Intelligent monitoring system for pharmaceutical supply logistics and transportation based on the Internet of Things

Abstract

The present invention relates to the field of data processing, and in particular to an intelligent monitoring system for medicine supply logistics transportation based on the Internet of Things, comprising a data processing module, for respectively collecting temperature data of a preset number of temperature sensors in a medical transport compartment at the i-th moment and a historical temperature data sequence before the i-th moment; a data analysis module, for obtaining, for any temperature sensor, the data stability and data difference of the temperature sensor at the i-th moment according to the data difference of the historical temperature data sequence corresponding to the temperature sensor, and obtaining the importance of the temperature sensor at the i-th moment; a temperature monitoring module, for obtaining the comprehensive temperature of the medical transport compartment at the i-th moment according to the importance of each temperature sensor at the i-th moment, and then monitoring the temperature in the medical transport compartment in real time, thereby improving the accuracy of real-time temperature monitoring in the medical transport vehicle during the medicine supply logistics transportation process.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to an intelligent monitoring system for medicine supply logistics and transportation based on the Internet of Things. Background Art

[0002] Pharmaceutical supply logistics and transportation is a key link in ensuring that drugs are delivered from production to the hands of end users. It is directly related to the quality, safety and effectiveness of drugs. With the aging of the global population and the improvement of health awareness, the demand for drugs continues to grow, which puts higher requirements on the efficiency and reliability of the pharmaceutical supply chain. Pharmaceutical logistics and transportation not only need to ensure the physical safety of drugs, but also must ensure that drugs are transported under appropriate temperature conditions, especially for temperature-sensitive biological products and vaccines.

[0003] In the prior art, the Internet of Things technology is used to realize real-time monitoring and management of pharmaceutical products in each link of the supply chain. Specifically, by monitoring the temperature and humidity of pharmaceuticals during transportation and transmitting these data back to the monitoring center, through the cloud computing platform, relevant technicians can remotely monitor the status of the drugs to ensure that the drugs are transported under suitable environmental conditions, thereby ensuring the quality and safety of the drugs. Among them, when monitoring the temperature of pharmaceuticals during transportation, multiple temperature sensors are installed in the medical transport compartment to obtain multiple temperature sensor data, and the average value of the multiple temperature sensor data is used as the temperature in the medical transport vehicle. However, due to the different installation positions of different sensors in the medical transport compartment, they may be affected by different environmental factors such as temperature, humidity, and airflow. For example, sensors close to the door or vents may be affected by external air flow, so that the average value of multiple temperature sensor data is directly used as the temperature in the medical transport compartment. It is not enough to exclude the abnormal data generated by a certain temperature sensor due to external environmental factors or due to abnormal conditions and unstable performance of a certain temperature sensor. The overall data is affected, and the temperature information in the medical transport vehicle cannot be accurately reflected, which affects the real-time temperature monitoring results in the process of pharmaceutical supply logistics transportation.

[0004] Therefore, how to improve the accuracy of real-time temperature monitoring in medical transport vehicles during the pharmaceutical supply logistics transportation process has become an urgent problem to be solved. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides an intelligent monitoring system for medicine supply logistics transportation based on the Internet of Things to solve the problem of how to improve the accuracy of temperature detection during the medicine supply logistics transportation process.

[0006] The present invention provides an intelligent monitoring system for medicine supply logistics and transportation based on the Internet of Things, which includes the following modules:

[0007] A data processing module is used to collect temperature data of each temperature sensor in the medical transport compartment at the i-th moment and a historical temperature data sequence before the i-th moment according to a preset number of temperature sensors during the medical supply logistics transportation process;

[0008] A data analysis module, for obtaining, for any temperature sensor, a degree of data stability of the temperature sensor at the i-th moment according to a data difference in a historical temperature data sequence corresponding to the temperature sensor, obtaining a degree of data difference of the temperature sensor at the i-th moment according to a data difference in a historical temperature data sequence corresponding to the temperature sensor and other temperature sensors, and obtaining a degree of importance of the temperature sensor at the i-th moment according to the data stability and the data difference;

[0009] The temperature monitoring module is used to obtain the importance of each temperature sensor at the i-th moment, obtain the comprehensive temperature of the medical transport compartment at the i-th moment according to the importance of each temperature sensor at the i-th moment, and monitor the temperature in the medical transport compartment in real time according to the comprehensive temperature at the i-th moment.

