Cloud-based collaborative logistics information updating method

Abstract

This invention relates to the field of intelligent logistics and cloud-based collaborative information processing technology, specifically a cloud-based collaborative logistics information update method, comprising: a cloud server acquiring a preset path topology and historical road conditions and speeds, constructing multiple time window sequences, generating an expected spatiotemporal envelope matrix, and sending it to a mobile terminal; the mobile terminal acquiring real-time spatial positioning coordinates, communication network signal strength, and current silent duration, calculating spatiotemporal deviation and signal attenuation gradient; waking up the communication module to upload status update data when trigger conditions are met; the cloud server receiving the data, regenerating the expected spatiotemporal envelope matrix for the next stage, and updating the trajectory status; this invention achieves controllable communication overhead, uninterrupted critical trajectories, and timely correction of scheduling status.

Description

Technical Field

[0001] This invention relates to the field of intelligent logistics and cloud-based collaborative information processing technology, specifically to a method for updating logistics information based on cloud-based collaboration. Background Technology

[0002] Existing logistics information update solutions typically involve the interaction of location and status data between vehicle terminals and cloud platforms. In related technologies, to track the on-the-go trajectory and delivery status of transport vehicles, mobile terminals can continuously upload location coordinates and related operational data at fixed intervals; alternatively, the cloud can uniformly monitor vehicle locations based on preset routes and then dispatch resources upon detecting anomalies. However, in scenarios involving cold chain transportation, mountainous delivery, or complex communication environments, the former method results in excessive communication resource consumption and struggles to balance preserving critical states in blind spots with terminal energy conservation, while the latter method is prone to inaccurate judgments due to changes in route timeliness, location jitter, or changes in road network conditions. Therefore, the logistics information update methods in related technologies struggle to simultaneously ensure controlled communication overhead, trajectory continuity, and accurate anomaly identification. Summary of the Invention

[0003] The purpose of this invention is to provide a cloud-based collaborative logistics information update method to solve the following technical problems: avoid the increased communication resource consumption caused by traditional fixed-period continuous uploading, and achieve uninterrupted key trajectories and timely correction of scheduling status.

[0004] The objective of this invention can be achieved through the following technical solutions:

[0005] The cloud-based collaborative logistics information update method includes: S100, obtaining a preset route topology and historical road conditions and speeds from a cloud server, constructing multiple time window sequences containing start and end times, calculating the target center latitude and longitude and tolerance radius for each time window sequence, matrix-encapsulating the start and end times, the target center latitude and longitude and the tolerance radius to generate an expected spatiotemporal envelope matrix, and sending the expected spatiotemporal envelope matrix to a mobile terminal;

[0006] S200: The mobile terminal locally acquires real-time spatial positioning coordinates, communication network signal strength within a continuous sampling period, and the current silence duration since the vehicle started or after the last data upload. Based on the real-time spatial positioning coordinates and the latitude and longitude of the target center in the expected spatiotemporal envelope matrix, the spatiotemporal deviation is calculated, and the signal attenuation gradient is calculated based on the communication network signal strength.

[0007] S300. Perform conditional branch determination for the spatiotemporal deviation and the signal attenuation gradient: In response to the spatiotemporal deviation being greater than the tolerance radius, or the signal attenuation gradient exceeding the preset attenuation threshold, or the current silence duration reaching the preset maximum keep-alive time, determine that the preset trigger condition is met, wake up the communication module to upload the status update data containing the real-time spatial positioning coordinates to the cloud server; otherwise, determine that the preset trigger condition is not met and maintain the local silence verification state.

[0008] S400: After receiving the status update data, the cloud server regenerates the expected spatiotemporal envelope matrix for the next stage and updates the trajectory status based on the expected spatiotemporal envelope matrix for the next stage.

[0009] Preferably, the calculation of spatiotemporal deviation based on the real-time spatial positioning coordinates and the target center latitude and longitude in the expected spatiotemporal envelope matrix, and the calculation of signal attenuation gradient based on the communication network signal strength, includes:

[0010] S201. Read the real-time spatial positioning coordinates and communication network signal strength locally on the mobile terminal at a preset sampling period;

[0011] S202. Extract the target center latitude and longitude corresponding to the current time window from the expected spatiotemporal envelope matrix;

[0012] S203. Call the local projection conversion ratio to convert the real-time spatial positioning coordinates and the latitude and longitude of the target center into displacement in the local plane rectangular coordinate system, and calculate the Euclidean distance difference between the two points based on the displacement to generate the spatiotemporal deviation.

[0013] S204. Extract the signal strength of the communication network within a continuous sampling period, calculate the rate of change of signal strength, and generate the signal attenuation gradient.

[0014] Preferably, in response to the fulfillment of a preset trigger condition, the wake-up communication module uploads status update data containing the real-time spatial positioning coordinates to the cloud server, including:

[0015] S301. In response to the satisfaction of the preset triggering condition, a wake-up command is triggered to activate the communication module of the mobile terminal;

[0016] S302. Obtain current moving speed and environmental perception data locally on the mobile terminal;

[0017] S303. Encapsulate the real-time spatial positioning coordinates, the current moving speed and the environmental perception data into a data packet to generate the status update data;

[0018] S304. Establish a data link with the cloud server and send the status update data to the cloud server.

[0019] Preferably, after receiving the status update data, the cloud server regenerates the expected spatiotemporal envelope matrix for the next stage and updates the trajectory status based on the expected spatiotemporal envelope matrix for the next stage, including:

[0020] S401. The cloud server parses the received status update data and extracts the real-time spatial positioning coordinates as the latest location node;

[0021] S402. Based on the latest location node and the preset path topology, recalculate the estimated arrival time;

[0022] S403. Combine the estimated arrival time with the remaining path topology to regenerate the expected spatiotemporal envelope matrix for the next stage;

[0023] S404. The expected spatiotemporal envelope matrix for the next stage is sent to the mobile terminal to cover the original expected spatiotemporal envelope matrix on the mobile terminal.

[0024] Preferred options also include:

[0025] S501. The cloud server monitors the trigger frequency of data uploads triggered by the mobile terminal within a preset time window due to the spatiotemporal deviation exceeding the tolerance radius;

[0026] S502. Determine whether the triggering frequency is higher than a preset frequency threshold.

