Block chain-based drug cross-regional transfer tracing and multi-subject data sharing system

Abstract

The invention relates to the technical field of block chains, in particular to a drug cross-region transfer tracing and multi-subject data sharing system based on a block chain. According to the temperature data distribution of each monitoring point at different moments, the temperature sudden change factor of each monitoring point at each moment is obtained; according to the temperature data distribution of different monitoring points at different moments and the temperature sudden change factors, in combination with the position distribution of different monitoring points, obtaining the chemical impact degree of transportation at each moment; according to the change trend of the vibration data at different moments and the vehicle speed data, the physical impact degree of transportation at each moment is obtained; according to the transportation chemical impact degree and the transportation physical impact degree at each moment, obtaining the transportation environment abnormity at each moment; and writing the data into the block chain in real time, and carrying out transfer tracing and multi-subject data sharing on the medicines. According to the method, the transportation environment abnormity at each moment is accurately obtained, so that the accuracy of block chain tracing and data sharing is improved.

Description

Technical Field

[0001] This invention relates to the field of blockchain technology, specifically to a blockchain-based system for tracing and sharing cross-regional drug transportation data across regions and among multiple entities. Background Technology

[0002] The transportation of medicines across regions involves the collaboration of multiple logistics entities. Some sensitive medicines have extremely high requirements for the transportation environment. Therefore, in order to ensure the safety of vaccines during transportation, data records need to be generated on the blockchain at every stage of transportation to provide traceable quality assurance information throughout the process.

[0003] In existing technologies, blockchain technology helps to solve the problems of information silos and information asymmetry in the traditional supply chain system, and strengthens the drug tracking and feedback mechanism with its inherent immutability and traceability. However, it lacks an adaptive design for the spatial distribution differences of monitoring points. Single or unreasonable monitoring points can easily lead to the omission of local exceedance risks, affecting the timeliness and accuracy of risk warnings, and the effect of transportation traceability and data sharing is poor. Summary of the Invention

[0004] To address the technical problems of missed risks of localized exceedances due to single or poorly distributed monitoring points, and the poor effectiveness of drug transport traceability and data sharing, the present invention aims to provide a blockchain-based drug cross-regional transport traceability and multi-entity data sharing system. The specific technical solution adopted is as follows: This invention proposes a blockchain-based system for cross-regional drug transport traceability and multi-entity data sharing, the system comprising: The data acquisition module is used to acquire vibration data, vehicle speed data, and temperature data from multiple monitoring points at every moment inside the drug transport vehicle. The temperature anomaly assessment module is used to obtain the temperature mutation factor of each monitoring point at each time moment based on the temperature data distribution of each monitoring point at different times moment; to obtain the degree of synchronization of temperature changes between different monitoring points at each time moment based on the temperature data distribution of different monitoring points at different times moment; and to obtain the authenticity of temperature anomalies at each monitoring point at each time moment based on the temperature data distribution, temperature mutation factor, and degree of synchronization of temperature changes of different monitoring points at each time moment. The transportation environment analysis module is used to obtain the degree of chemical shock to transportation at each moment based on the location distribution of different monitoring points, the authenticity of temperature anomalies at each moment, and temperature change factors; to obtain the degree of physical shock to transportation at each moment based on the changing trends of vibration data and vehicle speed data at different moments; and to obtain the transportation environment anomalies at each moment based on the degree of chemical shock and the degree of physical shock to transportation. The traceability and sharing module is used to write any anomalies in the transportation environment at any given moment into the blockchain in real time, enabling the traceability of drug transfers and the sharing of data among multiple entities.

[0005] Furthermore, the method for obtaining the temperature mutation factor includes: If the temperature data at a given time is greater than the maximum value within the preset temperature range, that time will be considered a positive anomaly; if the temperature data at a given time is less than the minimum value within the preset temperature range, that time will be considered a negative anomaly. For each historical moment, consecutively adjacent positive anomalous moments are considered as positive anomalous periods, and consecutively adjacent negative anomalous moments are considered as negative anomalous periods. The average temperature data of all moments in each anomalous period is obtained as the overall temperature level of each anomalous period. The maximum difference in overall temperature level between all adjacent abnormal time periods is obtained, and the product of the number of abnormal time periods is used as the temperature mutation factor for each monitoring point at each moment.

[0006] Furthermore, the method for obtaining the degree of synchronization of temperature changes includes: For any two monitoring points, if any moment corresponds to either a positive or negative anomaly, the corresponding moment shall be taken as the synchronization moment. A temperature curve is obtained by collecting temperature data from each monitoring point at all synchronous times, and the slope of the temperature curve at each synchronous time is obtained. If the slopes of two monitoring points are equal at each synchronous time, the consistent change direction factor between the corresponding monitoring points at each synchronous time is set as a preset first factor; otherwise, it is set as a preset second factor. The preset first factor is greater than the preset second factor. Within the historical range of each moment, the degree of synchronization of temperature changes between different monitoring points at different synchronization moments is obtained based on the temperature data differences between different monitoring points at different synchronization moments and the consistency factor of change direction. The temperature data differences are negatively correlated with the degree of temperature change synchronization, while the consistency factor of change direction is positively correlated with the degree of temperature change synchronization.