[0010] Preferably, the data analysis module obtains the data stability of the temperature sensor at the i-th moment according to the data difference in the historical temperature data sequence corresponding to the temperature sensor, including:

[0011] Respectively obtain the first-order difference of every two adjacent data in the historical temperature data sequence corresponding to the temperature sensor, and form a differential sequence corresponding to the temperature sensor together with the first data in the historical temperature data sequence corresponding to the temperature sensor;

[0012] Obtaining a standard deviation of the data in the differential sequence, and using an exponential function with a natural constant as a base to negatively map the standard deviation to obtain a corresponding first mapping value;

[0013] Obtaining a range of data in a historical temperature data sequence corresponding to the temperature sensor, and using an exponential function with a natural constant as a base to negatively map the range to obtain a corresponding second mapping value;

[0014] An average of the first mapping value and the second mapping value is obtained as the data stability of the temperature sensor at the i-th moment.

[0015] Preferably, the data analysis module obtains the degree of data difference of the temperature sensor at the i-th moment according to the data difference in the historical temperature data sequence corresponding to the temperature sensor and other temperature sensors, including:

[0016] The weighted summation results of the historical temperature data sequences corresponding to each of the temperature sensors are obtained respectively, and the absolute values ​​of the differences between the weighted summation results of the temperature sensor and each of the remaining temperature sensors are calculated, and the sum of the absolute values ​​of the differences is obtained as the data difference degree of the temperature sensor at the i-th moment.

[0017] Preferably, the data analysis module obtains the weighted sum of the historical temperature data sequences corresponding to each temperature sensor, including:

[0018] For any temperature sensor, if the total number of data in the historical temperature data sequence corresponding to the temperature sensor is less than a preset number threshold, the first number of data between each data in the historical temperature data sequence corresponding to the temperature sensor and the temperature data at the i-th moment is counted respectively, and the first difference between the total number of data and each of the first numbers is calculated to obtain the sum of the first differences. The ratio between each of the first differences and the sum of the first differences is obtained as the weight of the corresponding data, and the weight of each of the data is used to perform weighted summation on all the data in the historical temperature data sequence corresponding to the temperature sensor to obtain the weighted summation result of the historical temperature data sequence corresponding to the temperature sensor.

[0019] The data analysis module obtains the weighted sum of the historical temperature data sequences corresponding to each temperature sensor, and further includes:

[0020] For any temperature sensor, if the total number of data in the historical temperature data sequence corresponding to the temperature sensor is greater than or equal to a preset number threshold, a preset number of target data are obtained from the historical temperature data sequence corresponding to the temperature sensor in order from back to front;

[0021] The second quantity of data between each target data and the temperature data at the i-th moment is counted respectively, the second difference between the preset quantity and each second quantity is calculated, and the sum of the second differences is obtained. The ratio between each second difference and the sum of the second differences is obtained as the weight of the corresponding target data, and the weight of each target data is used to perform weighted summation on all target data to obtain the weighted summation result of the historical temperature data sequence corresponding to the temperature sensor.

[0022] Preferably, the data analysis module obtains the importance of the temperature sensor at the i-th moment according to the data stability and the data difference, including:

[0023] The inverse of the data difference degree is normalized to obtain a target normalized result, and the target normalized result and the data stability degree are weighted and summed to obtain the importance of the temperature sensor at the i-th moment.

[0024] Preferably, the temperature monitoring module obtains the comprehensive temperature of the medical transport compartment at the i-th moment according to the importance of each temperature sensor at the i-th moment, including:

[0025] The importance of each temperature sensor at the i-th moment is used as the adaptive weight of the temperature data of the corresponding temperature sensor at the i-th moment. Using each adaptive weight, the temperature data of all temperature sensors at the i-th moment are weighted summed to obtain the comprehensive temperature of the medical transport compartment at the i-th moment.

