An internet-of-things-based cold-chain logistics data traceability method and system

Abstract

The application 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, which specifically comprises the following steps: calculating the front and back flow degrees of each subsequence in the logistics node sequence of each batch of cold fresh products in cold-chain logistics by the number of subsequent nodes of each logistics node and the item retention time length of each logistics node; combining the order of each logistics node in the subsequence to calculate the flow smoothness influence degree of each logistics node in each batch of logistics; dividing each batch of cold fresh product cold-chain logistics into multiple tracing correlation stages through the flow smoothness influence degree; when the logistics node information is queried, the abnormal information of all logistics nodes in the tracing correlation stage where the logistics node is located is preferentially displayed; the accuracy and efficiency of tracing are improved, information interconnection between various links is ensured, errors caused by offline or delayed uploading of data are reduced, key node information can be timely mastered, and potential quality problems can be quickly responded to.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a cold chain logistics data traceability method and system based on Internet of Things. BACKGROUND

[0002] The cold chain logistics management based on Internet of Things is the information collection, recording, storage and query of the whole process of cold fresh product production, processing, circulation and sales, especially the recording of cold fresh product detection data, which can help different users of the corresponding cold chain logistics to accurately control the quality of cold fresh products, including temperature, humidity and other quality indicators.

[0003] Since the cold chain logistics process involves multi-stage transportation, that is, the cold chain logistics data of the whole link needs to be collected from multiple stages such as production, storage and transportation, and the data offline or delayed uploading in some vehicle transportation stages or storage stages will affect the accuracy of the actual cold chain logistics data traceability. SUMMARY

[0004] In order to solve the above technical problems, the purpose of the present application is to provide a cold chain logistics data traceability method and system based on Internet of Things, and the technical solution adopted is as follows:

[0005] In the first aspect, the present application provides a cold chain logistics data traceability method based on Internet of Things, which comprises the following steps:

[0006] In a preset sampling period, the information data of each logistics node in the cold chain logistics of each batch of cold fresh products is collected, including the residence time of goods at each logistics node;

[0007] The logistics node sequence of each batch of cold fresh products is constructed, the number of nodes after each logistics node in the logistics node sequence is recorded as the first subsequent node number of each logistics node, and the transportation range coefficient of each logistics node is calculated based on the difference between the first subsequent node number of each logistics node and other logistics nodes;

[0008] Each subsequence of the logistics node sequence is obtained by dividing the logistics node sequence according to the residence time of the logistics node; the front and back flow transfer degrees of each subsequence in the cold chain logistics are calculated based on the difference between the residence time of goods of adjacent two logistics nodes in each subsequence and combined with the transportation range coefficient;

[0009] Based on the front and back flow transfer degrees of each subsequence in any logistics node sequence and the order of each logistics node in the subsequence, the flow smoothness influence degree of each logistics node in each cold chain logistics is calculated;

[0010] The data traceability of each batch of cold fresh products in the cold chain logistics is carried out based on the flow smoothness influence degree.

[0011] In one embodiment, the expression of the transport range coefficient of each logistics node is:

[0012] , wherein, represents the transport range coefficient of the i-th logistics node in the logistics node sequence of the current batch of cold fresh products cold chain logistics; , respectively represent the number of logistics nodes after the i-th and i+1-th logistics nodes in the logistics node sequence of the current batch of cold fresh products cold chain logistics; represents the average of the first subsequent node number of all nodes in the logistics node sequence of the current batch of cold fresh products cold chain logistics; is a normalization function.

[0013] In one embodiment, the acquisition process of each sub-sequence of the logistics node sequence is:

[0014] In any logistics node sequence, the absolute value of the difference between the article retention time of each logistics node and the next 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 denoted as the first average. The sequence of logistics nodes whose first differences are successively greater than the first average or whose first differences are successively less than or equal to the first average is taken as each sub-sequence of the any logistics node sequence.

[0015] In one embodiment, the acquisition process of the front-back flow degree of each sub-sequence is:

[0016] The normalized value of the difference between the first average in the logistics node sequence and the first difference of each logistics node is calculated as the retention status difference of each logistics node in the logistics node sequence. The front-back flow degree of each sub-sequence is calculated based on the retention status difference and the transport range coefficient.