[0007] Furthermore, the method for obtaining the degree of synchronization of temperature changes includes: The temperature data difference between two monitoring points at each synchronization time is negatively correlated and mapped as temperature similarity. The cumulative value of the product of the temperature similarity and the consistency of the direction of change between the two monitoring points at all synchronization moments is obtained and normalized, which is used as the degree of synchronization of temperature change between the corresponding monitoring points at each moment.

[0008] Furthermore, the method for obtaining the authenticity of the temperature anomaly includes: If the degree of synchronization of temperature changes between different monitoring points at each moment is greater than or equal to the preset synchronization threshold, the corresponding monitoring point will be used as the synchronization monitoring point, and multiple synchronization monitoring points for each monitoring point at each moment will be obtained. The ratio of the number of synchronous monitoring points to the total number of monitoring points at each monitoring point at any given time is used as the first anomaly authenticity. Based on the degree of synchronization of temperature changes between each monitoring point and different synchronous monitoring points at each moment, the difference in temperature mutation factor, and the authenticity of the first anomaly, the authenticity of temperature anomalies at each monitoring point at each moment is taken as the authenticity of temperature anomalies. The degree of synchronization of temperature changes and the authenticity of the first anomaly are both positively correlated with the authenticity of temperature anomalies, while the difference in temperature mutation factor is negatively correlated with the authenticity of temperature anomalies.

[0009] Furthermore, the method for obtaining the degree of chemical impact during transport includes: If the temperature anomaly at a monitoring point at any given time is greater than or equal to the preset anomaly threshold, the corresponding monitoring point will be designated as a key monitoring point. Based on the temperature mutation factor, temperature anomaly authenticity, and relative distance between different monitoring key points at each moment, the degree of chemical shock to transportation at each moment is obtained. The temperature mutation factor, temperature anomaly authenticity, and relative distance are all positively correlated with the degree of chemical shock to transportation.

[0010] Furthermore, the method for obtaining the degree of physical impact during transportation includes: Based on the changing trends of vibration data at different times, multiple turbulence periods are obtained, and the transportation turbulence intensity of each turbulence period is obtained. The vehicle speed data at each moment is normalized to obtain the mean of the normalized vehicle speed data at all moments within each bumpy period, which is used as the overall vehicle speed level for each bumpy period. For each historical moment, the cumulative sum of the product of the transportation turbulence intensity and the overall vehicle speed level during all turbulence periods is obtained as the degree of physical impact on transportation at each moment.

[0011] Furthermore, the method for obtaining the turbulence period includes: Within the historical range of each moment, the difference in vibration data between adjacent moments is obtained and normalized as the vibration variability. If the vibration variability between adjacent moments is greater than or equal to a preset variability threshold, the corresponding moment is taken as the turbulence moment. All consecutive adjacent turbulence moments constitute the turbulence period. Furthermore, the method for obtaining the transport bump intensity includes: For any turbulence period, the product of the maximum value of the vibration variation between all adjacent moments and the number of moments within the turbulence period is obtained as the transportation turbulence intensity for each turbulence period.

[0012] Furthermore, the method for obtaining the anomalies in the transportation environment includes: The product of the degree of chemical impact and the degree of physical impact on transportation at each moment is obtained and normalized to represent the transportation environment anomaly at each moment.

[0013] The present invention has the following beneficial effects: This invention obtains the temperature mutation factor at each monitoring point at different times based on the temperature data distribution at each monitoring point, quantifying the degree of significant temperature change. Based on the temperature data distribution and temperature mutation factor at different monitoring points at different times, it obtains the authenticity of temperature anomalies at each monitoring point at each time, reflecting the credibility of temperature anomalies occurring in the corresponding area. Based on the location distribution of different monitoring points, the authenticity of temperature anomalies at each time, and the temperature mutation factor, it obtains the degree of chemical shock during transportation at each time, assessing the scope and severity of the impact of the anomaly on the drug. Based on the changing trends of vibration data and vehicle speed data at different times, it obtains the degree of physical shock during transportation at each time, assessing the physical stability of the drug. Based on the degree of chemical and physical shock during transportation at each time, it obtains the anomaly of the transportation environment at each time, comprehensively reflecting the abnormal state of the transportation environment at each time. This information is written to the blockchain in real time for drug transport traceability and multi-entity data sharing. This invention improves the accuracy of blockchain traceability and data sharing by accurately obtaining the anomalies of the transportation environment at each time. Attached Figure Description

[0014] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a structural block diagram of a blockchain-based cross-regional drug transport traceability and multi-entity data sharing system provided in one embodiment of the present invention. Figure 2 This is a flowchart illustrating a method for obtaining the degree of synchronization of temperature changes, as provided in one embodiment of the present invention. Detailed Implementation

[0016] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a blockchain-based cross-regional drug transport traceability and multi-entity data sharing system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0018] The following description, in conjunction with the accompanying drawings, details a specific solution for a blockchain-based cross-regional drug transport traceability and multi-entity data sharing system provided by this invention.

