination and synchronization priority ranking of multi-source data streams. Based on the timing index calculated from the offset residuals, each data stream can flexibly adjust its synchronization start and end times and transmission intervals according to the degree of spatial and temporal difference. Bandwidth and latency measurement results jointly constrain the queue slice length, forming an adaptive synchronization configuration. This mechanism ensures synchronization accuracy while avoiding resource contention, significantly reducing transmission latency and bandwidth waste, improving communication link stability during multi-node collaboration, balancing data transmission rhythm, and maintaining efficient data collaboration even under high load and multi-path conditions.

[0040] 3. The invention achieves dynamic balance control of error propagation by tracking the propagation path of offset residuals and adjusting the delay compensation ratio. Residual difference and path accumulation calculations reveal the direction and intensity of errors between nodes. The system can automatically identify nodes exceeding limits based on propagation trends and calculate delay compensation values. Then, it performs proportional adjustments according to the node transmission order, forming a balanced sequence across the entire time axis. This process enables rapid location and quantification of error sources, suppresses abnormal drift in the early stages of propagation, and prevents errors from accumulating and spreading during multiple rounds of synchronization. This improves the spatial consistency and temporal accuracy of mapping data, resulting in higher stability and traceability of results in continuous distributed mapping scenarios. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the overall implementation process of the present invention;

[0042] Figure 2 This is a schematic diagram illustrating the process of obtaining unified data sequence values ​​for mapping nodes in some embodiments of the present invention;

[0043] Figure 3 This is a schematic diagram illustrating the process of obtaining the data stream content relevance ratio in some embodiments of the present invention;

[0044] Figure 4 This is a schematic diagram of the process for generating multi-source data synchronization timing coefficients in some embodiments of the present invention;

[0045] Figure 5 This is a schematic diagram illustrating the process of obtaining the error propagation intensity value in some embodiments of the present invention;

[0046] Figure 6 This is a schematic diagram illustrating the process of obtaining the corrected timing information and conflict correction balance rate in some embodiments of the present invention. Detailed Implementation

[0047] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the linguistic context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0048] It should be understood that the terms "system," "device," "unit," and / or "module" as used in this specification are a method of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0049] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0050] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0051] The distributed collaborative processing and management platform for multi-source mapping data provided in the embodiments of this specification will be described in detail below with reference to the accompanying drawings.

[0052] In some embodiments, the distributed collaborative processing and management platform for multi-source mapping data can be executed through processing logic, which may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (instructions running on the processing device to execute hardware simulations), or any combination thereof. In some embodiments, Figure 1 One or more operations in the flowchart of the distributed collaborative processing and management platform for multi-source mapping data shown can be implemented by processing devices and / or terminal devices. For example, the distributed collaborative processing and management platform for multi-source mapping data can be stored in a storage device in the form of computer programs and / or instructions, and can be invoked and / or executed by processing devices and / or terminal devices.

[0053] Specifically, such as Figure 1As shown in the figure, an embodiment of the present invention discloses a distributed collaborative processing and management platform for multi-source mapping data, comprising:

[0054] The multi-source acquisition input module acquires node spatial coordinates, acquisition time, and signal strength parameters; performs coordinate transformation to obtain spatial location parameters; sorts the time to obtain time offset parameters; compares signals to obtain signal association identifiers; synchronously records node numbers and transmission order to generate node transmission path parameters; and integrates these to generate a unified data sequence value for the mapping nodes. The unified data sequence value for the mapping nodes includes a spatial location set, a time offset set, a signal association set, and a node path set.

[0055] The content relevance identification module calls the spatial location and timestamp in the unified data sequence value of the surveying node, calculates the offset difference and performs similarity comparison, and generates the data stream content relevance ratio based on the threshold; the data stream content relevance ratio is specifically the spatial offset ratio, the time offset ratio and the bandwidth occupancy ratio.

[0056] The distributed synchronization scheduling module calls the spatial offset ratio, time offset ratio, and bandwidth occupancy ratio in the data stream content correlation ratio, performs time difference and spatial difference operations, records the results as offset residual parameters, determines the synchronization priority based on the difference, adjusts the timing according to the bandwidth parameter, and generates multi-source data synchronization timing coefficients; the multi-source data synchronization timing coefficients include synchronization priority sequence, bandwidth scheduling coefficients, and residual record set.

[0057] The error propagation tracking module calls the offset residual parameter in the multi-source data synchronization time series coefficient and the node transmission path parameter and node number parameter in the unified data sequence value of the mapping node to calculate the residual difference between adjacent nodes and determine the propagation trend, and generate an error propagation intensity value; the error propagation intensity value specifically refers to the node residual increment, propagation direction identifier, and trend matching index.

[0058] The conflict correction management module calls the time offset parameter based on the error propagation intensity value, compares the ratio with the threshold, performs delay compensation on nodes that exceed the limit, and generates a conflict correction balance rate; the conflict correction balance rate includes the delay compensation coefficient, the time adjustment amount, and the node correction ratio.

[0059] like Figure 2 As shown, in some embodiments of the present invention, the multi-source acquisition input module is specifically composed of a spatial coordinate transformation submodule, a time series calculation submodule, a signal comparison and recognition submodule, and a path integration and generation submodule;

[0060] Among them, the spatial coordinate transformation submodule: obtains the node spatial coordinate parameters and geographic reference coordinates, performs coordinate system transformation on the latitude and longitude values ​​and calculates the position offset, compares with the reference benchmark positioning point to determine the spatially consistent interval, and generates spatial position parameter values;

[0061] The execution process of the spatial coordinate transformation submodule first obtains spatial coordinate parameters and geographic reference coordinates from multiple nodes. The node spatial coordinates can be raw coordinate information obtained from different measuring devices, sensors, or satellite positioning systems (such as GPS), while the geographic reference coordinates are usually a known standard coordinate system (such as WGS84). Next, a coordinate system transformation operation is performed on the obtained latitude and longitude coordinates.

