A primary medical low-altitude logistics whole-process traceability management method and system

Abstract

The application provides a kind of base-level medical low-altitude logistics whole-process traceability management method and system, multi-source parameters of medical supplies, unmanned aerial vehicle, handover site and signing link are collected, after being aligned by Beidou time service high-precision timestamp, push to full-factor digital twin model to realize virtual-real mapping;Adopt alliance chain stage-by-stage chain mechanism to encrypt storage of whole-process data and unique traceability identifier, determine each link adaptive early warning threshold by combining medical material risk level and historical data;Real-time monitoring of whole-process parameters, calculate multi-parameter fusion abnormality, when abnormality exceeds limit or compliance anomaly occurs, trigger smart contract to automatically execute abnormal disposal and generate whole-process traceability report.This scheme realizes the whole-process credible traceability of base-level medical low-altitude logistics from warehouse to signing, real-time management and control and intelligent disposal, effectively guarantees the safety and timeliness of medical material transportation.

Description

Technical Field

[0001] This invention relates to the field of low-altitude logistics and medical supplies management technology, and in particular to a method and system for full-process traceability and management of low-altitude logistics for primary healthcare. Background Technology

[0002] With the continuous improvement of my country's primary healthcare system, the demand for medical supplies distribution in remote areas, mountainous regions, and islands with poor transportation is increasing. Low-altitude drone logistics, with its advantages of speed, flexibility, and lack of terrain limitations, has become an effective means to solve the last-mile problem of primary healthcare supply distribution. However, medical supplies, especially cold chain supplies such as vaccines, blood products, and emergency medicines, have extremely high requirements for transportation environment, timeliness, and safety. Currently, the primary healthcare low-altitude logistics system still faces many problems that urgently need to be addressed.

[0003] Current technologies for managing medical supply logistics suffer from the following shortcomings: First, the traceability system is incomplete. Most systems can only record information at certain stages, and the data is stored in centralized databases, making it susceptible to tampering or loss, thus failing to guarantee the reliability of data throughout the entire process. Second, there is a lack of targeted real-time monitoring and early warning mechanisms. Early warning thresholds are mostly fixed values, unable to adapt to the risk levels of different types of medical supplies, and lack the ability to identify abnormal patterns involving multiple coupled parameters. Delayed early warnings can easily lead to damage to supplies. Third, there are gaps in the handover process. The handover between drones and community health stations relies heavily on manual verification, which is inefficient and prone to problems such as identity fraud and misdelivery of supplies. Fourth, the ability to handle anomalies is weak. Manual intervention is required when anomalies occur, resulting in slow response times and failing to meet the timeliness requirements of emergency supplies. Therefore, how to achieve reliable traceability, real-time control, and intelligent anomaly handling throughout the entire process of low-altitude logistics for primary healthcare has become a core challenge currently facing the industry. Summary of the Invention

[0004] Based on this, the present invention provides a method and system for full-process traceability and control of low-altitude logistics for primary healthcare, which can realize full-process reliable traceability, real-time control and intelligent anomaly handling.

[0005] In a first aspect, the present invention provides a method for full-process traceability and control of low-altitude logistics for primary healthcare, comprising the following steps: Collect multi-source parameter feature values ​​of medical supplies, transport drones, handover stations, and signing links in the entire low-altitude logistics chain for primary healthcare; All parameter feature values ​​are aligned with high-precision timestamps based on BeiDou time synchronization, and then all timetamp-aligned parameter feature values ​​are pushed to the full-element digital twin virtual model to achieve synchronous mapping and visualization of the entire logistics process in virtual space; Based on the phased on-chain mechanism of the consortium blockchain, the feature values ​​of the full-process parameters are encrypted and associated with the unique traceability identifier and stored in the corresponding node. At the same time, based on the historical health transportation data stored in the time series database associated with the digital twin virtual model, combined with the risk level of medical supplies, the adaptive early warning threshold of each link is determined by the sliding time window and weighted fusion algorithm. Real-time monitoring of parameter characteristic values ​​and corresponding adaptive early warning thresholds throughout the entire logistics process; when multiple parameter characteristic values ​​enter the warning zone outside the corresponding early warning threshold and show a continuous deviation trend, the multi-parameter fusion anomaly degree of the current logistics status relative to the historical health benchmark is determined. When the multi-parameter fusion anomaly exceeds the preset anomaly threshold or any key link experiences a compliance anomaly, the smart contract is triggered to automatically execute the anomaly handling process and generate a control report containing complete traceability information.

[0006] In some embodiments, aligning all parameter feature values ​​with high-precision timestamps based on BeiDou time synchronization specifically includes: Obtain the structured data packets with local timestamps corresponding to all parameter feature values. The data packets contain data source identifiers and unique material traceability codes. Based on the precise timing service of the BeiDou satellite navigation system, the local timestamps in all structured data packets are aligned at the nanosecond level. According to the preset time granularity, all parameter feature values ​​within the same time window are collected and completed to obtain a set of full-chain parameter feature values ​​after timestamp alignment.

[0007] In some embodiments, all timestamp-aligned parameter feature values ​​are pushed to a full-element digital twin virtual model to achieve synchronous mapping and visualization of the entire logistics process in virtual space. Specifically, this includes: All timestamp-aligned parameter feature values ​​are sent to the data access layer of the full-element digital twin virtual model via the 5G Industrial Internet protocol; Based on the source identifier and traceability code of the parameter feature values, update the status attributes of the virtual entities of medical supplies, drones, handover stations, and transportation routes in the digital twin virtual model respectively; The visualization engine that drives the digital twin virtual model renders and displays a three-dimensional dynamic mapping of the entire logistics process based on real-time changes in status attributes, while overlaying highlighted warning signs at abnormal locations.

