Medicine cold-chain logistics dynamic data cloud platform and route analysis method

Abstract

The invention relates to the technical field of medicine cold-chain logistics, and discloses a medicine cold-chain logistics dynamic data cloud platform and a route analysis method.The cloud platform comprises a data acquisition unit, a preprocessing unit, a data transmission unit, a storage management unit and a route analysis unit. Key data such as temperature, humidity and position are collected in real time through multiple sensors and are subjected to standardized preprocessing, so that the data are comprehensive, real-time and reliable, and a foundation is laid for subsequent analysis. In the aspect of storage management, partition storage is carried out based on time and space dimensions, and the data retrieval and management efficiency is greatly improved; in the aspect of route planning, factors such as time, temperature and humidity, congestion and the like are comprehensively considered, a scientific route cost evaluation function is constructed, costs such as transportation duration, temperature and humidity stability, road conditions and the like are covered, and in combination with a greedy algorithm, the medicine transportation quality can be guaranteed, the cost can be effectively reduced, and the logistics efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of pharmaceutical cold chain logistics, and in particular to a pharmaceutical cold chain logistics dynamic data cloud platform and a routing analysis method. Background Art

[0002] Pharmaceutical cold chain logistics plays a vital role in ensuring the quality and safety of drugs. The transportation process has extremely stringent requirements on environmental conditions such as temperature and humidity. Deviations in any link may cause the drugs to deteriorate and become ineffective, thus endangering the life and health of patients.

[0003] Traditional pharmaceutical cold chain logistics route planning methods often rely on experience or simple path calculation methods, and lack comprehensive consideration of various complex factors in the transportation process. For example, they fail to fully consider the impact of temperature and humidity fluctuations on drug quality, as well as the interference of road congestion, traffic restrictions and other road conditions on transportation time and cost. As a result, in actual transportation, drugs often take too long to be transported under unsuitable environmental conditions, or are delayed due to unreasonable route selection, increasing the risk of drug loss and logistics costs.

[0004] With the continuous development of the pharmaceutical industry and the increasingly stringent supervision of drug quality, there is an urgent need for a method and technology that can accurately and comprehensively consider multiple factors to plan the global optimal routing path for pharmaceutical cold chain logistics, so as to ensure the quality and safety of drugs throughout the transportation process, while improving logistics efficiency and reducing operating costs. In this context, a dynamic data cloud platform for pharmaceutical cold chain logistics and its routing analysis method are proposed to achieve efficient and accurate management and routing planning of pharmaceutical cold chain logistics. Summary of the invention

[0005] The purpose of the present invention is to provide a dynamic data cloud platform for pharmaceutical cold chain logistics and a routing analysis method, which solves the technical problems raised in the background technology.

[0006] The purpose of the present invention can be achieved through the following technical solutions: Pharmaceutical cold chain logistics dynamic data cloud platform, including: Data collection unit, which is used to deploy multiple sensors at key nodes of pharmaceutical cold chain logistics to obtain relevant data in real time; A preprocessing unit, used for preprocessing the collected relevant data and obtaining standardized relevant data; A data transmission unit, used for uploading the pre-processed relevant data to the storage management unit in the cloud platform; A storage management unit, used to partition and store relevant data according to time and space dimensions; The routing analysis unit is used to plan the global optimal routing path in pharmaceutical cold chain logistics based on factors corresponding to time, temperature, humidity and congestion.

[0007] As a further solution of the present invention: the relevant data includes a temperature data sequence collected by a temperature sensor, and is marked as T={T1, T2, ...T n}, the humidity data sequence collected by the humidity sensor is marked as H = {H1, H2, ... H n}, the position data sequence collected by the positioning sensor is marked as P = {P1 (x1, y1), P2 (x2, y2), ... P n (x n ,y n )}, where n represents the number of times the relevant data is collected.