[0062] For example, if the node data is originally based on a local coordinate system and needs to be converted to the global standard coordinate system (WGS84), the implementation first involves acquiring the latitude, longitude, and elevation parameters of each node using a multi-frequency GNSS positioning device (such as a Trimble R12 or Huace P5), while simultaneously using the geographic reference coordinates provided by RTK base stations deployed in the survey area as a reference. The raw coordinate data acquired by the device is usually in WGS84 or local coordinate system format and is received by a data acquisition control terminal (such as an industrial computer with a high-precision floating-point arithmetic module). A coordinate system transformation operation is then performed to ensure that the positional relationships remain consistent when transforming from one coordinate system to another.

[0063] To illustrate with a concrete example, suppose a node's original coordinates are longitude 120°, latitude 30°, and elevation 50 meters, while the reference coordinate system is WGS84. Through a coordinate transformation matrix, the transformed coordinates might be adjusted to longitude 120.01° and latitude 30.01°, resulting in a positional offset of 0.01°. This method effectively calculates the spatial offset and performs spatial consistency checks based on the reference benchmark, thus confirming the spatial consistency range for the area. If the offset exceeds a predetermined tolerance range (e.g., exceeding 0.05°), the node's coordinates are considered to violate the spatial consistency standard. Ultimately, the execution results in a specific spatial position parameter value, for example, using the transformed coordinates: longitude 120.01°, latitude 30.01°, and elevation 50.1 meters as the node's spatial position parameter.

[0064] Time series calculation submodule: Performs time difference calculation based on the collected time parameters and a unified time base, sorts the node timestamps according to the collection order and calculates the offset interval, pairs the time offset of each node with the sequence position, and generates time offset parameter values;

[0065] The execution of the time series calculation submodule relies on a high-precision clock module (such as the u-blox ZED-F9T time synchronization module) to provide a unified UTC time reference. Each mapping node synchronously acquires time via PPS signals. The data acquisition terminal of each node records the acquisition timestamp.

[0066] A unified time base typically refers to a standard time system (such as UTC time), and all collected timestamps need to be converted to a format consistent with this base. For each node, the time difference is first calculated, that is, the relative time offset of each node is determined by the time difference between the node's timestamp and the unified time base.

[0067] For example, if the timestamp of a node is 10:05:00 on November 13, 2025, and the unified time base is 10:00:00 on November 13, 2025, then the time offset of that node is +5 minutes. Then, the timestamps of all nodes are sorted according to the collection order to ensure time continuity. Next, the offset interval between the timestamps of each node is calculated; the offset interval refers to the time difference between two adjacent nodes. If the time difference between two nodes is 3 seconds, then the time offset interval between them is 3 seconds. Finally, the calculated time offset is paired with the sequence position of the nodes. For example, assuming that the time offset of the first node in a sequence is +5 minutes, the time offset of the second node is +8 minutes, and the time offset of the third node is +11 minutes, with offset intervals of 3 minutes and 3 minutes respectively, the generated time offset parameter values ​​can be recorded as: Node 1 offset time is +5 minutes, Node 2 offset time is +8 minutes, and Node 3 offset time is +11 minutes.

[0068] Signal comparison and identification submodule: Based on the node signal strength parameters, call the device identification code, compare the signal strength value with the threshold signal benchmark, filter signal data within the intensity fluctuation range, extract the matching relationship between the identification code and the intensity change trend, and generate signal association identification value;

[0069] The signal comparison and identification submodule's task is to analyze the stability and trend of the signal based on the signal strength parameters of each node. In this embodiment, the signal strength parameter RSSI (in dBm) is first obtained through the node's built-in radio frequency receiving module (such as a LoRa receiver or a USRPB210 software radio), and the data source is bound through the node device identification code (such as "ABC123"). Then, the signal strength value of each node is compared with a preset threshold signal benchmark, which may be set according to the device's technical standards or the normal fluctuation range of the signal.

[0070] For example, the main control system sets the signal strength reference threshold to the range of -75dBm to -90dBm, judging the signal to be within the normal range according to industrial communication standards. First, the signal strength of each node is sampled. For instance, if the signal strength of node 1 is -82dBm and that of node 2 is -95dBm, then the signal of node 2 exceeds the lower threshold and is judged as a weak signal. Subsequently, the signal fluctuation amplitude ΔS = |S| is calculated from the continuously sampled values. n -S n-1If ΔS ≤ 5dBm, it is classified as a stable band; if ΔS > 5dBm, it is recorded as a fluctuating region. The continuous signal samples at node 1 are {-81, -82, -83, -82}, with a maximum fluctuation amplitude of 2dBm, belonging to the stable region; the samples at node 2, {-88, -93, -91, -94}, have a fluctuation amplitude of 6dBm, belonging to the fluctuating region. The system filters out signals in the stable region and records their identification codes and intensity change trends. The trend is determined by calculating the mean change of the last three samples; for example, if the mean changes from -82 → -83 → -84, the trend is determined to be "weakening". Finally, a signal association identification value is generated, for example, the identification for node 1 is "ABC123 - Stable", and for node 2 it is "XYZ456 - Weakening". This identification value is used in subsequent path mapping to identify the data source and match communication quality.

[0071] The path integration and generation submodule calls the spatial location parameter value, time offset parameter value and signal association identifier value, performs mapping matching on the node number and transmission order and generates node path parameters, integrates all parameter sets into a unified structured sequence, and generates unified data sequence values ​​for surveying nodes.

[0072] The path integration and generation submodule's task is to integrate parameters from different submodules to generate a unified data sequence. In this embodiment, spatial location parameters, time offset parameters, and signal association identifiers are aggregated through distributed data acquisition node terminals. Node data is uploaded to the central data aggregation server via a high-speed communication module (such as 5G or fiber optic transmission link). The server uses a database management system (such as PostgreSQL+PostGIS) to perform mapping and matching between node numbers and transmission order. The matching logic is to sort the nodes in ascending order by time offset parameters and prioritize them based on signal strength stability.

