Warehouse distribution data optimization storage method and system based on Internet of Things

Abstract

The invention discloses a warehouse distribution data optimization storage method and system based on the Internet of Things, relates to the technical field of data storage, and solves the technical problems that data is repeatedly transmitted, the amount of data 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. Comprising the following steps that a plurality of data acquisition nodes are arranged in a shipment area, a storage area and a distribution transfer area of goods, each data acquisition node is provided with a corresponding data storage node, and goods information data, storage data and transportation data of the goods are acquired through the data acquisition nodes; according to the invention, the relay node can perform classification processing immediately after receiving the data, so that ordered storage of the data is ensured, and the situation that the data is sent to the edge node to be stored after being uploaded is avoided, thereby avoiding repeated transmission of the data, reducing the amount of data transmitted to the central server, and improving the data processing efficiency.

Description

Technical Field

[0001] This invention belongs to the field of data storage, specifically a method and system for optimizing the storage of warehousing and distribution data based on the Internet of Things. Background Technology

[0002] With the development of e-commerce and supply chain management, the amount of data in the warehousing and distribution industry has increased dramatically, making data security, integrity, and availability significant challenges. Traditional data management methods often suffer from problems such as data fragmentation, insufficient security, and low management efficiency. The widespread adoption and application of IoT technology offers a new approach to solving these problems.

[0003] IoT-based warehousing and distribution data optimization and storage technology refers to the technology of effectively managing and optimizing the storage of large amounts of data generated in the warehousing and distribution process using IoT technology. Traditional warehousing and distribution involves multiple stages such as order processing, inventory management, and cargo tracking, generating massive amounts of data, including real-time monitoring data, transaction records, and inventory information. IoT-based warehousing and distribution data optimization and storage systems utilize IoT devices such as sensors and RFID technology to monitor and collect data from the warehousing and distribution process in real time.

[0004] Existing warehousing and distribution data optimization and storage technologies typically involve uploading data, having a central server plan the data storage, and then sending the data to the corresponding storage location. This results in repeated data transmission, increases the amount of data transmitted to the central server, and is prone to data transmission delays and network congestion, thus reducing data processing efficiency. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes an optimized storage method and system for warehousing and distribution data based on the Internet of Things, which is used to solve the technical problem that repeated data transmission increases the amount of data transmitted to the central server, easily causes data transmission delay and network congestion risks, and reduces data processing efficiency.

[0006] To address the above problems, a first aspect of the present invention provides a method for optimizing the storage of warehousing and distribution data based on the Internet of Things, comprising the following steps: Several data collection nodes are set up in the cargo shipping area, storage area and distribution transfer area. Each data collection node is equipped with a corresponding data storage node. Cargo information data, storage data and transportation data are collected through the data collection nodes. The shipping area, warehousing area, and distribution transit area are divided into blocks according to geographical location. Each block is set up with a relay node. The data collection nodes of the shipping area, warehousing area, and distribution transit area will aggregate the collected data to the relay node of the corresponding block. The relay nodes in the corresponding areas classify the received cargo information data, warehousing data, and transportation data, and analyze the results of the classification to generate warehousing and distribution data storage solutions. The relay node will select data storage nodes according to the warehousing and distribution data storage plan, and transmit the data to the central server or data storage node for storage according to the warehousing and distribution data storage plan. The central server collects security monitoring log data from each data storage node, predicts the security level of the data storage node, and backs up or deletes the data stored in the data storage node based on the prediction results.

[0007] As a further aspect of the present invention: collecting cargo information data, warehousing data, and transportation data through data acquisition nodes includes the following steps: The message formats for cargo information data, warehousing data, and transportation data are defined separately through data acquisition nodes, including fields and data structures; Cargo information data, warehousing data, and transportation data are encapsulated into corresponding message formats and published to the appropriate message channels.

