Multi-party data collaborative logistics data management system

Abstract

The invention relates to the technical field of logistics data collaborative management, in particular to a multi-party data collaborative logistics data management system, which is characterized in that a multi-source data acquisition unit acquires cargo theoretical weight of a cargo owner end, vehicle-mounted weighing weight of a 3PL end and re-weighing weight data of a storage end, and binds a data aperture label for each type of data; the time sequence association processing unit aligns a time sequence through an asymmetric time window matching mechanism, sets a double-time-efficiency threshold value, triggers a time sequence fault mark and generates an association compensation signal when vehicle-mounted data exceeds tolerance, and the caliber time sequence two-dimensional storage unit binds three types of data through a transportation batch number by means of a dynamic association index. The cross-equipment benchmark is unified through an error transmission network, original and calibration data and compensation signals are stored, an intelligent difference analysis unit matches an allowable loss threshold according to an aperture label, and when a fault mark is activated, environmental parameters are called to carry out weighted calculation on data credibility, an attribution conclusion is output, and logistics data management efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of logistics data collaborative management, in particular to a logistics data management system with multi-party data collaboration. BACKGROUND

[0002] Logistics data collaborative management is an important technology, which is specifically applied to the whole-process management of cargo weight data in the multi-party collaboration scene of cargo owners, third-party logistics (3PL) and warehousing, and the core is to realize accurate judgment and responsibility tracing of logistics loss by integrating multi-party weight data, to adapt to the needs of modern logistics multi-party collaboration and data driving, and to promote the transformation of logistics data management from scattered records to integrated collaboration. In current logistics data management, the cargo physical theoretical weight of the cargo owner end, the vehicle-mounted weighing weight of the 3PL end and the reweighing weight of the warehousing end are often collected and stored separately, neither of which is forced to bind a unified data caliber label for various types of weight data, nor is the weighing reference of different devices chaotic, resulting in a lack of comparability of the data, and a lack of a time sequence alignment mechanism that adapts to the logistics scene, which easily causes data chain breakage due to delayed uploading of vehicle-mounted data, misplacement of reweighing data and loading event time, and without marking and compensating for time sequence anomalies, the storage time also does not establish a deep logical association of the three types of data through the transportation batch number, and the subsequent difference analysis only relies on a fixed loss threshold without considering the influence of time sequence anomalies and environmental interference on data reliability. These problems ultimately lead to distortion of the weight difference attribution, either misjudging device errors and time sequence deviations as cargo loss, or missing real transportation loss, making it difficult to support the loss responsibility definition of multi-party collaboration, and unable to meet the management needs of logistics data. In order to solve this technical problem, we provide a logistics data management system with multi-party data collaboration. SUMMARY

[0003] The present application aims to provide a logistics data management system with multi-party data collaboration to solve the problems raised in the background art.

[0004] To achieve the above-mentioned purpose, a logistics data management system with multi-party data collaboration is provided, comprising: The multi-source data acquisition unit 1 acquires the cargo physical theoretical weight data of the cargo owner end, the vehicle-mounted weighing weight data of the 3PL end and the reweighing weight data of the warehousing end, and forces to bind a preset data caliber label for each type of weight data; The time sequence association processing unit 2 realizes the time sequence alignment of each type of weight data through a non-symmetrical time window matching mechanism of dynamic time limit threshold, sets a first time limit threshold to match the loading event timestamp for the vehicle-mounted weighing weight data, sets a second time limit threshold to match the warehousing event timestamp for the reweighing weight data, and when it is detected that the vehicle-mounted data timestamp uploaded by the 3PL exceeds the preset tolerance range of the loading event timestamp, automatically triggers the time sequence fault marking and generates an associated compensation signal. The caliber time sequence dual-dimension storage unit 3 stores three types of weight data, and uses a dynamic correlation index structure to realize logical binding of the theoretical weight, the vehicle-mounted weighing weight and the reweighing weight of the same batch of goods through the transport batch number, and simultaneously stores original data with caliber labels, calibrated data and correlation compensation signals, wherein the calibrated data realizes cross-device reference unification through a preset caliber conversion coefficient; The intelligent difference analysis unit 4 automatically matches the allowable loss threshold based on the data caliber label of the current goods, and when the time sequence fault marker is activated, automatically calls the original environmental parameters of the vehicle-mounted weighing weight data in the loading period to perform data credibility weighted calculation, and outputs an attribution conclusion.

[0005] Compared with the prior art, the beneficial effects of the present application are: The present application realizes cross-device reference unification by forcibly binding data caliber labels for the theoretical weight of the consignee end, the vehicle-mounted weighing weight of the 3PL end and the reweighing weight of the warehouse end, and realizes cross-device reference unification through an error propagation network, uses a dynamic time threshold asymmetric time window matching mechanism to complete time alignment and generate a time sequence fault marker and a correlation compensation signal, binds three types of data through a dynamic correlation index structure with the transport batch number as the key, realizes difference attribution by combining data credibility weighted calculation and dynamic allowable loss threshold matching, and achieves the effects of caliber unification of multi-party weight data and accurate difference attribution, effectively solves the problem that multi-party weight data caliber is chaotic and cannot support the definition of logistics loss responsibility, and improves the efficiency of logistics data management. BRIEF DESCRIPTION OF DRAWINGS

[0006] Figure 1 The present application provides a kind of overall block diagram.

