Industrial digital information cloud storage system and method

Abstract

The present invention discloses an industrial digital information cloud storage system and method, which relate to the field of wireless communication networks, and include a digital information acquisition module, a multi-mode wireless communication module, a cloud cache module, a cloud storage module and a security management and control module. The output end of the digital information acquisition module is connected to the input end of the multi-mode wireless communication module, the output end of the multi-mode wireless communication module is connected to the input end of the cloud cache module, and the cloud cache module is bidirectionally connected to the cloud storage module, thereby solving the problems of slow transmission speed, long data response time and insufficient storage security of traditional cloud storage systems. The present invention improves data transmission speed, data access performance and security through multi-mode wireless communication, distributed cache, distributed cloud storage and security management and control technologies.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication networks, and more specifically to an industrial digital information cloud storage system and method. Background Art

[0002] With the advent of the information age and the continuous development of information technology, the amount of data involved in industrial production is increasing. Especially with the advancement of industrial informatization and intelligence, a large amount of industrial data needs to be stored, processed and analyzed, and enterprise digital transformation has become a trend. In the industrial field, data collection and analysis are becoming more and more important. There are many problems with traditional data storage methods, such as high data storage costs, poor data security, and low data processing efficiency. Therefore, it is particularly important to develop a new industrial digital information cloud storage system. The industrial digital information cloud storage system is a data storage and management system based on cloud computing and big data technology, which is mainly used to collect, store, manage and analyze various data generated in the industrial field. In the implementation process of the industrial digital information cloud storage system, wireless communication network is a very important technology. The technological development in the field of wireless communication networks has provided a lot of support for the implementation of industrial digital information cloud storage systems.

[0003] However, the data collection method is not efficient and automated enough. Traditional data collection methods usually require a lot of manpower and material costs and are inefficient. Existing industrial digital information cloud storage systems may face problems such as network congestion, insufficient bandwidth or weak signals during transmission, resulting in slow data transmission and even transmission errors. Traditional industrial digital information upload methods usually use wired communication methods, and the network coverage is limited, making it impossible to achieve full coverage. Data access speed is slow. In traditional cloud storage systems, the access speed is slow due to large data access volume and unbalanced load. The security protection level is relatively low. Traditional cloud storage systems have relatively low security protection levels and are vulnerable to threats such as hacker attacks and data leaks.

[0004] Therefore, the present invention discloses an industrial digital information cloud storage system and method, which uploads the pre-processed industrial digital information to the cloud through a multi-mode wireless communication module, and uses distributed cache, distributed cloud storage system and security management control module to cache, archive and control the permissions of the industrial digital information. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention discloses an industrial digital information cloud storage system and method, which are improved through multi-mode wireless communication, distributed caching, distributed cloud storage and security management control technologies, thereby improving data transmission speed, data access performance and security; the multi-mode wireless communication module uses an adaptive threshold value model to analyze the signal strength, bandwidth and transmission energy consumption of multiple communication networks, thereby controlling the wireless communication unit to automatically switch to the optimal communication network for data transmission. Compared with the transmission method of a single network, this module can improve the success rate and transmission speed of industrial digital information transmission, thereby realizing fast, efficient and reliable data transmission; the cloud cache module uses distributed cache and dynamic load balancing models, which can automatically distribute industrial digital information to multiple cache nodes according to data access conditions, and optimize data access performance through pre-reading and pre-loading strategies, thereby improving data reading speed and response speed; the cloud storage module uses a distributed cloud storage system to realize persistent storage, backup and data recovery of industrial digital information, which can ensure the security and stability of industrial digital information; the security management and control module uses a three-layer security firewall and permission authentication control mechanism to perform security protection and access control on cloud data, which can avoid the leakage of sensitive information and malicious attacks, thereby ensuring the security and reliability of industrial digital information; it has a high degree of automation and intelligence.

[0006] The present invention adopts the following technical solutions:

[0007] An industrial digital information cloud storage system, the cloud storage system comprising:

[0008] A digital information acquisition module is used to collect industrial digital information and perform preprocessing. The digital information acquisition module collects data information from industrial production equipment through a data acquisition card, and filters, reduces noise, verifies and standardizes the collected industrial digital information through a digital signal processor DSP. The data acquisition card performs differential acquisition of multiple analog input channels through an Ethernet interface, and converts the collected analog signals into digital signals through a built-in analog-to-digital converter ADC;

[0009] A multi-mode wireless communication module, used for uploading the pre-processed industrial digital information to the cloud, wherein the multi-mode wireless communication module transmits the pre-processed industrial digital information to the cloud for storage through a multi-mode wireless communication network, wherein the multi-mode wireless communication network comprises a network switching control unit, a wireless communication unit, a network management and optimization unit, and a security protection unit, wherein the output end of the network switching control unit is connected to the input end of the wireless communication unit, the network management and optimization unit is bidirectionally connected to the wireless communication unit, the output end of the security protection unit is connected to the input end of the wireless communication unit, and the network switching control unit performs signal strength, bandwidth, and transmission energy consumption analysis on the industrial digital information through a built-in adaptive threshold value model, and automatically controls the wireless communication unit to switch to the optimal communication network for data transmission;