[0019] Please see Figure 1 The diagram illustrates a structural block diagram of a blockchain-based cross-regional drug transport traceability and multi-entity data sharing system according to an embodiment of the present invention. The system specifically includes: a data acquisition module 101, a temperature anomaly assessment module 102, a transport environment analysis module 103, and a traceability sharing module 104. The data acquisition module 101 is used to acquire vibration data, vehicle speed data, and temperature data of multiple monitoring points at each moment inside the drug transport vehicle.

[0020] In the embodiments of the present invention, considering the spatial distribution differences in temperature, humidity, and vibration in cold chain truck compartments, it is easy to miss the risk of exceeding standards in other areas when analyzing a single monitoring point. Therefore, it is necessary to set up multiple monitoring points to collect transportation-related data. First, the temperature fluctuates easily at the door of the compartment due to opening and closing the door. The temperature may be lower at the rear near the refrigeration unit, and the humidity may be higher in the middle where the goods are stacked due to poor ventilation. In order to avoid missing the risk of exceeding standards in other areas, eight monitoring points are evenly arranged in the transport compartment. High-precision temperature sensors are installed at each monitoring point, and vibration sensors and high-reliability vehicle interfaces are installed in the middle of the compartment to collect data, obtaining vibration data, vehicle speed data, and temperature data of multiple monitoring points at each moment in the pharmaceutical transport compartment.

[0021] The temperature anomaly assessment module 102 is used to obtain the temperature mutation factor of each monitoring point at each time based on the temperature data distribution of each monitoring point at different times; to obtain the degree of synchronization of temperature changes between different monitoring points at each time based on the temperature data distribution of different monitoring points at different times; and to obtain the authenticity of temperature anomalies at each monitoring point at each time based on the temperature data distribution, temperature mutation factor, and degree of synchronization of temperature changes of different monitoring points at each time.

[0022] Vaccines are very sensitive to temperature. During transportation, the temperature at monitoring points may become unstable due to equipment failure, improper loading, or other reasons. By analyzing temperature data at different times, we can reflect the trend of temperature changes. Based on the temperature data distribution at each monitoring point at different times, we can obtain the temperature mutation factor at each monitoring point at each moment.

[0023] Preferably, in one embodiment of the present invention, the method for obtaining the temperature mutation factor includes: If the temperature data at a given time is greater than the maximum value within the preset temperature range, that time will be considered a positive anomaly; if the temperature data at a given time is less than the minimum value within the preset temperature range, that time will be considered a negative anomaly. For each historical moment, consecutively adjacent positive anomalous moments are considered as positive anomalous periods, and consecutively adjacent negative anomalous moments are considered as negative anomalous periods. The average temperature data of all moments in each anomalous period is obtained as the overall temperature level of each anomalous period. The maximum difference in overall temperature level between all adjacent abnormal time periods is obtained, along with the product of the number of abnormal time periods, and then normalized to serve as the temperature mutation factor for each monitoring point at each moment.

[0024] It should be noted that the difference represents the absolute value of the calculated difference.

[0025] It should be noted that, in the embodiments of the present invention, the implementers, based on relevant transportation experience, preset the temperature range to be 2 to 8°C, that is, a temperature greater than 8°C is considered a positive abnormal moment, and a temperature less than 2°C is considered a negative abnormal moment.

[0026] It should be noted that, in the embodiments of the present invention, normalization is performed by linear normalization or a normalization function, such as maximum and minimum value normalization; the specific means are well known to those skilled in the art and will not be described in detail here.

[0027] It should be noted that, in one embodiment of the present invention, the method for obtaining the historical range is to take each moment as a reference and form a range with a preset number of historical moments, the preset number being 60, wherein the range includes the reference moment; in other embodiments of the present invention, the size of the historical range can be specifically set according to the specific situation, and will not be limited or elaborated here.

[0028] Temperature fluctuations at a single monitoring point may be caused by local interference or by risks across the entire region. Therefore, based on the distribution of temperature data at different monitoring points at different times, the degree of synchronization of temperature changes between different monitoring points at each time can be obtained.

[0029] Preferably, in one embodiment of the present invention, the method for obtaining the degree of synchronization of temperature changes is described in [reference needed]. Figure 2 It illustrates a flowchart of a method for obtaining the degree of synchronization of temperature changes, including: Step S201: For any two monitoring points, if any time corresponds to either a positive or negative abnormal time, the corresponding time shall be taken as the synchronization time.

[0030] If positive or negative anomalies occur simultaneously, it indicates that anomalies in the same direction are occurring at the same monitoring points, and the temperatures are in the same state. The corresponding moments can be used as synchronization moments for subsequent analysis.

[0031] Based on this, we can analyze any two different monitoring points to obtain the synchronization time corresponding to each pair of monitoring points.

[0032] Step S202: Obtain the temperature curve composed of the temperature data of each monitoring point at all synchronization times, and obtain the slope of the temperature curve at each synchronization time; if the slopes of two monitoring points are equal at each synchronization time, set the consistent change direction factor between the corresponding monitoring points at each synchronization time to the preset first factor; otherwise, set it to the preset second factor, and the preset first factor is greater than the preset second factor.