[0008] As a further solution of the present invention: the pretreatment method is as follows: StepH1, temperature data preprocessing: Calculate the mean and standard deviation of n temperature data and mark them as T p 、T b ; The calculation formula is as follows: ; ; Where Ti refers to the temperature data collected for the i-th time, i=1, 2, ...n; Each temperature data T i Combine the corresponding mean and standard deviation to make abnormal judgment; When |T i -T p |>3×T b When the corresponding T i Abnormal value; When |T i -T p |≤3×T b When the corresponding T i The value is normal; When the corresponding T i If the value is abnormal, calculate the value with T i The average value of two adjacent temperature data is used to calculate the T i Replace the value; The formula is: TT i =(T i-1 +T i+1 ) / 2; Where, TT i Refers to the corresponding T iThe value to be replaced, that is, the standardized T i Value, T i-1 and T i+1 Refers to T i The values ​​of two adjacent temperature data; StepH2, humidity data preprocessing: The preprocessing method of humidity data is the same as that of temperature data; StepH3, position data preprocessing: Select the i-th collected position data, and according to the timestamp of the position data collection, obtain the position data set collected in real time within the specified processing period CT before and after the timestamp, and mark it as P j (x j ,y j )={P i-m (x i-m ,y i-m ), ... P i (x i ,y i ), ... P i+m (x i+m ,y i+m )}; Where j = 1, 2, ..., i + 2m, P i (x i ,y i ) represents the position data collected for the i-th time, and m represents the number of times the position data is collected in real time within the specified processing period CT; Then calculate P j (x j ,y j ) and use it as the position data collected for the i-th time after standardization; The calculation formula is as follows: ; ; Calculate the position data Pi (x p，i ,y p，i ).

[0009] As a further solution of the present invention: the partition storage method is as follows: Step C1, partition based on time dimension: Taking the specified standard time period BT as a unit, a number of time storage partitions are divided, and then according to the timestamp of relevant data collection, the relevant data is stored in a time storage partition containing the timestamp; Step C2, partition based on spatial dimension: The logistics area corresponding to the pharmaceutical cold chain logistics is divided into several grids, and then a spatial storage partition is established corresponding to each grid; then, based on the location data of the relevant data, the relevant data is stored in a spatial storage partition containing the location data.

[0010] As a further solution of the present invention: the routing path planning method is as follows: Step M1. Obtain the pharmaceutical cold chain logistics to be planned, extract the location information corresponding to the starting point and the end point of the logistics, and mark them as S (x s ,y s ) and E(x e ,y e ); Step M2, extract the historical logistics data in the spatial grid where the starting point and the end point are located and the adjacent grids from the storage management unit of the cloud platform; Historical logistics data includes the average transportation time, temperature fluctuation range, humidity fluctuation range and road condition information of each logistics section; And mark them as U k ,[T min，k , T max，k ]、[H min，k , H max，k ], L k ; k = 1, 2, ... e, e represents the number of logistics sections; Step M3: Construct a routing cost evaluation function, which is composed of the transportation time cost FU r , Temperature stability cost FT r , humidity stability cost FH r and road condition cost FL r Composition, namely F r =FU r +FT r +FH r +FL r ; Among them, F r is the routing cost evaluation value, r is the preset candidate routing path, and the logistics sections included in the candidate routing path are marked as r={tk1, tk2, ..., tk v}, where tk g represents the logistics segments included in the candidate routing path, and v represents the number of logistics segments included in the candidate routing path; Step M4: Combine the routing cost evaluation value and use the greedy algorithm to query the optimal routing path.

[0011] As a further solution of the present invention: wherein each logistics road section refers to the road section passed by the logistics transport vehicle in the spatial grid where the starting point and the end point are located and the adjacent grids; the road condition information is based on the road grade L of the relevant logistics road section X1，k 、Whether to restrict traffic L X2，k 、Road maintenance status X3，k OK, that is L k =L X1，k +L X2，k +L X3，k ; in: When the corresponding logistics section is a highway, L X1，k The value is 0.3; When the corresponding logistics section is an ordinary national highway, L X1，k The value is 0.5; When the corresponding logistics section is an urban road, L X1，k The value is 0.7; When the corresponding logistics section is an unrestricted section, L X2，k The value is 0.3; When the corresponding logistics section is a restricted section, L X2，k The value is 0.7; When the corresponding logistics section is a maintenance road, L X3，k The value is 0.9; When the corresponding logistics section is a normal road, L X3，k The value is 0.4.