[0010] A cloud cache module, used for caching the industrial digital information uploaded to the cloud. The cloud cache module distributes the industrial digital information to multiple cache nodes in a distributed cache manner, and realizes balanced distribution and access optimization of the industrial digital information through a dynamic load balancing model. The cloud cache module optimizes data access performance through pre-reading and pre-loading strategies;

[0011] A cloud storage module, used for archiving and managing the industrial digital information, and the cloud storage module realizes persistent storage, backup and data recovery of the industrial digital information through a distributed cloud storage system;

[0012] A security management and control module, which is used to perform security management and authority control on the industrial digital information stored in the cloud. The security management and control module performs security protection and access control on cloud data through a three-layer security firewall and authority authentication control mechanism;

[0013] The output end of the digital information acquisition module is connected to the input end of the multi-mode wireless communication module, the output end of the multi-mode wireless communication module is connected to the input end of the cloud cache module, the cloud cache module is bidirectionally connected to the cloud storage module, and the output end of the security management and control module is connected to the input ends of the cloud cache module and the cloud storage module.

[0014] As a further technical solution of the present invention, the adaptive threshold value model takes the signal strength, bandwidth and transmission energy consumption of the current network as input for calculation. The calculation formula of the signal strength of the current network is:

[0015]

[0016] In formula (1), S(t) is the signal strength of the current network, t represents the current time, P(t) represents the transmission power of the current network signal source, L(t) represents the loss of the current network signal during transmission, and N(t) represents the power of the current network environment noise. The bandwidth calculation formula of the current network is:

[0017]

[0018] In formula (2), B(t) is the bandwidth of the current network, C(t) represents the channel capacity of the current network, K(t) represents the signal power of the current network, and log2 represents the logarithmic operation with base 2. The transmission energy consumption calculation formula of the current network is:

[0019]

[0020] In formula (3), E(t) represents the transmission energy consumption of the current network, L(t) represents the data packet size of the current network, and t c Indicates the packet sending time, t r Indicates the packet receiving time, P c (t) represents the power consumption of the current network sending device during the transmission process, P r (t) represents the power consumption of the current network receiving device during the transmission process; the comprehensive evaluation formula of the signal strength, bandwidth and transmission energy consumption of the current network is:

[0021]

[0022] In formula (4), Z(t) is the comprehensive evaluation value of the signal strength, bandwidth and transmission energy consumption of the current network, and E max (t) is the maximum acceptable energy consumption of the current network. α1, α2, α3 and β1 are adjustable parameters used to weigh the impact of signal strength, bandwidth and energy consumption on the current network performance. The threshold value is calculated as follows:

[0023]

[0024] In formula (5), T represents the adaptive threshold value, S represents the network signal strength threshold, B represents the network bandwidth threshold, C represents the network channel capacity threshold, E represents the network transmission energy consumption threshold, and E represents the network transmission energy consumption threshold. max It represents the maximum acceptable energy consumption threshold of the network. It makes judgments and decisions based on the similarity between the comprehensive evaluation value and the adaptive threshold value, and selects the optimal communication network for data cloud transmission.

[0025] As a further technical solution of the present invention, the wireless communication unit includes a cellular network, a wireless local area network, a LoRa wide area network, a narrowband Internet of Things network, a Hummingbird network and a Sigfox network. The security protection unit uses the SSL / TLS encryption protocol to encrypt the transmission channel. The network management and optimization unit adjusts the transmission rate and priority of the industrial digital information through a flow control chip and a transmission control protocol TCP, and monitors and manages the multi-mode wireless communication network through a network traffic manager and a switch. The transmission control protocol TCP modifies the transmission rate and priority by adjusting the congestion window and sliding window parameters.

[0026] As a further technical solution of the present invention, the working method of the dynamic load balancing model includes:

[0027] S1, monitoring the load status of the cache node, monitoring the load status of the cache node through a task manager and a performance monitor, and inputting the monitoring data into the dynamic load balancing model, the load status including memory occupancy, disk occupancy, CPU utilization, node load and network bandwidth;

[0028] S2. Dynamically adjust the cache nodes. The dynamic load balancing model balances the data load between the cache nodes through a polling algorithm and performs adaptive adjustment of node failures by increasing the task load of normal nodes.

[0029] S3. Dynamically expand the cache nodes, and realize automatic expansion of the cache nodes according to the node load and the automatic expansion mechanism, wherein the automatic expansion mechanism realizes high concurrency or large-scale data caching by adding the cache nodes;

[0030] S4. Dynamic routing mechanism. The dynamic load balancing model realizes automatic allocation of data cache based on the dynamic routing mechanism. The dynamic routing mechanism selects the shortest path or the optimal path for data cache transmission through an intelligent routing algorithm.