[0017] In one embodiment, the expression of the front-back flow degree is:

[0018]

[0019] , wherein, represents the front-back flow degree of the j-th sub-sequence in the logistics node sequence of the current batch of cold fresh products; represents the number of logistics nodes in the j-th sub-sequence in the logistics node sequence of the current batch of cold fresh products; represents the transport range coefficient of the k-th logistics node in the j-th sub-sequence in the logistics node sequence of the current batch of cold fresh products; a difference in the stagnation of the kth logistics node in the jth sub-sequence in the logistics node sequence of the current batch of cold fresh products; is a preset minimum positive number; is a normalization function.

[0020] In one embodiment, the process of obtaining the flow smoothness influence degree of each logistics node in each cold chain logistics in which it is located is as follows:

[0021] obtaining the sub-sequence in which any logistics node is located in the logistics node sequence of each batch of cold fresh product cold chain logistics, obtaining the order number of the any logistics node in each sub-sequence in which it is located, and the forward and backward flow degree of each sub-sequence;

[0022] Based on the difference between the order number in each sub-sequence and the number of logistics nodes in the corresponding sub-sequence, and the difference between the forward and backward flow degree of each sub-sequence and the average of the forward and backward flow degree of all sub-sequences in which the any logistics node is located, the flow smoothness influence degree of the any logistics node in each cold chain logistics in which it is located is calculated.

[0023] In one embodiment, the expression of the flow smoothness influence degree is as follows:

[0024]

[0025] In the formula, represents the flow smoothness influence degree of logistics node A in the Jth batch of cold fresh product cold chain logistics; represents the order number of logistics node A in the sub-sequence in the Jth batch of cold fresh product cold chain logistics; represents the number of logistics nodes in the sub-sequence in which logistics node A is located in the Jth batch of cold fresh product cold chain logistics; represents the forward and backward flow degree of the sub-sequence in which logistics node A is located in the Jth batch of cold fresh product cold chain logistics; represents the average of the forward and backward flow degree of the sub-sequences in which logistics node A is located in all batches of cold fresh product cold chain logistics; represents an exponential function with the natural constant e as the base number.

[0026] In one embodiment, the data traceability of each batch of cold fresh product cold chain logistics based on the flow smoothness influence degree is as follows:

[0027] The flow smoothness influence degree of each logistics node of each batch of cold fresh product cold chain logistics is used to divide the corresponding logistics node sequence, obtaining multiple traceability association stages of each batch of cold fresh product cold chain logistics; for the logistics node that the user needs to query, the abnormal information of all logistics nodes in the traceability association stage in which the logistics node is located is preferentially displayed.

[0028] In one embodiment, the acquisition process of the traceability association stage is:

[0029] The logistics nodes in the logistics node sequence whose smoothness influence degree is continuously greater than a preset first threshold value are divided into one stage, and the logistics nodes whose smoothness influence degree is continuously less than or equal to the preset first threshold value are also divided into one stage, to obtain each traceability association stage of the cold chain logistics of the batch of cold fresh products.

[0030] In a second aspect, the embodiments of the present application also provide a cold chain logistics data traceability system based on the Internet of Things, which comprises a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the method according to any one of the preceding aspects when executing the computer program.

[0031] The embodiments of the present application have at least the following beneficial effects:

[0032] The present application can significantly improve the traceability accuracy and timeliness of data by in-depth analysis of the de-stocking in the cold chain logistics transportation process and accurate node information acquisition;

[0033] By calculating the transportation range coefficient of each logistics node, the transportation and distribution capacity of each logistics node is analyzed; by calculating the before-and-after transfer degree of each subsequence in the logistics node sequence of the batch of cold fresh products, the transportation and transfer status of the node in the cold chain logistics of the batch of cold fresh products under the influence of de-stocking is evaluated; and then the smoothness influence degree of each logistics node in the cold chain logistics of each batch of cold fresh products is calculated, each batch of cold fresh product cold chain logistics is divided into multiple traceability association stages through the smoothness influence degree, when querying the logistics node information, the abnormal information of all logistics nodes in the traceability association stage where the logistics node is located is preferentially displayed, the accuracy and efficiency of traceability are improved, information exchange between each link is ensured, and errors caused by offline or delayed uploading of data are reduced; this real-time monitoring and data transmission mechanism enables enterprises to timely grasp the temperature control state, product location and transportation progress, so as to quickly respond to potential quality problems; accurate node information acquisition will also enhance the transparency of cold chain logistics; consumers can track the source and transportation process of products through the traceability system, and improve the trust in product safety. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creating any creative labor.