[0073] In practice, the system calls upon the spatial location parameters (such as longitude, latitude, and elevation), time offset parameters (such as time differences between nodes), and signal association identifiers (such as signal change trends) obtained in the preceding steps. By mapping and matching these parameters with node numbers and their transmission order, it ensures that all data from each node can be associated in the correct order. For example, assuming that the spatial location of node 1 is (120.01°, 30.01°), the time offset is +5 minutes, and the signal association identifier is "ABC123-weakening", and the spatial location of node 2 is (120.02°, 30.02°), the time offset is +8 minutes, and the signal association identifier is "XYZ456-enhancing", the system integrates the information from node 1 and node 2 into a structured sequence. Finally, a unified data sequence value for the mapping nodes is generated, which includes the spatial location set, time offset set, signal association set, and node path set of all nodes. For example, the data for node 1 is {location: 120.01°, 30.01°, time offset: +5 minutes, signal association identifier: ABC123-weakening}, and the data for node 2 is {location: 120.02°, 30.02°, time offset: +8 minutes, signal association identifier: XYZ456-enhancing}. This data will be stored uniformly and processed and analyzed in sequence to provide basic data support for the module.

[0074] like Figure 3 As shown, in another embodiment of the present invention, the content relevance identification module includes a spatial offset identification submodule, a temporal correlation determination submodule, and a similarity filtering submodule. Specifically:

[0075] Spatial offset recognition submodule: calls the spatial position parameters in the unified data sequence value of the surveying nodes, detects the coordinate differences of each node based on the reference coordinate benchmark, performs distance difference calculation on adjacent coordinate points, judges the consistency of offset direction and counts the offset distribution interval, and generates spatial offset difference coefficient;

[0076] First, spatial location parameters are extracted from the unified data sequence of surveying nodes. The spatial location of each node is typically recorded in the form of longitude, latitude, and elevation, such as node 1's spatial location being (120.012°, 30.008°, 50.5m) and node 2's being (120.014°, 30.010°, 51.2m). The reference coordinate datum is the global standard coordinate system (such as WGS84), which is the unified reference standard for all node locations. The system first compares the spatial coordinates of each pair of adjacent nodes and calculates the spatial differences between them. The basic formula for calculating spatial differences is: in, and These represent the differences in longitude and latitude between the two nodes, with 111000 being a constant for converting longitude and latitude to meters. Assuming node 1 has longitude and latitude of (120.012°, 30.008°) and node 2 has longitude and latitude of (120.014°, 30.010°), then the difference in longitude between them is... Latitude difference The spatial difference between the two was calculated: .

[0077] Next, by calculating the spatial differences between adjacent nodes, the system determines whether these nodes belong to the same offset direction. At this point, by detecting the increment of the spatial difference between adjacent nodes, their directional consistency can be determined. If the increment of the spatial difference between adjacent nodes is less than a set threshold (e.g., 1 meter), they can be considered to be in the same offset direction; otherwise, their offset directions are different. By statistically analyzing the spatial offset differences between all nodes, a distribution range of spatial offsets is formed, and an offset difference coefficient is calculated. When calculating the offset difference coefficient, the system first normalizes the spatial difference of each node to ensure numerical consistency, and then generates a weighted average as the final spatial offset difference coefficient. This value is a representative indicator of the spatial offset of each pair of adjacent nodes, measuring the degree of consistency in the positional changes between nodes.

[0078] Time correlation determination submodule: Calls the timestamps collected in the time series calculation submodule, calculates the adjacent difference of the timestamp sequence and compares it with a unified time benchmark, judges the time correlation of nodes based on the time interval change trend, and generates a time synchronization matching degree value;

[0079] In the time correlation determination submodule, the timestamps of each node are first extracted from the time series calculation submodule. Each node's timestamp records the specific time when the node collected data; for example, node 1's timestamp is 10:05:12, node 2's is 10:05:15, and node 3's is 10:05:18. By calculating the adjacent differences between the timestamp sequences, the system calculates the time offset between the nodes. : The system then compares these time differences with a unified time base (e.g., UTC time). For each node, the system calculates the difference between its timestamp and the unified time base and performs trend analysis.

[0080] For example, if the time difference from node 1 to node 3 is positive, and the time interval between nodes varies by about 3 seconds, it can be determined that these nodes have good time synchronization. If the time difference continues to increase, it may indicate a synchronization error. The time synchronization matching degree value is calculated by averaging the time deviations between all nodes and standardizing it according to a set threshold range, ultimately obtaining an overall time matching degree. For example, if the node time deviations do not exceed 5 seconds, the matching degree value is 0.95; if it exceeds 10 seconds, the matching degree value drops to 0.8. This time synchronization matching degree value will be used as a basis for judging the data stream synchronization quality.

[0081] Similarity filtering submodule: Based on the spatial offset difference coefficient and the time synchronization matching degree value, the module performs a weighted comparison between the two, calculates the comprehensive similarity parameter, and performs filtering actions according to the set similarity threshold. It extracts the relevance intervals that meet the threshold conditions and generates the relevance ratio of the data stream content.

[0082] The similarity filtering submodule receives the spatial offset difference coefficient and time synchronization matching degree value output from the first two modules. First, the system performs a weighted comparison of the spatial offset difference coefficient and the time synchronization matching degree value. To calculate the comprehensive similarity parameter, the system assigns weights to these two values; for example, the spatial offset difference coefficient has a weight of 0.6, and the time synchronization matching degree value has a weight of 0.4. Assuming that node 1's spatial offset difference coefficient is 0.9 and node 1's time synchronization matching degree value is 0.8, the comprehensive similarity parameter is calculated as follows: Next, the system compares the comprehensive similarity parameter with a set similarity threshold, assuming the threshold is 0.85. If the comprehensive similarity parameter is less than the threshold, the node's data will be filtered out and will not participate in subsequent data stream calculations. If it is greater than the threshold, the node is considered to have a high similarity to other nodes and will continue to participate in subsequent data stream calculations.