[0031] As a further technical solution of the present invention, the distributed cloud storage system includes a data archiving unit, a data backup unit and a data recovery unit. The data archiving unit records the number of times the cache information item is called through an access counter, and uses a timer to trigger clearing and archiving operations. The timer clears and archives the cache information items based on the LRU cache strategy. The data backup unit divides the uploaded file into multiple data blocks through a copy mechanism and metadata backup, and stores them in multiple data nodes to achieve redundant backup of the data. The data recovery unit quickly restores the state of the distributed cloud storage system through a fault-tolerant mechanism and a snapshot backup function. The output end of the data archiving unit is connected to the input end of the data backup unit, and the output end of the data backup unit is connected to the input end of the data recovery unit.

[0032] As a further technical solution of the present invention, the three-layer security firewall includes an application layer firewall, a software firewall and a hardware firewall. The application layer firewall filters and manages cloud traffic based on the application protocol, and inspects and controls data packets, data streams and data content through protocol decoders and regular expressions. The software firewall filters and manages network traffic by monitoring and controlling network connections in and out of the host operating system. The hardware firewall achieves security protection by screening and filtering inbound and outbound data packets. The software firewall and hardware firewall use SSL Secure Sockets Layer acceleration cards to reduce the load of the firewall's internal server. The SSL Secure Sockets Layer acceleration card reduces the load of the firewall's internal server by accelerating the processing of secure sockets layer and transport layer connections.

[0033] As a further technical solution of the present invention, a method for cloud storage of industrial digital information is provided, the cloud storage method comprising the steps of:

[0034] Step 1: Collect the industrial digital information through the digital information acquisition module, and perform filtering, noise reduction, verification and standardization processing;

[0035] Step 2: uploading the pre-processed industrial digital information to the cloud through the multi-mode wireless communication module;

[0036] Step 3: Distributed caching of the industrial digital information uploaded to the cloud is performed through the cloud cache module;

[0037] Step 4: Persistently store, back up and restore the industrial digital information through the cloud storage module;

[0038] Step 5: Perform security management and authority control on the industrial digital information stored in the cloud through the security management and control module.

[0039] Positive beneficial effects:

[0040] The present invention discloses an industrial digital information cloud storage system and method, which are improved through multi-mode wireless communication, distributed caching, distributed cloud storage and security management control technologies, thereby improving data transmission speed, data access performance and security; the multi-mode wireless communication module uses an adaptive threshold value model to analyze the signal strength, bandwidth and transmission energy consumption of multiple communication networks, thereby controlling the wireless communication unit to automatically switch to the optimal communication network for data transmission. Compared with the transmission method of a single network, this module can improve the success rate and transmission speed of industrial digital information transmission, thereby realizing fast, efficient and reliable data transmission; the cloud cache module uses distributed cache and dynamic load balancing models, which can automatically distribute industrial digital information to multiple cache nodes according to data access conditions, and optimize data access performance through pre-reading and pre-loading strategies, thereby improving data reading speed and response speed; the cloud storage module uses a distributed cloud storage system to realize persistent storage, backup and data recovery of industrial digital information, which can ensure the security and stability of industrial digital information; the security management and control module uses a three-layer security firewall and permission authentication control mechanism to perform security protection and access control on cloud data, which can avoid the leakage of sensitive information and malicious attacks, thereby ensuring the security and reliability of industrial digital information; it has a high degree of automation and intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a schematic diagram of the overall architecture of an industrial digital information cloud storage system of the present invention;

[0042] Figure 2 This is a schematic diagram of a multi-mode wireless communication module architecture in an industrial digital information cloud storage system of the present invention;

[0043] Figure 3 A schematic diagram of the cloud storage module architecture in an industrial digital information cloud storage system of the present invention;

[0044] Figure 4 A process architecture diagram of a dynamic load balancing model in an industrial digital information cloud storage system of the present invention;

[0045] Figure 5 This is a process architecture diagram of an industrial digital information cloud storage method of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] An industrial digital information cloud storage system, the cloud storage system comprising:

[0048] A digital information acquisition module is used to collect industrial digital information and perform preprocessing. The digital information acquisition module collects data information from industrial production equipment through a data acquisition card, and filters, reduces noise, verifies and standardizes the collected industrial digital information through a digital signal processor DSP. The data acquisition card performs differential acquisition of multiple analog input channels through an Ethernet interface, and converts the collected analog signals into digital signals through a built-in analog-to-digital converter ADC;

[0049] A multi-mode wireless communication module, used for uploading the pre-processed industrial digital information to the cloud, wherein the multi-mode wireless communication module transmits the pre-processed industrial digital information to the cloud for storage through a multi-mode wireless communication network, wherein the multi-mode wireless communication network comprises a network switching control unit, a wireless communication unit, a network management and optimization unit, and a security protection unit, wherein the output end of the network switching control unit is connected to the input end of the wireless communication unit, the network management and optimization unit is bidirectionally connected to the wireless communication unit, the output end of the security protection unit is connected to the input end of the wireless communication unit, and the network switching control unit performs signal strength, bandwidth, and transmission energy consumption analysis on the industrial digital information through a built-in adaptive threshold value model, and automatically controls the wireless communication unit to switch to the optimal communication network for data transmission;