[0007] The meaning of each label in the figure is: 1, multi-source data acquisition unit; 2, time sequence correlation processing unit; 3, caliber time sequence dual-dimension storage unit; 4, intelligent difference analysis unit. DETAILED DESCRIPTION

[0008] The technical solutions in the embodiments of the present application will be described clearly and completely in the embodiments of the present application combined with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0009] The present application provides a kind of multi-party data collaborative logistics data management system, please refer to Figure 1 As shown in the figure, it includes: The multi-source data acquisition unit 1 respectively collects the theoretical weight data of the consignor end, the vehicle-mounted weighing weight data of the 3PL end and the reweighing weight data of the warehouse end, and forcibly binds a preset data caliber label for each type of weight data; The time sequence correlation processing unit 2 realizes the time sequence alignment of each type of weight data through a dynamic time threshold asymmetric time window matching mechanism, sets a first time threshold to match the loading event timestamp for the vehicle-mounted weighing weight data, sets a second time threshold to match the warehousing event timestamp for the reweighing weight data, when it is detected that the vehicle-mounted data timestamp uploaded by the 3PL exceeds the preset tolerance range of the loading event timestamp, automatically triggers the time sequence fault mark and generates a correlation compensation signal; The caliber time sequence two-dimensional storage unit 3 stores three types of weight data, and uses a dynamic correlation index structure to logically bind the theoretical weight, vehicle-mounted weighing weight and reweighing weight of the same batch of goods through the transportation batch number, and stores the original data, calibrated data and correlation compensation signal with caliber labels, wherein the calibrated data realizes cross-device reference unification through a preset caliber conversion coefficient; The intelligent difference analysis unit 4 automatically matches the allowable loss threshold based on the data caliber label of the current goods, when the time sequence fault mark is activated, automatically calls the original environmental parameters of the vehicle-mounted weighing weight data in the loading period for data credibility weighted calculation, and outputs the attribution conclusion.

[0010] The specific process of the time sequence correlation processing unit 2 to realize time sequence alignment is as follows: A time sequence coordination pipeline is created with the transportation batch number as the index, the theoretical data area, the vehicle-mounted data area and the reweighing data area are divided in the pipeline, the system submission timestamp of the consignor's delivery order is taken as the fixed anchor point of the theoretical data area, the loading completion moment recorded by the 3PL loading device is taken as the dynamic anchor point of the vehicle-mounted data area, and the moment when the warehouse weighbridge triggers stable weighing is taken as the rigid anchor point of the reweighing data area, and the non-symmetrical sliding window is expanded to align the interval, wherein the vehicle-mounted data area is expanded to form a first time threshold window to the pre-loading period with the dynamic anchor point as the center, the reweighing data area is compressed to form a second time threshold window to the post-warehousing period with the rigid anchor point as the center, when the actual arrival time of the vehicle-mounted data deviates from the dynamic anchor point, the window boundary is automatically contracted to the effective working period of the loading sensor, and the window starting point of the reweighing data area is adjusted to maintain the data chain continuity.

[0011] The construction and matching of the first time threshold value contains a triple dynamic adjustment layer, the basic threshold layer generates an initial time tolerance band according to the distribution law of historical vehicle-mounted device transmission delay, the environment adaptation layer real-time collects satellite positioning signal strength, vehicle-mounted weighing platform horizontal inclination and engine vibration frequency in the loading site, scales the tolerance band width, and the event calibration layer accurately locks the loading event time stamp through a double event stamp verification mechanism, that is, first, an operation event stamp is generated by scanning the electronic license plate of the transport tool by the 3PL operator, and then a physical event stamp is generated by the median time of three stable readings of the vehicle-mounted weighing sensor, when the deviation between the two event stamps exceeds the preset tolerance range, the sensor data playback verification is automatically triggered, the physical event stamp is used to cover the operation event stamp as the final dynamic anchor point, and the deviation value is injected into the time sequence coordination pipeline.

[0012] The matching of the second time threshold value is spatiotemporally fused: The time dimension takes the warehouse management system reservation storage time as the starting point, and the space dimension captures the transport tool parking coordinates through the platform laser positioning device, when the transport tool parking position deviates from the standard weighing area, the time threshold value is proportionally expanded according to the position offset, and the ground scale pressure sensor data stream is monitored, if the cargo unloading time exceeds the reservation period, the single time threshold window is split into a preparation window and an execution window, the execution window termination time is compared with the dynamic anchor point, if the interval exceeds the preset threshold, a conflict alarm is sent to the time sequence coordination pipeline.

[0013] The generation of the time sequence fault marker adopts an event chain driven strategy: When it is detected that the vehicle-mounted data time stamp exceeds the tolerance range, a four-stage fault evaluation process is started, first, it is judged whether the deviation is within the basic threshold layer range, if not, a conflict alarm signal is activated, and it is verified whether the vehicle-mounted data packet contains a physical event stamp, if not, it is marked as anchor point failure, then the execution window termination time is compared with the actual data arrival time, if there is reverse time sequence, it is marked as time sequence inversion, finally, a composite time sequence fault marker is generated by synthesizing the abnormal levels of the previous three types, the marker carries the fault type code, the deviation duration and the influence range coefficient, and is embedded in the time sequence coordination pipeline metadata layer.

[0014] The generation of the associated compensation signal executes double-source compensation fusion: The physical compensation source extracts the vibration frequency spectrum features at the loading moment through vehicle-mounted sensor playback data, converts it into a warehouse ground scale frequency response equivalent compensation value, and the time sequence compensation source calculates the time stamp correction increment according to the deviation duration, when it is marked as anchor point failure, the physical compensation source is preferentially used to reconstruct the vehicle-mounted data reference value, when it is marked as time sequence inversion, the time sequence compensation source is used to correct the weighing data storage sequence, and finally an associated signal carrying the compensation type identifier is generated, which is written into the time sequence coordination pipeline and the caliber time sequence two-dimensional storage unit 3 simultaneously.

