Storage configuration method, system and medium based on industrial Internet of Things data center

Abstract

The present invention provides a storage configuration method, system, and medium based on an industrial Internet of Things data center, relating to the field of data storage and management. The method comprises: an industrial Internet of Things management platform determines data upload parameters based on data attribute information, historical retrieval records, and historical processing records of the data to be processed; generates cache database configuration parameters and sub-library configuration parameters; generates a data upload instruction and sends it to an industrial Internet of Things sensor network platform; generates operating parameters of a second storage component and allocates storage space to the cache database; generates sub-library configuration instructions and sends them to the industrial Internet of Things sensor network platform. The industrial Internet of Things sensor network platform stores the data to be processed in the corresponding sub-library according to the data upload instruction and uploads it to the cache database or processing unit; generates operating parameters of the first storage component according to the sub-library configuration instructions and allocates storage space to the sub-library. This allows for more intelligent and efficient data storage and management.

Description

Technical Field

[0001] The present invention relates to the field of data storage and management, and in particular to a storage configuration method, system and medium based on an industrial Internet of Things data center. Background Art

[0002] With the advent of Industry 4.0, the Industrial Internet of Things (IIoT) has become a core driver of the intelligent transformation of manufacturing. In IIoT-enabled production lines, numerous sensors, intelligent devices, and control systems are interconnected, creating a highly collaborative, data-driven production environment. In such an environment, storing and managing large amounts of data is crucial to ensuring efficient production line operation. However, the amount of data generated by enterprises is growing exponentially with the increasing number of connected industrial devices. Different data storage strategies are required for different production processes, placing higher demands on data storage systems.

[0003] Therefore, it is crucial to develop a storage configuration method, system, and medium based on the industrial Internet of Things data center in order to store and manage data more intelligently and efficiently. Summary of the Invention

[0004] A storage configuration method based on an industrial Internet of Things (IIoT) data center is executed by an IIoT management platform of a storage configuration system based on the IIoT data center. The method includes: determining data upload parameters based on data attribute information, historical retrieval records, and historical processing records of the data to be processed; the data upload parameters indicating the storage strategy of the IIoT sensor network platform for the data to be processed; generating database configuration parameters based on the data upload parameters and data collection frequency; the database configuration parameters including cache database configuration parameters and sub-library configuration parameters; generating a data upload instruction based on the data upload parameters and sending the data upload instruction to the IIoT sensor network platform; generating operating parameters of a second storage component based on the cache database configuration parameters; controlling a second storage controller to allocate storage space to the cache database based on the operating parameters of the second storage component; generating a sub-library configuration instruction based on the sub-library configuration parameters, and sending the sub-library configuration instruction to the IIoT sensor network platform. The IIoT sensor network platform stores the data to be processed in the corresponding sub-library, either in the cache database or in the processing unit, based on the data upload instruction; generating operating parameters of a first storage component based on the sub-library configuration instruction; and controlling a first storage controller to allocate storage space to the sub-library based on the operating parameters of the first storage component. The industrial Internet of Things perception control platform collects the data to be processed and uploads it to the industrial Internet of Things sensor network platform. The first storage component and the second storage component correspond to different memories.

[0005] One or more embodiments of the present invention provide a storage configuration system based on an industrial Internet of Things data center, including: an industrial Internet of Things management platform, an industrial Internet of Things sensor network platform, and an industrial Internet of Things perception and control platform; the industrial Internet of Things management platform is communicatively connected with the industrial Internet of Things perception and control platform based on the industrial Internet of Things sensor network platform; the industrial Internet of Things management platform is configured to execute the above-mentioned storage configuration method based on the industrial Internet of Things data center.

[0006] One or more embodiments of the present invention provide a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the above-mentioned storage configuration method based on an industrial Internet of Things data center.

[0007] Beneficial effects: The storage configuration method, system and medium based on the industrial Internet of Things data center of the present invention determine the data upload parameters through the data attribute information, historical retrieval records and historical processing records of the data to be processed, and determine the data configuration parameters according to the data upload parameters, so as to dynamically configure the storage space size of the sub-database of the industrial Internet of Things sensor network platform and the cache database of the industrial Internet of Things management platform according to the data upload situation, which can avoid data loss caused by the fullness of some databases and the waste of resources caused by the idleness of some databases, and can effectively improve the utilization efficiency of storage space and alleviate storage pressure. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The present invention will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein:

[0009] Figure 1 is an exemplary schematic diagram of a storage configuration system based on an industrial Internet of Things data center according to some embodiments of the present invention;

[0010] Figure 2 is an exemplary flow chart of a storage configuration method based on an industrial Internet of Things data center according to some embodiments of the present invention;

[0011] Figure 3 is an exemplary schematic diagram of a data deployment model according to some embodiments of the present invention;

[0012] Figure 4 is an exemplary flow chart of determining database configuration parameters according to some embodiments of the present invention; DETAILED DESCRIPTION

[0013] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of the present invention. Those skilled in the art can apply the present invention to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0014] Flowcharts are used in this disclosure to illustrate the operations performed by systems according to embodiments of the present invention. It should be understood that the preceding and following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0015] Figure 1 This is an exemplary schematic diagram of a storage configuration system based on an industrial Internet of Things data center according to some embodiments of the present invention.

[0016] In some embodiments, as Figure 1 As shown, the storage configuration system based on the industrial Internet of Things data center may include an industrial Internet of Things user platform 110, an industrial Internet of Things service platform 120, an industrial Internet of Things management platform 130, an industrial Internet of Things sensor network platform 140, and an industrial Internet of Things perception control platform 150.

[0017] The IIoT user platform 110 is a platform for interacting with users. In some embodiments, the IIoT user platform 110 may include multiple users, including User 1, User 2, ..., User N. For example, an industrial enterprise may correspond to one or more users. In some embodiments, the IIoT user platform 110 is configured to receive user instructions and transmit them to the IIoT service platform 120.

[0018] In some embodiments, the IIoT user platform 110 may be a platform based on a desktop computer, a tablet computer, a laptop computer, a mobile phone, or other electronic devices capable of data processing and data communication.