[0008] As a further aspect of the present invention: the relay nodes in the corresponding regions classify the received cargo information data, warehousing data, and transportation data, including the following steps: Relay nodes subscribe to corresponding message channels for different types of data from data collection nodes within a block; The relay node performs preliminary processing on the received cargo information data, warehousing data, and transportation data, including data cleaning and data formatting. Add a cargo number field to the message format of cargo information data for warehousing and transportation data, and set the cargo number as a common field among cargo information data, warehousing data and transportation data for the same batch of goods; Identify the message formats of cargo information data, warehousing data, and transportation data; store cargo information data in a cargo information database table; store warehousing data in a warehousing data database table; and store transportation data in a transportation data database table. By establishing common fields, relationships are created between cargo information data, warehousing data, and transportation data in different database tables, forming complete warehousing and distribution data. For cargo data lacking cargo information in the warehousing and distribution data of the relay node, the warehousing data or transportation data in the warehousing and distribution data will be stored in the cache database; If the warehousing or transportation data in the warehousing and distribution data of the relay node is missing, a data query message is sent to the relay nodes of other blocks through the cargo number field in the cargo information data. Other relay nodes retrieve data stored in the cache database based on the cargo number field, and send the retrieved warehousing or transportation data to the relay node that sent the data query message.

[0009] As a further aspect of the present invention: the message format of cargo information data includes fields containing cargo number, cargo type, quantity and cargo storage conditions; the message format of warehousing data includes fields containing cargo number, warehouse location and warehouse remaining capacity; and the message format of transportation data includes fields containing cargo number, transportation time, transportation temperature and transportation humidity.

[0010] As a further aspect of the present invention: the results of the classification process are analyzed to generate separate warehousing and distribution data storage schemes, including the following steps: Obtain complete warehousing and distribution data, and analyze the security requirements of the warehousing and distribution data through the cargo information data in the cargo information database table; Based on the analysis results of the security requirements of cargo warehousing and distribution data, a warehousing and distribution data storage plan is generated.

[0011] As a further aspect of the present invention: based on the analysis results of the security requirements of cargo warehousing and distribution data, a warehousing and distribution data storage scheme is generated, including the following steps: The security requirements of the cargo warehousing and distribution data are normalized to obtain cargo information data with security requirements greater than or equal to the threshold, as well as the warehousing and transportation data associated with the cargo information data. The resulting warehousing and distribution data storage scheme is to upload the warehousing and distribution data to the central server. For goods information data where the safety requirement is less than the threshold, along with associated warehousing and transportation data, the resulting warehousing and distribution data storage solution is to transmit the warehousing and distribution data to a data storage node for storage.

[0012] As a further aspect of the present invention: the relay node will select data storage nodes according to the warehousing and distribution data storage scheme, and transmit the data to the central server or data storage node for storage according to the warehousing and distribution data storage scheme, including the following steps: The relay node obtains the warehousing and distribution data storage plan. If the warehousing and distribution data storage plan is to transmit the warehousing and distribution data to the data storage node for storage; Obtain the distance data from each data storage node to the relay node, sort them from closest to furthest from the relay node, and select the top 50% of data storage nodes as candidate nodes; The storage fitness of candidate nodes is evaluated using the following formula: Where K is the storage fitness evaluation value of the candidate node, α is the storage importance coefficient, d is the available storage of the candidate node, D is the maximum storage of the candidate node, L is the distance from the candidate node to the relay node, L0 is the average distance between the candidate node and the relay node, and L... max This represents the maximum distance between the candidate node and the relay node. Based on the evaluation results, data storage nodes with storage fitness evaluation values ​​greater than a preset threshold are selected as candidate nodes; If the warehousing and distribution data storage solution is to upload the warehousing and distribution data to the central server, then the warehousing and distribution data will be uploaded to the central server. If the warehousing and distribution data storage solution is to transmit the warehousing and distribution data to the data storage node for storage, then the warehousing and distribution data will be transmitted to the selected data storage node for storage.

[0013] As a further aspect of the present invention: the central server collects security monitoring log data from each data storage node, predicts the security coefficient of the data storage node, and backs up or deletes the data stored in the data storage node based on the prediction results, including the following steps: The central server collects security monitoring log data from each data storage node and divides the monitoring time period for each data storage node. After the monitoring time period ends for each data storage node, the security coefficient of the data storage node is predicted using the following formula: Where W is the predicted security coefficient of the data storage node, K0 is the evaluation value of the storage fitness of the candidate node obtained by using the data storage node as a candidate node during the monitoring period, β is the storage fitness coefficient of the data storage node, E is the number of times the data storage node generates error logs during the monitoring period, E0 is the average number of times all data storage nodes generate error logs during the monitoring period, and E max J represents the maximum number of error logs generated by all data storage nodes during the monitoring period; J0 represents the average number of network attacks on all data storage nodes during the monitoring period. max This is the maximum number of network attacks suffered by all data storage nodes within the monitoring period. Based on the predicted value of the security coefficient of the data storage node obtained from the calculation; If the predicted value of the security coefficient of the data storage node is less than the preset threshold, obtain the data collection time of the warehousing and distribution data stored in the data storage node, delete the data whose data collection time is earlier than the preset time node, and back up the data whose data collection time is at the preset time node and after the preset time node to the relay node in the block where the data storage node is located. Otherwise, there is no need to back up or delete the data stored in the data storage node.