[0050] A cloud cache module, used for caching the industrial digital information uploaded to the cloud. The cloud cache module distributes the industrial digital information to multiple cache nodes in a distributed cache manner, and realizes balanced distribution and access optimization of the industrial digital information through a dynamic load balancing model. The cloud cache module optimizes data access performance through pre-reading and pre-loading strategies;

[0051] A cloud storage module, used for archiving and managing the industrial digital information, and the cloud storage module realizes persistent storage, backup and data recovery of the industrial digital information through a distributed cloud storage system;

[0052] A security management and control module, which is used to perform security management and authority control on the industrial digital information stored in the cloud. The security management and control module performs security protection and access control on cloud data through a three-layer security firewall and authority authentication control mechanism;

[0053] The output end of the digital information acquisition module is connected to the input end of the multi-mode wireless communication module, the output end of the multi-mode wireless communication module is connected to the input end of the cloud cache module, the cloud cache module is bidirectionally connected to the cloud storage module, and the output end of the security management and control module is connected to the input ends of the cloud cache module and the cloud storage module.

[0054] In the above embodiment, the adaptive threshold value model takes the signal strength, bandwidth and transmission energy consumption of the current network as input for calculation. The calculation formula of the signal strength of the current network is:

[0055]

[0056] In formula (1), S(t) is the signal strength of the current network, t represents the current time, P(t) represents the transmission power of the current network signal source, L(t) represents the loss of the current network signal during transmission, and N(t) represents the power of the current network environment noise. The bandwidth calculation formula of the current network is:

[0057]

[0058] In formula (2), B(t) is the bandwidth of the current network, C(t) represents the channel capacity of the current network, K(t) represents the signal power of the current network, and log2 represents the logarithm operation with base 2;

[0059] Bandwidth refers to the maximum amount of data that can be transmitted by a network transmission line in a unit of time. It is usually expressed in bits per second (bit / s). When calculating bandwidth, you need to determine the amount of data first. The amount of data can be file size, network transmission rate, etc. Convert the amount of data from bits (bit) to bytes (B). Since the conversion relationship between bytes and bits is 1 byte = 8 bits, when calculating bandwidth, you need to divide the number of bits by 8. Then calculate the bandwidth: divide the converted amount of data by 1 second to get the number of bytes transmitted per second. This value is the network bandwidth, in bytes / second (B / s). The calculation result of bandwidth is usually expressed in MB / s, GB / s, etc. For example, if the calculated bandwidth is 1000000 bytes / second, it can be expressed as 1MB / s. The present invention fully considers parameter data information such as the bandwidth of the current network, the channel capacity of the current network, the signal power of the current network, and the logarithmic operation with base 2, and converts these data information into a function model to improve the data information calculation ability.

[0060] The current network transmission energy consumption calculation formula is:

[0061]

[0062] In formula (3), E(t) represents the transmission energy consumption of the current network, L(t) represents the data packet size of the current network, and t c Indicates the packet sending time, t r Indicates the packet receiving time, P c (t) represents the power consumption of the current network sending device during the transmission process, P r (t) represents the power consumption of the current network receiving device during the transmission process.

[0063] In a further specific embodiment, the calculation process of the network transmission energy consumption includes many aspects, such as: to calculate the amount of data, first of all, it is necessary to determine the amount of data transmitted by the network. The amount of data can be the file size, the number of data packets, etc. It is usually expressed in bytes (B). Then calculate the bandwidth: understand the bandwidth of the network, that is, the rate of the network transmission line. The bandwidth is usually expressed in bytes / second (B / s). Then calculate the transmission time: calculate the time required for data transmission. The time can be calculated by the following formula: transmission time (seconds) = amount of data (B) / bandwidth (B / s). Then calculate the energy consumption conversion coefficient. The network transmission energy consumption is related to factors such as bandwidth and transmission time. The energy consumption conversion coefficient is a parameter used to convert the transmission rate into energy consumption. Common energy consumption conversion coefficients are: Among them, electrical energy consumption: the unit is watt-hour / (bit / second) or watt-hour / (byte / second), among which thermal energy consumption: the unit is joule / (bit / second) or joule / (byte / second), and then calculate the energy consumption: according to the transmission time, bandwidth and energy consumption conversion coefficient, calculate the energy consumption of network transmission. For example, the calculation formula is as follows: Energy consumption (watt-hour / joule) = transmission time (seconds) × bandwidth (B / s) × energy consumption conversion factor (watt-hour / (bit / second) or joule / (bit / second). Finally, based on the calculated energy consumption value, the energy consumption performance of network transmission can be evaluated.