[0019] The IIoT service platform 120 is a platform for acquiring IIoT service data. In some embodiments, the IIoT service platform 120 can interact bidirectionally with the IIoT management platform 130. For example, after the IIoT service platform 120 sends a user's instruction information to the IIoT management platform 130, the IIoT management platform 130 uploads the execution data corresponding to the instruction information to the IIoT service platform 120.

[0020] The Industrial Internet of Things (IIoT) management platform 130 is a platform for managing and controlling the storage configuration system based on the IIoT data center. In some embodiments, the IIoT management platform 130 may include a data center 131, a processing unit 132, and a second storage controller. Data center 131 includes a cache database that communicates with the sub-databases of the Industrial Internet of Things sensor network platform 140; processing unit 132 is a central processing unit for processing pending data; and the second storage controller is a device that performs necessary control over memory access according to certain timing rules, including control of address signals, data signals, and various command signals, enabling the master device (the device accessing the memory) to use the memory resources according to its requirements.

[0021] In some embodiments, the IIoT management platform 130 can read data to be processed from the data center 131. The data to be processed may include production operation data, factory monitoring data, product inspection data, etc. In some embodiments, the processing unit 132 can be used to process the start and end time points of the data to be processed and cached data, as well as to record the use of the processed data (including calls by users and other steps / programs, including the number of calls, the object initiating each call, and the time of the call).

[0022] In some embodiments, the Industrial Internet of Things management platform 130 may include multiple sub-platforms, including sub-platform 1, sub-platform 2, ..., sub-platform n. For example, a sub-platform may include a device management platform, a quality control platform, a security monitoring platform, etc. In some embodiments, the Industrial Internet of Things management platform 130 may be implemented based on a processor or server.

[0023] In some embodiments, the IIoT management platform 130 may interact with the IIoT sensor network platform 140 .

[0024] In some embodiments, the industrial Internet of Things management platform 130 is configured to determine data upload parameters based on data attribute information, historical retrieval records and historical processing records of the data to be processed, and instruct the industrial Internet of Things sensor network platform 140 on the storage strategy for the data to be processed.

[0025] In some embodiments, the IIoT management platform 130 is configured to generate database configuration parameters based on the data upload parameters and the data collection frequency; generate a data upload instruction based on the data upload parameters, and send it to the IIoT sensor network platform 140. The database configuration parameters include cache database configuration parameters and sharding database configuration parameters.

[0026] In some embodiments, the industrial Internet of Things management platform 130 is further configured to generate working parameters of the second storage component based on the cache database configuration parameters, and control the second storage controller to allocate storage space to the cache database; generate sub-library configuration instructions based on the sub-library configuration parameters and send them to the industrial Internet of Things sensor network platform 140.

[0027] In some embodiments, the IIoT sensor network platform 140 is a platform for data transmission within a storage configuration system based on an IIoT data center. In some embodiments, the IIoT sensor network platform 140 may include a first storage controller and a sub-database for storing pending data. The first storage controller is a device that performs necessary control over memory access according to specific timing rules, including control of address signals, data signals, and various command signals, enabling the master device (the device accessing the memory) to utilize the memory resources as required.

[0028] In some embodiments, the sub-databases may include multiple sub-databases, including sub-database 1, sub-database 2, ..., sub-database n, for storing different types of data to be processed. For example, the sub-databases may include a device maintenance database, a quality monitoring database, a safety management database, etc. In some embodiments, the IIoT sensor network platform 140 may be configured with communication devices or servers.

[0029] In some embodiments, the cache database of the data center 131 (located in the main memory, i.e., the first storage component) and the multiple sub-databases (located in different secondary memories, i.e., the second storage components) of the industrial Internet of Things sensor network platform 140 communicate through a wireless link (for example, transmitting the data to be processed uploaded by the client (industrial Internet of Things perception control platform 150)), where the bandwidth of different wireless links is different; the cache database and the sub-database include free disk space of varying sizes, and the free disk space can be mapped to different virtual storages (which can be used to store different types of data to be processed), and different virtual storages have different physical addresses for establishing communication links.

[0030] In some embodiments, the industrial Internet of Things sensor network platform 140 is configured to store the data to be processed into the corresponding sub-database, or upload it to the cache database, or upload it to the processing unit according to the data upload instruction.

[0031] In some embodiments, the IIoT sensor network platform 140 is further configured to generate operating parameters for the first storage component based on the sub-database configuration instruction and control the first storage controller to allocate storage space to the sub-database. The first storage component and the second storage component correspond to different memories.

[0032] In some embodiments, the IIoT sensor network platform 140 may interact with the IIoT perception control platform 150 .

[0033] The Industrial Internet of Things (IIoT) perception and control platform 150 is used to monitor and control the production process. It includes a data acquisition device. In some embodiments, the data acquisition device may include at least one of production equipment and auxiliary equipment deployed on the production line. Production equipment refers to equipment required for the production process itself; auxiliary equipment refers to equipment outside the production process, such as monitoring equipment and quality inspection equipment.

[0034] In some embodiments, the IIoT perception control platform 150 is configured to collect data to be processed and upload it to the IIoT sensor network platform 140. For example, the platform collects business data such as production operation data, factory monitoring data, and product testing data and uploads it to the IIoT sensor network platform 140.

[0035] Figure 2 This is an exemplary flow chart of a storage configuration method based on an industrial Internet of Things data center according to some embodiments of the present invention. In some embodiments, the storage configuration method based on the industrial Internet of Things data center is executed by the industrial Internet of Things management platform 130 based on the storage configuration system of the industrial Internet of Things data center. Figure 2 As shown, the storage configuration method process based on the industrial Internet of Things data center includes the following steps:

[0036] Step 210: Determine data upload parameters based on data attribute information, historical retrieval records, and historical processing records of the data to be processed.

[0037] The data to be processed may include data collected by the industrial Internet of Things perception control platform 150. For example, one or more of production operation data, factory monitoring data, product testing data, etc.

[0038] Data attribute information refers to attribute information related to the data to be processed. For example, the data type, data volume, and collection time of the data to be processed. The data type may also include the production process to which the data to be processed belongs.

[0039] In some embodiments, the data attribute information can be determined based on the data acquisition device type, acquisition time, and storage space occupied by the data to be processed itself corresponding to the industrial Internet of Things perception control platform 150 that uploads the data to be processed.