[0083] In this example, the overall similarity parameter is 0.84, which is lower than the set threshold of 0.85, so the data from this node will be excluded. The data stream content relevance ratio is ultimately calculated by statistically analyzing all nodes that meet the threshold conditions, resulting in the spatial offset ratio, temporal offset ratio, and bandwidth occupancy ratio. For example, if the proportion of filtered node data is 80%, then the data stream content relevance ratio is 80%. These ratios will be used for subsequent multi-source data synchronization and optimization processing to improve the overall accuracy and synchronization of the data stream.

[0084] like Figure 4 As shown, in another embodiment of the present invention, the distributed synchronization scheduling module includes an offset calculation submodule, a priority determination submodule, and a timing adjustment submodule.

[0085] Offset Calculation Submodule: Calls the spatial offset ratio, time offset ratio and bandwidth occupancy ratio in the data stream content correlation ratio, calculates the time offset difference and performs differential on the spatial position sequence, compares it with the reference coordinate set according to the unified time base to obtain the residual sequence, and generates the offset residual coefficient;

[0086] Furthermore, in the offset calculation submodule, three key parameters are first extracted from the data stream content relevance ratio: spatial offset ratio, temporal offset ratio, and bandwidth utilization ratio. The spatial offset ratio represents the ratio of spatial location differences between nodes, the temporal offset ratio describes the proportion of temporal differences between nodes, and the bandwidth utilization ratio is used to measure the bandwidth consumption for data transmission.

[0087] Taking a specific scenario as an example, assume the spatial offset ratio is 0.8 (i.e., the spatial deviation between nodes accounts for 80% of the total spatial offset), the temporal offset ratio is 0.6 (i.e., the temporal deviation between nodes accounts for 60% of the total temporal offset), and the bandwidth utilization ratio is 0.4 (i.e., data transmission occupies 40% of the total bandwidth). These ratios will help the offset calculation submodule perform weighted calculations when performing differential operations.

[0088] In the calculation process, the time offset difference is first calculated using the following formula: in, The timestamp of the current node. To unify the time base timestamp. For example, assuming node 1's timestamp is 10:05:10 and the unified base time is 10:05:00, then the time offset difference is: Then, the spatial position sequence difference is calculated to obtain the spatial deviation between adjacent nodes. Assuming the spatial position of node 1 is (120.001°, 30.001°) and the spatial position of node 2 is (120.003°, 30.003°), their spatial deviation is: Next, the residual sequence for each node is obtained by comparing the temporal and spatial offset differences with a reference coordinate set. The residual sequence is a measure of the difference between the temporal and spatial offsets, indicating the degree of deviation between nodes. A large offset value indicates a significant synchronization problem.

[0089] Finally, by weighting the spatial offset difference coefficient, the temporal offset difference coefficient, and the bandwidth utilization ratio, the system generates an offset residual coefficient. This coefficient is used to measure the priority of data synchronization, ultimately ensuring the synchronization quality of the data stream. For example, assuming a node's residual value is 5 seconds and the bandwidth utilization rate is 40%, the generated offset residual coefficient would be: .

[0090] Priority determination submodule: Based on the offset residual coefficient, it calls the time offset difference sequence and the spatial difference sequence, compares the consistency of residual amplitude and direction according to the synchronization priority benchmark value, counts the sorting position of each data stream and normalizes it to obtain the synchronization priority index;

[0091] In the priority determination submodule, the temporal offset difference sequence and spatial difference sequence are first analyzed by calling the offset residual coefficient. The temporal offset difference sequence contains the time difference value of each node, and the spatial difference sequence records the spatial difference between every two nodes.

[0092] Based on a synchronization priority baseline value (e.g., set to 0.8, meaning higher priority is closer to 1), the system begins comparing the residual magnitude and directional consistency between nodes. For example, if the time deviations between node 1 and node 2 are 5 seconds and 6 seconds respectively, and their spatial deviations are 300 meters and 350 meters respectively, the time and spatial differences between the two nodes are similar in magnitude and consistent in direction, indicating good synchronization and high priority. When sorting all nodes, the residual magnitudes between nodes are first calculated, then the residual difference between each node and other nodes is calculated. Finally, based on the absolute value and sign of these residual differences, the directional consistency between nodes is determined. If the residual difference magnitudes of two nodes are similar and their directions are consistent, these nodes are considered to have higher synchronization priority.

[0093] Next, the system normalizes these priorities to ensure that the priority index values ​​are between 0 and 1. For example, assuming node 1 has a priority of 0.9 and node 2 has a priority of 0.7, after normalization, the final synchronization priority indices are as follows: Ultimately, the system determines the synchronous execution order of each node based on these priority indices.

[0094] Timing adjustment submodule: Based on the synchronization priority index, it calls the bandwidth occupancy ratio and network latency measurement value, determines the queue slice length according to the preset bandwidth threshold and latency threshold, adjusts the synchronization start and end time and sending interval, records the execution sequence, and generates multi-source data synchronization timing coefficients.

[0095] Specifically, adjusting the synchronization start and end times and sending intervals and recording the execution sequence involves: calling synchronization start and end time candidates and sending interval candidates based on the slice length range; selecting the starting point according to the priority index and performing arithmetic adjustments on the intervals; comparing the product constraint with the bandwidth occupancy ratio and the time delay measurement value; determining the time window boundaries and step size of each sequence; and generating timing configuration coefficients; after obtaining the timing configuration coefficients, calling the timing configuration coefficients and the queue slice length sequence; recording the start and end times and sending intervals according to the index order; merging adjacent splicable segments and removing conflicting segments; calculating the time axis occupancy ratio of each data stream and the stability of the synchronization sequence; summarizing them into a unified timing index; and generating multi-source data synchronization timing coefficients.

