Vehicle urea filling monitoring method based on internet of things data collection

Abstract

The present application belongs to the technical field of data collection of Internet of Things, and particularly relates to a method for monitoring vehicle urea filling based on data collection of Internet of Things. The method collects urea flow, cumulative filling amount, sampling serial number, local time stamp and previous message digest at the filling end, and collects urea liquid level sequence, vehicle running state, sampling serial number and local time stamp at the vehicle end; according to the continuity of sampling serial number, the connection relationship of message digest and the stability of sampling interval, the local time axis of both ends is recovered, the filling candidate event and the liquid level response candidate event are generated, and the elastic matching window is constructed in combination with time offset, flow change curve and liquid level hysteresis envelope; the filling start, flow duration, flow end, liquid level rise and liquid level stability are mapped into directed causal chain nodes, and the filling event credible record is output according to the time sequence relationship, the physical quantity change direction relationship and the cumulative amount consistency relationship. The present application can reduce the monitoring misjudgment caused by cross-end mismatch, repeated reporting and liquid level fluctuation.

Description

Technical Field

[0001] This invention belongs to the field of Internet of Things (IoT) data acquisition technology, specifically relating to a method for monitoring vehicle urea refueling based on IoT data acquisition. Background Technology

[0002] Monitoring of vehicle urea refueling typically relies on an IoT data acquisition link between the refueling equipment, the vehicle terminal, and the platform server. Vehicle urea is used for exhaust aftertreatment, and vehicles continuously consume urea solution during operation. Operators usually need to know whether the vehicle has completed refueling at the specified time and location, and whether the output at the refueling end corresponds to the received output at the vehicle end. The conventional implementation of existing systems involves collecting instantaneous flow rate, cumulative refueling volume, refueling start time, refueling end time, equipment number, vehicle binding information, and operation records at the refueling equipment side, and uploading this data to the platform via a station gateway or built-in communication terminal. At the vehicle side, the onboard terminal collects urea tank level, vehicle operating status, engine operating information, local sampling time, and terminal number, and then sends this data to the platform according to a fixed or triggered cycle. After receiving data from both ends, the platform typically uses the vehicle number, refueling equipment number, refueling order number, or time interval as the correlation basis to compare the cumulative volume at the refueling end with the incremental volume at the vehicle end and generate a refueling record. The implementation logic of this type of solution is relatively straightforward: the refueling terminal is regarded as the source of refueling volume, and the vehicle terminal is regarded as the source of receiving volume. The platform then merges the records in the data table. Since the sampling frequency, upload period, and local clock source of the two terminals are not consistent, conventional solutions often use a unified time window for tolerance matching, without establishing linkage constraints for the flow change stage, liquid level response stage, and network message sequence within the refueling process.

[0003] At the IoT communication link level, existing vehicle urea refueling monitoring solutions typically employ a combination of periodic reporting, event reporting, and offline retransmission. On the refueling device side, when the network is connected, data on the start, progress, and end of refueling are uploaded in event messages. During network fluctuations, the collected data is written to a local cache and retransmitted in cache order after the connection is restored. On the vehicle terminal side, liquid level data is typically uploaded according to a fixed sampling period, and event messages are added when changes in liquid level or vehicle status are detected. On the platform side, messages are generally sorted by arrival time or terminal timestamp, and deduplication is performed using the terminal number, sampling sequence number, and simple checksum. For out-of-order arrivals, delayed retransmissions, and short-term packet loss, conventional methods include reordering by timestamp, deleting by duplicate sequence number, backfilling by a fixed time window, or directly discarding incomplete segments. This type of communication processing can maintain the apparent continuity of the data table, but its focus remains on whether single-end messages arrive completely, failing to verify the flow change sequence at the refueling end and the liquid level response sequence at the vehicle end within the same event causal structure. If the terminal's local time drifts, or if the timestamp of the retransmitted message deviates from the actual collection order, the platform may still classify the historical retransmitted data into the current annotation event, or it may classify the late message in the current event into the next event, causing the time sequence boundary within the same event to be disrupted.

[0004] At the anomaly detection level, existing monitoring platforms mostly use fixed rules to determine the reliability of refueling records. For example, after refueling, the difference between the cumulative amount at the refueling end and the equivalent amount of liquid level at the vehicle end is calculated. If the difference falls within a preset range, the refueling record is considered normal; if the liquid level at the vehicle end does not rise within a specified time, or the rise is inconsistent with the refueling amount, the event is marked as abnormal. Some solutions introduce vehicle operating status for filtering, eliminating liquid level fluctuations during driving, or using liquid level changes in parked or idling states as the basis for refueling. Other solutions add message sequence number, terminal identity, and data digest verification on the platform side to detect duplicate reports or obviously tampered data. Although the above processing can eliminate some simple anomalies, it still mainly relies on single-point data or data differences within a fixed window, and does not fully utilize the sequential relationship between refueling start, flow continuity, flow end, liquid level rise, and liquid level stabilization. The liquid level of automotive urea itself also has quantitative jumps, liquid surface sloshing, and response lag; the liquid level rise does not always occur synchronously with the refueling flow. Using fixed thresholds can easily misidentify normal hysteresis as anomalies, and may also misidentify cross-end mismatch data as normal when the liquid level fluctuation happens to fall within the time window. Even if the platform saves anomaly markers, it often only focuses on the difference in results, making it difficult to explain whether the anomaly originates from out-of-order data collection, message retransmission, vehicle liquid level disturbance, or mismatch in the refueling object.

[0005] Therefore, the main technical problem with existing IoT-based vehicle urea refueling monitoring systems is that, under the condition of asynchronous data collection and transmission between the refueling end and the vehicle end, the platform lacks an event reconstruction mechanism that can simultaneously constrain the order of network messages and the order of the physical refueling process. This results in the inability to reliably organize the flow change data, cumulative volume data, liquid level response data, and stable data in the same urea refueling event into a single temporal causal chain. The technical reason for this problem is that local timestamps at both ends may drift, messages may be out of order, retransmitted, or duplicated in the IoT link, and there is an inherent response lag between flow changes on the refueling side and liquid level changes on the vehicle end. If matching is still performed according to fixed time windows, single-end sequence numbers, or result differences, the platform cannot determine whether a certain liquid level rise is caused by the corresponding refueling volume, nor can it determine whether a retransmitted message should be included in the current event. The resulting refueling records lack a foundation for cross-terminal consistency. While the data records store refueling volume, liquid level, and timestamps, they fail to express the sequential, corresponding, and closed-loop relationships between these data, making it impossible to technically distinguish between genuine refueling, duplicate reporting, delayed retransmission, and cross-terminal mismatches. This problem manifests primarily as the inability to verify cross-terminal data relationships, rather than insufficient accuracy in data collection from a single terminal. Therefore, it is necessary to construct a data foundation for refueling monitoring based on communication timing, event stages, and the closed-loop relationships of physical quantities. Summary of the Invention

[0006] The purpose of this invention is to provide a method for monitoring vehicle urea refueling based on Internet of Things (IoT) data acquisition, which can solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A vehicle urea refueling monitoring method based on IoT data acquisition includes: collecting urea flow rate, cumulative refueling amount, sampling sequence number, local timestamp, and preceding message summary at the refueling end; and collecting urea level sequence, vehicle operating status, sampling sequence number, and local timestamp at the vehicle end; recovering the local time axis at the refueling end and the vehicle end based on the continuity of sampling sequence number, the connection relationship of message summary, and the stability of sampling interval, respectively; generating refueling candidate events when the flow rate changes from zero to a continuous change state, and generating level response candidate events when the level continues to rise; constructing an elastic matching window based on the time offset between the two local time axes, the flow rate change curve of the refueling candidate events, and the hysteresis envelope of the level response candidate events; mapping the start of refueling, continuous flow, end of flow, level rise, and level stabilization as directed causal chain nodes, and outputting reliable refueling event records according to the temporal order, the direction of physical quantity change, and the consistency of cumulative amount.

