Cold-chain logistics data tracing method and system based on Internet of Things

Abstract

The invention relates to the technical field of data processing, in particular to a cold-chain logistics data tracing method and system based on the Internet of Things, and the method specifically comprises the steps: calculating the front-back flow degree of each subsequence in a logistics node sequence of cold-chain logistics of each batch of chilled fresh products through the number of subsequent nodes of each logistics node and the retention time of articles of each logistics node, calculating the fluency influence degree of each logistics node in each batch of logistics in combination with the sequence of each logistics node in the located sub-sequence, dividing each batch of cold-chain logistics of the chilled fresh products into a plurality of traceability association stages through the fluency influence degree, and when the logistics node information is inquired, obtaining the traceability association stage of each batch of cold-chain logistics of the chilled fresh products. The abnormal information of all the logistics nodes in the traceability association stage where the logistics nodes are located is preferentially displayed; according to the method, the accuracy and the efficiency of traceability are improved, information intercommunication among all links is ensured, errors caused by data offline or delayed uploading are further reduced, node key information can be grasped in time, and potential quality problems can be quickly solved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and specifically to a cold chain logistics data traceability method and system based on the Internet of Things. Background Art

[0002] Cold chain logistics management based on the Internet of Things is the collection, recording, storage and query of information on the entire process of production, processing, circulation and sales of fresh cold products, especially the recording of fresh cold product inspection data. It can help different users in the corresponding cold chain logistics to accurately control the quality of fresh cold products, including multiple quality indicators such as temperature and humidity.

[0003] Since the cold chain logistics process involves multi-stage transportation conditions, it is necessary to collect full-link cold chain logistics data from multiple stages such as production, warehousing, and transportation. Among them, some vehicle transportation stages or warehousing stages have offline or delayed data uploads, which will affect the traceability accuracy of the actual cold chain logistics data. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a cold chain logistics data traceability method and system based on the Internet of Things. The technical solutions adopted are as follows: In a first aspect, an embodiment of the present application provides a cold chain logistics data traceability method based on the Internet of Things, the method comprising the following steps: During the preset sampling period, collect information data of each logistics node in the cold chain logistics of each batch of fresh products, including the length of time the items are detained at each logistics node; Construct a logistics node sequence for the cold chain logistics of each batch of fresh products, and record the number of nodes after each logistics node in the logistics node sequence as the first subsequent node number of each logistics node; calculate the transportation range coefficient of each logistics node based on the difference between the first subsequent node numbers of each logistics node and other logistics nodes; The logistics node sequence is divided into subsequences of the logistics node sequence according to the retention time of the logistics nodes; based on the difference between the retention time of items of two adjacent logistics nodes in each subsequence and the transportation range coefficient, the forward and backward flow degree of each subsequence in the cold chain logistics is calculated; Based on the forward and backward flow of each subsequence in any logistics node sequence, as well as the order of each logistics node in the subsequence, the impact of each logistics node on the smoothness of each cold chain logistics in which it is located is calculated; Based on the fluency impact, data traceability of cold chain logistics of each batch of fresh chilled products is performed.

[0005] In one embodiment, the expression of the transport range coefficient of each logistics node is: , where Represents the transportation range coefficient of the i-th logistics node in the logistics node sequence of the cold chain logistics of the current batch of fresh products; 、 They respectively represent the number of logistics nodes after the i-th and i+1-th logistics nodes in the logistics node sequence of the cold chain logistics of the current batch of fresh products; Represents the mean number of first subsequent nodes of all nodes in the logistics node sequence of the cold chain logistics of the current batch of fresh products; is the normalization function.

[0006] In one embodiment, the process of obtaining each subsequence of the logistics node sequence is as follows: In any logistics node sequence, the absolute value of the difference between the length of time items are detained at each logistics node and the subsequent logistics node is calculated as the first difference of each logistics node; the average of the first differences of all logistics nodes in the any logistics node sequence is calculated and recorded as the first average; and a sequence consisting of logistics nodes whose first differences are continuously greater than the first average or whose first differences are continuously less than or equal to the first average is taken as each subsequence of the any logistics node sequence.

[0007] In one embodiment, the process of obtaining the forward and backward flow degree of each subsequence is as follows: Calculate the normalized value of the difference between the first mean in the logistics node sequence and the first difference of each logistics node as the retention status difference of each logistics node in the logistics node sequence; calculate the forward and backward flow degree of each subsequence based on the retention status difference and the transportation range coefficient.