[0026] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0027] The present invention provides an intelligent monitoring system for medicine supply logistics transportation based on the Internet of Things, comprising a data processing module, which is used for collecting the temperature data of each temperature sensor in a medical transportation compartment at the i-th moment and the historical temperature data sequence before the i-th moment according to a preset number of temperature sensors during the medicine supply logistics transportation process; a data analysis module, which is used for obtaining the data stability of any temperature sensor at the i-th moment according to the data difference in the historical temperature data sequence corresponding to the temperature sensor, obtaining the data difference degree of the temperature sensor at the i-th moment according to the data difference in the historical temperature data sequence corresponding to the temperature sensor and the remaining temperature sensors, and obtaining the importance of the temperature sensor at the i-th moment according to the data stability and the data difference; a temperature monitoring module, which is used for obtaining the importance of each temperature sensor at the i-th moment respectively, obtaining the comprehensive temperature of the medical transportation compartment at the i-th moment according to the importance of each temperature sensor at the i-th moment, and monitoring the temperature in the medical transportation compartment in real time according to the comprehensive temperature at the i-th moment. Among them, a preset number of temperature sensors are first used to collect the temperature data of each temperature sensor in the medical transport compartment at the current moment and the historical temperature data sequence before the current moment, so as to facilitate the subsequent real-time monitoring of the temperature in the medical transport compartment. Afterwards, for any temperature sensor, the temperature data of the temperature sensor at the current moment is assigned a corresponding adaptive weight, thereby reducing the influence of abnormal data generated by the temperature sensor on the overall data due to external environmental factors or abnormal conditions of the temperature sensor itself, and using the adaptive weight of the temperature data of each temperature sensor at the current moment, a more accurate comprehensive temperature of the medical transport compartment at the current moment is obtained, thereby improving the accuracy of real-time temperature monitoring in the medical transport vehicle during the medical supply logistics transportation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0029] Figure 1 This is a structural block diagram of an intelligent monitoring system for medicine supply logistics and transportation based on the Internet of Things provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0030] Embodiments of the present disclosure are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, but should not be understood as limiting the present disclosure.

[0031] It should be noted that the terms "first", "second", etc. in the specification of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0032] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0033] See also Figure 1 , is a structural block diagram of an intelligent monitoring system for medicine supply logistics and transportation based on the Internet of Things provided by the first embodiment of the present invention, such as Figure 1 As shown, the system may include:

[0034] The data processing module 11 is used to collect the temperature data of each temperature sensor in the medical transport compartment at the i-th moment and the historical temperature data sequence before the i-th moment according to a preset number of temperature sensors during the medical supply logistics transportation process.

[0035] During the medical supply logistics transportation process, multiple temperature sensors are first installed at different locations in the medical transport compartment, and each temperature sensor is used to collect the temperature data in the medical transport compartment in real time. The current moment is taken as the i-th moment. Taking the i-th moment as an example, a historical temperature data sequence consisting of the temperature data of each temperature sensor at the i-th moment and the temperature data of all moments before the i-th moment is obtained.

[0036] It should be noted that the number of temperature sensors set in the embodiment of the present invention is 3, and there is no limitation on the number of preset temperature sensors, which can be set according to the implementation scenario.

[0037] The data analysis module 12 is used to obtain, for any temperature sensor, the data stability of the temperature sensor at the i-th moment based on the data difference in the historical temperature data sequence corresponding to the temperature sensor, obtain the data difference degree of the temperature sensor at the i-th moment based on the data difference in the historical temperature data sequence corresponding to the temperature sensor and other temperature sensors, and obtain the importance of the temperature sensor at the i-th moment based on the data stability and the data difference.