[0009] Preferably, restoring the local timeline at the refueling end and the local timeline at the vehicle end includes: arranging consecutive messages from the same end in ascending order of sampling sequence number, mapping adjacent differences in local timestamps to differences in sampling intervals to obtain clock drift segments; for messages arriving out of order, determining the embedding position of the message in the sequence at this end based on the connection relationship of the preceding message digests; for sequences with short-term gaps, retaining the start and end sequence numbers, start and end timestamps, and cumulative boundary of the missing interval, and marking messages exceeding the IoT data acquisition and replenishment window as independent retransmission segments; and generating a continuous local timeline according to the clock drift segments and the embedding positions.

[0010] Preferably, constructing the elastic matching window includes: extracting the starting point of the first continuous non-zero flow rate, the cumulative increase in flow rate within the continuous flow rate segment, and the ending point of the flow rate returning to zero from the candidate refueling events to form the refueling-side event boundary; extracting the starting point of the continuous rise in liquid level, the segment of the change in the slope of the rise in liquid level, and the segment of the stable liquid level from the candidate liquid level response events to form the vehicle-side response boundary; projecting the refueling-side event boundary onto the vehicle-side local time axis according to the offset between the local time axis of the refueling end and the local time axis of the vehicle end; and generating an elastic matching window including a leading edge, a main body, and a trailing edge based on the hysteresis range of the vehicle-side response boundary relative to the refueling-side event boundary.

[0011] Preferably, the mapped directed causal chain nodes include: receiving the start-up sequence number, start-up timestamp, and preceding message digest of the refueling candidate event at the refueling start node; receiving the cumulative increment and stage identifier of the continuous flow segment at the flow continuity node; receiving the flow return-to-zero sequence number at the flow end node; receiving the liquid level increment sequence of the liquid level response candidate event at the liquid level rise node; and receiving the liquid level stability interval at the liquid level stability node; writing time sequence markers, physical quantity direction markers, and cumulative quantity corresponding markers between adjacent nodes, and retaining the node chain segment lacking any of the markers separately as the chain segment to be verified.

[0012] Preferably, processing the clock drift segment includes: selecting a segment with no gaps in the sampling sequence number and continuous preceding message digests from consecutive messages at the same end as a reference segment; establishing a segmented linear time mapping using the local timestamp sequence of the reference segment; defining the mapping endpoints for messages that cross missing intervals using the cumulative boundary and stage identifier before and after the missing interval; establishing a supplementary transmission offset label for messages that deviate from the segmented linear time mapping but have complete digest connections, based on the deviation between the arrival time and the local timestamp; and writing the supplementary transmission offset label into the continuous local time axis.

[0013] Preferably, the breakpoint processing for the short-term missing sequence includes: when the sampling sequence number, stage identifier, and cumulative quantity boundary at both ends of the missing interval satisfy a monotonic connection relationship, generating a placeholder sampling segment according to the start and end sequence numbers, and recording the missing direction, cumulative quantity boundary, and time uncertainty interval in the placeholder sampling segment; when the sampling sequence number of the retransmission segment falls into the placeholder sampling segment, replacing the corresponding placeholder position according to the monotonic relationship between the previous message digest and the cumulative quantity; when the retransmission segment has a duplicate sequence number, digest rollback, or stage inversion with the already connected message, excluding the retransmission segment from the continuous local time axis.

[0014] Preferably, generating the elastic matching window includes: using the flow start point to the flow end point of the refueling side event boundary as the main window; limiting the value range of the leading edge and the trailing edge according to the available liquid level range corresponding to the parking, idling, or low disturbance indicators in the vehicle's operating state; updating the hysteresis range based on the offset distribution of the liquid level rise start point relative to the flow start point in historical refueling events of the same vehicle; when the vehicle-side response boundary crosses multiple candidate main windows, determining the window affiliation based on the cumulative volume increment difference and the liquid level increment difference, and retaining the sampling sequence number of the unaffiliated boundary.

[0015] Preferably, the intra-chain consistency constraint for the chain segment to be verified includes: for adjacent nodes with missing time sequence markers, determining the order based on the sampling sequence number of the local time axis at the end and the digest of the preceding message; for adjacent nodes with missing physical quantity direction markers, restoring the direction markers based on the sign changes of the cumulative flow and liquid level increment; for adjacent nodes with missing cumulative quantity corresponding markers, establishing a correspondence based on the cumulative quantity increment at the filling end, the liquid level increment at the vehicle end, and the calibrated capacity range of the vehicle's urea tank; and keeping the chain segment with conflicts in a disconnected state and adding a conflict source field.

[0016] Preferably, before outputting the credible record of the refueling event, the method further includes: binding the supplementary transmission offset label, the placeholder sampling segment, the unassigned boundary, and the conflict source field to the corresponding directed causal chain node; establishing an abnormal evidence sequence according to the stage to which the node belongs, wherein the abnormal evidence sequence includes at least sequence number continuity evidence, summary connection evidence, time mapping evidence, physical quantity direction evidence, and cumulative quantity corresponding evidence; when there are multiple types of evidence conflicts for the same node, generating an abnormal location path according to the node order of refueling start, flow continuity, flow end, liquid level rise, and liquid level stabilization, and writing the abnormal location path into the credible record of the refueling event.

[0017] Preferably, generating the trusted record of the refueling event includes: configuring an event identifier for the completed directed causal chain, wherein the event identifier is jointly generated by the sampling sequence number range of the refueling end, the sampling sequence number range of the vehicle end, the time mapping segment numbers of both ends, and the message digest of the chain head; writing the trusted refueling amount as the intersection interval between the cumulative amount increment of the refueling end and the liquid level conversion amount of the vehicle end that satisfies the corresponding marker of the cumulative amount; assigning the incomplete chain segment to the category of repeated reporting, cross-end mismatch, delayed retransmission, or abnormal liquid level fluctuation according to the abnormal location path, and encapsulating the category field together with the event identifier into the IoT data collection result.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0019] 1. This invention collects urea flow rate, cumulative dispensing volume, sampling sequence number, local timestamp, and preceding message digest at the dispensing end, and collects urea level sequence, vehicle operating status, sampling sequence number, and local timestamp at the vehicle end. On the platform side, it reconstructs the local timelines at both ends based on the continuity of sampling sequence numbers, the connection relationship of message digests, and the stability of sampling intervals, so that the IoT data collection results no longer rely solely on message arrival time or a single terminal timestamp. Through the separate generation of dispensing candidate events and level response candidate events, the flow rate transitioning from zero to continuous change, continuous flow, flow rate returning to zero, continuous level rise, and level stabilization are classified into traceable event stages. The flexible matching window incorporates the time offset at both ends, the dispensing flow rate curve, and the level hysteresis envelope into the matching process, eliminating the need for the vehicle-end level response to be mechanically forced into a fixed window. After dispensing start, continuous flow, flow end, level rise, and level stabilization are mapped as directed causal chain nodes, adjacent nodes are simultaneously constrained by temporal sequence, physical quantity change direction, and cumulative quantity consistency. This processing enables the platform to restore asynchronously arriving refueling and level data into a data chain with a phased sequence. Retransmissions, out-of-order messages, and duplicate messages are only included in the event if the sequence number, summary, time mapping, and physical quantity relationship are all consistent. Therefore, the monitoring results can reflect the correspondence between the output from the refueling side and the reception from the vehicle side in the same vehicle urea refueling, reducing event mismatches caused by clock drift, network latency, and level response lag. This ensures that the reliable refueling volume, vehicle-side reception volume, and event integrity all have a clear data source. Compared to matching only according to a fixed time window, the resulting reliable refueling event record includes the message summary at the beginning of the chain, the sampling sequence number range at both ends, the time mapping segment, and node phase information, preserving continuous evidence from the start of refueling to level stabilization at the record level. The cumulative amount at the refueling end and the change in level at the vehicle end are no longer compared as isolated values, but are constrained by the sequence, direction of change, and correspondence of cumulative amounts within the same temporal causal chain. This transforms the judgment basis for refueling monitoring from result comparison to process closure verification.