[0033] It should be noted that, in the embodiments of the present invention, the temperature curve is obtained by fitting the temperature data at all synchronous moments using the least squares method or polynomial fitting method. The slope represents the rate of temperature change of the obtained temperature curve at each moment. The larger the slope, the greater the temperature change. The slope at each moment can be obtained by taking the derivative of the temperature curve at each moment. The specific means are well known to those skilled in the art and will not be described in detail here.

[0034] It should be noted that if the slopes of the two monitoring points are equal at each synchronization moment, it means that the temperature change rate of the monitoring points is more consistent at each synchronization moment, and the change direction consistency factor is larger. Therefore, the preset first factor is greater than the preset second factor. In one embodiment of the present invention, the preset first factor is set to 2 and the preset second factor is set to 1. In other embodiments of the present invention, the magnitudes of the preset first factor and the preset second factor can be set according to specific circumstances, and are not limited or elaborated here.

[0035] Step S203: For each historical moment, based on the temperature data differences between different monitoring points at different synchronization moments and the change direction consistency factor, the degree of temperature change synchronization between different monitoring points at each moment is obtained. The temperature data difference is negatively correlated with the degree of temperature change synchronization, and the change direction consistency factor is positively correlated with the degree of temperature change synchronization.

[0036] It should be noted that temperature data difference represents the absolute value of the difference between calculated temperature data. The greater the temperature data difference, the greater the temperature difference between different monitoring points, the less synchronized the temperature changes, and the lower the degree of temperature change synchronization. Conversely, the larger the consistency factor in the direction of change, the greater the degree of temperature change synchronization. Therefore, temperature data difference is negatively correlated with the degree of temperature change synchronization, while the consistency factor in the direction of change is positively correlated with the degree of temperature change synchronization.

[0037] In one embodiment of the present invention, a negative correlation mapping is performed on the temperature data differences between monitoring points at each synchronization moment to represent temperature similarity; the cumulative value of the product between the temperature similarity and the consistency factor of change direction between monitoring points at all synchronization moments is obtained and normalized to represent the degree of synchronization of temperature changes between corresponding monitoring points at each moment. The consistency factor of change direction is also mentioned.

[0038] It should be noted that, in the embodiments of the present invention, negative correlation mapping can be performed by taking the reciprocal or by using an exponential function exp(-) with the natural constant as the base. When taking the reciprocal, in order to avoid the formula being meaningless with a denominator of 0, a preset adjustment parameter is artificially added to the denominator. The preset adjustment parameter can be set to a non-zero value with the same dimensions as the denominator data. Its value can be set according to the range of values ​​of the denominator to minimize the impact on the denominator. The specific means are well known to those skilled in the art and will not be described in detail here.

[0039] The formula for the degree of synchronization of temperature changes is expressed as: ;in, Indicates the first The monitoring point and the first The monitoring point at the 1st The degree of synchronization of temperature changes over time; Indicates the first The monitoring point at the 1st Temperature data at each synchronous moment; Indicates the first The monitoring point at the 1st Temperature data at each synchronous moment; Indicates the first The monitoring point and the first Between the monitoring points on the 1st The absolute value of the difference between temperature data at each synchronous moment, i.e., the temperature data difference; Indicates the first The monitoring point and the first Between the monitoring points on the 1st The factor that ensures the consistent direction of change at each synchronous moment; Indicates the first The number of synchronized moments within the historical range of a given moment; Indicates the preset adjustment parameters; This represents the linear normalization function.

[0040] When a cooling failure causes the temperature in the entire vehicle compartment to rise, all monitoring points will experience sudden temperature changes at the same time. Therefore, the temperature at each monitoring point will be consistent within a short period of time. The presence of a monitoring point whose temperature is synchronized with that of most other monitoring points indicates a higher likelihood of a genuine anomaly. Based on the temperature data distribution, temperature mutation factor, and degree of temperature change synchronization at different monitoring points at each moment, the authenticity of the temperature anomaly at each monitoring point at each moment can be obtained.

[0041] Preferably, in one embodiment of the present invention, the method for obtaining the authenticity of temperature anomalies includes: If the degree of synchronization of temperature changes between different monitoring points at each moment is greater than or equal to the preset synchronization threshold, the corresponding monitoring point will be used as the synchronization monitoring point, and multiple synchronization monitoring points for each monitoring point at each moment will be obtained. It should be noted that the greater the degree of synchronization of temperature changes, the greater the possibility that the monitoring points are affected in the same way. Therefore, the preset synchronization threshold is set relatively high to screen out the monitoring points that are synchronized as much as possible. In one embodiment of the present invention, the preset synchronization threshold is set to 0.7 based on the relevant experience of the implementers. In other embodiments of the present invention, the preset synchronization threshold can be set according to the specific circumstances, and will not be limited or described in detail here.