[0027] S503. In response to the triggering frequency being higher than the preset frequency threshold, confirming that the current positioning error meets the preset frequent false triggering conditions, when generating the expected spatiotemporal envelope matrix for the next stage, multiply the tolerance radius by a preset compensation coefficient greater than 1 to generate an updated tolerance radius.

[0028] S504. In response to the trigger frequency being lower than or equal to the preset frequency threshold, maintain the current tolerance radius calculation logic.

[0029] Preferred options also include:

[0030] S601. The cloud server detects the validity status of the preset path topology in real time; S602. In response to the failure of the preset path topology, it is determined that a valid expected spatiotemporal envelope matrix cannot be generated.

[0031] S603, Send a downgrade control command to the mobile terminal;

[0032] S604. After receiving the degradation control instruction, the mobile terminal closes the conditional branch judgment logic and degrades the upload logic of the status update data to a fixed-period timed polling mode.

[0033] The beneficial effects of this invention are:

[0034] 1) This invention adopts an anomaly-driven mechanism that combines cloud-pre-generated spatiotemporal envelope with end-side silent verification; the terminal only wakes up to report when the spatiotemporal deviation exceeds the limit, the signal continuously attenuates, or the silent timeout occurs, effectively replacing the traditional high-frequency timed upload; it significantly reduces communication resource consumption, realizes the status update and retains valid data before entering the weak communication coverage area, and takes into account both controlled resource consumption and trajectory continuity.

[0035] 2) When the triggering conditions are met, the terminal encapsulates the real-time coordinates, movement speed and environmental perception data and uploads them synchronously; it upgrades the single location reporting to structured status reporting, enabling the cloud to accurately distinguish between different anomalies such as congestion and deviation, and to simultaneously monitor environmental risks such as cold chain temperature control, which significantly improves the interpretability and traceability efficiency of logistics scheduling.

[0036] 3) This invention introduces an event-driven trajectory replanning and envelope matrix coverage mechanism; after receiving updated data, the cloud recalculates the arrival time with the latest position and sends out a new spatiotemporal envelope, and the terminal updates the local comparison benchmark accordingly; this mechanism enables the expected trajectory to dynamically match the actual transportation process, avoiding continuous false alarms caused by outdated initial plans.

[0037] 4) This invention provides an adaptive compensation function for tolerance radius; by monitoring the frequency of deviation triggering in a short period of time, the cloud identifies false alarms of positioning drift caused by multipath reflection from tall buildings or complex terrain, and dynamically widens the tolerance radius of the area; effectively filtering environmental positioning noise, and improving the overall stability of the system while maintaining the sensitivity of anomaly identification;

[0038] 5) This invention has a degradation and fault tolerance control mechanism for path topology failure; when encountering extreme conditions such as sudden road closures or unrecorded sidewalks on the map that cause the preset path to fail, the cloud actively instructs the terminal to degrade to a fixed-cycle timed polling mode; ensuring that the basic trajectory and cargo status can still be updated even when a valid reference benchmark is lost, thus ensuring uninterrupted logistics supervision. Attached Figure Description

[0039] The invention will now be further described with reference to the accompanying drawings.

[0040] Figure 1 This is a flowchart illustrating the cloud-based collaborative logistics information update method provided in this application embodiment. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Please see Figure 1 A cloud-based collaborative logistics information update method includes: S100, obtaining a preset path topology and historical road conditions and speeds from a cloud server, constructing multiple time window sequences containing start and end times, calculating the target center latitude and longitude and tolerance radius for each time window sequence, matrix-encapsulating the start and end times, the target center latitude and longitude and the tolerance radius to generate an expected spatiotemporal envelope matrix, and sending the expected spatiotemporal envelope matrix to a mobile terminal;

[0043] S200: The mobile terminal locally acquires real-time spatial positioning coordinates, communication network signal strength within a continuous sampling period, and the current silence duration since the vehicle started or after the last data upload. Based on the real-time spatial positioning coordinates and the latitude and longitude of the target center in the expected spatiotemporal envelope matrix, the spatiotemporal deviation is calculated, and the signal attenuation gradient is calculated based on the communication network signal strength.

[0044] S300. Perform conditional branch determination for the spatiotemporal deviation and the signal attenuation gradient: In response to the spatiotemporal deviation being greater than the tolerance radius, or the signal attenuation gradient exceeding the preset attenuation threshold, or the current silence duration reaching the preset maximum keep-alive time, determine that the preset trigger condition is met, wake up the communication module to upload the status update data containing the real-time spatial positioning coordinates to the cloud server; otherwise, determine that the preset trigger condition is not met and maintain the local silence verification state.

[0045] S400: After receiving the status update data, the cloud server regenerates the expected spatiotemporal envelope matrix for the next stage and updates the trajectory status based on the expected spatiotemporal envelope matrix for the next stage.

[0046] This embodiment provides a cloud-based collaborative logistics information update mechanism; specifically, this embodiment takes the cold chain transportation scenario of provincial-level CDC cold storage delivering vaccines to county-level hospitals in mountainous areas as the main scenario; the transportation carrier is a refrigerated van, and a mobile terminal is installed inside the vehicle. The mobile terminal includes at least a satellite positioning module, a communication module, a baseband signal reading unit, a processor, and a local memory.

[0047] The cloud server is deployed in the logistics dispatch center to pre-generate the reasonable location range that the vehicle should appear in during a future transportation cycle by combining electronic maps, road network topology, historical traffic speed and in-transit order information, and to complete the trajectory update in collaboration with mobile terminals;

[0048] Before departure, the cloud server generates a preset transportation route based on the origin cold storage, destination hospital, highways, service areas, long tunnel sections, and off-ramp nodes. This route is not simply a broken line, but a description of the transportation process related to time.

[0049] Based on this, the cloud server will divide the entire process into multiple time window sequences, such as the outbound section, the ring road section, the smooth highway driving section, the section before the mountain tunnel, the recovery section after the tunnel, and the urban delivery section; each time window sequence corresponds to a central latitude and longitude and a tolerance radius;

[0050] The center latitude and longitude here represent the center position of the target path of the vehicle traveling according to the preset route within the time window. The tolerance radius is used to limit the range of position fluctuations allowed within the time window. For high-speed constant speed sections, the tolerance radius is set to the first preset threshold. For congested urban sections, near toll stations, or hospital loading and unloading areas, the tolerance radius is set to the second preset threshold, which is greater than the first preset threshold, to compensate for position fluctuations caused by temporary queuing, slow traffic, and positioning jitter.