[0020] 2. This invention provides a structured constraint on deep communication anomalies and acquisition gaps through piecewise linear time mapping, placeholder sampling segments, retransmission offset tags, unassigned boundaries, and conflict source fields. For segments with no gaps in the sampling sequence number and continuous preceding message summaries, a local time mapping can be established using these segments as reference segments. For messages spanning missing intervals, the mapping endpoints can be defined using the cumulative quantity boundaries and stage identifiers before and after the missing interval. For retransmission segments, the replacement placeholder position or exclusion from the local time axis can be determined based on the summary connection relationship, the monotonic relationship of the cumulative quantity, and the stage order. The leading edge, main body, and trailing edge of the elastic matching window are determined in conjunction with the vehicle's operating status and the historical refueling hysteresis range of the same vehicle, enabling the inclusion of liquid level data under parking, idling, or low-disturbance states into the available liquid level range, reducing the interference of driving disturbances on liquid level response judgment. The segments to be verified within the directed causal chain are completed using time sequence markers, physical quantity direction markers, and cumulative quantity corresponding markers. Segments with remaining conflicts remain disconnected, and the source of the conflict is recorded. The anomaly evidence sequence binds sequential continuity evidence, summary connection evidence, time mapping evidence, physical quantity direction evidence, and cumulative quantity correspondence evidence to corresponding nodes. The event identifier is generated by the sampling sequence number range at both ends, the time mapping segment number, and the message summary at the beginning of the chain. The resulting reliable refueling event record can retain the anomaly location path and distinguish unclosed chain segments into duplicate reporting, cross-end mismatch, delayed retransmission, or abnormal liquid level fluctuations, providing a verifiable technical basis for subsequent data verification. The reliable refueling quantity is represented by the intersection interval between the cumulative increment at the refueling end and the liquid level converted at the vehicle end, which satisfies the corresponding marker of the cumulative quantity. This avoids the one-sided bias that occurs when only the cumulative quantity at the refueling end or the liquid level converted at the vehicle end is used. For unassigned vehicle-side response boundaries, the platform retains their sampling sequence number and separates them from the candidate subject window to avoid forcibly merging liquid level fluctuations without corresponding refueling side boundaries into closed events. For retransmission segments with summary rollback, duplicate sequence numbers, or inverted stages, the local time axis directly excludes the corresponding segments, preventing replay data and duplicate data from entering the reliable record. Attached Figure Description

[0021] Figure 1 This is a flowchart of the overall process for monitoring vehicle urea refueling based on Internet of Things data acquisition, as described in this invention.

[0022] Figure 2 This is a flowchart of the local timeline restoration and retransmission segment processing of the present invention;

[0023] Figure 3 This is a flowchart illustrating the construction of the flexible matching window and the directed causal chain mapping of the present invention.

[0024] Figure 4 This is a flowchart illustrating the abnormal evidence sequence generation and event credibility record encapsulation process of the present invention. Detailed Implementation

[0025] refer to Figure 1 In one embodiment, a method for monitoring vehicle urea refueling based on IoT data acquisition is provided, comprising: collecting urea flow rate, cumulative refueling amount, sampling sequence number, local timestamp, and preceding message digest at the refueling end; and collecting urea level sequence, vehicle operating status, sampling sequence number, and local timestamp at the vehicle end; recovering the local time axis at the refueling end and the local time axis at the vehicle end based on the continuity of sampling sequence number, the connection relationship of message digest, and the stability of sampling interval, respectively; generating refueling candidate events based on the flow rate changing from zero to a continuous change state, and generating level response candidate events based on the continuous rise of level; and calculating the urea level response candidate events based on the time offset between the two local time axes, the flow rate change curve of the refueling candidate events, and the level response... The system constructs an elastic matching window based on the hysteresis envelope of candidate events; it maps the start of refueling, the duration of flow, the end of flow, the rise in liquid level, and the stabilization of liquid level as directed causal chain nodes, and outputs reliable records of refueling events according to the chronological order, the direction of change of physical quantities, and the consistency of cumulative quantities. The basic processing logic of this embodiment is to transform the asynchronously collected messages that were originally scattered at the refueling end and the vehicle end into a staged data chain within the same refueling event. This allows the platform to no longer merge data based solely on message arrival time or fixed time windows, but instead to use the sampling sequence number, the summary of the preceding message, the time offset, the flow rate change, and the liquid level response to jointly define the start and end boundaries and cross-end correspondence of the same refueling event.

[0026] In this embodiment, when the software acquisition program at the refueling end detects that the urea flow rate has changed from a zero-flow state to a continuous-flow state, it encapsulates the current sampling sequence number, local timestamp, instantaneous flow rate, cumulative refueling volume, equipment acquisition identifier, and previous message digest into a refueling end event message. The previous message digest is obtained by digesting the body fields of the previous message and written into the next message, thus forming a verifiable connection between consecutive messages. The software acquisition program at the vehicle end forms a urea level sequence at a fixed acquisition period, and simultaneously reads the vehicle's operating status and records the sampling sequence number, local timestamp, instantaneous flow rate, cumulative refueling volume, equipment acquisition identifier, and previous message digest into a refueling end event message. The timestamp, liquid level value, operating status identifier, and preceding message summary are encapsulated into a vehicle-side event message. After receiving messages from both ends, the platform does not directly determine the event order based on the arrival order. Instead, it establishes a local sequence within each of the two messages. If the sampling sequence number is continuous, the preceding message summary can be connected, and the adjacent sampling intervals are in the same stable segment, the corresponding messages are placed in the same local time axis. If the messages arrive out of order but the preceding message summary can point to an existing message, the embedding position is determined according to the summary relationship. If there is a short-term missing interval, the missing interval is retained and the platform waits for the supplementary message to fill it.