[0012] As a further solution of the present invention: In Step M3, the calculation method of each cost is as follows: Step M3.1, transportation time cost: StepM3.1.1. Extract each logistics section tk in the candidate routing path g The average transport time is denoted as U(tk g ); StepM3.1.2, then pass: ; Calculate the transportation time cost FU in the candidate routing path r ; Step M3.2, Temperature stability cost: StepM3.2.1. Extract each logistics section tk in the candidate routing path g The temperature fluctuation range is marked as [T (tk g，min ), T (tk g，max )]; StepM3.2.2, then according to each logistics section tk gThe temperature fluctuation range is used to determine the temperature stability cost of each logistics section; the method is as follows: If T (tk g，max )-T(tk g，min )>TY, then through: ; Calculate the temperature stability cost FT0 (tk g ); Among them, TY is the preset temperature fluctuation threshold; If T (tk g，max )-T(tk g，min )≤TY, then the temperature stability cost of each logistics section ; Step M3.2.3, then pass: ; Calculate the temperature stability cost FT in the candidate routing path r ; Step M3.3, humidity stability cost: StepM3.3.1. Extract each logistics section tk in the candidate routing path g The humidity fluctuation range is marked as [H (tk g，min ), H (tk g，max )]; StepM3.3.2, then according to each logistics section tk g The humidity fluctuation range is determined to determine the humidity stability cost of each logistics section; the method is as follows: If H (tk g，max )-H(tk g，min )>HY, then through ; Calculate the humidity stability cost FH0 (tk g ); Among them, HY is the preset humidity fluctuation threshold; If H (tk g，max )-H(tk g，min )≤HY, then the humidity stability cost of each logistics section ; Step M3.3.3, then pass: ; Calculate the humidity stability cost FH in the candidate routing path r ; Step M3.4, road condition cost: StepM3.4.1. Extract each logistics section tk in the candidate routing path g Traffic information and marked it as L (tkg ); Step M3.4.2, then pass: ; Calculate the road cost FL in the candidate routing path r .

[0013] As a further solution of the present invention: in Step M4, the query steps of the optimal routing path are as follows: Step M4.1. Mark the optimal routing path as ry and its corresponding routing cost value as F ry ; Initialize the optimal routing path ry to be empty, and its corresponding routing cost value F ry is infinite; StepM4.2, from the starting point S (x s ,y s ), traverse all its adjacent segments, and calculate the routing cost F(r) to reach the next node through each adjacent segment. c ), where r c represents the routing path to the next node through the relevant adjacent road segments; Step M4.3, combined with F (r c ), select the segment with the smallest cost and add it to the current routing path, and mark the current routing path as r0 ; Then the current routing cost value is updated and marked as F r0 ; Step M4.4, determine whether the current node is the end point E (x e ,y e ): If the current node is the end point, compare F r0 With F ry , if F r0 <F ry , then update ry=r0, F ry =F r0 ; If the current node is not the end point, continue to traverse the adjacent segments from the current node and repeat the contents of Step M4.2-Step M4.4 until the end point E (x e ,y e ).

[0014] A routing parsing method for a pharmaceutical cold chain logistics dynamic data cloud platform, the method being implemented by the pharmaceutical cold chain logistics dynamic data cloud platform, the method comprising the following steps: The first step is to obtain the pharmaceutical cold chain logistics to be planned, and extract the location information corresponding to the starting point and end point of the logistics; Step 2: Extract the historical logistics data in the spatial grid where the starting point and the end point are located and the adjacent grids from the storage management unit of the cloud platform; The third step is to construct a routing cost evaluation function, which is composed of transportation time cost, temperature stability cost, humidity stability cost and road condition cost; Step 4: Determine the routing cost evaluation value in combination with the routing cost evaluation function, and then use the greedy algorithm to query the optimal routing path.

[0015] Beneficial effects of the present invention: Comprehensive and real-time data collection: By deploying a variety of sensors at key nodes in the pharmaceutical cold chain logistics, such as temperature sensors, humidity sensors, positioning sensors, etc., it is possible to obtain relevant data such as temperature, humidity, and location in real time, providing comprehensive and real-time data support for the monitoring and management of cold chain logistics, which helps to detect potential problems in a timely manner.

[0016] Data standardization: The collected temperature, humidity, and location data are preprocessed to standardize them. Temperature and humidity data are judged as abnormal and corrected by calculating the average value and standard deviation. Location data is obtained by obtaining the data within the specified period based on the timestamp and calculating the average value for standardization, which improves the data quality and provides a reliable basis for subsequent data analysis and application.

[0017] Efficient partition storage: Adopt partition storage based on time dimension and space dimension. The time dimension divides the storage partitions into specified standard time periods, and the space dimension divides the logistics area into grids and establishes corresponding storage partitions, which facilitates the rapid retrieval and management of data and improves data access efficiency.

[0018] Comprehensive consideration of multiple factors: When planning the routing path, the routing analysis unit comprehensively considers factors such as time, temperature, humidity and congestion, and constructs a routing cost evaluation function that includes transportation time cost, temperature stability cost, humidity stability cost and road condition cost, so that the planned routing path is more in line with the actual needs of pharmaceutical cold chain logistics and ensures the quality of drug transportation.

