Intelligent medicine logistics tracing and dispatching system

Abstract

The invention belongs to the field of medicine logistics traceability scheduling, and particularly discloses an intelligent medicine logistics traceability scheduling system, which comprises a data acquisition module, a medicine information management module, an intelligent scheduling optimization module, a medicine traceability module, a cloud server and a local database. According to the method, the mixed integer multi-target scheduling model is constructed, the target function is hierarchically optimized by adopting the dictionary order method in combination with the medical logistics network nodes and the vehicle capacity constraints, so that global optimization of medical logistics planning is realized, distribution of high-priority medicines is preferentially guaranteed, and invalid transportation activities are reduced; by constructing a multi-level authentication system, leakage of real identities is avoided, interactive authentication and multi-round parameter verification are achieved through high-entropy secret key hash operation, dynamic random numbers and a timestamp verification mechanism, bidirectional identity authentication is achieved, and integrity and traceability of medicine logistics data are ensured.

Description

Technical Field

[0001] The present invention relates to the field of pharmaceutical logistics tracing and scheduling, and in particular to an intelligent pharmaceutical logistics tracing and scheduling system. Background Art

[0002] Intelligent pharmaceutical logistics traceability and scheduling refers to a system that leverages modern information technologies, such as the Internet of Things, big data, and artificial intelligence, to track and trace pharmaceutical information throughout the entire pharmaceutical logistics process and achieve efficient and rational scheduling. However, traditional pharmaceutical logistics scheduling often uses single-objective planning models, focusing solely on transportation costs or delivery times. This model fails to address the varying priorities of different pharmaceutical types, leading to delays in high-priority pharmaceutical needs and duplicated transportation route planning, resulting in high overall associated costs. Furthermore, traditional traceability systems often rely on simple encryption or single authentication, making it easy for attackers to obtain pharmaceutical data by forging reader identities or tampering with information such as temperature, humidity, and location during transportation, rendering traceability information unreliable. Summary of the Invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an intelligent pharmaceutical logistics traceability scheduling system. Traditional pharmaceutical logistics scheduling often adopts a single-objective planning model, which only focuses on transportation costs or delivery time, and cannot simultaneously handle the priority differences of different types of medicines, resulting in delays in high-priority medicine demands, repeated planning of transportation routes, and high total related costs. This solution constructs a mixed integer multi-objective scheduling model that includes minimizing the total number of unsatisfied medicine priority requests, minimizing the total amount of transportation activities, and minimizing the total related costs. Combined with the pharmaceutical logistics network nodes and vehicle capacity constraints, the lexicographic order method is used to hierarchically optimize the objective function to achieve global optimization of pharmaceutical logistics planning. It can not only give priority to the delivery of high-priority medicines, but also reduce invalid routes through path optimization. Transportation activities can significantly improve scheduling efficiency and cost control capabilities; traditional traceability systems mostly use simple encryption or single identity authentication, and attackers can easily obtain medical data by forging the identity of the reader, or tamper with information such as temperature, humidity, and location during transportation, resulting in unreliable traceability information. This solution builds a multi-level authentication system. The cloud server generates a high-entropy key as a security root, and stores the hash values ​​of managers, tags, and readers through an encrypted hash table to avoid real identity leakage. Interactive authentication and multi-round parameter verification are achieved through high-entropy key hashing, dynamic random numbers, and timestamp verification mechanisms, realizing two-way identity authentication for managers, RFID readers, and RFID tags, effectively resisting man-in-the-middle attacks, replay attacks, and forgery attacks, and ensuring the integrity and traceability of medical logistics data.