[0064] Based on the above principle, the present invention improves the calculation of the transmission energy consumption of the current network to improve the data information calculation ability. In specific applications, the following data factors need to be considered: the transmission energy consumption of the current network, the data packet size of the current network, the data packet sending time, the data packet receiving time, the power consumption of the current network sending device during the transmission process, the power consumption of the current network receiving device during the transmission process and other data information, which are factors affecting the transmission energy consumption of the current network. Converting these data information into a function representation has a positive and beneficial effect.

[0065] The comprehensive evaluation formula of the current network's signal strength, bandwidth, and transmission energy consumption is:

[0066]

[0067] In formula (4), Z(t) is the comprehensive evaluation value of the signal strength, bandwidth and transmission energy consumption of the current network, and Emax (t) is the maximum acceptable energy consumption of the current network. α1, α2, α3 and β1 are adjustable parameters used to weigh the impact of signal strength, bandwidth and energy consumption on the current network performance. The threshold value is calculated as follows:

[0068]

[0069] In formula (5), T represents the adaptive threshold value, S represents the network signal strength threshold, B represents the network bandwidth threshold, C represents the network channel capacity threshold, E represents the network transmission energy consumption threshold, and E represents the network transmission energy consumption threshold. max It represents the maximum acceptable energy consumption threshold of the network. It makes judgments and decisions based on the similarity between the comprehensive evaluation value and the adaptive threshold value, and selects the optimal communication network for data cloud transmission.

[0070] In a specific embodiment, the network switching control unit analyzes the signal strength, bandwidth and transmission energy consumption of the industrial digital information through a built-in adaptive threshold model, and then automatically controls the wireless communication unit to switch to the optimal communication network for data cloud transmission according to the calculation results. Specifically, the workflow is as follows: obtain parameters such as the signal strength, bandwidth and transmission energy consumption of the current network. These parameters are sent as input to the adaptive threshold model for calculation to obtain the score of each available network. Select the communication network with the highest score as the currently used network, and send the switching instruction to the wireless communication unit. After receiving the switching instruction, the wireless communication unit can automatically transmit the data cloud to the optimal communication network.

[0071] It should be noted that this process is carried out in real time. The network switching control unit will continuously monitor the current network environment and adjust the communication network used according to real-time changes. In this way, it can ensure that industrial digital information can be quickly and stably transmitted to the cloud in different scenarios.

[0072] For the adaptive threshold value model built into the network switching control unit, its hardware working environment includes the following:

[0073] Signal strength detector: used to detect changes in signal strength in the current network, and needs to have high sensitivity and high precision. Usually implemented using hardware devices such as RF front-end chips and antennas.

[0074] Bandwidth detector: used to monitor bandwidth changes in the current network, and needs to have high speed and high accuracy. Usually implemented using hardware devices such as digital signal processors and FPGAs.

[0075] Energy consumption meter: used to evaluate the energy consumed by data transmission in the current network, which needs to have the characteristics of high accuracy and low power consumption. It is usually implemented by hardware devices such as energy meters and power sensors.

[0076] Control unit: Make judgments and decisions based on the comprehensive evaluation value and adaptive threshold value calculated by the adaptive threshold value model, and select the optimal communication network for data cloud transmission. Usually implemented using hardware devices such as microprocessors and embedded systems.

[0077] In summary, the adaptive threshold model needs to monitor and calculate various indicators in the hardware working environment, and make judgments and decisions through the control unit to achieve the selection of the optimal communication network. Therefore, it is necessary to reasonably design the hardware system to ensure that it has the characteristics of high performance, low power consumption and high reliability.

[0078] Different types of wireless communication network equipment, 4G mobile phones, Wi-Fi routers and LoRa modules, were selected, and a complete real environment was established in the laboratory to simulate the industrial digital information transmission scenario. The industrial digital information data was randomly generated and sent through the wireless communication unit. The transmission data was monitored and managed by the network management and optimization unit. The signal strength, bandwidth and transmission energy consumption were analyzed and evaluated in real time through the adaptive threshold value model (Group A) and the weighted comprehensive evaluation model (Group B), and the network switching control unit was automatically controlled to select the optimal network and transmit data. During the data transmission process, the network switching duration, transmission rate and success rate parameter data were recorded and recorded in Table 1.

[0079] Table 1 Results Statistics

[0080]

[0081] According to the analysis of experimental results, after analysis and evaluation of the adaptive threshold value model and the weighted comprehensive evaluation model, Group A has a higher success rate and shorter network switching time in selecting the optimal network for data transmission than Group B. In the experimental results of 4G mobile phones, Wi-Fi routers and LoRa modules, Group A has a shorter network switching time, higher transmission rate and higher success rate than Group B; based on the experimental results and the requirements of actual industrial application scenarios, the effectiveness and reliability of the adaptive threshold value model in industrial digital information transmission are higher than the weighted comprehensive evaluation model.

