Cold-chain logistics information distribution and tracing method and system for data management

Abstract

The invention relates to the technical field of cold-chain logistics and information processing, and provides a cold-chain logistics information distribution and tracing system for data management, which comprises a server, a distributed verification module, a node sampling prediction module, a logistics data push module and an environment monitoring module, the distributed verification module is used for collecting data port states of a plurality of logistics nodes and performing consistency verification on node data so as to form an inter-node trusted data set; the node sampling prediction module is used for carrying out periodic sampling on a freight vehicle, storage equipment and a temperature control device, calculating a freight priority index Freight Priority Index and generating prediction data; the logistics data pushing module is used for dynamically pushing node data according to a freight transport priority index and a system load index (Network Load), and generating a delivery feedback index (Delivery Feedback) on the basis of feedback information; the environment monitoring module is used for monitoring cold chain transportation and storage environment parameters in real time, and recording and tracing temperature control states and safety alarm information of all nodes.

Description

Technical Field

[0001] This invention relates to the field of cold chain logistics and information processing technology, and in particular to a method and system for data management, cold chain logistics information distribution, and traceability. Background Technology

[0002] In the process of cold chain logistics transportation and warehousing management, it is necessary to collect real-time data on the temperature control, location, environment, and operational status of transport vehicles and warehousing equipment to ensure the quality, safety, and traceability of temperature-sensitive goods (such as food, medicine, and fresh produce) throughout the entire logistics chain. However, most traditional cold chain information systems adopt a centralized data processing architecture, which still has significant shortcomings in terms of data real-time performance, accuracy, node collaboration, and cross-link tracking.

[0003] For example, the prior art disclosed in foreign patent EP3437040A1 discloses a method for prioritizing the distribution of cold chain logistics data. This method proposes storing cold chain data in a cold chain database and user data in a network database, and then prioritizing the distribution of cold chain data based on user roles. While this scheme enhances user role matching from a data priority perspective, it still relies on a centralized database structure and lacks systematic processing of temperature and humidity changes at logistics nodes, authentication of warehousing / transportation equipment, load collaboration between nodes, and multi-node synchronous verification mechanisms.

[0004] Another typical example is the data warehouse method and system for a cold chain system disclosed in foreign patent WO2017123854A1, which proposes to integrate multiple cold chain information sources into a data warehouse to assemble traceable "farm-to-fork" product records. Although this solution improves data integration and traceability, it mainly focuses on product record integration and does not deeply implement logistics information distribution systems such as node sampling prediction, distributed data verification, data push priority control, and feedback-traceability closed-loop mechanisms.

[0005] In addition, the following defects still exist in the existing technology:

[0006] 1. Most cold chain information systems are still centralized or based on a single node, lacking distributed node data management and inter-node synchronization verification mechanisms. This leads to data silos, high verification costs, and difficulty in ensuring authenticity when transmitting data across nodes and links.

[0007] 2. Although traditional systems can collect temperature control, location, or status data, they do not have a dynamic push mechanism, meaning they cannot automatically adjust the data distribution order and frequency based on node status, equipment load, and freight priority.

[0008] 3. Most systems lack a closed-loop control process from "data acquisition - node verification - priority push - feedback traceability", making it difficult to monitor temperature fluctuations and process abnormalities in real time and automatically trigger safety alarms and accountability.

[0009] 4. In cold chain logistics applications, there is still a lack of an information distribution and tracking system that can simultaneously achieve multi-node distributed data management, data priority push, temperature control environment monitoring, and complete traceability capabilities.

[0010] This invention was developed to address the common problems in this field, such as fragmented data management, crude push mechanisms, weak node verification, and insufficient traceability loops. Summary of the Invention

[0011] The purpose of this invention is to address the shortcomings of current methods by proposing a data management method and system for cold chain logistics information distribution and traceability.

[0012] To overcome the shortcomings of the existing technology, the present invention adopts the following technical solution: a data management cold chain logistics information distribution and traceability system, including a server, a distributed verification module, a node sampling prediction module, a logistics data push module, and an environmental monitoring module; the server is connected to the distributed verification module, the node sampling prediction module, the logistics data push module, and the environmental monitoring module respectively;

[0013] The distributed verification module is used to collect the data port status of multiple logistics nodes and perform consistency verification on the node data to form a trusted dataset among nodes; the node sampling and prediction module is used to periodically sample freight vehicles, warehousing equipment, and temperature control devices, calculate the freight priority index Freight_Priority_Index, and generate prediction data; the logistics data push module is used to dynamically push node data according to the freight priority index and the system load index Network_Load, and generate the delivery feedback index Delivery_Feedback based on feedback information; the environmental monitoring module is used to monitor cold chain transportation and warehousing environment parameters in real time, record and trace the temperature control status and safety alarm information of each node;

[0014] The logistics data push module includes a sequence decision unit, a load adaptation unit, and a hierarchical push execution unit. The sequence decision unit acquires a verified node trusted dataset, sorts the data items to be pushed according to the freight priority index, and dynamically adjusts the sorting results based on the node's operational and environmental characteristics. The load adaptation unit adaptively filters the sorted data items according to the system load index Network_Load to dynamically determine the data transmission volume for each push cycle. The hierarchical push execution unit pushes the target data items to the management terminal according to a hierarchical scheduling mechanism to ensure that high-priority data is prioritized for transmission under high load conditions.

[0015] Optionally, the distributed verification module includes a node detection unit, a data consistency verification unit, and a trusted set generation unit; the node detection unit is used to collect the data port status of each logistics node; the data consistency verification unit is used to perform cross-node consistency verification on data from different nodes; and the trusted set generation unit is used to generate a node trusted dataset from the verified data records.

[0016] Optionally, the node sampling prediction module includes a node sampling unit, a priority calculation unit, and a prediction generation unit; the node sampling unit is used to periodically collect vehicle operating parameters, temperature control parameters, and storage equipment status; the priority calculation unit is used to calculate the freight priority index Freight_Priority_Index based on the operating frequency and delay characteristics within the sampling period; and the prediction generation unit is used to generate prediction data for the next push period based on historical node behavior.

[0017] Optionally, the sequence decision unit includes a sorting subunit and an adjustment subunit. The sorting subunit is used to initially sort the data items to be pushed according to the freight priority index; the adjustment subunit is used to dynamically adjust the initial sorting result based on the node operation characteristics and environmental characteristics.

[0018] Optionally, the load adaptive unit includes a load monitoring subunit and an adaptive filtering subunit; the load monitoring subunit is used to monitor the system load index Network_Load in real time; the adaptive filtering subunit is used to determine the number of corresponding push data items according to the system load index in different load ranges.

[0019] Optionally, the hierarchical push execution unit adopts a hierarchical scheduling mechanism to divide the target data item into at least two priority levels, and sends the data sequentially in the push time slot according to the priority level, so as to ensure that high priority data is still transmitted first under high load conditions.

[0020] Optionally, the environmental monitoring module includes a temperature control monitoring unit, an identity traceability unit, and a safety alarm unit; the temperature control monitoring unit is used to acquire and record the temperature and humidity parameters of the cold chain nodes; the identity traceability unit is used to record the unique identity information of the node devices; and the safety alarm unit is used to generate alarm information when abnormal temperature control, abnormal equipment, or abnormal node data is detected.

[0021] In addition, this application also provides a method for cold chain logistics information distribution and traceability in data management, the method comprising the following steps:

[0022] S1. Collect the data port status of each logistics node and perform consistency verification on the node data to generate a trusted node dataset.

[0023] S2. Periodically collect vehicle and warehousing equipment operation data and calculate the freight priority index Freight_Priority_Index;

[0024] S3. Sort, filter, and determine the data push order based on Freight_Priority_Index and the system load index Network_Load;

[0025] S4. Push the target data items to the management terminal according to the hierarchical scheduling mechanism, and record the push log for traceability.

[0026] Optionally, the cold chain logistics information distribution and traceability method further includes: the sub-steps performed in step S3 include:

[0027] S31. Perform initial sorting based on freight priority indicators;

[0028] S32. Dynamically adjust the sorting results based on node operation characteristics and environmental characteristics;

[0029] S33. Adaptively filter the number of data items to be pushed based on the system load index Network_Load.