[0008] In one embodiment, the expression of the front-to-back flow degree is:

[0009] Where, Indicates the forward and backward flow degree of the jth subsequence in the logistics node sequence of the current batch of fresh products; Indicates the number of logistics nodes in the jth subsequence of the logistics node sequence of the current batch of fresh products; Indicates the transportation range coefficient of the kth logistics node in the jth subsequence in the logistics node sequence of the current batch of fresh products; Indicates the difference in the retention status of the kth logistics node in the jth subsequence in the logistics node sequence of the current batch of fresh products; is a preset minimum positive number; is the normalization function.

[0010] In one embodiment, the process of obtaining the smoothness influence of each logistics node in each cold chain logistics is as follows: Obtain the subsequence of any logistics node in the logistics node sequence of each batch of cold chain logistics of fresh products, obtain the sequence number of any logistics node in each subsequence, and the forward and backward flow degree of each subsequence; Based on the difference between the sequence number in each subsequence and the number of logistics nodes in the corresponding subsequence, and the difference between the front-to-back flow rate of each subsequence and the average front-to-back flow rate of all subsequences in which any logistics node is located, the impact of the smoothness of any logistics node in each cold chain logistics in which it is located is calculated.

[0011] In one embodiment, the expression of the fluency impact is:

[0012] Where, It represents the impact of logistics node A on the smoothness of the cold chain logistics of the Jth batch of fresh products; Represents the sequence number of logistics node A in the subsequence of the cold chain logistics of batch J of fresh products; Represents the number of logistics nodes in the subsequence of the Jth batch of fresh products in the cold chain logistics of logistics node A; Represents the forward and backward flow degree of the subsequence of the Jth batch of fresh products in the cold chain logistics of logistics node A; Represents the mean of the forward and backward flow degrees of the subsequences in the cold chain logistics of all batches of fresh products at logistics node A; Represents an exponential function with the natural constant e as its base.

[0013] In one embodiment, the data traceability of cold chain logistics of each batch of fresh products based on the fluency impact is specifically as follows: The corresponding logistics node sequence is divided according to the fluency impact of each logistics node in the cold chain logistics of each batch of fresh products, and multiple traceability association stages of the cold chain logistics of each batch of fresh products are obtained; for the logistics nodes that users need to query, the abnormal information of all logistics nodes on the traceability association stage where the logistics node is located will be displayed first.

[0014] In one embodiment, the acquisition process of the traceability association stage is: The logistics nodes in the logistics node sequence whose fluency impact is continuously greater than the preset first threshold are divided into one stage, and the logistics nodes whose fluency impact is continuously less than or equal to the preset first threshold are also divided into one stage, and the various traceability association stages of the cold chain logistics of the corresponding batch of fresh products are obtained.

[0015] In the second aspect, an embodiment of the present application also provides a cold chain logistics data traceability system based on the Internet of Things, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above methods are implemented.

[0016] The embodiments of the present application have at least the following beneficial effects: This application can significantly improve the accuracy and timeliness of data traceability by conducting in-depth analysis of destocking in the cold chain logistics transportation process and implementing accurate node information collection; By calculating the transportation range coefficient of each logistics node, the transportation and distribution capacity of each logistics node was analyzed. By calculating the forward and backward flow rate of each subsequence in the logistics node sequence of each batch of fresh products' cold chain logistics, the transportation flow status of each node during the cold chain logistics transportation of that batch of fresh products under the influence of destocking was evaluated. Then, the smoothness impact of each logistics node in the cold chain logistics of each batch of fresh products was calculated. The cold chain logistics of each batch of fresh products was divided into multiple traceability association stages based on the smoothness impact. When querying logistics node information, abnormal information of all logistics nodes in the traceability association stage where the logistics node is located is prioritized for display, thereby improving the accuracy and efficiency of traceability, ensuring information exchange between various links, and reducing errors caused by offline or delayed data upload. This real-time monitoring and data transmission mechanism enables enterprises to keep abreast of temperature control status, product location and transportation progress, so as to quickly respond to potential quality issues. Accurate node information collection will also enhance the transparency of cold chain logistics. Consumers can track the source and transportation process of products through the traceability system, increasing their trust in product safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 A flowchart of a cold chain logistics data traceability method based on the Internet of Things provided in one embodiment of the present application; Figure 2 Schematic diagram of the acquisition process of each subsequence. DETAILED DESCRIPTION

[0019] In order to further illustrate the technical means and effects adopted by this application to achieve the predetermined invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, describes in detail the specific implementation method, structure, features and effects of a cold chain logistics data traceability method and system based on the Internet of Things proposed in this application. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0020] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0021] The following describes in detail a cold chain logistics data traceability method and system based on the Internet of Things provided by this application with reference to the accompanying drawings.

