A method for real-time transmission and verification of 5G inspection data

Abstract

This invention discloses a real-time backhaul verification method for 5G inspection data, relating to the field of inspection data transmission technology. The method involves collecting raw fragmented signals during the backhaul process from the inspection terminal, embedding continuous time identifiers and dynamic source identifiers into each fragment to construct a time series; rearranging the fragmented signals in chronological order based on the time series and continuously comparing them to generate a temporary backhaul sequence consistent with the acquisition rhythm; extracting time interval mutation points and identifying misalignment risk areas based on the temporary backhaul sequence; performing time extension balancing and buffering on mutation segments within the risk areas to form a time-stable backhaul sequence; and combining the stable sequence with full-cycle time integration and continuous mapping to obtain a complete data sequence. This invention, through full-cycle time integration and continuous mapping, forms a complete time series corresponding to the actual inspection rhythm, improving the reliability and application stability of inspection results.

Description

Technical Field

[0001] This invention relates to the field of inspection data transmission technology, specifically to a real-time feedback verification method for 5G inspection data. Background Technology

[0002] Real-time data transmission and verification during equipment inspection refers to the process of simultaneously transmitting operational data such as temperature, vibration, current, images, and sound waves collected by inspection terminals (e.g., intelligent inspection instruments, mobile terminals, or embedded sensor nodes) to a backend or cloud platform via wireless communication links (e.g., 4G, 5G, or industrial Wi-Fi) during equipment inspection. The backend system then verifies the integrity, consistency, and authenticity of the uploaded data based on timestamps, device numbers, and geographical locations. Its core objective is to ensure that inspection data is not lost, duplicated, tampered with, or misaligned in time throughout the entire process of collection, transmission, and storage. The verification process typically includes data format standardization checks, temporal continuity comparisons, matching of acquisition device identifiers, removal of abnormal values, and comparison of differences between the original signal and historical benchmarks. This enables real-time confirmation and traceability of inspection results, ensuring the timeliness and reliability of inspection conclusions.

[0003] The existing technology has the following shortcomings:

[0004] During the data transmission process, to adapt to the high-bandwidth, dynamically changing communication channels, the inspection terminal typically transmits the collected data in fragments. When the network is under high load, some fragmented data packets may arrive out of order due to link congestion or node switching, causing cross-frame mismatches at frame boundaries in the dynamic reassembly algorithm. Such mismatches can misassemble data from different time segments into a logically continuous sequence, forming a data chain that appears complete but is actually time-displaced. Since the system typically relies on frame order continuity as the basis for data integrity judgment during the verification phase, this type of hidden mismatch is difficult to identify in a timely manner, which may lead to deviations in subsequent status identification, trend analysis, and anomaly judgment, ultimately affecting the accuracy and reliability of the inspection results.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a real-time feedback verification method for 5G inspection data to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a real-time feedback verification method for 5G inspection data, comprising the following steps:

[0008] Step 1: Collect raw data fragments from the inspection terminal during the transmission process, embed continuous time identifiers and dynamic source identifiers into each fragment to build a complete time series foundation and establish a unified time reference for subsequent sorting of fragments.

[0009] Step 2: Based on the constructed time series, the acquired segmented signals are rearranged in time order according to the embedded continuous time identifiers, and continuous comparison is performed during the rearrangement process to generate a temporary feedback sequence that matches the rhythm of the inspection data acquisition, which is used to provide continuous temporal basis for the identification of subsequent boundary segments.

[0010] Step 3: Based on the generated temporary backhaul sequence, perform time stretching analysis on the continuous segments, extract the time interval mutation points between adjacent segments, and mark the mutation point locations as misalignment risk areas to provide a precise positioning basis in the subsequent splicing and correction process;

[0011] Step 4: Based on the identified misalignment risk areas, perform time-stretching balancing processing on adjacent continuous segments around the time interval abrupt change point, and perform buffering connection according to the acquisition rhythm before and after to restore the natural temporal continuity between segments, thereby forming a time-stable data back transmission sequence.

[0012] Step 5: Combine the formed stable data feedback sequence to perform full-cycle time integration processing on the entire inspection process. The back and forth feedback sequences after time stretching and balancing are continuously mapped to obtain a complete data sequence corresponding to the actual inspection rhythm, which is used to provide accurate time series support for subsequent operation status identification.

[0013] Preferably, the process of collecting fragmented raw data signals from the inspection terminal during the data transmission includes the following steps:

[0014] When performing data acquisition operations, the inspection terminal collects the operational data generated by the inspection target in segments. When the segments are generated, the local time control unit outputs a continuous time identifier and embeds the continuous time identifier into the header of the segmented signal data. At the same time, based on the operating environment information and the acquisition task information, a dynamic source identifier is generated and embedded into the tail of the segmented signal data, so that the segmented signal has time information and source information.

[0015] The fragmented signals embedded with continuous time identifiers and dynamic source identifiers are pushed to the backhaul channel in the order of time identifiers, and the fragment time identifiers and corresponding source identifiers are recorded during transmission. The correspondence between the two is used to maintain the recognizability and timing correspondence of the fragments during transmission.

[0016] Based on the continuity of time signatures, all segmented signals are arranged, segments with the same source signature are categorized, and segments with varying time intervals are sequentially filled, forming a two-layer time series with time as the main axis and source as the secondary axis, which reflects the actual collection order and source distribution of inspection data.

[0017] Preferably, the steps for time-series-based time-series rearrangement include:

[0018] The time stamps of all the segmented signals are read, the time stamps of the segmented signal data headers are extracted and stored in the time index table according to the acquisition order. The time index table is arranged in the primary order of time stamps and the secondary order of dynamic source identifiers, so that time and source correspond in the data structure and the time stamps of the segmented signals are consistent with the source identifiers.

[0019] The fragmented signals are rearranged according to the time identifier order recorded in the time index table. Fragmented signals with the same source identifier are established as independent time series trajectories and arranged continuously according to the time identifier. When fragmented signals with different source identifiers intersect in time, they are inserted into the corresponding positions according to the global order of the time identifiers to form a complete time chain.

[0020] Continuous comparison is performed on the rearranged segmented signals. By comparing the time interval between adjacent segmented signals with the time interval corresponding to the inspection and acquisition cycle, continuous segments are divided and segment boundary points are marked. The source identification and comparison records are used for parallel mapping.

[0021] Based on the continuous comparison results, the segments are integrated and spliced ​​sequentially according to the time identifier. Segments with time intervals that match the collection rhythm are directly connected, while segments with abrupt time intervals retain the interval information. At the same time, the source identifier information is arranged synchronously to generate a temporary feedback sequence that matches the inspection data collection rhythm.