[0042] The ratio of the number of synchronous monitoring points to the total number of monitoring points at each monitoring point at any given time is used as the first anomaly authenticity. Based on the degree of synchronization of temperature changes between each monitoring point and different synchronous monitoring points at each moment, the difference in temperature mutation factor, and the authenticity of the first anomaly, the authenticity of temperature anomalies at each monitoring point at each moment is taken as the authenticity of temperature anomalies. The degree of synchronization of temperature changes and the authenticity of the first anomaly are both positively correlated with the authenticity of temperature anomalies, while the difference in temperature mutation factor is negatively correlated with the authenticity of temperature anomalies.

[0043] It should be noted that the greater the degree of synchronization of temperature changes, the more consistent the temperature changes among monitoring points, and the more credible the anomaly. The difference represents the absolute value of the calculated difference. The greater the difference in the temperature abrupt change factor, the more inconsistent the temperature changes among monitoring points, and the less likely it is a global temperature change, making the anomaly unreliable. The greater the authenticity of the first anomaly, the more synchronized the monitoring points, indicating that the temperature changes are more consistent and not isolated events. Therefore, the degree of synchronization of temperature changes and the authenticity of the first anomaly are both positively correlated with the authenticity of temperature anomalies, while the difference in the temperature abrupt change factor is negatively correlated with the authenticity of temperature anomalies.

[0044] In one embodiment of the present invention, the difference in temperature abrupt change factors between each monitoring point and each synchronous monitoring point is negatively correlated and mapped as temperature abrupt change similarity; the average product of the degree of synchronization of temperature changes and the temperature abrupt change similarity between each monitoring point and all synchronous monitoring points at each moment is obtained as temperature change similarity; the product of the first anomaly authenticity and the temperature change similarity is obtained and normalized as the temperature anomaly authenticity of each monitoring point at each moment; the formula for temperature anomaly authenticity is expressed as: ;in, Indicates the first The monitoring point at the 1st The realism of temperature anomalies at any given moment; Indicates the first The monitoring point at the 1st The number of synchronous monitoring points at any given time; This indicates the total number of monitoring points; Indicates the first The monitoring point and the first The monitoring point at the 1st The degree of synchronization of temperature changes over time; Indicates the first The monitoring point and the first The monitoring point at the 1st Differences in temperature mutation factors over time; Indicates the preset adjustment parameters; This represents the linear normalization function.

[0045] The transportation environment analysis module 103 is used to obtain the degree of chemical shock to transportation at each moment based on the location distribution of different monitoring points, the authenticity of temperature anomalies at each moment, and temperature change factors; to obtain the degree of physical shock to transportation at each moment based on the changing trend of vibration data and vehicle speed data at different moments; and to obtain the transportation environment anomaly at each moment based on the degree of chemical shock and the degree of physical shock to transportation.

[0046] Some drugs may decompose at high temperatures, while low temperatures may cause crystallization or structural changes. Therefore, the greater the change in the temperature mutation factor, the greater the authenticity of the temperature anomaly, the more likely the chemical composition of the drug will change during transportation, the larger the distribution range of the monitoring points affected by the anomaly, the more likely it is to be a global risk, and the greater the possibility of chemical shock. Based on the location distribution of different monitoring points, the authenticity of the temperature anomaly at each moment, and the temperature mutation factor, the degree of chemical shock during transportation at each moment can be obtained.

[0047] Preferably, in one embodiment, the method for obtaining the degree of chemical impact during transport includes: If the temperature anomaly at a monitoring point at any given time is greater than or equal to the preset anomaly threshold, the corresponding monitoring point will be designated as a key monitoring point. It should be noted that the greater the authenticity of the temperature anomaly, the more likely an anomaly is to occur at the monitoring point, requiring close attention. Setting a larger preset anomaly threshold will allow for greater focus on monitoring points with higher anomaly authenticity, thus improving computational efficiency. In one embodiment of the present invention, the preset anomaly threshold is set to 0.7 based on the relevant experience of the implementers. In other embodiments of the present invention, the preset anomaly threshold can be set according to specific circumstances, and will not be limited or elaborated here.

[0048] Based on the temperature mutation factor, temperature anomaly authenticity, and relative distance between different monitoring key points at each moment, the degree of chemical shock to transportation at each moment is obtained. The temperature mutation factor, temperature anomaly authenticity, and relative distance are all positively correlated with the degree of chemical shock to transportation.

[0049] It should be noted that the larger the temperature mutation factor, the more likely the chemical composition of the drug will change, the greater the degree of chemical shock during transportation, and the greater the authenticity of the temperature anomaly. Temperature changes occur at most monitoring points, and the degree of chemical shock during transportation is greater. The relative distance reflects the size of the temperature anomaly distribution range. The greater the relative distance, the wider the anomaly distribution range and the greater the degree of chemical shock during transportation. Therefore, the temperature mutation factor, the authenticity of the temperature anomaly, and the relative distance are all positively correlated with the degree of chemical shock during transportation.

[0050] In one embodiment of the present invention, the cumulative sum of the temperature mutation factor and the temperature anomaly authenticity at different monitoring key points at each moment is obtained; the mean of the relative distance between all monitoring key points at each moment is obtained as the anomaly distribution breadth; the product between the cumulative sum and the anomaly distribution breadth is calculated as the degree of chemical shock to transportation at each moment; the formula for the degree of chemical shock to transportation is expressed as: ;in, Indicates the first The degree of chemical impact on transportation at any given time; Indicates the first The average relative distance between all monitored key points at any given time, i.e., the breadth of anomaly distribution; Indicates the first The key monitoring points are in the first Temperature mutation factor at time; Indicates the first The key monitoring points are in the first The realism of temperature anomalies at any given moment; This indicates the number of key monitoring points.