[0040] The historical retrieval record refers to the historical record of the retrieval of the data to be processed by the processing unit, cache database, etc. In some embodiments, the historical retrieval record may also include information such as data attribute information, retrieval method, and retrieval time of the retrieved data to be processed.

[0041] In some embodiments, the industrial Internet of Things management platform 130 can obtain historical retrieval records from the data reading logs of each sub-database of the industrial Internet of Things sensor network platform 140.

[0042] Historical processing records refer to records of processing data to be processed by the data center of the Industrial Internet of Things management platform 130. In some embodiments, historical processing records may include the start and end time points of processing the data to be processed and cached data in the cache database, as well as usage records of the processing results. The usage records of processing results include the number of times users or other computer programs or instructions have called the processing results, the object initiating each call, and the time of the call.

[0043] In some embodiments, the IIoT management platform 130 may obtain historical processing records from a data center.

[0044] In some embodiments, the data upload parameter indicates a storage strategy for the IIoT sensor network platform 140 for the data to be processed. The storage strategy may include storing the data to be processed in a corresponding sub-database, uploading the data to a cache database in the data center of the IIoT management platform 130, or uploading the data to a processing unit of the IIoT management platform 130.

[0045] In some embodiments, the industrial Internet of Things sensor network platform 140 can store or upload the data to be processed to a corresponding database or storage unit according to the data upload parameters.

[0046] In some embodiments, the industrial Internet of Things management platform 130 can filter out historical retrieval records and historical processing records of historical data to be processed that have the same or similar data type as the data to be processed based on the data attribute information of the data to be processed; determine the waiting time and the retrieval index based on the historical retrieval records and historical processing records; calculate the timeliness requirement of the data to be processed based on the waiting time and the call index; and determine the data upload parameters based on the timeliness requirement.

[0047] The waiting processing time is the time from when the historical data to be processed is uploaded to the sub-database of the industrial Internet of Things sensor network platform 140 to when the industrial Internet of Things management platform 130 starts processing the historical data to be processed.

[0048] The call index is used to indicate how frequently the data to be processed is called. It is positively correlated with the frequency of historical call records and negatively correlated with the initial call interval. The initial call interval refers to the time between the time the historical data to be processed is stored in the Industrial Internet of Things sensor network platform 140 and when it is first called.

[0049] The timeliness requirement represents the timeliness of the data to be processed. In some embodiments, the timeliness requirement can be calculated using the following formula:

[0050] (1)

[0051] in, is a coefficient less than 0, is a coefficient greater than 0. It can be understood that the shorter the waiting time, the greater the called index and the higher the timeliness requirement.

[0052] In some embodiments, when the timeliness requirement is greater than or equal to a first threshold, the data upload parameter is to upload the pending data to the processing unit of the industrial Internet of Things management platform 130. When the first threshold is greater than the timeliness requirement and greater than or equal to a second threshold, the data upload parameter is to upload the pending data to a cache database in the data center of the industrial Internet of Things management platform 130. When the timeliness requirement is less than the second threshold, the data upload parameter is to store the pending data in the corresponding sub-database. The first and second thresholds are determined by technicians based on prior experience, and the first threshold is greater than the second threshold.

[0053] Step 220: Generate database configuration parameters based on the data upload parameters and the data collection frequency.

[0054] The data collection frequency refers to the frequency at which the data collection device collects the data to be processed.

[0055] In some embodiments, the industrial Internet of Things management platform 130 can obtain the current data collection frequency from the industrial Internet of Things perception control platform 150.

[0056] Database configuration parameters are parameters used to configure the storage space of a database. In some embodiments, database configuration parameters include cache database configuration parameters and shard database configuration parameters. The cache database configuration parameters are used to configure the storage space of the cache database in the data center of the Industrial Internet of Things management platform 130, while the shard database configuration parameters are used to configure the storage space of at least one shard database of the Industrial Internet of Things sensor network platform 140.

[0057] In some embodiments, the industrial Internet of Things management platform 130 can determine the database (such as a cache database, a sub-database, etc.) for storing the data to be processed based on the data upload parameters; determine the amount of data to be stored in the future time period based on the data collection frequency; calculate the future remaining space of the database for storing the data to be processed based on the amount of data stored in the future time period and the current remaining space of the database for storing the data to be processed; in response to the future remaining space being less than a preset space threshold (such as 10% of the database), reallocate the storage space size of the database so that its future remaining space exceeds the preset space threshold.

[0058] In some embodiments, in each preset cycle, the industrial Internet of Things management platform 130 can update the data collection frequency of the current cycle according to the database configuration parameters of the previous cycle to obtain an update frequency; based on the update frequency, the industrial Internet of Things perception control platform 150 is used to control the data collection device to perform data collection.

[0059] The preset period refers to the period for updating the database configuration parameters. In some embodiments, the preset period is negatively correlated with the frequency of collecting the data to be processed.

[0060] Update frequency refers to the frequency of data collection after update.

[0061] In some embodiments, the industrial Internet of Things management platform 130 can query the collection frequency table according to the database configuration parameters of the previous cycle to obtain the update frequency.

[0062] In some embodiments, the collection frequency table is preset by a technician. In the collection parameter table, the closer the storage space occupied by the cache database and sub-databases in the database configuration parameters is to the total storage space of the storage device, the lower the data collection frequency. It is understood that the closer the storage space occupied by the cache database and sub-databases is to the total storage space of the storage device, the greater the storage pressure and the more likely it is to encounter insufficient storage space. Therefore, reducing the data collection frequency can alleviate storage pressure on the storage device.

[0063] In some embodiments of the present invention, the data collection frequency of the data collection parameters is adjusted by determining the update parameters based on the database configuration parameters of the previous cycle, which can effectively avoid the problem of data loss due to insufficient storage space.

[0064] Step 230: Generate a data upload instruction based on the data upload parameters and send the data upload instruction to the industrial Internet of Things sensor network platform.

[0065] In some embodiments, the industrial Internet of Things management platform 130 may send the data upload parameters as data upload instructions to the industrial Internet of Things sensor network platform 140 .

[0066] In some embodiments, the industrial Internet of Things sensor network platform 140 can store the data to be processed in the corresponding sub-database, or upload it to the cache database, or upload it to the processing unit according to the data upload instruction.

[0067] Step 240: Generate operating parameters of the second storage component according to the cache database configuration parameters.