[0022] Preferably, during the time sequence reordering process, when the time interval between adjacent segment signals is inconsistent with the time interval corresponding to the inspection and acquisition rhythm, the position of the time interval change is marked as the segment boundary point, and the time interval is filled in according to the position of the boundary point during the integration stage, so that the temporary backhaul sequence remains continuous on the time axis, and the source identification information and time identification are updated synchronously to ensure that segments from different sources are arranged in a corresponding manner within a unified time frame.

[0023] Preferably, the steps for performing time-spread analysis based on temporary backhaul sequences include:

[0024] The continuous segments in the temporary return sequence are expanded segment by segment. The time stamp in the data header of the segmented signal is read and the time interval between adjacent segments is recorded in sequence. At the same time, the dynamic source identifier is recorded synchronously, so that the time curves corresponding to different sources are independently distributed on the time axis, and adjacent time stamps are connected to form a time extension curve.

[0025] Extract the temporal connection between consecutive segments, record the time difference between the last segment time identifier of each consecutive segment and the first segment time identifier of the next segment, forming a time interval set covering the entire inspection cycle, and attach source identification information and segment index to each interval item;

[0026] The time interval set is subjected to mutation feature identification in chronological order. The change range of time interval between adjacent segments is compared to determine the location of a sudden jump or drop. The corresponding segment boundary is marked as the time interval mutation point, and the mutation point location, the time interval value before and after, the segment index and the source identifier are recorded to form a time mutation mapping table.

[0027] Centered on the time location of the mutation point, risk segments covering the time range before and after the mutation point are delineated according to a fixed extension ratio. The start time of the risk segment is set as the time identifier of the segment before the mutation point, and the end time is set as the time identifier of the segment after the mutation point. Each risk segment is assigned a number and a source identifier to form a misaligned risk distribution table.

[0028] Preferably, the risk segments in the misalignment risk distribution table are arranged sequentially in chronological order, and the risk segments corresponding to different source identifiers are displayed independently on the time axis. The time range of each risk segment covers the time identifier interval of adjacent segments before and after the mutation point, and the time interval change record is retained within the risk segment so as to achieve accurate positioning and independent tracking of the misalignment risk area based on the time mutation mapping table during the subsequent splicing and correction process.

[0029] Preferably, the steps for performing time-delay balancing based on misalignment risk regions include:

[0030] The identified misalignment risk areas are located and their boundaries are extracted over time. The time range is determined by reading the end time identifier of the preceding continuous segment and the start time identifier of the following continuous segment. The time identifier, source identifier, and arrangement position of all segmented signals within the risk area are extracted. The start and end times, time span, number of segments, and source relationship are recorded to form a continuously adjustable connection interval.

[0031] Time extension correction is performed on adjacent mutation segments within the misalignment risk area. The time markers of the segments before and after are read and the time offset is determined. If the time difference is greater than the acquisition interval, continuous time markers are inserted. If the time difference is less than the acquisition interval, the overlapping time markers are redistributed to make the time distribution correspond to the acquisition rhythm.

[0032] The extended and corrected segments are buffered and connected. The end time marker of the previous segment and the start time marker of the subsequent segment are determined and a transition time interval is generated. The time interval gradually changes according to the acquisition rhythm to form a continuous curve, ensuring that the time interval is continuous and consistent with the acquisition rhythm.

[0033] The corrected and connected segmented signals are integrated into a time series. The signals are rearranged according to the time identifier and the source identifier is mapped synchronously, so that the collected data from different sources are spread in parallel on a unified time axis, forming a continuous and error-free time-stability data backhaul sequence.

[0034] Preferably, during the time extension correction process, the redistribution of time markers is performed according to the fixed time interval of the acquisition rhythm, and the connection between the previous and subsequent segments is smoothly connected through the transition time interval. The time interval of the transition time interval gradually changes from the end time interval of the previous segment to the start time interval of the next segment, ensuring that the segmented signals after time extension are continuously arranged on the time axis and consistent with the acquisition rhythm.

[0035] Preferably, the steps of performing full-cycle time integration processing in conjunction with stable data backhaul sequences include:

[0036] The stable data backhaul sequence is divided into time intervals and ordered. The start time and end time identifiers are extracted from each stable data backhaul sequence. The start and end times of all sequences are summarized and the start and end points of the entire cycle are determined. The time offset of each stable data backhaul sequence is calculated within the entire cycle and its position on the time axis is determined. When there is a time gap between adjacent stable data backhaul sequences, a virtual time segment is inserted. When there is time overlap, the start time position of the next stable data backhaul sequence is adjusted to form a continuous full-cycle time distribution.

[0037] A continuous mapping is performed on the stable data backhaul sequences before and after time-stretched balancing. The stable data backhaul sequence with the earliest time stamp is selected as the starting point of the main time sequence. The subsequent stable data backhaul sequences are connected sequentially according to the increasing order of time stamps. The connection points are aligned and distributed according to the time interval of the acquisition rhythm, so that all stable data backhaul sequences form an increasing time main chain on a unified time axis.

[0038] After continuous mapping is completed, time integration and summarization are performed on the main time sequence. The fragmented signals from each source are inserted into the corresponding time period in sequence according to the order of the main time chain. The fragmented signals from the same source are arranged continuously, and the fragmented signals from different sources are arranged interleaved according to the time identifier. The time periods with overlap or gaps are corrected by adjusting the order or supplementing with virtual time identifiers to form a complete data sequence corresponding to the actual inspection rhythm.

[0039] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0040] This invention embeds continuous time identifiers and dynamic source identifiers during the inspection data fragmentation generation stage, and performs rearrangement, extension, and integration processing based on the time series, ensuring that the inspection data maintains a consistent time reference relationship throughout the entire process of collection, transmission, and reconstruction. Through time sequence rearrangement and time extension balancing, it effectively avoids the erroneous splicing of data from different time segments into a logically continuous sequence during transmission, fundamentally eliminating hidden time misalignment problems. This ensures that the resulting data transmission sequence truly reflects the inspection data collection rhythm in the time dimension, thereby improving the reliability of inspection data in terms of time consistency.

[0041] This invention performs full-cycle time integration processing on stable data transmission sequences, continuously mapping the time-stretched and balanced transmission sequences to obtain a complete time series covering the entire inspection process. This enables the inspection data to form a continuous and traceable distribution structure on the global time axis. Based on this complete data series, subsequent operational status identification can be analyzed with precise time support, avoiding misjudgments and trend deviations caused by time misalignments. This helps improve the reliability and stability of inspection results in practical applications, providing a reliable data foundation for intelligent analysis and refined management of the inspection process. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0043] Figure 1 This is a flowchart of the method of the present invention;

[0044] Figure 2 This is a flowchart illustrating the time sequence rearrangement based on time series data in this invention.