[0008] In some embodiments, based on the phased on-chain mechanism of the consortium blockchain, storing the full-process parameter feature values ​​and unique traceability identifiers in encrypted association to the corresponding nodes specifically includes: A globally unique traceability identifier is generated for each batch of medical supplies, and this identifier is used throughout the entire process of outbound warehousing, transportation, handover, and receipt. The entire process data is divided into outbound data segment, transportation data segment, handover data segment and receipt data segment according to the logistics stage, and a corresponding hash value is generated for each data segment. After encrypting and associating the hash value of each data segment with the traceability identifier, the data is stored on the blockchain at pharmacy nodes, drone platform nodes, community health station nodes, and regulatory department nodes respectively. The smart contract automatically verifies the consistency of the data uploaded to the chain by each node. If data tampering is found, an alarm is immediately triggered and the corresponding data segment is locked.

[0009] In some embodiments, the adaptive early warning threshold for each stage is determined by combining the risk level of medical supplies with a sliding time window and a weighted fusion algorithm, specifically including: Based on the type and storage requirements of medical supplies, they are divided into three levels: high risk, medium risk and low risk, with different initial warning coefficients for each level. Historical health transportation data of medical supplies with corresponding risk levels are extracted from the time-series database associated with the digital twin virtual model; For each parameter feature value, the sliding time window data sequence is extracted according to the set window length and sliding step size, and its exponentially weighted moving mean and standard deviation are calculated; By combining the initial warning coefficient with the calculated mean and standard deviation, the adaptive warning upper and lower limits of the characteristic values ​​of each parameter in each stage are determined, and these limits are stored in association with the traceability identifier and the transportation stage.

[0010] In some embodiments, when multiple parameter feature values ​​enter the warning zone outside the corresponding warning threshold and show a continuous deviation trend, determining the multi-parameter fusion anomaly degree of the current logistics status relative to the historical health benchmark specifically includes: For each parameter feature value, determine whether its real-time value enters the warning zone outside the corresponding warning threshold; For the parameter characteristic values ​​of entering the warning zone, linear regression analysis is used to determine whether they show a continuous deviation trend; If multiple parameter feature values ​​are determined to show a continuous deviation trend, the weighted Mahalanobis distance algorithm is used to calculate the multi-parameter fusion anomaly degree of the current logistics status relative to the historical health benchmark, based on the weight coefficients and deviation degree of all abnormal parameters.

[0011] In some embodiments, when the multi-parameter fusion anomaly degree exceeds a preset anomaly threshold or a compliance anomaly occurs in any key step, triggering the smart contract to automatically execute the anomaly handling process specifically includes: Continuously monitor whether the anomaly degree of multi-parameter fusion exceeds the preset anomaly threshold, and monitor whether there are compliance anomalies in key links such as outbound verification, drone airworthiness, handover identity authentication, and receipt confirmation; When any monitoring condition is met, the smart contract automatically generates corresponding handling instructions based on the anomaly type and level. If the transportation parameters are abnormal, the nearest backup drone will be automatically dispatched to transfer the supplies, and the transportation route in the digital twin model will be updated; if the compliance is abnormal, the corresponding supplies will be immediately locked and the regulatory authorities and relevant responsible persons will be notified. All data from the anomaly handling process is stored on the blockchain in real time to ensure the traceability of the handling process.

[0012] In some embodiments, generating a control report containing complete traceability information specifically includes: Based on the source identification of the triggered anomaly, the corresponding full-process on-chain data of medical supplies is pulled from each node of the consortium blockchain; Integrate logistics trajectories, parameter change curves, and anomaly handling process data recorded in the digital twin virtual model; Generate a standardized control report that includes basic information about materials, time nodes throughout the entire process, parameter change trends, explanations of abnormal situations, and handling results; The hash value of the control report is stored on the blockchain and made available to regulatory authorities, medical institutions and patients for query and verification.

[0013] In some embodiments, the parameter characteristics include the temperature and humidity of medical supplies, vibration amplitude, drone flight altitude, flight speed, remaining battery power, handover station identity authentication information, and biometric data of the recipient.

[0014] Secondly, this invention provides a full-process traceability and control system for low-altitude logistics in primary healthcare, comprising: The data acquisition module is used to collect multi-source parameter feature values ​​of medical supplies, transport drones, handover stations, and signing links in the entire chain of low-altitude logistics for primary healthcare. The processing module is used to perform high-precision timestamp alignment of all parameter feature values ​​based on BeiDou time synchronization, and then push all timetamp aligned parameter feature values ​​to the full-element digital twin virtual model to realize the synchronous mapping and visualization of the entire logistics process in virtual space. The blockchain module is used to store the full-process parameter feature values ​​and unique traceability identifiers in encrypted association to the corresponding nodes based on the phased on-chain mechanism of the consortium blockchain. The processing module is also used to determine the adaptive early warning threshold for each link by combining historical health transportation data stored in the time-series database associated with the digital twin virtual model with the risk level of medical supplies and using a sliding time window and weighted fusion algorithm. The processing module is also used to monitor the parameter feature values ​​of the entire logistics process and the corresponding adaptive early warning thresholds in real time. When multiple parameter feature values ​​enter the warning zone outside the corresponding early warning threshold and show a continuous deviation trend, the multi-parameter fusion anomaly degree of the current logistics status relative to the historical health benchmark is determined. The execution module is used to trigger the smart contract to automatically execute the exception handling process and generate a control report containing complete traceability information when the multi-parameter fusion anomaly degree exceeds the preset anomaly threshold or when any key link has a compliance anomaly.