[0030] Optionally, the cold chain logistics information distribution and traceability method further includes: the sub-steps performed in step S4 include:

[0031] S41. Divide the target data items into at least two priority levels;

[0032] S42. Data is sent sequentially within the push time slot according to priority.

[0033] S43. During the push process, the real-time monitoring data of the environmental monitoring module is associated with and stored with the push record to achieve full-process traceability.

[0034] The beneficial effects achieved by this invention are:

[0035] 1. By cooperating with the node sampling prediction module and the environmental monitoring module, the system can simultaneously acquire the node's operating characteristics and real-time temperature control status, and transform information such as temperature control fluctuations and delay risks into quantifiable predictive indicators, ensuring that the system can identify potential high-risk nodes in advance, overcoming the shortcomings of existing technologies that cannot dynamically assess node status.

[0036] 2. By cooperating with the node sampling prediction module and the logistics data push module, the system can dynamically sort and filter the data to be pushed according to Freight_Priority_Index, realize the priority transmission of high-priority data, and ensure that the system can prioritize the distribution of time-sensitive or temperature-sensitive data, thus solving the shortcomings of the existing system that the push order is fixed and cannot reflect the urgency.

[0037] 3. Through the cooperation of the distributed verification module and the node sampling prediction module, the system can immediately verify the consistency of data between nodes after data collection, and predict the node operation trend based on historical behavior. This ensures that the entire system has a reliable node trust foundation before making data decisions, avoiding the decision bias problem caused by data inconsistency in traditional solutions.

[0038] 4. Through the cooperation of the logistics data push module and the environmental monitoring module, the system can dynamically adjust the push time slot allocation according to Network_Load under high load conditions, and automatically improve the push level of data from temperature control abnormal nodes, ensuring that the system can still prioritize the transmission of critical data when the load increases, thus overcoming the problem of delayed push of critical data in traditional cold chain systems when the network is congested.

[0039] 5. Through the cooperation of the distributed verification module, logistics data push module and environmental monitoring module, the system can simultaneously record the node's trust status, push order, push time slot number and temperature control snapshot information, realizing full-process traceability of each push data, ensuring that the system has complete auditability and reviewability, and avoiding the problems of lack of source evidence for push behavior and inability to trace the source in traditional solutions. Attached Figure Description

[0040] The invention will be further understood from the following description taken in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but rather the emphasis is on illustrating the principles of the embodiments. In different views, the same reference numerals designate the same parts.

[0041] Figure 1 This is a schematic diagram of the overall block shape of the present invention.

[0042] Figure 2This is a schematic diagram of the control flow of the trusted set generation unit of the present invention.

[0043] Figure 3 This is a schematic diagram of the control flow of the node sampling prediction module of the present invention.

[0044] Figure 4 This is a schematic diagram of the control flow of the environmental monitoring module of the present invention.

[0045] Figure 5 This is a schematic diagram of the control flow of the load adaptive unit of the present invention.

[0046] Figure 6 This is a schematic diagram of the control flow of the sequential decision-making unit of the present invention. Detailed Implementation

[0047] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated beforehand. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0048] Example 1: According to Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown, this embodiment provides a cold chain logistics information distribution and traceability system for data management. The cold chain logistics information distribution and traceability system includes a server, a distributed verification module, a node sampling prediction module, a logistics data push module, and an environmental monitoring module. The server is connected to the distributed verification module, the node sampling prediction module, the logistics data push module, and the environmental monitoring module respectively, and stores the intermediate data and process data of the distributed verification module, the node sampling prediction module, the logistics data push module, and the environmental monitoring module in the database of the server.

[0049] The distributed verification module is used to collect the data port status of multiple logistics nodes and perform consistency verification on the node data to form a trusted dataset among nodes; the node sampling and prediction module is used to periodically sample freight vehicles, warehousing equipment, and temperature control devices, calculate the freight priority index Freight_Priority_Index, and generate prediction data; the logistics data push module is used to dynamically push node data according to the freight priority index and the system load index Network_Load, and generate the delivery feedback index Delivery_Feedback based on feedback information; the environmental monitoring module is used to monitor cold chain transportation and warehousing environment parameters in real time, record and trace the temperature control status and safety alarm information of each node;

[0050] The cold chain logistics information distribution and traceability system also includes a central processing unit (CPU). The CPU is connected to the server, distributed verification module, node sampling and prediction module, logistics data push module, and environmental monitoring module via air control. The CPU is used to centrally control the distributed verification module, node sampling and prediction module, logistics data push module, and environmental monitoring module.

[0051] In this embodiment, a node refers to a terminal device that is physically deployed in the cold chain logistics link, has an independent device identifier, and is capable of collecting cold chain-related data and uploading it to a server. A node is a specific hardware device, including at least a data acquisition unit, a communication unit, and a unique device identifier, used to generate and upload temperature control, location, or operational status data during cold chain transportation, storage, or handover.

[0052] The specific form of a node can be an on-board terminal on a transport vehicle, an environmental monitoring terminal in cold chain storage equipment, a temperature control recording device on a refrigerated container, or a temperature and humidity sensor gateway on a mobile temperature control vehicle. Any device with data acquisition and communication capabilities can constitute a node.

[0053] Optionally, the distributed verification module includes a node detection unit, a data consistency verification unit, and a trusted set generation unit; the node detection unit is used to collect the data port status of each logistics node to determine the data upload capability and communication health status of each node; the data consistency verification unit is used to perform cross-node consistency verification on data from different nodes; the trusted set generation unit is used to generate a node trusted dataset from the verified data records.

[0054] The node detection unit periodically establishes a communication handshake mechanism with registered logistics nodes, including sending heartbeat packets, port activation requests, or executing MQTT / TCP connection confirmations to obtain the node's online status and port reachability. If a node does not respond to the handshake request within a preset time, it is determined that the node is offline or unreachable, and the corresponding communication anomaly information is recorded. Registered logistics nodes refer to physical terminal devices that have completed identity registration, network verification, and have been confirmed by the server as capable of normal communication within the cold chain logistics system.

[0055] The node detection unit parses the port operation status field reported by the node to obtain the specific status of the data port, including whether the port is open, closed, occupied, and any error codes. The node detection unit performs structural verification or CRC verification on the returned data packets to identify whether there are transmission errors or abnormal data formats on the port. If an error code or structural abnormality is detected, the node is marked as having an abnormal port status.

[0056] The node detection unit monitors data update status based on node upload timestamps. It determines whether a node is uploading data normally by calculating the time difference between the current time and the node's most recent successful upload time. If this time difference exceeds a set threshold, or if continuous packet loss occurs, the node is recorded as having an abnormal data update status. The node detection unit can also configure different data upload frequency requirements based on node type to adapt to the collection characteristics of different nodes such as transport vehicles, warehousing equipment, or container terminals.

[0057] The data consistency verification unit performs consistency verification on data from different nodes according to a fixed execution order to ensure the reliability of multi-source data during cold chain transportation. This process does not rely on abstract analysis models, but is completed step by step based on the node's own historical records, actual operating conditions during transportation, and mutual verification relationships between multiple nodes. The specific process is as follows.

[0058] The verification unit performs a preliminary check on the data of a single node. It reads the node's temperature control records, upload frequency, and device status information from previous acquisition cycles to determine whether the node's current data is continuous with its previous records. For example, if a node's temperature has remained relatively stable for several recent cycles, but the temperature suddenly jumps significantly in the current cycle, this anomaly is recorded as: the node itself is discontinuous. This type of data will be directly marked as abnormal without proceeding to the cross-node comparison stage. If the current record is consistent with the node's previous records in terms of the magnitude and direction of change, it is considered continuous and allowed to proceed to the next stage.