[0068] The second storage component refers to a component within the Industrial Internet of Things (IIoT) management platform 130 that implements data storage functions, such as memory. In some embodiments, the first storage component and the second storage component correspond to different memories. The first storage component corresponds to the sub-databases of the Industrial Internet of Things (IIoT) sensor network platform 140, while the second storage component corresponds to the cache database of the IIoT management platform 130. Different sub-databases are located in different first storage components. Different sub-databases and cache databases are configured as free disk space within different first and second storage components, respectively, to store different types of data to be processed.

[0069] In some embodiments, the cache database of the IIoT management platform 130 and one or more sub-databases of the IIoT sensor network platform 140 are connected via wired and / or wireless communication. The data transmission bandwidth of the communication link between the cache database and different sub-databases is different.

[0070] The operating parameter of the second storage component refers to a parameter for allocating the storage space size of the cache database. In some embodiments, the industrial Internet of Things management platform 130 can use the storage space size of the cache database in the cache database configuration parameter as the operating parameter of the second storage component.

[0071] Step 250: Based on the operating parameters of the second storage component, control the second storage controller to allocate storage space to the cache database.

[0072] In some embodiments, the second storage component can determine the space difference based on the storage space size of the cache database in the working parameters and the current storage space size of the cache database; divide the unused storage space from the second storage component and incorporate it into the cache database according to the space difference to allocate storage space to the cache database.

[0073] Step 260: Generate a sub-library configuration instruction based on the sub-library configuration parameters, and send the sub-library configuration instruction to the industrial Internet of Things sensor network platform.

[0074] In some embodiments, the industrial Internet of Things management platform 130 may use sub-library configuration parameters as sub-library configuration instructions.

[0075] In some embodiments, the industrial Internet of Things sensor network platform 140 can generate working parameters of the first storage component according to the sub-database configuration instruction; and control the first storage controller to allocate storage space to the sub-database based on the working parameters of the first storage component.

[0076] The operating parameter of the first storage component refers to the operating parameter for allocating the storage space size of the sub-database. In some embodiments, the industrial Internet of Things management platform 130 can use the storage space size of at least one sub-database included in the sub-database configuration instruction as the operating parameter of the first storage component.

[0077] The specific method of controlling the first storage controller to allocate storage space to the sub-database is similar to the method of controlling the second memory to allocate storage space to the cache database. For details, please refer to the above-mentioned related content.

[0078] In some embodiments of the present invention, data upload parameters are determined by data attribute information, historical retrieval records and historical processing records of the data to be processed, and data configuration parameters are determined based on the data upload parameters, so as to dynamically configure the storage space size of the sub-databases of the industrial Internet of Things sensor network platform 140 and the cache database of the industrial Internet of Things management platform 130 according to the data upload situation. This can avoid data loss caused by the fullness of some databases and resource waste caused by the idleness of some databases, effectively improve the utilization efficiency of storage space, and alleviate storage pressure.

[0079] In some embodiments, the industrial Internet of Things management platform 130 can also obtain the total amount of storage space allocated or released by the sub-database for the task process within a predetermined time period from the first storage controller, and use the sub-database whose total amount of allocated or released storage space is greater than a set threshold as the target database; modify the configuration file and structured query language of the target sub-database to modify the maximum number of connections allowed by the target sub-database.

[0080] The predetermined time period refers to a past time period. In some embodiments, the predetermined time period can be preset by a technician, such as the past hour, the past day, etc.

[0081] A task process refers to a task or instruction executed by the Industrial Internet of Things, such as querying data or analyzing data.

[0082] The storage space allocated to a task process refers to the storage space allocated to all task processes for executing instructions. The storage space released to a task process refers to the storage space released after the task process ends.

[0083] The size of the threshold can be preset by technicians.

[0084] The target database refers to the sub-database that needs to adjust the configuration file and structured query language.

[0085] A configuration file is a computer file that divides the maximum number of database connections based on the hardware. For example, the MySQL database configuration file my.ini or my.cnf file.

[0086] Structured Query Language refers to the language that determines the maximum number of connections in the database configuration file, for example, the max_connections language in the mysqld topic.

[0087] In some embodiments, when the total amount of allocated or released storage space exceeds a set threshold, the IIoT management platform 130 can modify the programming language in the configuration file that determines the maximum number of connections to increase the maximum number of connections allowed by the target sub-database. For example, the program code in the programming language that determines the maximum number of connections can be modified to increase the maximum number of connections allowed by the target sub-database.

[0088] It can be understood that when the total amount of allocated or released storage space exceeds the set threshold, it means that the sub-database is read and written more frequently, and the maximum number of connections for the sub-database can be increased to improve access concurrency.

[0089] In some embodiments of the present invention, the maximum number of connections allowed by a sub-database is adjusted by the total amount of storage space allocated or released by the sub-database for the task process, thereby avoiding congestion and queuing when accessing the sub-database and improving the access efficiency of the sub-database.

[0090] It should be noted that the above description of the storage configuration method process for an Industrial Internet of Things data center is for illustrative purposes only and does not limit the scope of application of the present invention. Those skilled in the art will appreciate that various modifications and variations to the storage configuration method process for an Industrial Internet of Things data center can be made under the guidance of the present invention. However, such modifications and variations remain within the scope of the present invention.

[0091] Figure 3 is an exemplary schematic diagram of a data deployment model according to some embodiments of the present invention.

[0092] In some embodiments, the industrial Internet of Things management platform 130 can determine the uploaded analysis data based on the data popularity, data attribute information of the data to be processed, historical retrieval records, historical processing records, the processing capability strength of the industrial Internet of Things management platform 130, and the data collection frequency through a data allocation model; the data allocation model is a neural network model; based on the uploaded analysis data, the data upload parameters are determined.

[0093] Data heat refers to a value that represents the frequency with which the data to be processed will be called in the future. In some embodiments, the IIoT management platform 130 may use the average frequency of historical calls for data to be processed of the same data type as the data heat of the data to be processed.

[0094] Processing capacity intensity is data that represents the processing performance of the processing devices (e.g., processors, servers, etc.) of the IIoT management platform 130. For example, the computing frequency of the processing devices can be measured. In some embodiments, the IIoT management platform 130 can determine the processing capacity intensity based on the performance parameters of the processing devices. These performance parameters are provided by the manufacturers of the processing devices.

