CN-122019544-A - Bin allocation data optimized storage method and system based on internet of things

CN122019544ACN 122019544 ACN122019544 ACN 122019544ACN-122019544-A

Abstract

The invention discloses a bin allocation data optimized storage method and system based on the Internet of things, relates to the technical field of data storage, and solves the technical problems that repeated data transmission is performed, the data quantity transmitted to a central server is increased, the risks of data transmission delay and network congestion are easily caused, and the data processing efficiency is reduced; the method comprises the following steps that a plurality of data acquisition nodes are arranged in a cargo delivery area, a storage area and a distribution transfer area of cargoes, corresponding data storage nodes are arranged in each data acquisition node, cargo information data, storage data and transportation data of cargoes are acquired through the data acquisition nodes, the relay nodes can immediately conduct classification processing after receiving the data, ordered storage of the data is guaranteed, the data is prevented from being uploaded and then sent to an edge node for storage, repeated transmission of the data is avoided, the data quantity transmitted to a central server is reduced, and data processing efficiency is improved.

Inventors

LI DEYUE
DONG YADAN

Assignees

北京众和易优科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260202

Claims (10)

1. The bin allocation data optimized storage method based on the Internet of things is characterized by comprising the following steps of: A plurality of data acquisition nodes are arranged in a cargo discharging area, a storage area and a delivery transfer area of cargoes, each data acquisition node is provided with a corresponding data storage node, and cargo information data, storage data and transportation data of cargoes are acquired through the data acquisition nodes; Dividing a shipment area, a storage area and a distribution transfer area into blocks according to geographic positions, wherein each block is provided with a relay node respectively, and data acquisition nodes of the shipment area, the storage area and the distribution transfer area gather acquired data to relay nodes of corresponding blocks; The relay nodes in the corresponding areas perform classification processing on the received goods information data, warehouse data and transportation data, analyze the classification processing results and respectively generate warehouse data storage schemes; The relay node screens the data storage nodes according to the bin allocation data storage scheme, and transmits the data to a central server or the data storage nodes for storage according to the bin allocation data storage scheme; the central server collects the safety monitoring log data of each data storage node, predicts the safety coefficient of the data storage node, and backups or deletes the data stored in the data storage node according to the prediction result.
2. The bin allocation data optimized storage method based on the internet of things according to claim 1, wherein the cargo information data, the storage data and the transportation data of cargoes are collected through the data collection node, and the method comprises the following steps: respectively defining message formats of cargo information data, storage data and transportation data through data acquisition nodes, wherein the message formats comprise fields and data structures; and packaging the goods information data, the warehouse data and the transportation data into corresponding message formats respectively, and issuing the corresponding message formats into corresponding message channels.
3. The bin allocation data optimized storage method based on the internet of things according to claim 2, wherein the relay nodes in the corresponding areas perform classification processing on the received cargo information data, the stored data and the transportation data, and the method comprises the following steps: the relay node subscribes to corresponding message channels respectively aiming at different types of data of the data acquisition nodes in the blocks; The relay node performs preliminary processing on the received goods information data, warehouse data and transportation data, and comprises data cleaning and data formatting; adding a cargo number field of cargo information data into message formats of storage data and transportation data, and setting the cargo number as a common field among the cargo information data, storage data and transportation data of cargoes in the same batch; Identifying message formats of the goods information data, the storage data and the transportation data, storing the goods information data into a goods information database table, storing the storage data into a storage data database table, and storing the transportation data into a transportation data database table; establishing association relations among cargo information data, warehouse data and transportation data in different database tables through common fields to form complete warehouse allocation data; For cargo data lacking cargo information data in the bin allocation data in the relay node, storing the storage data or the transportation data in the bin allocation data into a cache database; For the lack of warehouse data or transportation data in warehouse data in the relay node, sending a data query message to other partitioned relay nodes through a cargo number field in cargo information data; and the other partitioned relay nodes search the data stored in the cache database according to the goods number field and send the searched warehouse data or transport data to the relay node which sends the data query message.
4. The optimal storage method of warehouse allocation data based on the internet of things according to claim 3, wherein the message format of the cargo information data is a field containing a cargo number, a cargo type, a number and a cargo preservation condition, the message format of the warehouse data is a field containing a cargo number, a warehouse location and a warehouse remaining capacity, and the message format of the transportation data is a field containing a cargo number, a transportation time, a transportation temperature and a transportation humidity.
5. The method for optimizing and storing the bin allocation data based on the internet of things according to claim 3, wherein the analysis of the classification processing results respectively generates a bin allocation data storage scheme, and the method comprises the following steps: Acquiring complete cabin allocation data, and analyzing the safety requirement degree of the cargo cabin allocation data through cargo information data in a cargo information database table; And generating a warehouse data storage scheme according to the analysis result of the safety demand of the cargo warehouse data.
6. The optimal storage method of the bin allocation data based on the internet of things according to claim 5, wherein the safety requirement of the cargo bin allocation data is analyzed through cargo preservation conditions in cargo information data in a cargo information database table by the following formula: Wherein Q is the initial safety demand of the cargo warehouse allocation data, T is the specified special storage temperature in the cargo storage condition, P is the specified special storage pressure in the cargo storage condition, T0 is the preset conventional cargo storage temperature, and P0 is the preset conventional cargo storage pressure; the system comprises a distribution personnel, a backup policy coefficient, a alpha, beta and gamma, wherein the SR is the safety requirement degree of the data of the cargo warehouse, the Ku is a data importance level, the distribution personnel is divided into three levels according to the cargo value and the timeliness, the common importance, the medium importance and the high importance are assigned to different importance, the B is the backup policy coefficient, the backup policy coefficient takes a value of 1 to indicate that the backup is needed, the backup policy coefficient takes a value of 0 to indicate that the backup is deletable, and the alpha, the beta and the gamma are weight coefficients.