[0004] The technical solution adopted by the present invention is as follows: The present invention provides an intelligent pharmaceutical logistics tracing and scheduling system, which specifically includes a data acquisition module, a pharmaceutical information management module, an intelligent scheduling optimization module, a pharmaceutical tracing module, a cloud server and a local database;

[0005] The data acquisition module embeds RFID tags on the outer packaging of medicines. The RFID tags collect and record the temperature, humidity, location and transportation speed during the transportation of medicines, and store the pseudo-identity and reader identification. The data information in the RFID tags is stored in the cloud server. Monitoring sensors are installed in the medicine warehouse to obtain the demand for medicines;

[0006] The medicine information management module records the basic information and in-and-out records of medicines, sets priorities according to the types of medicines, obtains the number of medicine requests and transmits the number of medicine requests to the intelligent scheduling optimization module;

[0007] The intelligent scheduling optimization module determines the pharmaceutical logistics planning by constructing a pharmaceutical logistics scheduling model and adopting mixed integer, multi-type and multi-objective models;

[0008] The pharmaceutical traceability module installs an RFID reader on the pharmaceutical logistics vehicle to read the data information in the RFID tag and upload it to the cloud server, while storing the manager's identity and key. The manager traces the pharmaceutical logistics information through the cloud server.

[0009] Furthermore, the intelligent scheduling optimization module determines the pharmaceutical logistics plan through a pharmaceutical logistics multi-objective scheduling method, and the pharmaceutical logistics multi-objective scheduling method specifically includes the following steps:

[0010] Step A1: Construct a pharmaceutical logistics network. The nodes of the pharmaceutical logistics network include pharmaceutical supplier nodes, demand nodes, and transit nodes. The edges of the pharmaceutical logistics network are pharmaceutical transportation routes. Capacity is set for pharmaceutical logistics vehicle types.

[0011] Step A2: Establish a pharmaceutical logistics scheduling model. The pharmaceutical logistics scheduling model uses a mixed integer, multi-type, and multi-objective model to determine pharmaceutical logistics planning. The objective function of the pharmaceutical logistics scheduling model is defined and hierarchical optimization is performed. The objective functions are to minimize the total number of unmet pharmaceutical priority requests, minimize the total amount of transportation activities, and minimize the total relevant costs. The total relevant costs include transportation costs, pharmaceutical supplier node construction costs, vehicle occupancy costs, capacity configuration costs, node operation costs, and transit costs. The formula used is as follows: ; ; ;

[0012] Where, is the first objective function value, i.e. the total number of unsatisfied pharmaceutical priority requests, is the second objective function value, i.e. the total amount of transportation activities, is the third objective function value, i.e. the total relevant cost, It is the total amount of unmet priority requests accumulated during the previous pharmaceutical logistics planning. is the total amount of transportation activities during the previous planning of pharmaceutical logistics, The total cost of the previous pharmaceutical logistics planning, It is a collection of medical types. is an index of medicine type, is a collection of demand nodes, is the index of the demand node, It is the total cycle when planning pharmaceutical logistics. It is the cycle index of the current planning of pharmaceutical logistics. are all nodes in the logistics network, and Both are correct The traversal represents the index of the starting node and the index of the end node respectively. is a collection of logistics vehicle types, is the index of the logistics vehicle type, is a collection of pharmaceutical supplier nodes, is the index of the pharmaceutical supplier node, It is a collection of logistics vehicle configuration capacity levels, is the index of the logistics vehicle configuration capacity level, Is a medical type The priority weight, yes Always Demand nodes regarding medicine types demand, Is a logistics vehicle type The transport importance coefficient of yes Time logistics vehicle type Slave nodes To Node The number of transports, Is a logistics vehicle type from arrive The unit transportation cost, It is a pharmaceutical supplier node The unit fixed cost, Used to determine whether the current node is a pharmaceutical supplier node. is a node Parking and maintenance of logistics vehicle types The unit time cost, yes Time Node Logistics vehicle type Available quantity, is a node Logistics vehicle type Configuring capacity levels construction costs, Used to determine whether it is a logistics vehicle type Configuring capacity levels , It is at the node The unit operating cost of managing medicines, Used to determine whether the current node is a required node. Used to determine whether the current node is a transit node. Is a medical type Vehicle types in logistics The unit cost of transit, yes At the node at all times Logistics vehicle type Type of transferred medicine the number of yes At the node at all times Type of logistics vehicles transferred in Type of medicine the number of

[0013] Step A3: Constraints are set based on the total number of nodes, the total number of pharmaceutical logistics vehicles, the number of pharmaceutical requests, and the priority of pharmaceutical types. The objective function is solved hierarchically using a lexicographic ordering method to obtain the pharmaceutical logistics planning path in real time.