[0082] In the above embodiment, the wireless communication unit includes a cellular network, a wireless local area network, a LoRa wide area network, a narrowband Internet of Things network, a Hummingbird network and a Sigfox network. The security protection unit uses the SSL / TLS encryption protocol to encrypt the transmission channel. The network management and optimization unit adjusts the transmission rate and priority of the industrial digital information through the flow control chip and the transmission control protocol TCP, and monitors and manages the multi-mode wireless communication network through the network traffic manager and the switch. The transmission control protocol TCP modifies the transmission rate and priority by adjusting the congestion window and sliding window parameters.

[0083] In a specific embodiment, cellular network: mainly refers to mobile communication networks such as 4G and 5G, which can provide high-speed data transmission and stable network connection. Wireless local area network: refers to a Wi-Fi network, which can provide high-speed data transmission and wireless connection within a certain range. LoRa wide area network: refers to a long-distance, low-power radio network that can provide wide-area coverage and low-power transmission. Narrowband Internet of Things network: refers to a low-power, narrowband radio network designed for Internet of Things applications, suitable for the connection and data transmission of Internet of Things devices. Hummingbird network: refers to a wireless access network based on optical communication, which can provide high-speed broadband access and wireless connection. Sigfox network: refers to an Internet access network based on satellite communication, suitable for remote areas and application scenarios requiring remote monitoring.

[0084] The flow control chip and the transmission control protocol TCP can control the transmission rate and priority by dynamically adjusting the transmission parameters, ensuring the timely transmission of high-priority data and the appropriate delay of low-priority data, and ensuring the reliability and stability of data transmission in the event of network congestion or abnormal conditions. At the same time, the network management and optimization unit can achieve effective utilization and control of network resources through traffic monitoring and management, improving transmission efficiency and network performance.

[0085] In the above embodiment, the working method of the dynamic load balancing model includes:

[0086] S1, monitoring the load status of the cache node, monitoring the load status of the cache node through a task manager and a performance monitor, and inputting the monitoring data into the dynamic load balancing model, the load status including memory occupancy, disk occupancy, CPU utilization, node load and network bandwidth;

[0087] S2. Dynamically adjust the cache nodes. The dynamic load balancing model balances the data load between the cache nodes through a polling algorithm and performs adaptive adjustment of node failures by increasing the task load of normal nodes.

[0088] S3. Dynamically expand the cache nodes, and realize automatic expansion of the cache nodes according to the node load and the automatic expansion mechanism, wherein the automatic expansion mechanism realizes high concurrency or large-scale data caching by adding the cache nodes;

[0089] S4. Dynamic routing mechanism. The dynamic load balancing model realizes automatic allocation of data cache based on the dynamic routing mechanism. The dynamic routing mechanism selects the shortest path or the optimal path for data cache transmission through an intelligent routing algorithm.

[0090] In a specific embodiment, the cloud cache module is a module for caching industrial digital information uploaded to the cloud, which uses a distributed cache method to distribute the industrial digital information to multiple cache nodes, and realizes balanced distribution and access optimization of industrial digital information through a dynamic load balancing model. Through the working method of the above dynamic load balancing model, the cloud cache module can achieve efficient, fast and secure caching and access management of industrial digital information, improve the performance and stability of the system, and thus realize reliable industrial digital information cloud storage and access services.

[0091] In the above embodiment, the distributed cloud storage system includes a data archiving unit, a data backup unit and a data recovery unit. The data archiving unit records the number of times the cache information item is called through an access counter, and uses a timer to trigger clearing and archiving operations. The timer clears and archives the cache information items based on the LRU cache strategy. The data backup unit divides the uploaded file into multiple data blocks through a copy mechanism and metadata backup, and stores them in multiple data nodes to achieve redundant backup of the data. The data recovery unit quickly restores the state of the distributed cloud storage system through a fault-tolerant mechanism and a snapshot backup function. The output end of the data archiving unit is connected to the input end of the data backup unit, and the output end of the data backup unit is connected to the input end of the data recovery unit.

[0092] In a specific embodiment, the copy mechanism: the distributed file system will divide the uploaded file into multiple data blocks, and then store these data blocks on multiple DataNodes respectively to achieve redundant backup of data. By default, each data block has at least 3 copies stored on different DataNodes to ensure data reliability and availability. Metadata backup: In order to enhance the reliability and fault tolerance of the file system, some distributed file systems will use SecondaryNameNode to assist NameNode in metadata backup and edit log merging. Secondary NameNode will regularly copy metadata information from NameNode and store it on the local file system to back up metadata. Fault tolerance mechanism: Once a DataNode fails or the network is interrupted, the distributed file system will copy copies from other backup locations to ensure data integrity and accessibility. In the case of data block loss or damage, the distributed file system will re-copy the data block from other DataNodes to ensure data reliability. Snapshot backup: The distributed file system supports the file system snapshot function, which can save the current file system state as a read-only snapshot. When catastrophic damage occurs, the snapshot backup function can be used to quickly restore the file system state.