[0045] Figure 3 This is a flowchart illustrating the time extension analysis of the temporary return sequence in this invention.

[0046] Figure 4 This is a schematic diagram of the system modules of the present invention. Detailed Implementation

[0047] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0048] This invention provides, for example Figures 1 to 3 The method for real-time transmission and verification of 5G inspection data, as shown, includes the following steps:

[0049] Step 1: Collect raw data fragments from the inspection terminal during the transmission process, embed continuous time identifiers and dynamic source identifiers into each fragment to build a complete time series foundation and establish a unified time reference for subsequent sorting of fragments.

[0050] The specific implementation process of this step is as follows:

[0051] During the data acquisition process, the inspection terminal performs real-time segmented acquisition of operational data generated by the inspection target. The terminal sequentially acquires temperature, vibration, current, sound, and image signals at a fixed sampling period, and after sampling, divides these acquisition results into multiple independent segmented signals according to time sequence. The generation time of each segmented signal corresponds to a specific sampling time point, and at the instant of segment generation, a continuous time identifier is output through the local time control unit. This time identifier uses a strictly incremental time sequence format to accurately reflect the acquisition order. After generating the time identifier, the inspection terminal embeds it into the data header of the segmented signal, ensuring that each segmented signal carries unique time information from the moment of generation. Simultaneously, at the same moment of segment generation, the inspection terminal generates a dynamic source identifier based on its own operating environment information and acquisition task information. This identifier consists of the inspection terminal number, acquisition channel number, and inspection area number. By embedding this identifier into the data tail of the segmented signal, each segment is assigned a corresponding source identity during the generation stage. By embedding continuous time identifiers and dynamic source identifiers in the data header and tail respectively, each segment signal has a definite acquisition time and a unique source identity from the beginning of its generation, thereby ensuring its identifiability and traceability in subsequent transmission stages.

[0052] It should be noted that:

[0053] The local time control unit can employ existing technologies such as real-time clock circuits, embedded processor internal timers, system-level clock chips, or high-precision time counting mechanisms based on the operating system kernel. For example, a timestamp can be generated using the RTC circuit built into a microcontroller or embedded processor, or an incremental time value can be output using the system timer or high-precision timer / counter of an ARM processor. In scenarios with network synchronization capabilities, a unified time reference can also be generated by combining a GPS time synchronization module or a network time synchronization protocol.

[0054] These existing time control methods can all provide continuously incrementing time markers to mark the generation time of segmented signals.

[0055] After embedding the identifiers of the segmented signals, the generated segmented signals are organized in an orderly manner according to the transmission process of the backhaul channel to ensure that the time and source information remain consistent during transmission. The inspection terminal pushes the segments sequentially to the backhaul channel based on the time identifiers carried in the segmented signals, ensuring that the transmission order matches the acquisition order. During transmission, when the load of the backhaul channel fluctuates, the signal coverage area switches, or there are time delay differences in the transmission path, some segmented signals may arrive out of order. To prevent out-of-order segments from losing their time correspondence in subsequent stages, the time identifier of the segment is recorded in a local cache when the segment is sent, and the corresponding source identifier is recorded simultaneously, so that any subsequent segment can find a matching entry in the cache record based on the time identifier. When multiple inspection terminals collect data in parallel and transmit it back simultaneously, the dynamic source identifier of each segmented signal plays a distinguishing role, ensuring that segments from different inspection terminals or different acquisition channels are not confused during transmission. In this way, even under multi-channel parallel or asynchronous backhaul conditions, the identifiability and temporal correspondence of the fragments can still be maintained through the combination of time stamps and source stamps. The result of this step is a set of fragments with time stamps and source stamps, which are arranged in the order of acquisition time, providing a complete raw data set for subsequent time series foundation construction.

[0056] After establishing the matching relationship between time stamps and source stamps, a time series basic construction operation is performed on all stamped signals to form a unified time reference framework for subsequent sorting and reassembly. This process arranges all stamped signals one by one according to the continuity of time stamps, connecting stamps from the same inspection terminal end-to-end based on time stamps, and integrating stamps from different terminals in parallel based on time stamps, ensuring that all stamps have a unique position on the overall timeline. To ensure the continuity of the timeline, the intervals between time stamps are checked during the arrangement process. When a change in the interval between time stamps of adjacent stamps is found, sequential filling is performed while maintaining the original acquisition interval on the timeline, making the entire timeline logically continuous. For the correspondence of source stamps, classification is performed simultaneously with time series construction, assigning stamps with the same source stamp to the corresponding source sequence, ensuring that each source's timeline has a precise position on the global timeline, thus forming a two-layer time series structure with time as the main axis and source as the secondary axis. This construction method not only ensures the continuous temporal mapping of the segmented signals but also enables the synchronous expression of data from different inspection terminals within the same time dimension. After this step is completed, all segmented signals form a complete time series basis under a unified time reference. This time series covers the entire inspection cycle and can accurately reflect the actual collection order and source distribution of the inspection data at each moment.

[0057] Through the above steps, the raw data fragments collected by the inspection terminal during the backhaul process possess dual identifiers of time and source at the time of generation, maintaining their correspondence throughout transmission and integration. The continuous time identifier provides a precise acquisition time reference for each fragment, enabling any fragment to find a unique position on the overall timeline based on its time identifier. The dynamic source identifier ensures that data generated by different inspection terminals retains its independent identity attributes after converging into a unified backhaul link, preventing confusion due to network out-of-order delivery or node switching. Ultimately, the time identifier and source identifier work together at the data level, forming a traceable, locatable, and mappable time sequence foundation for the entire inspection data backhaul process. Through this embedding and mapping method of continuous time identifiers and dynamic source identifiers, the inspection data maintains a consistent temporal logical relationship throughout the entire process of acquisition, transmission, and reassembly, establishing a complete temporal foundation for real-time backhaul and reliable correction of inspection data.

[0058] Step 2: Based on the constructed time series, the acquired segmented signals are rearranged in time order according to the embedded continuous time identifiers, and continuous comparison is performed during the rearrangement process to generate a temporary feedback sequence that matches the rhythm of the inspection data acquisition, which is used to provide continuous temporal basis for the identification of subsequent boundary segments.