[0022] See also Figure 1 , which shows a flowchart of a cold chain logistics data traceability method based on the Internet of Things provided by an embodiment of the present application, the method comprising the following steps: Step S1, within a preset sampling period, collect information data of each logistics node in the cold chain logistics of each batch of fresh products, including the retention time of items at each logistics node.

[0023] Taking the cold chain logistics process of any batch of chilled fresh products as an example, the logistics process includes production and inspection links, processing links, warehousing links, transportation links and sales links. The type of information data that can be collected in each link is different. First, different logistics nodes are set for different links. In the embodiment of the present application, the production and inspection link is regarded as a logistics node, each processing link is regarded as a logistics node, and each storage point for each warehousing is regarded as a logistics node; each distribution point passed during transportation is regarded as a logistics node, and each sales point is regarded as a logistics node; since the transportation routes and sales locations of different batches of chilled fresh products may be different, the logistics nodes passed by different batches of chilled fresh products may also be different.

[0024] It should be noted that for the setting of logistics nodes, this application only provides one setting method. The implementer can adjust the logistics nodes according to the requirements of different categories of cold fresh products for processing, transportation and storage. For cold fresh products with more sophisticated requirements, the division of logistics nodes of the corresponding cold logistics needs to be more refined. This application does not impose specific restrictions on the specific setting method.

[0025] Then, obtain the information data generated by each batch of fresh products as they pass through each logistics node: Taking any cold chain logistics process as an example, during the production inspection phase, temperature and humidity sensors are used to monitor the temperature and humidity of the cold storage environment, identify the types of fresh products, and assign a unique identification code to each fresh product for tracking. At the same time, the appearance characteristics of the fresh product at that time and the preliminary inspection results are recorded; Record the processing time, methods and process parameters at each processing step, and collect the specifications and grades of the processed fresh products; In the warehousing process, temperature and humidity sensors installed at the storage points collect the temperature and humidity of the storage environment in real time, and record the storage time, storage location information and inventory quantity of the batch of fresh products; During the transportation process, the transportation route and time are collected through vehicle positioning equipment, as well as the temperature and humidity of the environment in which the fresh and cold products are transported through temperature and humidity sensors; The sales process collects sales location, sales time, sales price and feedback information given by consumers.

[0026] During the entire cold chain logistics process, information data from all logistics nodes is uploaded to a dedicated information database in real time for aggregation.

[0027] The sampling period is set. Preferably, in the embodiment of the present application, the sampling period is set to 24 hours. As other embodiments of the present application, the implementer can set the sampling period according to the actual situation.

[0028] Step S2: construct a logistics node sequence for the cold chain logistics of each batch of fresh products, and record the number of nodes after each logistics node in the logistics node sequence as the first subsequent node number of each logistics node; calculate the transportation range coefficient of each logistics node based on the difference between the first subsequent node number of each logistics node and other logistics nodes.

[0029] Because cold chain logistics involves multiple stages of transportation, it requires full-chain collection of cold chain logistics data from production, warehousing, and transportation. The warehousing process occurs across multiple distribution locations, and data discontinuities are particularly common between warehouses and transport vehicles. Data offline or delayed uploads during the transportation or warehousing stages of some vehicles can affect the accuracy and timeliness of cold chain logistics data traceability. Therefore, it is necessary to analyze the flow of inventory-driven transactions throughout the cold chain logistics transportation process and obtain accurate node information for rapid and efficient cold chain logistics data traceability.