[0015] Compared with the prior art, the present invention has the following advantages: The present invention provides a method and system for full-process traceability and control of low-altitude logistics for primary healthcare. First, it collects multi-source parameter feature values ​​from the entire low-altitude logistics chain for primary healthcare and aligns them with high-precision timestamps using BeiDou time synchronization. This data is then pushed to a full-element digital twin virtual model to achieve synchronous mapping between the virtual and real worlds. This step establishes a comprehensive digital mirror covering medical supplies, drones, handover points, and transportation routes, providing a unified and intuitive data foundation for subsequent control. Second, it employs a consortium blockchain phased on-chain mechanism to encrypt and associate the entire process data with a unique traceability identifier. Smart contracts automatically verify data consistency, fundamentally solving the problems of tampering and untrustworthiness in centralized data storage. This invention addresses the problem of ensuring the immutability and traceability of data throughout the entire process. Then, by combining the risk level of medical supplies with historical health transportation data, a sliding time window and weighted fusion algorithm are used to determine adaptive early warning thresholds for each stage. This overcomes the drawback of fixed thresholds being unable to adapt to different material needs, improving the accuracy and targeting of early warnings. Next, through multi-parameter fusion anomaly calculation, it can keenly capture early anomaly trends caused by multi-parameter coupling, achieving early warning of potential risks. Finally, when an anomaly occurs, a smart contract is triggered to automatically execute the handling process and generate a full-process traceability report, achieving automation and standardization of anomaly handling and significantly improving response speed and handling efficiency. In summary, the solution of this invention achieves reliable traceability, real-time control, and intelligent anomaly handling throughout the entire process of low-altitude logistics for primary healthcare, from pharmacy delivery to patient receipt, effectively ensuring the transportation safety and timeliness of primary healthcare supplies. Attached Figure Description

[0016] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is an exemplary flowchart of a method for full-process traceability and control of low-altitude logistics for primary healthcare, as shown in some embodiments of the present invention. Figure 2 This is a schematic diagram illustrating the application scenario of a full-process traceability and control system for low-altitude logistics in primary healthcare, as shown in some embodiments of the present invention. Figure 3 This is a schematic diagram of the phased on-chain process of a consortium blockchain according to some embodiments of the present invention; Figure 4 This is a schematic diagram of the structure of a full-process traceability and control system for low-altitude logistics in primary healthcare, as shown in some embodiments of the present invention. Figure 5 This is a schematic diagram of the structure of a computer device for implementing a method for full-process traceability and control of low-altitude logistics in primary healthcare, as shown in some embodiments of the present invention. Detailed Implementation

[0017] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0018] The present invention will be further described in detail below with reference to the accompanying drawings.

[0019] refer to Figure 1 The figure is an exemplary flowchart of a method for full-process traceability and control of low-altitude logistics for primary healthcare, according to some embodiments of the present invention. This method mainly includes the following steps: In step 101, multi-source parameter feature values ​​are collected for medical supplies, transport drones, handover stations, and signing links in the entire chain of low-altitude logistics for primary healthcare.

[0020] In practical implementation, the collection of parameter characteristics at each stage of the low-altitude logistics chain for primary healthcare can be achieved in the following ways: First, smart tags integrating temperature and humidity sensors, vibration sensors, and RFID tags are affixed to the packaging of medical supplies. These smart tags can collect parameters such as the temperature and humidity of the environment in which the medical supplies are located and the vibration amplitude during transportation in real time, and periodically transmit data via wireless communication. Next, GPS / BeiDou positioning modules, flight status sensors, and battery management systems are installed on the transport drones to collect flight parameters such as flight altitude, flight speed, geographical location, remaining battery power, and motor speed in real time. NFC card readers and facial recognition terminals are deployed at community health station handover points to collect the identity authentication information of the handover personnel. Finally, mobile scanning terminals and facial recognition devices are deployed at the patient signing stage to collect the biometric data of the signatory and the signing time information. All the collected parameter characteristics are encapsulated into structured data packets with data source identifiers and unique traceability codes for the supplies, awaiting subsequent transmission and processing.

[0021] In some embodiments, reference Figure 2As shown in the figure, this is a schematic diagram of an application scenario for a grassroots medical low-altitude logistics full-process traceability and control system, as illustrated in some embodiments of the present invention. The figure includes smart tags for medical supplies, transport drones, community health station handover terminals, patient signature terminals, edge computing gateways, cloud servers, consortium blockchain node clusters, and a digital twin platform. Data collected by the smart tags and drones is sent to the edge computing gateway via a 5G network for preprocessing. The preprocessed data is then uploaded to the cloud server, which synchronizes the data to the consortium blockchain node cluster and the digital twin platform, ultimately achieving full-process control and traceability.

[0022] In step 102, all parameter feature values ​​are aligned with high-precision timestamps based on BeiDou time synchronization, and then all timetamp-aligned parameter feature values ​​are pushed to the full-element digital twin virtual model to achieve synchronous mapping and visualization of the entire logistics process in virtual space.

[0023] In some embodiments, high-precision timestamp alignment of all parameter feature values ​​based on BeiDou time synchronization can be achieved by the following steps: Obtain the structured data packets with local timestamps corresponding to all parameter feature values. The data packets contain data source identifiers and unique material traceability codes. Based on the precise timing service of the BeiDou satellite navigation system, the local timestamps in all structured data packets are aligned at the nanosecond level. According to the preset time granularity, all parameter feature values ​​within the same time window are collected and completed to obtain a set of full-chain parameter feature values ​​after timestamp alignment.