[0014] As another aspect of the present invention: a warehouse and distribution data optimization and storage system based on the Internet of Things, comprising: Data acquisition module: Several data acquisition nodes are set up in the cargo shipping area, storage area and distribution transfer area. Each data acquisition node is equipped with a corresponding data storage node. The cargo information data, storage data and transportation data are collected through the data acquisition nodes. Data aggregation module: The shipping area, warehousing area and delivery transit area are divided into blocks according to geographical location. Each block is set up with a relay node. The data collection nodes of the shipping area, warehousing area and delivery transit area will aggregate the collected data to the relay node of the corresponding block. Data classification module: The relay nodes in the corresponding area classify the received cargo information data, warehousing data and transportation data, analyze the classification results, and generate warehousing and distribution data storage solutions respectively. Data storage and distribution module: The relay node will select data storage nodes according to the warehousing and distribution data storage plan, and transmit the data to the central server or data storage nodes for storage according to the warehousing and distribution data storage plan; Data maintenance module: The central server collects security monitoring log data from each data storage node, predicts the security level of the data storage node, and backs up or deletes the data stored in the data storage node based on the prediction results.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention reduces data transmission distance and mitigates data transmission latency and network congestion risks by dividing the system into blocks and setting up relay nodes. Simultaneously, it enables data processing and transmission within localized areas, reducing the overall system load and improving data processing efficiency. Furthermore, the localization of data during block division and transmission reduces the risk of attacks on the overall system and enhances data security. The block division and relay node setup provides the system with a degree of fault tolerance; even if a block or node fails, it will not affect the operation of the entire system.

[0016] This invention uses relay nodes in corresponding regions to classify and process received cargo information data, warehousing data, and transportation data, and analyzes the classification results to generate separate warehousing and distribution data storage schemes. The relay nodes then select data storage nodes based on these schemes and transmit the data to a central server or data storage node for storage. Relay nodes can classify data immediately upon receipt, ensuring orderly data storage and avoiding repeated transmissions of data to edge nodes after uploading. This reduces the amount of data transmitted to the central server and improves data processing efficiency. Furthermore, only classified warehousing and distribution data is transmitted to the central server or data storage node, reducing network transmission pressure and the risk of network congestion. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the system framework of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figures 1-2 The first aspect of this invention provides a method for optimizing the storage of warehousing and distribution data based on the Internet of Things, comprising the following steps: Several data collection nodes are set up in the cargo shipping area, storage area and distribution transfer area. Each data collection node is equipped with a corresponding data storage node. Cargo information data, storage data and transportation data are collected through the data collection nodes. The shipping area, warehousing area, and distribution transit area are divided into blocks according to geographical location. Each block is set up with a relay node. The data collection nodes of the shipping area, warehousing area, and distribution transit area will aggregate the collected data to the relay node of the corresponding block. The relay nodes in the corresponding areas classify the received cargo information data, warehousing data, and transportation data, and analyze the results of the classification to generate warehousing and distribution data storage solutions. The relay node will select data storage nodes according to the warehousing and distribution data storage plan, and transmit the data to the central server or data storage node for storage according to the warehousing and distribution data storage plan. The central server collects security monitoring log data from each data storage node, predicts the security level of the data storage node, and backs up or deletes the data stored in the data storage node based on the prediction results.