[0014] Furthermore, the pharmaceutical traceability module implements authentication of the RFID reader and the administrator through a cloud service interaction method, and the cloud service interaction method specifically includes the following steps:

[0015] Step B1: Initialization: The cloud server generates a high entropy key, generates an RFID tag pseudo ID for the RFID tag, and establishes an encrypted hash table that stores the administrator identity information, the RFID tag, and the hash value of the RFID reader;

[0016] Step B2: Reader registration: The RFID reader generates a reader random number and sends it to the cloud server through a secure channel. After receiving the reader random number, the cloud server uses the high entropy key to perform a hash operation on the reader random number to obtain a hash random number. The cloud server returns the reader random number and the hash random number to the RFID reader and stores the administrator ID, reader random number, and hash random number in the local database.

[0017] Step B3: User registration. The administrator selects an identity ID and password, calculates the hash values ​​of the identity ID and password respectively, selects a user random number, uses the user random number to hash the identity ID hash value again to obtain the identity ciphertext, sends the identity ID hash value, identity ciphertext and reader random number to the cloud server, the cloud server retrieves the hash random number based on the reader random number, uses the hash random number to hash the identity ciphertext to obtain the encrypted identity ciphertext, hashes the identity ciphertext with the high entropy key to obtain the hashed identity ciphertext, and the cloud server randomly generates a pseudo-identity ID for the administrator, stores the pseudo-identity ID, the hash value of the identity ID and the identity ciphertext on the cloud server, and returns the pseudo-identity ID, encrypted identity ciphertext and hashed identity ciphertext to the administrator;

[0018] Step B4: Interactive authentication, specifically including the following steps:

[0019] Step B41: Administrator authentication is initiated. The administrator enters his / her identity ID and password, and performs a hash operation to obtain the identity ID verification hash and password verification hash. The cloud server verifies the identity ID verification hash and password verification hash. If they are different from the hash values ​​of the identity ID and password, the administrator's identity is invalid. Otherwise, two random numbers are randomly generated, all hash parameters related to the high entropy key are calculated and timestamp is added, recorded as message M0, and message M0 is uploaded to the cloud server.

[0020] Step B42: Cloud server verification. After receiving message M0, the cloud server verifies whether the time stamp in message M0 is within the preset time difference. It verifies all hash parameters related to the high entropy key through the pseudo-identity ID. After verification, it continues to randomly generate a random number, extract the RFID reader identifier and RFID tag identifier, calculate all key parameters related to the RFID reader and add a timestamp, record it as message M1, and send message M1 to the RFID reader.

[0021] Step B43: RFID reader verification. After receiving message M1, the RFID reader verifies the timestamp and key parameters in sequence. If the verification is successful, it continues to randomly generate a random number, extract the RFID tag pseudo-ID, calculate the authentication message M2, and send the authentication message M2 to the RFID tag.

[0022] Step B44: The RFID tag responds. After receiving the authentication message M2, the RFID tag performs a hash verification on the authentication message M2. If the verification is successful, it continues to randomly generate a random number, updates the tag pseudo-identity ID, calculates the response message M3, and returns the response message M3 to the RFID reader.

[0023] Step B45: RFID reader update and feedback. After receiving the response message M3, the RFID reader uses the high entropy key to encrypt the new RFID tag parameters, generates a random number, calculates the session parameters related to the session key, recorded as message M4, and sends message M4 to the cloud server. After the cloud server verifies message M4, it updates the RFID tag status and generates a new administrator pseudo-identity ID, recorded as message M5. Message M5 is sent to the administrator. After receiving message M5, the administrator calculates the session key and updates the administrator pseudo-identity to complete the authentication.

[0024] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0025] (1) Traditional pharmaceutical logistics scheduling often adopts a single-objective planning model, which only focuses on transportation costs or delivery time and cannot simultaneously handle the priority differences of different types of medicines, resulting in delays in high-priority medicine demands, repeated planning of transportation routes, and high total related costs. This solution constructs a mixed integer multi-objective scheduling model that includes minimizing the total number of unsatisfied medicine priority requests, minimizing the total amount of transportation activities, and minimizing the total related costs. Combined with the constraints of pharmaceutical logistics network nodes and vehicle capacity, this solution adopts a lexicographic ordering method to hierarchically optimize the objective function to achieve global optimization of pharmaceutical logistics planning. This not only gives priority to the delivery of high-priority medicines, but also reduces ineffective transportation activities through path optimization, significantly improving scheduling efficiency and cost control capabilities.