[0024] In practice, high-precision timestamp alignment based on BeiDou time synchronization can be achieved in the following way: all data acquisition devices and edge computing gateways are connected to the BeiDou Precision Time Synchronization System to obtain a unified high-precision time reference; after receiving each structured data packet, the edge computing gateway converts the local timestamp in the data packet into a BeiDou standard timestamp; a 1-second time alignment period is set, and all data packets arriving in each period are collected. For cases where multiple data packets from the same source exist within a period, the data packet with the timestamp closest to the center point of the period is selected as the representative; for sources of missing data within a period, linear interpolation is used to complete the data, and finally, a time-aligned dataset containing all parameter feature values ​​of the entire chain is generated.

[0025] In some embodiments, pushing all timestamp-aligned parameter feature values ​​to a full-element digital twin virtual model to achieve synchronous mapping and visualization of the entire logistics process in virtual space can be achieved through the following steps: All timestamp-aligned parameter feature values ​​are sent to the data access layer of the full-element digital twin virtual model via the 5G Industrial Internet protocol; Based on the source identifier and traceability code of the parameter feature values, update the status attributes of the virtual entities of medical supplies, drones, handover stations, and transportation routes in the digital twin virtual model respectively; The visualization engine that drives the digital twin virtual model renders and displays a three-dimensional dynamic mapping of the entire logistics process based on real-time changes in status attributes, while overlaying highlighted warning signs at abnormal locations.

[0026] In practical implementation, the construction and updating of the full-element digital twin virtual model can be achieved in the following way: Virtual entities corresponding one-to-one with the physical world are pre-constructed in the virtual space, including virtual entities for medical supplies, drones, community health stations, geographical environment, and transportation routes; after receiving the time-aligned parameter feature values, the data access layer of the digital twin platform parses out the source identifier and traceability code corresponding to each parameter; based on the source identifier, the corresponding virtual entity is located, and the parameter feature values ​​are assigned to the corresponding state attributes of the virtual entity. For example, the temperature and humidity values ​​are assigned to the environmental temperature and humidity attributes of the medical supplies virtual entity, and the geographical location is assigned to the current location attribute of the drone virtual entity; the visualization engine monitors the changes in the state attributes of all virtual entities in real time. When the state attributes are updated, the 3D rendering pipeline is driven to update the virtual scene, realizing dynamic visualization of the entire logistics process; when abnormal parameters are detected, a red highlighted flashing indicator is superimposed at the location of the corresponding virtual entity, and a detailed abnormal information prompt box pops up.

[0027] In step 103, based on the phased on-chain mechanism of the consortium blockchain, the feature values ​​of the full-process parameters and the unique traceability identifier are encrypted and associated and stored in the corresponding nodes. At the same time, based on the historical health transportation data stored in the time-series database associated with the digital twin virtual model, combined with the risk level of medical supplies, the adaptive early warning threshold of each link is determined by a sliding time window and a weighted fusion algorithm.

[0028] In some embodiments, reference Figure 3 As shown in the figure, this is a schematic diagram of the phased on-chain process of a consortium blockchain as illustrated in some embodiments of the present invention. The data storage based on the phased on-chain mechanism of the consortium blockchain specifically includes: A globally unique traceability identifier is generated for each batch of medical supplies, and this identifier is used throughout the entire process of outbound warehousing, transportation, handover, and receipt. The entire process data is divided into outbound data segment, transportation data segment, handover data segment and receipt data segment according to the logistics stage, and a corresponding hash value is generated for each data segment. After encrypting and associating the hash value of each data segment with the traceability identifier, the data is stored on the blockchain at pharmacy nodes, drone platform nodes, community health station nodes, and regulatory department nodes respectively. The smart contract automatically verifies the consistency of the data uploaded to the chain by each node. If data tampering is found, an alarm is immediately triggered and the corresponding data segment is locked.

[0029] In practical implementation, the deployment and data upload of the consortium blockchain can be achieved in the following way: A consortium blockchain is constructed, consisting of pharmacies, drone operation platforms, community health stations, health regulatory departments, and medical institutions. Each participant acts as an independent node with corresponding read and write permissions. When medical supplies leave the pharmacy, the system generates a globally unique traceability identifier for that batch of supplies and generates a hash value for the outbound data, including outbound time, supply name, specifications, quantity, and production batch number. This hash value is then associated with the traceability identifier and stored on the blockchain at the pharmacy node. During transportation, the drone generates hash values ​​for the collected transportation parameters every 5 minutes, associates them with the traceability identifier, and stores them on the blockchain. The system connects to the drone platform node. When supplies arrive at the community health station for handover, a hash value is generated from the handover data, including the identity information of the personnel, the handover time, and the status of the supplies. This hash value is then associated with the traceability identifier and stored on the blockchain at the community health station node. When a patient signs for the package, a hash value is generated from the signatory's biometric data and the signing time. This hash value is then associated with the traceability identifier and stored on the blockchain at the regulatory department node. The smart contract monitors the on-chain data of each node in real time and periodically verifies the consistency of hash values ​​for the same traceability identifier corresponding to different nodes. If a hash value mismatch is found, it is immediately determined to be data tampering, triggering a system alarm and locking the corresponding data segment to prevent the spread of tampered data.