[0035] Figure 1 A step flow chart of a cold chain logistics data traceability method based on an Internet of Things is provided for an embodiment of the present application;

[0036] Figure 2 An acquisition process diagram for each sub-sequence is shown. DETAILED DESCRIPTION

[0037] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined object, the specific implementation, structure, features and effects of the cold chain logistics data traceability method and system based on an Internet of Things according to the present application are described in detail as follows in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0039] The specific scheme of the cold chain logistics data traceability method and system based on an Internet of Things provided by the present application is described in detail below in combination with the drawings.

[0040] Please refer to Figure 1 which shows a step flow chart of a cold chain logistics data traceability method based on an Internet of Things provided for an embodiment of the present application. The method includes the following steps:

[0041] Step S1, in a preset sampling period, information data of each logistics node in the cold chain logistics of each batch of chilled products is collected, including the residence time of the goods at each logistics node.

[0042] Taking the cold chain logistics process of any batch of chilled products as an example, the logistics process includes a production detection link, a processing link, a warehousing link, a transportation link and a sales link. The types of information data that can be collected are different for each link. First, different logistics nodes are set for different links. In the present embodiment, the production detection link is taken as a logistics node, each processing link is taken as a logistics node, and each warehousing point for warehousing is taken as a logistics node. Each distribution point passed during transportation is taken as a logistics node, and each sales point is taken as a logistics node. Since the transportation routes and sales locations of different batches of chilled products may be different, the logistics nodes passed by different batches of chilled products may also be different.

[0043] It should be noted that for the setting of the logistics nodes, the application only provides one setting mode, and the implementer can adjust the logistics nodes according to the requirement fineness of different categories of cold fresh products for processing, transportation and storage. The cold fresh products with higher requirement fineness need more fine division of the logistics nodes of the cold logistics, and the specific setting mode is not limited in the application.

[0044] Then, the information data generated when each batch of cold fresh products passes through each logistics node is acquired:

[0045] Taking any cold chain logistics process as an example, the temperature and humidity of the cold storage environment are monitored by a temperature and humidity sensor in the production and detection link, the variety of the cold fresh products is acquired, and a unique identification code is allocated to each cold fresh product for tracking, and the appearance characteristics and preliminary inspection results of the cold fresh products at that time are recorded;

[0046] The time, method and process parameters of each processing link are recorded, and the specifications and grades of the cold fresh products after processing are collected;

[0047] The temperature and humidity of the storage environment are collected in real time by the temperature and humidity sensor installed at the storage point in the storage link, and the storage time, storage location information and storage quantity of the batch of cold fresh products are recorded;

[0048] The transportation route and transportation time are collected by the vehicle positioning device in the transportation link, and the temperature and humidity of the environment of the cold fresh products during transportation are collected by the temperature and humidity sensor;

[0049] The sales location, sales time, sales price and feedback information given by the consumer are collected in the sales link.

[0050] The information data of all the logistics nodes in the whole cold chain logistics process are uploaded to a special information database in real time for collection.

[0051] The sampling period is set, and preferably, the sampling period is set to 24h in the embodiment of the application. As other embodiments of the application, the implementer can set the sampling period according to the actual situation.

[0052] Step S2, constructing the logistics node sequence of each batch of cold fresh products cold chain logistics, recording the number of nodes after each logistics node in the logistics node sequence as the first subsequent node number of each logistics node; calculating 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.

[0053] Since the cold chain logistics process involves multi-stage transportation conditions, that is, the cold chain logistics data of multiple stages such as production, storage, transportation, etc. need to be collected throughout the link, and the storage process will exist in multiple distribution location networks, especially the data interconnection between the warehouse and the transport vehicle group is prone to data gaps; the data offline or delayed upload in part of the vehicle transportation stage or storage stage will affect the accuracy and timeliness of the actual cold chain logistics data traceability. Therefore, it is necessary to analyze the flow condition caused by de-stocking in the whole cold chain logistics transportation process, and obtain accurate node information for rapid and efficient cold chain logistics data traceability.