[0015] The logical binding of the dynamic association index structure realizes three-level event-driven: The main index layer responds to the shipment end event to create an index framework containing theoretical data slots with the transport batch number as the key. The sub-index layer captures the loading completion event to create a vehicle-mounted data slot and mount a compensation signal slot in the framework. The meta-index layer responds to the warehousing completion event to create a re-calling data slot and establish a bidirectional link with the compensation signal slot. The binding process uses a data slot activity detection mechanism. When the time sequence fault marker is updated, the associated data slot recalibration process is automatically activated, and the compensation signal is called to recalculate the logical association strength of the vehicle-mounted and re-calling data, and the index tree branch weight is dynamically adjusted.

[0016] The cross-device benchmarking is realized through an error propagation network: A device precision level chain is constructed, with the shipper inventory system as the first-level benchmark node, the 3PL vehicle-mounted device as the second-level propagation node, and the warehouse weight bridge as the third-level terminal node. The unified process is executed in two stages. The static stage generates a theoretical error propagation matrix based on the device calibration certificate, and the dynamic stage injects a compensation signal as a real-time correction factor. Finally, the calibration data is output, and the original node precision label and error propagation path are retained in the index structure.

[0017] The automatic matching of allowable loss thresholds uses a rule engine coupling strategy: The basic rule layer loads the preset threshold template based on the cargo classification code, and the dynamic rule layer selects the judgment mode based on the caliber label combination. The theoretical caliber applies a full-range loss threshold to the re-calling caliber, and the vehicle-mounted caliber applies a section loss threshold to the re-calling caliber. When the time sequence fault marker is detected, the threshold boundary is dynamically expanded according to the marker level, and the expansion amplitude is positively correlated with the compensation signal strength. At the same time, the device precision level chain data is called to add a device reliability attenuation coefficient to the threshold calculation of the second-level node.

[0018] The data reliability weighted calculation executes three-dimensional factor fusion: The time dimension factor generates a time decay coefficient based on the time sequence fault marker level, the device dimension factor assigns a benchmark weight based on the device level chain, and the environment dimension factor extracts abnormal parameters of the loading sensor to generate an environmental interference index. The vehicle-mounted data is used as the main input stream, and the re-calling data is used as the verification stream. The three-dimensional factors dynamically adjust the decision weight of the main input stream. When the threshold expansion mechanism is activated, the environmental dimension factor's decision priority is automatically reduced, and the re-calling data after compensation is used as the final attribution benchmark.

[0019] It needs to be further explained that after the multi-source data acquisition unit 1 completes the collection of the consignor end physical weight data, the 3PL end vehicle-mounted weighing weight data and the warehouse end reweighing weight data, and forcibly binds the preset data caliber label for each type of data, the time sequence correlation processing unit 2 needs to eliminate the time deviation of different source data through time sequence alignment. If the three types of weight data are not synchronized in time, the subsequent difference analysis will misjudge the numerical change caused by the time difference as the loss of goods. The specific implementation is as follows: A time sequence coordination pipeline is created with a transportation batch number as an index, the transportation batch number is a unique identification code of each batch of goods in the whole logistics process, which can accurately associate three types of weight data of the same batch of goods, and the time sequence coordination pipeline is a special data processing channel for integrating multi-source data of the same batch, which can avoid confusion of different batch data. In the pipeline, it is divided into three independent partitions of theoretical data area, vehicle-mounted data area and reweighing data area according to the data source, and the corresponding type of weight data and the associated caliber label and timestamp information are stored respectively to ensure clear data classification. The time anchor points of the three partitions are set respectively, the anchor point is the reference time point of time sequence alignment, which needs to be set differently according to the generation characteristics of different data. The theoretical data area adopts a fixed anchor point, and the submission timestamp of the consignor's delivery order in the system is used as the reference, which is automatically generated by the consignor's system and does not change with subsequent operations, ensuring the stability of the time reference of the theoretical data. The vehicle-mounted data area adopts a dynamic anchor point, and the completion moment of loading recorded by the 3PL loading equipment is used as the reference. The completion moment of loading refers to the moment when the last piece of goods is loaded into the vehicle and triggers the first stable reading of the vehicle-mounted weighing sensor, which changes dynamically with the actual loading progress and fits the generation logic of the vehicle-mounted data. The reweighing data area adopts a rigid anchor point, and the moment of stable weighing triggered by the warehouse weighbridge is used as the reference. The stable weighing moment refers to the moment when the goods are completely unloaded to the weighbridge and the pressure sensor value is continuously stable for 3 seconds without fluctuation, which is automatically determined by the weighbridge system and has strong objectivity. After completing the anchor point setting, the alignment interval is extended through the asymmetric sliding window, which refers to adjusting the window expansion direction and range according to the data characteristics to avoid alignment deviation caused by uniform window. Among them, the vehicle-mounted data area is expanded to the pre-loading period with the dynamic anchor point as the center to form a first time threshold window. Since there may be pre-weighing and batch weighing during the loading process, expanding to the pre-loading period can cover all vehicle-mounted weighing data related to the current loading to ensure no omission. The reweighing data area is compressed to the post-warehouse period with the rigid anchor point as the center to form a second time threshold window. The reweighing data needs to be collected as soon as possible after the goods are warehoused, otherwise the weight may change due to environmental factors, so it is compressed to the post-warehouse period to avoid invalid data. When the actual arrival time of the vehicle-mounted data deviates from the dynamic anchor point, the system will automatically contract the boundary of the first time threshold window to the effective working period of the loading sensor, which is the time range when the vehicle-mounted weighing sensor is powered on and in normal working state, excluding invalid data during the non-working period of the sensor. At the same time, the window start point of the reweighing data area is adjusted, such as postponing the start point of the second time threshold window from "11:40:15" to "11:42:15", to ensure the continuity of the time chain of vehicle-mounted data and reweighing data, and avoid the disassociation of the two types of data due to the delay of vehicle-mounted data. After determining the core anchor point and window logic of time sequence alignment, the construction and matching mechanism of the first time threshold need to be further refined, the first time threshold directly determines the effective range of the vehicle-mounted data,It needs to be ensured through three dynamic adjustment layers that it is adapted to different working conditions, and false judgment or missed judgment caused by a single threshold is avoided. The specific implementation is as follows: The first is a basic threshold layer, the core of which is to determine an initial time tolerance band based on historical data. The system will call the transmission delay data of the same 3PL and the same type of vehicle-mounted device in the past three months, count the distribution of these delay data, and expand the upper limit of the distribution range by 20% as the initial time tolerance band. This ensures that the initial threshold can cover most normal delay situations and provides a basic range for subsequent adjustment. The second is an environment adaptation layer. The system will collect three key environmental parameters in real time. The first is the satellite positioning signal strength. If the signal strength is lower than -90dBm, the tolerance band width will be expanded by 30%. The second is the horizontal inclination angle of the vehicle-mounted weighing platform. For every 0.1° of inclination, the tolerance band width will be expanded by 5%. The third is the engine vibration frequency. For every 10Hz of frequency, the tolerance band width will be expanded by 10%. Through real-time feedback of these parameters, the tolerance band can dynamically adapt to the on-site environment, avoiding the delay caused by environmental interference from being misjudged as a time sequence anomaly. The third is an event calibration layer. Through a double-event stamp verification mechanism, the loading event timestamp is accurately locked, and human operation errors are eliminated. First, the 3PL operator scans the electronic license plate of the transport tool with a handheld terminal after loading is completed to generate an operation event stamp. This timestamp reflects the time when the operator confirms that the loading is completed. At the same time, the vehicle-mounted weighing sensor will continuously collect weight readings three times after loading is completed. The median value of these three readings corresponds to the time as the physical event stamp. This timestamp directly reflects the physical time when the weight is stable, and is more objective. The system will compare the deviation between the operation event stamp and the physical event stamp. If the deviation exceeds the preset tolerance range, the sensor data playback verification will be automatically triggered, the original data of the vehicle-mounted sensor in this period will be retrieved, and the median time when the weight is stable will be re-confirmed. Finally, the physical event stamp is used to cover the operation event stamp as the final dynamic anchor point of the vehicle-mounted data area, and the deviation value of the two event stamps is injected into the time sequence coordination pipeline to provide a reference for subsequent data credibility analysis.