[0030] Due to the requirement of destocking, the data correlation between logistics data of each link is enhanced, that is, the flow speed in time series is accelerated. Therefore, the impact of destocking requirements on cold chain logistics data is mainly manifested in the enhancement of the correlation between multiple links. For example, in the three links from inventory points to logistics transport vehicles and then to inventory distribution points, the transportation or storage detention time of cold chain logistics products between these links is short, there are few abnormal conditions, and it is also affected by the transportation and storage capacity of the node itself. For example, the distribution range of lower-level district and county cold chain logistics points is limited by the delivery range of the cold chain equipment of the logistics node.

[0031] In order to obtain the flow status of the cold chain logistics transportation stage, we first determine the transportation and storage capacity range of the cold chain logistics nodes, specifically: First, the logistics node sequence of the cold chain logistics of each batch of fresh products is obtained: all the logistics nodes passed by the cold chain logistics of any batch of fresh products are arranged according to the arrival order of the fresh products to obtain the logistics node sequence of the batch of fresh products.

[0032] Then, the various types of information data generated by the batch of fresh products at its i-th logistics node are compared with the corresponding standard values. The absolute values ​​of the differences between the various types of information data and the corresponding standard values ​​are obtained and normalized to eliminate the influence of units and dimensions. This results in the change in the various types of information data for the batch of fresh products at its i-th logistics node. For example, assuming the i-th logistics node is a storage point, the information data generated by the batch of fresh products at the storage point includes the ambient temperature of the storage point detected by the temperature sensor. The normalized value of the absolute value of the difference between this temperature and the standard storage ambient temperature is obtained as the temperature change.

[0033] Afterwards, any batch of fresh products is taken as the current batch of fresh products, and the number of logistics nodes after the i-th logistics node in the logistics node sequence of the cold chain logistics of the current batch of fresh products is obtained, which is recorded as the first subsequent node number of the i-th logistics node Finally, calculate the transportation range coefficient of each logistics node in the logistics node sequence of the cold chain logistics of this batch of fresh products. The expression is:

[0034] Where, Represents the transportation range coefficient of the i-th logistics node in the logistics node sequence of the cold chain logistics of the current batch of fresh products; 、 They respectively represent the number of logistics nodes after the i-th and i+1-th logistics nodes in the logistics node sequence of the cold chain logistics of the current batch of fresh products; Represents the mean number of first subsequent nodes of all nodes in the logistics node sequence of the cold chain logistics of the current batch of fresh products; is the normalization function.

[0035] The larger the ratio, the greater the transportation and distribution capacity of the node during the cold chain logistics transportation of the batch of fresh products.

[0036] Step S3, dividing the logistics node sequence by the retention time of the logistics nodes to obtain subsequences of the logistics node sequence; based on the difference between the retention time of items in two adjacent logistics nodes in each subsequence, combined with the transportation range coefficient, calculating the forward and backward flow degree of each subsequence in the cold chain logistics.

[0037] Since only the differences between nodes in the same link of the cold chain logistics are meaningful for analysis, the data correlation between the logistics data of each link is enhanced, that is, the flow speed in time series is accelerated. The specific manifestation is that the transportation or storage detention time of the cold chain logistics products between links is short and there are fewer abnormal conditions. The flow status of the logistics node is mainly expressed as the consistency relationship with the detention performance at all previous nodes. That is, during the cold chain logistics transportation of this batch of fresh products, if the detention performance starting from a certain node is lower than the average level, it can be considered that the flow status of the cold chain logistics data performance of the node is better.

[0038] Therefore, the length of time items are detained at each logistics node in the cold chain logistics of each batch of fresh products is obtained. In the logistics node sequence of any batch of fresh products, the absolute value of the difference between the length of time items are detained at each logistics node and the next logistics node is calculated as the first difference of each logistics node. Furthermore, the mean of the first differences of all logistics nodes in the logistics node sequence is calculated and recorded as the first mean, which represents the general detention status of this cold chain logistics transportation process. In the logistics node sequence, a sequence consisting of logistics nodes whose first differences are continuously greater than the first mean or whose first differences are continuously less than or equal to the first mean is taken as each subsequence of the logistics node sequence.

[0039] Calculate a normalized value of the difference between the first mean value in the logistics node sequence and the first difference of each logistics node as the retention status difference of each logistics node in the logistics node sequence, where the normalization range is 0 to 1.