[0027] Specifically, the refueling candidate event is triggered by a change in flow rate. The platform searches for the position in the local time axis at the refueling end where the flow rate changes from a stable segment of zero or near zero to a continuous non-zero segment. The corresponding sampling sequence number is used as the candidate point for refueling start, and the cumulative increase of subsequent continuous flow segments and the position where the flow rate returns to zero are used as the event boundary. The liquid level response candidate event is triggered by a continuous rise in the liquid level at the vehicle end. The platform searches for the position in the local time axis at the vehicle end where the liquid level sequence changes from a stable or declining trend to a continuous upward trend, and excludes sampling segments with strong driving disturbances based on the vehicle's operating status. The refueling candidate event and the liquid level response candidate event are not required to be completely synchronized. The platform projects the flow rate curve on the refueling side onto the time range on the vehicle side based on the offset between the local time axes at both ends, and uses the hysteresis range of the liquid level response as the extension part of the elastic matching window, so that the process of the refueling flow rate changing first and the liquid level changing subsequently can be accommodated by the same event.

[0028] To facilitate the expression of event-based data collection content, the following table 1, which corresponds to the event stage and the data collection field, is set up in this embodiment. All fields in Table 1 are parsed from the IoT data collection messages by the software collection program and written into the node records of the subsequent directed causal chain.

[0029] Table 1. Correspondence between event stages and data collection fields

[0030] Event Phase Adding field Vehicle-side fields Node records content Betting begins First consecutive non-zero sampling sequence number of traffic, local timestamp, preceding message digest Vehicle operating status current liquid level sampling sequence number Chain head digest starting point sequence number time mapping fragment Traffic Continuous Continuous flow segment cumulative injection volume stage identifier Liquid level sequence within the corresponding time range Cumulative volume increment flow continuity boundary End of traffic Flow rate return to zero, sampling sequence number, cumulative refill amount Vehicle operating status within the corresponding time range End point number of the refueling side Liquid level rise Projection time range The slope change segment at the starting point of the continuous rise in liquid level sequence Liquid level increment sequence response boundary Liquid level stable Refueling end closing message digest End of sampling sequence of stable liquid level range Stable segment boundary closed state

[0031] In this embodiment, the local timeline recovery is represented by a piecewise time mapping method. Let the end-side identifier be u, where u=a represents the refueling end, u=c represents the vehicle end, the sampling point number is k, and the local timestamp is... The corrected local time is The stable time period to which the sampling points belong is Then the local time recovery expression of the same end message is:

[0032]

[0033] in, This indicates the ratio of the local clock on the device side to the platform reference time during a stable period. This indicates the shift offset between the local time on the device and the platform reference time. This represents the offset correction amount of the retransmitted message relative to the normal arrival sequence at the same end. If the retransmitted message has an offset correction amount relative to the normal arrival sequence at the same end within a certain stable time period... , Local timestamp of a certain sampling point , Retransmit offset tag ,but If the sampling point at the vehicle end , , , ,but The platform compares the event boundaries at both ends based on this instead of directly comparing uncorrected local timestamps.

[0034] After restoring the local timelines at both ends, the platform sets the refueling start, flow continuity, flow end, liquid level rise, and liquid level stabilization as nodes in a directed causal chain. Each node stores its own source, sampling sequence range, correction time range, summary connection information, stage identifier, and physical quantity boundary. Directed edges are established between nodes according to the natural chronological order of the refueling process. Three types of constraint markers are written between adjacent nodes: time sequence markers indicate that the correction time of the previous stage is not later than the correction time of the next stage; physical quantity direction markers indicate that the direction of change of the cumulative flow and liquid level increment is consistent with the stage attribute; and cumulative quantity correspondence markers indicate that there is an acceptable intersection range between the cumulative quantity increment at the refueling end and the liquid level conversion at the vehicle end. When any constraint marker is missing, the corresponding chain segment is not directly output as a closed event, but enters a pending verification state, which is then judged by subsequent retransmission processing and intra-chain consistency processing.

[0035] The message digest connection relationship solves the problem of uncertain intra-terminal order caused by message out-of-order and replay; the segmented time mapping solves the problem of local time drift on the terminal side; the elastic matching window solves the problem of hysteresis between the refueling flow rate change and the vehicle liquid level response; and the directed causal chain solves the problem of cross-terminal data acquisition only comparing the result value and lacking process constraints. The above processing together enables the vehicle urea refueling record to express the stage sequence and data correspondence from the output of the refueling terminal to the reception of the vehicle terminal.

[0036] refer to Figure 2 In a preferred embodiment, restoring the local timeline at the refueling end and the local timeline at the vehicle end includes: arranging consecutive messages from the same end in ascending order of sampling sequence number, mapping adjacent differences in local timestamps to differences in sampling intervals to obtain clock drift segments; for messages arriving out of order, determining the embedding position of the message in the local sequence based on the concatenation relationship of preceding message summaries; for sequences with short-term gaps, retaining the start and end sequence numbers, start and end timestamps, and cumulative boundary of the missing interval, and marking messages exceeding the IoT data acquisition and replenishment window as independent retransmission segments; according to the time... The clock drift segment and the embedded position generate a continuous local time axis. In specific implementation, the platform maintains end-side message buffers for the refueling end and the vehicle end respectively. The messages in the buffer are first grouped by terminal identifier, and then sorted by sampling sequence number. After sorting, the difference between adjacent sampling sequence numbers and the difference of local timestamp are compared. If the sampling sequence numbers are continuous and the difference of local timestamp changes smoothly around the same sampling interval, they are assigned to the same clock drift segment. If the sampling sequence number goes back but the previous message digest points to an existing message, the out-of-order message is inserted into the corresponding successor position instead of being appended according to the arrival time.

[0037] In this embodiment, short-term missing values ​​are not directly filled with fictitious sampled values. Instead, a missing description record is formed using the start and end sequence numbers, start and end timestamps, and cumulative quantity boundaries of the missing interval. For example, if the sampling sequence number at the injection end jumps from 215 to 219, the missing interval is recorded as 216 to 218. If the cumulative injection quantity corresponding to sequence number 215 is 12.6 and the cumulative injection quantity corresponding to sequence number 219 is 14.1, then the cumulative quantity boundary of the missing interval is recorded as 12.6 to 14.1. If the subsequent retransmission message contains 216 to 218 and the preceding message digests are sequentially connected, the retransmission message can replace the placeholder position in the missing description record. If the arrival time of the retransmission message exceeds the IoT data collection and replenishment window set by the platform and cannot be connected with the current event digest chain, an independent retransmission segment is formed. The independent retransmission segment does not participate in the closure of the current injection event.

[0038] The intra-terminal sorting uses the sampling sequence number, the difference between adjacent local timestamps, and the connection relationship of the previous message digest. This can distinguish between real sampling gaps, out-of-order messages, and historical retransmissions. Short-term missing intervals are preserved with boundary records to retain verifiable information, and independent retransmission segments are isolated outside the current local timeline, so that the platform can still restore the intra-terminal collection order after network fluctuations.

[0039] Further, in one embodiment, processing the clock drift segment includes: selecting a segment with no gaps in the sampling sequence number and continuous digests of preceding messages from consecutive messages at the same end as a reference segment; establishing a segmented linear time mapping using the local timestamp sequence of the reference segment; defining the mapping endpoints for messages that cross missing intervals using the cumulative boundary and stage identifier before and after the missing interval; establishing a supplementary transmission offset label according to the deviation between the arrival time and the local timestamp for messages that deviate from the segmented linear time mapping but have complete digest connection; and writing the supplementary transmission offset label into the continuous local time axis. Specifically, the platform searches for continuous digest segments in the local sequence at each end, selects segments with continuous sampling sequence numbers and monotonically increasing local timestamps as reference segments, maps the start and end points of the reference segments to the stable reference time during the platform's reception process, and then calculates the correction time of other sampling points within the segment using a linear relationship.