[0054] Due to the requirement of de-stocking, the data correlation between the logistics data of each link is enhanced, that is, the flow speed in time sequence is accelerated, so the requirement of de-stocking affects the performance of cold chain logistics data, mainly the enhancement of correlation in multiple links, for example, from the inventory point to the logistics transport vehicle to the inventory distribution point, the transportation or storage residence time of the cold chain logistics products between the links is short, the abnormal condition is less, and it is also affected by the transportation and storage capacity of the node itself, for example, the distribution range of the lower level county cold chain logistics point is limited by the cold chain equipment preservation delivery range of the logistics node.

[0055] In order to obtain the flow condition performance of the cold chain logistics transportation stage, first determine the transportation and storage capacity range of the cold chain logistics node, specifically:

[0056] Firstly, the logistics node sequence of each batch of cold fresh product cold chain logistics is obtained: arrange all the logistics nodes passed by any batch of cold fresh product cold chain logistics in the arrival order of the cold fresh product to obtain the logistics node sequence of the batch of cold fresh product.

[0057] Then, the various types of information data generated by the batch of cold fresh product at the i-th logistics node are compared with the corresponding standard value, the absolute value of the difference between the various types of information data and the corresponding standard value is obtained, and it is normalized to avoid the influence of units and dimensions, to obtain the change amount of various types of information data of the batch of cold fresh product at the i-th logistics node. For example, assuming that the i-th logistics node is a storage point, the information data generated by the batch of cold fresh product at the storage point includes the environment temperature of the storage point detected by the temperature sensor, then the normalized value of the absolute value of the difference between the temperature and the standard storage environment temperature is obtained as the temperature change amount.

[0058] After that, any batch of cold fresh product is taken as the current batch of cold fresh product, the number of logistics nodes after the i-th logistics node in the logistics node sequence of the current batch of cold fresh product cold chain logistics is obtained, denoted as the first subsequent node number of the i-th logistics node ; finally, the transportation range coefficient of each logistics node in the logistics node sequence of the batch of cold fresh product cold chain logistics is calculated, the expression is:

[0059]

[0060] In the formula, represents the transport range coefficient of the i-th logistics node in the logistics node sequence of the current batch of cold fresh product cold chain logistics; 、 respectively represent the number of logistics nodes after the i-th and i+1-th logistics nodes in the logistics node sequence of the current batch of cold fresh product cold chain logistics; represents the average of the first subsequent node number of all nodes in the logistics node sequence of the current batch of cold fresh product cold chain logistics; is a normalization function.

[0061] The larger the ratio is, the greater the transport and distribution capacity of the node in the cold chain logistics transportation process of the batch of cold fresh products is.

[0062] Step S3, dividing the logistics node sequence by the residence time of the logistics node to obtain each sub-sequence of the logistics node sequence; based on the difference between the residence time of the goods of the adjacent two logistics nodes in each sub-sequence, and combining the transport range coefficient, the front and back transfer degrees of each sub-sequence in the cold chain logistics are calculated.

[0063] Since only the difference between the nodes in the same link of the cold chain logistics has analytical significance, the data correlation degree between the logistics data of each link is enhanced, that is, the flow rate in time sequence is accelerated, and the specific performance is that the transport or storage residence time of the cold chain logistics products between links is short, and the abnormal conditions are few, and the flow condition of the logistics node is mainly represented as the consistency relationship with the residence performance of all previous nodes, that is, the residence performance of the batch of cold fresh products in the cold chain logistics transportation process from a certain node is lower than the average level, and it is considered that the flow condition of the cold chain logistics data performance of the node is better.

[0064] Therefore, the residence time of the goods at each logistics node in each batch of cold fresh product cold chain logistics is obtained, and in the logistics node sequence of any batch of cold fresh product cold chain logistics, the absolute value of the difference between the residence time of the goods of each logistics node and the residence time of the goods of the next logistics node is calculated as the first difference of each logistics node; further, the average of the first difference of all logistics nodes in the logistics node sequence is calculated, denoted as the first average, representing the general residence condition of this cold chain logistics transportation process;

[0065] In the logistics node sequence, the sequence composed of the logistics nodes whose first difference is successively greater than the first average or whose first difference is successively less than or equal to the first average is regarded as each sub-sequence of the logistics node sequence.