[0026] (2) In view of the technical problem that traditional traceability systems mostly use simple encryption or single authentication, attackers can easily obtain medical data by forging the identity of the reader, or tamper with the temperature, humidity, location and other information during transportation, resulting in unreliable traceability information, this solution builds a multi-level authentication system. The cloud server generates a high-entropy key as a security root, and stores the hash values ​​of the manager, tag, and reader through an encrypted hash table to avoid the leakage of the real identity. The interactive authentication and multi-round parameter verification are realized through the high-entropy key hash operation, dynamic random number and timestamp verification mechanism, realizing two-way identity authentication of the manager, RFID reader, and RFID tag, effectively resisting man-in-the-middle attacks, replay attacks and forgery attacks, and ensuring the integrity and traceability of medical logistics data. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1This is a module connection diagram of an intelligent pharmaceutical logistics tracing and scheduling system provided by the present invention;

[0028] Figure 2 A flowchart of the cloud service interaction method provided by the present invention.

[0029] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0031] Example 1, see Figure 1 The present invention provides an intelligent pharmaceutical logistics tracing and scheduling system, which specifically includes a data acquisition module, a pharmaceutical information management module, an intelligent scheduling optimization module, a pharmaceutical tracing module, a cloud server and a local database;

[0032] The data acquisition module embeds RFID tags on the outer packaging of medicines. The RFID tags collect and record the temperature, humidity, location and transportation speed during the transportation of medicines, and store the pseudo-identity and reader identification. The data information in the RFID tags is stored in the cloud server. Monitoring sensors are installed in the medicine warehouse to obtain the demand for medicines;

[0033] The medicine information management module records the basic information and in-and-out records of medicines, sets priorities according to the types of medicines, obtains the number of medicine requests and transmits the number of medicine requests to the intelligent scheduling optimization module;

[0034] The intelligent scheduling optimization module determines the pharmaceutical logistics planning by constructing a pharmaceutical logistics scheduling model and adopting mixed integer, multi-type and multi-objective models;

[0035] The pharmaceutical traceability module installs an RFID reader on the pharmaceutical logistics vehicle to read the data information in the RFID tag and upload it to the cloud server, while storing the manager's identity and key. The manager traces the pharmaceutical logistics information through the cloud server.

[0036] Example 2, see Figure 1This embodiment is based on the above embodiment. The intelligent scheduling optimization module determines the pharmaceutical logistics plan through a pharmaceutical logistics multi-objective scheduling method. The pharmaceutical logistics multi-objective scheduling method specifically includes the following steps:

[0037] Step A1: Construct a pharmaceutical logistics network. The nodes of the pharmaceutical logistics network include pharmaceutical supplier nodes, demand nodes, and transit nodes. The edges of the pharmaceutical logistics network are pharmaceutical transportation routes. Capacity is set for pharmaceutical logistics vehicle types.

[0038] Step A2: Establishing a pharmaceutical logistics scheduling model, wherein the pharmaceutical logistics scheduling model uses a mixed integer, multi-type, and multi-objective model to determine pharmaceutical logistics planning, defining objective functions of the pharmaceutical logistics scheduling model and performing hierarchical optimization, wherein the objective functions are minimizing the total number of unmet pharmaceutical priority requests, minimizing the total amount of transportation activities, and minimizing total relevant costs, wherein the total relevant costs include transportation costs, pharmaceutical supplier node construction costs, vehicle occupancy costs, capacity configuration costs, node operation costs, and transit costs;

[0039] Step A3: Set constraints based on the total number of nodes, the total number of pharmaceutical logistics vehicles, the number of pharmaceutical requests, and the priority of pharmaceutical types, use the lexicographic method to hierarchically solve the objective function, and obtain the pharmaceutical logistics planning path in real time.

