Data acquisition method and device of industrial Internet of Things, and storage medium

Abstract

The invention relates to the field of technical data acquisition, and particularly discloses a data acquisition method and device for an industrial Internet of Things and a storage medium, and the method comprises the following steps: S1, constructing a dynamic sensor network, and collecting the operation data of industrial equipment in real time through a multi-modal sensor; s2, dynamically adjusting a data acquisition frequency # imgabs2 # based on an environment noise coefficient # imgabs0 # and an equipment health index # imgabs1 #; s3, performing hierarchical compression and encryption on the acquired data, and allocating a compression rate # imgabs4 # and encryption strength # imgabs5 # according to data type importance # imgabs3 #; s4, executing load balancing through an edge computing node, and distributing a computing task to an optimal node; and S5, storing the processed data into a distributed database according to spatial-temporal correlation, and generating a multi-copy disaster recovery index. Through dynamic acquisition, intelligent processing, optimized storage and adaptive strategy adjustment, accurate and efficient data acquisition is realized, data security and processing performance are flexibly guaranteed, storage query efficiency and disaster tolerance capability are improved, and storage resources are utilized to the maximum extent.

Description

Technical Field

[0001] The present invention relates to the field of technical data collection, and in particular to a data collection method, device and storage medium for industrial Internet of Things. Background Art

[0002] With the rapid development of industrial Internet of Things, data collection, processing and storage in industrial production are facing unprecedented challenges. Traditional data collection methods have exposed many problems in complex industrial environments.

[0003] In the data collection process, the previous sensor networks are often static and fixed, and it is difficult to adapt to the dynamic changes in the operating status of industrial equipment. For example, in some large factories, equipment requires data collection at different frequencies under different working conditions, but the traditional method cannot be adjusted in real time, resulting in either insufficient data collection affecting the equipment monitoring accuracy, or excessive data collection causing waste of resources. In addition, environmental noise interference seriously affects data quality, and traditional methods lack effective response strategies, making it difficult to ensure the accuracy and reliability of collected data.

[0004] For data processing, on the one hand, different types of data have different importance in industrial production, but the traditional data compression and encryption methods adopt a unified standard, which cannot specifically guarantee the security of important data, nor can they reasonably allocate storage resources. On the other hand, the task allocation of edge computing nodes lacks an effective load balancing mechanism, which is prone to single-point overload, resulting in performance degradation or even collapse of the entire system.

[0005] In terms of data storage, traditional distributed database storage methods lack sufficient consideration of the temporal and spatial characteristics of industrial data, and data query efficiency is low. When it is necessary to query device data at a specific time and location, it is often necessary to traverse a large amount of irrelevant data, which consumes a lot of time and resources. At the same time, the data's disaster recovery capacity is insufficient. Once the data is lost or damaged, it is difficult to recover quickly, which brings serious risks to industrial production.

[0006] To sum up, the existing industrial Internet of Things data collection, processing and storage technologies have many defects and cannot meet the industrial production's needs for efficient, reliable and secure data management. Summary of the invention

[0007] The purpose of the present invention is to provide a data collection method, device and storage medium for industrial Internet of Things to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: a data collection method for industrial Internet of Things, comprising the following steps: S1. Real-time collection: Build a dynamic sensor network to collect the operating data of industrial equipment in real time through multimodal sensors; S2. Frequency adjustment: based on environmental noise coefficient and device health index , dynamically adjust the data collection frequency , the formula is: ; in, is the reference frequency, is the noise change rate, is the noise threshold, is the maximum value of the health index; S3. Encryption and compression: Perform layered compression and encryption on the collected data, based on the importance of the data type. Allocate compression ratio and encryption strength ,satisfy: ; in, is the maximum value of the data type's importance; is the maximum value of encryption strength; S4. Load balancing: Load balancing is performed through edge computing nodes, and computing tasks are allocated to the optimal nodes to avoid single point overload; S5. Data storage: Store the processed data in a distributed database according to time and space correlation, and generate a multi-copy disaster recovery index.

[0009] Preferably, the device health index The calculation formula is: ; in, For the sensor parameter weights, For the Real-time values ​​of sensors, For the The nominal value of the sensor, For the The maximum value of the sensor parameter, For the The minimum value of a sensor parameter.