[0040] Therefore, the forward and backward flow degree of each subsequence is calculated, and the expression is:

[0041] Where, Indicates the forward and backward flow degree of the jth subsequence in the logistics node sequence of the current batch of fresh products; Indicates the number of logistics nodes in the jth subsequence of the logistics node sequence of the current batch of fresh products; Indicates the transportation range coefficient of the kth logistics node in the jth subsequence in the logistics node sequence of the current batch of fresh products; Indicates the difference in the retention status of the kth logistics node in the jth subsequence in the logistics node sequence of the current batch of fresh products; is a preset minimum positive number. In the embodiment of the present application, The value of is set to 0.001; is the normalization function.

[0042] In each cold chain logistics subsequence, the larger the node's transportation range coefficient, the smaller the difference in detention conditions, and the better the forward and backward flow conditions within the subsequence. The larger the ratio, the better the transportation flow condition of this node under the influence of destocking during the cold chain logistics transportation of this batch of fresh chilled products.

[0043] Step S4, based on the forward and backward flow degree of each subsequence in any logistics node sequence and the order of each logistics node in the subsequence, calculate the smoothness influence of each logistics node in each cold chain logistics in which it is located.

[0044] The forward and backward flow rates of the cold chain logistics transport subsequences obtained above are analyzed based on the cold chain logistics transport process of a single batch. This is affected not only by the batch type but also by the intercommunication between warehousing and transportation data, as well as incidental factors at each stage. Specifically, data gaps can occur in the intercommunication between warehouses and transport vehicles, and some vehicles experience offline or delayed data uploads during the transport or warehousing stages. Therefore, the impact of forward and backward flow rates on the smoothness of logistics nodes is calculated by analyzing the impact of changes in the smoothness of cold chain logistics transport sequences across multiple batches.

[0045] Taking logistics node A as an example, obtain the logistics node sequence of all batches of cold chain logistics of fresh products passing through logistics node A during the sampling period; obtain the subsequence of logistics node A in the logistics node sequence of each batch of cold chain logistics of fresh products, and obtain the sequence number of logistics node A in each subsequence it is in, as well as the forward and backward flow degree of each subsequence it is in.

[0046] Furthermore, the smoothness impact of logistics node A is calculated as follows:

[0047] Where, It represents the impact of logistics node A on the smoothness of the cold chain logistics of the Jth batch of fresh products; Represents the sequence number of logistics node A in the subsequence of the cold chain logistics of batch J of fresh products; The maximum value of the sequence numbers of all elements in the subsequence of the Jth batch of fresh products in the cold chain logistics of logistics node A, that is, the number of logistics nodes in the subsequence; Represents the forward and backward flow degree of the subsequence of the Jth batch of fresh products in the cold chain logistics of logistics node A; Represents the mean of the forward and backward flow degrees of the subsequences in the cold chain logistics of all batches of fresh products at logistics node A; Represents an exponential function with the natural constant e as its base.

[0048] The greater the change in the sequence number of each node in all its subsequences, and the more consistent the flow rate of the cold chain logistics transportation subsequence, the smaller the impact of the node on the smoothness of the overall cold chain logistics transportation process.

[0049] Step S5: tracing the data of the cold chain logistics of each batch of fresh products based on the fluency impact.

[0050] In this way, the fluency impact of all logistics nodes can be obtained. In the actual cold chain logistics data traceability process, the entire cold chain logistics transportation process needs to be divided into multiple stages according to the common fluency impact of the transportation nodes of the corresponding batches of products. Each stage can be regarded as a traceability-related node with strong associated information. When there are abnormal conditions, the synchronous information extraction and display of multiple related nodes should be carried out to improve the accuracy and efficiency of the single traceability process.

[0051] Therefore, for the traceability requirements of any batch of fresh products, we first extract the logistics node sequence of its cold chain logistics and calculate the impact of each logistics node on the smoothness of the cold chain logistics of this batch of fresh products; A first threshold is set. Preferably, in the embodiment of the present application, the first threshold is set to 0.85. As other embodiments of the present application, the implementer can set the value of the first threshold according to the actual situation. The logistics node sequence of the cold chain logistics of the batch of fresh products is divided into stages by the first threshold. Specifically, the logistics nodes in the logistics node sequence whose fluency impact is continuously greater than the first threshold are divided into one stage, and the logistics nodes that are continuously less than or equal to the first threshold are divided into one stage, thereby obtaining multiple traceability association stages of the cold chain logistics of the batch of fresh products. The stage division process is illustrated by an example: assuming that there are 7 logistics nodes from 1 to 7 in the logistics node sequence, and the fluency impact of the 2nd, 3rd, 5th, 6th, and 7th logistics nodes is greater than the first threshold, then the logistics information collected by the 1st logistics node is taken as the first stage, the logistics information collected by the 2nd and 3rd logistics nodes is taken as the second stage, the logistics information collected by the 4th logistics node is taken as the third stage, and the logistics information collected by the 5th, 6th and 7th logistics nodes is taken as the fourth stage.