[0030] In some embodiments, determining the adaptive early warning threshold based on the risk level of medical supplies specifically includes: Based on the type and storage requirements of medical supplies, they are divided into three levels: high risk, medium risk and low risk, with different initial warning coefficients for each level. Historical health transportation data of medical supplies with corresponding risk levels are extracted from the time-series database associated with the digital twin virtual model; For each parameter feature value, the sliding time window data sequence is extracted according to the set window length and sliding step size, and its exponentially weighted moving mean and standard deviation are calculated; By combining the initial warning coefficient with the calculated mean and standard deviation, the adaptive warning upper and lower limits of the characteristic values ​​of each parameter in each stage are determined, and these limits are stored in association with the traceability identifier and the transportation stage.

[0031] In practical implementation, the adaptive warning threshold can be calculated as follows: First, risk levels are classified according to the characteristics of medical supplies. Vaccines, blood products, and emergency medicines, which are highly temperature-sensitive and have extremely high time-sensitivity requirements, are classified as high-risk, with an initial warning coefficient set to 2; ordinary medicines and medical devices are classified as medium-risk, with an initial warning coefficient set to 2.5; medical consumables and office supplies are classified as low-risk, with an initial warning coefficient set to 3. Then, historical health transportation data for the most recent three months of medical supplies corresponding to their risk levels are extracted from the time-series database, and abnormal data is removed to form a training dataset; the data is then adjusted based on temperature and humidity. For each parameter characteristic value such as vibration amplitude and flight altitude, a window length of 100 sampling points and a sliding step size of 1 sampling point are set to extract the sliding time window data sequence. The mean and standard deviation of each window data sequence are calculated using an exponentially weighted moving average algorithm, with a smoothing coefficient set to 0.3. Finally, the adaptive warning upper and lower limits of each parameter are calculated according to the formulas: upper warning limit = mean + initial warning coefficient standard deviation and lower warning limit = mean - initial warning coefficient standard deviation. These limits are then associated with the material traceability identifier and the current transportation stage and stored in the system configuration library. As the transportation process progresses, the system will dynamically update the warning thresholds based on the real-time collected data.

[0032] In step 104, the parameter feature values ​​and corresponding adaptive early warning thresholds of the entire logistics process are monitored in real time. When multiple parameter feature values ​​enter the warning zone outside the corresponding early warning threshold and show a continuous deviation trend, the multi-parameter fusion anomaly degree of the current logistics status relative to the historical health benchmark is determined.

[0033] In some embodiments, real-time monitoring of parameter characteristic values ​​and corresponding adaptive early warning thresholds throughout the entire logistics process can be achieved through the following steps: Real-time acquisition of full-chain parameter feature values ​​aligned with timestamps; Based on the traceability identifier of the materials and the current transportation stage, the corresponding adaptive warning upper and lower limits are retrieved from the system configuration library.

[0034] In some embodiments, determining the multi-parameter fusion anomaly degree specifically includes: For each parameter feature value, determine whether its real-time value enters the warning zone outside the corresponding warning threshold; For the parameter characteristic values ​​of entering the warning zone, linear regression analysis is used to determine whether they show a continuous deviation trend; If multiple parameter feature values ​​are determined to show a continuous deviation trend, the weighted Mahalanobis distance algorithm is used to calculate the multi-parameter fusion anomaly degree of the current logistics status relative to the historical health benchmark, based on the weight coefficients and deviation degree of all abnormal parameters.

[0035] In specific implementation, the calculation of multi-parameter fusion anomaly degree can be achieved as follows: First, set the warning zone ratio to 0.9 for each parameter feature value, and calculate the upper and lower boundaries of the warning zone. For example, if the upper warning limit is T, then the upper boundary of the warning zone is T / 0.9; if the lower warning limit is t, then the lower boundary of the warning zone is t0.9. Compare the real-time parameter value with the warning zone boundary. If the parameter value is between the warning threshold and the warning zone boundary, it is determined to have entered the warning zone. For parameters that have entered the warning zone, extract the time series data of the most recent 20 sampling periods, use the least squares method to perform linear regression fitting, and calculate the slope of the trend line. If the absolute value of the slope is greater than the preset trend line... If the sensitivity threshold is reached and the direction of change points away from the normal range, it is determined to be a continuous deviation trend. When two or more parameters are detected to show a continuous deviation trend, the multi-parameter fusion anomaly calculation is triggered. Weighting coefficients are set according to the degree of influence of each parameter on the safety of medical supplies. For example, the weighting coefficient for temperature and humidity is 0.4, the weighting coefficient for vibration amplitude is 0.3, the weighting coefficient for remaining battery power is 0.2, and the weighting coefficient for flight altitude is 0.1. The weighted Mahalanobis distance algorithm is used to calculate the distance between the current observation vector and the historical health benchmark vector. This distance value is the multi-parameter fusion anomaly degree. The larger the value, the higher the degree of deviation of the logistics status from the health benchmark.

[0036] In step 105, when the multi-parameter fusion anomaly degree exceeds the preset anomaly threshold or any key link has a compliance anomaly, the smart contract is triggered to automatically execute the anomaly handling process and generate a control report containing complete traceability information.