[0093] Therefore, data backup, data recovery unit, and data recovery unit are key components in the distributed file system, which can ensure the backup, recovery, and reliability of system data, thereby realizing persistent storage, snapshot backup, and data recovery of industrial digital information.

[0094] In the above embodiment, the three-layer security firewall includes an application layer firewall, a software firewall and a hardware firewall. The application layer firewall filters and manages cloud traffic based on the application protocol, and inspects and controls data packets, data streams and data content through protocol decoders and regular expressions. The software firewall filters and manages network traffic by monitoring and controlling network connections in and out of the host operating system. The hardware firewall implements security protection by screening and filtering inbound and outbound data packets. The software firewall and the hardware firewall use SSL Secure Sockets Layer acceleration cards to reduce the load of the server inside the firewall. The SSL Secure Sockets Layer acceleration card reduces the load of the server inside the firewall by accelerating the processing of secure sockets layer and transport layer connections.

[0095] In a specific embodiment, software firewall: a software firewall is generally an application or system used to monitor and control network traffic on a computer network and prevent network attacks. It achieves traffic control and security protection by screening inbound and outbound data packets. Hardware firewall: a hardware firewall is generally an independent device that consists of hardware components and embedded software. Hardware firewalls are usually used in conjunction with network devices such as network switches or routers and have higher security and performance. It can achieve more flexible and effective security protection by screening and filtering inbound and outbound data packets. Application layer firewall refers to a firewall located at the top of the network protocol stack, which performs traffic filtering and management based on the protocol of the application. Unlike traditional network layer firewalls, application layer firewalls can go deep into the application layer to inspect and control data packets, data flows and data content, thereby providing more fine-grained network security protection. In summary, the three-layer security firewall is an important component of the security management and control module. It achieves dual security protection of industrial digital information stored in the cloud through a combination of software, hardware, and application layer firewalls, protects confidentiality and integrity, and promotes secure access and data exchange.

[0096] In the above embodiment, an industrial digital information cloud storage method is provided, the cloud storage method comprising the steps of:

[0097] Step 1: Collect the industrial digital information through the digital information acquisition module, and perform filtering, noise reduction, verification and standardization processing;

[0098] Step 2: uploading the pre-processed industrial digital information to the cloud through the multi-mode wireless communication module;

[0099] Step 3: Distributed caching of the industrial digital information uploaded to the cloud is performed through the cloud cache module;

[0100] Step 4: Persistently store, back up and restore the industrial digital information through the cloud storage module;

[0101] Step 5: Perform security management and authority control on the industrial digital information stored in the cloud through the security management and control module.

[0102] Although the specific embodiments of the present invention are described above, it should be understood by those skilled in the art that these specific embodiments are only illustrative, and those skilled in the art may omit, replace, and change the details of the above methods and systems in various ways without departing from the principles and essence of the present invention. For example, merging the above method steps so as to perform substantially the same functions in substantially the same manner to achieve substantially the same results is within the scope of the present invention. Therefore, the scope of the present invention is limited only by the appended claims.

Claims

1. An industrial digital information cloud storage system, characterized by: The cloud storage system comprises: Digital information acquisition module, used to collect industrial digital information and perform pre-processing; A multi-mode wireless communication module, used for uploading the pre-processed industrial digital information to the cloud; A cloud cache module, used for caching the industrial digital information uploaded to the cloud; A cloud storage module for archiving and managing the industrial digital information; A security management and control module, used for security management and authority control of the industrial digital information stored in the cloud; The output end of the digital information acquisition module is connected to the input end of the multi-mode wireless communication module, the output end of the multi-mode wireless communication module is connected to the input end of the cloud cache module, the cloud cache module is bidirectionally connected to the cloud storage module, and the output end of the security management and control module is connected to the input ends of the cloud cache module and the cloud storage module; the digital information acquisition module collects data information from industrial production equipment through a data acquisition card, and filters, reduces noise, verifies and standardizes the collected industrial digital information through a digital signal processor DSP, the data acquisition card performs differential acquisition of multiple analog input channels through an Ethernet interface, and converts the collected analog signals into digital signals through a built-in analog-to-digital converter ADC; The multi-mode wireless communication module transmits the pre-processed industrial digital information to the cloud for storage through a multi-mode wireless communication network. The multi-mode wireless communication network includes a network switching control unit, a wireless communication unit, a network management and optimization unit, and a security protection unit. The output end of the network switching control unit is connected to the input end of the wireless communication unit, the network management and optimization unit is bidirectionally connected to the wireless communication unit, and the output end of the security protection unit is connected to the input end of the wireless communication unit. The network switching control unit analyzes the signal strength, bandwidth and transmission energy consumption of the industrial digital information through a built-in adaptive threshold value model, and automatically controls the wireless communication unit to switch to the optimal communication network for data transmission; the cloud cache module distributes the industrial digital information to multiple cache nodes in a distributed cache manner, and realizes balanced distribution and access optimization of the industrial digital information through a dynamic load balancing model. The cloud cache module optimizes data access performance through pre-reading and pre-loading strategies; The cloud storage module realizes the persistent storage, backup and data recovery of the industrial digital information through a distributed cloud storage system; The security management and control module performs security protection and access control on cloud data through a three-layer security firewall and an authorization authentication control mechanism; the adaptive threshold value model uses the signal strength, bandwidth and transmission energy consumption of the current network as input for calculation. The comprehensive evaluation formula of the signal strength, bandwidth and transmission energy consumption of the current network is: ; in, is the signal strength of the current network, is the current network bandwidth, Indicates the channel capacity of the current network, Indicates the current network transmission energy consumption, is the maximum acceptable energy consumption of the current network, , , and It is an adjustable parameter used to weigh the impact of signal strength, bandwidth and energy consumption on the current network performance. The calculation formula of the adaptive threshold is: ; in, represents the adaptive threshold value, is the network signal strength threshold, Indicates the network bandwidth threshold. represents the network channel capacity threshold, Indicates the network transmission energy consumption threshold, It represents the maximum acceptable energy consumption threshold of the network. It makes judgments and decisions based on the similarity between the comprehensive evaluation value and the adaptive threshold value, and selects the optimal communication network for data cloud transmission.