[0052] Taking the cold chain logistics of any batch of fresh produce as an example, for the logistics node the user needs to query, the abnormal information of all logistics nodes in the traceability associated stage where the logistics node is located is prioritized for display, meeting the traceability requirements of accuracy and efficiency, and realizing the traceability of cold chain logistics data. The abnormal information mentioned above refers to the information data of all items of information of the logistics node that exceed the standard value.

[0053] The schematic diagram of the acquisition process of each subsequence is as follows Figure 2 shown.

[0054] Based on the same inventive concept as the above method, an embodiment of the present application also provides a cold chain logistics data traceability system based on the Internet of Things, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned cold chain logistics data traceability methods based on the Internet of Things.

[0055] In summary, the embodiments of the present application provide a cold chain logistics data traceability method based on the Internet of Things. By conducting in-depth analysis of destocking during cold chain logistics transportation and implementing accurate node information collection, the accuracy and timeliness of data traceability can be significantly improved. By calculating the transportation range coefficient of each logistics node, the transportation and distribution capacity of each logistics node was analyzed. By calculating the forward and backward flow rate of each subsequence in the logistics node sequence of each batch of fresh products' cold chain logistics, the transportation flow status of each node during the cold chain logistics transportation of that batch of fresh products under the influence of destocking was evaluated. Then, the smoothness impact of each logistics node in the cold chain logistics of each batch of fresh products was calculated. The cold chain logistics of each batch of fresh products was divided into multiple traceability association stages based on the smoothness impact. When querying logistics node information, abnormal information of all logistics nodes in the traceability association stage where the logistics node is located is prioritized for display, thereby improving the accuracy and efficiency of traceability, ensuring information exchange between various links, and reducing errors caused by offline or delayed data upload. This real-time monitoring and data transmission mechanism enables enterprises to keep abreast of temperature control status, product location and transportation progress, so as to quickly respond to potential quality issues. Accurate node information collection will also enhance the transparency of cold chain logistics. Consumers can track the source and transportation process of products through the traceability system, increasing their trust in product safety.

[0056] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the above descriptions are of specific embodiments of the present application. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0057] The various embodiments in this application are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0058] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present application should be included in the scope of protection of the present application.

Claims

1. A cold chain logistics data tracing method based on the Internet of Things, characterized in that: The method comprises the following steps: During the preset sampling period, collect information data of each logistics node in the cold chain logistics of each batch of fresh products, including the length of time the items are detained at each logistics node; Construct a logistics node sequence for the cold chain logistics of each batch of fresh products, and record the number of nodes after each logistics node in the logistics node sequence as the first subsequent node number of each logistics node; calculate the transportation range coefficient of each logistics node based on the difference between the first subsequent node numbers of each logistics node and other logistics nodes; The logistics node sequence is divided into subsequences of the logistics node sequence according to the retention time of the logistics nodes; based on the difference between the retention time of items of two adjacent logistics nodes in each subsequence and the transportation range coefficient, the forward and backward flow degree of each subsequence in the cold chain logistics is calculated; Based on the forward and backward flow of each subsequence in any logistics node sequence, as well as the order of each logistics node in the subsequence, the impact of each logistics node on the smoothness of each cold chain logistics in which it is located is calculated; Based on the fluency impact, data traceability of cold chain logistics of each batch of fresh chilled products is performed.

2. The cold chain logistics data tracing method based on the Internet of Things according to claim 1, characterized in that: The expression of the transportation range coefficient of each logistics node is: , where Represents the transportation range coefficient of the i-th logistics node in the logistics node sequence of the cold chain logistics of the current batch of fresh products; 、 They respectively represent the number of logistics nodes after the i-th and i+1-th logistics nodes in the logistics node sequence of the cold chain logistics of the current batch of fresh products; Represents the mean number of first subsequent nodes of all nodes in the logistics node sequence of the cold chain logistics of the current batch of fresh products; is the normalization function.