[0040] In this embodiment, messages that cross missing intervals do not directly participate in linear fitting. Instead, the endpoints of the mapping are constrained by two known sampling points before and after the missing interval. For example, if sequence numbers 300 to 305 are continuous, sequence numbers 306 to 308 are missing, and sequence numbers 309 to 315 are continuous during the traffic continuity phase, then sequence numbers 300 to 305 and 309 to 315 are respectively established as piecewise linear mappings. The front boundary of the missing interval is taken from the cumulative amount and stage identifier of sequence number 305, and the back boundary of the missing interval is taken from the cumulative amount and stage identifier of sequence number 309. The platform only allows the retransmission message to replace the placeholder position between the above cumulative amount boundary and stage identifier. If a message digest is complete but its arrival time is significantly later than the adjacent message at the same end, the sampling sequence number order is not changed. Instead, a retransmission offset label is written at the corresponding sampling point. The retransmission offset label participates in the local time axis reconstruction and serves as time mapping evidence for subsequent abnormal evidence sequences.

[0041] Piecewise linear time mapping is supported by continuous segments of intra-terminal summaries. Missing intervals are restricted by cumulative boundary and stage identifiers to determine the attribution of retransmitted messages. Retransmission offset labels retain the difference between message acquisition time and arrival time, enabling the platform to separate clock drift and offline retransmission at the time axis level.

[0042] Further, in a preferred embodiment, the breakpoint processing for the short-term missing sequence includes: when the sampling sequence number, stage identifier, and cumulative quantity boundary at both ends of the missing interval satisfy a monotonic connection relationship, a placeholder sampling segment is generated according to the start and end sequence numbers, and the missing direction, cumulative quantity boundary, and time uncertainty interval are recorded in the placeholder sampling segment; when the sampling sequence number of the retransmitted segment falls into the placeholder sampling segment, the corresponding placeholder position is replaced according to the monotonic relationship between the previous message digest and the cumulative quantity; when the retransmitted segment has a duplicate sequence number, digest rollback, or stage inversion with the already joined message, the retransmitted segment is excluded from the continuous local time axis. Specifically, the missing direction indicates that the missing interval belongs to the continuous flow direction at the refueling end, the rising liquid level direction at the vehicle end, or the stable direction; the cumulative quantity boundary indicates the cumulative quantity value range at the beginning and end of the missing interval; and the time uncertainty interval indicates the correction time range that the missing sampling point may fall into.

[0043] In this embodiment, the placeholder sampling segment only saves the boundaries and uncertain intervals, and does not generate false instantaneous flow rate or liquid level values. After the supplementary transmission segment enters the platform, the platform checks whether the preceding message summary of the first message of the supplementary transmission segment corresponds to the preceding message summary of the placeholder sampling segment, checks whether the sampling sequence number inside the supplementary transmission segment is continuous, and checks whether the cumulative injection amount or liquid level change of the supplementary transmission segment falls within the cumulative amount boundary of the placeholder sampling segment. If the above conditions are met, the placeholder position is replaced with the supplementary transmission segment. If the sampling sequence number of the supplementary transmission segment is the same as that of the already entered message but the summary is different, or the preceding message summary of the supplementary transmission segment points to an earlier message to form a summary rollback, or the stage identifier of the supplementary transmission segment rolls back from liquid level stability to liquid level rise, the platform writes the supplementary transmission segment into the exclusion record and prevents it from entering the continuous local time axis.

[0044] The placeholder sampling segment retains the boundary constraints of the sampling gap. The supplementary transmission segment only enters the local time axis when the summary chain and the monotonic relationship of the physical quantity can be connected. The repetition of the sequence number, the summary rollback and the stage inversion are directly blocked, so that the historical playback data, the repeated reported data and the stage disordered data cannot destroy the intra-terminal order of the current annotation event.

[0045] refer to Figure 3 In one embodiment, constructing an elastic matching window includes: extracting the starting point of the first continuous non-zero flow rate, the cumulative increase in flow rate within a continuous segment, and the ending point of the flow rate returning to zero from the candidate events for refueling, forming a refueling-side event boundary; extracting the starting point of the continuous rise in liquid level, the segment of change in the slope of the rise in liquid level, and the segment of stable liquid level from the candidate events for liquid level response, forming a vehicle-side response boundary; projecting the refueling-side event boundary onto the vehicle-side local time axis according to the offset between the local time axis of the refueling end and the local time axis of the vehicle end; and generating an elastic matching window including a leading edge, a main body, and a trailing edge based on the hysteresis range of the vehicle-side response boundary relative to the refueling-side event boundary. Specifically, the platform records the starting point of the first continuous non-zero flow rate as the refueling-side starting point, the ending point of the flow rate returning to zero as the refueling-side ending point, the difference in the cumulative refueling amount between the refueling-side starting point and the ending point as the cumulative increase in flow rate on the refueling side, and the starting point of the continuous rise in liquid level on the vehicle end to the ending point of the stable liquid level segment as the vehicle-side response boundary.

[0046] In this embodiment, the flexible matching window is represented by the following expression, where the correction time of the injection-side starting point of the m-th injection candidate event is . The correction time for the end point of the injection side is The formula for calculating the flexible matching window is:

[0047]

[0048] in, This represents the matchable time interval of the m-th candidate event on the vehicle's local timeline. This indicates the historical average lag of the rise in fluid level at the vehicle end relative to the change in flow rate at the refueling end. This represents the leading edge expansion amount set to accommodate minor fluctuations in the liquid level in advance. This represents the trailing edge extension amount set after the liquid level stabilizes, for example... , , , , ,but Only when the vehicle-end liquid level response boundary falls within this interval will the subsequent cumulative amount corresponding to the m-th candidate event be checked.

[0049] The flexible matching window is formed by the flow boundary on the refueling side, the time offset at both ends, and the liquid level hysteresis at the vehicle end. The leading edge, the main body, and the trailing edge correspond to the small liquid level disturbance before refueling is triggered, the continuous refueling process, and the liquid level stabilization process after refueling is completed, respectively. The response hysteresis that cannot be expressed by the fixed window is included in the verifiable range, while irrelevant liquid level fluctuations that cross the window are excluded.

[0050] Further, in a preferred embodiment, generating the elastic matching window includes: using the flow start point to the flow end point of the refueling side event boundary as the main window; limiting the value range of the leading edge and the trailing edge according to the available liquid level interval corresponding to the parking, idling, or low disturbance indicators in the vehicle operation state; updating the hysteresis range according to the offset distribution of the liquid level rise start point relative to the flow start point in historical same vehicle refueling events; when the vehicle side response boundary crosses multiple candidate main windows, the window affiliation is determined by the cumulative volume increment difference and the liquid level increment difference, and the sampling sequence number of the unaffiliated boundary is retained. Specifically, the platform divides the vehicle operation state into a liquid level available state and a liquid level disturbance state. The parking, idling, or low disturbance indicators correspond to the liquid level available state, and the sampling points in the liquid level disturbance state cannot be used as the boundary points of the leading edge or the trailing edge.