[0021] Specifically, in this embodiment, several data collection nodes are set up in the cargo shipping area, warehousing area, and distribution transit area. Each data collection node has a corresponding data storage node. These nodes collect cargo information, warehousing data, and transportation data. The shipping area, warehousing area, and distribution transit area are divided into blocks according to geographical location, and each block has a relay node. The data collection nodes in these areas aggregate the collected data to the corresponding block's relay node. By dividing the area into blocks and setting relay nodes, the data transmission distance is reduced, lowering the risk of data transmission latency and network congestion. Simultaneously, data is processed and transmitted locally, reducing the overall system load and improving data processing efficiency. The data is more localized during the block division and transmission process, reducing the risk of attacks on the overall system and improving data security. By dividing the data into blocks and setting relay nodes, edge computing resources can be better utilized, reducing dependence on cloud resources and improving the overall system's resource utilization. The block division and relay node setup gives the system a certain degree of fault tolerance; even if a block or node fails, it will not affect the operation of the entire system.

[0022] The received cargo information, warehousing data, and transportation data are categorized and processed by relay nodes in the corresponding regions. The results of the categorization and processing are analyzed to generate separate warehousing and distribution data storage plans. The relay nodes then select data storage nodes according to the warehousing and distribution data storage plans and transmit the data to the central server or data storage nodes for storage. The relay nodes can immediately categorize and process the data upon receipt, ensuring that the data is stored in an orderly manner. This avoids data being uploaded and then sent to edge nodes for storage, thereby avoiding repeated data transmission, reducing the amount of data transmitted to the central server, and improving data processing efficiency. At the same time, only the categorized warehousing and distribution data is transmitted to the central server or data storage nodes, reducing network transmission pressure and the risk of network congestion.

[0023] By collecting security monitoring logs from each data storage node through a central server, the security level of each node is predicted. Based on the prediction results, data stored in these nodes is backed up or deleted. Analysis and prediction of security monitoring log data facilitates the timely identification of potential security threats, effectively protecting data security and reducing the risk of data leakage and damage. Simultaneously, based on the prediction results, targeted data backup or deletion can be performed, optimizing data management and storage, and ensuring data integrity and availability. By predicting security levels and taking corresponding data management measures, the potential costs associated with security incidents can be reduced, including costs related to data recovery, legal proceedings, and reputational damage.

[0024] In one embodiment of the present invention, the collection of cargo information data, warehousing data, and transportation data through data acquisition nodes includes the following steps: The message formats for cargo information data, warehousing data, and transportation data are defined separately through data acquisition nodes, including fields and data structures; Cargo information data, warehousing data, and transportation data are encapsulated into corresponding message formats and published to the appropriate message channels.

[0025] In one embodiment of the present invention, the relay node in the corresponding region performs classification processing on the received cargo information data, warehousing data, and transportation data, including the following steps: Relay nodes subscribe to corresponding message channels for different types of data from data collection nodes within a block; The relay node performs preliminary processing on the received cargo information data, warehousing data, and transportation data, including data cleaning and data formatting. Add a cargo number field to the message format of cargo information data for warehousing and transportation data, and set the cargo number as a common field among cargo information data, warehousing data and transportation data for the same batch of goods; Identify the message formats of cargo information data, warehousing data, and transportation data; store cargo information data in a cargo information database table; store warehousing data in a warehousing data database table; and store transportation data in a transportation data database table. By establishing common fields, relationships are created between cargo information data, warehousing data, and transportation data in different database tables, forming complete warehousing and distribution data. For cargo data lacking cargo information in the warehousing and distribution data of the relay node, the warehousing data or transportation data in the warehousing and distribution data will be stored in the cache database; If the warehousing or transportation data in the warehousing and distribution data of the relay node is missing, a data query message is sent to the relay nodes of other blocks through the cargo number field in the cargo information data. Other relay nodes retrieve data stored in the cache database based on the cargo number field, and send the retrieved warehousing or transportation data to the relay node that sent the data query message.

[0026] Specifically, for goods lacking cargo information in the warehousing and distribution data of relay nodes, the warehousing or transportation data is stored in a cache database. For goods lacking warehousing or transportation data in the warehousing and distribution data of relay nodes, a data query message is sent to other relay nodes using the cargo number field in the cargo information data. Other relay nodes retrieve the data stored in the cache database based on the cargo number field and send the retrieved warehousing or transportation data to the relay node that sent the data query message. Through these relay nodes, the data can undergo preliminary processing, and the data can be aggregated and planned at the relay nodes. This reduces the data transmission distance, lowers the risk of network congestion, and ensures that only the complete, categorized warehousing and distribution data is transmitted to the central server or data storage node, reducing network transmission pressure and the risk of data corruption.