[0059] Simultaneously, the verification unit compares the node data with the actual operating conditions of the current transportation link. Since different links in the cold chain operation have different allowable temperature change ranges—for example, there may be a short-term temperature rise during the initial loading stage, while the temperature should remain stable during long-distance operation—the verification unit judges whether the node data conforms to the reasonable changes of that link based on the allowable change range corresponding to the current link. For example, during the initial start-up stage of the refrigeration unit, the temperature of nodes near the refrigeration port drops rapidly, while the temperature of nodes farther from the refrigeration port drops slowly; in the stage where the temperature has stabilized, the temperature change amplitude of each node should be small. If the node data matches the objective operating characteristics of that link, it is considered to meet the link requirements; otherwise, it is marked as abnormal.

[0060] In addition, the verification unit cross-verifies data from multiple nodes within the same time frame. This step does not require the absolute values ​​of each node to be identical, but rather checks whether they conform to the natural laws of the cold chain space. For example, two adjacent nodes should maintain consistency in the direction and magnitude of temperature changes; nodes at different positions in the front and back of the cargo compartment may have some differences, but these differences must be within the normal range of temperature differences recorded in historical data; if a node is located near a heat source or an open area, its temperature fluctuation should be greater than that of other nodes, and the verification unit can use this to determine whether the changes at that node are reasonable. Through cross-verification between nodes, it is possible to further confirm whether the data from multiple nodes within the same time frame exhibit a coordinated relationship, thereby determining their consistency.

[0061] If a piece of data shows obvious non-continuity, does not meet the conditions of the transportation process, or cannot be corroborated with other nodes in any of the three stages mentioned above, the verification unit marks the data as inconsistent; if no abnormality occurs in any of the three stages, the data is marked as consistent and written into the subsequent process as reliable data.

[0062] The trusted set generation unit is used to organize and store data records marked as verified as passed after the data consistency verification unit completes the consistency determination, in order to form a node trusted dataset that can be used for subsequent sorting, pushing, and tracing. Specifically, the trusted set generation unit first receives the verification result output by the data consistency verification unit, and reads the node identifier, time information, temperature control value, location information, device status, and consistency flag for each data record. For example, for a data record from node number "Node_3" with a temperature of 4.8℃, GPS location (23.152°N, 113.278°E), and device status of "normal" collected at 10:32:15, if its consistency flag is "passed", then this data will be included as candidate trusted data in subsequent processing; conversely, if data uploaded by the same node at 10:32:20 is judged as "inconsistent" due to a sudden jump, then this data will be removed and will not participate in trusted set generation.

[0063] The trusted set generation unit categorizes and organizes candidate trusted data according to pre-set grouping rules. These grouping rules can include one or more methods, such as grouping by node identifier, grouping by transport batch, or grouping by time period. For example, when using transport batch "Batch_20241108_A" as a baseline, all verified data belonging to that batch and originating from multiple nodes (such as Node_1, Node_2, and Node_5) can be grouped into the same batch set. Similarly, when grouping by time period, verified data uploaded continuously by the same node between 10:30:00 and 10:40:00 can be grouped into a single group, forming a trusted data segment for that node during that time period, allowing for rapid location of the data during subsequent tracing.

[0064] After grouping, the trusted set generation unit generates corresponding index information for each trusted data record. This index information includes at least a node index, a batch index, and a time index. For example, data from Node_3, batch_20241108_A, and time 10:32:15 will generate an index entry in the database similar to (Node_3, Batch_20241108_A, 10:32:15). This allows the server to locate the trusted data record within milliseconds when performing Freight_Priority_Index sorting, Network_Load filtering, or historical temperature control tracing.

[0065] Finally, the trusted set generation unit writes the grouped and indexed trusted data into the server's database or cache area and archives it in the storage structure with the identifier "Node Trusted Dataset". For example, for batch Batch_20241108_A, a trusted dataset composed of multiple nodes such as Node_1, Node_2, and Node_3 will be formed for subsequent priority sorting and pushing; for node Node_3, node trusted data fragments covering multiple time periods (such as 10:30–10:40, 10:40–10:50) can be formed for subsequent temperature control anomaly tracing and alarm cause location. The trusted set generation unit organizes the originally discrete verification into a structured, searchable data set with time and batch correlation, thereby achieving seamless connection between data consistency verification results and push distribution and traceability management.

[0066] Optional, such as Figure 3As shown, the node sampling prediction module includes a node sampling unit, a priority calculation unit, and a prediction generation unit. The node sampling unit is used to periodically collect vehicle operating parameters, temperature control parameters, and storage equipment status. The priority calculation unit is used to calculate the freight priority index Freight_Priority_Index based on the operating frequency and delay characteristics within the sampling period. The prediction generation unit is used to generate prediction data for the next push period based on historical node behavior.

[0067] The node sampling unit is used to periodically acquire vehicle operating parameters, temperature control parameters, and the status of storage equipment. The acquisition process is automatically executed based on a preset sampling period and the equipment's built-in detection interface. Specifically, when the system starts, the node sampling unit reads the sampling period setting issued by the server, such as 3 seconds, 5 seconds, or 10 seconds, and triggers a acquisition task at the end of each sampling period. The acquisition task includes polling the sensors installed in the vehicle, storage equipment, or transport container. These sensors typically include temperature sensors, humidity sensors, vehicle speed sensors, GPS location modules, power status detectors, and door magnetic opening / closing detectors.

[0068] During the data collection process, the node sampling unit first reads the operating parameters of the vehicle or warehousing equipment through the device interface, such as the vehicle's current speed, engine speed, battery voltage, and refrigeration unit's operating status via the CAN bus interface. Subsequently, it acquires real-time temperature control data of the cargo compartment or warehousing environment through locally connected temperature and humidity sensors. If the node is warehousing equipment, it obtains information such as the refrigeration equipment's operating status, fan speed, and compressor operation through the equipment's status interface. After completing the above data acquisition, the sampling unit encapsulates the collected timestamps, node identifiers, temperature values, humidity values, vehicle speed, and equipment status raw data in one go, forming a structured sampling record.

[0069] In a specific example, when the sampling period is 5 seconds, the node sampling unit will trigger data collection at consecutive time points such as 14:32:10, 14:32:15, and 14:32:20. For instance, during the data collection at 14:32:15, the system obtains the cargo compartment temperature of 4.6℃ through the temperature sensor, the vehicle location (23.150°N, 113.296°E) through the GPS module, the vehicle speed of 65 km / h and the normal operation of the refrigeration unit through the vehicle interface, and reads that the cargo compartment door is closed through the door magnetic sensor. The sampling unit combines the above information into a sampling record and either temporarily stores it or uploads it to the server.

[0070] By periodically repeating the above data collection process, the node sampling unit can continuously acquire the node's continuous operating parameters, environmental temperature control data, and equipment operating status during transportation, providing continuous and traceable node data input for the subsequent priority calculation unit and data push module.

[0071] The priority calculation unit calculates the Freight_Priority_Index corresponding to a node within each push cycle, based on multiple sampling records reported by the node sampling unit, thereby reflecting the urgency of the freight tasks belonging to that node in the current cycle. Specifically, the priority calculation unit first reads sampling data associated with the same transportation task or the same node within the current statistical cycle from the cache or database. The statistical cycle can be set to, for example, 5 minutes, 10 minutes, or a scheduling cycle. In the read sampling data, the priority calculation unit counts the number of times a node is scheduled or participates in cold chain tasks, the number of status updates, and other information to characterize the operating frequency. For example, within a 10-minute cycle, if a transportation task corresponding to a node is continuously written to the status of "in transit," "awaiting loading," or "arriving soon," it is recorded as having a high operating frequency, while tasks with only a few status changes are recorded as having a low operating frequency.