[0038] Since different sensors are installed at different locations in the medical transport compartment, they may be affected by different environmental factors. For example, sensors near the door or vents may be affected by external air flow, so that directly taking the average value of multiple temperature sensor data as the temperature in the medical transport compartment is not enough to exclude the possibility that the abnormal data generated by the temperature sensor due to external environmental factors or abnormal conditions and unstable performance of the temperature sensor itself has a great impact on the accuracy of the temperature data at the i-th moment to be monitored. Therefore, the data of a single sensor is first compared, that is, the data stability of the temperature sensor at the i-th moment is obtained according to the discreteness and compactness of the temperature data in the historical temperature data sequence corresponding to any temperature sensor. Therefore, in the embodiment of the present invention, for any temperature sensor, the data stability of the temperature sensor at the i-th moment is obtained, including:

[0039] Respectively obtain the first-order difference of every two adjacent data in the historical temperature data sequence corresponding to the temperature sensor, and form a differential sequence corresponding to the temperature sensor together with the first data in the historical temperature data sequence corresponding to the temperature sensor;

[0040] Obtaining a standard deviation of the data in the differential sequence, and using an exponential function with a natural constant as a base to negatively map the standard deviation to obtain a corresponding first mapping value;

[0041] Obtaining a range of data in a historical temperature data sequence corresponding to the temperature sensor, and using an exponential function with a natural constant as a base to negatively map the range to obtain a corresponding second mapping value;

[0042] An average of the first mapping value and the second mapping value is obtained as the data stability of the temperature sensor at the i-th moment.

[0043] In one embodiment, taking the kth temperature sensor as an example, the first-order difference of each two adjacent data in the historical temperature data sequence corresponding to the kth temperature sensor is obtained respectively, and the first data in the historical temperature data sequence corresponding to the kth temperature sensor is used to form a differential sequence corresponding to the kth temperature sensor, and the standard deviation of the differential sequence is calculated, and the range of the historical temperature data sequence corresponding to the kth temperature sensor is calculated. According to the standard deviation and the range, the data stability of the kth temperature sensor at the i-th moment is obtained, and the expression of the data stability of the kth temperature sensor at the i-th moment is:

[0044]

[0045] in, Indicates the data stability of the kth temperature sensor at the i-th moment, represents the standard deviation of the differential sequence corresponding to the kth temperature sensor, represents the range of the historical temperature data sequence corresponding to the kth temperature sensor, L represents the historical temperature data sequence corresponding to each temperature sensor, represents the differential sequence corresponding to each temperature sensor, and exp() represents the exponential function with a natural constant as the base.

[0046] It should be noted that The smaller the value of , the smaller the discreteness of the data in the differential sequence corresponding to the kth temperature sensor is, indicating that the stability of the temperature data measured by the kth temperature sensor during transportation is higher, and the data stability of the kth temperature sensor at the i-th moment is greater; The smaller the value is, the greater the compactness of the data in the historical temperature data sequence corresponding to the kth temperature sensor is, indicating that the stability of the temperature data measured by the kth temperature sensor during transportation is higher, and the data stability corresponding to the kth temperature sensor at the i-th moment is greater.

[0047] After obtaining the data stability of a single temperature sensor at the i-th moment, it is necessary to further determine whether the temperature sensor performs well in the temperature measurement process of all temperature sensors, that is, whether the temperature detection is accurate. Since the reference value of temperature data closer to the temperature data at the i-th moment is greater, in order to avoid the polarization of data weight allocation in the historical temperature data sequence corresponding to the temperature sensor due to excessive temperature data in the later stage, therefore, in the embodiment of the present invention, according to the data difference in the historical temperature data sequence corresponding to the temperature sensor and the other temperature sensors, the data difference degree of the temperature sensor at the i-th moment is obtained, and the specific method is as follows:

[0048] (1) Obtain the weighted summation result of the historical temperature data sequence corresponding to each temperature sensor respectively.

[0049] Specifically, for any temperature sensor, if the total number of data in the historical temperature data sequence corresponding to the temperature sensor is less than a preset number threshold, the first number of data between each data in the historical temperature data sequence corresponding to the temperature sensor and the temperature data at the i-th moment is counted respectively, and the first difference between the total number of data and each of the first numbers is calculated to obtain the sum of the first differences. The ratio between each of the first differences and the sum of the first differences is obtained as the weight of the corresponding data, and the weight of each of the data is used to perform weighted summation on all the data in the historical temperature data sequence corresponding to the temperature sensor to obtain the weighted summation result of the historical temperature data sequence corresponding to the temperature sensor.