[0010] Preferably, in the layered compression encryption process, a dynamic switching strategy between the lightweight encryption algorithm LEA and the advanced encryption standard AES is adopted. Exceeding the preset threshold When the AES encryption algorithm is enabled, the lightweight encryption algorithm LEA is used for encryption. The condition for dynamic switching strategy is determined by the following formula: ; in, represents the safety factor, Indicates the current idle computing power of the edge node, Indicates the total computing power of edge nodes.

[0011] Preferably, the load balancing is performed by assigning weights to tasks. Dynamically select edge nodes, the weight formula is: ; in, To optimize the weight coefficient, For the The latency of the edge nodes, For the The idle CPU power of edge nodes, For the The length of the edge node task queue, For the The maximum length of the task queue of an edge node.

[0012] Preferably, the generation of the multi-copy disaster recovery index specifically includes the following steps: and importance , combined with the system preset critical safety factor , calculate the number of copies , the formula is: .

[0013] The data collection device of the industrial Internet of Things includes: a dynamic perception module, an edge computing gateway and a distributed storage engine; the dynamic perception module is used to perform data collection and frequency adjustment; the edge computing gateway integrates a lightweight encryption chip and an FPGA accelerator to achieve layered compression encryption and load balancing; the distributed storage engine supports time and space partitioning and automatic expansion and contraction of replicas, and the storage structure meets the following requirements: , the data will be divided into time slices , Equipment location identification and data priority Get organized.

[0014] Preferably, the edge computing gateway has a built-in fault self-recovery mechanism. When a node fails, the redundant path weight Rerouting data: ; in, represents the bandwidth of the path, Refers to the signal strength, is the number of hops, that is, the number of intermediate nodes that data needs to pass through from the source node to the target node. Represents delay.

[0015] Preferably, the distributed storage engine adopts column storage and time window partitioning, and the data query efficiency optimization formula is: ; in, Indicates the actual data query time. Indicates the benchmark query time, Indicates the cache hit rate, which refers to the proportion of data that can be directly obtained from the cache when querying data. Indicates the size of the data to be queried, that is, the amount of data involved in the query operation. Indicates I / O speed, which refers to the data reading and writing speed of the storage device.

[0016] The data storage medium of the industrial Internet of Things stores a computer program executable by a processor, and when executed by the processor, the steps of the data collection method of the industrial Internet of Things can be implemented, and the computer program also has a function based on data heat. Dynamically adjust storage strategies to meet data heat The calculation method is as follows: ; in, Indicates that in a specific time window The number of accesses to data within the Indicates the time that has passed since the data was last accessed; It is a data half-life parameter, which is used to describe the speed at which data popularity decays over time.

[0017] The data collection method, device and storage medium of the industrial Internet of Things proposed by the present invention have the following beneficial effects: 1. Dynamic and efficient data collection: By building a dynamic sensor network and dynamically adjusting the data collection frequency based on the environmental noise coefficient and equipment health index, data can be accurately collected in complex industrial environments, which not only ensures the integrity and accuracy of the data, but also avoids the waste of resources, effectively improves the efficiency and quality of data collection, and provides strong support for the accurate monitoring and fault prediction of industrial equipment.

[0018] 2. Intelligent and flexible data processing: The layered compression and encryption strategy allocates compression rate and encryption strength according to the importance of data type, which can not only ensure the security of important data, but also make rational use of storage resources. At the same time, the dynamic switching of lightweight encryption algorithm and advanced encryption standard, combined with the load balancing mechanism of edge computing nodes, dynamically allocates tasks according to the node's delay, idle computing power and task queue load, effectively avoiding single point overload, greatly improving the overall performance and stability of the system, and ensuring efficient and reliable data processing.

[0019] 3. Optimized data storage and management: Columnar storage and time window partitioning are used to store data in combination with time-space correlation, which significantly improves data query efficiency. Multi-copy disaster recovery indexes are generated based on data access frequency and importance, which enhances data disaster recovery capabilities and ensures data security and reliability. The distributed storage engine supports time-space partitioning and automatic expansion and reduction of replicas, further optimizing the data storage structure and facilitating data management and use.

[0020] 4. Adaptive storage strategy adjustment: Dynamically adjust the storage strategy based on data popularity, and reasonably allocate storage resources according to the actual usage of the data. For frequently accessed hot data, a more efficient storage method is provided to improve data access speed; for less popular data, a more resource-saving storage method is adopted to reduce storage costs and maximize the utilization of storage resources.