[0019] Optimize transportation costs: The calculation methods of transportation time cost, temperature stability cost, humidity stability cost and road condition cost are used to quantitatively evaluate the routing cost from different aspects. Combined with the greedy algorithm to query the optimal routing path, it helps to reduce transportation costs and improve logistics efficiency while meeting the conditions for drug transportation.

[0020] Integration with real-time traffic conditions: Traffic condition information is determined based on road grade, traffic restrictions, and road maintenance conditions, so that route planning can reflect actual road conditions in real time, further optimize routing paths, and reduce the risk of delays during transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention will be further described below in conjunction with the accompanying drawings.

[0022] Figure 1 It is a system block diagram of the pharmaceutical cold chain logistics dynamic data cloud platform of the present invention.

[0023] Figure 2 It is a flow chart of the routing parsing method of the pharmaceutical cold chain logistics dynamic data cloud platform of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] Embodiment 1 See also Figure 1 and Figure 2 As shown, the present invention is a pharmaceutical cold chain logistics dynamic data cloud platform, comprising: Data collection unit, which is used to deploy multiple sensors at key nodes of pharmaceutical cold chain logistics to obtain relevant data in real time; In this embodiment, multiple key nodes such as warehouse entrances and exits, inside of transport vehicles, distribution sites, etc.; Various sensors include temperature sensors, humidity sensors, and positioning sensors; The relevant data includes the temperature data sequence collected by the temperature sensor, which is marked as T={T1, T2, ...T n}, the humidity data sequence collected by the humidity sensor is marked as H = {H1, H2, ... H n}, the position data sequence collected by the positioning sensor is marked as P = {P1 (x1, y1), P2 (x2, y2), ... P n (x n ,y n )}, where n represents the number of times relevant data are collected; The preprocessing unit is used to preprocess the collected relevant data and obtain standardized relevant data; the preprocessing method is as follows: StepH1, temperature data preprocessing: Calculate the mean and standard deviation of n temperature data and mark them as T p 、T b ; The calculation formula is as follows: ; ; Where Ti refers to the temperature data collected for the i-th time, i=1, 2, ...n; Each temperature data T i Combine the corresponding mean and standard deviation to make abnormal judgment; When |T i -T p |>3×T b When the corresponding T i Abnormal value; When |T i -T p |≤3×T b When the corresponding T i The value is normal; Among them, i=1, 2, ... n, and they are marked according to the time trend; When the corresponding T i If the value is abnormal, calculate the value with T i The average value of two adjacent temperature data is used to calculate the T i Replace the value; The formula is: TT i =(T i-1 +T i+1 ) / 2; Where, TT i Refers to the corresponding T i The value to be replaced, that is, the standardized T i Value, T i-1 and T i+1 Refers to T i The values ​​of two adjacent temperature data; StepH2, humidity data preprocessing: The preprocessing method of humidity data is the same as that of temperature data; StepH3, position data preprocessing: Select the i-th collected position data, and according to the timestamp of the position data collection, obtain the position data set collected in real time within the specified processing period CT before and after the timestamp, and mark it as P j (x j ,y j )={P i-m (x i-m ,y i-m ), ... P i (x i ,y i ), ... P i+m (x i+m ,y i+m )}; Where j = 1, 2, ..., i + 2m, P i (x i ,y i ) represents the position data collected for the i-th time, and m represents the number of times the position data is collected in real time within the specified processing period CT; In this embodiment, the value of CT is 10 seconds, and the acquisition frequency preset by the positioning sensor is 2 seconds each time; Then calculate P j (x j ,y j ) and use it as the position data collected for the i-th time after standardization; The calculation formula is as follows: ; ; Calculate the position data Pi (x p，i ,y p，i ); A data transmission unit, used for uploading the pre-processed relevant data to the storage management unit in the cloud platform; The storage management unit is used to adopt a distributed database architecture to partition and store relevant data according to the time dimension and space dimension; the partition storage method is as follows: Step C1, partition based on time dimension: Taking the specified standard time period BT as a unit, a number of time storage partitions are divided, and then according to the timestamp of relevant data collection, the relevant data is stored in a time storage partition containing the timestamp; In this embodiment, the value of BT is 1 day, for example, the relevant data corresponding to each day is stored in an independent database table; Step C2, partition based on spatial dimension: The logistics area corresponding to the pharmaceutical cold chain logistics is divided into several grids, and then a spatial storage partition is established corresponding to each grid; then, based on the location data of the relevant data, the relevant data is stored in a spatial storage partition containing the location data.