[0059] The specific implementation process of this step is as follows:

[0060] First, time stamps are read from all acquired segmented signals based on time series data. Each segmented signal is embedded with a unique continuous time stamp during generation, recording the acquisition time of that segmented signal. The time stamp values ​​are extracted by reading the data header information of the segmented signals, and the time stamps of all segmented signals are stored in a time index table according to the acquisition order. This time index table is arranged in primary order by time stamp and secondary order by dynamic source identifier, ensuring a one-to-one correspondence between time and source in the data structure. For segmented signals corresponding to the same source identifier, the time stamps are arranged sequentially according to the sampling period; for segmented signals corresponding to different source identifiers, they are sorted overall according to the chronological order of the time stamps. Through this reading and sorting operation, all segmented signals are logically positioned in a comparable time series, providing a basic reference for subsequent sorting and comparison operations. Throughout this process, the time stamp and source identifier of each segmented signal remain consistent with the original data content without any modification, ensuring the accuracy of the time correspondence in subsequent processing.

[0061] After reading and organizing the time stamps, the segmented signals are rearranged according to their temporal relationship. This rearrangement process uses a time index table as its basis, starting with the segmented signal with the earliest time stamp and arranging the segments sequentially in ascending order of time. During the arrangement, independent time series trajectories are established for segments corresponding to the same source identifier, and these segments are continuously arranged on the time axis according to their time stamps. When segments from different source identifiers intersect in time, they are inserted into their corresponding positions according to the global order of their time stamps, ensuring the continuity of the overall time axis. This arrangement method enables the formation of a complete time chain under multi-source concurrent acquisition conditions, achieving a unified mapping of all segmented signals in the global time dimension. During the arrangement process, the time stamp of each segmented signal is used as the unique sorting criterion, and dynamic source identifiers are used to mark the source relationship of the segments, so as to accurately distinguish the data segments corresponding to different inspection terminals during subsequent continuous comparison and integration. After the arrangement is completed, all segmented signals are arranged in order from the earliest acquisition time to the latest acquisition time according to their time stamps, forming a preliminary time-ordered dataset.

[0062] After obtaining the initially sorted set of segmented signals, a continuous comparison operation is performed on adjacent segmented signals. This comparison process compares the time intervals of two adjacent segmented signals one by one to determine their continuity in the time dimension. During the comparison, starting from the beginning of the time axis, the time stamp values ​​of adjacent segments are read sequentially, the time difference between them is calculated, and compared with the time interval corresponding to the inspection and acquisition cycle. When the time interval of adjacent segments matches the time interval corresponding to the inspection and acquisition rhythm, they are marked as continuous segments; when the time interval deviates from the time interval corresponding to the inspection and acquisition rhythm, the position is identified as a potential segment boundary, and the position of the corresponding segment on the time axis is recorded. At the same time, the source identifiers of adjacent segments are also compared. When the source identifiers are different but the time intervals match the acquisition rhythm, they are still considered as continuous segments, but the record information of source switching is retained for parallel mapping in the subsequent integration stage. Throughout the continuous comparison process, the time stamp is always used as the judgment basis, and the source identifier is used as an auxiliary identification item to ensure that the correlation between data sources is preserved while judging the continuity of time. After continuous comparison, the initially arranged fragment set was divided into several continuous segments. The time interval within each continuous segment was stable, and the segments were distinguished by a dividing point, laying the foundation for subsequent rhythmic integration.

[0063] After continuous comparison is completed, the comparison results are integrated to form a temporary feedback sequence that matches the inspection data collection rhythm. This integration process uses the continuous segments defined in the comparison phase as basic units, sequentially splicing all continuous segments according to their time identifiers. During splicing, the temporal connection between adjacent segments is maintained. Segments with time intervals matching the collection rhythm are directly connected, while segments with abrupt changes in time intervals retain the interval information during splicing, ensuring the entire sequence logically presents a temporal rhythm distribution consistent with the inspection data collection rhythm. Simultaneously, source identifier information is synchronized, keeping segments from the same source independent and continuous on the time axis, while segments from different sources are alternately arranged according to their time identifiers, ensuring data from different inspection terminals correspond within a unified time frame. Through this integration method, the generated temporary feedback sequence not only completely corresponds to the inspection collection process in time sequence but also maintains consistency with the actual inspection scenario in source distribution. After integration, the resulting temporary feedback sequence covers the entire inspection cycle, with the time intervals between each internal segment corresponding to the actual collection rhythm, forming a data sequence with complete temporal continuity.

[0064] Through the aforementioned continuous steps, the acquired fragmented signals are read from time markers, arranged in chronological order, and then continuously compared and integrated with adjacent fragments, gradually constructing a temporary backhaul sequence corresponding to the inspection data acquisition rhythm. This temporary backhaul sequence is continuous in the time dimension, distinguishable in the source dimension, and logically reflects the data acquisition process of the inspection terminal, providing continuous temporal basis for subsequent boundary segment identification. Through the unified sorting and continuous comparison of time markers, regardless of out-of-order arrival or source overlap in the backhaul link, the fragmented signals can be restored to a time arrangement consistent with the actual acquisition rhythm during this rearrangement and integration process.

[0065] Step 3: Based on the generated temporary backhaul sequence, perform time stretching analysis on the continuous segments, extract the time interval mutation points between adjacent segments, and mark the mutation point locations as misalignment risk areas to provide a precise positioning basis in the subsequent splicing and correction process;

[0066] The specific implementation process of this step is as follows:

[0067] The generated temporary feedback sequence contains continuous segments that are expanded segment by segment to form a time-extended basis. Each continuous segment consists of multiple segmented signals arranged in ascending order of time identifiers. Ideally, the time intervals of these segmented signals should match the acquisition intervals of the inspection terminal. To ensure the integrity of subsequent analysis, the header information of each segmented signal is read one by one during the expansion process, the time identifier field is extracted, and the time interval values ​​between adjacent segments are recorded sequentially according to the numerical order of the time identifiers. Through this method of reading and recording item by item, the originally discrete time identifiers are connected into a continuous time curve, making the time variation relationship within the segment intuitively visible. Simultaneously, the dynamic source identifiers in the segmented signals are recorded synchronously, ensuring that the time curves corresponding to different sources are independently distributed on the time axis, and accurately distinguishing the time trajectories of different acquisition sources in subsequent steps. In this process, the start and end points of each time interval are composed of the time identifiers of two adjacent segmented signals, and the expansion order of the time axis strictly follows the segment acquisition order, without jumps or omissions, ensuring the continuity and comparability of the time-extended basis.

[0068] After establishing the time-extended foundation, the temporal connections between consecutive segments are extracted to form a time interval sequence between adjacent segments. This operation uses the last segment signal of each consecutive segment as the termination point and the first segment signal of the next segment as the starting point, recording the time difference between the two as the time interval between the adjacent segments. In this way, the entire temporary backhaul sequence is divided into multiple time-connected segments, each containing the time distance between two consecutive segments. During the extraction process, all segments are traversed in the order of their time identifiers, starting from the first segment of the sequence, and the time interval between each pair of adjacent segments is calculated and recorded sequentially. For segments under the same source identifier, this time interval directly reflects the continuity of the acquisition activity; for segments between different source identifiers, this time interval reflects the synchronization of different acquisition terminals in the time dimension. To ensure the accuracy of the time interval recording, source identifier information and segment sequence index are attached to each interval item during the extraction process, enabling accurate tracing of the segment position and source attribute corresponding to the interval during subsequent mutation analysis. Through this process, a set of time intervals covering the entire inspection cycle can be formed, which fully describes the temporal connections between segments and provides basic data for identifying time mutations.

