Logistics transportation information real-time query system based on data large model

Abstract

The invention relates to the technical field of logistics data query, in particular to a logistics transportation information real-time query system based on a data big model, which comprises an information management module, a cloud platform, an information service module and an input module, and is characterized in that the information service module is connected with a user side; the input module is used for inputting logistics information and logistics archive information to the cloud platform, and the cloud platform is used for sorting and managing information data of the logistics information and the logistics archive information and completing real-time updating and processing of information parameters; the information service module is used for feeding back logistics transaction information, reminding logistics information and querying stored logistics information, and distributing the logistics transaction information, the logistics information and the stored logistics information to the cloud platform and an update application host of the information management module; and the information management module is used for real-time query of the logistics order information, the logistics transportation information and the logistics storage information. The real-time updating response efficiency of the system is higher, the data transmission time consumption is shorter, and user query is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics data query, and particularly to a real-time query system for logistics transportation information based on a large data model. Background Art

[0002] Logistics information is the general term for knowledge, materials, images, data, documents, etc. that reflect the contents of various logistics activities. In recent years, with the relatively rapid growth of the logistics industry, the logistics system has been continuously improved. In the logistics information management system, as the total volume of goods to be transported increases, the response timeliness of the pre-applied information has gradually declined. The traditional logistics information statistics platform determines the actual positions of each level of management nodes by sorting out user characteristic parameters, and then changes the real-time input volume of information data to achieve timely update of logistics transmission parameters. However, the response efficiency of this system is relatively low, and the consumption time required for data transmission is also relatively long, making it difficult to achieve real-time and clear tracking of goods to be transported. Summary of the Invention

[0003] The purpose of the present invention is to provide a real-time query system for logistics transportation information based on a large data model to solve the problems raised in the above background art.

[0004] To achieve the above purpose, the present invention provides the following technical solutions: A real-time query system for logistics transportation information based on a large data model includes an information management module, a cloud platform, an information service module, and an input module. Among them, the cloud platform is respectively connected to the information service module, the information management module, and the input module in the system. The information service module is connected to the user terminal, and the cloud platform stores and manages information parameters; The input module is used to input logistics information and logistics file information into the cloud platform. The cloud platform is used to sort out the logistics information and logistics file information, and perform real-time update and processing of information parameters according to the sorting; The information service module is used to feedback logistics transaction information and generate reminder logistics information, update the logistics transaction information and reminder logistics information to the logistics information, and then allocate them to the update application hosts of the cloud platform and the information management module; The information management module is used to perform real-time query on the updated logistics information. The updated logistics information also includes logistics order information, logistics transportation information, and logistics warehousing information; The cloud platform transfers all information parameters to the database of the equipped cloud storage for easy query of the system application data.

[0005] Further, the information service module includes a management and search unit, a feedback unit and a reminder unit connected to the management and search unit. The feedback unit is used for information feedback and service feedback, and feeds back the goods transmission data to the host in the form of information flow to determine the current transmission position of the logistics information. The reminder unit is used to allocate the reminder logistics information to the cloud platform and the updated application host of the information management module in the form of data flow through the management and search unit.

[0006] Further, the input module includes Host Ⅰ, the cloud platform includes Host Ⅱ and Host Ⅲ, and Host Ⅰ is interconnected with Host Ⅱ and Host Ⅲ. Host Ⅰ inputs logistics information to Host Ⅱ, and Host Ⅰ inputs logistics file information to Host Ⅲ.

[0007] Further, the information management module includes information processing equipment for logistics. The information processing equipment is connected to the data information interface and is used to query information parameters and release the information parameters for direct control management of logistics to obtain accurate queried goods information.

[0008] Further, the client includes a third-party platform, and the third-party platform includes a third-party host and a third-party mobile device.

[0009] Further, the cloud platform is equipped with a database for cloud storage, which is used to obtain asymmetric data information that has been encrypted to achieve consistency in data information between the information management module and the information service module.

[0010] Further, the data between the cloud platform and the database is transmitted in a compressed manner.