[0021] In summary, the present invention realizes accurate and efficient data collection, flexible protection of data security and processing performance, improved storage query efficiency and disaster recovery capabilities, and maximizes the use of storage resources through dynamic collection, intelligent processing, optimized storage, and adaptive strategy adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of the data collection method of the industrial Internet of Things of the present invention; Figure 2 This is a principle block diagram of the data acquisition device of the industrial Internet of Things of the present invention; Figure 3 A schematic diagram of the dynamic adjustment of data heat and storage strategy of the present invention. DETAILED DESCRIPTION

[0023] 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.

[0024] See also Figure 1-Figure 3 The present invention provides a technical solution: a data collection method for industrial Internet of Things, comprising the following steps: S1. Real-time collection: Build a dynamic sensor network to collect the operating data of industrial equipment in real time through multimodal sensors.

[0025] S2. Frequency adjustment: based on environmental noise coefficient and device health index , dynamically adjust the data collection frequency , the formula is: ;in, is the base frequency, which is the initial reference value of the data collection frequency; is the noise change rate, that is, the degree of change of environmental noise within a certain period of time; is the noise threshold. When the noise change rate exceeds this threshold, it indicates that the interference of environmental noise on data collection has reached the level that the collection frequency needs to be adjusted. The maximum value of the health index, used to set the current device health index Normalization is performed; this formula comprehensively considers the impact of environmental noise and equipment health on the acquisition frequency. When the environmental noise changes greatly ( Increased) or poor device health ( When the data rate decreases, the collection frequency should be appropriately increased to more closely monitor the equipment operation status; otherwise, the collection frequency should be reduced to save resources.

[0026] S3. Encryption and compression: Perform layered compression and encryption on the collected data, based on the importance of the data type. Allocate compression ratio and encryption strength ,satisfy: ;in, is the maximum value of the data type's importance; is the maximum value of encryption strength; For data of lower importance ( smaller), assigning a higher compression ratio ( The smaller, The larger the size), the less storage space is needed, but the encryption strength is relatively low ( , The smaller, The smaller the data is); while for data with high importance ( If the compression ratio is large, the compression ratio is reduced to ensure data integrity, while the encryption strength is increased to ensure data security. Is the maximum value of the data type importance, used to convert the actual importance value Perform normalization processing; It is the maximum value of encryption strength and serves as a reference for determining the upper limit of encryption strength for data of different importance.

[0027] S4. Load balancing: Load balancing is performed through edge computing nodes, and computing tasks are allocated to the optimal nodes to avoid single-point overload.

[0028] S5. Data storage: Store the processed data in a distributed database according to time and space correlation, and generate a multi-copy disaster recovery index.

[0029] The device health index The calculation formula is: ;in, For the sensor parameter weights, For the Real-time values ​​of sensors, For the The nominal value of the sensor, For the The maximum value of the sensor parameter, For the The minimum value of a sensor parameter; Equipment Health Index It is an index that comprehensively considers multiple sensor parameters and is obtained by weighted summing the evaluation results of each sensor parameter. The value range of this index is between 0 and 1. The closer the value is to 1, the better the health of the device; the closer the value is to 0, the more serious the fault or abnormality of the device is and the worse the health condition is. Represents the number of sensors. In industrial equipment, multiple sensors of different types are usually installed to monitor the operating status of different aspects of the equipment. It is the total number of these sensors used to assess the health of the equipment; For the Sensor parameter weights: Different sensors have different importance in determining the overall health of the device, so each sensor is assigned a weight. , the weight range is from 0 to 1, and the sum of all sensor weights satisfies ; For example, for a motor device, the weight of the temperature sensor is relatively high, because excessive motor temperature may cause serious failures, while some auxiliary sensors have lower weights; For the Real-time value of a sensor: that is, the data collected by the sensor at the current moment, such as the motor temperature value collected by the temperature sensor in real time, the internal pressure value of the device collected by the pressure sensor in real time, etc. For the The nominal value of a sensor, which is the value that the sensor parameter should reach when the device is in normal operation; More specifically, , which calculates the The deviation between the real-time value of each sensor and the nominal value is normalized to the range of 0 to 1. hour, =0, then =0, indicating that the sensor parameters are in normal state; when Deviation The farther, The larger the value of The closer the value is to 1, the more serious the deviation of the sensor parameter from the normal state is; The above deviation degree is converted so that the closer the value is to 1, the The closer a sensor parameter is to 0, the more abnormal it is. The weight of the sensor is taken into account, and the normality of each sensor is weighted to highlight the impact of important sensors on the equipment health index. For example, when the parameters of a sensor with a larger weight are abnormal, the impact on the equipment health index will be greater; By summing the weighted normality of all sensors, we can finally get the equipment health index, which can intuitively reflect the overall health of the equipment and can be used in the data collection frequency adjustment formula mentioned above to dynamically adjust the data collection frequency according to the equipment health status in order to more closely monitor the equipment status. For example, when the equipment health index is low, the data collection frequency can be appropriately increased to promptly detect possible equipment failures; when the equipment health index is high, the data collection frequency can be reduced to save system resources.