[0051] It should be noted that, in the embodiments of the present invention, the relative distance can be obtained by Euclidean distance or Manhattan distance calculation methods; the specific means are well known to those skilled in the art and will not be described in detail here.

[0052] Considering that the active ingredients in vaccines are extremely sensitive to physical shock and continuous vibration, continuous and severe vibration during transportation can lead to structural damage and failure of the active ingredients. The greater the fluctuation in vibration data, the more likely it is to cause damage to the drug packaging and failure of the seal, which in turn leads to moisture and contamination. Moreover, the vibration is more severe when the vehicle is traveling at high speed than when it is traveling at low speed, resulting in a greater physical impact. Therefore, the degree of physical impact during transportation at each moment is obtained based on the changing trend of vibration data and vehicle speed data at different times.

[0053] Preferably, in one embodiment of the present invention, the method for obtaining the degree of physical impact during transportation includes: Based on the changing trends of vibration data at different times, multiple turbulence periods are obtained, and the transportation turbulence intensity of each turbulence period is obtained. Preferably, in one embodiment of the present invention, the method for obtaining the turbulence period includes: Within the historical range of each moment, the difference in vibration data between adjacent moments is obtained and normalized as the vibration variability. If the vibration variability between adjacent moments is greater than or equal to a preset variability threshold, the corresponding moment is taken as the turbulence moment. All consecutive adjacent turbulence moments constitute the turbulence period.

[0054] It should be noted that the difference in vibration data represents the absolute value of the difference between the calculated vibration data. The greater the difference between the vibration data, the greater the degree of vibration change, and the more likely it is to cause turbulence. Therefore, the larger the preset change threshold is set, the greater the probability of turbulence will occur. In one embodiment of the present invention, the preset change threshold is set to 0.7 based on the relevant experience of the implementers. In other embodiments of the present invention, the preset change threshold can be set according to specific circumstances, and will not be limited or elaborated here.

[0055] Preferably, in one embodiment of the present invention, the method for obtaining the transport bump intensity includes: For any turbulence period, the product of the maximum value of the vibration variation between all adjacent moments and the number of moments within the turbulence period is obtained as the transportation turbulence intensity for each turbulence period.

[0056] The vehicle speed data at each moment is normalized to obtain the mean of the normalized vehicle speed data at all moments within each bumpy period, which is used as the overall vehicle speed level for each bumpy period. It should be noted that the maximum and minimum values ​​of vehicle speed data are obtained at all times, and the maximum and minimum values ​​of vehicle speed data at each time are normalized. The specific methods are well known to those skilled in the art and will not be elaborated here.

[0057] For each historical moment, the cumulative sum of the product of the transportation turbulence intensity and the overall vehicle speed level during all turbulence periods is obtained as the degree of physical impact on transportation at each moment.

[0058] In one embodiment of the present invention, the formula for the degree of physical impact on transportation is expressed as: ;in, Indicates the first The degree of physical impact on transportation at any given moment; Indicates the first The maximum value of the degree of vibration change between all adjacent moments during the turbulent period; Indicates the first The number of moments within the period of turbulence; Indicates the first The intensity of transport turbulence is the product of the maximum value of the degree of vibration change between all adjacent moments during the turbulence period and the number of moments. Indicates the first The average of the normalized vehicle speed data at all times within each bumpy period, i.e., the overall vehicle speed level. Indicates the first The number of periods of turbulence within the historical range of a given moment.

[0059] Chemical and physical shocks may not be isolated risks; they may have a synergistic effect. Severe vibration may cause micro-cracks in drug packaging, allowing outside air or moisture to enter, which in turn exacerbates the impact of temperature fluctuations on the chemical properties of the drug, leading to more serious drug quality problems. The abnormality of the transportation environment is relatively poor. The abnormality of the transportation environment at each moment can be obtained based on the degree of chemical and physical shock during transportation.

[0060] Preferably, in one embodiment of the present invention, the method for obtaining anomalies in the transportation environment includes: The product of the degree of chemical impact and the degree of physical impact on transportation at each moment is obtained and normalized to represent the transportation environment anomaly at each moment.

[0061] It should be noted that, in the embodiments of the present invention, normalization is performed by linear normalization or a normalization function, such as maximum and minimum value normalization; the specific means are well known to those skilled in the art and will not be described in detail here.

[0062] Therefore, the greater the degree of chemical impact during transportation, the greater the degree of physical impact during transportation. Severe vibration may cause micro-cracks in the drug packaging, allowing outside air or moisture to enter and have a significant impact on the drug. The more severe the vibration caused by bumps, the worse the abnormality of the transportation environment.

[0063] The traceability and sharing module 104 is used to write the abnormalities of the transportation environment at each moment into the blockchain in real time, so as to trace the transfer of medicines and share data among multiple entities.