[0096] First, the synchronization order of each node is determined based on the synchronization priority index. The synchronization priority index reflects the importance of each node's synchronization; for example, node 1 has a priority of 0.9, and node 2 has a priority of 0.7, meaning node 1 will be synchronized first. Next, the bandwidth utilization ratio and network latency measurement are used to help determine if the queue slice length needs adjustment. The bandwidth utilization ratio represents the proportion of network bandwidth occupied by the current data stream, while the network latency measurement represents the delay in data transmission. In this process, bandwidth and latency thresholds are used as system-defined standards; assuming a bandwidth threshold of 0.8 and a latency threshold of 100ms. When the bandwidth utilization ratio is lower than the bandwidth threshold (e.g., 0.5) and the latency measurement is lower than the latency threshold (e.g., 80ms), a longer queue slice length can be selected, such as 50ms; if the bandwidth utilization ratio is higher than the threshold, the queue slice length is shortened to 20ms to ensure that data transmission does not exceed network capacity limits. Finally, the synchronization start and end times and the transmission interval are adjusted. Assuming node 1 has a synchronization priority of 0.9, the system will allocate it a shorter transmission interval, say 30ms. Node 2, with a synchronization priority of 0.7, will be allocated a longer transmission interval, say 50ms. The start times of all nodes are sorted according to their priorities; higher-priority nodes will begin synchronization in a shorter time, while lower-priority nodes will synchronize with a slight delay. Finally, the execution sequence of each node is recorded, and a multi-source data synchronization timing coefficient is calculated. This coefficient integrates factors such as time synchronization, bandwidth usage, and network latency, reflecting the overall synchronization efficiency of the system.

[0097] In the specific synchronization adjustment process, the system first sets the slice length range, assuming it's [20ms, 50ms]. Based on the synchronization priority index, the system assigns an appropriate slice length and transmission interval to each node. If node 1 has a higher priority (0.9), its slice length will be a smaller value in the range, such as 20ms, while node 2 has a lower priority (0.7), so its slice length will be a larger value, such as 40ms. Next, the system assigns candidate synchronization start and end times to each node and selects the start point based on priority. For example, node 1's synchronization start time is 10:00:00.000, and node 2's synchronization start time is 10:00:00.200. The transmission interval is then adjusted arithmetically. Assuming node 1's transmission interval is 30ms and node 2's is 50ms, the system adjusts the interval differences according to priority to ensure that higher-priority nodes have shorter synchronization intervals and lower-priority nodes have longer synchronization intervals. At this point, the system will also compare bandwidth utilization and network latency. Assuming a bandwidth utilization of 0.4 and a latency measurement of 90ms, the product of the bandwidth utilization and latency is 0.4 × 20ms = 8ms, which is lower than the set latency threshold. This means that the bandwidth usage is reasonable and the latency meets the requirements. Finally, the system will calculate the timing configuration coefficient based on these parameters, representing the optimal synchronization scheme under the current network environment and node priority conditions.

[0098] Once the timing configuration coefficients are generated, the system combines them with the queue slice length sequence to generate the final timing configuration for each node. Assuming a timing configuration coefficient of 0.85, a slice length of 20ms for node 1, and 40ms for node 2, the system allocates specific synchronization window boundaries and step sizes to each node based on these configurations. First, the start and end times of the time window and the synchronization transmission interval for each node are calculated based on the timing configuration coefficients. For example, the start and end times of the time window for node 1 are [10:00:00.000, 10:00:00.020], and the start and end times of the time window for node 2 are [10:00:00.200, 10:00:00.240]. Next, the system merges the synchronization segments of adjacent nodes. If the time windows of node 1 and node 2 do not overlap, their time windows are considered independent; if the time windows partially overlap, the system merges these overlapping segments according to the synchronization priority and timing configuration coefficients, eliminating unnecessary conflicting segments to ensure accurate and stable synchronization of each data stream. Finally, the system calculates the timeline occupancy ratio of each data stream and the synchronization sequence stability of the entire system. Higher stability indicates more accurate synchronization between nodes. After summarizing the timing configurations of all nodes, the system generates a unified timing metric, which reflects the multi-source data synchronization capability and efficiency of the entire system. Ultimately, the multi-source data synchronization timing coefficients generated based on these timing configurations will be used for subsequent data processing and transmission optimization.

[0099] like Figure 5 As shown, in some embodiments of the present invention, the error propagation tracking module includes a residual calculation submodule, a propagation path submodule, and a trend judgment submodule.

[0100] The residual calculation submodule calls the offset residual coefficient in the multi-source data synchronization time series coefficient and the node number parameter in the unified data sequence value of the mapping node. For each node, it calculates the residual difference between adjacent nodes, records the difference direction according to the residual amplitude and sign, statistically analyzes and normalizes the amplitude distribution of the residual sequence, and generates the node residual difference value.

[0101] In the residual calculation submodule, the system first obtains the offset residual coefficient from the multi-source data synchronization time series coefficients. The offset residual coefficient represents the synchronization accuracy of each node. For example, assuming the offset residual coefficient of node 1 is 3 seconds and that of node 2 is 5 seconds, it indicates the time error between these two nodes during synchronization. Then, the system extracts the node number parameter from the unified data sequence values ​​of the mapping nodes. The node number parameter is used to identify the uniqueness of each node. For example, node 1 is numbered A001 and node 2 is numbered A002, ensuring that the data of each node can be associated with its corresponding synchronization time series data. Next, the system calculates the residual difference between adjacent nodes, which is the difference in the offset residual coefficients of adjacent nodes. For example, the offset residuals of node 1 and node 2 are 3 seconds and 5 seconds respectively, so the residual difference between them is: ;

[0102] For each node, the system records the difference direction based on the residual magnitude and sign. If the residual difference is positive, it indicates an error in node synchronization, and the offset direction of that node is recorded. For example, assuming the residual difference between node 1 and node 2 is positive, it means that the synchronization delay of node 2 is greater than that of node 1, and the direction sign is "+". Next, the system statistically analyzes the magnitude distribution of the residual sequences among all nodes. For example, among multiple nodes, 60% have a residual difference of 0.5 seconds, 30% have a residual difference of 1 second, and 10% have a residual difference of 2 seconds. Finally, the system normalizes these residual magnitude distributions. The normalized residual difference values ​​will be used as input parameters for subsequent error propagation analysis. For example, the normalized residual difference values ​​are 0.3 for node 1 and 0.7 for node 2. In this way, the system can accurately record the residual difference value of each node, ensuring further improvement in synchronization accuracy.