[0051] In this embodiment, historical refueling events in the same vehicle are used to calculate the hysteresis distribution of the liquid level rise starting point relative to the flow start point. The platform saves the hysteresis value of each closed event according to the vehicle identifier. During the update, events with conflict source fields are removed, and only closed events are used to calculate the current hysteresis range. When the time distance between two refueling candidate main windows is close and the liquid level response boundary at the vehicle end spans multiple main windows, the platform calculates the difference between the cumulative volume increment and the liquid level increment at the refueling end of each candidate window. The window with the smaller difference and complete time sequence marking is selected as the assigned window. The liquid level response boundary that cannot be assigned retains its sampling sequence number and writes it into the unassigned boundary field. Unassigned boundaries are not included in any closed events.

[0052] Vehicle operating status limits the available liquid level range, historical vehicle lag distribution limits the response delay range, multiple candidate window assignments use both cumulative volume increment difference and liquid level increment difference, and unassigned boundaries are preserved rather than forcibly merged, enabling adjacent refueling events, liquid level disturbances and delayed responses to be processed separately.

[0053] In one embodiment, the mapped directed causal chain nodes include: a starting node receiving the start number, start timestamp, and preceding message digest of the candidate event for refueling; a continuous flow node receiving the cumulative increment and stage identifier of the continuous flow segment; and a flow end node receiving the flow return-to-zero sequence number; a rising liquid level node receiving the liquid level increment sequence of the candidate event for liquid level response; and a stable liquid level node receiving the stable liquid level interval; time sequence markers, physical quantity direction markers, and cumulative quantity corresponding markers are written between adjacent nodes, and the node chain segment lacking any marker is reserved separately as a chain segment to be verified. In specific implementation, the platform assigns each node a node type, source end, sampling sequence number range, correction time range, stage identifier, digest entry, and digest exit. The directed edges between nodes are established in the order of refueling start pointing to continuous flow, continuous flow pointing to end flow, end flow pointing to rising liquid level, and rising liquid level pointing to stable liquid level.

[0054] In this embodiment, the time sequence marker is obtained by comparing the correction time range of adjacent nodes. If the end correction time of the previous node is not later than the start correction time of the next node, or if the two only touch within the time uncertainty interval, a connectable marker is written. The physical quantity direction marker is obtained by comparing the direction of the cumulative flow and the liquid level increment. The flow continuity node requires that the cumulative filling amount is not decreasing, the liquid level rise node requires that the liquid level sequence is rising within the available interval, and the liquid level stability node requires that the liquid level change falls into the stability zone. The cumulative amount correspondence marker is obtained by comparing the cumulative amount increment at the filling end with the liquid level converted at the vehicle end. If there is an intersection between the two, a correspondent marker is written. If any marker is missing, the corresponding chain segment is reserved separately as a chain segment to be verified and awaits the result of the supplementary transmission of the segment, the replacement result of the placeholder sampling segment, or the result of the intra-chain consistency processing.

[0055] The cumulative amount can be represented by a normalized difference, with the starting point of the liquid level at the vehicle end being... The end point of the liquid level at the vehicle end is The formula for calculating the cumulative difference is:

[0056]

[0057] in, This represents the normalized difference between the cumulative increase in liquid level at the refueling end and the converted liquid level at the vehicle end. This represents the difference between the start and end points of the cumulative refueling volume at the refueling end within the same event. This represents the coefficient used to convert a unit change in urea level at the vehicle end into urea volume. This indicates the increase in liquid level at the vehicle end. Used to avoid a denominator of zero, for example , , , , Therefore, the liquid level at the vehicle end is calculated to be 14.5. This value then enters the determination process for the corresponding marker of the cumulative amount.

[0058] Directed causal chains transform cross-end events from tabular records into a structure of nodes and edges. Time sequence markers, physical quantity direction markers, and cumulative quantity corresponding markers constrain the collection order, stage changes, and volume relationships, respectively. The chain segment to be verified is not pre-confirmed as a closed event, enabling the platform to retain the detailed state of incomplete events.

[0059] Furthermore, in a preferred embodiment, the intra-chain consistency constraint on the chain segment to be verified includes: for adjacent nodes with missing time sequence markers, determining the order based on the sampling sequence number of the local time axis at the respective end and the digest of the preceding message; for adjacent nodes with missing physical quantity direction markers, restoring the direction markers based on the sign changes of the cumulative flow and liquid level increment; for adjacent nodes with missing cumulative quantity corresponding markers, establishing a correspondence based on the cumulative quantity increment at the filling end, the liquid level increment at the vehicle end, and the calibrated capacity range of the vehicle's urea tank; and keeping the chain segment with remaining conflicts disconnected and adding a conflict source field. Specifically, the intra-chain consistency constraint is only executed within the existing node chain segment and does not introduce new event boundaries from the outside.

[0060] In this embodiment, the missing time sequence marker usually comes from the arrival time disorder caused by the retransmission of messages. The platform uses the same-end sampling sequence number and the previous message digest to restore the order. If the digest output of the continuous flow node at the filling end can be connected to the digest input of the flow end node, the intra-end order can still be restored even if the flow end node is late. The missing physical quantity direction marker usually comes from the missing sampling segment not being fully backfilled. The platform uses the cumulative filling amount difference and the change of the liquid level increment sign to determine the corresponding direction. If the cumulative flow amount changes from a low value to a high value and the stage identifier is still in the continuous flow stage, the continuous flow direction marker is restored. When the marker corresponding to the cumulative amount is missing, the platform uses the vehicle urea tank calibration capacity range as the constraint boundary of the liquid level conversion amount, and calculates the intersection of the cumulative amount increment at the filling end and the liquid level increment conversion range at the vehicle end. If there is no intersection, the chain segment remains disconnected and a conflict source field is added.

[0061] To facilitate the public disclosure of the consistency processing and abnormal evidence binding relationship within the chain, the following Table 2 field table is set up in this embodiment. The fields are all derived from the end-side message, time axis recovery result, elastic matching result, and directed causal chain processing result.

[0062] Table 2 In-chain consistency fields and anomaly evidence fields

[0063] Serial number continuity evidence Sorting by sampling sequence number within the terminal Missing interval start and end numbers, duplicate numbers Packet loss duplicate reporting Abstract coherence evidence Comparison of preceding message digests Abstract Entry Abstract Exit Abstract Back Misconnection during playback message stage Time-mapping evidence Piecewise linear time mapping Time-mapped fragment retransmission offset tag Delayed transmission clock drift Evidence of physical quantity direction Comparison of cumulative flow and liquid level increment Direction marker stage inverted field Disorder of abnormal liquid level fluctuation stages Cumulative amount corresponding evidence Comparison of injection volume and liquid level conversion Intersection difference value Cross-end mismatch injection volume inconsistency

[0064] The completion of the chain segment to be verified does not rely on a single rule. The time sequence is restored by connecting the sampling sequence number and the summary. The physical quantity direction is restored by the cumulative flow and the liquid level increment. The correspondence of the cumulative quantities is jointly limited by the cumulative flow at the filling end, the liquid level increment at the vehicle end, and the calibrated capacity range. The chain segment that still cannot be closed retains the broken state and the source of the conflict to avoid abnormal segments being mistakenly merged into reliable events.