[0030] In the layered compression encryption process, a dynamic switching strategy between the lightweight encryption algorithm LEA and the advanced encryption standard AES is adopted. Exceeding the preset threshold When the AES encryption algorithm is enabled, the lightweight encryption algorithm LEA is used for encryption. The condition for dynamic switching strategy is determined by the following formula: ;in, Represents the safety factor, which is a constant set according to actual needs. Indicates the current idle computing power of the edge node, Indicates the total computing power of edge nodes.

[0031] The load balancing is done by assigning weights to tasks. Dynamically select edge nodes, the weight formula is: ;in, To optimize the weight coefficient, For the The latency of the edge nodes, For the The idle CPU power of edge nodes, For the The length of the edge node task queue, For the The maximum length of the edge node task queue; Threshold By formula Determine, among which, is the safety factor, which is a constant set according to actual needs. If the data security requirements are high, it can be appropriately increased. value; if you pay more attention to resource utilization efficiency, you can reduce value; Represents the current idle computing power of the edge node, reflecting the amount of resources currently available for encryption computing on the node; is the total computing power of the edge node. When the idle computing power of the edge node is higher and the safety factor is set higher, the threshold The higher the value, the more likely it is to use the AES encryption algorithm, and vice versa, the more likely it is to use the LEA algorithm; Weight formula Multiple factors that affect the processing capabilities of edge nodes are considered comprehensively; It is The lower the latency of the edge nodes, the lower the latency The larger the value, the more it is multiplied by the optimization weight coefficient After that, in the weight The larger the proportion of the node, the more suitable it is for processing tasks; this reflects the consideration of real-time data processing. The larger it is, the more the system values ​​node latency. Indicates The idle CPU computing power of edge nodes. The more idle computing power there is, the more it is multiplied by the optimization weight coefficient. Post-weight The greater the contribution, the more resources the node has to process new tasks. The size reflects the emphasis the system places on the node's CPU resource utilization. It is The length of the edge node task queue, is the maximum length of the node's task queue, Indicates the load level of the current task queue. It represents the remaining processing capacity, multiplied by the optimization weight coefficient After inclusion weight Calculation of The larger the value, the more the system pays attention to the load of the node task queue, avoiding assigning tasks to nodes with full task queues to prevent task backlogs.

[0032] The generation of the multi-copy disaster recovery index specifically includes the following steps: and importance , combined with the system preset critical safety factor , calculate the number of copies , the formula is: ; More specifically, Multiply the data access frequency, importance, and critical safety factor to get a value that comprehensively considers these three factors. The larger the value, the higher the importance and access demand of the data, and the more copies are needed to ensure its security and availability; The logarithm with base 2 is taken for the above product. The purpose of taking the logarithm is to scale the product result so that the number of copies calculated in the end is within a reasonable range. The base 2 is taken because in computer storage and data processing, data is usually stored and transmitted in binary form. This choice is in line with the characteristics of data storage and management. Use the ceiling function Process the logarithmic results to get the final number of copies , rounded up because the number of replicas must be an integer, and there must be enough replicas to meet disaster recovery requirements. For example, if the calculated result is 2.3, then rounded up to 3, that is, 3 replicas need to be created; According to the characteristics of different data, the appropriate number of copies can be dynamically allocated to each data item. For data with high access frequency and great importance, more copies will be generated to ensure that data can be quickly restored in the event of a failure to ensure business continuity; for data with low access frequency and low importance, fewer copies will be generated, thereby ensuring data security while rationally utilizing storage resources and reducing storage costs.

[0033] The data collection device of the industrial Internet of Things includes: a dynamic perception module, an edge computing gateway and a distributed storage engine; the dynamic perception module is used to perform data collection and frequency adjustment; the edge computing gateway integrates a lightweight encryption chip and an FPGA accelerator to achieve layered compression encryption and load balancing; the distributed storage engine supports time and space partitioning and automatic expansion and contraction of replicas, and the storage structure meets the following requirements: , the data will be divided into time slices , Equipment location identification and data priority Get organized.