[0020] After the construction and matching of the first time limit threshold are completed, the time sequence correlation processing unit 2 needs to perform spatiotemporal fusion constraints on the second time limit threshold of the complex weighing data. The effectiveness of the complex weighing data is not only related to time, but also affected by the space factors such as the location of the transport tool and the unloading progress of the goods. Relying on time dimension constraints alone is easy to miss the time sequence dislocation caused by spatial deviation. The specific implementation is as follows: The matching of the second aging threshold takes the spatio-temporal two-dimensional constraint as the core. First, the reference starting point is established in the time dimension, taking the warehouse entry time pre-ordered by the consignor in the warehouse management system as the reference. This time is generated by the consignor side according to the estimated transportation distance, confirmed by the warehouse system, and serves as the time reference for data collection. The initial aging window range is determined from this starting point. The spatial dimension is captured by the platform laser positioning device, which captures the stopping coordinates of the transport tool. The platform laser positioning device is a laser ranging sensor installed on both sides of the warehouse platform, which can capture the three-dimensional coordinates of the transport tool carriage edge in real time. The standard weighing area is the pre-determined effective weighing range of the platform. When the captured stopping coordinates deviate from this range, the system will proportionally expand the time threshold according to the position offset. For every 0.1m increase in the offset, the time threshold is expanded by 2 minutes. In the above case of 0.5m deviation, the initial 30-minute window is expanded to 40 minutes, ensuring that the transport tool still has sufficient time to complete the reweighing after adjusting the stopping position. At the same time, the system will monitor the data flow of the platform pressure sensor in real time. If the unloading time of the goods exceeds the pre-ordered period, the single aging window will be split into a preparation window and an execution window. The preparation window corresponds to the goods unloading preparation phase, and the execution window corresponds to the actual reweighing phase. After splitting, only the data within the execution window participates in the time sequence alignment. Finally, the termination time of the execution window is compared with the dynamic anchor point of the vehicle-mounted data area across the area. If the interval between the two exceeds the pre-set threshold, it is determined that the reweighing data and the vehicle-mounted data time sequence are out of sync, and a conflict alarm is sent to the time sequence coordination pipeline, prompting the staff to check whether there is an abnormality in the transportation link. After the spatio-temporal fusion constraint matching of the second aging threshold is completed, if the time sequence association processing unit 2 detects that the vehicle-mounted data timestamp uploaded by the 3PL exceeds the tolerance range of the first aging threshold, an event chain driven strategy is used to generate a time sequence fault marker. This strategy uses multi-stage evaluation to progressively identify fault types, ensuring accurate identification and avoiding abnormal mislabeling caused by single-dimensional determination. The specific implementation is as follows: When the timestamp deviation is detected, the system immediately starts a four-stage fault evaluation process. The first stage is to determine whether the deviation is within the basic threshold layer range. The basic threshold layer range is the initial time tolerance band generated by the first aging threshold value. If the on-board data timestamp deviation is 610 seconds, it is not within the range, and the conflict warning signal is automatically activated, prompting that there is a serious delay in data transmission. The second stage checks whether the on-board data packet contains a physical event timestamp. The physical event timestamp is the median time of three consecutive stable readings of the on-board weighing sensor. If the event timestamp is missing in the data packet, it is marked as anchor point failure, indicating that the on-board data has lost a reliable time reference. The third stage compares the execution window termination time after the second aging threshold value is split with the actual arrival time of the on-board data. If the execution window termination time is earlier than the actual arrival time of the on-board data, i.e., there is reverse timing, it is marked as timing inversion. The fourth stage generates a composite timing fault marker by synthesizing the abnormal level of the previous three types. If there is a deviation beyond the basic threshold and timing inversion, the type code is 03. The preset type code is 01 for deviation beyond threshold, 02 for anchor point failure, and 03 for deviation and timing inversion. The deviation duration and impact range coefficient are calculated, and the timing fault marker carrying these information is embedded in the metadata layer of the timing coordination pipeline and stored in association with the batch of transportation data, providing abnormal basis for subsequent data compensation and difference analysis.