[0103] The propagation path submodule calls the node transmission path parameters in the unified data sequence value of the mapping nodes based on the node residual difference value, matches the difference value and path index in the order of node number, calculates the cumulative residual amount of each path segment and compares the consistency of the difference sign direction, establishes the cumulative residual sequence within the path, and obtains the error path increment value.

[0104] For example, the execution process of the propagation path submodule is as follows: The system first calls the node transmission path parameters in the unified data sequence of the mapping nodes based on the node residual difference values. The node transmission path parameters are used to describe the physical path of data transmission and the arrangement order of the nodes. For example, assuming that the transmission path of node 1 is P1→P2→P3 and that of node 2 is P4→P5, the system associates the node residual difference values ​​with the node transmission paths. Next, the system matches the residual difference values ​​with the path indices according to the node number order. For example, the residual difference values ​​of node 1 and node 2 are 0.3 and 0.7, respectively, the path index of node 1 is P1→P2→P3, and the path index of node 2 is P4→P5. Then, the system calculates the cumulative residual amount for each path segment. For example, in the path P1→P2→P3, the residual difference value of node 1 is 0.3 seconds, the cumulative residual amount of path P1→P2 is 0.3 seconds, and the cumulative residual amount of path P2→P3 is 0.3 seconds. Next, the system compares the sign of the residual differences between adjacent nodes. Assuming the residual differences between nodes 1 and 2 are both positive, their signs are consistent, and the system continues to calculate the cumulative value of the residuals within the path. If the signs of the residual differences between two adjacent nodes are opposite, it indicates a synchronization error, requiring readjustment. Finally, based on the cumulative residual amount and sign consistency of the path segment, the system obtains the error path increment value. For example, the error path increment value for path P1→P2→P3 is 0.6 seconds, representing the cumulative synchronization difference between the nodes.

[0105] Trend Judgment Submodule: Calculates the incremental difference between adjacent path segments based on the incremental value of the error path, judges the continuity and magnitude of the incremental direction, performs difference smoothing on discontinuous segments and rearranges the path sequence, calculates the propagation direction ratio and intensity quantization value, and generates the error propagation intensity value.

[0106] Specifically, the system first calculates the increment difference between adjacent path segments based on the error path increment value. The increment difference represents the amount of change in error increment between path segments. For example, if the error increment value for path P1→P2→P3 is 0.6 seconds, while the error increment value for path P2→P3→P4 is 0.8 seconds, then the increment difference is: Next, the system determines the continuity and magnitude of the increment direction, checking if the increment direction is continuous and if the magnitude of the change is within a preset range. If the increment difference has a large magnitude, it indicates an unstable trend in the synchronization error, and the system will make adjustments. If the increment difference is small and the direction is consistent, it indicates that the synchronization error is relatively stable, and the system will continue data processing. For discontinuous path segments, the system will perform difference smoothing, that is, interpolating the increment difference to smooth out the error fluctuations between path segments. For example, if the increment difference of path P1→P2→P3 is 0.5 seconds, while the increment difference of path P2→P3→P4 is 0.9 seconds, the system will perform smoothing between these two path segments, interpolating to obtain a more consistent increment difference. Subsequently, the system rearranges the path sequence to ensure the stability of synchronization accuracy. Finally, the system calculates the propagation direction ratio and intensity quantization value. The propagation direction ratio represents the consistency of error propagation on the synchronization path, and the intensity quantization value represents the intensity of error propagation. For example, if the propagation direction ratio of path segment P1→P2→P3 is 0.8 and the intensity quantization value is 0.6, then the final generated error propagation intensity value is: This error propagation strength value is used for subsequent synchronization adjustments and optimizations.

[0107] Please see Figure 6 In another embodiment of the present invention, the conflict correction management module includes a delay compensation identification submodule, an over-limit node comparison submodule, and a conflict correction calculation submodule.

[0108] Delay compensation identification submodule: Obtains the error propagation intensity value, compares it with the time offset parameter, analyzes the time offset of each node, determines whether it exceeds the set time offset threshold, if it exceeds the threshold, records the node that exceeds the threshold and generates a delay compensation requirement; generates the delay compensation requirement value based on the information of the node that exceeds the threshold.

[0109] Specifically, when obtaining the error propagation strength value and time offset parameter, it is necessary to first retrieve the error propagation strength value of each node from the node dataset. Its corresponding time offset parameter ,in The calculation results, derived from the error propagation tracking module, represent the magnitude of the accumulated error at each node in the propagation path. This represents the offset of a node relative to a unified time base. During execution, the system reads the time offset values ​​of the most recent three sampling periods from the historical synchronization records of each node, and calculates the current time offset change rate by averaging the time differences between two adjacent sampling periods. This is used to determine the node synchronization status. Subsequently, the rate of change is compared with the error propagation intensity value, and the ratio of the two is used to determine the synchronization status. Characterizes the strength of the effect of node errors on time offset. When If the error exceeds a preset threshold, the node synchronization status is considered abnormal. The threshold is set based on the statistical results of the overall system error distribution. For example, in a monitoring sample of 100 nodes, the 80th percentile of the error intensity distribution is taken as the upper limit threshold. .

[0110] In a specific surveying scenario, assuming the error propagation intensity of node A is 2.4 and the time offset rate of change is 1.0, then the calculated... Therefore, node A is determined to be an out-of-limit node. The system then records node number A and its corresponding error and offset values, and generates the delay compensation requirement value. At this point, the compensation demand value As input parameters for subsequent correction calculations, they guide the adjustment of synchronization delay, ultimately yielding the delay compensation requirements.

[0111] The over-limit node comparison submodule: Based on the delay compensation requirement value, it calls the error propagation intensity value and time offset parameter to calculate the ratio of the over-limit nodes, determine the deviation magnitude of the over-limit nodes, compare it with the set threshold, determine whether the nodes need additional compensation, and finally generate a list of over-limit nodes and obtain the deviation value of the over-limit nodes.