[0072] Within this statistical period, all status change records or scheduling records for the same freight task i are summarized, and the number of times the task's status is updated, scheduled, or operated within the statistical period is recorded as the original value F of the operating frequency. i Meanwhile, for the current set of freight tasks participating in the comparison, the average operating frequency μ of each task is calculated. F and standard deviation σ F Based on this, the standard score of the running frequency of task i is calculated: ;

[0073] The priority calculation unit extracts delay features based on task time limit information. For each freight task i, the system pre-stores its planned completion time T. planned,i (e.g., planned arrival time or planned delivery time), the priority calculation unit obtains the current time T at the end of the current statistical period. current Alternatively, the actual timestamp of the latest status of the task can be used as the difference between the two, which is recorded as the original delay value L. i L i = T current - T planned,i L i A positive value indicates that the task is nearing or has exceeded its planned deadline, while a negative value indicates that there is still time remaining. For the same batch of tasks, L... i Calculate the average value μ L and standard deviation σ L And obtain the standard delay score for task i: ;

[0074] After obtaining the standard score of operating frequency Z_freq,i and the standard score of delay Z_delay,i, the priority calculation unit combines the two and calculates the freight priority index of task i according to the following formula:

[0075] ;

[0076] The larger the Freight_Priority_Index value, the higher the task's running frequency within the current statistical period, and the closer it is to or beyond the planned deadline. Therefore, it should be given a higher priority in subsequent data push and scheduling. The priority calculation unit can directly write the calculated Freight_Priority_Index into the priority field of the corresponding task, which can be called by the logistics data push module when determining the push order, thereby achieving comprehensive priority control based on running frequency and delay characteristics.

[0077] Specifically, the priority calculation unit extracts delay features within the same period. These delay features are obtained by comparing the planned transportation time with the actual time. For example, based on the pre-set planned arrival time, planned loading time, or planned delivery time of the task, the difference is calculated with the actual timestamp in the sampling record to determine whether the current task is approaching its deadline or has already exceeded it. When the actual time of a transportation task approaches or exceeds the predetermined deadline, the Z-value corresponding to its delay feature is... delay,i The value will be too large; when the state time is significantly earlier than the predetermined time limit, the Z value corresponding to the delay feature will be too large. delay,i The value is too small.

[0078] For example, in a specific instance, the planned arrival time for a cold chain delivery task is preset to 14:30. If the task is continuously recorded as "in transit" within the statistical period from 14:25 to 14:30, the original delay value L_i will be positive, resulting in a higher standard delay score Z. delay,i The Freight_Priority_Index will increase accordingly as the Freight_Priority_Index is higher than the average level, reflecting the high urgency of the task in the current cycle.

[0079] Furthermore, after obtaining the standard scores corresponding to the operating frequency and latency characteristics, the priority calculation unit also interprets the actual priority meaning of Freight_Priority_Index in segments according to preset priority determination rules. These rules can be stored as segmented intervals and corresponding priority values. For example, the operating frequency can be divided into high, medium, and low levels, and the latency characteristics into normal, critical, and timeout levels, with different priority intervals set for each combination: high-frequency tasks in a critical or timeout state are assigned a higher Freight_Priority_Index interval; low-frequency tasks with normal latency are assigned a lower priority interval; and medium-frequency tasks with relatively high latency are assigned a medium-to-high priority interval.

[0080] In this embodiment, the priority calculation unit can map frequency level and delay level to a numerical range. In this embodiment, the combination of high frequency and critical delay is mapped to above 90 points, the combination of medium frequency and normal delay is mapped to 60 to 80 points, and the combination of low frequency and no delay is mapped to below 40 points, so as to form the numerical interpretation of Freight_Priority_Index, so that the system can distinguish priorities based on this in subsequent data push sorting.

[0081] The prediction generation unit utilizes historical node behavior records to calculate the next push cycle and the corresponding predicted data, enabling more reasonable push rhythm and priority control in subsequent data pushes. Specifically, the prediction generation unit first retrieves the historical records of the target node within several completed push cycles from the server. These historical records include the node's temperature control parameters, vehicle operating status, warehouse equipment status, and the Freight_Priority_Index value calculated within the corresponding cycle, as well as the time points of each actual push.

[0082] After acquiring the aforementioned historical records, the prediction generation unit statistically analyzes the push intervals of the most recent push cycles, calculates the interval between two adjacent actual push times, and thereby obtains the push interval range and typical interval values ​​that usually occur under normal operating conditions for this node. At the same time, the prediction generation unit analyzes the temperature control variation and Freight_Priority_Index variation within the same time period. When it is found that the temperature fluctuates greatly or Freight_Priority_Index is at a high level multiple times in a certain time period, the time period is marked as a "relatively sensitive" interval.

[0083] When the temperature control changes are small and Freight_Priority_Index remains at a low or medium level for an extended period, the corresponding interval will be marked as a "stable" interval.

[0084] Based on this, the prediction generation unit determines the next push cycle by combining typical push intervals with recent change characteristics: when the most recent cycles are all within the "stable change" range, the prediction generation unit can set the next push cycle to be close to or slightly larger than the historical typical interval value; when there is an increase in temperature control fluctuations or a rapid rise in Freight_Priority_Index in the most recent cycle, the prediction generation unit appropriately shortens the next push cycle to make subsequent pushes more frequent, so as to refresh the data on the management terminal in a timely manner during periods of rapid status changes. At the same time, the prediction generation unit also predicts the temperature range that nodes may appear in the next push cycle and the approximate direction of Freight_Priority_Index change based on historical temperature control change trends and the proximity of task deadlines, and generates prediction data in the form of "expected push time and expected priority range", which is written into the prediction result field of the server for the logistics data push module to refer to when deciding on the actual push timing.

[0085] By using historical node behavior records, actual push intervals, and changes in temperature control and priority, the prediction generation unit can reasonably estimate the next push cycle and related prediction data. This enables the entire system to reduce unnecessary pushes when the node status is stable, and to push earlier or more frequently when the node status changes rapidly or the task is close to the deadline, thereby improving the targeting and timeliness of cold chain logistics information distribution.

[0086] The system utilizes the cooperation between the distributed verification module and the node sampling prediction module to immediately verify the consistency of data between nodes after data collection and predict the node operation trend based on historical behavior. This ensures that the entire system has a reliable node trust foundation before making data decisions, avoiding the decision bias problem caused by data inconsistency in traditional solutions.

[0087] The logistics data push module includes a sequence decision unit, a load adaptation unit, and a hierarchical push execution unit. The sequence decision unit acquires a verified node trusted dataset, sorts the data items to be pushed according to the freight priority index, and dynamically adjusts the sorting results based on the node's operational and environmental characteristics. The load adaptation unit adaptively filters the sorted data items according to the system load index Network_Load to dynamically determine the data transmission volume for each push cycle. The hierarchical push execution unit pushes the target data items to the management terminal according to a hierarchical scheduling mechanism to ensure that high-priority data is prioritized for transmission under high load conditions.

[0088] Optional, such as Figure 6 As shown, the sequence decision unit includes a sorting subunit and an adjustment subunit. The sorting subunit is used to initially sort the data items to be pushed according to the freight priority index; the adjustment subunit is used to dynamically adjust the initial sorting result based on the node operation characteristics and environmental characteristics.

[0089] In this embodiment, the adjustment subunit dynamically adjusts the sorting position of each data item to be pushed after the sorting subunit completes the initial sorting based on Freight_Priority_Index, so that the final sorting result can reflect the real-time operating status and environmental conditions of the node in the current period. Specifically, the adjustment subunit reads the node operating information reported by the node sampling unit and the environmental parameters reported by the environmental monitoring module, verifies each data item corresponding to each node, and determines its forward or backward movement in the initial sorting according to preset adjustment rules.

[0090] The adjustment subunit corrects the sorting position based on the node's operational characteristics. These characteristics include changes in vehicle speed, the current operating status of the vehicle or storage equipment, the magnitude of temperature control changes within a continuous sampling period, and the stability of data reporting. For example, if the vehicle speed of a node significantly increases within the current period and the node is approaching its delivery destination, the adjustment subunit moves the corresponding data item of that node forward in the initial sorting to ensure it is processed in earlier batches. Similarly, if the temperature control data of a node shows a continuous increase or sudden jump over multiple sampling periods, the adjustment subunit will also move the node's data item forward so that the management terminal can obtain the latest data for that node in a timely manner. Conversely, if a node has stable temperature control, low vehicle speed, and normal equipment status within a continuous period, the adjustment subunit can keep its sorting unchanged or move it backward appropriately, allowing system resources to be prioritized for more urgent data items.