[0066] The normalized value of the difference between the first mean value and the first difference of each logistics node in the sequence of logistics nodes is calculated as the difference in the retention status of each logistics node in the sequence of logistics nodes. The normalized range is 0-1.

[0067] Therefore, the pre-post flow degree of each sub-sequence is calculated, and the expression is:

[0068]

[0069] In the formula, represents the pre-post flow degree of the jth sub-sequence in the sequence of logistics nodes of the current batch of chilled products; represents the number of logistics nodes in the jth sub-sequence in the sequence of logistics nodes of the current batch of chilled products; represents the transportation range coefficient of the kth logistics node in the jth sub-sequence in the sequence of logistics nodes of the current batch of chilled products; represents the retention status difference of the kth logistics node in the jth sub-sequence in the sequence of logistics nodes of the current batch of chilled products; is a preset minimum positive number, and in the embodiments of the present application, the value of is set to 0.001; is a normalization function.

[0070] In each cold chain logistics sub-sequence, the greater the transportation range coefficient of the node, the smaller the retention status difference value, and the better the pre-post flow status in the sub-sequence. The greater the ratio, the better the transportation flow status of the node in the cold chain logistics transportation process of the batch of chilled products under the influence of destocking.

[0071] Step S4, based on the pre-post flow degree of each sub-sequence in any logistics node sequence and the order of each logistics node in the sub-sequence, the flow smoothness influence degree of each logistics node in each cold chain logistics is calculated.

[0072] The pre-post flow degree of the above-mentioned obtained cold chain logistics transportation sub-sequence is analyzed in the cold chain logistics transportation process of a single batch, which is not only affected by the batch type, but also affected by the mutual communication between the respective storage and transportation data and the occasional factors in the stage, that is, there is a data fault in the data communication between the warehouse and the transportation vehicle group; there are data offline or delayed uploading conditions in the transportation stage or storage stage of part of the vehicles. Therefore, the flow smoothness influence degree of the logistics node is calculated by the flow smoothness influence of the pre-post flow degree under the change of multiple batches and different cold chain logistics transportation sequences.

[0073] Taking the logistics node A as an example, the logistics node sequence of all batches of cold fresh products passing through the logistics node A in the sampling period is obtained; the subsequence of the logistics node A in the logistics node sequence of each batch of cold fresh products is obtained, and the order number of the logistics node A in each subsequence and the front and back flow transfer degree of each subsequence are obtained.

[0074] Further, the fluency influence degree of the logistics node A is calculated, and the expression is as follows:

[0075]

[0076] In the formula, denotes the fluency influence degree of the logistics node A in the Jth batch of cold fresh product cold chain logistics; denotes the order number of the logistics node A in the subsequence in the Jth batch of cold fresh product cold chain logistics; denotes the maximum value of the order number of all elements in the subsequence of the Jth batch of cold fresh product cold chain logistics of the logistics node A, that is, the number of logistics nodes in the subsequence; denotes the front and back flow transfer degree of the subsequence of the Jth batch of cold fresh product cold chain logistics of the logistics node A; denotes the average value of the front and back flow transfer degrees of the subsequence in all batches of cold fresh product cold chain logistics of the logistics node A; denotes an exponential function with the natural constant e as the base number.

[0077] The greater the order number variation of each node in all subsequences and the more consistent the front and back flow transfer degrees of the cold chain logistics transportation subsequence, the smaller the influence of the node on the fluency of the whole cold chain logistics transportation process.

[0078] In step S5, data tracing of each batch of cold fresh product cold chain logistics is performed based on the fluency influence degree.

[0079] Thus, the fluency influence degrees of all logistics nodes are obtained, and in the actual cold chain logistics data tracing process, the whole cold chain logistics transportation process needs to be divided into multiple stages according to the common fluency influence degrees of the transportation nodes of the corresponding batches of products, each stage can be regarded as a strong correlation information tracing correlation node, and when there is an abnormal condition, synchronous information extraction and display of multiple correlation nodes should be performed to improve the accuracy and efficiency of single tracing process.