[0026] This embodiment can obtain relevant data in real time by deploying multiple sensors such as temperature, humidity, and positioning at multiple key nodes of pharmaceutical cold chain logistics, such as warehouse entrances and exits, inside transport vehicles, and distribution sites, providing a comprehensive and accurate data basis for the monitoring and management of cold chain logistics. The collected temperature, humidity, and location data are preprocessed, and specific abnormality judgment and processing methods are used to improve the quality and reliability of the data, providing more effective support for subsequent analysis and decision-making. For example, temperature data determines abnormal values ​​by calculating the mean and standard deviation, and replaces abnormal values ​​with the mean of adjacent data, ensuring the stability and accuracy of the data. A distributed database architecture is used to partition and store relevant data according to the time dimension and space dimension, which facilitates rapid retrieval and management of data. The time dimension is partitioned in days, and the space dimension is partitioned according to the logistics area grid. This storage method can improve the efficiency of data storage and query, and facilitate the analysis of cold chain logistics data in different time periods and locations.

[0027] Embodiment 2 As the second embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the technical solution of the present embodiment is different from that of the first embodiment only in that the present embodiment further includes: The routing analysis unit is used to plan the global optimal routing path in pharmaceutical cold chain logistics based on factors corresponding to time, temperature, humidity and congestion; In this embodiment, the routing analysis unit is used to improve logistics efficiency and reduce transportation costs while ensuring the quality of the medicines; The routing path planning method is as follows: Step M1. Obtain the pharmaceutical cold chain logistics to be planned, extract the location information corresponding to the starting point and the end point of the logistics, and mark them as S (x s ,y s ) and E(x e ,y e ); Step M2, extract the historical logistics data in the spatial grid where the starting point and the end point are located and the adjacent grids from the storage management unit of the cloud platform; Historical logistics data includes the average transportation time, temperature fluctuation range, humidity fluctuation range and road condition information of each logistics section; And mark them as U k ,[T min，k , T max，k ]、[H min，k , H max，k ], L k ; k = 1, 2, ... e, e represents the number of logistics sections; Among them, each logistics section refers to the road section that logistics transportation vehicles pass through in the spatial grid where the starting point and the end point are located and their adjacent grids. These sections are the basic units that constitute the transportation routing path; the road condition information is based on the road grade L of the relevant logistics section. X1，k 、Whether to restrict traffic L X2，k 、Road maintenance status X3，k OK, that is L k =L X1，k +L X2，k +L X3，k ; When the corresponding logistics section is a highway, L X1，k The value is 0.3; When the corresponding logistics section is an ordinary national highway, L X1，k The value is 0.5; When the corresponding logistics section is an urban road, L X1，k The value is 0.7; When the corresponding logistics section is an unrestricted section, L X2，k The value is 0.3; When the corresponding logistics section is a restricted section, L X2，k The value is 0.7; When the corresponding logistics section is a maintenance road, L X3，k The value is 0.9; When the corresponding logistics section is a normal road, L X3，k The value is 0.4; Step M3: Construct a routing cost evaluation function, which is composed of the transportation time cost FU r , Temperature stability cost FT r , humidity stability cost FH r and road condition cost FL r Composition, namely F r =FU r +FT r +FH r +FL r ; Among them, F r is the routing cost evaluation value, r is the preset candidate routing path, and the logistics sections included in the candidate routing path are marked as r={tk1, tk2, ..., tk v}, where tk g represents the logistics segments included in the candidate routing path, and v represents the number of logistics segments included in the candidate routing path; The calculation method of each cost is as follows: Step M3.1, transportation time cost: StepM3.1.1. Extract each logistics section tk in the candidate routing path gThe average transport time is denoted as U(tk g ); StepM3.1.2, then pass: ; Calculate the transportation time cost FU in the candidate routing path r ; Step M3.2, Temperature stability cost: StepM3.2.1. Extract each logistics section tk in the candidate routing path g The temperature fluctuation range is marked as [T (tk g，min ), T (tk g，max )]; StepM3.2.2, then according to each logistics section tk g The temperature fluctuation range is determined to determine the temperature stability cost of each logistics section; The method is as follows: If T (tk g，max )-T(tk g，min )>TY, then through: ; Calculate the temperature stability cost FT0 (tk g ); Wherein, TY is a preset temperature fluctuation threshold, which is preset according to the characteristics of the drug in this embodiment; If T (tk g，max )-T(tk g，min )≤TY, then the temperature stability cost of each logistics section ; Step M3.2.3, then pass: ; Calculate the temperature stability cost FT in the candidate routing path r ; Step M3.3, humidity stability cost: StepM3.3.1. Extract each logistics section tk in the candidate routing path g The humidity fluctuation range is marked as [H (tk g，min ), H (tk g，max )]; StepM3.3.2, then according to each logistics section tk g The humidity fluctuation range is determined to determine the humidity stability cost of each logistics section; The method is as follows: If H (tk g，max )-H(tk g，min )>HY, then through: ; Calculate the humidity stability cost FH0 (tk g ); Wherein, HY is a preset humidity fluctuation threshold, which is preset according to the characteristics of the drug in this embodiment; If H (tk g，max )-H(tk g，min )≤HY, then the humidity stability cost of each logistics section ; Step M3.3.3, then pass: ; Calculate the humidity stability cost FH in the candidate routing path r ; Step M3.4, road condition cost: StepM3.4.1. Extract each logistics section tk in the candidate routing path g Traffic information and marked it as L (tk g ); Step M3.4.2, then pass: ; Calculate the road cost FL in the candidate routing path r ; Step M4, combined with the routing cost evaluation value, use the greedy algorithm to query the optimal routing path; The specific steps are as follows: Step M4.1. Mark the optimal routing path as ry and its corresponding routing cost value as F ry ; Initialize the optimal routing path ry to be empty, and its corresponding routing cost value F ry is infinite; StepM4.2, from the starting point S (x s ,y s ), traverse all its adjacent segments, and calculate the routing cost F(r) to reach the next node through each adjacent segment. c ), where r c represents the routing path to the next node through the relevant adjacent road segments; Step M4.3, combined with F (r c ), select the segment with the smallest cost and add it to the current routing path, and mark the current routing path as r0 ; Then the current routing cost value is updated and marked as F r0 ; Step M4.4, determine whether the current node is the end point E (x e,y e ): If the current node is the end point, compare F r0 With F ry , if F r0 <F ry , then update ry=r0, F ry =F r0 ; If the current node is not the end point, continue to traverse the adjacent segments from the current node and repeat the contents of Step M4.2-Step M4.4 until the end point E (x e ,y e ).