[0037] In some embodiments, triggering the automatic execution of the exception handling process of the smart contract specifically includes: Continuously monitor whether the anomaly degree of multi-parameter fusion exceeds the preset anomaly threshold, and monitor whether there are compliance anomalies in key links such as outbound verification, drone airworthiness, handover identity authentication, and receipt confirmation; When any monitoring condition is met, the smart contract automatically generates corresponding handling instructions based on the anomaly type and level. If the transportation parameters are abnormal, the nearest backup drone will be automatically dispatched to transfer the supplies, and the transportation route in the digital twin model will be updated; if the compliance is abnormal, the corresponding supplies will be immediately locked and the regulatory authorities and relevant responsible persons will be notified. All data from the anomaly handling process is stored on the blockchain in real time to ensure the traceability of the handling process.

[0038] In practice, the anomaly handling process can be implemented as follows: A threshold of 3 for the multi-parameter fusion anomaly score is preset. When the anomaly score exceeds 3, it is considered a serious security risk. Simultaneously, the system monitors the compliance of key processes in real time, including whether the materials and order information are consistent upon shipment, whether the drone has passed airworthiness checks before takeoff, whether the handover personnel are authorized, and whether the signatory matches the order information. When any monitoring condition is met, the smart contract is automatically triggered, generating a handling instruction according to preset rules. For example, when the temperature and humidity of cold chain materials are detected to be continuously exceeding the standard, the smart contract first queries the current... The system monitors the status of all available backup drones within the area, selects the nearest drone with cold chain transportation capabilities, sends a transfer instruction to it, updates the transportation route in the digital twin model, marks the location and status of any abnormal drones in red, and notifies nearby maintenance personnel to handle the situation. When the identity verification of the handover personnel fails, the smart contract immediately locks the batch of materials, prohibits the handover operation, and sends an alarm message to the head of the community health station and the regulatory department. All data related to instruction sending, execution status, and processing results during the abnormal handling process are stored on the blockchain in real time, ensuring that the entire handling process is traceable and auditable.

[0039] In some embodiments, generating a control report containing complete traceability information specifically includes: Based on the source identification of the triggered anomaly, the corresponding full-process on-chain data of medical supplies is pulled from each node of the consortium blockchain; Integrate logistics trajectories, parameter change curves, and anomaly handling process data recorded in the digital twin virtual model; Generate a standardized control report that includes basic information about materials, time nodes throughout the entire process, parameter change trends, explanations of abnormal situations, and handling results; The hash value of the control report is stored on the blockchain and made available to regulatory authorities, medical institutions and patients for query and verification.

[0040] In practice, the generation and querying of control reports can be achieved as follows: After the anomaly is handled, the system automatically retrieves all on-chain data of the batch of materials from each node of the consortium blockchain based on the material's traceability identifier; simultaneously, it exports the complete logistics trajectory of the material, historical change curves of each parameter, and a 3D visualization record of the anomaly handling process from the digital twin platform; the above data are integrated to generate a standardized PDF format control report, which includes the material name, specifications, production batch number, traceability identifier, time nodes of each stage, parameter change trend graph, time and cause of the anomaly, handling measures and results, etc.; the hash value of the control report is stored on the blockchain to the regulatory department's node to ensure the report's authenticity and immutability; regulatory departments, medical institutions, and patients can scan the QR code on the material and enter the traceability identifier to query the corresponding control report, verifying the transportation process and quality safety of medical supplies.

[0041] Furthermore, in another aspect, in some embodiments, the present invention provides a traceability and control system for the entire process of low-altitude logistics in primary healthcare, as described in reference... Figure 4 The figure is a schematic diagram of the structure of a full-process traceability and control system for low-altitude logistics in primary healthcare, according to some embodiments of the present invention. This system includes: a data acquisition module, a processing module, a blockchain module, and an execution module, which are described below: The data acquisition module in this invention is mainly used to collect multi-source parameter feature values ​​of medical supplies, transport drones, handover stations and signing links in the entire chain of low-altitude logistics for primary healthcare. The processing module in this invention is mainly used to perform high-precision timestamp alignment of all parameter feature values ​​based on BeiDou time synchronization, and then push all timetamp aligned parameter feature values ​​to the full-element digital twin virtual model to realize the synchronous mapping and visualization of the entire logistics process in virtual space. The blockchain module in this invention is mainly used to store the full-process parameter feature values ​​and unique traceability identifiers in encrypted association to the corresponding nodes based on the phased on-chain mechanism of the consortium blockchain. The processing module is also used to determine the adaptive early warning threshold for each link by combining historical health transportation data stored in the time-series database associated with the digital twin virtual model with the risk level of medical supplies and using a sliding time window and weighted fusion algorithm. The processing module is also used to monitor the parameter feature values ​​and corresponding adaptive early warning thresholds of the entire logistics process in real time. When multiple parameter feature values ​​enter the warning zone outside the corresponding early warning threshold and show a continuous deviation trend, the multi-parameter fusion anomaly degree of the current logistics status relative to the historical health benchmark is determined. The execution module in this invention is mainly used to trigger the smart contract to automatically execute the exception handling process and generate a control report containing complete traceability information when the multi-parameter fusion anomaly degree exceeds the preset anomaly threshold or any key link has a compliance anomaly.

[0042] Each module in the aforementioned grassroots medical low-altitude logistics full-process traceability and control system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0043] In another embodiment, the present invention provides a computer device, which may be a cloud server, and its internal structure diagram may be as follows. Figure 5 As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores historical transportation data, digital twin model data, and consortium blockchain node data for low-altitude logistics in primary healthcare. The network interface communicates with external terminals, drones, data collection devices, and consortium blockchain nodes via a network. When the computer program is executed by the processor, it implements a method for full-process traceability and control of low-altitude logistics in primary healthcare.

[0044] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0045] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above embodiment of the method for full-process traceability and control of low-altitude logistics for primary healthcare.