[0051] Furthermore, the cloud server organizes each time window into a single row of spatiotemporal description records; the three rows of matrix data can be represented by the following data structure: the first row is 08:00 to 08:20, center point P1, tolerance radius R1; the second row is 08:20 to 09:10, center point P2, tolerance radius R2; the third row is 09:10 to 09:40, center point P3, tolerance radius R3;

[0052] After receiving the matrix, the mobile terminal does not need to continuously connect to the cloud server to query the cloud server. Instead, it matches the corresponding time window according to the current time and performs silent verification on the mobile terminal side.

[0053] During transportation, the mobile terminal periodically reads the real-time spatial positioning coordinates, the communication network signal strength within the current continuous sampling period, and the current silence duration since the vehicle started or after the last data upload was completed; the spatiotemporal deviation degree characterizes the degree of deviation between the vehicle's current actual position and the planned position at that moment.

[0054] The signal attenuation gradient characterizes whether the signal attenuation rate of the communication environment in which the mobile terminal is located exceeds a set threshold; the former is used to identify situations such as deviation, abnormal delay, and unexpected road congestion, while the latter is used to identify weak coverage environments such as tunnels, underground parking areas, and mountain-obstructed areas.

[0055] Because cold chain transportation requires ensuring continuous tracking at key nodes, the system does not use high-frequency uploading throughout the entire process. Instead, it allows the mobile terminal to make local judgments first and only wakes up the communication module to report when necessary.

[0056] The mobile terminal determines that the preset triggering conditions are met when any of the following conditions occur: First, the spatiotemporal deviation of the vehicle is greater than the tolerance radius; second, the communication signal shows a rapid decline during continuous sampling, indicating that the stable Internet access conditions are about to be lost, that is, the signal attenuation gradient exceeds the preset attenuation threshold.

[0057] Third, although the first two items have not been triggered, the current silent duration has reached the preset maximum keep-alive time. At this time, it is necessary to resend the status update data to prevent the cloud server from not receiving the mobile terminal's heartbeat for a long time.

[0058] When none of the three conditions are met, the mobile terminal continues to maintain the local silent verification state, only performing local reading and comparison, and does not perform the reporting action.

[0059] The logical basis for constructing the above three types of triggering conditions is that the information update mechanism of cold chain logistics does not need to maintain high-frequency continuous location reporting at the minute level, but instead adopts an event-driven mode, and only performs status synchronization at key nodes such as deviating from the predetermined path, about to enter a weak communication coverage area, or the silent keep-alive cycle timeout.

[0060] For example, if a vehicle travels at a constant speed for tens of kilometers on a highway and remains within a reasonable envelope, continuous uploading will only increase the consumption of communication resources and will not generate new state representation data.

[0061] Conversely, when a vehicle approaches the entrance of a long tunnel and the signal continuously weakens, even if it has not yet deviated from the route, it should prioritize uploading once before the communication connection is interrupted in order to preserve a reliable state before entering the blind zone.

[0062] Furthermore, if the positioning module is unable to output valid coordinates for a short period of time, such as when satellite positioning is intermittently inaccurate due to multipath reflections under an overpass in a mountainous area, the mobile terminal can temporarily store the most recent valid coordinates and mark this sample as low-confidence data, without immediately determining deviation based on this.

[0063] If the communication signal fluctuates momentarily but does not form a continuous attenuation trend, it will not be regarded as a reliable basis for entering the blind zone, so as to avoid false triggering due to brief electromagnetic interference; if the mobile terminal has determined that it needs to report but the communication module fails to activate, it can enter the retry queue and cache a pending status update data locally.

[0064] If the retry still fails, the silent keep-alive mechanism will handle it and retransmit it when the next communication is restored. If the cloud server fails to return a new expected spatiotemporal envelope matrix in a short time, the mobile terminal can continue to use the current matrix that has not expired to complete the local verification, so as to avoid losing the judgment basis due to a single network jitter.

[0065] In the vaccine cold chain delivery task of this embodiment, the vehicle departs from the provincial CDC cold storage at 8:00 a.m. and is scheduled to be delivered to the county hospital in the mountainous area before noon; the cloud server divides the entire journey into several time windows before departure: outbound road, urban ring road, highway main road, approach road before long mountain tunnel, mountain road after tunnel, and county hospital receiving area;

[0066] When the vehicle is on the main highway, it remains within the expected envelope, and the mobile terminal performs only silent verification without reporting. When approaching a long tunnel in the mountainous area, the baseband unit detects a continuous drop in signal, and the mobile terminal actively uploads its current location before entering the tunnel.

[0067] If a vehicle detours via a national highway due to temporary traffic control, its actual location will exceed the original envelope range, and the mobile terminal will trigger a report again. After receiving the report, the cloud server will recalculate the subsequent arrival time at the hospital and send the new expected spatiotemporal envelope matrix for the next stage to the mobile terminal, so that the subsequent trajectory judgment will continue to be executed according to the detour route.

[0068] The purpose of this step is to transform the traditional one-sided data collection mode of fixed-period uploading into a collaborative mode where the cloud server provides expected data, the end-side is locally autonomous, and communication is only initiated in case of anomalies. This will enable the logistics information to be updated in a way that ensures controlled communication resource usage, uninterrupted critical trajectories, and timely correction of scheduling status.

[0069] In a preferred embodiment of the present invention, the spatiotemporal deviation is calculated based on the real-time spatial positioning coordinates and the latitude and longitude of the target center in the expected spatiotemporal envelope matrix, and the signal attenuation gradient is calculated based on the communication network signal strength, including:

[0070] S201. Read the real-time spatial positioning coordinates and communication network signal strength locally on the mobile terminal at a preset sampling period;

[0071] S202. Extract the target center latitude and longitude corresponding to the current time window from the expected spatiotemporal envelope matrix;

[0072] S203. Call the local projection conversion ratio to convert the real-time spatial positioning coordinates and the latitude and longitude of the target center into displacement in the local plane rectangular coordinate system, and calculate the Euclidean distance difference between the two points based on the displacement to generate the spatiotemporal deviation.

[0073] S204. Extract the signal strength of the communication network within a continuous sampling period, calculate the rate of change of signal strength, and generate the signal attenuation gradient.