[0095] Upload analysis data refers to analyzing the degree of recommendation for uploading the data to be processed to different databases. In some embodiments, the upload analysis data is represented as a vector (x, y, z), where x, y, and z can be values ​​between 0 and 1, representing the degree of recommendation for uploading the data to be processed to the sub-database of the IIoT sensor network platform 140, the cache database of the IIoT management platform 130, and the processing unit of the IIoT management platform 130, respectively. A larger value indicates a higher recommendation.

[0096] The data allocation model refers to a model used to predict uploaded analysis data. In some embodiments, the data allocation model can be a neural network model (NN).

[0097] like Figure 3 As shown, in some embodiments, the input of the data allocation model 370 includes the data heat 310 of the data to be processed, data attribute information 320, historical retrieval records 330, historical processing records 340, the sample data collection frequency 350 of the collection device that collects the sample data to be processed, and the processing capacity strength 360 of the industrial Internet of Things management platform, and the output includes uploaded analysis data 380. For relevant descriptions of the data attribute information of the data to be processed, historical retrieval records, historical processing records, and data collection frequency, see Figure 2 and its related descriptions.

[0098] In some embodiments, the data allocation model can be trained based on a large number of first training samples with first labels. The first training samples include sample data popularity, sample data attribute information, sample historical retrieval records, sample historical processing records, sample processing capability strength of the industrial Internet of Things management platform 130, and sample data collection frequency.

[0099] In some embodiments, the IIoT management platform 130 can use historical data, including the popularity of the sample data to be processed, data attribute information, historical retrieval records, historical processing records, the processing capacity of the IIoT management platform 130, and the sample data collection frequency of the collection device that collected the sample data to be processed, as the first training sample. The first label can be constructed based on the historical data upload parameters of the sample data to be processed. For example, if the sample data to be processed was uploaded to the processing platform's cache database during the second historical period, the first label would be (0, 1, 0).

[0100] In some embodiments, the industrial Internet of Things management platform 130 may also construct a first label based on the timeliness requirement of the sample data to be processed. For example, for sample data to be processed whose timeliness requirement is greater than the first threshold, the first label may be (0, 0, 1), which indicates that the data to be processed is uploaded to the processing unit of the industrial Internet of Things management platform 130; for sample data to be processed whose timeliness requirement is between the first threshold and the second threshold, the first label may be (0, 1, 0), which indicates that the data to be processed is uploaded to the cache database of the industrial Internet of Things management platform 130; for sample data to be processed whose timeliness requirement is less than the second threshold, the first label may be (1, 0, 0), which indicates that the data to be processed is uploaded to the sub-database of the industrial Internet of Things sensor network platform 140; For relevant descriptions of the timeliness requirement, the first threshold, and the second threshold, see Figure 2 and its related descriptions.

[0101] In some embodiments, the industrial Internet of Things management platform 130 can input one or more first training samples into the initial data allocation model to obtain uploaded analysis data output by the initial data allocation model; based on the uploaded analysis data output by the initial data allocation model and the first label corresponding to the one or more first training samples, substitute them into the formula of a predefined loss function to calculate the value of the loss function; based on the value of the loss function, reversely update the model parameters in the initial data allocation model, and the model parameter updating method may include gradient descent method, etc.; when the iteration completion condition is met, the model training ends and a trained data allocation model is obtained. The iteration completion condition may include the loss value being less than the loss threshold, the number of iterations reaching the maximum number of iterations, etc.

[0102] In some embodiments, the training phase of the data deployment model may include an initial phase and an enhanced phase; in the initial training phase, the training sample data set is obtained based on a general data set on the cloud platform; in the enhanced training phase, the training sample data set is obtained based on a historical data set based on the target enterprise.

[0103] The initial stage refers to the stage in which the initial data allocation model is trained for the first time to obtain the data allocation model.

[0104] The general data set includes historical data to be processed collected by the industrial Internet of Things systems from different enterprises, as well as its data popularity, data attribute information, historical retrieval records, historical processing records, the processing capability strength of the industrial Internet of Things management platform 130, the sample data collection frequency of the collection device, and historical data upload parameters.

[0105] In some embodiments, the general data set is stored in a cloud platform, which can be implemented based on a cloud server connected to the industrial Internet of Things systems of different enterprises. The industrial Internet of Things systems of different enterprises can upload the above data to the cloud platform regularly or irregularly.

[0106] In some embodiments, the industrial Internet of Things management platform 130 can obtain the initial stage training data set based on the general data set according to the aforementioned method of determining the first training sample and its first label.

[0107] The reinforcement phase is the phase where the data allocation model trained in the initial phase is personalized. For example, the training aims to enhance the applicability of the data allocation model to the current enterprise.

[0108] In some embodiments, the target enterprise may include an enterprise of the same or similar type as the enterprise described in the current industrial Internet of Things system. The same or similar refers to the same or similar business scope, enterprise product type, etc.

[0109] A historical dataset refers to a dataset generated by the target enterprise's IIoT system during historical periods of time when it collected data to be processed. In some embodiments, the historical dataset includes the data popularity, data attribute information, historical retrieval records, historical processing records, the processing capacity of the IIoT management platform 130, and the sample data collection frequency and data upload parameters of the collection device used to collect the sample data to be processed.

[0110] In some embodiments, the industrial Internet of Things management platform 130 can obtain the training data set for the reinforcement phase based on the historical data set of the target enterprise according to the aforementioned method of determining the first training sample and its first label.

[0111] In some embodiments, the training sample data set includes multiple sample subsets, and the labels corresponding to the training data in the same sample subset are the same; and the learning rates corresponding to the training data in different sample subsets are different.

[0112] A sample subset refers to a set consisting of a portion of training sample data. For example, a set consisting of a portion of first training samples and their corresponding first labels. In some embodiments, the IIoT management platform 130 may treat first training samples with the same first label as a sample subset.

[0113] In some embodiments, when training a data deployment model based on different sample subsets, the IIoT management platform 130 can set different learning rates for different sample subsets. For example, the IIoT management platform 130 can set the learning rate of the sample subset with the first label (0, 0, 1) to be the sample subset with the highest learning rate, and set the learning rate of the sample subset with the first label (1, 0, 0) to be the sample subset with the lowest learning rate.