[0091] The adjustment subunit supplements and adjusts the ranking results based on environmental characteristics. These environmental characteristics include the spatial location of nodes within the cold chain transport container, external temperature changes, temperature zone differences within the cargo compartment, and cargo category. For example, nodes located near the cargo compartment door or those more susceptible to external temperature fluctuations typically have a higher risk of temperature control fluctuations than nodes located in the middle of the container. After identifying such location characteristics, the adjustment subunit will move the data items of these nodes forward. When the environmental monitoring module detects a significant increase in the external ambient temperature during transport, the adjustment subunit will also move the positions of nodes located at the edge of the temperature zone forward to increase the data push priority of these nodes. If the cargo corresponding to a node belongs to a category requiring strict temperature control (such as vaccines or perishable biological products), the adjustment subunit will further improve its position in the ranking.

[0092] The adjustment subunit can make the final sorting result more consistent with the actual situation of the current transportation process without changing the main sorting logic of Freight_Priority_Index. When the node's operating status changes or the external environment fluctuates, the system can adjust the sorting result in real time through the adjustment subunit, thereby ensuring that the data of high-risk nodes or critical nodes are prioritized during the push process, enhancing the timeliness and responsiveness of cold chain data push.

[0093] Optional, such as Figure 5 As shown, the load adaptive unit includes a load monitoring subunit and an adaptive filtering subunit; wherein: the load monitoring subunit is used to monitor the system load index Network_Load in real time; the adaptive filtering subunit is used to determine the number of corresponding push data items according to the system load index in different load ranges.

[0094] The load monitoring subunit is used to obtain the server's current communication usage, task processing status, and cache queue status in real time, and calculates the three types of load factors into a system load index, Network_Load, using a unified quantification method. Specifically, the load monitoring subunit reads: Link_Usage, Server_Concurrency, and Queue_Length_Ratio.

[0095] Among them, the ratio of the current bandwidth used by the communication link to the maximum available bandwidth, for example, 0.25 means that the communication link is using 25%; the server concurrency ratio is the ratio of the number of parsing and push tasks currently being processed by the server to the maximum concurrency processing capacity; and the processing queue ratio is the ratio of the length of the queue of data to be pushed to the maximum queue capacity.

[0096] In addition, the load monitoring subunit normalizes the above three load factors and generates the system load index Network_Load as follows:

[0097]

[0098] The value of Network_Load ranges from 0 to 1, and a value close to 1 indicates a high system load.

[0099] The adaptive filtering subunit selects different data filtering strategies based on the load range of Network_Load to ensure that the push volume is reduced when the system load is high and increased when the load is low. The adaptive filtering subunit selects the corresponding filtering strategy based on the range to which Network_Load belongs:

[0100] When the system load index Network_Load is in the range of 0 to 0.4, and is in the low load range, the adaptive filtering sub-unit allows 80% to 100% of the data items in the sorted list to be pushed.

[0101] When the system load index Network_Load is in the range of 0.4 to 0.7, which is in the medium load range, the adaptive filtering sub-unit will only select the top 40% to 60% of the data items for push.

[0102] When the system load index Network_Load is in the range of 0.7 to 1.0, and is in the high load range, the adaptive filtering sub-unit will only select the top 10% to 20% of the data items to push.

[0103] The adaptive filtering subunit calculates the number of data items to be transmitted in this cycle based on the filtering ratio, using the following formula:

[0104] ;

[0105] In the formula, Total_Sorted_Items is the total number of sorted data items output by the sequential decision unit; Selection_Ratio(Network_Load) is determined by the ratios corresponding to the three load intervals mentioned above (in this embodiment, the ratios corresponding to the three intervals mentioned above are set to 0.9, 0.5, and 0.15).

[0106] For example: If the sorted list has 120 data entries, and the current Network_Load = 0.78 (high load range), corresponding to Selection_Ratio = 0.15, then the push volume for this period is: Transmit_Count = 120 × 0.15 = 18 data entries.

[0107] In addition, the adaptive filtering subunit performs adaptive pushing based on the system load index Network_Load. Specifically, the setting has a clear inverse relationship with the allowed pushing amount per cycle as follows:

[0108] A higher Network_Load indicates a busier system and fewer push notifications; a lower Network_Load indicates an idle system and more push notifications.

[0109] Optionally, the hierarchical push execution unit adopts a hierarchical scheduling mechanism to divide the target data item into at least two priority levels, and sends the data sequentially in the push time slot according to the priority level, so as to ensure that high priority data is still transmitted first under high load conditions.

[0110] After receiving the data items filtered by the load adaptation unit, the tiered push execution unit first classifies them into different priority levels based on the Freight_Priority_Index corresponding to each data item. In this embodiment, the sorted data items are divided into three priority levels:

[0111] 1) Data items that meet any of the following conditions are classified as first-level priority:

[0112] The Freight_Priority_Index of the corresponding task is higher than the set threshold (e.g., above 85 points).

[0113] The node's operational characteristics indicate recent temperature fluctuations, abnormal jumps, or equipment malfunctions.

[0114] Environmental characteristics indicate that the node is located in a temperature-sensitive area (such as near the cargo door or near the refrigeration vent).

[0115] 2) If any of the following conditions are met, it will be classified as a secondary priority level:

[0116] Freight_Priority_Index is in the middle range (e.g., 60-85 points);

[0117] Both the node's operating status and environmental characteristics are stable.

[0118] Cold chain products are relatively common, but regular monitoring is still necessary.

[0119] 3) Meeting any of the following conditions will result in being classified as a Level 3 priority level:

[0120] Freight_Priority_Index is below a set threshold (e.g., below 60 points);

[0121] The node status is continuous and stable with minimal temperature control variation;

[0122] The goods are classified as low-risk or are less affected by environmental changes.

[0123] By using the above three priority levels, the hierarchical push execution unit can clearly group data items, making it easier to schedule execution according to priority in the future.

[0124] In addition, in this embodiment, to achieve orderly scheduling of data items with different priorities during the push process, the hierarchical push execution unit presets the available sending time slots within the push cycle and forms a "hierarchical scheduling time slot table" for controlling the push order. This time slot table is loaded during system initialization and is used to define the number of time slots in the push cycle, the proportion of time slots occupied by different priorities, and the arrangement order of each time slot, so that data push can be executed hierarchically according to priority levels on a fixed structure.

[0125] Specifically, the hierarchical scheduling time slot table includes parameters such as push cycle length, number of priority levels, number of available time slots corresponding to each priority level, and time slot arrangement order. The push cycle length is used to limit the total number of data transmission time slots that can be filled in each push cycle, for example, it can be set to 20, 30, or a number dynamically adjusted by the system; the number of priority levels can be determined according to the application scenario. This embodiment uses three priority levels, including first-level priority (high priority), second-level priority (medium priority), and third-level priority (low priority).

[0126] After the push cycle length is determined, the system server allocates a corresponding number of transmission time slots to each priority level according to a pre-set ratio. For example, when the push cycle contains 20 time slots, 12 time slots can be allocated to Level 1 priority, 6 time slots to Level 2 priority, and 2 time slots to Level 3 priority. Subsequently, the hierarchical push execution unit arranges the above time slots in a fixed order and records the priority corresponding to each time slot in the time slot table. For example, the time slot table can be arranged in the following order: the first 10 time slots are occupied by Level 1 priority, the middle 5 time slots are occupied by Level 2 priority, then 2 time slots are occupied by Level 1 priority, and the last 2 time slots are allocated to Level 3 priority. Time slots that exceed the current push demand can be set as idle time slots, and no data will be sent during actual execution.

[0127] During the push execution process, the hierarchical push execution unit reads the time slots sequentially according to the hierarchical scheduling time slot table and selects data from the data queue of the corresponding priority for push. When there is no data to push in a time slot for a certain priority, the hierarchical push execution unit can postpone the time slot to the next priority or keep it in an idle state, thereby ensuring the continuity of the push execution process.