[0074] This embodiment provides a local generation step for spatiotemporal deviation and signal attenuation gradient; specifically, in the aforementioned cold chain vaccine transportation scenario, it is not enough to rely solely on whether the current location is near the map route, because the same geographical location has different business significance at different transportation times;

[0075] For example, although the two vehicles are geographically close, the impact on dispatching and signing preparation is different: a vehicle arrives near a toll station 20 minutes in advance, and a vehicle arrives late due to taking a detour.

[0076] Therefore, this embodiment binds location determination with time window, so that the mobile terminal is exempt from performing macroscopic traversal of the global path, and instead performs precise matching and verification on the local spatial road segment corresponding to the current time window;

[0077] The mobile terminal reads real-time spatial positioning coordinates and current network signal strength according to a preset sampling period; this sampling period can be set to seconds or several seconds depending on the terminal's power conditions, transportation type, and line characteristics.

[0078] For refrigerated trucks with continuous power supply, a first preset sampling period is configured to enhance the response sensitivity to tunnel entrances and detour events; for battery-powered limited power tracking devices, a second preset sampling period longer than the first preset sampling period is configured to reduce local energy consumption; the sampled data first enters the local cache, and the terminal extracts the latitude and longitude of the target center in the time window corresponding to the current system time.

[0079] To illustrate the local comparison process, a specific data processing example can be used; assume that the current matrix contains three time windows, corresponding to center points P1, P2, and P3 respectively; if the terminal's current time falls within the second time window, then only P2 is taken as the target center point, and no horizontal comparison is made with P1 or P3.

[0080] The terminal then calculates the distance difference between the real-time positioning point Q and P2, and the result represents the degree of deviation between the current vehicle and the center area of ​​the planned route.

[0081] The distance difference is positively correlated with the degree to which the vehicle deviates from the expected planning range; if the distance difference is greater than a preset difference threshold, it indicates that the vehicle is at risk of deviating from its course or exceeding the expected arrival time; if the distance difference is less than or equal to the preset difference threshold, it indicates that the vehicle is within the expected planning range of the corresponding time window.

[0082] It should be noted that, since both the real-time spatial positioning coordinates and the target center latitude and longitude are expressed in spherical latitude and longitude coordinates, and the tolerance radius is usually expressed in physical units of absolute length, in order to avoid calculation distortion, when the local terminal performs the above Euclidean distance difference calculation, it will call the local projection conversion ratio according to the latitude of the vehicle, convert the longitude difference and latitude difference into the displacement in the local plane rectangular coordinate system, and then calculate the Euclidean distance between the two points as the final spatiotemporal deviation.

[0083] This projection conversion mechanism overcomes the fundamental error in latitude convergence caused by directly treating latitude and longitude as planar coordinates to calculate Euclidean distance, ensuring that the spatiotemporal deviation can be compared with the absolute physical length and tolerance radius in the same dimension.

[0084] Regarding the signal attenuation gradient, the terminal does not only look at the absolute strength of the signal at a certain moment, but observes the trend of change within a continuous sampling period.

[0085] The reason is that the weak signals in mountain transportation do not necessarily indicate communication loss. Only when they continue to decrease over several consecutive sampling periods do they have a strong indication of blind zone approach.

[0086] For example, if three sets of signal values ​​S1, S2, and S3 are continuously sampled, and S1 to S2 produces a first attenuation and S2 to S3 produces a second attenuation greater than the first attenuation, then it can be determined that the attenuation trend of the current communication environment meets the preset deterioration conditions.

[0087] If S1, S2, and S3 fluctuate but remain generally stable, it is more likely to be a normal cell handover or a momentary fluctuation caused by vehicle obstruction, and should not be mistakenly triggered to send a data reporting command.

[0088] Compared to the basic solution, making judgments based on a single location or a single signal is prone to false alarms; for example, when a vehicle passes under an overpass, the location point may change instantaneously; or when a base station switches, the signal value may drop briefly but recover quickly.

[0089] Therefore, this embodiment improves the judgment stability by using the current time window matching and continuous sampling trend extraction method to upgrade the judgment object from single-point observation to local state observation with time continuity.

[0090] Furthermore, if the current time happens to be at the intersection of two adjacent time windows, the terminal can prioritize selecting the time window that covers the current time.

[0091] If the matching is unstable due to clock drift, a short-term transition tolerance can be introduced to allow the terminal to prioritize the use of the previous valid time window and avoid frequent window jumping. If one or two distorted values ​​appear in continuous sampling, such as invalid positioning points or missing signal values, the terminal can wait for the next valid sample to fill in the gaps before generating the trend result instead of directly calculating the attenuation trend.

[0092] If the current matrix has expired and the new matrix has not yet been issued, the terminal can only retain silent keep-alive sampling and not output deviation judgment based on the failed matrix to avoid incorrect reference;

[0093] Before the vaccine cold chain vehicle entered the mountainous area, the terminal read the location and cellular signal once per second; when the time fell within the time window corresponding to the tunnel approach road, the terminal only extracted the center point of that time window as a reference.

[0094] Although the vehicle is still on the planned road, it is slowly changing lanes along the temporary access road due to the construction ahead, and its actual position is gradually deviating from the original center area, with the degree of deviation continuing to increase.

[0095] Meanwhile, the baseband unit detected a step-like decrease in signal strength over several consecutive sampling cycles; the two local indicators reflect, from the perspectives of spatial and communication environments, that the vehicle has entered a stage requiring close attention.

[0096] The purpose of this step is to enable the terminal to generate deviation representations and blind zone approximation representations with business meaning locally, thereby achieving early identification of location anomalies and network degradation, and providing an executable and interpretable data foundation for subsequent branch triggering.

[0097] In a preferred embodiment of the present invention, in response to a preset trigger condition being met, the communication module is woken up to upload status update data containing the real-time spatial positioning coordinates to a cloud server, including:

[0098] S301. In response to the satisfaction of the preset triggering condition, a wake-up command is triggered to activate the communication module of the mobile terminal;

[0099] S302. Obtain current moving speed and environmental perception data locally on the mobile terminal;

[0100] S303. Encapsulate the real-time spatial positioning coordinates, the current moving speed and the environmental perception data into a data packet to generate the status update data;

[0101] S304. Establish a data link with the cloud server and send the status update data to the cloud server;

[0102] This embodiment provides a data encapsulation and communication wake-up mechanism for triggering reporting; specifically, based on the aforementioned local silent verification, uploading only location coordinates is sometimes insufficient to support the cloud server in making effective scheduling judgments; because if the spatiotemporal deviation is greater than the tolerance radius, it may be that the vehicle detours at high speed or that the vehicle is stuck in a traffic jam at low speed.