[0069] To avoid confusion in the processing logic between the time extension analysis process and the time connection extraction process, the relevant processing methods are further clarified as follows:

[0070] When processing continuous segments segment by segment, the time intervals between adjacent segments are recorded sequentially according to time markers. This processing targets the segments within a single continuous segment, constructing the temporal distribution relationship within that segment and forming a time extension curve. This belongs to the segment-internal processing stage of the time extension analysis phase. However, when extracting the temporal connection relationships between consecutive segments, the difference between the end and start time markers of adjacent segments is recorded as the time interval. This processing addresses the boundary relationships between different consecutive segments, describing the temporal connection state between segments and providing a basis for identifying time interval abrupt changes. This belongs to the inter-segment temporal relationship extraction processing. The aforementioned intra-segment time intervals are used for time extension analysis, while the inter-segment time intervals described later are used for time interval abrupt change identification.

[0071] After obtaining the complete set of time intervals, these time intervals are identified for abrupt change features in chronological order. This identification process uses the overall time-varying curve as the basis for analysis, comparing the time interval values ​​between adjacent segments one by one. Starting from the beginning of the time series, the time intervals of two consecutive segments are selected, and the magnitude of change between them is determined. If the time interval of the current segment jumps or drops abruptly compared to the previous segment's interval, it indicates a possible segment connection anomaly at that time position. The segment boundary corresponding to this position is then marked as a time interval abrupt change point. To improve the accuracy of identification, the time distribution within adjacent segments is simultaneously referenced during the identification process. When a segment has a uniform time distribution but the time intervals between adjacent segments change abnormally, it can be determined that the anomaly originates from time misalignment rather than differences in acquisition rate. When identifying each abrupt change point, the location of the abrupt change, the preceding and following time interval values, the corresponding segment index, and the source identifier are recorded simultaneously to form a traceable time abrupt change mapping table. The identification process continues along the time axis until the entire inspection cycle is covered, ensuring that all points with time interval abrupt changes are completely identified and located on the time axis.

[0072] After identifying all time interval abrupt changes, misalignment risk areas are marked for these abrupt changes. This marking process centers on the time location of the abrupt change and extends the time range before and after it according to a fixed ratio, forming risk segments covering a certain time range before and after the abrupt change. Each risk segment contains the location of the abrupt change and several adjacent segment signals, indicating the potential misalignment risk in the data within that time interval. During marking, the start time of the risk segment is set to the time identifier of the segment preceding the abrupt change, and the end time is set to the time identifier of the segment following the abrupt change, ensuring that the risk area completely covers the potentially misaligned time range. Simultaneously, each risk segment is assigned a unique number and its corresponding source identifier is recorded, allowing misalignment risks from different acquisition sources to be tracked independently. After all risk areas are marked, they are arranged chronologically to form a complete misalignment risk distribution table. This table clearly reflects the location and range of potential time misalignments in the entire temporary backhaul sequence, providing precise time positioning data for subsequent splicing and correction stages.

[0073] Through the above steps, relying on the temporary backhaul sequence, a complete analysis of its continuous segments was performed, from time extension to interval extraction, and from mutation identification to risk labeling. Each continuous segment was unfolded into a complete time trajectory during time extension; the temporal relationship between adjacent segments was quantified into specific time distances during interval extraction; anomalous changes in the temporal structure were captured and located during mutation identification; and the temporal range of potential misalignments was clearly defined during risk labeling. This layer-by-layer refinement approach transformed the temporal structure of the temporary backhaul sequence from continuous description to risk localization at the data level, enabling misalignment repair to be performed based on precise time coordinates during the subsequent splicing and correction stage.

[0074] Step 4: Based on the identified misalignment risk areas, perform time-stretching balancing processing on adjacent continuous segments around the time interval abrupt change point, and perform buffering connection according to the acquisition rhythm before and after to restore the natural temporal continuity between segments, thereby forming a time-stable data back transmission sequence.

[0075] The specific implementation process of this step is as follows:

[0076] Precise location and boundary extraction of time intervals are performed for the previously identified misalignment risk areas. The starting point of each misalignment risk area is determined by the end time marker of the last consecutive segment before the mutation point, and the ending point is determined by the start time marker of the first consecutive segment after the mutation point. By reading these time markers, the exact time range of the time misalignment can be obtained. Based on this, the time markers, source markers, and their positions in the temporary return sequence of all segmented signals within the risk area are extracted. This information is recorded item by item in the order of the time markers so that subsequent processing can proceed according to the original order. To ensure the integrity of the extended processing, the two consecutive segments before and after the mutation point are included in the analysis range simultaneously during extraction, so that the two segments form a continuously adjustable connection interval on the time axis. After completing this operation, a set of data records containing start and end times, time span, number of segments, and source correspondence is obtained. This set of data provides complete boundary conditions and basic information for subsequent time extension correction.

[0077] After extracting the boundaries of the misalignment risk area, time stretching correction is performed on adjacent abrupt segments within this area. The key to this process is redistributing the time-misaligned portions to time positions corresponding to the actual acquisition rhythm. Specifically, the end time marker of the preceding segment and the start time marker of the following segment are first read, and the time difference between them is used to determine the time offset within the risk area. If the time difference is greater than the normal acquisition interval, it indicates a time discontinuity at that location. In this case, continuous time markers are re-inserted within the discontinuity interval according to the acquisition rhythm time interval, restoring the time interval distribution consistent with the acquisition rhythm. If the time difference is less than the normal acquisition interval, it indicates time overlap at that location. In this case, the time markers within the area are rearranged, redistributing the overlapping portions onto the time axis, restoring the time markers to a monotonically increasing order. During the adjustment process, the time markers of each segment signal remain consistent with their original acquisition order, without changing the segment content; only the time positions are remapped. In this way, the time distribution within the misalignment risk area is stretched or compressed again, restoring the time relationship between adjacent segments to a continuous acquisition rhythm.