[0021] After generating the timing fault marker to determine the abnormal type, the timing correlation processing unit 2 needs to generate a correlation compensation signal by fusing double-source compensation to correct the weight data deviation caused by timing deviation. This signal corrects the data from the physical characteristics and time dimensions to ensure that the on-board data and the reweighing data still maintain logical correlation. The specific implementation is as follows: The dual-source compensation fusion includes two compensation paths of physical compensation source and timing compensation source. The physical compensation source focuses on the weight reference correction of the vehicle-mounted data. The vehicle-mounted sensor plays back the data to extract the vibration spectrum features at the loading moment. Then, the vibration spectrum features are converted into the frequency response equivalent compensation value of the warehouse weighbridge through the preset frequency response conversion algorithm. The timing compensation source focuses on the timestamp correction. According to the deviation duration in the timing fault marker, the timestamp correction increment is calculated. The application of the compensation signal needs to be executed according to the differences of the timing fault marker types. When the marker is an anchor point failure, the vehicle-mounted data has no physical event timestamp, and the weight reference is inaccurate. The physical compensation source is preferentially used to reconstruct the vehicle-mounted data reference value. For example, the vehicle-mounted original weighing data is 1000 kg, and the frequency response equivalent compensation value is 0.3 kg. After the reconstruction, it is 1000.3 kg, which ensures that the weighing reference of the vehicle-mounted data is consistent with the reweighing data. When the marker is a timing inversion, the reweighing data and the vehicle-mounted data time sequence are chaotic. The timing compensation source is used to correct the reweighing data storage sequence. For example, the original timestamp of the reweighing data is “11:45:00”, and the timestamp correction increment is +610 seconds. After the adjustment, it is “11:55:10”, which makes the reweighing data timestamp later than the vehicle-mounted data, restores the normal timing logic, and finally generates the associated signal carrying the compensation type identifier. The signal is written into the timing coordination pipeline for correcting the subsequent timing alignment logic, and is written into the caliber timing two-dimensional storage unit 3 for storing the original data, the calibrated data and the compensation type identifier of the batch, which ensures that the compensation process is traceable and provides a correction basis for the loss judgment of the subsequent intelligent difference analysis unit 4.