[0011] Further, the logistics file information includes logistics transaction information, logistics order information, logistics transportation information and logistics warehousing information after each transaction is completed.

[0012] Compared with the prior art, the beneficial effects of the present invention are: The system of the present invention takes the cloud platform as the core of system application, and is respectively connected to the information management module and the information service module in the system. On this basis, it analyzes and processes the logistics information and logistics file information of logistics users, and transfers all information parameters to the equipped database for cloud storage, which is convenient for querying system application data. The database for cloud storage is used to integrate logistics information related to third-party software, and can obtain asymmetric data information that has been encrypted under the support of the cloud platform. By means of asymmetric information encryption, the security of relevant logistics information in the system is guaranteed. Compared with the traditional logistics information update system, the system of the present invention has a higher response efficiency for real-time update of the applied system and a shorter data transmission time-consuming, which is very convenient for users to query. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a schematic diagram of the system structure of the present invention.

[0014] Figure 2 This is a structure diagram of the cloud platform of the present invention.

[0015] Figure 3 This is a structure diagram of the information management module of the present invention.

[0016] Figure 4 This is the structure of the information service module of the present invention.

[0017] Figure 5 This is a flow chart of the logistics data compression of the present invention. Detailed implementation manners

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0019] Please refer to Figures 1 to 5 , the present invention provides a technical solution: As Figure 1 shown, the hardware execution environment of a real-time query system for logistics transportation information based on a data large model is composed of a cloud platform, an information management module, an information service module, etc. The specific construction method is as follows: The cloud platform is the basis of the hardware device, which is jointly composed of host II and host III at all levels and cooperates with host I to work. When logistics information is input into the surface application structure of the cloud platform, all hosts simultaneously open the application connection state. And to ensure the timeliness of information parameter transmission, the hosts at the bottom layer need to have the ability to sort out logistics files and manage information data at the same time. On the one hand, it can complete the real-time update and processing of information parameters according to the application requirements of third-party logistics equipment. On the other hand, it can also establish a practical physical connection from the host to the bottom-layer information management module while meeting the connection instructions of the logistics system, so as to realize the update and maintenance of logistics information. The cloud platform structure is as Figure 2 shown.

[0020] In the present invention, information parameters can be regarded as a collection of logistics information and logistics file information. The cloud platform stores and manages information parameters through a database. When logistics information and logistics file information are input into the cloud platform through the input module, the cloud platform then processes them in real time and updates the currently stored information parameters in real time. Logistics information also includes logistics transaction information, reminder logistics information, logistics order information, logistics transportation information, logistics warehousing information, etc.

[0021] The information management module is a lower-level physical execution structure of the cloud platform. It can establish a theoretical connection between the information processing equipment of logistics and the interface unit of data information according to the real-time update requirements of third-party logistics information. Under the action of the terminal platform, it realizes the real-time query of logistics order information, logistics transportation information, and logistics warehousing information. The structure of the information management module is as Figure 3 shown.

[0022] The information service module is located at the bottom layer of the system and consists of three physical structures: a transaction feedback unit, an information reminder unit, and a query unit. Among them, the transaction feedback unit has the application capabilities of information feedback and service feedback. Under the action of the system execution software, it can feedback the cargo transmission data to the system host in the form of information flow, and determine the current transmission position of the logistics information according to the feedback of various hardware devices (such as positioning sensors). The information reminder unit distributes the reminder logistics information generated by the information service module to each level of the host in the form of data flow. The query unit is capable of communicating with third-party software to upgrade logistics information and perform information update and information processing. The structure of the information service module is as Figure 4 shown.