[0114] It's understandable that if the data to be processed needs to be uploaded to the processing unit of the Industrial Internet of Things management platform 130, it means that the data to be processed needs to be processed more quickly. Therefore, setting the highest learning rate for the subset of samples with the first label (0,0,1) (i.e., the data to be processed is uploaded to the processing unit of the Industrial Internet of Things management platform 130) allows the trained data allocation model to more quickly perceive the data to be processed that needs to be uploaded to the processing unit as soon as possible.

[0115] In some embodiments of the present invention, by setting different learning rates for different sample subsets, the training efficiency of the data allocation model can be improved, and the perception effect of the data allocation model on different data to be processed can be enhanced.

[0116] In some embodiments of the present invention, the data allocation model is initially trained using a training data set determined by a general data set, which can improve the versatility of the data allocation model; based on the data allocation model trained in the initial stage, intensive training is performed using a training data set determined by the historical data set of the target enterprise, which can more quickly obtain a data allocation model that is compatible with the current enterprise, improve the model's pertinence, and thus improve the accuracy of the uploaded analysis data output by the model.

[0117] In some embodiments, the IIoT management platform 130 may use the data upload parameter with the highest value among the uploaded analysis data as the data upload parameter for the currently processed data. For example, if the uploaded analysis data is (0.23, 0.76, 0.37), the value (0.76) corresponding to the cache database uploaded to the IIoT management platform 130 is the highest, and therefore the data upload parameter is the cache database uploaded to the IIoT management platform 130.

[0118] In some embodiments, the industrial Internet of Things management platform 130 can determine the data collection quality based on the device operating status, device health status, and collection bandwidth quality; and determine the data upload parameters based on the data collection quality and uploaded analysis data.

[0119] The device operating status refers to the operating status of the data acquisition device. In some embodiments, the device operating status may include voltage data, current data, and environmental data (such as temperature and humidity) from the data acquisition device. In some embodiments, the Industrial Internet of Things management platform 130 can directly read the voltage and current data from the data acquisition device through the Industrial Internet of Things perception and control platform 150. By acquiring environmental data from temperature sensors, humidity sensors, and other sensors in the same environment as the data acquisition device, the device operating status can be determined.

[0120] The device health status is used to characterize the stability of the data acquisition device. In some embodiments, the IIoT management platform 130 can obtain fault records and maintenance records of the data acquisition device. The lower the fault frequency of the data acquisition device in the fault records and the more maintenance records, the better the device health status.

[0121] Collection bandwidth quality indicates the quality of the data collection device uploading the collected, unprocessed data to the IIoT sensor network platform 140. In some embodiments, the IIoT management platform 130 can determine the collection bandwidth quality based on the packet loss rate and transmission delay when the IIoT sensor network platform 140 receives the unprocessed data collected by the data collection device. The lower the packet loss rate and transmission delay, the higher the collection bandwidth quality.

[0122] Data collection quality represents the quality of data collected and uploaded by data collection equipment.

[0123] In some embodiments, the industrial Internet of Things management platform 130 can construct a collection quality vector based on the device operating status, device health status, and collection bandwidth quality; retrieve the reference vector with the highest similarity in the quality vector database based on the collection quality vector; and use the reference collection quality corresponding to the reference vector as the data collection quality.

[0124] The quality vector database includes a large number of reference vectors constructed based on historical data and their corresponding reference acquisition qualities. In some embodiments, the IIoT management platform 130 can construct reference vectors based on the historical device operating status, historical health status, and historical acquisition bandwidth quality corresponding to the unprocessed data in the historical data, and mark the reference acquisition quality corresponding to the reference vectors based on whether the unprocessed data contains missing data or data errors during use, thereby constructing the quality vector database.

[0125] In some embodiments, the industrial Internet of Things management platform 130 can determine a quality threshold based on the uploaded analysis data, where the data upload parameter is the value of the recommended degree of the processing unit uploaded to the industrial Internet of Things management platform 130, and the quality threshold is positively correlated with the value of the recommended degree of the processing unit uploaded to the industrial Internet of Things management platform 130; the data upload parameter of the to-be-processed data whose data collection quality is lower than the quality threshold is set to be uploaded to the corresponding sub-database in the industrial Internet of Things sensor network platform 140.

[0126] In some embodiments of the present invention, the data collection quality is determined by the device operating status, device health status and collection bandwidth quality of the data collection device, thereby determining the data upload parameters. This can avoid uploading the processed data with poor data collection quality directly to the industrial Internet of Things management platform 130, which causes the industrial Internet of Things management platform 130 to spend a lot of computing resources for data cleaning, and can reduce the operating pressure of the industrial Internet of Things management platform 130.

[0127] In some embodiments of the present invention, by determining the upload analysis data through a data allocation model and determining the data upload parameters based on the upload analysis parameters, more reasonable data upload parameters can be obtained, so that the unprocessed data that needs to be processed by the processing unit in a timely manner is uploaded to the industrial Internet of Things management platform 130 in a timely manner, thereby improving the efficiency of data flow.

[0128] Figure 4 FIG. 1 is an exemplary flow chart of a process for determining database configuration parameters according to some embodiments of the present invention. The process for determining database configuration parameters includes the following steps:

[0129] Step 410: Determine the estimated call time based on the historical call records and historical processing records.

[0130] The estimated call time refers to the estimated time when the data to be processed in the database is called. For example, it refers to the time point in the future when the processing unit of the IIoT management platform 130 calls the data to be processed from the cache database or the sub-database of the IIoT sensor network platform 140.

[0131] In some embodiments, the industrial Internet of Things management platform 130 can use the average of the time points between the time points when historical to-be-processed data and cached data of the same or similar data types in historical retrieval records and historical processing records are retrieved or processed and the time points when the historical to-be-processed data are collected as the estimated call duration; and determine the estimated call time based on the time points when the to-be-processed data are collected and the estimated call duration.

[0132] In some embodiments, since the data to be processed may be called multiple times, the industrial Internet of Things management platform 130 can determine the first estimated call time of the data to be processed based on the time when multiple historical data to be processed were first called or processed; determine the second estimated call time of the current data to be processed based on the time when multiple historical data to be processed were called or processed for the second time; and so on; thereby obtaining multiple estimated call times of the data to be processed.