[0103] The impact of entering the blind zone on general freight and cold chain vaccine transportation is different. Therefore, after confirming that the preset triggering conditions are met, the mobile terminal also collects the current movement speed and environmental perception data simultaneously, so that the uploaded information is expanded from a single location point to status update data that can be used for scheduling and analysis.

[0104] Mobile terminals can keep their communication modules in a low-activity or sleep state under normal circumstances to reduce power consumption and unnecessary link occupation. When the local branch determines that the preset trigger conditions are met, the processor sends a wake-up command to the communication module, so that the data link of the communication module switches from standby to transmit state.

[0105] The mobile terminal reads the current moving speed corresponding to the current location, as well as environmental perception data; the environmental perception data here may be additional data related to the transportation task, such as the temperature of the compartment, the humidity of the compartment, the opening and closing status of the door magnet, the working status of the refrigeration unit, and the vibration level.

[0106] In cold chain scenarios, location changes and temperature control status often need to be analyzed in conjunction; for example, if a vehicle stays in a service area for a long time and the temperature inside the vehicle begins to fluctuate, the dispatch center needs to determine as soon as possible whether it is a normal resupply stop or a risky stop caused by a malfunction of the refrigeration unit.

[0107] To illustrate the data packet encapsulation process, a specific data packet structure example can be used; suppose the status update data includes field A for recording real-time spatial positioning coordinates, field B for recording the current movement speed, field C for recording the environmental perception data summary, and field D for recording the trigger reason identifier;

[0108] If the triggering reason is that the signal attenuation gradient exceeds the preset attenuation threshold, then field D is recorded as the corresponding mark; if the triggering reason is that the spatiotemporal deviation is greater than the tolerance radius, then field D is recorded as another mark; after receiving the record, the cloud server can directly adopt different processing strategies based on the mark.

[0109] This encapsulation method eliminates the need for the cloud server to communicate repeatedly with the mobile terminal to confirm the triggering cause. Instead, it can directly perform logical parsing based on the triggering identifier and the current transportation stage and environmental status.

[0110] Compared to the solution that only uploads location points, this embodiment, by supplementing the current moving speed and environmental perception data, significantly improves the interpretability of the cloud server for the same event; for example, if the vehicle's spatial displacement is less than a preset displacement threshold, and the current moving speed is lower than a preset low speed threshold for more than a preset duration, and the compartment temperature remains stable, then it is closer to a congestion waiting situation.

[0111] If the position deviates significantly, the speed remains constant, and the door magnet opens and closes abnormally, it may indicate a temporary change of the handover point or loading and unloading operations. For goods with high time sensitivity and strict regulatory requirements, such status update data helps to identify cargo risks in addition to trajectory anomalies.

[0112] Furthermore, if the mobile terminal cannot obtain certain environmental perception data when it triggers reporting, such as the temperature and humidity sensor being offline for a short time, it can still use the method of real-time spatial positioning coordinates + current moving speed + acquired environmental perception data + missing marker to encapsulate the data packet, so that the entire packet is not blocked from being sent due to the abnormality of a single sensor.

[0113] If the communication module successfully wakes up but fails to establish a connection with the cloud server, the mobile terminal can put the status update data into the local sending queue and generate a timestamp so that it can be retransmitted in order later.

[0114] If the triggering cause occurs repeatedly within a short period of time, the mobile terminal can locally merge adjacent repeated events, for example, retaining only the latest report within tens of seconds, in order to suppress communication storms caused by dense jitter.

[0115] In the mountain transportation phase of this embodiment, when the vaccine cold chain vehicle approaches the entrance of a long tunnel, the mobile terminal detects continuous signal attenuation, so it wakes up the communication module and reads the current moving speed, compartment temperature and door magnetic status. At this time, the vehicle speed remains stable, the compartment temperature is maintained within the set cold chain range, and the door magnetic is in the closed state.

[0116] The mobile terminal encapsulates this information and sends it to the cloud server. The cloud server can then confirm that the vehicle is entering the signal blind zone in a normal driving state and that there are no abnormalities in the cargo environment. Therefore, this point can be used as a trusted node before entering the tunnel for subsequent trajectory display and transportation traceability.

[0117] The purpose of this step is to expand the trigger upload from sending data from a single coordinate to a structured status report for logistics scheduling and quality supervision, thereby achieving more targeted link wake-up, more comprehensive cloud server analysis, and easier traceability of cold chain anomalies.

[0118] In a preferred embodiment of the present invention, after receiving the state update data, the cloud server regenerates the expected spatiotemporal envelope matrix for the next stage and updates the trajectory state based on the expected spatiotemporal envelope matrix for the next stage, including:

[0119] S401. The cloud server parses the received status update data and extracts the real-time spatial positioning coordinates as the latest location node;

[0120] S402. Based on the latest location node and the preset path topology, recalculate the estimated arrival time;

[0121] S403. Combine the estimated arrival time with the remaining path topology to regenerate the expected spatiotemporal envelope matrix for the next stage;

[0122] S404. The expected spatiotemporal envelope matrix for the next stage is sent to the mobile terminal to cover the original expected spatiotemporal envelope matrix on the mobile terminal.

[0123] This embodiment provides a mechanism for trajectory replanning and matrix retransmission after receiving events in the cloud. Specifically, in the aforementioned scheme, if the vehicle is only responsible for reporting anomalies to the cloud, and the cloud continues to use the old matrix generated before departure, then as the transportation process evolves, data deviation will occur between the old matrix and the actual transportation status, resulting in a decrease in the accuracy of subsequent trajectory comparison.

[0124] Therefore, this embodiment adds an event-driven regeneration step on the cloud side, so that each valid report can become the basis for refreshing subsequent expectations;

[0125] After receiving the status update data, the cloud server parses the real-time coordinates, speed, environmental perception data and triggering reasons, and extracts the real-time coordinates as the latest location node. In terms of data processing logic, this node is used to remap the spatial coordinates of the deviated expected transportation trajectory to the current valid location.