3. The cold chain logistics data tracing method based on the Internet of Things according to claim 1, characterized in that: The process of obtaining each subsequence of the logistics node sequence is as follows: In any logistics node sequence, the absolute value of the difference between the item detention time of each logistics node and the next logistics node is calculated as the first difference of each logistics node; the mean of the first differences of all logistics nodes in the said logistics node sequence is calculated and recorded as the first mean; A sequence of logistics nodes whose first differences are continuously greater than the first mean or whose first differences are continuously less than or equal to the first mean is taken as each subsequence of any logistics node sequence.

4. The cold chain logistics data tracing method based on the Internet of Things according to claim 3, characterized in that: The process of obtaining the forward and backward flow degree of each subsequence is as follows: Calculating a normalized value of the difference between the first mean value in the logistics node sequence and the first difference of each logistics node as the detention status difference of each logistics node in the logistics node sequence; The forward and backward flow degree of each subsequence is calculated based on the retention status difference and the transportation range coefficient.

5. The cold chain logistics data tracing method based on the Internet of Things according to claim 4, characterized in that: The expression of the front-to-back flow degree is: Where, Indicates the forward and backward flow degree of the jth subsequence in the logistics node sequence of the current batch of fresh products; Indicates the number of logistics nodes in the jth subsequence of the logistics node sequence of the current batch of fresh products; Indicates the transportation range coefficient of the kth logistics node in the jth subsequence in the logistics node sequence of the current batch of fresh products; Indicates the difference in the retention status of the kth logistics node in the jth subsequence in the logistics node sequence of the current batch of fresh products; is a preset minimum positive number; is the normalization function.

6. The cold chain logistics data tracing method based on the Internet of Things according to claim 1, characterized in that: The process of obtaining the smoothness influence of each logistics node in each cold chain logistics is as follows: Obtain the subsequence of any logistics node in the logistics node sequence of each batch of cold chain logistics of fresh products, obtain the sequence number of any logistics node in each subsequence, and the forward and backward flow degree of each subsequence; Based on the difference between the sequence number in each subsequence and the number of logistics nodes in the corresponding subsequence, and the difference between the front-to-back flow rate of each subsequence and the average front-to-back flow rate of all subsequences in which any logistics node is located, the impact of the smoothness of any logistics node in each cold chain logistics in which it is located is calculated.

7. The cold chain logistics data tracing method based on the Internet of Things according to claim 6, characterized in that: The expression of the fluency influence is: Where, It represents the impact of logistics node A on the smoothness of the cold chain logistics of the Jth batch of fresh products; Represents the sequence number of logistics node A in the subsequence of the cold chain logistics of batch J of fresh products; Represents the number of logistics nodes in the subsequence of the Jth batch of fresh products in the cold chain logistics of logistics node A; Represents the forward and backward flow degree of the subsequence of the Jth batch of fresh products in the cold chain logistics of logistics node A; Represents the mean of the forward and backward flow degrees of the subsequences in the cold chain logistics of all batches of fresh products at logistics node A; Represents an exponential function with the natural constant e as its base.

8. The cold chain logistics data tracing method based on the Internet of Things according to claim 1, characterized in that: The data traceability of cold chain logistics of each batch of fresh products based on the fluency impact is specifically as follows: The corresponding logistics node sequence is divided according to the fluency impact of each logistics node in the cold chain logistics of each batch of fresh products, and multiple traceability association stages of the cold chain logistics of each batch of fresh products are obtained; for the logistics nodes that users need to query, the abnormal information of all logistics nodes on the traceability association stage where the logistics node is located will be displayed first.

9. The cold chain logistics data tracing method based on the Internet of Things according to claim 8, characterized in that: The acquisition process of the traceability association stage is as follows: The logistics nodes in the logistics node sequence whose fluency impact is continuously greater than the preset first threshold are divided into one stage, and the logistics nodes whose fluency impact is continuously less than or equal to the preset first threshold are also divided into one stage, and the various traceability association stages of the cold chain logistics of the corresponding batch of fresh products are obtained.

10. A cold chain logistics data traceability system based on the Internet of Things, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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