[0050] In one embodiment, taking the kth temperature sensor as an example, the quantity threshold set in the embodiment of the present invention is 10, which is not limited and can be set according to the implementation scenario. If the total number of data in the historical temperature data sequence corresponding to the kth temperature sensor is Less than the preset quantity threshold, that is , then the expression for obtaining the weighted sum of the historical temperature data sequence corresponding to the kth temperature sensor is:

[0051]

[0052] in, represents the weighted sum of the historical temperature data sequence corresponding to the kth temperature sensor, represents the temperature data at the jth moment in the historical temperature data sequence corresponding to the kth temperature sensor, Represents the total number of data in the historical temperature data sequence corresponding to the kth temperature sensor, represents the first quantity of data between the temperature data at the j-th moment and the temperature data at the ith moment, and L represents a historical temperature data sequence corresponding to each temperature sensor.

[0053] It should be noted that The smaller the value is, the smaller the number of data between the temperature data at the jth moment and the temperature data at the ith moment is, that is, the closer the temperature data at the jth moment is to the temperature data at the ith moment, indicating that the reference value of the temperature data at the jth moment is greater, and the more it is necessary to increase the weight of the temperature data at the jth moment.

[0054] If the total number of data in the historical temperature data sequence corresponding to the temperature sensor is greater than or equal to a preset number threshold, then obtaining a preset number of target data in the historical temperature data sequence corresponding to the temperature sensor in a backward order;

[0055] The second quantity of data between each target data and the temperature data at the i-th moment is counted respectively, the second difference between the preset quantity and each second quantity is calculated, and the sum of the second differences is obtained. The ratio between each second difference and the sum of the second differences is obtained as the weight of the corresponding target data, and the weight of each target data is used to perform weighted summation on all target data to obtain the weighted summation result of the historical temperature data sequence corresponding to the temperature sensor.

[0056] In one embodiment, taking the kth temperature sensor as an example, if the total number of data in the historical temperature data sequence corresponding to the kth temperature sensor is greater than or equal to 10, then in order from back to front, 10 target data equal to the preset number threshold are obtained in the historical temperature data sequence corresponding to the kth temperature sensor, and the 10 target data are combined into a target data sequence. According to the target data sequence, the weighted summation result of the historical temperature data sequence corresponding to the kth temperature sensor is obtained, and the expression of the weighted summation result of the historical temperature data sequence corresponding to the kth temperature sensor is:

[0057]

[0058] in, represents the weighted sum of the historical temperature data sequence corresponding to the kth temperature sensor, represents the temperature data at the jth moment in the target data sequence corresponding to the kth temperature sensor, represents the total number of data in the target data sequence corresponding to the kth temperature sensor, represents the second quantity of data between the temperature data at the jth moment and the temperature data at the ith moment, t represents the time corresponding to the first data in the target data sequence corresponding to the kth temperature sensor, and L represents the historical temperature data sequence corresponding to each temperature sensor.

[0059] It should be noted that The larger the value is, the larger the number of data between the temperature data at the jth moment and the temperature data at the ith moment is, that is, the farther the temperature data at the jth moment is from the temperature data at the ith moment, indicating that the reference value of the temperature data at the jth moment is smaller, and the more it is necessary to reduce the weight of the temperature data at the jth moment.

[0060] (2) Calculate the absolute value of the difference between the weighted summation results of the temperature sensor and each of the other temperature sensors, and obtain the sum of the absolute values ​​of the differences as the data difference degree of the temperature sensor at the i-th moment.

[0061] In one embodiment, taking the kth temperature sensor as an example, the expression for obtaining the degree of data difference of the kth temperature sensor at the i-th moment is:

[0062]

[0063] in, Indicates the degree of data difference of the kth temperature sensor at the i-th moment, represents the weighted sum of the historical temperature data sequence corresponding to the kth temperature sensor, represents the weighted sum of the historical temperature data sequences corresponding to the nth temperature sensor except the kth temperature sensor, L represents the historical temperature data sequence corresponding to each temperature sensor, K represents the total number of temperature sensors, and || represents the absolute value.