[0112] The out-of-limit node comparison submodule performs a ratio calculation operation on each identified out-of-limit node based on the delay compensation requirement value, starting by calling the error propagation strength value. With time offset parameter Normalization is performed, and the error intensity is calculated based on the average error of all nodes in the network. Convert to proportionality coefficient and normalize the time offset to When performing ratio calculations, the magnitude of the node deviation is calculated. and compare it with the system-set threshold. The threshold is set based on the system's allowable synchronization deviation ratio. For example, in a multi-source mapping scenario, the allowable deviation is no more than 5%. If a node's deviation exceeds this value, it is recorded as a node requiring additional compensation. Taking node A as an example, assuming the average error intensity across the entire network... Maximum time offset The parameters of node A are ,but Calculated Node A is marked as requiring additional compensation. The system then compiles all out-of-limit nodes' numbers and deviation values, forming an out-of-limit node list { And save the deviation magnitude results for each node. This list provides the out-of-limit deviation value for each node, which can be used for conflict correction calculations in the next stage.

[0113] The conflict correction calculation submodule calls the deviation value of the out-of-limit node and the delay compensation requirement value to perform correction calculations for each out-of-limit node. It optimizes the synchronization of the nodes by adjusting the time offset, and finally generates the corrected timing information to obtain the conflict correction balance rate.

[0114] The conflict correction calculation submodule calls the out-of-limit node deviation value. Delay compensation demand value During the correction calculation, the system retrieves the current time offset parameters of each out-of-limit node. And calculate the adjustment amount. This adjustment is used to correct the synchronization timing of the nodes. Subsequently, a time offset reset operation is performed on each node, and the new timing parameters are defined as follows: Taking node A as an example, suppose... ,but Corrected time offset After this calculation, the timing parameters of all nodes exceeding the limit are updated to the new synchronization configuration table. The system then calculates the corrected average offset difference of each node based on a unified timing reference. The adjusted synchronization sequence is recorded. The conflict correction balance rate is calculated by cross-referencing the corrected offset difference of each node with bandwidth and delay parameters. ,in To correct the average time offset value, This represents the maximum corrected time offset. Taking nodes A, B, and C as examples, the corrected offsets are 1.56, 1.47, and 1.52 respectively. The calculated values ​​are... ,but The system records this balance rate as the final output, yielding the conflict-corrected balance rate.

[0115] Through the execution of these sub-modules, the system can accurately calculate the delay compensation value of each node based on parameters such as error propagation strength, time offset, and network latency, and adjust the synchronization time according to the transmission order of the nodes, thereby ultimately generating a balanced synchronization timing coefficient to ensure stable data synchronization.

[0116] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0117] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0118] Furthermore, those skilled in the art will understand that various aspects of this specification can be described and illustrated in several patentable ways or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Accordingly, various aspects of this specification can be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. All of the above hardware or software may be referred to as a “data block,” “module,” “engine,” “unit,” “component,” or “system.” Furthermore, various aspects of this specification may be represented as a computer product located on one or more computer-readable media, including computer-readable program code.

[0119] Computer storage media may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and suitable combinations thereof. Computer storage media can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer storage medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0120] The computer program code required for the operation of each part of this manual can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc.; conventional procedural programming languages ​​such as C, Visual Basic, Fortran2003, Perl, COBOL2002, PHP, ABAP; dynamic programming languages ​​such as Python, Ruby, and Groovy; or other programming languages. This program code can run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or processing device. In the latter case, the remote computer can be connected to the user's computer through any network, such as a local area network (LAN) or wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as Software as a Service (SaaS).

[0121] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented by hardware devices, they can also be implemented solely by software solutions, such as installing the described system on existing processing devices or mobile devices.

[0122] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0123] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be consistent with the teachings of this specification, rather than as examples or limitations. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A distributed collaborative processing and management platform for multi-source surveying and mapping data, characterized in that, The platform includes: The multi-source acquisition input module acquires node spatial coordinates, acquisition time and signal strength parameters, performs coordinate transformation to obtain spatial position parameters, sorts time to obtain time offset parameters, compares signals to obtain signal association identifiers, synchronously records node numbers and transmission order to generate node transmission path parameters, and integrates them to generate a unified data sequence value for mapping nodes. The content relevance identification module calls the spatial location and timestamp in the unified data sequence value of the surveying and mapping nodes, calculates the offset difference and performs similarity comparison, and generates the content relevance ratio of the data stream based on the threshold. The distributed synchronization scheduling module calls the spatial offset ratio, time offset ratio, and bandwidth occupancy ratio in the data stream content correlation ratio, performs time difference and spatial difference operations, records the results as offset residual parameters, determines the synchronization priority based on the differences, adjusts the timing according to the bandwidth parameters, and generates multi-source data synchronization timing coefficients. The error propagation tracking module calls the offset residual parameter in the multi-source data synchronization time series coefficient and the node transmission path parameter and node number parameter in the unified data sequence value of the mapping node to calculate the residual difference between adjacent nodes and determine the propagation trend, and generate the error propagation intensity value. The conflict correction management module calls the time offset parameter based on the error propagation intensity value, compares the ratio with the threshold, performs delay compensation on nodes that exceed the limit, and generates a conflict correction balance rate.

2. The distributed collaborative processing and management platform for multi-source mapping data according to claim 1, characterized in that, The unified data sequence value of the mapping nodes includes a spatial location set, a time offset set, a signal association set, and a node path set; the data stream content correlation ratio specifically includes the spatial offset ratio, the time offset ratio, and the bandwidth occupancy ratio; the multi-source data synchronization timing coefficient includes the synchronization priority sequence, the bandwidth scheduling coefficient, and the residual record set; the error propagation intensity value specifically refers to the node residual increment, the propagation direction identifier, and the trend matching index; the conflict correction balance rate includes the delay compensation coefficient, the time adjustment amount, and the node correction ratio.