[0028] In the pharmaceutical cold chain logistics, this embodiment plans the global optimal routing path based on factors such as time, temperature, humidity and congestion, which can improve logistics efficiency and reduce transportation costs while ensuring the quality of medicines. By comprehensively considering multiple influencing factors, a more scientific and reasonable path selection can be provided for logistics transportation. A routing cost evaluation function including transportation time cost, temperature stability cost, humidity stability cost and road condition cost is constructed, which comprehensively considers all aspects affecting the route selection, making the evaluation of the routing path more accurate and reasonable. The calculation of each cost has a clear basis and formula, such as calculating the transportation time cost based on the average transportation time of the logistics section, calculating the stability cost based on the temperature and humidity fluctuation range, and calculating the road condition cost based on the road condition information. The greedy algorithm is used to query the optimal routing path. The algorithm can continuously select the section with the lowest cost during the traversal process, gradually construct the optimal routing path, and improve the efficiency and accuracy of routing planning. By continuously comparing and updating the current optimal path and its cost value, the global optimal routing path that meets the requirements can be found.

[0029] Embodiment 3 As the third embodiment of the present invention, when the present application is specifically implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments mentioned above for implementation.

[0030] This embodiment combines the advantages of data collection, preprocessing and storage management of embodiment one, and the advantages of route analysis and planning of embodiment two, which not only ensures the comprehensive collection, efficient processing and orderly storage of pharmaceutical cold chain logistics data, but also can carry out scientific route planning based on these data, comprehensively improve the management level of pharmaceutical cold chain logistics, and maximize logistics efficiency and reduce costs while ensuring the quality of drugs.

[0031] The present invention also provides a routing parsing method for a pharmaceutical cold chain logistics dynamic data cloud platform, which is implemented by the pharmaceutical cold chain logistics dynamic data cloud platform. The method includes the following steps: The first step is to obtain the pharmaceutical cold chain logistics to be planned, and extract the location information corresponding to the starting point and end point of the logistics; Step 2: Extract the historical logistics data in the spatial grid where the starting point and the end point are located and the adjacent grids from the storage management unit of the cloud platform; The third step is to construct a routing cost evaluation function, which is composed of transportation time cost, temperature stability cost, humidity stability cost and road condition cost; Step 4: Determine the routing cost evaluation value in combination with the routing cost evaluation function, and then use the greedy algorithm to query the optimal routing path.