[0046] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps described in the embodiment of the method for full-process traceability and control of low-altitude logistics for primary healthcare.

[0047] In one embodiment, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps described in the embodiment of the method for full-process traceability and control of low-altitude logistics in primary healthcare.

[0048] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0049] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0050] The technical solutions provided by the embodiments disclosed in this invention have the following beneficial effects: The present invention provides a method and system for full-process traceability and control of low-altitude logistics for primary healthcare. First, it collects multi-source parameter feature values ​​from the entire low-altitude logistics chain for primary healthcare and aligns them with high-precision timestamps using BeiDou time synchronization. This data is then pushed to a full-element digital twin virtual model to achieve synchronous mapping between the virtual and real worlds. This step establishes a comprehensive digital mirror covering medical supplies, drones, handover points, and transportation routes, providing a unified and intuitive data foundation for subsequent control. Second, it employs a consortium blockchain phased on-chain mechanism to encrypt and associate the entire process data with a unique traceability identifier. Smart contracts automatically verify data consistency, fundamentally solving the problems of tampering and untrustworthiness in centralized data storage. This invention addresses the problem of ensuring the immutability and traceability of data throughout the entire process. Then, by combining the risk level of medical supplies with historical health transportation data, a sliding time window and weighted fusion algorithm are used to determine adaptive early warning thresholds for each stage. This overcomes the drawback of fixed thresholds being unable to adapt to different material needs, improving the accuracy and targeting of early warnings. Next, through multi-parameter fusion anomaly calculation, it can keenly capture early anomaly trends caused by multi-parameter coupling, achieving early warning of potential risks. Finally, when an anomaly occurs, a smart contract is triggered to automatically execute the handling process and generate a full-process traceability report, achieving automation and standardization of anomaly handling and significantly improving response speed and handling efficiency. In summary, the solution of this invention achieves reliable traceability, real-time control, and intelligent anomaly handling throughout the entire process of low-altitude logistics for primary healthcare, from pharmacy delivery to patient receipt, effectively ensuring the transportation safety and timeliness of primary healthcare supplies.

[0051] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for full-process traceability and control of low-altitude logistics for primary healthcare, characterized in that, Includes the following steps: Collect multi-source parameter feature values ​​of medical supplies, transport drones, handover stations, and signing links in the entire low-altitude logistics chain for primary healthcare; All parameter feature values ​​are aligned with high-precision timestamps based on BeiDou time synchronization, and then all timetamp-aligned parameter feature values ​​are pushed to the full-element digital twin virtual model to achieve synchronous mapping and visualization of the entire logistics process in virtual space; Based on the phased on-chain mechanism of the consortium blockchain, the feature values ​​of the full-process parameters are encrypted and associated with the unique traceability identifier and stored in the corresponding node. At the same time, based on the historical health transportation data stored in the time series database associated with the digital twin virtual model, combined with the risk level of medical supplies, the adaptive early warning threshold of each link is determined by the sliding time window and weighted fusion algorithm. Real-time monitoring of parameter characteristic values ​​and corresponding adaptive early warning thresholds throughout the entire logistics process; when multiple parameter characteristic values ​​enter the warning zone outside the corresponding early warning threshold and show a continuous deviation trend, the multi-parameter fusion anomaly degree of the current logistics status relative to the historical health benchmark is determined. When the multi-parameter fusion anomaly exceeds the preset anomaly threshold or any key link experiences a compliance anomaly, the smart contract is triggered to automatically execute the anomaly handling process and generate a control report containing complete traceability information.

2. The method for full-process traceability and control of low-altitude logistics for primary healthcare as described in claim 1, characterized in that, The high-precision timestamp alignment of all parameter feature values ​​based on BeiDou time synchronization specifically includes: Obtain the structured data packets with local timestamps corresponding to all parameter feature values. The data packets contain data source identifiers and unique material traceability codes. Based on the precise timing service of the BeiDou satellite navigation system, the local timestamps in all structured data packets are aligned at the nanosecond level. According to the preset time granularity, all parameter feature values ​​within the same time window are collected and completed to obtain a set of full-chain parameter feature values ​​after timestamp alignment.

3. The method for full-process traceability and control of low-altitude logistics for primary healthcare as described in claim 1, characterized in that, Pushing all timestamp-aligned parameter feature values ​​to the full-element digital twin virtual model to achieve synchronous mapping and visualization of the entire logistics process in virtual space specifically includes: All timestamp-aligned parameter feature values ​​are sent to the data access layer of the full-element digital twin virtual model via the 5G Industrial Internet protocol; Based on the source identifier and traceability code of the parameter feature values, update the status attributes of the virtual entities of medical supplies, drones, handover stations, and transportation routes in the digital twin virtual model respectively; The visualization engine that drives the digital twin virtual model renders and displays a three-dimensional dynamic mapping of the entire logistics process based on real-time changes in status attributes, while overlaying highlighted warning signs at abnormal locations.

4. The method for full-process traceability and control of low-altitude logistics for primary healthcare as described in claim 1, characterized in that, Based on the phased on-chain mechanism of the consortium blockchain, the characteristic values ​​of the entire process parameters are encrypted and associated with the unique traceability identifier and then stored in the corresponding node. Specifically, this includes: A globally unique traceability identifier is generated for each batch of medical supplies, and this identifier is used throughout the entire process of outbound warehousing, transportation, handover, and receipt. The entire process data is divided into outbound data segment, transportation data segment, handover data segment and receipt data segment according to the logistics stage, and a corresponding hash value is generated for each data segment. After encrypting and associating the hash value of each data segment with the traceability identifier, the data is stored on the blockchain at pharmacy nodes, drone platform nodes, community health station nodes, and regulatory department nodes respectively. The smart contract automatically verifies the consistency of the data uploaded to the chain by each node. If data tampering is found, an alarm is immediately triggered and the corresponding data segment is locked.