[0126] Based on the latest location node, the cloud platform recalculates the estimated arrival time by combining the current preset route topology, remaining road segment length, historical speed model, traffic status interface, and destination pickup appointment time period;

[0127] The significance of this recalculation is that it no longer relies on the static prediction at the start, but instead relies on the prediction of the remaining process under the current known state; the cloud takes the latest position node as the starting point, redivides the time window of the remaining path, and generates a new subsequent spatiotemporal envelope matrix;

[0128] A specific data example can be used to illustrate this: if the remaining part of the original matrix... Includes time boundaries based on the corresponding time window sequences. , , central position , , and tolerance radius , , The constructed data structure:

[0129]

[0130] If a vehicle needs to detour during T3, the cloud will no longer mechanically reuse the data. , , Instead, it updates the time boundary, center position, and tolerance range based on the new location node, generating a new data structure:

[0131]

[0132] After receiving the data, the terminal overwrites the old local matrix with the new matrix, and all subsequent silent verifications are based on the updated transportation plan.

[0133] The fundamental purpose of adopting an overlay-based distribution mechanism to replace the multi-version matrix coexistence strategy is to ensure the uniqueness of the instruction source during the logistics execution process; if the old matrix and the new matrix coexist, the mobile terminal is prone to referencing different benchmarks at adjacent sampling times, leading to a fork in the judgment logic.

[0134] Furthermore, if the cloud receives a status packet and finds that key fields are missing, such as an invalid location node or an abnormal timestamp, it can temporarily refrain from generating a new matrix and instead request the terminal to resend the data packet or reuse the most recent valid matrix.

[0135] If the cloud recalculates and finds that the remaining road segment length is less than the preset road segment threshold, for example, if the vehicle has entered the hospital's receiving area, then a complex matrix will no longer be generated, and only a short-cycle closing envelope will be issued until the delivery is completed.

[0136] If a new matrix is ​​sent from the cloud but the terminal confirms the receipt and the timeout occurs, the cloud can push it several times. If the receipt is still not received, the version number is retained and resent when the terminal actively reports next time, so as to avoid inconsistencies in the matrix versions on both sides of the vehicle cloud.

[0137] After the refrigerated truck completed a status update before entering the tunnel, the cloud identified the point as the latest location node and, combined with real-time traffic on the mountain road, discovered that a ramp ahead was closed and that it was necessary to detour via another county road;

[0138] The cloud then recalculated the vehicle's arrival time at the hospital, replacing the original subsequent time window based on highway straight travel with a new time window based on county road detours, and reissued a new subsequent envelope;

[0139] After the vehicle exits the tunnel and continues to run, the terminal no longer compares it with the old route, but performs silent verification according to the updated detour route, thereby avoiding subsequent continuous misjudgment and deviation.

[0140] The purpose of this step is to enable the cloud to shift from passively recording locations to actively correcting transportation expectations, thereby ensuring that the trajectory reference continuously matches the actual transportation process and guaranteeing consistency between subsequent deviation judgments, arrival predictions, and trajectory displays on the user end.

[0141] In a preferred embodiment of the present invention, it further includes: S501, a cloud server monitors the triggering frequency of data upload triggered by the mobile terminal within a preset time window due to the spatiotemporal deviation being greater than the tolerance radius;

[0142] S502. Determine whether the triggering frequency is higher than a preset frequency threshold.

[0143] S503. In response to the triggering frequency being higher than the preset frequency threshold, confirming that the current positioning error meets the preset frequent false triggering conditions, when generating the expected spatiotemporal envelope matrix for the next stage, multiply the tolerance radius by a preset compensation coefficient greater than 1 to generate an updated tolerance radius.

[0144] S504. In response to the trigger frequency being lower than or equal to the preset frequency threshold, maintain the current tolerance radius calculation logic.

[0145] This embodiment provides an adaptive compensation mechanism for tolerance radius for frequent false triggering; specifically, in the aforementioned scheme, if the expected spatiotemporal envelope matrix is ​​set too ideally, the problem of repeated data uploads may occur in a short period of time in certain special road sections;

[0146] Especially on urban elevated roads, mountain roads, and between hospital buildings, positioning signals are easily affected by multipath reflections. The vehicle is actually still traveling along the correct road, but the positioning point coordinates fluctuate alternately on both sides of the preset tolerance range. If the original tolerance radius is still used, the mobile terminal will be forced to report frequently, which not only increases the communication pressure, but also makes the cloud server mistakenly think that the vehicle is continuously abnormal.

[0147] To this end, the cloud server monitors the trigger frequency of data uploads triggered by a mobile terminal within a preset time window due to the spatiotemporal deviation exceeding the tolerance radius;

[0148] The frequency statistics do not cover all data upload events, but are limited to upload actions caused by exceeding the spatiotemporal deviation limit. This is because this specific event type can intuitively represent the severity of the current tolerance radius setting.

[0149] If the triggering frequency is determined to be higher than the preset frequency threshold, the cloud server does not immediately determine that the vehicle is actually repeatedly veering off course, but further determines that there are frequent false reports of deviation.

[0150] In this case, when the cloud server regenerates the expected spatiotemporal envelope matrix for the next stage, it multiplies the tolerance radius of the current stage or adjacent stages by a preset compensation coefficient greater than 1 to generate an updated tolerance radius, thereby appropriately widening the envelope boundary and absorbing positioning jitter.

[0151] This can be illustrated with a specific data compensation example; suppose that for a certain road segment, two rows of matrix data were originally issued, namely [time window sequence A, center point], ... Tolerance radius [Time window sequence B, center point] and [time window sequence B, center point] Tolerance radius ];

[0152] Within a short period of time, the mobile terminal repeatedly triggered uploads due to the spatiotemporal deviation exceeding the tolerance radius. However, after verification by the cloud server, it was found that the vehicle speed, road direction, and subsequent location nodes were generally consistent with the planned route, indicating that there were frequent false alarms and deviations.

[0153] At this point, when the cloud server generates the next version of the matrix, it adjusts the tolerance radius to the updated value. and ,in and The values ​​are respectively greater than and The initial value; the envelope matrix is ​​updated to match the dynamic positioning error characteristics of the road segment by updating the tolerance radius;

[0154] Furthermore, the setting of the aforementioned preset compensation coefficient does not rely on unexplainable static empirical values, but is dynamically driven by the actual drift state.