[0040] Example 3, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. The pharmaceutical traceability module implements authentication of the RFID reader and the administrator through a cloud service interaction method. The cloud service interaction method specifically includes the following steps:

[0041] Step B1: Initialization: The cloud server generates a high entropy key, generates an RFID tag pseudo ID for the RFID tag, and establishes an encrypted hash table that stores the administrator identity information, the RFID tag, and the hash value of the RFID reader;

[0042] Step B2: Reader registration: The RFID reader generates a reader random number and sends it to the cloud server through a secure channel. After receiving the reader random number, the cloud server uses the high entropy key to perform a hash operation on the reader random number to obtain a hash random number. The cloud server returns the reader random number and the hash random number to the RFID reader and stores the administrator ID, reader random number, and hash random number in the local database.

[0043] Step B3: User registration. The administrator selects an identity ID and password, calculates the hash values ​​of the identity ID and password respectively, selects a user random number, uses the user random number to hash the identity ID hash value again to obtain the identity ciphertext, sends the identity ID hash value, identity ciphertext and reader random number to the cloud server, the cloud server retrieves the hash random number based on the reader random number, uses the hash random number to hash the identity ciphertext to obtain the encrypted identity ciphertext, hashes the identity ciphertext with the high entropy key to obtain the hashed identity ciphertext, and the cloud server randomly generates a pseudo-identity ID for the administrator, stores the pseudo-identity ID, the hash value of the identity ID and the identity ciphertext on the cloud server, and returns the pseudo-identity ID, encrypted identity ciphertext and hashed identity ciphertext to the administrator;

[0044] Step B4: Interactive authentication, specifically including the following steps:

[0045] Step B41: Administrator authentication is initiated. The administrator enters his / her identity ID and password, and performs a hash operation to obtain the identity ID verification hash and password verification hash. The cloud server verifies the identity ID verification hash and password verification hash. If they are different from the hash values ​​of the identity ID and password, the administrator's identity is invalid. Otherwise, two random numbers are randomly generated, all hash parameters related to the high entropy key are calculated and timestamp is added, recorded as message M0, and message M0 is uploaded to the cloud server.

[0046] Step B42: Cloud server verification. After receiving message M0, the cloud server verifies whether the time stamp in message M0 is within the preset time difference. It verifies all hash parameters related to the high entropy key through the pseudo-identity ID. After verification, it continues to randomly generate a random number, extract the RFID reader identifier and RFID tag identifier, calculate all key parameters related to the RFID reader and add a timestamp, record it as message M1, and send message M1 to the RFID reader.

[0047] Step B43: RFID reader verification. After receiving message M1, the RFID reader verifies the timestamp and key parameters in sequence. If the verification is successful, it continues to randomly generate a random number, extract the RFID tag pseudo-ID, calculate the authentication message M2, and send the authentication message M2 to the RFID tag.

[0048] Step B44: The RFID tag responds. After receiving the authentication message M2, the RFID tag performs a hash verification on the authentication message M2. If the verification is successful, it continues to randomly generate a random number, updates the tag pseudo-identity ID, calculates the response message M3, and returns the response message M3 to the RFID reader.

[0049] Step B45: RFID reader update and feedback. After receiving the response message M3, the RFID reader uses the high entropy key to encrypt the new RFID tag parameters, generates a random number, calculates the session parameters related to the session key, recorded as message M4, and sends message M4 to the cloud server. After the cloud server verifies message M4, it updates the RFID tag status and generates a new administrator pseudo-identity ID, recorded as message M5. Message M5 is sent to the administrator. After receiving message M5, the administrator calculates the session key and updates the administrator pseudo-identity to complete the authentication.

[0050] Example 4: This example is based on the above example. In actual implementation, vehicle fault signals, including engine abnormality and insufficient tire pressure, are collected in real time through on-board sensors to generate a vehicle availability status identifier. The real-time congestion index of each transportation route is obtained through the API, converted into a road section travel time correction coefficient and added to the multi-objective function to make real-time adjustments to the logistics planning.