[0032] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0033] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. The pharmaceutical cold chain logistics dynamic data cloud platform is characterized by: include: Data collection unit, which is used to deploy multiple sensors at key nodes of pharmaceutical cold chain logistics to obtain relevant data in real time; A preprocessing unit, used for preprocessing the collected relevant data and obtaining standardized relevant data; A data transmission unit, used to upload the pre-processed relevant data to the storage management unit in the cloud platform; A storage management unit, used to partition and store relevant data according to time and space dimensions; The routing analysis unit is used to plan the global optimal routing path in pharmaceutical cold chain logistics based on factors corresponding to time, temperature, humidity and congestion.

2. The pharmaceutical cold chain logistics dynamic data cloud platform according to claim 1 is characterized in that: The relevant data includes the temperature data sequence collected by the temperature sensor, which is marked as T={T1, T2, ...T n }, the humidity data sequence collected by the humidity sensor is marked as H = {H1, H2, ... H n }, the position data sequence collected by the positioning sensor is marked as P = {P1 (x1, y1), P2 (x2, y2), ... P n (x n ,y n )}, where n represents the number of times the relevant data is collected.

3. The pharmaceutical cold chain logistics dynamic data cloud platform according to claim 2 is characterized in that: The preprocessing method is as follows: StepH1, temperature data preprocessing: Calculate the mean and standard deviation of n temperature data and mark them as T p , T b ; Each temperature data T i Combine the corresponding mean and standard deviation to make abnormal judgment; When |T i -T p |>3×T b When the corresponding T i Abnormal value; When |T i -T p |≤3×T b When the corresponding T i The value is normal; Among them, i=1, 2, ... n, and they are marked according to the time trend; When the corresponding T i If the value is abnormal, calculate the value with T i The average value of two adjacent temperature data is used to calculate the T i Replace the value; The formula is: TT i =(T i-1 +T i+1 ) / 2; Where, TT i Refers to the corresponding T i The value to be replaced, that is, the standardized T i Value, T i-1 and T i+1 Refers to T i The values ​​of two adjacent temperature data; StepH2, humidity data preprocessing: The preprocessing method of humidity data is the same as that of temperature data; StepH3, position data preprocessing: Select the i-th collected position data, and according to the timestamp of the position data collection, obtain the position data set collected in real time within the specified processing period CT before and after the timestamp, and mark it as P j (x j ,y j )={P i-m (x i-m ,y i-m ), ... P i (x i ,y i ), ... P i+m (x i+m ,y i+m )}; Where j = 1, 2, ..., i + 2m, P i (x i ,y i ) represents the position data collected for the i-th time, and m represents the number of times the position data is collected in real time within the specified processing period CT; Then calculate P j (x j ,y j ) and use it as the position data collected for the i-th time after standardization; The calculation formula is as follows: ； ; Calculate the position data Pi (x p，i ,y p，i ).

4. The pharmaceutical cold chain logistics dynamic data cloud platform according to claim 3 is characterized in that: The partition storage method is as follows: Step C1, partition based on time dimension: Taking the specified standard time period BT as a unit, a number of time storage partitions are divided, and then according to the timestamp of relevant data collection, the relevant data is stored in a time storage partition containing the timestamp; Step C2, partition based on spatial dimension: The logistics area corresponding to the pharmaceutical cold chain logistics is divided into several grids, and then a spatial storage partition is established corresponding to each grid; then, based on the location data of the relevant data, the relevant data is stored in a spatial storage partition containing the location data.

5. The pharmaceutical cold chain logistics dynamic data cloud platform according to claim 1 is characterized in that: The routing path planning method is as follows: Step M1. Obtain the pharmaceutical cold chain logistics to be planned, extract the location information corresponding to the starting point and the end point of the logistics, and mark them as S (x s ,y s ) and E(x e ,y e ); Step M2, extract the historical logistics data in the spatial grid where the starting point and the end point are located and the adjacent grids from the storage management unit of the cloud platform; Historical logistics data includes the average transportation time, temperature fluctuation range, humidity fluctuation range and road condition information of each logistics section; And mark them as U k ,[T min，k , T max，k ]、[H min，k , H max，k ], L k ; k = 1, 2, ... e, e represents the number of logistics sections; Step M3: Construct a routing cost evaluation function, which is composed of the transportation time cost FU r , Temperature stability cost FT r , humidity stability cost FH r and road condition cost FL r Composition, namely F r =FU r +FT r +FH r +FL r ; Among them, F r is the routing cost evaluation value, r is the preset candidate routing path, and the logistics sections included in the candidate routing path are marked as r={tk1, tk2, ..., tk v }, where tk g represents the logistics segments included in the candidate routing path, and v represents the number of logistics segments included in the candidate routing path; Step M4: Combine the routing cost evaluation value and use the greedy algorithm to query the optimal routing path.