[0034] The edge computing gateway has a built-in fault self-recovery mechanism. When a node fails, the redundant path weight Rerouting data: ;in, represents the bandwidth of the path, Refers to the signal strength, is the number of hops, that is, the number of intermediate nodes that data needs to pass through from the source node to the target node. represents delay; molecular Indicates the transmission capacity and stability of the path. The larger the bandwidth and the stronger the signal strength, the larger the numerator value, indicating that the path has better performance in data transmission. Represents the transmission cost of the path. The more hops and the greater the delay, the larger the denominator value, which means that the path will bring more overhead and delay during data transmission. Therefore, the weight The larger the value is, the better the comprehensive performance of the path in terms of transmission capacity, stability and transmission cost is, and the more suitable the path is for data transmission. When a node in the edge computing gateway fails, the system will calculate the weight of each redundant path according to the above formula ; Then, choose the weight The largest path is used as the new data transmission path to reroute the data to the target node. In this way, data transmission can be restored quickly and automatically to ensure the normal operation of the industrial Internet of Things system and improve the reliability and stability of the system. At the same time, the mechanism can also dynamically select the optimal path according to the real-time status of the network, make full use of network resources and improve the efficiency of data transmission.

[0035] The distributed storage engine adopts column storage and time window partitioning, and the data query efficiency optimization formula is: ;in, Indicates the actual data query time. Indicates the benchmark query time, Indicates the cache hit rate, which refers to the proportion of data that can be directly obtained from the cache when querying data. Indicates the size of the data to be queried, that is, the amount of data involved in the query operation. Indicates I / O speed, which refers to the data reading and writing speed of the storage device; More specifically, This part reflects the impact of cache on query time. When the cache hit rate is 100%, , this part of the time overhead is 0, which means that all data can be quickly obtained from the cache; when the cache hit rate is 0%, , at this time, the query time includes the benchmark query time; It reflects the time required to read data from the storage device. The larger the data volume and the slower the I / O speed, the longer this time will be. Through this formula, we can analyze that measures such as improving cache hit rate, reducing query data volume, and increasing the I / O speed of storage devices can effectively reduce the actual data query time and improve the data query efficiency of the distributed storage engine.

[0036] The data storage medium of the industrial Internet of Things stores a computer program executable by a processor, and when executed by the processor, the steps of the data collection method of the industrial Internet of Things can be implemented, and the computer program also has a function based on data heat. Dynamically adjust storage strategies to meet data heat The calculation method is as follows: ;in, Indicates that in a specific time window The number of accesses to the data within a preset time period (which is used to count the number of accesses to the data). This parameter reflects the recent level of attention paid to the data. The more accesses, the more popular the data. Indicates the time since the data was last accessed. As time goes by, if the data is not accessed for a long time, its popularity will gradually decrease; is the data half-life parameter, which is used to describe the speed at which data heat decays over time. The smaller the value, the faster the data heat decays; The larger the value, the slower the data heat decays.

[0037] The computer program stored in the storage medium has two functions: Implementing the steps of data collection method: The program can execute various steps of the data collection method of the industrial Internet of Things, build a dynamic sensor network, collect industrial equipment operation data in real time, dynamically adjust the collection frequency according to the environmental noise coefficient and equipment health index, compress and encrypt the collected data in layers, realize load balancing through edge computing nodes, and store the processed data in a distributed database according to time and space correlation and generate multiple copies of disaster recovery indexes, etc. These steps together constitute a complete data collection, processing and storage process, ensuring that the industrial Internet of Things system can efficiently and accurately obtain and manage equipment operation data; Dynamically adjust storage policies based on data popularity: This is an important feature of the program, which aims to optimize the allocation of storage resources based on the actual usage of data.

[0038] More specifically, The average access frequency of data in a specific time window is calculated, which reflects the current activity of the data; is an exponential decay function. As increases, the function value gradually decreases, so that the data heat decreases over time. By multiplying the two, we get an index that comprehensively reflects the data heat. .

[0039] The computer program will calculate the The value can be used to dynamically adjust the data storage strategy. For data with high popularity, since it is frequently accessed, a more efficient storage method can be used, such as storing it in a high-speed storage device (such as a solid-state drive) to increase the data access speed and reduce the query response time; for data with low popularity, a more space-saving storage method can be used, such as migrating it to a low-cost storage device such as a tape library to reduce storage costs. In this way, while meeting data access requirements, the optimal configuration of storage resources can be achieved, thereby improving the overall performance and economy of the industrial Internet of Things system.