[0023] The system of the present invention belongs to an online logistics application processing platform, and its purpose is to achieve complete and secure logistics information processing through the mutual connection between the information management module, the cloud platform, the information service module, and the input module. The realization of each processing function requires the common cooperation of several types of application hosts such as the information management module, the information service module, and the cloud platform. In the platform, the existence of user characteristic parameters can ensure the stable operation of each hardware execution device, and while completely searching for third-party logistics information, it can provide a relatively perfect execution space for the database of cloud storage: that is, the logistics user is the hardware device host existing in the third-party application software, with strong independent operation ability. Under the action of multiple execution functions such as order processing and order forwarding, the logistics information at all levels is divided into multiple different data structures, thus ensuring the application viscosity of information data.

[0024] Let q max represent the maximum storage capacity of third-party logistics application information, e max represent the maximum reference weight value of information update, λ represent the system update duration, y ′ represent the application coefficient. By combining the above physical quantities, the user characteristic parameter P of third-party logistics information can be defined as:

[0025] In the formula,u 1 、 u 0 respectively represent the maximum and minimum update application coefficients of logistics transmission information.

[0026] In the system of the present invention, the database of cloud storage is used to integrate logistics information related to third-party software. With the support of the cloud platform, it can obtain the asymmetric data information that has been encrypted, and then, according to the analysis requirements of user characteristics, achieve the consistency of data information between the information management module and the information service module, so as to maintain the synchronous application of the logistics information management module and the information service module. To ensure the timeliness of logistics information update, the storage parameter lengths matching different data are also different. In short, the more important the physical location occupied by the information to be transmitted, the shorter the corresponding storage code length, and vice versa. Moreover, to ensure the mutual independence of data, only one type of third-party logistics information can be placed in one storage compartment.

[0027] In the system of the present invention, the cloud platform is used as the core of system application, and is respectively connected to the information management module and the information service module in the system. On this basis, it analyzes and processes the logistics information and logistics file information of logistics users, and transfers all information parameters (data parameters) to the equipped cloud storage database for easy query of system application data. Through the method of asymmetric information encryption, the security of relevant logistics information in the system is ensured. Compared with the traditional logistics information update system, the system of the present invention has a higher response efficiency for real-time update of the applied system and a shorter data transmission time, and can fundamentally solve the problem of unclear target tracking behavior of goods to be transported.

[0028] In the present invention, in order to improve the data transmission efficiency between the cloud platform and the cloud storage database, data compression transmission is adopted. During the logistics transportation process, since the data stream data mainly covers logistics order information, logistics transportation information (transportation start information and time information), logistics warehousing information, route information, receipt information, etc., and due to the different attributes and units of logistics data, in order to perform effective comparison and analysis, these data need to be normalized, and the formula is as follows:

[0029] Among them, x is the normalized data, x' is the original data, x min and x max are the minimum and maximum values of the data respectively. After processing, a real-time logistics data matrix X is constructed, which can be expressed as:

[0030] Next, set the compression threshold to further optimize the transmission efficiency of logistics data, and design a compression and transmission process for logistics data based on the integer wavelet threshold compression algorithm to meet the real-time requirements. The logistics data compression process design is as follows Figure 5 shown. Above, the design of logistics data compression and transmission is completed. After the real-time compression and transmission of logistics data, it is stored in the database of cloud storage.

[0031] In the present invention, before the data is normalized, the data stream data is first preprocessed as follows: The data set of the data stream data can be expressed as: , where and are the input text instance and its corresponding label set respectively, is the target text instance index, represents the total number of text instances included in the target data set, and the samples in are preprocessed as follows: (1), Remove all HTML tags, URLs, and non-alphabetic characters in the text and replace them with spaces; (2), Convert the uppercase letters in the text to lowercase; (3) Create a set of label names to obtain a multi-label text data set, where is the target label name, is the target label index, represents the number of labels in the target data set.

[0032] Then, the text data set is classified, and the specific method is as follows: S1, Input the text data set; S2, Initialize, set the minimum support r and the minimum confidence z ; S3, Take each word in the corpus formed by the text data set as an item to form an item set; S4, Scan the foregoing item set and calculate the support of each item; S5, Judge whether the support of all proper subsets of each item is greater than r , if so, retain and merge to generate the next-level item set, and then jump to step S4, otherwise output the finally obtained frequent item set F ; S6, For all non-empty subsets F of f i , calculate its confidence according to the calculation formula and judgef i Whether the confidence level is greater than the minimum confidence level z If so, output the association relationship f i ⇒( F - f i );Otherwise, eliminate the non-empty subsets f i 。

[0033] The calculation formula is as follows:

[0034] In the formula, A ( f i ) is the f i confidence level; Q ( F ) and Q ( f i ) are respectively the F and f i support degrees.