6. The pharmaceutical cold chain logistics dynamic data cloud platform according to claim 5 is characterized in that: in, Each logistics section refers to the road section that logistics transport vehicles pass through within the spatial grid where the starting point and the end point are located and their adjacent grids; the road condition information is based on the road grade L of the relevant logistics section. X1，k 、Whether to restrict traffic L X2，k 、Road maintenance status X3，k OK, that is L k =L X1，k +L X2，k +L X3，k .

7. The pharmaceutical cold chain logistics dynamic data cloud platform according to claim 6, characterized in that: When the corresponding logistics section is a highway, L X1，k The value is 0.3; When the corresponding logistics section is an ordinary national highway, L X1，k The value is 0.5; When the corresponding logistics section is an urban road, L X1，k The value is 0.7; When the corresponding logistics section is an unrestricted section, L X2，k The value is 0.3; When the corresponding logistics section is a restricted section, L X2，k The value is 0.7; When the corresponding logistics section is a maintenance road, L X3，k The value is 0.9; When the corresponding logistics section is a normal road, L X3，k The value is 0.

4.

8. The pharmaceutical cold chain logistics dynamic data cloud platform according to claim 6, characterized in that: In StepM3, the calculation method of each cost is as follows: Step M3.1, transportation time cost: StepM3.1.

1. Extract each logistics section tk in the candidate routing path g The average transport time is denoted as U(tk g ); StepM3.1.2, then pass: ; Calculate the transportation time cost FU in the candidate routing path r ; Step M3.2, Temperature stability cost: StepM3.2.

1. Extract each logistics section tk in the candidate routing path g The temperature fluctuation range is marked as [T (tk g，min ), T (tk g，max )]; StepM3.2.2, then according to each logistics section tk g The temperature fluctuation range is used to determine the temperature stability cost of each logistics section; the method is as follows: If T (tk g，max )-T(tk g，min )>TY, then through: ; Calculate the temperature stability cost FT0 (tk g ); Among them, TY is the preset temperature fluctuation threshold; If T (tk g，max )-T(tk g，min )≤TY, then the temperature stability cost of each logistics section ; Step M3.2.3, then pass: ; Calculate the temperature stability cost FT in the candidate routing path r ; Step M3.3, humidity stability cost: The humidity stability cost FH in the candidate routing path r Calculated according to the temperature stability cost in the candidate routing path: Step M3.4, road condition cost: StepM3.4.

1. Extract each logistics section tk in the candidate routing path g Traffic information and marked it as L (tk g ); Step M3.4.2, then pass: ; Calculate the road cost FL in the candidate routing path r .

9. The pharmaceutical cold chain logistics dynamic data cloud platform according to claim 5, characterized in that: In StepM4, the query steps for the optimal routing path are as follows: Step M4.

1. Mark the optimal routing path as ry and its corresponding routing cost value as F ry ; Initialize the optimal routing path ry to be empty, and its corresponding routing cost value F ry is infinite; StepM4.2, from the starting point S (x s ,y s ), traverse all its adjacent segments, and calculate the routing cost F(r) to reach the next node through each adjacent segment. c ), where r c represents the routing path to the next node through the relevant adjacent road segments; Step M4.3, combined with F (r c ), select the segment with the smallest cost and add it to the current routing path, and mark the current routing path as r0 ; Then the current routing cost value is updated and marked as F r0 ; Step M4.4, determine whether the current node is the end point E (x e ,y e ): If the current node is the end point, compare F r0 With F ry , if F r0 <F ry , then update ry=r0, F ry =F r0 ; If the current node is not the end point, continue to traverse the adjacent segments from the current node and repeat the contents of Step M4.2-Step M4.4 until the end point E (x e ,y e ).

10. A routing parsing method for a pharmaceutical cold chain logistics dynamic data cloud platform, characterized in that: The method is implemented through the pharmaceutical cold chain logistics dynamic data cloud platform described in any one of claims 1-9.

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