[0078] The time stretching correction process is further clarified, specifically as follows:

[0079] Using the time interval corresponding to the inspection and data collection rhythm as a unified benchmark, and taking the segment boundary time marker as the starting point, when the time difference is greater than the time interval, incremental time markers are generated sequentially at fixed intervals within the corresponding interval and mapped to the segment signals in order; when the time difference is less than the time interval, the time markers are redistributed according to the original segment order, so that the time intervals of adjacent segment signals are equal to the time interval, while ensuring that the time markers are monotonically increasing overall. The generated or redistributed time markers correspond one-to-one with the segment signals according to their arrangement on the time axis, so that each segment signal corresponds to only one time marker.

[0080] After time stretching correction, a buffering process is performed on adjacent segments to ensure a natural transition in the acquisition rhythm after the time stretching is balanced. This process establishes a transition time interval on the corrected timeline, allowing segments to connect continuously in time. Specifically, the end time marker of the previous segment is determined as the starting point of the buffer, and the start time marker of the next segment is determined as the end point of the buffer. Then, a transition time interval is generated between these two time markers. This transition time interval is allocated according to the time pattern of the acquisition rhythm, with its time interval gradually changing from the end time interval of the previous segment to the start time interval of the next segment, making the time rhythm between the two continuous and smooth within the transition time interval. During the buffering process, all involved segmented signals maintain the continuous change characteristics of the time markers, without jumps or overlaps, thus forming a complete acquisition rhythm curve on the timeline. After buffering, the time stretching intervals of the segments are logically connected, the time interval transition is natural, eliminating the boundary abruptness caused by the stretching correction, and maintaining the continuity of the entire time series in terms of acquisition rhythm.

[0081] It should be noted that:

[0082] The termination time interval of the previous segment refers to the time interval maintained between adjacent segment signals within the continuous segment before the segment boundary point during continuous comparison. This termination time interval has been determined to be consistent with the time interval corresponding to the inspection acquisition rhythm, and therefore represents the time rhythm of that segment under stable acquisition conditions. The start time interval of the next segment refers to the time interval maintained between adjacent segment signals within the continuous segment after the segment boundary point. It has also been determined to be consistent with the time interval corresponding to the inspection acquisition rhythm, and is used to represent the acquisition rhythm corresponding to the new continuous segment. The transition time interval is set to connect these two time intervals that have been determined to be continuous, so that the time distribution on both sides of the boundary point gradually transitions from the stable time interval of the previous continuous segment to the stable time interval of the next continuous segment, thereby realizing the continuous processing of rhythm transition on the time axis.

[0083] After completing time-delay correction and buffering for all misalignment risk areas, the processed segmented signals are integrated into a unified time series to form a time-stable data return sequence. In this stage, all corrected and connected segments are first rearranged according to their time identifiers to ensure the timeline is continuous from the first acquisition moment to the last. Then, the segmented signals corresponding to different source identifiers are synchronously mapped, ensuring that the acquired data from multiple sources are distributed in parallel on a unified timeline. The time trajectory corresponding to each source identifier is continuously unfolded on the global timeline, and the time positions between different sources strictly correspond without overlap or misalignment. During the integration process, the start and end times of each segment are calibrated to ensure that the connection points between segments are consistent with the acquisition rhythm. Through this unified integration method, all processed segmented signals form a complete continuous data chain in the time dimension, with the time interval between any two adjacent segments consistent with the acquisition rhythm, ensuring a smooth distribution and rhythmic consistency of the time series. The time-stable data return sequence obtained after this integration accurately reflects the time progress of the entire inspection process, providing a reliable time basis for subsequent full-cycle time integration and operational status identification.

[0084] Through the execution of the above steps, based on the identified misalignment risk areas, a complete time-stretching and balancing process was completed for adjacent mutation fragments, from time boundary extraction to stretching correction, from buffering and joining to sequence integration. Time interval positioning determined the range and location of the misalignment, stretching correction restored the continuity of the time sequence, buffering and joining balanced the transition between the preceding and following acquisition rhythms, and sequence integration achieved a unified global time mapping. Through this continuous operation, time misalignments caused by network latency, node switching, or fragment out-of-order processing were repaired, and the final time-stable data return sequence completely corresponds to the actual acquisition rhythm in terms of time distribution.

[0085] Step 5: Combine the formed stable data feedback sequence to perform full-cycle time integration processing on the entire inspection process. The back and forth feedback sequences after time stretching and balancing are continuously mapped to obtain a complete data sequence corresponding to the actual inspection rhythm, which is used to provide accurate time series support for subsequent operation status identification.

[0086] The specific implementation process of this step is as follows:

[0087] After a stable data return sequence is formed, it is divided into time intervals and positioned sequentially. Each stable data return sequence consists of multiple segments that have been extended, corrected, and buffered. These segments remain continuous within a local time range, but still need to be uniformly arranged on the overall time axis to achieve correspondence throughout the entire cycle. Therefore, starting from the initial segment of each stable data return sequence, time stamp information is extracted one by one, and the start and end time stamps of the sequence are recorded. After summarizing the start and end times of all sequences, the complete start and end range of the inspection cycle is determined based on the magnitude of the time stamp values. The earliest time stamp in all sequences is taken as the start of the entire cycle, and the latest time stamp is taken as the end of the entire cycle. Then, each stable data return sequence is positioned within this complete time range, the time offset of each sequence relative to the start of the entire cycle is calculated, and its exact position on the entire cycle time axis is determined accordingly. To maintain continuity between adjacent sequences, the end time marker of the preceding sequence is compared with the start time marker of the following sequence. When a time gap exists between them, a virtual time segment is inserted at the gap according to the time interval corresponding to the acquisition rhythm, ensuring a coherent time distribution. When time overlap occurs, the start time position of the following stable data transmission sequence is adjusted to eliminate the overlap and redetermine its arrangement. After this processing, all stable transmission sequences form a continuous distribution from the start time to the end time on the full-cycle time axis, ensuring that each time period has a corresponding data segment.

[0088] After completing the time interval division and sequential positioning, the backhaul sequences after time stretching and balancing are continuously mapped. The backhaul sequences are the stable data backhaul sequences. The purpose of this mapping operation is to connect the backhaul sequences of all time periods sequentially on the time axis to form a complete main time sequence. In the specific implementation, the stable data backhaul sequence with the smallest starting time identifier is first selected as the starting point of the main time sequence. The next stable data backhaul sequence is then connected to the time boundary of the previous stable data backhaul sequence in ascending order of time identifiers. During connection, the ending time identifier of the previous sequence is used as the connection reference point, and the starting time identifier of the next sequence is aligned with its adjacent time position to maintain continuity of time identifiers at the connection point. For backhaul sequences with the same source identifier, they are connected one by one according to the acquisition order to ensure that the segmented signals from the same source are continuously distributed in time. For backhaul sequences with different source identifiers, they are embedded into the main sequence according to the order of time identifiers, enabling parallel mapping of data from multiple sources on the same time axis. During continuous mapping, the segmented signals within each sequence are rearranged according to their time signatures to ensure that each acquisition point on the time axis can find its corresponding segment in the global time series. To prevent time discontinuities or overlaps at connection points, the time distribution of adjacent sequences is aligned according to the time interval of the acquisition rhythm during mapping, ensuring that the time signatures on the entire time axis are continuous and without jumps. After mapping is completed, all returned sequences form a main time chain over the entire cycle. Segmented signals from different sources are arranged in chronological order within a unified time frame, with the time signature continuously increasing from the start to the end of the inspection, and the time interval always consistent with the inspection rhythm.