[0064] Anomalies in the transportation environment can reflect the likelihood of risks occurring during transportation; the greater the anomalies in the transportation environment, the greater the likelihood of risks occurring. It should be noted that the anomalies in the transportation environment at any given moment are written to the blockchain in real time, and the response is driven by smart contracts on the blockchain. The process is as follows: A first and a second danger threshold are preset. If the first danger threshold is less than the second, the contract automatically compares the transportation environment anomaly with the danger threshold. If the transportation environment anomaly is greater than or equal to the second danger threshold, a serious risk has occurred during transportation. The contract calls the off-chain IoT control interface to send a "immediately decelerate and stop heating" command to the vehicle's onboard system, and simultaneously pushes emergency notifications to logistics companies, regulatory authorities, and pharmaceutical companies. If the transportation environment anomaly is greater than or equal to the first danger threshold but less than the second danger threshold, a minor risk has occurred during transportation. The contract issues a "reduce vehicle speed to a safe threshold" command. The system executes commands such as "below the value" and "reduce heating power by 10%-15%" and writes them to the "risk adjustment" log on the chain. If the anomaly of the transportation environment is greater than the preset first danger threshold, and no risk has occurred during transportation, the contract will not intervene and will continue to record and analyze the anomaly of the transportation environment at each moment. It should be noted that the greater the anomaly of the transportation environment, the greater the possibility of risk. The greater the preset second danger threshold is than the preset first danger threshold, the greater the relative anomaly of the transportation environment. In one embodiment of the present invention, the preset first danger threshold is set to 0.5 and the preset second danger threshold is set to 0.8 based on relevant experience. In other embodiments of the present invention, the preset first danger threshold and the preset second danger threshold can be set according to specific circumstances, which will not be limited or elaborated here.

[0065] Based on this, every processing step in the smart contract is written into the consortium blockchain in the form of a transaction. The consortium blockchain's consensus mechanism enables cross-regional node collaboration. According to hierarchical access control rules, the system only allows complete logs to pharmaceutical companies and regulatory authorities for the specific batches handled by the enterprise. Only the anonymized risk level and processing status are displayed to the public to prevent the leakage of trade secrets. This satisfies regulatory requirements for end-to-end traceability while protecting corporate data privacy. Through this end-to-end mechanism, the continued circulation of drugs can be quickly halted in the event of dual risks such as packaging damage or temperature runaway, minimizing the risk of quality incidents spreading. It should be noted that blockchain is a well-known technology in the field and will not be elaborated upon here.

[0066] In summary, this invention obtains the temperature mutation factor of each monitoring point at each moment based on the temperature data distribution at different times; it obtains the degree of chemical shock during transportation at each moment based on the temperature data distribution and temperature mutation factor of different monitoring points at different times, combined with the location distribution of different monitoring points; it obtains the degree of physical shock during transportation at each moment based on the changing trend of vibration data and vehicle speed data at different times; and it obtains the anomaly of the transportation environment at each moment based on the degree of chemical shock and the degree of physical shock during transportation. This information is then written to the blockchain in real time for drug transport traceability and multi-entity data sharing. This invention improves the accuracy of blockchain traceability and data sharing by accurately obtaining the anomaly of the transportation environment at each moment.