[0022] After the timing correlation processing unit 2 generates the timing fault marker and the associated compensation signal, and completes the spatiotemporal fusion constraint of the second time threshold, the caliber timing two-dimensional storage unit 3 needs to realize the logical binding of the three types of weight data through the dynamic association index structure. If there is no unified index association, the theoretical weight, the vehicle-mounted weighing weight and the reweighing weight of the same batch of goods will be in a scattered storage state. The subsequent difference analysis needs to be repeatedly searched, which is extremely low in efficiency. Therefore, a three-level driven index system needs to be constructed based on the key events in the logistics process. The specific implementation is as follows: The logical binding of the dynamic association index structure takes three-level event-driven as the core, each index layer corresponds to a key node of the whole logistics process, ensuring that the data binding is synchronized with the actual business rhythm, the first level is the main index layer, responding to the shipment event of the consignor side, when the consignor submits the shipping order and generates the transport batch number in the system, the main index layer immediately creates an index framework containing a theoretical data slot with the transport batch number as the index key, the theoretical data slot is a unit specially storing the theoretical weight data of the consignor side, at the same time, it is associated with the timestamp of the shipping order submitted by the consignor and the basic information of the cargo classification code, forming the top layer framework of the index structure, providing a basis for subsequent data mounting, the second level is the sub-index layer, capturing the loading completion event of the 3PL side, when the 3PL completes loading and triggers the dynamic anchor point of the vehicle data area, the sub-index layer automatically creates a vehicle data slot in the framework of the main index layer, stores the original data of the vehicle weighing weight and timestamp, and synchronously mounts the compensation signal slot, the compensation signal slot is used to store the associated compensation signal generated by the timing association processing unit 2, and establishes a one-way association with the vehicle data slot, ensuring that the compensation signal can accurately act on the vehicle data, the third level is the meta-index layer, responding to the warehousing completion event of the warehousing side, when the warehouse load cell completes the reweighing and generates a rigid anchor point, the meta-index layer creates a reweighing data slot on the basis of the sub-index layer, stores the reweighing weight data and execution window information, and establishes a bidirectional link with the compensation signal slot, the bidirectional link means that the reweighing data slot can call the compensation signal, and the compensation signal slot can also trace the reweighing data, avoiding the disconnection of the compensation signal and the reweighing data, during the whole binding process, the system uses a data slot activity detection mechanism, when the timing fault marker is updated, the mechanism automatically activates all associated data slots to enter the recalibration process, calls the compensation signal to recalculate the logical association strength of the vehicle data and the reweighing data, and dynamically adjusts the index tree branch weight according to the association strength, the higher the association strength, the greater the index branch weight where the data slot is located, ensuring that the data with close association and high credibility is preferentially obtained when subsequent data is called, on the basis of completing the data logical binding, to solve the weight data deviation caused by the weighing reference difference of different devices, the error propagation network is needed to realize the cross-device reference unification, ensuring that the three types of data are compared based on the same reference, the specific implementation is as follows: First, the device precision hierarchy chain is constructed, which is divided into levels according to the reliability and role positioning of the device in weight data generation. The shipper manifest system is set as a first-level reference node. The physical theoretical weight of the cargo stored in the shipper manifest system is calculated based on the cargo specifications and quantity, and there is no physical measurement error, so it is the most reliable reference. The 3PL vehicle-mounted weighing device is set as a second-level transmission node. The vehicle-mounted device directly measures the actual weight of the cargo, but there is a certain error due to vibration and inclination, and needs to be corrected based on the first-level node. The warehouse weighbridge is set as a third-level terminal node. The weighbridge is used for re-measurement verification and is at the end of data collection. The error sources include the device itself and the unloading process, which need to be calibrated through the previous two levels. The reference unification process is executed in two levels. The static level generates a theoretical error transmission matrix based on the device verification certificate. The device verification certificate is a device error report issued by the measurement agency. The theoretical error transmission matrix records the correlation of the error of each level of device, providing a theoretical basis for reference unification. The dynamic level injects the generated correlation compensation signal as a real-time correction factor. For example, the vehicle-mounted data has a -3kg error due to vibration, and the physical compensation value in the correlation compensation signal is +3kg. The dynamic level injects this compensation value into the theoretical error transmission matrix to correct the vehicle-mounted data. The corrected vehicle-mounted data = original vehicle-mounted data + 3kg. At the same time, the time base of the re-measurement data is adjusted according to the time stamp in the compensation signal to ensure the reference unification in the time dimension. Finally, the calibrated data is output, and the original node precision label and error transmission path are retained in the dynamic correlation index structure to facilitate the tracing of error sources during subsequent difference analysis and to avoid misjudging device errors as cargo loss. When the caliber time series double-dimensional storage unit 3 completes data storage and reference unification, the intelligent difference analysis unit 4 needs to automatically match the allowable loss threshold based on the cargo characteristics and data state. The allowable loss threshold is the core standard for judging whether the cargo weight difference is reasonable. If a fixed threshold is used, it will lead to misjudgment due to different cargo types, data caliber, and device state. Therefore, a rule engine coupling strategy is adopted to balance universality and dynamic adaptability. The specific implementation is as follows: The rule engine coupling strategy is divided into a basic rule layer and a dynamic rule layer, and the two layers cooperatively realize accurate matching of the threshold value. The basic rule layer loads a preset threshold value template according to a cargo classification code. The cargo classification code is an encoding that identifies the type of cargo. The transportation loss rate of different types of cargo is significantly different. The system configures a preset threshold value template for each type of code in advance. When the cargo classification code is obtained, the corresponding template is automatically loaded to ensure the basic rationality of the threshold value matching. The dynamic rule layer selects a judgment mode based on a data caliber label combination. The caliber label combination is the data source involved in the comparison. Different combinations correspond to different loss calculation ranges. The theoretical caliber is suitable for the full-process loss threshold value for the repeated caliber. The full-process loss threshold value covers the whole process from the consignor to the warehouse. The loss includes transportation and loading and unloading links. For example, if the theoretical weight is 1000 kg and the full-process threshold value is 0.5%, the loss is allowed to be less than or equal to 5 kg. The on-board caliber is suitable for the section loss threshold value for the repeated caliber. The section loss threshold value only covers the transportation section from loading to warehousing. The loss is mainly a small amount of scattering in the transportation process. For example, if the on-board weight is 998 kg and the section threshold value is 0.3%, the loss is allowed to be less than or equal to 2.994 kg. When the system detects that the time sequence fault marker is activated, it means that there is a certain deviation in the data. The threshold value boundary needs to be dynamically expanded according to the marker level. The higher the marker level, the greater the expansion range. The expansion range is positively correlated with the associated compensation signal strength. The higher the compensation signal strength, the greater the data deviation, and the greater the threshold value expansion range. This avoids the reasonable loss being misjudged as an abnormality due to data deviation. At the same time, the system calls the built device accuracy level chain data to add a device reliability attenuation coefficient to the threshold value calculation of the secondary node (3PL on-board device). If the on-board device has a recent verification error of 3 kg, the reliability attenuation coefficient is set to 0.9. The section loss threshold value is corrected from 0.3% to 0.3% x 0.9 = 0.27%. This reduces the problem of loose threshold value caused by unreliable devices and ensures that the allowed loss threshold value can adapt to data deviation and take into account device reliability, providing accurate basis for subsequent weight difference attribution.

[0023] The specific implementation of the data credibility weighted calculation of the three-dimensional factor fusion is as follows: The data credibility weighting calculation takes the vehicle-mounted data as the main input stream and the reweighing data as the verification stream. Through the synergistic effect of three-dimensional factors of time, equipment and environment, the decision weight of the main input stream is dynamically adjusted. Among them, the time dimension factor generates a time decay coefficient based on the time sequence fault marker level generated by the time sequence correlation processing unit 2. The time sequence fault marker level is a classification of the severity of data time sequence deviation. The time decay coefficient is negatively correlated with the marker level. The equipment dimension factor gives the reference weight according to the equipment accuracy level chain constructed based on the cross-equipment reference uniform time. The equipment accuracy level chain sets the cargo manifest system as a first reference node, the 3PL vehicle-mounted equipment as a second transmission node, and the warehouse weight bridge as a third terminal node. Based on the equipment level and historical calibration error data, a fixed reference weight is given to the vehicle-mounted equipment. If the recent calibration report of the vehicle-mounted equipment shows that the error exceeds the normal range, the reference weight is further adjusted downward to ensure that the equipment dimension factor can reflect the reliability of the hardware itself. The environmental dimension factor generates an environmental interference index by extracting sensor abnormal parameters during loading. The loading sensor abnormal parameters include the horizontal inclination of the vehicle-mounted weighing platform, the engine vibration frequency, and the satellite positioning signal strength. The system will count the number and severity of abnormal parameters. A single slight abnormality (inclination 0.6°) corresponds to an interference index of 0.3. Two moderate abnormalities (inclination 0.8° and vibration frequency 55Hz) correspond to an interference index of 0.6. Three serious abnormalities (inclination 1.0° and vibration frequency 60Hz and signal strength -95dBm) correspond to an interference index of 0.9. The larger the interference index, the stronger the interference of the loading environment on the vehicle-mounted weighing data, and the lower the data credibility. After obtaining the specific values of the three-dimensional factors, the three are multiplied to obtain a comprehensive adjustment coefficient, which is used to adjust the initial decision weight of the vehicle-mounted data, and at the same time, the verification weight of the reweighing data is correspondingly improved, realizing the dynamic adaptation of the decision weight of the main input stream. When the threshold expansion mechanism of the allowable loss threshold is activated, i.e., the time sequence fault marker is detected, the threshold boundary needs to be expanded. The system will automatically reduce the decision priority of the environmental dimension factor. For example, the proportion of the environmental factor in the comprehensive adjustment coefficient is originally 30%, which is now reduced to 10%, reducing the influence of environmental interference on weight adjustment. At the same time, the compensated reweighing data output by the caliber time sequence two-dimensional storage unit 3 is taken as the final attribution reference. If the decision weight of the vehicle-mounted data after three-dimensional factor adjustment is less than 0.2, the difference between the compensated reweighing data and the theoretical weight data is directly used for loss attribution, avoiding the interference of low credibility vehicle-mounted data on the conclusion, and ensuring that the output attribution conclusion accurately reflects the actual transportation loss of the cargo.