[0155] Specifically, the cloud server will calculate the average deviation distance of multiple positioning points that cause false triggering relative to the current expected center position within a preset time window, and perform a proportional calculation or difference with the basic positioning error baseline corresponding to the road segment to derive the preset compensation coefficient greater than 1.

[0156] This structured processing ensures that the compensation relaxation operation closely matches the actual severity of environmental interference, so that the updated tolerance radius can effectively cover and filter out high-frequency positioning noise interference in the area, thereby suppressing false triggering while preventing the envelope range from expanding indefinitely and becoming ineffective.

[0157] Compared to the basic solution, this embodiment adds a layer of negative feedback regulation capability to the cloud server; the system eliminates the static logic that absolutely determines each time the spatiotemporal deviation exceeds the limit as a path deviation, and instead allows the cloud server to identify environmental errors from the statistical phenomenon of trigger frequency;

[0158] For cold chain transportation, the technical effect of the mechanism is to accurately identify route deviation, time delay or cold chain environment abnormalities, thereby eliminating position drift noise within a preset distance range generated by the positioning module;

[0159] Furthermore, if the triggering frequency is high but is accompanied by abnormal vehicle speed, sudden change in direction, or abnormal door sensor, it is not advisable to simply relax the tolerance radius, because it is more likely to be a real yaw or abnormal stop.

[0160] If a mobile terminal is in a state of high false triggering for a long time, the cloud server can also add a device health abnormality flag to the terminal, prompting the maintenance personnel to check its positioning module antenna, installation location, or firmware status.

[0161] If the trigger frequency is lower than or equal to the preset frequency threshold, the current tolerance radius calculation logic is maintained without compensation, so as to prevent the envelope from being amplified without principle and reducing the sensitivity of anomaly identification.

[0162] When the vaccine cold chain truck entered the vicinity of the county hospital campus, there were many high-rise buildings and overpasses around the hospital area, and the location point repeatedly shifted between the main road and the auxiliary road of the hospital area.

[0163] The mobile terminal therefore repeatedly determined that the spatiotemporal deviation was greater than the tolerance radius in a short period of time and uploaded status update data. However, the cloud server, based on the current moving speed, subsequent arrival nodes and door sensor status, found that the vehicle was actually moving along the delivery channel and had not actually deviated from its course.

[0164] Therefore, when the cloud server regenerates the expected spatiotemporal envelope matrix for the next stage, it compensates and relaxes the tolerance radius near the hospital's receiving area, so that the mobile terminal will not trigger repeatedly due to small-scale drift.

[0165] The purpose of this mechanism is to suppress invalid concurrency caused by positioning noise through statistical feedback from the cloud server, thereby achieving a dynamic balance between anomaly identification sensitivity and system stability, and reducing link occupation and scheduling interference caused by false alarms.

[0166] In a preferred embodiment of the present invention, it further includes: S601, the cloud server detects the validity status of the preset path topology in real time;

[0167] S602. In response to the failure of the preset path topology, it is determined that a valid expected spatiotemporal envelope matrix cannot be generated.

[0168] S603, Send a downgrade control command to the mobile terminal;

[0169] S604. After receiving the degradation control instruction, the mobile terminal closes the conditional branch judgment logic and degrades the upload logic of the status update data to a fixed-period timed polling mode.

[0170] This embodiment provides a degradation control mechanism in the case of path topology failure; specifically, the aforementioned scheme is based on the premise that the cloud can continuously provide an effective spatiotemporal envelope;

[0171] However, in real-world logistics scenarios, extreme conditions may still occur that cause the entire route topology to fail, such as sudden landslides in mountainous areas leading to the closure of the original highway, temporary detours opened by local authorities, temporary changes to the unloading entrance at the destination hospital, and newly built connecting roads not yet included in electronic maps.

[0172] In this situation, if the cloud continues to forcibly generate envelopes according to the old topology, the terminal will be in a state of deviating from the limit almost continuously, which will make the abnormal driving mechanism meaningless.

[0173] To address this issue, the cloud server monitors the validity of the preset route topology in real time. This validity monitoring is based on a combination of factors, including road accessibility data from the map service, road closure notices from the traffic management platform, consistency between historically reported routes and the current planned route, and collective deviations by other transport vehicles in the same area.

[0174] Once the cloud confirms that the original path topology has failed or is no longer sufficient to support the generation of a reliable spatiotemporal envelope matrix, it will no longer send new envelopes to the vehicle, but will instead actively send downgrade control commands.

[0175] After receiving the data, the mobile terminal disables the conditional branch decision logic based on spatiotemporal deviation and signal attenuation gradient, and degrades the upload mode to fixed-period timed polling.

[0176] The reason for choosing to degrade to a fixed period, rather than letting the terminal completely stop reporting during the failure period, is that logistics supervision requires basic continuity more during abnormal periods; at this time, although the cloud cannot send the expected location reference to the terminal, it still needs to obtain the real-time spatial positioning coordinates of the mobile terminal.

[0177] Although fixed-period polling is less efficient, it is reliable when reference benchmarks are missing, ensuring that the trajectory remains unbroken and the cold chain status is continuously reported, making it easier for dispatchers to continuously monitor the status of the route with manual intervention.

[0178] This switching relationship can be illustrated with a specific example. Under normal circumstances, the terminal executes a local sampling-condition judgment-on-demand upload mode. After entering the degraded state, the terminal executes a local sampling-upload mode at fixed intervals. For example, in the collaborative mode, long-cycle sparse reporting can be maintained, while in the degraded mode, it degenerates into high-frequency continuous polling based on fixed short cycles until the cloud restores the effective topology and reissues the usable matrix. After the terminal receives the recovery instruction, it switches back to the abnormal-driven collaborative mode.

[0179] Furthermore, if the cloud detects that the topology may be invalid but the evidence is not yet sufficient, a short-term transition strategy can be issued first, such as temporarily shortening the keep-alive time or increasing the polling frequency, instead of immediately downgrading to a full version; if the terminal still receives the old version matrix in the downgraded state, the downgrade control command should be given priority to prevent the simultaneous execution of two sets of mutually exclusive logic.