[0035] Use a long short-term memory artificial neural network to conduct in-depth learning and analysis on the association relationship between text vectors and text classification, and finally obtain accurate text classification results. The calculation process is as follows: Let f ( x ; θ ) represent a neural network, θ is the network parameter, and each time K training samples are selected. At the t th iteration, the partial derivative of the loss function with respect to the parameter θ is:

[0036] Among them, is a differentiable loss function, S t is called the batch size. The gradient t updated at the th time is defined as:

[0037] Use gradient descent to update the parameters:

[0038] Among them, is the learning rate and is greater than 0.

[0039] In the present invention, in order to reduce the data response time between the system cloud platform and the database of cloud storage, the following method is specifically adopted: Set the matrix of the cloud platform H The number of hidden nodes is mv , then we can get:

[0040] Wherein, H † is the Moore-Penrose generalized inverse of the matrix, H T represents the node function of the matrix of the system cloud platform, H T · H represents the matrix product.

[0041] Suppose H T · H becomes a singular matrix, and the weight matrix formula is obtained as follows:

[0042] Wherein, Y represents the two-times solution of the system cloud platform.

[0043] Set the training data set D 0 in the cloud platform of the system: , i = 1, 2, …, n 0 , and n 0 > mv . If D 0 is optimal, then , where H 0 , Y 0 represent the data transformation coefficients of the system database. To distinguish these two coefficients, H 0 can be defined as the first system database data transformation coefficient, and Y 0 is defined as the second system database data transformation coefficient. Then H 0 and Y 0 respectively satisfy:

[0044]

[0045] In the above formula, w 1 , w 2 ,…, w mv and b 1 , b 2 , …, b mv represent the input weights and biases respectively, then the solution of B (0) can be expressed as:

[0046] where, .

[0047] An adaptive method is used to determine the fuzzy density of different meta-classifiers of the system cloud platform. Using the confusion matrix to determine the initial fuzzy density , based on the classifier s i for c i the initial fuzzy density of class can be expressed by the formula as:

[0048] where, i = 1, 2, …, N , j = 1, 2, …, M . represents the proportion of the classifier s i identifying the sample of class c j as the class c j , represents the proportion of the classifier s i identifying the sample of class c k as the class c j , and k ≠ j . When the value of the former is larger, the value of the latter is smaller, and the value of is larger.

[0049] Set p ij ( x ) represents the classifier s i for the test samplex In the classification results, the probabilities of each category are directly proportional to the accuracy of the classification results. As p ij ( x ) the difference increases, s i for x correct classification becomes easier, and the classification of data in the system database can be more easily achieved. The discriminability s i of the classifier a i can be expressed as:

[0050] where represents p ij normalization, so: , 1 ≤ i ≤ N , 1 ≤ j ≤ N , assuming then .

[0051] The support represents the difference in the recognition results of each classifier for the test samples on the system cloud platform x . The difference in recognition between classifier s i and s j is defined using the probabilistic Euclidean distance and can be expressed as:

[0052] where 1 ≤ i , i ′ ≤ N . The total average distance TD i is set to represent the difference in the recognition results between classifier s i and other classifiers, so:

[0053] where TD i ∈ [0, 1]. Through analysis, TD i the value of TD i is inversely proportional to the support. When s iThe greater the difference from the recognition results of other classifiers, the lower the support. According to the above formula, the support β i can be expressed as:

[0054] Using 、 a i 、 β i the adaptive fuzzy density is obtained and expressed by the formula as:

[0055] Thus, according to the adaptive fuzzy density, the system database is integrated, and based on the integrated database, the design of the system cloud platform and database is realized.