[0024] The multi-source data acquisition unit 1 in the application collects the physical theoretical weight of the consignor terminal, the vehicle-mounted weighing weight of the 3PL terminal and the warehouse terminal reweighing weight data, binds the data caliber label for each type of data, the time sequence correlation processing unit 2 aligns the time sequence through the asymmetric time window matching mechanism, sets the double time threshold, triggers the time sequence fault mark and generates the correlation compensation signal when the vehicle-mounted data exceeds the tolerance, the caliber time sequence two-dimensional storage unit 3 binds the three types of data by the dynamic correlation index with the transportation batch number, unifies the cross-device reference through the error transmission network, stores the original, calibrated data and compensation signal, the intelligent difference analysis unit 4 matches the allowable loss threshold according to the caliber label, calls the environmental parameter weighted calculation data reliability when the fault mark is activated, outputs the attribution conclusion, and improves the logistics data management efficiency.

[0025] The basic principles, main features and advantages of the application are shown and described above. It should be understood by those skilled in the art that the application is not limited by the above examples, and the above examples and descriptions in the specification are only preferred examples of the application and are not intended to limit the application. Without departing from the spirit and scope of the application, various changes and improvements can be made to the application, and these changes and improvements all fall within the scope of the claimed application. The scope of protection of the application is defined by the appended claims and their equivalents.

Claims

1. A logistics data management system for multi-party data collaboration, characterized in that, include: The multi-source data acquisition unit (1) collects the theoretical weight data of goods from the cargo owner, the vehicle weighing weight data from the 3PL end, and the re-weighing weight data from the warehouse end, and forcibly binds a preset data caliber label to each type of weight data. The time-series correlation processing unit (2) achieves time-series alignment of each type of weight data through the asymmetric time window matching mechanism of dynamic time threshold. It sets a first time threshold to match the loading event timestamp for vehicle weighing weight data and sets a second time threshold to match the warehousing event timestamp for reweighing weight data. When it is detected that the timestamp of the vehicle data uploaded by 3PL exceeds the preset tolerance range of the loading event timestamp, it automatically triggers the time-series tomography marker and generates a correlation compensation signal. The time-series dual-dimensional storage unit (3) stores three types of weight data and uses a dynamic association index structure to logically bind the theoretical weight, vehicle weighing weight and re-weighing weight of the same batch of goods through the transportation batch number. At the same time, it stores the original data with caliber label, the calibrated data and the associated compensation signal. The calibrated data achieves cross-device benchmark unification through the preset caliber conversion coefficient. The intelligent difference analysis unit (4) automatically matches the allowable loss threshold based on the data caliber label of the current cargo. When the time sequence fault mark is activated, it automatically calls the original environmental parameters of the vehicle weighing weight data during the loading period to perform data credibility weighting calculation and outputs the attribution conclusion.

2. The logistics data management system for multi-party data collaboration according to claim 1, characterized in that: The specific process for the timing alignment implemented by the timing association processing unit (2) is as follows: A time-series collaborative pipeline is created using the transport batch number as an index. Within the pipeline, three data areas are defined: a theoretical data area, a vehicle-mounted data area, and a re-weighing data area. The system submission timestamp of the shipper's shipping order serves as the fixed anchor point for the theoretical data area. The moment of loading completion recorded by the 3PL loading equipment serves as the dynamic anchor point for the vehicle-mounted data area. The moment when the warehouse weighbridge triggers stable weighing serves as the rigid anchor point for the re-weighing data area. An asymmetric sliding window is used to expand the alignment interval. Specifically, the vehicle-mounted data area expands towards the pre-loading period centered on the dynamic anchor point to form the first time-sensitive threshold window, while the re-weighing data area compresses towards the post-warehouse period centered on the rigid anchor point to form the second time-sensitive threshold window. When the actual arrival time of the vehicle-mounted data deviates from the dynamic anchor point, the window boundary automatically shrinks to the effective working period of the loading sensor. Simultaneously, the starting point of the window for the re-weighing data area is adjusted to maintain the continuity of the data chain.