[0180] If the cloud restores a valid path but the terminal fails to receive the restoration command in time, the terminal will continue to operate in degraded mode until it receives a new valid matrix and mode switching flag, so as to ensure that the system behavior is predictable. If the terminal itself has a storage abnormality that prevents it from saving the degrade flag, it can include the current mode status when uploading at each fixed period, so that the cloud can verify it and resend the control command if necessary.

[0181] In the latter part of the vaccine transportation in this embodiment, a sudden rockfall in the mountainous area caused the original planned county road to be closed. The traffic police temporarily guided all cold chain vehicles to detour via an emergency access road that was not yet included in the electronic map. The cloud, combining multiple vehicle trajectories and road announcements, determined that the original path topology was invalid and could not continue to generate a reliable envelope. Therefore, it immediately issued a downgrade control command to the vehicle.

[0182] Once the vehicle receives the information, it stops making conditional judgments based on spatiotemporal deviation and signal attenuation, and instead uploads location, speed, and compartment temperature information at fixed intervals. After the cloud obtains a new valid route, it reissues a new spatiotemporal envelope matrix and restores the abnormal driving mode of cloud-edge collaboration.

[0183] The purpose of this mechanism is to provide the system with a clear fallback path for exceptions, so as to maintain the continuous updating of basic logistics information and ensure uninterrupted transportation supervision and cold chain traceability even in scenarios such as the failure of preset routes, map reference distortion, or sudden road changes.

[0184] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A cloud-based collaborative logistics information updating method, characterized in that, include: S100. Obtain the preset path topology and historical road conditions and speed from the cloud server, construct multiple time window sequences including start and end times, calculate the target center latitude and longitude and tolerance radius for each time window sequence, encapsulate the start and end times, the target center latitude and longitude and the tolerance radius into a matrix, generate the expected spatiotemporal envelope matrix, and send the expected spatiotemporal envelope matrix to the mobile terminal. S200: The mobile terminal locally acquires real-time spatial positioning coordinates, communication network signal strength within a continuous sampling period, and the current silence duration since the vehicle started or after the last data upload. Based on the real-time spatial positioning coordinates and the latitude and longitude of the target center in the expected spatiotemporal envelope matrix, the spatiotemporal deviation is calculated, and the signal attenuation gradient is calculated based on the communication network signal strength. S300. Perform conditional branch determination for the spatiotemporal deviation and the signal attenuation gradient: In response to the spatiotemporal deviation being greater than the tolerance radius, or the signal attenuation gradient exceeding the preset attenuation threshold, or the current silence duration reaching the preset maximum keep-alive time, determine that the preset trigger condition is met, wake up the communication module to upload the status update data containing the real-time spatial positioning coordinates to the cloud server; otherwise, determine that the preset trigger condition is not met and maintain the local silence verification state. S400: After receiving the status update data, the cloud server regenerates the expected spatiotemporal envelope matrix for the next stage and updates the trajectory status based on the expected spatiotemporal envelope matrix for the next stage. 2.The cloud-based collaborative logistics information updating method of claim 1, wherein, The spatiotemporal deviation is calculated based on the real-time spatial positioning coordinates and the target center latitude and longitude in the expected spatiotemporal envelope matrix, and the signal attenuation gradient is calculated based on the communication network signal strength, including: S201. Read the real-time spatial positioning coordinates and communication network signal strength locally on the mobile terminal at a preset sampling period; S202. Extract the target center latitude and longitude corresponding to the current time window from the expected spatiotemporal envelope matrix; S203. Call the local projection conversion ratio to convert the real-time spatial positioning coordinates and the latitude and longitude of the target center into displacement in the local plane rectangular coordinate system, and calculate the Euclidean distance difference between the two points based on the displacement to generate the spatiotemporal deviation. S204. Extract the signal strength of the communication network within a continuous sampling period, calculate the rate of change of signal strength, and generate the signal attenuation gradient. 3.The cloud-based collaborative logistics information updating method of claim 1, wherein, In response to the fulfillment of preset trigger conditions, the wake-up communication module uploads status update data containing the real-time spatial positioning coordinates to the cloud server, including: S301. In response to the satisfaction of the preset triggering condition, a wake-up command is triggered to activate the communication module of the mobile terminal; S302. Obtain current moving speed and environmental perception data locally on the mobile terminal; S303. Encapsulate the real-time spatial positioning coordinates, the current moving speed and the environmental perception data into a data packet to generate the status update data; S304. Establish a data link with the cloud server and send the status update data to the cloud server.

4. The cloud-based collaborative logistics information update method according to claim 1, characterized in that, After receiving the status update data, the cloud server regenerates the expected spatiotemporal envelope matrix for the next stage and updates the trajectory status based on the expected spatiotemporal envelope matrix for the next stage, including: S401. The cloud server parses the received status update data and extracts the real-time spatial positioning coordinates as the latest location node; S402. Based on the latest location node and the preset path topology, recalculate the estimated arrival time; S403. Combine the estimated arrival time with the remaining path topology to regenerate the expected spatiotemporal envelope matrix for the next stage; S404. The expected spatiotemporal envelope matrix for the next stage is sent to the mobile terminal to cover the original expected spatiotemporal envelope matrix on the mobile terminal.

5. The cloud-based collaborative logistics information update method according to claim 1, characterized in that, Also includes: S501. The cloud server monitors the trigger frequency of data uploads triggered by the mobile terminal within a preset time window due to the spatiotemporal deviation exceeding the tolerance radius; S502. Determine whether the triggering frequency is higher than a preset frequency threshold. S503. In response to the triggering frequency being higher than the preset frequency threshold, confirming that the current positioning error meets the preset frequent false triggering conditions, when generating the expected spatiotemporal envelope matrix for the next stage, multiply the tolerance radius by a preset compensation coefficient greater than 1 to generate an updated tolerance radius. S504. In response to the trigger frequency being lower than or equal to the preset frequency threshold, maintain the current tolerance radius calculation logic.

6. The cloud-based collaborative logistics information update method according to claim 1, characterized in that, Also includes: S601. The cloud server detects the validity status of the preset path topology in real time; S602. In response to the failure of the preset path topology, it is determined that a valid expected spatiotemporal envelope matrix cannot be generated. S603, Send a downgrade control command to the mobile terminal; S604. After receiving the degradation control instruction, the mobile terminal closes the conditional branch judgment logic and degrades the upload logic of the status update data to a fixed-period timed polling mode.

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