[0040] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The data collection method of the industrial Internet of Things is characterized by: The following steps are involved: S1. Real-time collection: Build a dynamic sensor network to collect the operating data of industrial equipment in real time through multimodal sensors; S2. Frequency adjustment: based on environmental noise coefficient and device health index , dynamically adjust the data collection frequency , the formula is: ； in, is the reference frequency, is the noise change rate, is the noise threshold, is the maximum value of health index; S3. Encryption and compression: Perform layered compression and encryption on the collected data, based on the importance of the data type. Allocate compression ratio and encryption strength ,satisfy: ； in, is the maximum value of the data type's importance; is the maximum value of encryption strength; S4. Load balancing: Load balancing is performed through edge computing nodes, and computing tasks are allocated to the optimal nodes to avoid single point overload; S5. Data storage: Store the processed data in a distributed database according to time and space correlation, and generate a multi-copy disaster recovery index.

2. The data collection method of the industrial Internet of Things according to claim 1 is characterized in that: The device health index The calculation formula is: ； in, For the sensor parameter weights, For the Real-time values ​​of sensors, For the The nominal value of the sensor, For the The maximum value of the sensor parameter, For the The minimum value of a sensor parameter.

3. The data collection method of the industrial Internet of Things according to claim 2 is characterized in that: In the layered compression encryption process, a dynamic switching strategy between the lightweight encryption algorithm LEA and the advanced encryption standard AES is adopted. Exceeding the preset threshold When the AES encryption algorithm is enabled, the lightweight encryption algorithm LEA is used for encryption. The condition for dynamic switching strategy is determined by the following formula: ； in, represents the safety factor, Indicates the current idle computing power of the edge node, Indicates the total computing power of edge nodes.

4. The data collection method of the industrial Internet of Things according to claim 3 is characterized in that: The load balancing is done by assigning weights to tasks. Dynamically select edge nodes, the weight formula is: ； in, To optimize the weight coefficient, For the The latency of the edge nodes, For the The idle CPU power of edge nodes, For the The length of the edge node task queue, For the The maximum length of the task queue of an edge node.

5. The data collection method of the industrial Internet of Things according to claim 4 is characterized in that: The generation of the multi-copy disaster recovery index specifically includes the following steps: and importance , combined with the system preset critical safety factor , calculate the number of copies , the formula is: 。 6. A data acquisition device for industrial Internet of Things, which is applied to the data acquisition method for industrial Internet of Things according to claim 5, characterized in that: include: Dynamic sensing module, used to perform data collection and frequency adjustment; Edge computing gateway, integrating lightweight encryption chip and FPGA accelerator to achieve layered compression encryption and load balancing; Distributed storage engine, supports time-space partitioning and automatic expansion and contraction of replicas, and the storage structure meets the following requirements: , the data will be divided into time slices , Equipment location identification and data priority Get organized.

7. The data acquisition device of the industrial Internet of Things according to claim 6, characterized in that: The edge computing gateway has a built-in fault self-recovery mechanism. When a node fails, the redundant path weight Rerouting data: ； in, represents the bandwidth of the path, Refers to the signal strength, is the number of hops, that is, the number of intermediate nodes that data needs to pass through from the source node to the target node. Represents delay.

8. The data acquisition device of the industrial Internet of Things according to claim 7, characterized in that: The distributed storage engine adopts column storage and time window partitioning, and the data query efficiency optimization formula is: ； in, Indicates the actual data query time. Indicates the benchmark query time, Indicates the cache hit rate, which refers to the proportion of data that can be directly obtained from the cache when querying data. Indicates the size of the data to be queried, that is, the amount of data involved in the query operation. Indicates I / O speed, which refers to the data reading and writing speed of the storage device.

9. A data storage medium for industrial Internet of Things, characterized in that: The data storage medium of the industrial Internet of Things stores a computer program that can be executed by a processor. When executed by the processor, the steps of the data collection method of the industrial Internet of Things described in any one of claims 1 to 5 can be implemented, and the computer program also has a function based on data heat. Dynamically adjust storage strategies to meet data heat The calculation method is as follows: ； in, Indicates that in a specific time window The number of accesses to data within the Indicates the time that has passed since the data was last accessed; It is a data half-life parameter, which is used to describe the speed at which data popularity decays over time.

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