[0051] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0052] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0053] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. An intelligent pharmaceutical logistics tracing and scheduling system, characterized by: Specifically, it includes data collection module, medical information management module, intelligent scheduling optimization module, medical traceability module, cloud server and local database; The data acquisition module embeds RFID tags on the outer packaging of medicines, stores the data information in the RFID tags to the cloud server, and installs monitoring sensors in the medicine warehouse to obtain the demand for medicines; The medicine information management module records the basic information and in-and-out records of medicines, sets priorities according to the types of medicines, obtains the number of medicine requests and transmits the number of medicine requests to the intelligent scheduling optimization module; The intelligent scheduling optimization module determines the pharmaceutical logistics planning by constructing a pharmaceutical logistics scheduling model and adopting mixed integer, multi-type and multi-objective models; The pharmaceutical traceability module installs an RFID reader on the pharmaceutical logistics vehicle to read the data information in the RFID tag and upload it to the cloud server, while storing the manager's identity and key. The manager traces the pharmaceutical logistics information through the cloud server.

2. The intelligent pharmaceutical logistics tracing and scheduling system according to claim 1 is characterized in that: The intelligent scheduling optimization module determines the pharmaceutical logistics plan through a pharmaceutical logistics multi-objective scheduling method, and the pharmaceutical logistics multi-objective scheduling method specifically includes the following steps: Step A1: Construct a pharmaceutical logistics network. The nodes of the pharmaceutical logistics network include pharmaceutical supplier nodes, demand nodes, and transit nodes. The edges of the pharmaceutical logistics network are pharmaceutical transportation routes. Capacity is set for pharmaceutical logistics vehicle types. Step A2: Establish a pharmaceutical logistics scheduling model, define the objective function of the pharmaceutical logistics scheduling model, and perform hierarchical optimization. The objective functions are to minimize the total number of unmet pharmaceutical priority requests, minimize the total amount of transportation activities, and minimize the total relevant costs. The total relevant costs include transportation costs, pharmaceutical supplier node construction costs, vehicle occupancy costs, capacity configuration costs, node operation costs, and transit costs. Step A3: Set constraints based on the total number of nodes, the total number of pharmaceutical logistics vehicles, the number of pharmaceutical requests, and the priority of pharmaceutical types, use the lexicographic method to hierarchically solve the objective function, and obtain the pharmaceutical logistics planning path in real time.

3. The intelligent pharmaceutical logistics tracing and scheduling system according to claim 1 is characterized in that: The pharmaceutical traceability module implements authentication of the RFID reader and the administrator through a cloud service interaction method, wherein the cloud service interaction method specifically includes the following steps: Step B1: Initialization, the cloud server generates a high entropy key, generates an RFID tag pseudo ID for the RFID tag, and establishes an encrypted hash table; Step B2: Reader registration: The RFID reader generates a reader random number and sends it to the cloud server through a secure channel. After receiving the reader random number, the cloud server uses the high entropy key to perform a hash operation on the reader random number to obtain a hash random number. The cloud server returns the reader random number and the hash random number to the RFID reader and stores the administrator ID, reader random number, and hash random number in the local database. Step B3: User registration. The administrator selects an identity ID and password, calculates the hash values ​​of the identity ID and password respectively, selects a user random number, uses the user random number to hash the identity ID hash value again to obtain the identity ciphertext, sends the identity ID hash value, identity ciphertext and reader random number to the cloud server, the cloud server retrieves the hash random number based on the reader random number, uses the hash random number to hash the identity ciphertext to obtain the encrypted identity ciphertext, hashes the identity ciphertext with the high entropy key to obtain the hashed identity ciphertext, and the cloud server randomly generates a pseudo-identity ID for the administrator, stores the pseudo-identity ID, the hash value of the identity ID and the identity ciphertext on the cloud server, and returns the pseudo-identity ID, encrypted identity ciphertext and hashed identity ciphertext to the administrator; Step B4: Mutual authentication.

4. The intelligent pharmaceutical logistics tracing and scheduling system according to claim 1 is characterized in that: Step B4: Interactive authentication, specifically including the following steps: Step B41: Administrator authentication initiated; Step B42: Cloud server verification; Step B43: RFID reader verification; Step B44: RFID tag responds; Step B45: RFID reader update and feedback.

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