2. The industrial digital information cloud storage system according to claim 1, characterized in that: The wireless communication unit includes a cellular network, a wireless local area network, a LoRa wide area network, a narrowband Internet of Things network, a Hummingbird network and a Sigfox network. The security protection unit uses the SSL / TLS encryption protocol to encrypt the transmission channel. The network management and optimization unit adjusts the transmission rate and priority of the industrial digital information through a flow control chip and a transmission control protocol TCP, and monitors and manages the multi-mode wireless communication network through a network traffic manager and a switch. The transmission control protocol TCP modifies the transmission rate and priority by adjusting the congestion window and sliding window parameters.

3. The industrial digital information cloud storage system according to claim 2 is characterized in that: The working method of the dynamic load balancing model includes: S1, monitoring the load status of the cache node, monitoring the load status of the cache node through a task manager and a performance monitor, and inputting the monitoring data into the dynamic load balancing model, the load status including memory occupancy, disk occupancy, CPU utilization, node load and network bandwidth; S2. Dynamically adjust the cache nodes. The dynamic load balancing model balances the data load between the cache nodes through a polling algorithm, and performs adaptive adjustment of node failures by increasing the task load of normal nodes. S3. Dynamically expand the cache nodes, and realize automatic expansion of the cache nodes according to the node load and the automatic expansion mechanism, wherein the automatic expansion mechanism realizes high concurrency or large-scale data caching by adding the cache nodes; S4. Dynamic routing mechanism. The dynamic load balancing model realizes automatic allocation of data cache based on the dynamic routing mechanism. The dynamic routing mechanism selects the shortest path or the optimal path for data cache transmission through an intelligent routing algorithm.

4. The industrial digital information cloud storage system according to claim 2, characterized in that: The distributed cloud storage system includes a data archiving unit, a data backup unit and a data recovery unit. The data archiving unit records the number of times a cache information item is called through an access counter, and uses a timer to trigger clearing and archiving operations. The timer clears and archives cache information items based on an LRU cache strategy. The data backup unit divides an uploaded file into multiple data blocks through a copy mechanism and metadata backup, and stores them in multiple data nodes to achieve redundant backup of data. The data recovery unit quickly recovers the state of the distributed cloud storage system through a fault-tolerant mechanism and a snapshot backup function. The output end of the data archiving unit is connected to the input end of the data backup unit, and the output end of the data backup unit is connected to the input end of the data recovery unit.

5. The industrial digital information cloud storage system according to claim 2 is characterized in that: The three-layer security firewall includes an application layer firewall, a software firewall and a hardware firewall. The application layer firewall filters and manages cloud traffic based on the application protocol, and inspects and controls data packets, data streams and data content through protocol decoders and regular expressions. The software firewall filters and manages network traffic by monitoring and controlling network connections in and out of the host operating system. The hardware firewall implements security protection by screening and filtering inbound and outbound data packets. The software firewall and hardware firewall use SSL secure socket layer acceleration cards to reduce the load of the firewall's internal server. The SSL secure socket layer acceleration card reduces the load of the firewall's internal server by accelerating the processing of secure socket layer and transport layer connections.

6. An industrial digital information cloud storage method, characterized in that: An industrial digital information cloud storage system as described in any one of claims 1 to 5, wherein the cloud storage method comprises the steps of: Step 1: Collect the industrial digital information through the digital information acquisition module, and perform filtering, noise reduction, verification and standardization processing; Step 2: uploading the pre-processed industrial digital information to the cloud through the multi-mode wireless communication module; Step 3: Distributed caching of the industrial digital information uploaded to the cloud is performed through the cloud cache module; Step 4: Persistently store, back up and restore the industrial digital information through the cloud storage module; Step 5: Perform security management and authority control on the industrial digital information stored in the cloud through the security management and control module.

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