[0128] Furthermore, the tiered scheduling time slot table also includes time slot adjustment strategies corresponding to the load. When the load adaptation unit determines that the system is in a high-load range, the tiered push execution unit reduces or cancels all low-priority time slots according to the adjustment strategies in the time slot table, while the number of time slots for first-priority data remains unchanged or is reduced by the smallest amount, to ensure that high-priority data can still be pushed in the first few time slots. Conversely, when the system is in a low-load range, the tiered push execution unit can allow second- or third-priority data to restore its original number of time slots, or even temporarily increase the number of time slots when resources are sufficient, to improve overall push efficiency.

[0129] In addition, to adapt to the resource constraints caused by high load, the tiered push execution unit adopts a low-priority automatic yielding strategy. Under this strategy, when the number of available time slots in the system is insufficient to meet the data transmission requirements of all priorities, the time slots occupied by second- and third-priority items will be automatically reduced to zero according to a preset ratio, while the data items of first-priority items will not be affected. For example, under high load, all third-priority time slots can be canceled, second-priority time slots can be reduced to 20% of their original proportion, while first-priority time slots will not be reduced or will only be reduced by a minimal amount, thus reserving more transmission opportunities for first-priority items.

[0130] In this embodiment, the hierarchical push execution unit also introduces a cross-level occupancy strategy. When the number of first-priority data items exceeds their original quota in the time slot table, the system allows them to occupy idle time slots not used by lower-priority items or data spaces not allocated to second- or third-priority items. For example, in a certain push cycle, if the number of second- or third-priority data items is lower than their corresponding time slot capacity, first-priority items can directly occupy those unused time slots, allowing all high-priority data to be transmitted within the current cycle.

[0131] In this embodiment, through the cooperation of the node sampling prediction module and the logistics data push module, the system can dynamically sort and filter the data to be pushed according to Freight_Priority_Index, realize the priority transmission of high-priority data, and ensure that the system can prioritize the distribution of time-sensitive or temperature-sensitive data, thus solving the shortcomings of the existing system where the push order is fixed and cannot reflect the degree of urgency.

[0132] Optional, such as Figure 4 As shown, the environmental monitoring module includes a temperature control monitoring unit, an identity tracing unit, and a safety alarm unit; the temperature control monitoring unit is used to acquire and record the temperature and humidity parameters of the cold chain nodes; the identity tracing unit is used to record the unique identity information of the node devices; and the safety alarm unit is used to generate alarm information when abnormal temperature control, abnormal equipment, or abnormal node data is detected.

[0133] The temperature control monitoring unit acquires current environmental parameters through temperature and humidity sensors installed in transport vehicles, refrigerated containers, storage equipment, or temperature control devices. The sensors can continuously report temperature and humidity data according to a fixed sampling period (e.g., 10 seconds, 30 seconds, or 1 minute).

[0134] The temperature control monitoring unit may include multiple digital temperature sensors and integrated temperature and humidity probes installed inside the cargo compartment of the refrigerated vehicle. Each sensor is connected to the node data acquisition module via RS485 bus or BLE wireless connection. Each sensor collects temperature and humidity parameters at a fixed sampling period and uploads them to the server through a data acquisition gateway. The server records, analyzes trends, and assesses stability of the data from each monitoring point, enabling the system to achieve real-time and continuous temperature control monitoring of the cold chain environment within a given space.

[0135] After receiving the data reported by the sensor, the temperature control monitoring unit reads the timestamp, node number and sampling location corresponding to the data, and writes each data record into the temperature control log of the server to ensure the traceability of the temperature control records.

[0136] Subsequently, the temperature control monitoring unit calculates the variation range of temperature and humidity data from multiple consecutive sampling periods to determine whether the temperature control status of the current node is stable. For example, if the temperature variation range is lower than the set stability threshold in the past 5 sampling periods, it is determined that the temperature control is stable; if the temperature rises continuously above the set threshold in a short period of time, it is marked as temperature control fluctuation.

[0137] The temperature control monitoring unit can also send temperature and humidity trend information as auxiliary features to the node sampling prediction module or the safety alarm unit for subsequent prediction or risk assessment.

[0138] When a node first connects to the system, the identity tracing unit reads the unique identification information of that node device, such as the device serial number, RFID code, QR code, device factory number, or installation number, and writes this unique identifier into the node's registry. Secondly, each time data is reported, the identity tracing unit reads the device's unique identification field from the data packet and matches it with the node's registry to verify the validity of the data source node's identity. For example, if a node reports an identification field that does not have a corresponding record in the registry, the identity tracing unit can mark it as unregistered node data.

[0139] In the process of pushing, verifying and tracing, the identity tracing unit associates data with the corresponding unique device identifier, enabling the server to perform historical queries, location tracing, temperature control record backtracking and device maintenance record queries on nodes based on device identity, thereby ensuring the traceability of the entire cold chain monitoring process.

[0140] The system utilizes the cooperation between the logistics data push module and the environmental monitoring module to dynamically adjust the push time slot allocation based on Network_Load under high load conditions, and automatically upgrades the push level for data from nodes with abnormal temperature control. This ensures that the system can still prioritize the transmission of critical data when the load increases, overcoming the problem of delayed push of critical data in traditional cold chain systems when the network is congested.

[0141] The safety alarm unit receives operational information from the temperature control monitoring unit and the node detection submodule, including temperature and humidity data, equipment operating status, and data validity detection results, and makes a preliminary judgment based on preset alarm thresholds. For example, when the temperature exceeds the set upper limit or falls below the set lower limit, it is judged as a temperature control abnormality.

[0142] When a device reports a status of "fault", "offline", or "low battery", it is considered an abnormal device. When a node reports continuously missing data, disordered timestamps, or failed verification, it is considered a abnormal data.

[0143] When the security alarm unit detects any of the above-mentioned anomalies, it will generate corresponding alarm information. The alarm information includes, but is not limited to: alarm type, node to which it belongs, trigger time, trigger reason, and anomaly parameter value fields.

[0144] Subsequently, the safety alarm unit issues notifications to the public using different methods based on the alarm level. For example, alarms for minor temperature control deviations can be recorded in the system log or sent to the management terminal via a regular notification, while alarms for serious temperature control overruns or equipment malfunctions can be pushed out via SMS, voice, or high-priority system messages.

[0145] In addition, the safety alarm unit writes all historical alarm records into the alarm database, enabling the system to achieve comprehensive risk tracing based on the behavior of historical alarm-related nodes, push records, and temperature control change trends in subsequent tracing and analysis.

[0146] The management terminal is used to receive node data, alarm information, and traceability content pushed by the system server, and provides interactive functions such as data display, task confirmation, and anomaly handling. The specific form of the management terminal is not limited to a single hardware type; various terminal devices with information processing and communication capabilities can be used depending on the application scenario of cold chain logistics.

[0147] Specifically, the management terminal can be a mobile operating terminal such as a smartphone or tablet. The aforementioned management terminal is usually equipped with a matching application or a browser-based management interface. It can receive data pushed by the server through cellular network (4G / 5G) or wireless local area network (WiFi), enabling transport drivers and on-site operators to view the node temperature control status, vehicle operation status and abnormal alarm information in real time in a mobile environment.

[0148] The management terminal can also be a handheld data acquisition terminal (PDA). These devices typically integrate barcode scanning modules, RFID reading modules, and industrial-grade protective structures, making them suitable for warehousing, loading / unloading, and distribution scenarios. Through the handheld terminal, operators can receive the latest temperature control data and transportation status pushed by the system while reading node identification information, facilitating on-site verification and task confirmation. On the vehicle side, the management terminal can also be an onboard information terminal or onboard monitoring screen. This terminal maintains a real-time connection with the server via an onboard communication module, displaying the temperature and humidity inside the cargo compartment, node operating status, and system alarms to the driver, enabling the driver to obtain critical cold chain status information in a timely manner during transportation.

[0149] In this embodiment, the management terminal can also be a fixed management workstation or desktop computer, used in a dispatch center or warehouse management office. This type of terminal connects to the server via Ethernet or a wireless network and can be used to view global transportation routes, task scheduling status, historical temperature control curves, and alarm records, making it suitable for managers to perform comprehensive analysis and task allocation.