[0089] After completing continuous mapping, the integrated time master sequence is time-integrated and summarized to obtain a complete data sequence corresponding to the actual inspection rhythm. This integration process is based on the time master chain, starting from the beginning of the entire cycle, and sequentially inserting the time-ordered segmented signals from each source's feedback sequence into their corresponding time periods, forming a complete data chain covering the entire inspection process. During integration, the identifier value of each time point on the time master chain is first read, and the corresponding segmented signal is matched and inserted into the appropriate position in the time sequence. For segmented signals from the same source, their original acquisition rhythm is maintained, and they are continuously arranged on the time trajectory corresponding to that source. For segmented signals from different sources, they are sequentially and interleaved on the time axis according to the time identifier, ensuring all data unfolds synchronously under a unified time reference. During integration, if a slight time overlap is found between adjacent segments, the overlapping portion is allocated to adjacent time intervals through order adjustment. If time gaps exist, virtual time identifiers are inserted according to the acquisition rhythm to maintain time continuity. After integration, all segmented signals form a continuous sequence on the time axis, with corresponding data from the first acquisition moment to the last, and the time intervals perfectly matching the acquisition cycle. The temporal distribution of this complete data sequence corresponds to the actual acquisition rhythm of the inspection terminal, and each segmented signal accurately reflects the true acquisition status at a specific moment during the inspection process. Through this full-cycle time integration method, the final complete data sequence not only possesses temporal continuity and consistency but also reflects the rhythm changes and state transitions during the inspection process, providing accurate and traceable time series support for operational status identification.

[0090] Through the above steps, combined with a stable data feedback sequence, the entire inspection process was fully processed, from time interval division to continuous mapping and then to full-cycle time integration. Time interval division and sequential positioning ensured complete coverage of the entire cycle time range, continuous mapping realized the logical connection and time extension unification between feedback sequences, and time integration and summarization arranged data from different sources into a continuous arrangement on a unified time axis. Through this series of operations, the inspection data was completely reconstructed in the time dimension, all segmented signals maintained continuous distribution throughout the entire cycle, and finally formed a complete data sequence that perfectly corresponded to the actual inspection rhythm.

[0091] Beneficial effect 1:

[0092] This invention embeds continuous time identifiers and dynamic source identifiers during the inspection data fragmentation generation stage, and performs rearrangement, extension, and integration processing based on the time series, ensuring that the inspection data maintains a consistent time reference relationship throughout the entire process of collection, transmission, and reconstruction. Through time sequence rearrangement and time extension balancing, it effectively avoids the erroneous splicing of data from different time segments into a logically continuous sequence during transmission, fundamentally eliminating hidden time misalignment problems. This ensures that the resulting data transmission sequence truly reflects the inspection data collection rhythm in the time dimension, thereby improving the reliability of inspection data in terms of time consistency.

[0093] Benefit 2:

[0094] This invention performs full-cycle time integration processing on stable data transmission sequences, continuously mapping the time-stretched and balanced transmission sequences to obtain a complete time series covering the entire inspection process. This enables the inspection data to form a continuous and traceable distribution structure on the global time axis. Based on this complete data series, subsequent operational status identification can be analyzed with precise time support, avoiding misjudgments and trend deviations caused by time misalignments. This helps improve the reliability and stability of inspection results in practical applications, providing a reliable data foundation for intelligent analysis and refined management of the inspection process.

[0095] This invention provides, for example Figure 4 The system shown is a real-time feedback and verification system for 5G inspection data, which includes a time stamp construction module, a time sequence rearrangement and comparison module, a time abrupt change identification module, a time stretching and balancing module, and a full-cycle integration module.

[0096] Time stamp construction module: Collects raw data fragments from the inspection terminal during the transmission process, embeds continuous time stamps and dynamic source stamps into each fragment to build the time series foundation;

[0097] Time-series rearrangement and comparison module: Based on the constructed time series, the acquired fragmented signals are rearranged in time sequence according to the embedded continuous time identifiers, and continuous comparison is performed during the rearrangement process to generate a temporary feedback sequence that matches the rhythm of inspection data acquisition.

[0098] The time abrupt change identification module: Based on the generated temporary backhaul sequence, it performs time extension analysis on the continuous segments, extracts the time interval abrupt change points between adjacent segments, and marks the location of the abrupt change points as misalignment risk areas;

[0099] Time-spacing balancing module: Based on the identified misalignment risk areas, time-spacing balancing is performed on adjacent continuous segments around the time interval abrupt change point, and buffering is performed according to the acquisition rhythm to form a time-stable data back transmission sequence;

[0100] Full-cycle integration module: Combining the generated data feedback sequence, it performs full-cycle time integration processing on the entire inspection process, continuously mapping the back and forth feedback sequences after time extension and balancing to obtain a complete data sequence corresponding to the actual inspection rhythm.

[0101] The present invention provides a real-time feedback verification method for 5G inspection data, which is implemented through the aforementioned real-time feedback verification system for 5G inspection data. For details of the specific method and process of the real-time feedback verification system for 5G inspection data, please refer to the embodiment of the above-mentioned real-time feedback verification method for 5G inspection data, which will not be repeated here.

[0102] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the present invention.