5. The method for full-process traceability and control of low-altitude logistics for primary healthcare as described in claim 1, characterized in that, Based on the risk level of medical supplies, an adaptive early warning threshold for each stage is determined using a sliding time window and a weighted fusion algorithm, specifically including: Based on the type and storage requirements of medical supplies, they are divided into three levels: high risk, medium risk and low risk, with different initial warning coefficients for each level. Historical health transportation data of medical supplies with corresponding risk levels are extracted from the time-series database associated with the digital twin virtual model; For each parameter feature value, the sliding time window data sequence is extracted according to the set window length and sliding step size, and its exponentially weighted moving mean and standard deviation are calculated; By combining the initial warning coefficient with the calculated mean and standard deviation, the adaptive warning upper and lower limits of the characteristic values ​​of each parameter in each stage are determined, and these limits are stored in association with the traceability identifier and the transportation stage.

6. The method for full-process traceability and control of low-altitude logistics for primary healthcare as described in claim 1, characterized in that, When multiple parameter feature values ​​enter the warning zone outside the corresponding early warning threshold and show a continuous deviation trend, the determination of the multi-parameter fusion anomaly degree of the current logistics status relative to the historical health benchmark specifically includes: For each parameter feature value, determine whether its real-time value enters the warning zone outside the corresponding warning threshold; For the parameter characteristic values ​​that enter the warning zone, linear regression analysis is used to determine whether they show a continuous deviation trend in the whole process traceability and control method of low-altitude logistics for primary medical care. If multiple parameter feature values ​​are determined to show a continuous deviation trend, the weighted Mahalanobis distance algorithm is used to calculate the multi-parameter fusion anomaly degree of the current logistics status relative to the historical health benchmark, based on the weight coefficients and deviation degree of all abnormal parameters.

7. The method for full-process traceability and control of low-altitude logistics for primary healthcare as described in claim 1, characterized in that, When the multi-parameter fusion anomaly degree exceeds the preset anomaly threshold or any key link experiences a compliance anomaly, the smart contract is triggered to automatically execute the anomaly handling process, specifically including: Continuously monitor whether the anomaly degree of multi-parameter fusion exceeds the preset anomaly threshold, and monitor whether there are compliance anomalies in key links such as outbound verification, drone airworthiness, handover identity authentication, and receipt confirmation; When any monitoring condition is met, the smart contract automatically generates corresponding handling instructions based on the anomaly type and level. If the transportation parameters are abnormal, the nearest backup drone will be automatically dispatched to transfer the supplies, and the transportation route in the digital twin model will be updated; if the compliance is abnormal, the corresponding supplies will be immediately locked and the regulatory authorities and relevant responsible persons will be notified. All data from the anomaly handling process is stored on the blockchain in real time to ensure the traceability of the handling process.

8. The method for full-process traceability and control of low-altitude logistics for primary healthcare as described in claim 1, characterized in that, Generating a control report containing complete traceability information specifically includes: Based on the source identification of the triggered anomaly, the corresponding full-process on-chain data of medical supplies is pulled from each node of the consortium blockchain; Integrate logistics trajectories, parameter change curves, and anomaly handling process data recorded in the digital twin virtual model; Generate a standardized control report that includes basic information about materials, time nodes throughout the entire process, parameter change trends, explanations of abnormal situations, and handling results; The hash value of the control report is stored on the blockchain and made available to regulatory authorities, medical institutions and patients for query and verification.

9. The method for full-process traceability and control of low-altitude logistics for primary healthcare as described in claim 1, characterized in that, The parameter characteristics include the temperature and humidity of medical supplies, vibration amplitude, drone flight altitude, flight speed, remaining battery power, handover station identity authentication information, and biometric data of the signatory.

10. A traceability and control system for the entire process of low-altitude logistics in primary healthcare, characterized in that, include: The data acquisition module is used to collect multi-source parameter feature values ​​of medical supplies, transport drones, handover stations, and signing links in the entire chain of low-altitude logistics for primary healthcare. The processing module is used to perform high-precision timestamp alignment of all parameter feature values ​​based on BeiDou time synchronization, and then push all timetamp aligned parameter feature values ​​to the full-element digital twin virtual model to realize the synchronous mapping and visualization of the entire logistics process in virtual space. The blockchain module is used to store the full-process parameter feature values ​​and unique traceability identifiers in encrypted association to the corresponding nodes based on the phased on-chain mechanism of the consortium blockchain. The processing module is also used to determine the adaptive early warning threshold for each link by combining historical health transportation data stored in the time-series database associated with the digital twin virtual model with the risk level of medical supplies and using a sliding time window and weighted fusion algorithm. The processing module is also used to monitor the parameter feature values ​​of the entire logistics process and the corresponding adaptive early warning thresholds in real time. When multiple parameter feature values ​​enter the warning zone outside the corresponding early warning threshold and show a continuous deviation trend, the multi-parameter fusion anomaly degree of the current logistics status relative to the historical health benchmark is determined. The execution module is used to trigger the smart contract to automatically execute the exception handling process and generate a control report containing complete traceability information when the multi-parameter fusion anomaly degree exceeds the preset anomaly threshold or when any key link has a compliance anomaly.

Images