[0027] In one embodiment of the present invention, the message format of cargo information data includes fields containing cargo number, cargo type, quantity and cargo storage conditions; the message format of warehousing data includes fields containing cargo number, warehouse location and warehouse remaining capacity; and the message format of transportation data includes fields containing cargo number, transportation time, transportation temperature and transportation humidity.

[0028] In one embodiment of the present invention, the results of the classification process are analyzed to generate warehousing and distribution data storage schemes, including the following steps: Obtain complete warehousing and distribution data, and analyze the security requirements of the warehousing and distribution data through the cargo information data in the cargo information database table; Based on the analysis results of the security requirements of cargo warehousing and distribution data, a warehousing and distribution data storage plan is generated.

[0029] In one embodiment of the present invention, the security requirements of cargo warehousing and distribution data are analyzed based on the cargo storage conditions in the cargo information database table using the following formula: Where Q is the initial security requirement of the cargo warehousing and distribution data, T is the special storage temperature specified in the cargo storage conditions, P is the special storage pressure specified in the cargo storage conditions, T0 is the preset normal cargo storage temperature, and P0 is the preset normal cargo storage pressure. SR represents the security requirement of the cargo warehousing and distribution data, predicted by the central server through security monitoring log data (such as abnormal access frequency, encryption strength, etc.). Ku represents the data importance level, which is divided into three levels—ordinary importance, medium importance, and high importance—by delivery personnel based on the value and timeliness of the goods. Different importance levels are assigned values. In this embodiment, the data importance level of ordinary importance is assigned a value of 2, the data importance level of medium importance is assigned a value of 3, and the data importance level of high importance is assigned a value of 4. B represents the backup strategy coefficient, where a backup strategy coefficient of 1 indicates that backup is required, and a backup strategy coefficient of 0 indicates that deletion is permissible. α, β, and γ are weighting coefficients (in this embodiment, α=0.5, β=0.3, γ=0.2), which can be adjusted according to business priorities.

[0030] In one embodiment of the present invention, a warehousing and distribution data storage scheme is generated based on the analysis results of the security requirements of the cargo warehousing and distribution data, including the following steps: The security requirements of the cargo warehousing and distribution data are normalized to obtain cargo information data with security requirements greater than or equal to the threshold, as well as the warehousing and transportation data associated with the cargo information data. The resulting warehousing and distribution data storage scheme is to upload the warehousing and distribution data to the central server. For cargo information data with a safety requirement level below a threshold, along with associated warehousing and transportation data, a warehousing and distribution data storage solution is generated: the warehousing and distribution data is transmitted to a data storage node for storage. In this embodiment, the safety requirement level threshold is set to 0.5.

[0031] In one embodiment of the present invention, the relay node will select data storage nodes according to the warehousing and distribution data storage scheme, and transmit the data to the central server or data storage node for storage according to the warehousing and distribution data storage scheme, including the following steps: The relay node obtains the warehousing and distribution data storage plan. If the warehousing and distribution data storage plan is to transmit the warehousing and distribution data to the data storage node for storage; Obtain the distance data from each data storage node to the relay node, sort them from closest to furthest from the relay node, and select the top 50% of data storage nodes as candidate nodes; The storage fitness of candidate nodes is evaluated using the following formula: Where K is the storage fitness evaluation value of the candidate node, α is the storage importance coefficient, d is the available storage of the candidate node, D is the maximum storage of the candidate node, L is the distance from the candidate node to the relay node, L0 is the average distance between the candidate node and the relay node, and L... max This represents the maximum distance between the candidate node and the relay node. Based on the evaluation results, data storage nodes with storage fitness evaluation values ​​greater than a preset threshold are selected as candidate nodes; If the warehousing and distribution data storage solution is to upload the warehousing and distribution data to the central server, then the warehousing and distribution data will be uploaded to the central server. If the warehousing and distribution data storage solution is to transmit the warehousing and distribution data to the data storage node for storage, then the warehousing and distribution data will be transmitted to the selected data storage node for storage.

[0032] Specifically, the importance coefficient α of storage quantity can be set according to requirements. In this embodiment, the importance coefficient α of storage quantity is set to 1.2. Meanwhile, when the relay node selects data storage nodes according to the warehousing and distribution data storage plan, it selects data storage nodes with a storage fitness evaluation value greater than 1 for the candidate nodes. If the warehousing and distribution data storage solution is to upload the warehousing and distribution data to the central server, then the warehousing and distribution data will be uploaded to the central server. If the warehousing and distribution data storage solution is to transmit the warehousing and distribution data to the data storage node for storage, then the warehousing and distribution data will be transmitted to the selected data storage node for storage.