7. The optimal storage method of the cabin allocation data based on the internet of things according to claim 5, wherein the method is characterized in that according to the analysis result of the safety demand of the cargo cabin allocation data, a cabin allocation data storage scheme is generated, and the method comprises the following steps: Normalizing the safety demand of the cargo warehouse data to obtain cargo information data corresponding to the safety demand greater than or equal to a threshold value, and warehouse data and transportation data associated with the cargo information data, wherein the generation warehouse data storage scheme comprises the steps of uploading the warehouse data to a central server; And (3) transmitting the goods information data corresponding to the safety demand degree smaller than the threshold value and the storage data and the transportation data related to the goods information data to a data storage node for storage.
8. The method for optimizing and storing the bin allocation data based on the internet of things according to claim 7, wherein the relay node screens the data storage nodes according to the bin allocation data storage scheme and transmits the data to the central server or the data storage nodes for storage according to the bin allocation data storage scheme, comprising the following steps: The relay node acquires a bin allocation data storage scheme, and if the bin allocation data storage scheme is that bin allocation data is transmitted to the data storage node for storage; acquiring distance data from each data storage node to the relay node, sorting from the near to the far according to the distance between each data storage node and the relay node, and selecting the first 50% of data storage nodes as candidate nodes; the storage fitness of the candidate node is evaluated by the following formula: Wherein K is an evaluation value of storage fitness of the candidate node, alpha is a storage quantity importance coefficient, D is an available storage quantity of the candidate node, D is a maximum storage quantity of the candidate node, L is a distance from the candidate node to the relay node, L 0 is an average value of distances between the candidate node and the relay node, and L max is a maximum value of distances between the candidate node and the relay node; Screening data storage nodes with storage adaptability evaluation values of the candidate nodes larger than a preset threshold according to the evaluation result; if the bin allocation data storage scheme is that bin allocation data is uploaded to a central server, the bin allocation data is uploaded to the central server; if the bin allocation data storage scheme is that the bin allocation data is transmitted to the data storage nodes for storage, the bin allocation data is transmitted to the screened data storage nodes for storage.
9. The bin allocation data optimizing and storing method based on the internet of things according to claim 8, wherein the central server collects security monitoring log data of each data storage node, predicts security coefficients of the data storage nodes, and backups or deletes data stored in the data storage nodes according to a prediction result, comprising the following steps: The central server collects the safety monitoring log data of each data storage node, divides the monitoring time period of the data storage node, predicts the safety coefficient of the data storage node after the monitoring time period of each data storage node is ended, and is carried out according to the following formula: Wherein, W is the predicted value of the security coefficient of the data storage node, K 0 is the estimated value of the storage fitness of the data storage node as the candidate node in the monitoring time period, beta is the storage fitness coefficient of the data storage node, E is the number of times the data storage node generates error logs in the monitoring time period, E 0 is the average value of the number of times the data storage node generates error logs in the monitoring time period, E max is the maximum value of the number of times the data storage node generates error logs in the monitoring time period, J is the number of times the data storage node is attacked by the network in the monitoring time period, J 0 is the average value of the number of times the data storage node is attacked by the network in the monitoring time period, and J max is the maximum value of the number of times the data storage node is attacked by the network in the monitoring time period; according to the calculated predicted value of the safety coefficient of the data storage node; if the predicted value of the safety coefficient of the data storage node is smaller than a preset threshold value, acquiring the data acquisition time of the bin allocation data stored in the data storage node, deleting the data of which the data acquisition time is earlier than that of the preset time node, locating the data acquisition time of the bin allocation data at the preset time node and backing up the data after the preset time node to a relay node in a block where the data storage node is located; Otherwise, the data stored in the data storage node does not need to be backed up or deleted.
10. Storehouse is joined in marriage data optimization storage system based on thing networking, its characterized in that includes: The data acquisition module is used for arranging a plurality of data acquisition nodes in a cargo discharging area, a storage area and a distribution transfer area of cargoes, arranging corresponding data storage nodes in each data acquisition node, and acquiring cargo information data, storage data and transportation data of the cargoes through the data acquisition nodes; The data summarizing module is used for partitioning the shipment area, the storage area and the distribution transfer area according to geographic positions, wherein each partition is provided with a relay node respectively, and the data acquisition nodes of the shipment area, the storage area and the distribution transfer area summarize acquired data to the relay nodes of the corresponding partition; The data classification module is used for classifying the received cargo information data, the warehouse data and the transportation data by the relay nodes in the corresponding areas, analyzing the classification result and respectively generating a warehouse data storage scheme; The relay node screens the data storage nodes according to the bin allocation data storage scheme and transmits the data to the central server or the data storage nodes for storage according to the bin allocation data storage scheme; And the data maintenance module is used for collecting the safety monitoring log data of each data storage node by the central server, predicting the safety coefficient of the data storage node, and backing up or deleting the data stored in the data storage node according to the prediction result.