[0067] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0068] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1.A blockchain-based medicine cross-regional transfer traceability and multi-agent data sharing system, characterized in that, The system comprises: a data acquisition module configured to acquire vibration data, vehicle speed data, and temperature data of a plurality of monitoring points at each time in a drug transport vehicle compartment; a temperature anomaly evaluation module configured to obtain a temperature mutation factor of each monitoring point at each time according to temperature data distribution of each monitoring point at different times, obtain a temperature change synchronization degree between different monitoring points at each time according to temperature data distribution of different monitoring points at different times, and obtain a temperature anomaly authenticity of each monitoring point at each time according to temperature data distribution of different monitoring points at each time, the temperature mutation factor, and the temperature change synchronization degree; a transport environment analysis module configured to obtain a chemical impact degree of transport at each time according to position distribution of different monitoring points, the temperature anomaly authenticity at each time, and the temperature mutation factor, obtain a physical impact degree of transport at each time according to change trend of vibration data at different times and vehicle speed data, and obtain a transport environment anomaly at each time according to the chemical impact degree of transport at each time and the physical impact degree of transport at each time; a traceability sharing module configured to write the transport environment anomaly at each time into a blockchain in real time, and perform drug transfer traceability and multi-subject data sharing. 2.The medicine cross-region transfer tracing and multi-agent data sharing system based on blockchain according to claim 1, wherein, The method for obtaining the temperature mutation factor comprises: if there is a time point at which the temperature data is greater than the maximum value in a preset temperature range, the corresponding time point is regarded as a positive abnormal time point; if there is a time point at which the temperature data is less than the minimum value in the preset temperature range, the corresponding time point is regarded as a negative abnormal time point; for a historical range of each time point, consecutive adjacent positive abnormal time points are regarded as a positive abnormal period, and consecutive adjacent negative abnormal time points are regarded as a negative abnormal period; a mean value of temperature data of all time points in each abnormal period is obtained as an overall temperature level of each abnormal period; a maximum value of difference between overall temperature levels of adjacent abnormal periods and a product between numbers of abnormal periods are obtained as the temperature mutation factor of each monitoring point at each time. 3.The medicine cross-region transfer tracing and multi-agent data sharing system based on blockchain according to claim 2, wherein, The method for obtaining the temperature change synchronization degree comprises: for any two monitoring points, if there is any time point corresponding to a positive abnormal time point or a negative abnormal time point, the corresponding time point is regarded as a synchronization time point; a temperature curve formed by temperature data of each monitoring point at all synchronization time points is obtained, a slope of the temperature curve at each synchronization time point is obtained, if the slopes of the two monitoring points at each synchronization time point are equal, a change direction consistency factor between the corresponding monitoring points at each synchronization time point is set as a preset first factor, otherwise, the change direction consistency factor is set as a preset second factor, the preset first factor is greater than the preset second factor; for a historical range of each time point, a temperature change synchronization degree between different monitoring points at each time is obtained according to temperature data difference between different monitoring points at different synchronization time points and the change direction consistency factor, the temperature data difference is negatively correlated with the temperature change synchronization degree, and the change direction consistency factor is positively correlated with the temperature change synchronization degree. 4.The medicine cross-region transfer tracing and multi-agent data sharing system based on blockchain according to claim 3, wherein, The method for obtaining the temperature change synchronization degree comprises: temperature data difference between two monitoring points at each synchronization time point is negatively correlated and mapped as temperature similarity. The cumulative value of the product of the temperature similarity and the consistency of the direction of change between the two monitoring points at all synchronization moments is obtained and normalized, which is used as the degree of synchronization of temperature change between the corresponding monitoring points at each moment. 5.The medicine cross-region transfer tracing and multi-agent data sharing system based on blockchain according to claim 1, wherein, The method for obtaining the authenticity of the temperature anomaly includes: If the degree of synchronization of temperature changes between different monitoring points at each moment is greater than or equal to the preset synchronization threshold, the corresponding monitoring point will be used as the synchronization monitoring point, and multiple synchronization monitoring points for each monitoring point at each moment will be obtained. The ratio of the number of synchronous monitoring points to the total number of monitoring points at each monitoring point at any given time is used as the first anomaly authenticity. Based on the degree of synchronization of temperature changes between each monitoring point and different synchronous monitoring points at each moment, the difference in temperature mutation factor, and the authenticity of the first anomaly, the authenticity of temperature anomalies at each monitoring point at each moment is taken as the authenticity of temperature anomalies. The degree of synchronization of temperature changes and the authenticity of the first anomaly are both positively correlated with the authenticity of temperature anomalies, while the difference in temperature mutation factor is negatively correlated with the authenticity of temperature anomalies. 6.The medicine cross-region transfer tracing and multi-agent data sharing system based on blockchain according to claim 1, wherein, The method for obtaining the degree of chemical impact during transport includes: If the temperature anomaly at a monitoring point at any given time is greater than or equal to the preset anomaly threshold, the corresponding monitoring point will be designated as a key monitoring point. Based on the temperature mutation factor, temperature anomaly authenticity, and relative distance between different monitoring key points at each moment, the degree of chemical shock to transportation at each moment is obtained. The temperature mutation factor, temperature anomaly authenticity, and relative distance are all positively correlated with the degree of chemical shock to transportation. 7.The medicine cross-region transfer tracing and multi-agent data sharing system based on blockchain according to claim 1, wherein, The method for obtaining the degree of physical impact on the transport includes: Based on the changing trends of vibration data at different times, multiple turbulence periods are obtained, and the transportation turbulence intensity of each turbulence period is obtained. The vehicle speed data at each moment is normalized to obtain the mean of the normalized vehicle speed data at all moments within each bumpy period, which is used as the overall vehicle speed level for each bumpy period. For each historical moment, the cumulative sum of the product of the transportation turbulence intensity and the overall vehicle speed level during all turbulence periods is obtained as the degree of physical impact on transportation at each moment. 8.The medicine cross-region transportation tracing and multi-agent data sharing system based on blockchain according to claim 7, wherein, The method for obtaining the turbulence period includes: Within the historical range of each moment, the difference in vibration data between adjacent moments is obtained and normalized as the vibration variability. If the vibration variability between adjacent moments is greater than or equal to a preset variability threshold, the corresponding moment is taken as the turbulence moment. All consecutive adjacent turbulence moments constitute the turbulence period. 9.The medicine cross-region transfer tracing and multi-agent data sharing system based on blockchain according to claim 7, wherein, The method for obtaining the transport bump intensity includes: For any turbulence period, the product of the maximum value of the vibration variation between all adjacent moments and the number of moments within the turbulence period is obtained as the transportation turbulence intensity for each turbulence period. 10.The medicine cross-region transfer tracing and multi-agent data sharing system based on blockchain according to claim 1, wherein, The method for obtaining the anomalies in the transportation environment includes: The product of the degree of chemical impact and the degree of physical impact on transportation at each moment is obtained and normalized to represent the transportation environment anomaly at each moment.

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