[0033] In one embodiment of the present invention, the central server collects security monitoring log data from each data storage node, predicts the security coefficient of the data storage node, and backs up or deletes the data stored in the data storage node based on the prediction result, including the following steps: The central server collects security monitoring log data from each data storage node and divides the monitoring time period for each data storage node. After the monitoring time period ends for each data storage node, the security coefficient of the data storage node is predicted using the following formula: Where W is the predicted security coefficient of the data storage node, K0 is the evaluation value of the storage fitness of the candidate node obtained by using the data storage node as a candidate node during the monitoring period, β is the storage fitness coefficient of the data storage node, E is the number of times the data storage node generates error logs during the monitoring period, E0 is the average number of times all data storage nodes generate error logs during the monitoring period, and E max J represents the maximum number of error logs generated by all data storage nodes during the monitoring period; J0 represents the average number of network attacks on all data storage nodes during the monitoring period. max This is the maximum number of network attacks suffered by all data storage nodes within the monitoring period. Based on the predicted value of the security coefficient of the data storage node obtained from the calculation; If the predicted value of the security coefficient of the data storage node is less than the preset threshold, obtain the data collection time of the warehousing and distribution data stored in the data storage node, delete the data whose data collection time is earlier than the preset time node, and back up the data whose data collection time is at the preset time node and after the preset time node to the relay node in the block where the data storage node is located. Otherwise, there is no need to back up or delete the data stored in the data storage node.

[0034] Specifically, the storage fitness coefficient β of the data storage node can be set according to requirements. In this embodiment, the storage fitness coefficient β of the data storage node is set to 0.5. If the data storage node has not been used as a candidate node during the monitoring period and the evaluation value of the storage fitness of the candidate node has not been calculated, then K here is 0. Meanwhile, in this embodiment, if the predicted value of the security coefficient of the data storage node is less than -3, the data collection time of the warehousing and distribution data stored in the data storage node is obtained, the data whose data collection time is earlier than the preset time node is deleted, and the data whose data collection time is at the preset time node and after the preset time node is backed up to the relay node in the block where the data storage node is located. Otherwise, there is no need to back up or delete the data stored in the data storage node.

[0035] As another embodiment of the present invention, an IoT-based warehouse distribution data optimization and storage system is provided, comprising: Data acquisition module: Several data acquisition nodes are set up in the cargo shipping area, storage area and distribution transfer area. Each data acquisition node is equipped with a corresponding data storage node. The cargo information data, storage data and transportation data are collected through the data acquisition nodes. Data aggregation module: The shipping area, warehousing area and delivery transit area are divided into blocks according to geographical location. Each block is set up with a relay node. The data collection nodes of the shipping area, warehousing area and delivery transit area will aggregate the collected data to the relay node of the corresponding block. Data classification module: The relay nodes in the corresponding area classify the received cargo information data, warehousing data and transportation data, analyze the classification results, and generate warehousing and distribution data storage solutions respectively. Data storage and distribution module: The relay node will select data storage nodes according to the warehousing and distribution data storage plan, and transmit the data to the central server or data storage nodes for storage according to the warehousing and distribution data storage plan; Data maintenance module: The central server collects security monitoring log data from each data storage node, predicts the security level of the data storage node, and backs up or deletes the data stored in the data storage node based on the prediction results.

[0036] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for optimizing the storage of warehousing and distribution data based on the Internet of Things, characterized in that, Includes the following steps: Several data collection nodes are set up in the cargo shipping area, storage area and distribution transfer area. Each data collection node is equipped with a corresponding data storage node. Cargo information data, storage data and transportation data are collected through the data collection nodes. The shipping area, warehousing area, and distribution transit area are divided into blocks according to geographical location. Each block is set up with a relay node. The data collection nodes of the shipping area, warehousing area, and distribution transit area will aggregate the collected data to the relay node of the corresponding block. The relay nodes in the corresponding areas classify the received cargo information data, warehousing data, and transportation data, and analyze the results of the classification to generate warehousing and distribution data storage solutions. The relay node will select data storage nodes according to the warehousing and distribution data storage plan, and transmit the data to the central server or data storage node for storage according to the warehousing and distribution data storage plan. The central server collects security monitoring log data from each data storage node, predicts the security level of the data storage node, and backs up or deletes the data stored in the data storage node based on the prediction results.