3. The logistics data management system for multi-party data collaboration according to claim 2, characterized in that: The construction and matching of the first timeliness threshold includes three dynamic adjustment layers. The basic threshold layer generates an initial time tolerance band based on the distribution pattern of historical transmission delay of on-board equipment. The environmental adaptation layer collects the satellite positioning signal strength, horizontal tilt angle of the on-board weighing platform and engine vibration frequency at the loading site in real time and scales the tolerance band width. The event calibration layer accurately locks the loading event timestamp through a dual event stamp verification mechanism. First, the 3PL operator scans the electronic license plate of the transport vehicle to generate an operation event stamp. Then, the physical event stamp is generated by the median of three consecutive stable readings of the on-board weighing sensor. When the deviation between the two event stamps exceeds the preset tolerance range, the sensor data playback verification is automatically triggered. The physical event stamp is used to cover the operation event stamp as the final dynamic anchor point, and the deviation value is injected into the timing coordination pipeline.

4. A logistics data management system for multi-party data collaboration according to claim 3, characterized in that: The second timeliness threshold is matched to perform spatiotemporal fusion constraints: The time dimension is based on the scheduled entry time of the warehouse management system. The spatial dimension captures the docking coordinates of the transport vehicle through the platform laser positioning device. When the docking position of the transport vehicle deviates from the standard weighing area, the time threshold is extended proportionally according to the position offset. At the same time, the data stream of the weighbridge pressure sensor is monitored. If the unloading time of the goods exceeds the scheduled time period, the single time window is split into a preparation window and an execution window. The termination time of the execution window is compared with the dynamic anchor point across the region. If the interval between the two exceeds the preset threshold, a conflict alarm is sent to the time sequence coordination pipeline.

5. A logistics data management system for multi-party data collaboration according to claim 4, characterized in that: The generation of the temporal tomographic markers adopts an event chain-driven strategy: When the vehicle data timestamp is detected to be outside the tolerance range, the fourth-order tomography assessment process is initiated. First, it is determined whether the deviation is within the basic threshold layer. If not, a conflict alarm signal is activated, and it is verified whether the vehicle data packet contains a physical event stamp. If it is missing, it is marked as anchor point failure. Then, the execution window termination time is compared with the actual data arrival time. If there is a reverse timing sequence, it is marked as timing inversion. Finally, a composite timing tomography marker is generated by combining the first three anomaly levels. This marker carries the tomography type code, deviation duration, and impact range coefficient, and is embedded in the timing collaborative pipeline metadata layer.

6. A logistics data management system for multi-party data collaboration according to claim 5, characterized in that: The generation of the associated compensation signal involves dual-source compensation fusion: The physical compensation source extracts the vibration spectrum characteristics at the moment of loading by playing back data from the vehicle-mounted sensor and converts them into the equivalent compensation value of the frequency response of the warehouse weighbridge. The time-series compensation source calculates the timestamp correction increment based on the duration of the deviation. When the anchor point is marked as failed, the physical compensation source is used to reconstruct the vehicle-mounted data reference value. When the time sequence is marked as reversed, the time-series compensation source is used to correct the reconstructed data storage sequence. Finally, an associated signal carrying the compensation type identifier is generated. This signal is synchronously written into the time-series collaborative pipeline and caliber time-series dual-dimensional storage unit (3).

7. A logistics data management system for multi-party data collaboration according to claim 6, characterized in that: Logical binding of dynamically associated index structures implements a three-level event-driven approach: The main index layer responds to the shipper's shipment event, creating an index framework containing theoretical data slots using the transport batch number as the key. The sub-index layer captures the loading completion event, creates vehicle data slots within the framework, and attaches compensation signal slots. The meta-index layer responds to the warehousing completion event, creates a duplicate data slot, and establishes a bidirectional link with the compensation signal slot. The binding process uses a data slot activity detection mechanism, that is, when the time-series fault marker is updated, the recalibration process of the associated data slot is automatically activated, and the compensation signal is called to recalculate the logical association strength between the vehicle and duplicate data, and the index tree branch weights are dynamically adjusted.

8. A logistics data management system for multi-party data collaboration according to claim 7, characterized in that: Cross-device benchmark unification is achieved through an error propagation network: A hierarchical chain of equipment accuracy is constructed, with the cargo owner list system as the first-level benchmark node, the 3PL vehicle-mounted equipment as the second-level transmission node, and the warehouse weighbridge as the third-level terminal node. The unified process is executed in two levels: the static level generates a theoretical error transmission matrix based on the equipment calibration certificate, while the dynamic level injects compensation signals as real-time correction factors, and finally outputs calibration data, while retaining the original node accuracy labels and error transmission paths in the index structure.

9. A logistics data management system for multi-party data collaboration according to claim 8, characterized in that: The automatic matching of the allowable loss threshold adopts a rule engine coupling strategy: The basic rule layer loads a preset threshold template based on the cargo classification code, while the dynamic rule layer selects a judgment mode based on the combination of caliber labels. The theoretical caliber applies the full-process loss threshold to the nominal caliber, while the vehicle-mounted caliber applies the section loss threshold to the nominal caliber. When a time-series tomography marker is detected, the threshold boundary is dynamically expanded according to the marker level. The expansion range is positively correlated with the compensation signal strength. At the same time, the equipment accuracy hierarchical chain data is called to calculate the additional equipment reliability attenuation coefficient for the thresholds involving the secondary nodes.

10. A logistics data management system for multi-party data collaboration according to claim 9, characterized in that: The data credibility weighted calculation performs three-dimensional factor fusion: The time dimension factor generates a time decay coefficient based on the time series tomography marker level. The equipment dimension factor assigns a baseline weight according to the equipment hierarchy chain. The environmental dimension factor extracts abnormal parameters of the on-board sensors to generate an environmental interference index. The on-board data is used as the main input stream, and the re-evaluation data is used as the verification stream. The decision weight of the main input stream is dynamically adjusted through the three-dimensional factors. When the threshold expansion mechanism is activated, the decision priority of the environmental dimension factor is automatically reduced, and the compensated re-evaluation data is used as the final attribution benchmark.

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