[0064] It should be noted that The larger the value of , the greater the difference between the temperature data measured by the k-th temperature sensor during transportation and the temperature data measured by other sensors, indicating that the accuracy of the temperature measured by the k-th temperature sensor during transportation is lower, that is, the worse the temperature monitoring effect is, and the greater the difference in data corresponding to the k-th temperature sensor at the i-th moment.

[0065] Since the stronger the data stability of a single temperature sensor is, the higher the stability of the temperature data measured by the temperature sensor during transportation is, and the smaller the difference between the temperature data collected by the temperature sensor and other temperature sensors is, the higher the accuracy of the temperature measurement by the temperature sensor is, the higher the importance of the temperature sensor is, and vice versa, the lower the importance of the temperature sensor is. Therefore, in the embodiment of the present invention, obtaining the importance of the temperature sensor at the i-th moment includes:

[0066] The inverse of the data difference degree is normalized to obtain a target normalized result, and the target normalized result and the data stability degree are weighted and summed to obtain the importance of the temperature sensor at the i-th moment.

[0067] In one embodiment, taking the kth temperature sensor as an example, the expression for obtaining the importance of the kth temperature sensor at the i-th moment is:

[0068]

[0069] in, represents the importance of the kth temperature sensor at the i-th moment, Indicates the data stability of the kth temperature sensor at the i-th moment, Indicates the degree of data difference of the kth temperature sensor at the i-th moment, The weight representing the stability of the data, It represents the weight of the data difference, and norm() represents the normalization function.

[0070] Preferably, the weights of the data stability and the data difference set in the embodiment of the present invention are empirical values, that is, There is no restriction on this, and it can be set according to the implementation scenario.

[0071] It should be noted that The larger the value of is, the higher the stability of the temperature data measured by the k-th temperature sensor during transportation is, and the greater the weight should be given to the temperature data of the k-th temperature sensor at the i-th moment, and the greater the importance of the k-th temperature sensor at the i-th moment is. The smaller the value is, the smaller the difference between the temperature data measured by the k-th temperature sensor during transportation and the temperature data measured by other sensors is, indicating that the accuracy of the temperature measured by the k-th temperature sensor during transportation is higher, and it is more necessary to give a greater weight to the temperature data of the k-th temperature sensor at the i-th moment, and the greater the importance of the k-th temperature sensor at the i-th moment is.

[0072] According to the above method for obtaining the importance of the kth temperature sensor at the i-th moment, the importance of each temperature sensor at the i-th moment is obtained respectively.

[0073] The temperature monitoring module 13 is used to obtain the importance of each temperature sensor at the i-th moment, obtain the comprehensive temperature of the medical transport compartment at the i-th moment according to the importance of each temperature sensor at the i-th moment, and monitor the temperature in the medical transport compartment in real time according to the comprehensive temperature at the i-th moment.

[0074] Due to external environmental factors or due to abnormal conditions or unstable performance of a certain temperature sensor, the abnormal data generated by the temperature sensor affects the overall data. Therefore, in an embodiment of the present invention, according to the importance of each temperature sensor at the i-th moment, a corresponding adaptive weight is assigned to the temperature data of each temperature sensor at the i-th moment. That is, if the temperature sensor generates abnormal temperature data at the i-th moment due to external environmental factors or due to abnormal conditions of the temperature sensor itself, the weight of the abnormal temperature data generated by the temperature sensor at the i-th moment is reduced to reduce the impact of the abnormal temperature data on the overall temperature data. For temperature sensors with outstanding performance, the weight of the temperature data of the temperature sensor at the i-th moment is increased to obtain the comprehensive temperature of the medical transport compartment at the i-th moment, thereby making the temperature monitoring in the medical transport compartment at the i-th moment more accurate.