[0133] Step 420: Determine the space release rate based on the estimated call time.

[0134] The space release rate refers to the rate at which old data in the database is cleaned up. For example, the amount of old data cleaned up per unit time in the next preset cycle. For more information about the preset cycle, see Figure 2 and its related descriptions.

[0135] In some embodiments, the industrial Internet of Things management platform 130 will use the pending data that has been called a maximum number of times within the next preset cycle as the pending data that needs to be cleaned within the next cycle, where the maximum number of times can be preset by technical personnel; determine the amount of pending data that needs to be cleaned in the next preset cycle based on the amount of pending data that needs to be cleaned; calculate the space release rate based on the amount of pending data that needs to be cleaned and the duration of the next preset cycle.

[0136] In some embodiments, the industrial Internet of Things management platform 130 can also determine the data call threshold based on data attribute information, current database type, and processing capability strength; and determine the space release rate based on the data call threshold and estimated call time.

[0137] The current database type refers to the type of database where the data to be processed is located, for example, cache database, distributed database, etc.

[0138] The data call threshold refers to the maximum number of times that the pending data is called. In some embodiments, when the number of times the pending data in a database (such as a cache database, a sub-database, etc.) is called reaches the data call threshold, the database will clear the pending data.

[0139] In some embodiments, the industrial Internet of Things management platform 130 can construct a call feature vector based on data attribute information, current database type, and processing capability; retrieve the reference call vector with the highest similarity from the call vector database based on the call feature vector; and use the reference call threshold corresponding to the reference call vector as the data call threshold. Figure 2 For a description of the processing power, see Figure 3 and its related descriptions.

[0140] The call vector database includes a large number of reference call vectors and their corresponding reference call thresholds. In some embodiments, the IIoT management platform 130 can construct reference call vectors based on historical data attribute information, historical database type, and historical processing capacity strength corresponding to the historical data to be processed. The actual number of calls corresponding to the historical data to be processed is used as the reference call threshold for the reference call vector, thereby constructing the call vector database.

[0141] In some embodiments, the industrial Internet of Things management platform 130 may treat the pending data whose call times reach the data call threshold within the next preset period as the pending data that needs to be cleaned up, and determine the space release rate according to the above method.

[0142] In some embodiments of the present invention, a more reasonable space release rate can be obtained by determining a database data call threshold through data attribute information, current database type and processing capability strength and using it to determine a space release rate.

[0143] Step 430: Determine database configuration parameters based on data upload parameters, space release rate, and data collection frequency.

[0144] In some embodiments, if the data upload parameter is a sub-database uploaded to the Industrial Internet of Things sensor network platform 140, the Industrial Internet of Things management platform 130 can calculate the data storage rate of the sub-database based on the data collection frequency corresponding to all the data to be processed in the sub-database as needed; based on the current remaining storage space of the sub-database, the data storage rate and the space release rate, calculate whether there is a moment in the next preset period when the remaining storage space of the sub-database is less than 0. If not, it means that there is sufficient storage space in the next preset period and there is no need to adjust the sub-database configuration parameters of the sub-database; if it exists, it means that there is insufficient storage space in the next preset period, then the total data volume is calculated based on the current data volume, data storage rate and space release rate of the sub-database, and the storage space required for the total data volume is used as the sub-database configuration parameter. For example, there are currently 3GB of data in the sub-database, the data storage rate in the next preset period is 0.4GB / hour, the space release rate is 0.2GB / hour, and the duration of the preset period is 24 hours. Then the storage space required by the end of the next preset period is , then configure the sub-database to 7.8GB.

[0145] In some embodiments, the database configuration parameters also include cache update parameters. The industrial Internet of Things management platform 130 can also determine the cache update parameters based on the data attribute information corresponding to the cache data in the cache database, the user enterprise type, the historical retrieval records, the historical processing records, the device health status, and the device operation status; and, based on the cache update parameters, update the update configuration parameters of the second storage component.

[0146] Cache update parameters refer to parameters related to reconfiguring the storage space of the cache database. In some embodiments, cache update parameters may include an update period and an update amount. The update amount refers to the amount of adjustment to the storage space of the cache database, such as the amount of data deleted / overwritten during the update process.

[0147] Cache data refers to data stored in a cache database, such as data to be processed and / or its processing results stored in the cache database.

[0148] The user enterprise type refers to the type of enterprise in the industrial Internet of Things system, such as a hardware processing enterprise, an electronic component manufacturing enterprise, etc. In some embodiments, the user enterprise type can be set by the user.

[0149] In some embodiments, the industrial Internet of Things management platform 130 can input data attribute information, user enterprise type, historical retrieval records, historical processing records, device health status, and device operation status into an update parameter determination model to obtain cache update parameters. The update parameter determination model can be a recurrent neural network (RNN) model. For more information about data attribute information, historical retrieval records, and historical processing records, see Figure 2 For details about the device health status and device operation status, see Figure 3 and its related descriptions.

[0150] In some embodiments, the update parameter determination model can be trained based on a large number of second training samples with second labels. The second training samples can include sample data attribute information of sample to-be-processed data in the sample cache database, sample user and enterprise type, sample historical retrieval records, sample historical processing records, sample device health status, and sample device operating status. The second label can be the actual cache update parameter corresponding to the sample cache database.

[0151] In some embodiments, the industrial Internet of Things management platform 130 can construct a second training sample based on the historical data attribute information, historical user enterprise type, historical retrieval record, historical processing record, historical equipment health status, and historical equipment operation status corresponding to the cache data of different historical periods in the historical data, and use the validity period of the historical cache data of the historical period corresponding to the second training sample as the update period, and use the amount of data in the cache database when the update period is reached as the update amount, thereby obtaining a second label. Among them, the validity period refers to the length of time from the time the historical cache data is stored in the cache database to the time it is deleted. The training process of the model is determined by updating the parameters. Figure 3 The training process of the data allocation model is similar. For more information, please refer to Figure 3 and its related descriptions.

[0152] In some embodiments, the cache update parameter is further related to data acquisition quality, acquisition bandwidth quality, and data storage rate.

[0153] The data storage rate refers to the amount of data stored in the database per unit time. In some embodiments, the industrial Internet of Things management platform 130 can calculate the data storage rate based on the data collection frequency of the data stored in the cache database and the amount of data collected each time. For example, the data collection device collects 50MB of data each time, and the data collection frequency is 3 times / hour, then the data storage rate can be calculated as .