Claims

1. A method for real-time transmission and verification of 5G inspection data, characterized in that, Includes the following steps: The raw data fragments from the inspection terminal during the transmission process are collected, and continuous time identifiers and dynamic source identifiers are embedded in each fragment to construct a time series basis. Based on the constructed time series, the acquired fragmented signals are rearranged in time order according to the preceding and following order of the embedded continuous time identifiers, and continuous comparison is performed during the rearrangement process to generate a temporary feedback sequence that matches the rhythm of inspection data acquisition. Based on the generated temporary backhaul sequence, time extension analysis is performed on the continuous segments to extract the time interval mutation points between adjacent segments, and the locations of the mutation points are marked as misalignment risk areas; Based on the identified misalignment risk areas, time-stretching balancing is performed on adjacent continuous segments around the time interval abrupt change point, and buffering is performed according to the acquisition rhythm before and after to form a time-stable data back transmission sequence. By combining the generated data feedback sequence, the entire inspection process is processed through full-cycle time integration. The back-and-forth feedback sequences after time stretching and balancing are continuously mapped to obtain a complete data sequence corresponding to the actual inspection rhythm. The steps for time-extended balancing based on misaligned risk regions include: The identified misalignment risk areas are located within a time interval and their boundaries are extracted. For adjacent mutation segments within the misalignment risk area, time extension correction is performed. If the time difference is greater than the acquisition interval, continuous time markers are inserted. If the time difference is less than the acquisition interval, overlapping time markers are redistributed. The extended and corrected segments are subjected to buffering and joining processing. The end time marker of the previous segment and the start time marker of the subsequent segment are determined and a transition time interval is generated. The time interval gradually changes according to the acquisition rhythm to form a continuous curve. The corrected and connected fragmented signals are integrated into a time series, rearranged according to the time identifier order and synchronously mapped to the source identifier to form a data return sequence. The steps involved in performing time stretching analysis based on temporary backhaul sequences include: The continuous segments in the temporary return sequence are expanded segment by segment. The time stamp in the data header of the segmented signal is read and the time interval between adjacent segments is recorded in sequence. At the same time, the dynamic source identifier is recorded synchronously, so that the time curves corresponding to different sources are independently distributed on the time axis, and adjacent time stamps are connected to form a time extension curve. Extract the temporal connection between consecutive segments, record the time difference between the last segment time identifier of each consecutive segment and the first segment time identifier of the next segment, forming a time interval set covering the entire inspection cycle, and attach source identification information and segment index to each interval item; The time interval set is subjected to mutation feature identification in chronological order. The change range of time interval between adjacent segments is compared to determine the location of a sudden jump or drop. The corresponding segment boundary is marked as the time interval mutation point, and the mutation point location, the time interval value before and after, the segment index and the source identifier are recorded to form a time mutation mapping table. Centered on the time location of the mutation point, risk segments covering the time range before and after the mutation point are delineated according to a fixed extension ratio. The start time of the risk segment is set as the time identifier of the segment before the mutation point, and the end time is set as the time identifier of the segment after the mutation point. Each risk segment is assigned a number and source identifier to form a misaligned risk distribution table. The steps involved in performing full-cycle time integration processing using stable data backhaul sequences include: The stable data backhaul sequence is divided into time intervals and ordered. The start time and end time identifiers are extracted from each stable data backhaul sequence. The start and end times of all sequences are summarized and the start and end points of the entire cycle are determined. The time offset of each stable data backhaul sequence is calculated within the entire cycle and its position on the time axis is determined. When there is a time gap between adjacent stable data backhaul sequences, a virtual time segment is inserted. When there is time overlap, the start time position of the next stable data backhaul sequence is adjusted to form a continuous full-cycle time distribution. A continuous mapping is performed on the stable data backhaul sequences before and after time stretching and balancing. The stable data backhaul sequence with the earliest time stamp is selected as the starting point of the main time sequence. The subsequent stable data backhaul sequences are connected sequentially according to the increasing order of the time stamps, and the connection points are aligned and distributed according to the time interval of the acquisition rhythm. After continuous mapping is completed, time integration and summarization are performed on the main time sequence. The fragmented signals from each source are inserted into the corresponding time period in sequence according to the order of the main time chain. The fragmented signals from the same source are arranged continuously, and the fragmented signals from different sources are arranged interleaved according to the time identifier. The time periods with overlap or gaps are corrected by adjusting the order or supplementing with virtual time identifiers to form a complete data sequence corresponding to the actual inspection rhythm.

2. The real-time feedback verification method for 5G inspection data according to claim 1, characterized in that, The process of collecting fragmented raw data signals from the inspection terminal during transmission includes the following steps: When performing data acquisition operations, the inspection terminal collects the operational data generated by the inspection target in segments. When the segments are generated, the local time control unit outputs a continuous time identifier and embeds the continuous time identifier into the header of the segmented signal data. At the same time, based on the operating environment information and the acquisition task information, a dynamic source identifier is generated and embedded into the tail of the segmented signal data, so that the segmented signal has time information and source information. The fragmented signals embedded with continuous time identifiers and dynamic source identifiers are pushed to the backhaul channel in the order of time identifiers, and the fragment time identifiers and corresponding source identifiers are recorded during transmission; Based on the continuity of time signatures, all segmented signals are arranged, segments with the same source signature are classified, and segments with varying time intervals are sequentially filled, forming a two-layer time series with time as the main axis and source as the secondary axis.

3. The real-time transmission and verification method for 5G inspection data according to claim 1, characterized in that, The steps for reordering time based on time series data include: Read the time stamp of all the segmented signals, extract the time stamp of the segmented signal data header and store it in the time index table according to the acquisition order. The time index table is arranged in the primary order of time stamp and the secondary order of dynamic source identifier. The fragmented signals are rearranged according to the time identifier order recorded in the time index table. Fragmented signals with the same source identifier are established as independent time series trajectories and arranged continuously according to the time identifier. When fragmented signals with different source identifiers intersect in time, they are inserted into the corresponding positions according to the global order of the time identifier. Continuous comparison is performed on the rearranged segmented signals. By comparing the time interval between adjacent segmented signals with the time interval corresponding to the inspection and acquisition cycle, continuous segments are divided and segment boundary points are marked. Based on the continuous comparison results, the segments are integrated and spliced ​​sequentially according to the time identifier. Segments with time intervals that match the collection rhythm are directly connected, while segments with abrupt time intervals retain the interval information. At the same time, the source identifier information is arranged synchronously to generate a temporary feedback sequence that matches the inspection data collection rhythm.

4. The real-time feedback verification method for 5G inspection data according to claim 3, characterized in that, During the time sequence reordering process, when the time interval between adjacent segments is inconsistent with the time interval corresponding to the inspection and acquisition rhythm, the position of the time interval change is marked as the segment boundary point, and the time interval is filled in according to the position of the boundary point during the integration stage.

5. The real-time transmission and verification method for 5G inspection data according to claim 1, characterized in that, The risk segments in the misalignment risk distribution table are arranged in chronological order. Risk segments corresponding to different source identifiers are displayed independently on the time axis. The time range of each risk segment covers the time identifier interval of adjacent segments before and after the mutation point.

6. The real-time feedback verification method for 5G inspection data according to claim 1, characterized in that, During the time extension correction process, the redistribution of time markers is performed at fixed time intervals according to the acquisition rhythm. The connection between the preceding and following segments is smoothly achieved through a transition time interval, and the time interval of the transition time interval gradually changes from the end time interval of the previous segment to the start time interval of the next segment.

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