Description

Bin allocation data optimized storage method and system based on internet of things Technical Field The invention belongs to the field of data storage, and particularly relates to a bin allocation data optimal storage method and system based on the Internet of things. Background With the development of electronic commerce and supply chain management, the data volume of the warehouse industry has increased dramatically, and the security, integrity and availability of data have become important challenges. The traditional data management method often faces the problems of data dispersion, insufficient security, low management efficiency and the like. The popularization and application of the internet of things provide a new approach for solving the problems. The bin allocation data optimized storage technology based on the Internet of things is a technology for effectively managing and optimizing storage of a large amount of data generated in a bin allocation and distribution link by utilizing the Internet of things technology. In the traditional warehouse matching links, a plurality of links such as order processing, inventory management, goods tracking and the like are involved, and massive data including real-time monitoring data, transaction records, inventory information and the like are generated. The bin allocation data optimizing and storing system based on the Internet of things utilizes the Internet of things equipment such as sensors, RFID technology and the like to monitor and collect data of the bin allocation and allocation links in real time. In the existing bin allocation data optimizing and storing technology, after data is uploaded, a central server plans the data storage, and then the data is sent to a corresponding storage position for storage, so that the data is repeatedly transmitted, the data quantity transmitted to the central server is increased, the risks of delay of data transmission and network congestion are easily caused, and the data processing efficiency is reduced. Disclosure of Invention The invention aims to at least solve one of the technical problems in the prior art, and therefore, the invention provides a bin allocation data optimizing and storing method and system based on the Internet of things, which are used for solving the technical problems that the repeated transmission of data is realized, the data quantity transmitted to a central server is increased, the risks of data transmission delay and network congestion are easily caused, and the data processing efficiency is reduced. In order to solve the above problems, a first aspect of the present invention provides a bin allocation data optimization storage method based on the internet of things, comprising the following steps: A plurality of data acquisition nodes are arranged in a cargo discharging area, a storage area and a delivery transfer area of cargoes, each data acquisition node is provided with a corresponding data storage node, and cargo information data, storage data and transportation data of cargoes are acquired through the data acquisition nodes; Dividing a shipment area, a storage area and a distribution transfer area into blocks according to geographic positions, wherein each block is provided with a relay node respectively, and data acquisition nodes of the shipment area, the storage area and the distribution transfer area gather acquired data to relay nodes of corresponding blocks; The relay nodes in the corresponding areas perform classification processing on the received goods information data, warehouse data and transportation data, analyze the classification processing results and respectively generate warehouse data storage schemes; The relay node screens the data storage nodes according to the bin allocation data storage scheme, and transmits the data to a central server or the data storage nodes for storage according to the bin allocation data storage scheme; the central server collects the safety monitoring log data of each data storage node, predicts the safety coefficient of the data storage node, and backups or deletes the data stored in the data storage node according to the prediction result. The invention further provides a method for acquiring cargo information data, warehouse data and transportation data of cargoes through a data acquisition node, comprising the following steps: respectively defining message formats of cargo information data, storage data and transportation data through data acquisition nodes, wherein the message formats comprise fields and data structures; and packaging the goods information data, the warehouse data and the transportation data into corresponding message formats respectively, and issuing the corresponding message formats into corresponding message channels. The invention further provides a method for classifying the received goods information data, warehouse data and transportation data by the relay nodes in the corresponding areas, comprising the