2. The method for optimizing warehousing and distribution data storage based on the Internet of Things according to claim 1, characterized in that, The process of collecting cargo information, warehousing data, and transportation data through data collection nodes includes the following steps: The message formats for cargo information data, warehousing data, and transportation data are defined separately by the data acquisition nodes, including fields and data structures; Cargo information data, warehousing data, and transportation data are encapsulated into corresponding message formats and published to the appropriate message channels.

3. The method for optimizing warehousing and distribution data storage based on the Internet of Things according to claim 2, characterized in that, The relay nodes in the corresponding area classify and process the received cargo information data, warehousing data, and transportation data, including the following steps: Relay nodes subscribe to corresponding message channels for different types of data from data collection nodes within a block; The relay node performs preliminary processing on the received cargo information data, warehousing data, and transportation data, including data cleaning and data formatting. Add a cargo number field to the message format of cargo information data for warehousing and transportation data, and set the cargo number as a common field among cargo information data, warehousing data and transportation data for the same batch of goods; Identify the message formats of cargo information data, warehousing data, and transportation data; store cargo information data in a cargo information database table; store warehousing data in a warehousing data database table; and store transportation data in a transportation data database table. By establishing common fields, relationships are created between cargo information data, warehousing data, and transportation data in different database tables, forming complete warehousing and distribution data. For cargo data lacking cargo information in the warehousing and distribution data of the relay node, the warehousing data or transportation data in the warehousing and distribution data will be stored in the cache database; If the warehousing or transportation data in the warehousing and distribution data of the relay node is missing, a data query message is sent to the relay nodes of other blocks through the cargo number field in the cargo information data. Other relay nodes retrieve data stored in the cache database based on the cargo number field, and send the retrieved warehousing or transportation data to the relay node that sent the data query message.

4. The method for optimizing the storage of warehousing and distribution data based on the Internet of Things according to claim 3, characterized in that, The message format for cargo information data includes fields for cargo number, cargo type, quantity, and cargo storage conditions. The message format for warehousing data includes fields for cargo number, warehouse location, and remaining warehouse capacity. The message format for transportation data includes fields for cargo number, transportation time, transportation temperature, and transportation humidity.

5. The method for optimizing the storage of warehousing and distribution data based on the Internet of Things according to claim 3, characterized in that, The results of the classification process are analyzed, and separate warehousing and distribution data storage solutions are generated, including the following steps: Obtain complete warehousing and distribution data, and analyze the security requirements of the warehousing and distribution data through the cargo information data in the cargo information database table; Based on the analysis results of the security requirements of cargo warehousing and distribution data, a warehousing and distribution data storage plan is generated.

6. The method for optimizing the storage of warehousing and distribution data based on the Internet of Things according to claim 5, characterized in that, By analyzing the cargo storage conditions in the cargo information database tables, the security requirements of cargo warehousing and distribution data are determined using the following formula: Where Q is the initial security requirement of the cargo warehousing and distribution data, T is the special storage temperature specified in the cargo storage conditions, P is the special storage pressure specified in the cargo storage conditions, T0 is the preset normal cargo storage temperature, and P0 is the preset normal cargo storage pressure. Among them, SR is the security requirement of cargo warehousing and distribution data, Ku is the data importance level, which is divided into three levels by delivery personnel according to the value and timeliness of the goods: ordinary importance, medium importance and high importance. Values ​​are assigned to different importance levels. B is the backup strategy coefficient. A backup strategy coefficient of 1 indicates that backup is required, and a backup strategy coefficient of 0 indicates that deletion is allowed. α, β and γ are weight coefficients.

7. The method for optimizing the storage of warehousing and distribution data based on the Internet of Things according to claim 5, characterized in that, Based on the analysis results of the security requirements of cargo warehousing and distribution data, a warehousing and distribution data storage plan is generated, including the following steps: The security requirements of the cargo warehousing and distribution data are normalized to obtain cargo information data with security requirements greater than or equal to the threshold, as well as the warehousing and transportation data associated with the cargo information data. The resulting warehousing and distribution data storage scheme is to upload the warehousing and distribution data to the central server. For goods information data where the safety requirement is less than the threshold, along with associated warehousing and transportation data, the resulting warehousing and distribution data storage solution is to transmit the warehousing and distribution data to a data storage node for storage.