[0150] The management terminal enables real-time reception and display of cold chain node data at different application locations, allowing operators, managers, and drivers in the cold chain logistics process to obtain key data in their respective scenarios, thereby improving the system's visualization capabilities, response speed, and overall coordination. The specific type of management terminal can be selected by those skilled in the art based on the actual situation; this is a method well-known to those skilled in the art, and therefore will not be described in detail in this embodiment.

[0151] In this embodiment, through the cooperation of the node sampling prediction module and the environmental monitoring module, the system can simultaneously acquire the operating characteristics and real-time temperature control status of the nodes, and transform information such as temperature control fluctuations and delay risks into quantifiable predictive indicators, ensuring that the system can identify potential high-risk nodes in advance, thus overcoming the shortcomings of the prior art in that it cannot dynamically assess the node status.

[0152] In addition, this application also provides a method for cold chain logistics information distribution and traceability in data management, the method comprising the following steps:

[0153] S1. Collect the data port status of each logistics node and perform consistency verification on the node data to generate a trusted node dataset.

[0154] Step S1 further includes performing sub-steps such as S11 to S13:

[0155] S11. Verify the identity of registered nodes. That is, before collecting data, the system reads the unique identifier of the node (such as RFID code, device serial number or sensor tag code) to confirm that the node belongs to a registered and legitimate node device.

[0156] S12. Determine the range of nodes participating in the verification based on the node's trust weight. That is, when some nodes have low trust scores, the system can choose to skip their data or reduce their weight in the consistency verification to improve the overall quality of the trust set.

[0157] S13. Generate index records for the data that passes the verification. The index records include time index, node index, and batch index, so that subsequent traceability queries can quickly locate the source node.

[0158] S2. Periodically collect vehicle and warehousing equipment operation data and calculate the freight priority index Freight_Priority_Index;

[0159] S3. Sort, filter, and determine the data push order based on Freight_Priority_Index and the system load index Network_Load;

[0160] Optionally, the cold chain logistics information distribution and traceability method further includes: the sub-steps performed in step S3 include:

[0161] S31. Perform initial sorting based on freight priority indicators;

[0162] S32. Dynamically adjust the sorting results based on node operation characteristics and environmental characteristics;

[0163] S33. Adaptively filter the number of data items to be pushed based on the system load index Network_Load.

[0164] S34. After sorting, mark the priority level of each data item for easy use by S4.

[0165] S35. After filtering, generate a candidate push set as input for S4, making the entire process clearly segmented.

[0166] S36. Write the reasons for the sorting adjustment process (such as temperature control fluctuations, vehicle speed changes, etc.) into an additional field for subsequent traceability and explanation of the decision logic of the push behavior.

[0167] S4. Push the target data items to the management terminal according to the hierarchical scheduling mechanism, and record the push log for traceability.

[0168] Optionally, the cold chain logistics information distribution and traceability method further includes: the sub-steps performed in step S4 include:

[0169] S41. Divide the target data items into at least two priority levels;

[0170] The division is based on the Freight_Priority_Index value of the target data item, dividing the data item into at least two priority queues, such as a high-priority queue, a second-highest-priority queue, and a normal-priority queue. To enhance the system's adaptability to changing environments, the priority division is not only based on Freight_Priority_Index but can also be dynamically adjusted by combining node operating characteristics and environmental characteristics.

[0171] Specifically, when the temperature control monitoring unit detects an upward trend, sudden jump, or abnormal fluctuation in temperature at a node within a recent sampling period, the data item corresponding to that node will be automatically and temporarily moved to a higher priority queue, even if its Freight_Priority_Index has not reached the high priority threshold. This ensures that such potentially risky data receives priority processing during the push phase. Similarly, when a node's operational characteristics indicate that the vehicle is accelerating, about to reach a critical delivery point, or the equipment has just recovered from a fault, the system will also upgrade its data priority, placing it at the top of the push queue.

[0172] Furthermore, for perishable goods, vaccines, biological agents, and other products with strict temperature control requirements, their corresponding data items can be identified by the system as special task types and automatically assigned a higher priority during the priority mapping process. Through this mapping and dynamic enhancement mechanism, priority allocation is not only based on static numerical judgments but can also be quickly adjusted according to real-time risks, thereby ensuring that critical data maintains a leading position in subsequent push notifications.

[0173] S42. Data is sent sequentially within the push time slot according to priority.

[0174] In this embodiment, the hierarchical push execution unit uses a preset hierarchical scheduling time slot table for push scheduling. The push period is divided into several consecutive transmission time slots, each time slot being used to send one piece of data or a group of data. The hierarchical scheduling time slot table reserves a fixed number of transmission time slots for different priorities, with high-priority data occupying the earliest and most numerous time slots in the push period.

[0175] During push execution, the system strictly follows the time slot table order to read time slots sequentially and selects data from the priority queue corresponding to each time slot for transmission. When a priority queue has no data to push in the current time slot, the system allows higher-priority queues to occupy that time slot across levels, but does not allow lower-priority queues to preempt higher-priority time slots, thus ensuring the strictness of priority scheduling.

[0176] To prevent high-priority data from being diluted under high load conditions, the system has a built-in priority time slot reservation strategy. When Network_Load is high, leading to a reduction in the number of available time slots, the system prioritizes reserving time slots occupied by high-priority queues and correspondingly reduces or cancels low-priority time slots. For example, when the number of available time slots decreases from 20 to 6, the system allocates all 6 time slots to high-priority data, while low-priority data no longer participates in the scheduling for the current cycle. This strategy ensures that high-priority data still has a stable transmission opportunity even under high system load.

[0177] S43. During the push process, the real-time monitoring data of the environmental monitoring module is associated with and stored with the push record to achieve full-process traceability.

[0178] After each successful data transmission, the hierarchical push execution unit synchronizes and stores information such as the push slot number, transmission timestamp, priority level, and Network_Load index at the time of push execution into the push log. Simultaneously, during push execution, the system also associates and stores fields from the environmental monitoring module, including the temperature and humidity sampling snapshots, device identification information, and the corresponding transportation batch, for that node.

[0179] If, after a push is executed, it is found that a high-priority data has not been sent in time due to insufficient time slots, the system will trigger a high-priority resend mechanism to reserve an earlier time slot for the data in the next push cycle, so as to ensure that critical data is not missed due to system resource constraints.

[0180] By cooperating with the distributed verification module, logistics data push module, and environmental monitoring module, the system can simultaneously record the node's trust status, push order, push time slot number, and temperature control snapshot information, enabling full-process traceability of each push data. This ensures that the system has complete auditability and re-verification capabilities, avoiding the problems of lacking evidence of the source of push behavior and being unable to trace the source in traditional solutions.

[0181] Example 2: This example should be understood as including all the features of any of the foregoing examples, and further improving upon them, according to... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6Furthermore, the distributed verification module also includes a trusted set generation unit. After obtaining the node operation behavior data uploaded by the node detection unit and the verification results generated by the data consistency verification unit, the trusted set generation unit performs periodic cumulative calculations on the node's trusted weight based on the node's historical temperature control stability, data consistency pass rate, equipment anomaly occurrence, and data reporting continuity, and generates an updated Node_Trust_Score after each verification cycle is completed.

[0182] Among them, the node trust weight Node_Trust_Score is calculated according to the evaluation period. The update is performed using the following formula:

[0183] ;

[0184] In the formula, k represents the kth evaluation period. The node credibility weight score (0-100) for the current evaluation period (the kth period); The node credibility weight score for the previous evaluation period; This is the positive cumulative score for this period, used to reflect the stable and normal performance of the nodes; This is the deduction item for this period, used to reflect negative behaviors such as abnormalities and malfunctions; the outer min(100, …) and max(0, …) are used to limit the score between 0 and 100.

[0185] When a node first connects to the system and has not yet generated a trusted weight from the previous evaluation period, the system will assign an initial default value to that node. .