[0080] Therefore, for the tracing demand of any batch of cold fresh products, the logistics node sequence of the cold chain logistics of the batch of cold fresh products is extracted first, and the fluency influence degrees of each logistics node in the batch of cold fresh product cold chain logistics are calculated;

[0081] The first threshold is set, preferably, the first threshold is set to 0.85 in the embodiment of the application. As other embodiments of the application, the implementer can set the value of the first threshold according to the actual situation. The logistics node sequence of the batch of cold fresh product cold chain logistics is divided into stages by the first threshold, specifically, the logistics nodes with the flow smoothness influence degree greater than the first threshold are divided into a stage, and the logistics nodes with the flow smoothness influence degree less than or equal to the first threshold are divided into a stage, thereby obtaining a plurality of traceability correlation stages of the batch of cold fresh product cold chain logistics. The stage division process is exemplified: assuming that there are 1-7 logistics nodes in the logistics node sequence, and the flow smoothness influence degrees of the 2nd, 3rd, 5th, 6th, and 7th logistics nodes are greater than the first threshold, 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.

[0082] Taking any batch of cold fresh product cold chain logistics as an example, for the logistics node that the user needs to query, the abnormal information of all the logistics nodes in the traceability correlation stage where the logistics node is located is preferentially displayed to meet the accuracy and efficiency requirements of traceability, and the traceability of cold chain logistics data is realized. The abnormal information is the information data whose data value exceeds the standard value in all items of information of the logistics node.

[0083] The acquisition process of each subsequence is shown in the schematic diagram Figure 2 .

[0084] Based on the same inventive concept as the above method, the embodiment of the application also provides a cold chain logistics data traceability system based on the Internet of Things, which comprises a memory, a processor, and a computer program stored in the memory and running on the processor. The processor executes the computer program to realize the steps of any one of the above methods.

[0085] In summary, the embodiment of the application provides a cold chain logistics data traceability method based on the Internet of Things. Through in-depth analysis of the de-stocking in the cold chain logistics transportation process and implementation of accurate node information collection, the accuracy and timeliness of data traceability can be significantly improved.

[0086] The transportation range coefficient of each logistics node is calculated to analyze the transportation and distribution capacity of each logistics node; the front and rear transfer degrees of each sub-sequence in the logistics node sequence of the batch of chilled product cold chain logistics are calculated to evaluate the transportation and transfer status of the node in the chilled product cold chain logistics under the influence of destocking; and then the flow smoothness influence degree of each logistics node in each batch of chilled product cold chain logistics is calculated, each batch of chilled product cold chain logistics is divided into multiple traceability association stages through the flow smoothness influence degree, when the logistics node information is queried, the abnormal information of all logistics nodes in the traceability association stage where the logistics node is located is preferentially displayed, the accuracy and efficiency of traceability are improved, information interconnection between each link is ensured, and errors caused by offline or delayed uploading of data are reduced; the real-time monitoring and data transmission mechanism enables enterprises to timely grasp the temperature control state, product position and transportation progress, so as to quickly respond to potential quality problems; 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, and improve the trust in product safety.

[0087] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the above describes the specific embodiments of the present application. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0088] Each embodiment in the present application is described in a progressive manner, and the same and similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments.

[0089] The above only describes the preferred embodiments of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. An Internet of Things-based cold-chain logistics data traceability method, characterized in that, The method comprises the following steps: In a preset sampling period, information data of each logistics node in the cold chain logistics of each batch of chilled products is collected, including the residence time of goods at each logistics node; a logistics node sequence of the cold chain logistics of each batch of chilled products is constructed, and the number of nodes after each logistics node in the logistics node sequence is recorded as the first subsequent node number of each logistics node; a transport range coefficient of each logistics node is calculated based on the difference between the first subsequent node number of each logistics node and the first subsequent node number of other logistics nodes; Each subsequence of the logistics node sequence is obtained by dividing the logistics node sequence according to the residence time of the logistics node; the front-back flow degree of each subsequence in the cold chain logistics is calculated based on the difference between the residence time of goods of adjacent two logistics nodes in each subsequence and the transport range coefficient; the obtaining process of each subsequence of the logistics node sequence is as follows: in any logistics node sequence, the absolute value of the difference between the residence time of goods of each logistics node and the residence time of goods of the next logistics node is calculated as the first difference of each logistics node; the average value of the first difference of all logistics nodes in any logistics node sequence is calculated and recorded as the first average value; the sequence composed of the logistics nodes whose first difference is continuously greater than the first average value or whose first difference is continuously less than or equal to the first average value is taken as each subsequence of any logistics node sequence; the obtaining process of the front-back flow degree of each subsequence is as follows: the normalized value of the difference between the first average value in the logistics node sequence and the first difference of each logistics node is calculated as the residence status difference of each logistics node in the logistics node sequence; the front-back flow degree of each subsequence is calculated based on the residence status difference and the transport range coefficient; The flow smoothness influence degree of each logistics node in each cold chain logistics in which the logistics node is located is calculated based on the front-back flow degree of each subsequence in any logistics node sequence and the order of each logistics node in the subsequence in which the logistics node is located; Data tracing of the cold chain logistics of each batch of chilled products is performed based on the flow smoothness influence degree, specifically as follows: each logistics node sequence is divided into multiple trace correlation stages of the cold chain logistics of each batch of chilled products by using the flow smoothness influence degree of each logistics node in the logistics node sequence; for a logistics node to be queried by a user, the abnormal information of all logistics nodes in the trace correlation stage in which the logistics node is located is preferentially displayed.