[0075] The specific method for obtaining the comprehensive temperature of the medical transport compartment at the i-th moment includes:

[0076] The importance of each temperature sensor at the i-th moment is used as the adaptive weight of the temperature data of the corresponding temperature sensor at the i-th moment. Using each adaptive weight, the temperature data of all temperature sensors at the i-th moment are weighted summed to obtain the comprehensive temperature of the medical transport compartment at the i-th moment.

[0077] In one embodiment, the expression for obtaining the comprehensive temperature of the medical transport compartment at the i-th moment is:

[0078]

[0079] in, represents the comprehensive temperature of the medical transport compartment at the i-th moment, represents the importance of the kth temperature sensor at the i-th moment, Represents the temperature data of the kth temperature sensor at the i-th moment.

[0080] According to the above method for obtaining the comprehensive temperature of the medical transport compartment at the i-th moment, the comprehensive temperature of the medical transport compartment at the i+1-th moment is obtained, and so on. According to the temperature data collected in real time by each temperature sensor, the temperature in the medical transport compartment is monitored in real time.

[0081] In summary, the present invention provides an intelligent monitoring system for pharmaceutical supply logistics transportation based on the Internet of Things, including a data processing module, which is used to collect the temperature data of each temperature sensor in the medical transport compartment at the i-th moment and the historical temperature data sequence before the i-th moment according to a preset number of temperature sensors during the pharmaceutical supply logistics transportation process; a data analysis module, which is used to obtain the data stability of the temperature sensor at the i-th moment according to the data difference in the historical temperature data sequence corresponding to the temperature sensor for any temperature sensor, obtain the data difference degree of the temperature sensor at the i-th moment according to the data difference in the historical temperature data sequence corresponding to the temperature sensor and the remaining temperature sensors, and obtain the importance of the temperature sensor at the i-th moment according to the data stability and the data difference; a temperature monitoring module, which is used to obtain the importance of each temperature sensor at the i-th moment respectively, obtain the comprehensive temperature of the medical transport compartment at the i-th moment according to the importance of each temperature sensor at the i-th moment, and monitor the temperature in the medical transport compartment in real time according to the comprehensive temperature at the i-th moment. Among them, a preset number of temperature sensors are first used to collect the temperature data of each temperature sensor in the medical transport compartment at the current moment and the historical temperature data sequence before the current moment, so as to facilitate the subsequent real-time monitoring of the temperature in the medical transport compartment. Afterwards, for any temperature sensor, the temperature data of the temperature sensor at the current moment is assigned a corresponding adaptive weight, thereby reducing the influence of abnormal data generated by the temperature sensor on the overall data due to external environmental factors or abnormal conditions of the temperature sensor itself, and using the adaptive weight of the temperature data of each temperature sensor at the current moment, a more accurate comprehensive temperature of the medical transport compartment at the current moment is obtained, thereby improving the accuracy of real-time temperature monitoring in the medical transport vehicle during the medical supply logistics transportation process.

[0082] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. The intelligent monitoring system for pharmaceutical supply logistics and transportation based on the Internet of Things is characterized by: The system comprises: A data processing module is used to collect temperature data of each temperature sensor in the medical transport compartment at the i-th moment and a historical temperature data sequence before the i-th moment according to a preset number of temperature sensors during the medical supply logistics transportation process; A data analysis module, for obtaining, for any temperature sensor, a degree of data stability of the temperature sensor at the i-th moment according to a data difference in a historical temperature data sequence corresponding to the temperature sensor, obtaining a degree of data difference of the temperature sensor at the i-th moment according to a data difference in a historical temperature data sequence corresponding to the temperature sensor and other temperature sensors, and obtaining a degree of importance of the temperature sensor at the i-th moment according to the data stability and the data difference; A temperature monitoring module, for respectively obtaining the importance of each temperature sensor at the i-th moment, obtaining the comprehensive temperature of the medical transport compartment at the i-th moment according to the importance of each temperature sensor at the i-th moment, and performing real-time monitoring of the temperature in the medical transport compartment according to the comprehensive temperature at the i-th moment; The data analysis module obtains the importance of the temperature sensor at the i-th moment according to the data stability and the data difference, including: Normalizing the inverse of the data difference degree to obtain a target normalization result, performing weighted summation on the target normalization result and the data stability degree to obtain the importance of the temperature sensor at the i-th moment; The temperature monitoring module obtains the comprehensive temperature of the medical transport compartment at the i-th moment according to the importance of each temperature sensor at the i-th moment, including: The importance of each temperature sensor at the i-th moment is used as the adaptive weight of the temperature data of the corresponding temperature sensor at the i-th moment. Using each adaptive weight, the temperature data of all temperature sensors at the i-th moment are weighted summed to obtain the comprehensive temperature of the medical transport compartment at the i-th moment.