[0186] In addition, in this embodiment, the positive cumulative score of this period Calculate according to the following formula:

[0187] ;

[0188] Where k represents the kth evaluation period, Bonus points are awarded for consistency. This contributes to improved temperature control stability.

[0189] In this embodiment, the total number of data records collected for consistency verification is: The number of records that passed the verification was ,but:

[0190] ;

[0191] Where k represents the kth evaluation period;

[0192] Based on pass rate Points will be awarded in segments, specifically:

[0193] ;

[0194] Where k represents the kth evaluation period;

[0195] Meanwhile, a consistency pass rate of ≥99% indicates that the data of this node is very reliable in this period, and 3 points are added; a pass rate of 95% to 99% is added, and no points are added if it is below 95%.

[0196] In addition, the difference between the maximum and minimum temperature records collected during this period is: Points will be awarded based on temperature fluctuations, specifically:

[0197] ;

[0198] In the above formula, if the temperature fluctuation is very small (≤0.5℃) and the environment is stable, a maximum of 2 points will be added; if the temperature fluctuates to a certain extent (0.5℃≤1.0℃) but is still acceptable, 1 point will be added; if the fluctuation is too large (exceeding 1.0℃), no points will be added.

[0199] Furthermore, in this embodiment, the deductions for this period can be calculated using the formula "number of abnormalities × corresponding deduction coefficient," specifically: ;

[0200] in, This represents the number of serious equipment failures (such as chiller failure or sensor offline) that occurred at this node during the current period. This value is collected by the equipment status detection unit in the environmental monitoring module. This represents the number of times the temperature control over-limit or high-level alarm is triggered within this cycle. Its value is the number of high-level alarms in this cycle as counted in the alarm record table. This represents the number of times data was lost or not reported consecutively within this period. Its value is obtained by the server automatically analyzing the data reporting status of the nodes.

[0201] In this embodiment, the generated Node_Trust_Score will be output to the server along with the node trusted dataset and provided as a unified trusted parameter to the node sampling prediction module, logistics data push module and environmental monitoring module. This enables the system to dynamically adjust based on the long-term trusted performance of the nodes in multiple stages such as sampling, verification, sorting and alarm, thereby forming a full-process trusted control mechanism oriented towards node behavior.

[0202] By periodically statistically and cumulatively calculating node operation behavior, consistency verification results, temperature control change characteristics, and equipment status information through the trusted set generation unit, the system can generate a dynamic trusted weight Node_Trust_Score for each cold chain node, reflecting its long-term operational stability. This forms a unified trusted basis in multiple modules such as subsequent data collection frequency adjustment, verification threshold setting, push sorting optimization, and alarm level adjustment. It has the advantages of objective node trust assessment, continuous updates, and full-process linkage, and can effectively improve the stability, judgment accuracy, and anomaly response capabilities of the cold chain logistics data management process.

[0203] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. A cold chain logistics information distribution and traceability system for data management, characterized in that, The system comprises a server, a distributed verification module, a node sampling prediction module, a logistics data pushing module and an environment monitoring module; the server is connected with the distributed verification module, the node sampling prediction module, the logistics data pushing module and the environment monitoring module respectively; The distributed verification module is used for collecting data port states of multiple logistics nodes and performing consistency verification on node data to form a trusted data set among nodes; the node sampling prediction module is used for periodically sampling freight vehicles, storage equipment and temperature control devices, calculating a freight priority index Freight_Priority_Index and generating prediction data; the logistics data pushing module is used for dynamically pushing node data according to the freight priority index and a system load index Network_Load, and generating a delivery feedback index Delivery_Feedback based on feedback information; the environment monitoring module is used for real-time monitoring of cold chain transportation and storage environment parameters, recording and tracing the temperature control state and safety alarm information of each node; The logistics data pushing module comprises a sequence decision unit, a load adaptive unit and a hierarchical pushing execution unit; the sequence decision unit is used for obtaining a verified node trusted data set, sorting to-be-pushed data items according to the freight priority index, and dynamically adjusting the sorting result based on the running characteristics and environmental characteristics of the nodes; the load adaptive unit is used for adaptively filtering the sorted data items according to the system load index Network_Load to dynamically determine the data transmission amount of each pushing period; and the hierarchical pushing execution unit is used for pushing target data items to a management terminal according to a hierarchical scheduling mechanism to ensure that high-priority data is given priority in high-load conditions.

2. The data managed cold-chain logistics information distribution and traceability system according to claim 1, characterized in that, The distributed verification module comprises a node detection unit, a data consistency verification unit and a trusted set generation unit; the node detection unit is used for collecting data port states of each logistics node; the data consistency verification unit is used for performing cross-node consistency verification on data from different nodes; and the trusted set generation unit is used for generating a node trusted data set from the data records that pass the verification.

3. The data-managed cold-chain logistics information distribution and traceability system according to claim 2, characterized in that, The node sampling prediction module comprises a node sampling unit, a priority calculation unit and a prediction generation unit; the node sampling unit is used for periodically collecting vehicle operating parameters, temperature control parameters and storage equipment states; the priority calculation unit is used for calculating the freight priority index Freight_Priority_Index according to the operating frequency and delay characteristics in the sampling period; and the prediction generation unit is used for generating prediction data for the next pushing period based on historical node behaviors.

4. The data managed cold chain logistics information distribution and traceability system according to claim 3, wherein, The sequence decision unit comprises a sorting subunit and an adjustment subunit; the sorting subunit is used for initially sorting to-be-pushed data items according to the freight priority index; and the adjustment subunit is used for dynamically adjusting the initial sorting result based on the node running characteristics and environmental characteristics.

5. The data managed cold-chain logistics information distribution and traceability system according to claim 4, characterized in that, The load adaptive unit comprises a load monitoring subunit and an adaptive screening subunit; the load monitoring subunit is used for monitoring the system load index Network_Load in real time; the adaptive screening subunit is used for determining the corresponding number of pushed data items in different load intervals according to the system load index.

6. The data managed cold-chain logistics information distribution and traceability system according to claim 5, characterized in that, The hierarchical push execution unit adopts a hierarchical scheduling mechanism, divides the target data items into at least two priority levels, and sends the data in the push time slot in turn according to the priority, so as to ensure that the high-priority data is still preferentially transmitted under high-load conditions.

7. The data managed cold-chain logistics information distribution and traceability system according to claim 6, characterized in that, The environment monitoring module comprises a temperature control monitoring unit, an identity tracing unit and a safety alarm unit; the temperature control monitoring unit is used for acquiring and recording the temperature and humidity parameters of the cold chain node; the identity tracing unit is used for recording the unique identity information of the node equipment; and the safety alarm unit is used for generating alarm information when detecting temperature control abnormalities, equipment abnormalities or node data abnormalities.

8. A data management cold-chain logistics information distribution and traceability method according to the data management cold-chain logistics information distribution and traceability system of claim 7, characterized in that, The cold chain logistics information distribution and tracing method comprises the following steps: S1, collecting the data port state of each logistics node, and performing consistency verification on the node data to generate a node trusted data set; S2, periodically collecting vehicle and warehouse equipment operation data, and calculating a freight priority index Freight_Priority_Index; S3, sorting, screening and determining the data push sequence according to Freight_Priority_Index and system load index Network_Load; S4, pushing the target data items to the management terminal according to the hierarchical scheduling mechanism, and recording the push log for tracing.

9. The data managed cold-chain logistics information distribution and traceability method and system according to claim 8, characterized in that, The cold chain logistics information distribution and tracing method further comprises the following sub-steps performed in step S3: S31, performing initial sorting according to the freight priority index; S32, dynamically adjusting the sorting result based on the node operation characteristics and environmental characteristics; S33, adaptively screening the number of data items to be pushed according to the system load index Network_Load.

10. The data managed cold-chain logistics information distribution and traceability method and system according to claim 9, characterized in that, The cold chain logistics information distribution and tracing method further comprises the following sub-steps performed in step S4: S41, dividing the target data items into at least two priority levels; S42, sending the data in the push time slot in turn according to the priority; S43, associating and storing the real-time monitoring data of the environment monitoring module with the push record in the push process to realize whole-process tracing.

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