2. The cold-chain logistics data traceability method based on the Internet of Things according to claim 1, characterized in that, The expression of the transport range coefficient of each logistics node is as follows: , wherein, represents the transport range coefficient of the i-th logistics node in the logistics node sequence of the cold chain logistics of the current batch of cold fresh products; 、 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 cold fresh products; represents the average of the first subsequent node number of all nodes in the logistics node sequence of the cold chain logistics of the current batch of cold fresh products; is a normalization function.

3. The cold chain logistics data traceability method based on the Internet of Things according to claim 1, characterized in that, The expression of the front-back flow degree is as follows: In the formula, represents the flow transfer degree of the jth sub-sequence in the sequence of the logistics nodes of the current batch of cold fresh products; represents the number of logistics nodes in the jth sub-sequence in the sequence of the logistics nodes of the current batch of cold fresh products; represents the transportation range coefficient of the kth logistics node in the jth sub-sequence in the sequence of the logistics nodes of the current batch of cold fresh products; represents the difference in the stagnation state of the kth logistics node in the jth sub-sequence in the sequence of the logistics nodes of the current batch of cold fresh products; is a preset minimum positive number; is a normalization function.

4. The cold chain logistics data traceability method based on the Internet of Things according to claim 1, characterized in that, The obtaining process of the flow smoothness influence degree of each logistics node in each cold chain logistics in which the logistics node is located is as follows: The subsequence in which any logistics node is located in the logistics node sequence of each batch of chilled products is obtained, the order number of any logistics node in each subsequence in which the logistics node is located is obtained, and the front-back flow degree of each subsequence is obtained; The flow smoothness influence degree of any logistics node in each cold chain logistics in which the logistics node is located is calculated based on the difference between the order number of any logistics node in each subsequence and the number of logistics nodes in the corresponding subsequence, and the difference between the front-back flow degree of each subsequence and the average value of the front-back flow degree of all subsequences in which any logistics node is located.

5. The cold chain logistics data traceability method based on the Internet of Things according to claim 4, characterized in that, The expression of the flow smoothness influence degree is as follows: In the formula, represents the flow smoothness influence degree of logistics node A in the Jth batch of chilled product cold chain logistics; represents the order number of logistics node A in the subsequence in the Jth batch of chilled product cold chain logistics; represents the number of logistics nodes in the subsequence of the Jth batch of chilled product cold chain logistics of logistics node A; represents the before-and-after flow degree of the subsequence of the Jth batch of chilled product cold chain logistics of logistics node A; represents the average of the before-and-after flow degrees of the subsequences in all batches of chilled product cold chain logistics of logistics node A; represents an exponential function with the natural constant e as the base.

6. The cold chain logistics data traceability method based on the Internet of Things according to claim 1, characterized in that, The obtaining process of the trace correlation stage is as follows: The logistics nodes with the continuous flow smoothness influence degree greater than the preset first threshold value in the logistics node sequence are divided into one stage, and the logistics nodes with the continuous flow smoothness influence degree less than or equal to the preset first threshold value are also divided into one stage, to obtain each traceability correlation stage of the cold chain logistics of the batch of cold fresh products.

7. An Internet of Things based cold chain logistics data traceability system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor, when executing the computer program, implements the steps of the method according to any one of claims 1-6.

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