8. The method for optimizing the storage of warehousing and distribution data based on the Internet of Things according to claim 7, characterized in that, The relay node will select data storage nodes according to the warehousing and distribution data storage plan, and then transmit the data to the central server or data storage nodes for storage according to the plan, including the following steps: The relay node obtains the warehousing and distribution data storage plan. If the warehousing and distribution data storage plan is to transmit the warehousing and distribution data to the data storage node for storage; Obtain the distance data from each data storage node to the relay node, sort them from closest to furthest from the relay node, and select the top 50% of data storage nodes as candidate nodes; The storage fitness of candidate nodes is evaluated using the following formula: Where K is the storage fitness evaluation value of the candidate node, α is the storage importance coefficient, d is the available storage of the candidate node, D is the maximum storage of the candidate node, L is the distance from the candidate node to the relay node, L0 is the average distance between the candidate node and the relay node, and L... max This represents the maximum distance between the candidate node and the relay node. Based on the evaluation results, data storage nodes with storage fitness evaluation values ​​greater than a preset threshold are selected as candidate nodes; If the warehousing and distribution data storage solution is to upload the warehousing and distribution data to the central server, then the warehousing and distribution data will be uploaded to the central server. If the warehousing and distribution data storage solution is to transmit the warehousing and distribution data to the data storage node for storage, then the warehousing and distribution data will be transmitted to the selected data storage node for storage.

9. A method for optimizing the storage of warehousing and distribution data based on the Internet of Things according to claim 8, characterized in that, The central server collects security monitoring log data from each data storage node, predicts the security level of the data storage nodes, and backs up or deletes the data stored in the data storage nodes based on the prediction results, including the following steps: The central server collects security monitoring log data from each data storage node and divides the monitoring time period for each data storage node. After the monitoring time period ends for each data storage node, the security coefficient of the data storage node is predicted using the following formula: Where W is the predicted security coefficient of the data storage node, K0 is the evaluation value of the storage fitness of the candidate node obtained by using the data storage node as a candidate node during the monitoring period, β is the storage fitness coefficient of the data storage node, E is the number of times the data storage node generates error logs during the monitoring period, E0 is the average number of times all data storage nodes generate error logs during the monitoring period, and E max J represents the maximum number of error logs generated by all data storage nodes during the monitoring period; J0 represents the average number of network attacks on all data storage nodes during the monitoring period. max This refers to the maximum number of network attacks suffered by all data storage nodes within the monitoring period. Based on the predicted value of the security coefficient of the data storage node obtained from the calculation; If the predicted value of the security coefficient of the data storage node is less than the preset threshold, obtain the data collection time of the warehousing and distribution data stored in the data storage node, delete the data whose data collection time is earlier than the preset time node, and back up the data whose data collection time is at the preset time node and after the preset time node to the relay node in the block where the data storage node is located. Otherwise, there is no need to back up or delete the data stored in the data storage node.

10. A warehouse and distribution data optimization and storage system based on the Internet of Things, characterized in that, include: Data acquisition module: Several data acquisition nodes are set up in the cargo shipping area, storage area and distribution transfer area. Each data acquisition node is equipped with a corresponding data storage node. The cargo information data, storage data and transportation data are collected through the data acquisition nodes. Data aggregation module: The shipping area, warehousing area and delivery transit area are divided into blocks according to geographical location. Each block is set up with a relay node. The data collection nodes of the shipping area, warehousing area and delivery transit area will aggregate the collected data to the relay node of the corresponding block. Data classification module: The relay nodes in the corresponding area classify the received cargo information data, warehousing data and transportation data, analyze the classification results, and generate warehousing and distribution data storage solutions respectively. Data storage and distribution module: The relay node will select data storage nodes according to the warehousing and distribution data storage plan, and transmit the data to the central server or data storage nodes for storage according to the warehousing and distribution data storage plan; Data maintenance module: The central server collects security monitoring log data from each data storage node, predicts the security level of the data storage node, and backs up or deletes the data stored in the data storage node based on the prediction results.

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