[0056] In the present invention, as an optional but non-essential implementation manner, the foregoing neural network is optimized to improve the accuracy of text mining, and the following formula is used for modeling:

[0057] In the formula, x mm 、 x zz respectively refer to the opinions of the mm, zz th randomly selected user; x mm,new represents the new opinion of the user formed after optimization.

[0058] Then communication is carried out, which is expressed as follows:

[0059] Among them, x ll is the ll th randomly selected conversation partner; Sign The function determines the f mm 、 f zz direction of x ll by comparing.

[0060] After deliberation, by selecting a random number of comments or group members, the following formula is obtained:

[0061] In the formula, x e is the e th randomly selected user; N eIt is the comment or group size, which is a random number between 1 and the total number of users.

[0062] The update causes an overall update by updating a certain dimension, as shown in the following formula:

[0063] Where, d refers to the d th dimension randomly selected in the search space; ub d and lb d are the upper and lower limits corresponding to the d th dimension respectively.

[0064] When optimizing, the minimum of the average error percentage FF of the test set is taken as the goal, as shown in the following formula:

[0065] In the formula, y L,f is the f th fitting output value of the neural network; y test,f is the true value in the test set.

[0066] For the present invention, the un-described parts are prior art.

[0067] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A real-time query system for logistics and transportation information based on a big data model, characterized in that: It includes an information management module, a cloud platform, an information service module and an input module, wherein the cloud platform is respectively connected to the information service module, the information management module and the input module in the system; The input module is used to input logistics information and logistics archive information to the cloud platform, and the cloud platform is used to organize the logistics information and logistics archive information, and complete the real-time update and processing of information parameters according to the organization; The information service module is used to feedback logistics transaction information and generate reminder logistics information, and update the logistics transaction information and reminder logistics information to the logistics information, and then distribute it to the cloud platform and the update application host of the information management module; The information management module is used to query the updated logistics information in real time, and the updated logistics information also includes logistics order information, logistics transportation information and logistics warehousing information; The cloud platform transfers all information parameters to the equipped cloud storage database to facilitate the query of system application data.

2. A real-time query system for logistics and transportation information based on a large data model as claimed in claim 1, characterized in that: The information service module includes a management and search unit and a feedback unit and a reminder unit connected to the management and search unit. The feedback unit is used for information feedback and service feedback, and feeds back the cargo transmission data to the host in the form of information flow to determine the current transmission position of the logistics information. The reminder unit is used to distribute the reminder logistics information in the form of data flow through the management and search unit to the cloud platform and the update application host of the information management module.

3. A real-time query system for logistics and transportation information based on a data big model as claimed in claim 1, characterized in that: The input module includes a host I, and the cloud platform includes a host II and a host III. The host I is connected to the host II and the host III. The host I inputs logistics information to the host II, and the host I inputs logistics file information to the host III.

4. A real-time query system for logistics and transportation information based on a data big model as claimed in claim 1, characterized in that: The information management module includes a logistics information processing device, which is connected to a data information interface and is used to query information parameters to obtain accurate information about the queried goods.

5. A real-time query system for logistics and transportation information based on a large data model as claimed in claim 4, characterized in that: The information processing device communicates with a user terminal, the user terminal includes a third-party platform, and the third-party platform includes a third-party host and a third-party mobile device.

6. A real-time query system for logistics and transportation information based on a large data model as claimed in claim 1, characterized in that: The database is used to obtain the asymmetric data information that has completed the encryption process, so as to maintain the consistency of the data information of the information management module and the information service module.

7. A real-time query system for logistics and transportation information based on a large data model as claimed in claim 6, characterized in that: The data between the cloud platform and the database is transmitted in a compressed manner.

8. A real-time query system for logistics and transportation information based on a large data model as claimed in claim 1, characterized in that: The logistics file information includes logistics transaction information, logistics order information, logistics transportation information and logistics warehousing information after each transaction is completed.

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