[0065] refer to Figure 4 In a preferred embodiment, before outputting the credible record of the refueling event, the method further includes: binding the supplementary transmission offset tag, the placeholder sampling segment, the unassigned boundary, and the conflict source field to the corresponding directed causal chain node; establishing an abnormal evidence sequence according to the stage to which the node belongs, wherein the abnormal evidence sequence includes at least sequence number continuity evidence, summary connection evidence, time mapping evidence, physical quantity direction evidence, and cumulative quantity corresponding evidence; when there are multiple types of evidence conflicts for the same node, generating an abnormal location path according to the node order of refueling start, flow continuity, flow termination, liquid level rise, and liquid level stabilization, and writing the abnormal location path into the credible record of the refueling event. Specifically, the platform binds the abnormal information to the node rather than to the entire event, so that the source of the abnormality can be located to the refueling start, flow continuity, flow termination, liquid level rise, or liquid level stabilization stage.

[0066] In this embodiment, the supplementary transmission offset label is bound to the node where the sampling point that generated the supplementary transmission is located, the placeholder sampling segment is bound to the node where the missing interval is located, the unassigned boundary is bound to the liquid level rising node or the liquid level stable node, and the conflict source field is bound to the adjacent nodes that cannot be closed; the abnormal evidence sequence is arranged in the order of nodes, and each node can simultaneously have sequence number continuity evidence, summary connection evidence, time mapping evidence, physical quantity direction evidence, and cumulative quantity corresponding evidence; if there are multiple types of conflicts in the same node, such as the continuous flow node having both sequence number missing and summary rollback, the abnormal location path takes the chain segment from the start of refueling to continuous flow as the entry point, and records the sequential occurrence positions of sequence number missing and summary rollback; if the liquid level rising node has an unassigned boundary and the cumulative quantity corresponding evidence conflicts, the abnormal location path continues to extend to the liquid level rising node and retains the liquid level sampling sequence number.

[0067] The sequence of anomalous evidence can be expressed using a chain closure score. Let the sequence number of the m-th event be the continuity evidence. The abstract coherence evidence is Time mapping evidence is The evidence for the direction of physical quantities is The evidence corresponding to the cumulative amount is The formula for calculating the chain closure score is:

[0068]

[0069] in, , , , , The value indicates whether the corresponding evidence satisfies the closure condition; 1 indicates satisfaction, and 0 indicates non-satisfaction. Indicates the number of conflict source fields retained. to This indicates the data processing weights pre-configured by the platform, for example... , , , , , If all five types of evidence for an event satisfy the criteria and the number of conflicts is 0, then If the time-mapping evidence and the cumulative evidence do not meet the requirements and the number of conflicts is 2, then This result is used to generate the closed state field in a trusted record.

[0070] The abnormal evidence sequence binds communication layer anomalies, time mapping anomalies, physical quantity direction anomalies, and cumulative quantity corresponding anomalies to specific stages. The anomaly location path unfolds in the order of the directed causal chain nodes, enabling the platform to distinguish collection problems at different locations within the same event and avoid outputting only a single anomaly marker without being able to trace back the data source.

[0071] Further, in one embodiment, generating the trusted record of the refueling event includes: configuring an event identifier for the completed directed causal chain, wherein the event identifier is jointly generated by the sampling sequence number range of the refueling end, the sampling sequence number range of the vehicle end, the time mapping segment numbers of both ends, and the message digest of the chain head; writing the trusted refueling amount as the intersection interval between the cumulative amount increment of the refueling end and the liquid level conversion amount of the vehicle end that satisfies the corresponding marker of the cumulative amount; assigning the chain segments that have not completed the closure to the categories of repeated reporting, cross-end mismatch, delayed retransmission, or abnormal liquid level fluctuation according to the abnormal location path, and encapsulating the category field together with the event identifier as the IoT data collection result. In specific implementation, the event identifier is not generated by order number or arrival time alone, but by concatenating the sampling sequence number range of the refueling end, the sampling sequence number range of the vehicle end, the time mapping segment number, and the message digest of the chain head to generate a digest, so that the event identifier is bound to the data chain itself.

[0072] In this embodiment, the reliable refueling quantity is not directly taken from the cumulative increment at the refueling end, nor from the converted liquid level at the vehicle end. Instead, it is calculated from the intersection range of the two under the condition that the corresponding markers of the cumulative quantity are satisfied. For example, after processing the missing interval, the cumulative increment at the refueling end is 14.8 to 15.2, and after processing the liquid level conversion at the vehicle end is 14.5 to 15.0, the reliable refueling quantity range is 14.8 to 15.0. If the directed causal chain is not closed, the platform reads the conflict source field along the abnormal location path. If the sampling sequence number is repeated at the same end and the summary entry is different, it is assigned as duplicate reporting. If the cumulative increment at the refueling end and the converted liquid level at the vehicle end have no intersection and the time sequence is complete, it is assigned as cross-end mismatch. If the re-transmitted offset label is concentrated at the continuous flow or flow end node and causes time mapping evidence conflict, it is assigned as delayed re-transmission. If the liquid level rise node has an unassigned boundary and the vehicle operation state is in a disturbed state, it is assigned as abnormal liquid level fluctuation.

[0073] Event identifiers are generated by sampling sequence range, time mapping segment, and chain head message digest, which can bind the end-side collection order and cross-end matching process. The reliable refill amount is represented by the intersection range of the cumulative increase at the refill end and the liquid level converted at the vehicle end, which can reduce the impact of one-sided data deviation on the results. Unclosed chain segments form category fields according to the abnormal location path, so that the IoT data collection results simultaneously include event identity, reliable amount range, and abnormal source.

[0074] During the overall operation of this embodiment, neither the refueling end nor the vehicle end needs to change the hardware structure of the urea refueling equipment or the vehicle's urea tank. The end-side software acquisition program generates IoT data acquisition messages with sampling sequence numbers, local timestamps, and previous message summaries according to the existing acquisition objects. The platform side completes message buffering, intra-end sorting, timeline recovery, candidate event identification, flexible window matching, directed causal chain construction, intra-chain consistency verification, abnormal evidence sequence generation, and trusted record encapsulation. For events with stable network conditions, the platform can directly generate closed causal chains from continuous messages. For events with out-of-order, short-term missing, or retransmission events, the platform restores the intra-end order through summary concatenation, placeholder sampling segments, and retransmission offset tags. For events with liquid level lag or liquid level disturbance, the platform distinguishes between available responses and irrelevant fluctuations through flexible matching windows, vehicle operating status, and unassigned boundaries.

[0075] Each processing stage revolves around the same core data structure, namely the local timeline and the directed causal chain. The end-side message digest provides the order basis for the local timeline, the time mapping provides a common time reference for cross-end matching, the flexible matching window provides a hysteresis tolerance range for the injection flow rate and liquid level response, the cumulative quantity corresponding mark provides a limit for the closure of physical quantities, and the abnormal evidence sequence provides the location basis for the unclosed chain segment. This enables the vehicle urea injection monitoring based on IoT data acquisition to form a reviewable event record under asynchronous acquisition and asynchronous transmission conditions.