3. The distributed collaborative processing and management platform for multi-source mapping data according to claim 2, characterized in that, The multi-source acquisition input module includes: Spatial coordinate transformation submodule: obtains node spatial coordinate parameters and geographic reference coordinates, performs coordinate system transformation on latitude and longitude values ​​and calculates position offset, compares with reference benchmark positioning points to determine spatially consistent intervals, and generates spatial position parameter values; Time series calculation submodule: Performs time difference calculation based on the collected time parameters and a unified time base, sorts the node timestamps according to the collection order and calculates the offset interval, pairs the time offset of each node with the sequence position, and generates time offset parameter values; Signal comparison and identification submodule: Based on the node signal strength parameters, call the device identification code, compare the signal strength value with the threshold signal benchmark, filter signal data within the intensity fluctuation range, extract the matching relationship between the identification code and the intensity change trend, and generate signal association identification value; The path integration and generation submodule calls the spatial location parameter value, time offset parameter value and signal association identifier value, performs mapping matching on the node number and transmission order and generates node path parameters, integrates all parameter sets into a unified structured sequence, and generates unified data sequence values ​​for surveying nodes.

4. The distributed collaborative processing and management platform for multi-source mapping data according to claim 3, characterized in that, The content relevance identification module includes: Spatial offset recognition submodule: calls the spatial position parameters in the unified data sequence value of the surveying nodes, detects the coordinate differences of each node based on the reference coordinate benchmark, performs distance difference calculation on adjacent coordinate points, judges the consistency of offset direction and counts the offset distribution interval, and generates spatial offset difference coefficient; Time correlation determination submodule: Calls the timestamps collected in the time series calculation submodule, calculates the adjacent difference of the timestamp sequence and compares it with a unified time benchmark, judges the time correlation of nodes based on the time interval change trend, and generates a time synchronization matching degree value; Similarity filtering submodule: Based on the spatial offset difference coefficient and the time synchronization matching degree value, the module performs a weighted comparison of the two, calculates the comprehensive similarity parameter, and performs filtering actions according to the set similarity threshold. It extracts the relevance intervals that meet the threshold conditions and generates the relevance ratio of the data stream content.

5. The distributed collaborative processing and management platform for multi-source mapping data according to claim 4, characterized in that, The distributed synchronization scheduling module includes: Offset Calculation Submodule: Calls the spatial offset ratio, time offset ratio and bandwidth occupancy ratio in the data stream content correlation ratio, calculates the time offset difference and performs differential on the spatial position sequence, compares it with the reference coordinate set according to the unified time base to obtain the residual sequence, and generates the offset residual coefficient; Priority determination submodule: Based on the offset residual coefficient, it calls the time offset difference sequence and the spatial difference sequence, compares the consistency of residual amplitude and direction according to the synchronization priority benchmark value, counts the sorting position of each data stream and normalizes it to obtain the synchronization priority index; Timing adjustment submodule: Based on the synchronization priority index, it calls the bandwidth occupancy ratio and network latency measurement value, determines the queue slice length according to the preset bandwidth threshold and latency threshold, adjusts the synchronization start and end time and sending interval, records the execution sequence, and generates multi-source data synchronization timing coefficients.

6. The distributed collaborative processing and management platform for multi-source mapping data according to claim 5, characterized in that, The specific steps for adjusting the synchronization start and end times and sending intervals and recording the execution sequence are as follows: based on the slice length range, call the synchronization start and end time candidates and the sending interval candidates, select the starting point according to the priority index and perform arithmetic adjustment on the interval, compare with the bandwidth occupancy ratio product constraint and the delay measurement value, determine the time window boundary and step size of each sequence, and generate timing configuration coefficients.

7. The distributed collaborative processing and management platform for multi-source mapping data according to claim 6, characterized in that, The specific method for generating multi-source data synchronization timing coefficients is as follows: call the timing configuration coefficients and queue slice length sequence, record the start and end times and sending intervals in index order, merge adjacent splicable segments and remove conflicting segments, calculate the time axis occupancy ratio and synchronization sequence stability of each data stream, summarize them into a unified timing index, and generate multi-source data synchronization timing coefficients.

8. The distributed collaborative processing and management platform for multi-source mapping data according to claim 5, characterized in that, The error propagation tracking module includes: The residual calculation submodule calls the offset residual coefficient in the multi-source data synchronization time series coefficient and the node number parameter in the unified data sequence value of the mapping node. For each node, it calculates the residual difference between adjacent nodes, records the difference direction according to the residual amplitude and sign, statistically analyzes and normalizes the amplitude distribution of the residual sequence, and generates the node residual difference value. The propagation path submodule calls the node transmission path parameters in the unified data sequence value of the mapping nodes based on the node residual difference value, matches the difference value and path index in the order of node number, calculates the cumulative residual amount of each path segment and compares the consistency of the difference sign direction, establishes the cumulative residual sequence within the path, and obtains the error path increment value. Trend Judgment Submodule: Calculates the incremental difference between adjacent path segments based on the incremental value of the error path, judges the continuity and magnitude of the incremental direction, performs difference smoothing on discontinuous segments and rearranges the path sequence, calculates the propagation direction ratio and intensity quantization value, and generates the error propagation intensity value.

9. The distributed collaborative processing and management platform for multi-source mapping data according to claim 8, characterized in that, The conflict correction management module includes: Delay compensation identification submodule: Obtain the error propagation intensity value, compare it with the time offset parameter, analyze the time offset of each node, determine whether it exceeds the set time offset threshold, if it exceeds the threshold, record the node that exceeds the threshold and generate the delay compensation requirement, and generate the delay compensation requirement value based on the information of the node that exceeds the threshold. The over-limit node comparison submodule: Based on the delay compensation requirement value, it calls the error propagation intensity value and time offset parameter to calculate the ratio of over-limit nodes, determine the deviation magnitude of over-limit nodes, compare it with the set threshold, determine whether the node needs additional compensation, and finally generate a list of over-limit nodes and obtain the deviation value of over-limit nodes. The conflict correction calculation submodule calls the deviation value of the out-of-limit node and the delay compensation requirement value to perform correction calculations for each out-of-limit node. It optimizes the synchronization of the nodes by adjusting the time offset, and finally generates the corrected timing information to obtain the conflict correction balance rate.

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