2. According to the Internet of Things-based intelligent monitoring system for pharmaceutical supply logistics and transportation according to claim 1, it is characterized in that: The data analysis module obtains the data stability of the temperature sensor at the i-th moment according to the data difference in the historical temperature data sequence corresponding to the temperature sensor, including: Respectively obtain the first-order difference of every two adjacent data in the historical temperature data sequence corresponding to the temperature sensor, and form a differential sequence corresponding to the temperature sensor together with the first data in the historical temperature data sequence corresponding to the temperature sensor; Obtaining a standard deviation of the data in the differential sequence, and using an exponential function with a natural constant as a base to negatively map the standard deviation to obtain a corresponding first mapping value; Obtaining a range of data in a historical temperature data sequence corresponding to the temperature sensor, and using an exponential function with a natural constant as a base to negatively map the range to obtain a corresponding second mapping value; An average of the first mapping value and the second mapping value is obtained as the data stability of the temperature sensor at the i-th moment.

3. According to the Internet of Things-based intelligent monitoring system for pharmaceutical supply logistics and transportation according to claim 1, it is characterized in that: The data analysis module obtains the degree of data difference of the temperature sensor at the i-th moment according to the data difference in the historical temperature data sequence corresponding to the temperature sensor and other temperature sensors, including: The weighted summation results of the historical temperature data sequences corresponding to each of the temperature sensors are obtained respectively, and the absolute values ​​of the differences between the weighted summation results of the temperature sensor and each of the remaining temperature sensors are calculated, and the sum of the absolute values ​​of the differences is obtained as the data difference degree of the temperature sensor at the i-th moment.

4. The intelligent monitoring system for medicine supply logistics and transportation based on the Internet of Things according to claim 3 is characterized in that: The data analysis module obtains the weighted summation result of the historical temperature data sequence corresponding to each temperature sensor, including: For any temperature sensor, if the total number of data in the historical temperature data sequence corresponding to the temperature sensor is less than a preset number threshold, the first number of data between each data in the historical temperature data sequence corresponding to the temperature sensor and the temperature data at the i-th moment is counted respectively, and the first difference between the total number of data and each of the first numbers is calculated to obtain the sum of the first differences. The ratio between each of the first differences and the sum of the first differences is obtained as the weight of the corresponding data, and the weight of each of the data is used to perform weighted summation on all the data in the historical temperature data sequence corresponding to the temperature sensor to obtain the weighted summation result of the historical temperature data sequence corresponding to the temperature sensor.

5. The intelligent monitoring system for medicine supply logistics and transportation based on the Internet of Things according to claim 3 is characterized in that: The data analysis module obtains the weighted sum of the historical temperature data sequences corresponding to each temperature sensor, and further includes: For any temperature sensor, if the total number of data in the historical temperature data sequence corresponding to the temperature sensor is greater than or equal to a preset number threshold, a preset number of target data are obtained from the historical temperature data sequence corresponding to the temperature sensor in order from back to front; The second quantity of data between each target data and the temperature data at the i-th moment is counted respectively, the second difference between the preset quantity and each second quantity is calculated, and the sum of the second differences is obtained. The ratio between each second difference and the sum of the second differences is obtained as the weight of the corresponding target data, and the weight of each target data is used to perform weighted summation on all target data to obtain the weighted summation result of the historical temperature data sequence corresponding to the temperature sensor.

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