Claims

1. A method for monitoring vehicle urea refueling based on Internet of Things (IoT) data acquisition, characterized in that, include: At the filling end, collect urea flow rate, cumulative filling amount, sampling sequence number, local timestamp and previous message summary; at the vehicle end, collect urea level sequence, vehicle operating status, sampling sequence number and local timestamp. The local time axis of the refueling end and the local time axis of the vehicle end are restored based on the continuity of the sampling sequence number, the connection relationship of the message digest, and the stability of the sampling interval, respectively. Candidate events for refueling are generated when the flow rate changes from zero to a continuously changing state, and candidate events for liquid level response are generated when the liquid level continues to rise. An elastic matching window is constructed based on the time offset between two local time axes, the flow rate change curve of the injection candidate event, and the hysteresis envelope of the liquid level response candidate event. The start of refueling, continuous flow, end of flow, rise in liquid level, and stabilization of liquid level are mapped as directed causal chain nodes, and reliable records of refueling events are output according to the chronological order, the direction of change of physical quantities, and the consistency of cumulative quantities.

2. The method for monitoring vehicle urea refueling based on Internet of Things data acquisition according to claim 1, characterized in that, Restoring the partial timeline at the refueling end and the partial timeline at the vehicle end includes: For consecutive messages from the same end, arrange them in ascending order of sampling sequence number, and match the adjacent differences of local timestamps with the differences of sampling intervals to obtain the clock drift segment; For messages that arrive out of order, the embedding position of the message in the local sequence is determined based on the concatenation relationship of the preceding message digests; For sequences with short-term gaps, retain the start and end sequence numbers, start and end timestamps, and cumulative quantity boundaries of the missing interval, and mark messages that exceed the IoT data acquisition and replenishment window as independent retransmission fragments; A continuous local time axis is generated according to the clock drift segment and the embedding position.

3. The method for monitoring vehicle urea refueling based on IoT data acquisition according to claim 2, characterized in that, Building a flexible matching window includes: Extract the starting point of the first consecutive non-zero flow rate, the cumulative increase in flow rate within the continuous flow rate segment, and the ending point of the flow rate returning to zero from the candidate events for refueling, and form the event boundary on the refueling side; Extract the starting point of continuous liquid level rise, the segment of liquid level rise slope change, and the segment of liquid level stability from the candidate liquid level response events to form the vehicle side response boundary; Based on the offset between the local time axis of the refueling end and the local time axis of the vehicle end, the event boundary of the refueling side is projected onto the local time axis of the vehicle end; Based on the hysteresis range of the vehicle-side response boundary relative to the refueling-side event boundary, an elastic matching window including the leading edge, the main body, and the trailing edge is generated.

4. The method for monitoring vehicle urea refueling based on Internet of Things data acquisition according to claim 3, characterized in that, Mapped directed causal chain nodes include: The starting node receives the starting sequence number, starting timestamp, and preceding message digest of the candidate event for the injection; the continuous flow node receives the cumulative increment and stage identifier of the continuous flow segment; and the end flow node receives the zero-return sequence number of the flow. The liquid level rise node receives the liquid level increment sequence of the liquid level response candidate events, and the liquid level stabilization node receives the liquid level stabilization interval. Write time sequence markers, physical quantity direction markers, and cumulative quantity corresponding markers between adjacent nodes, and keep the node chain segment that lacks any of the markers as a separate chain segment to be verified.

5. The method for monitoring vehicle urea refueling based on Internet of Things data acquisition according to claim 4, characterized in that, Processing the clock drift segment includes: In the continuous messages at the same end, a segment with no gaps in the sampling sequence number and continuous digest of the preceding message is selected as the reference segment, and a segmented linear time mapping is established using the local timestamp sequence of the reference segment; For messages that cross missing intervals, the mapping endpoints are defined by the cumulative boundary before and after the missing interval and the stage identifier, respectively. For messages that deviate from the segmented linear time mapping but have complete digest connections, a supplementary transmission offset label is established according to the deviation between the arrival time and the local timestamp; Write the retransmission offset label into the continuous local time axis.

6. The method for monitoring vehicle urea refueling based on Internet of Things data acquisition according to claim 5, characterized in that, Breakpoint processing of the short-term missing sequence includes: When the sampling sequence number, stage identifier, and cumulative quantity boundary at both ends of the missing interval satisfy the monotonic connection relationship, a placeholder sampling segment is generated according to the start and end sequence number, and the missing direction, cumulative quantity boundary, and time uncertainty interval are recorded in the placeholder sampling segment. When the sampling sequence number of the supplementary segment falls into the placeholder sampling segment, the corresponding placeholder position is replaced according to the monotonic relationship between the preceding message digest and the cumulative amount. When a retransmitted segment has the same sequence number as an already linked message, or has a digest rollback or phase inversion, the retransmitted segment will be excluded from the continuous local timeline.

7. The method for monitoring vehicle urea refueling based on Internet of Things data acquisition according to claim 6, characterized in that, Generating the elastic matching window includes: The main window is defined as the flow start point to the flow end point at the boundary of the refueling side event. The range of values ​​for the leading edge and the trailing edge is limited based on the available liquid level range corresponding to the vehicle's parking, idling, or low-disturbance indicators during vehicle operation. The hysteresis range is updated based on the offset distribution of the liquid level rise starting point relative to the flow rate starting point in historical same-vehicle refueling events; When the vehicle-side response boundary spans multiple candidate subject windows, the window assignment is determined by the cumulative increment difference and the liquid level increment difference, and the sampling sequence number of the unassigned boundary is retained.

8. The method for monitoring vehicle urea refueling based on Internet of Things data acquisition according to claim 7, characterized in that, Performing intra-chain consistency constraints on the chain segment to be verified includes: For adjacent nodes with missing time sequence markers, the order is determined based on the sampling sequence number of the local time axis at the end and the digest of the preceding message; For adjacent nodes where the physical quantity direction marker is missing, the direction marker is restored based on the sign changes of the cumulative flow and liquid level increment; For adjacent nodes where the cumulative amount is missing a marker, establish a corresponding relationship based on the cumulative amount increment at the filling end, the liquid level increment at the vehicle end, and the calibrated capacity range of the vehicle's urea tank. For segments that still have conflicts, keep them disconnected and append a conflict source field.

9. The method for monitoring vehicle urea refueling based on Internet of Things data acquisition according to claim 8, characterized in that, Before outputting the trusted record of the injection event, it also includes: The retransmission offset label, the placeholder sampling segment, the unassigned boundary, and the conflict source field are respectively bound to the corresponding directed causal chain nodes; An abnormal evidence sequence is established according to the stage to which the node belongs. The abnormal evidence sequence includes at least the sequence number continuity evidence, the summary connection evidence, the time mapping evidence, the physical quantity direction evidence, and the cumulative quantity corresponding evidence. When multiple types of conflicting evidence exist for the same node, an abnormal location path is generated in the order of node start of refueling, continuous flow, end of flow, liquid level rise, and liquid level stabilization, and the abnormal location path is written into the refueling event trusted record.

10. The method for monitoring vehicle urea refueling based on Internet of Things data acquisition according to claim 9, characterized in that, Generating the trusted record of the injection event includes: To complete the configuration of the closed directed causal chain, the event identifier is generated by the sampling sequence number range of the refueling end, the sampling sequence number range of the vehicle end, the time mapping segment numbers of both ends, and the message digest of the chain head. The reliable refill amount is written as the intersection interval between the cumulative increment at the refilling end and the liquid level converted at the vehicle end, which satisfies the corresponding marker of the cumulative amount; The incompletely closed chain segments are assigned to categories such as repeated reporting, cross-end mismatch, delayed retransmission, or abnormal liquid level fluctuation according to the abnormal location path, and the category field and the event identifier are encapsulated together as IoT data collection results.

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