[0154] In some embodiments, the update period in the cache update parameters is positively correlated with the data acquisition quality and the acquisition bandwidth quality, and negatively correlated with the data storage rate. For example, the industrial Internet of Things management platform 130 can adjust the cache update parameters based on the data acquisition quality, the acquisition bandwidth quality and the data storage rate to obtain the adjusted cache update parameters. For more information about data acquisition quality and acquisition bandwidth quality, see Figure 3 and its related descriptions.

[0155] It can be understood that for cached data with high data collection quality and collection bandwidth quality, the industrial Internet of Things management platform 130 may call it multiple times, so the update cycle can be extended; for cached data with a high data storage rate, the storage space of the cache database may be filled up more quickly, so the update cycle can be shortened to avoid data loss caused by insufficient cache database.

[0156] In some embodiments of the present invention, cache update parameters are adjusted based on data acquisition quality, acquisition bandwidth quality, and data storage rate. The adjustment requirements of the cache database can be combined with actual needs to retain high-quality data while ensuring sufficient storage space in the cache database.

[0157] In some embodiments, the industrial Internet of Things management platform 130 can send the cache update parameters to the second storage component, so that the second storage controller of the second storage component updates the storage space of the cache database according to the cache update parameters. For relevant instructions on the second storage component, please refer to Figure 2 Relevant description of step 240.

[0158] In some embodiments of the present invention, cache update parameters are determined by using data attribute information corresponding to the cached data, user enterprise type, historical retrieval records, historical processing records, device health status, and device operating status, thereby obtaining more reasonable cache update parameters. In some embodiments of the present invention, the estimated call time of the data to be processed is determined by using historical data retrieval records and historical data processing records, and the space release rate is further determined to obtain data configuration parameters. The storage space of the database can be flexibly adjusted according to the storage, retrieval, and deletion of data to ensure the normal operation of different databases and avoid data loss.

[0159] While the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure is merely illustrative and does not limit the present invention. Although not explicitly described herein, those skilled in the art may make various modifications, improvements, and revisions to the present invention. Such modifications, improvements, and revisions are suggested in the present invention and remain within the spirit and scope of the exemplary embodiments of the present invention.

[0160] In addition, unless expressly stated in the claims, the order of the processing elements and sequences, the use of alphanumeric characters, or the use of other names described in the present invention are not intended to limit the order of the processes and methods of the present invention. Although the above disclosure discusses some embodiments of the invention that are currently considered useful through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the spirit and scope of the embodiments of the present invention. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0161] Similarly, it should be noted that, in order to simplify the presentation of the present disclosure and facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of the invention sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not imply that the subject matter of the invention requires more features than those recited in the claims. In practice, an embodiment may have fewer features than the totality of the features of a single embodiment disclosed above.

[0162] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, and documents, cited herein is hereby incorporated by reference in its entirety. This excludes any application history that is inconsistent with or conflicts with the present disclosure, including any document (currently or subsequently appended to this disclosure) that limits the broadest scope of the claims of this disclosure. It should be noted that if the descriptions, definitions, and / or terminology used in the accompanying materials are inconsistent or conflicting with the present disclosure, the descriptions, definitions, and / or terminology used in this disclosure will control.

[0163] Finally, it should be understood that the embodiments described herein are intended only to illustrate the principles of the present invention. Other variations may also fall within the scope of the present invention. Therefore, by way of example and not limitation, alternative configurations of the embodiments of the present invention may be considered consistent with the teachings of the present invention. Accordingly, the embodiments of the present invention are not limited to the embodiments explicitly described and illustrated herein.

Claims

1. A storage configuration method based on an industrial Internet of Things data center, characterized in that: The method is executed by an industrial Internet of Things management platform of a storage configuration system of an industrial Internet of Things data center, The method comprises: Determine data upload parameters based on the data attribute information, historical retrieval records, and historical processing records of the data to be processed, including: Based on the data popularity, the data attribute information, the historical retrieval records, the historical processing records, the processing capability strength of the industrial Internet of Things management platform and the data collection frequency, the data allocation model is used to determine the uploaded analysis data; the data allocation model is a neural network model; Determining the data upload parameters based on the uploaded analysis data includes: Determine the data collection quality based on the device operation status, device health status and collection bandwidth quality; determine the data upload parameters based on the data collection quality and the uploaded analysis data; Based on the data upload parameters and the data collection frequency, database configuration parameters are generated. The database configuration parameters also include cache update parameters of the cache database. The cache update parameters include an update cycle and an update amount, including: Determining an estimated call time based on the historical call records and the historical processing records; Determining a space release rate based on the estimated call time includes: Determining a data call threshold based on the data attribute information, the current database type, and the processing capability strength; treating pending data whose call counts reach the data call threshold within the next preset period as pending data to be cleared; and determining the space release rate based on the data call threshold and the estimated call time; Determining the database configuration parameters based on the data upload parameters, the space release rate, and the data collection frequency; Based on the data attribute information corresponding to the cached data in the cache database, the user enterprise type, the historical retrieval record, the historical processing record, the device health status, and the device operation status, the cache update parameter is determined by updating a parameter determination model, wherein the update parameter determination model is a recurrent neural network model, wherein: The update period in the cache update parameter is positively correlated with the data acquisition quality and the acquisition bandwidth quality, and negatively correlated with the data storage rate; updating the update configuration parameters of the second storage component according to the cache update parameters; In response to the future remaining space being less than a preset space threshold, reallocating the storage space size of the database so that the future remaining space exceeds the preset space threshold; According to the database configuration parameters of the previous cycle, query the data collection frequency table and update the data collection frequency of the current cycle; Generate a data upload instruction according to the data upload parameters and send the data upload instruction to the industrial Internet of Things sensor network platform; generating operating parameters of the second storage component according to the cache database configuration parameters; Based on the operating parameter of the second storage component, controlling the second storage controller to allocate storage space to the cache database; Generate a sub-library configuration instruction based on the sub-library configuration parameters, and send the sub-library configuration instruction to the industrial Internet of Things sensor network platform; obtaining, from the first storage controller, a total amount of storage space allocated or released by the sub-databases for the task process within a predetermined time period, and selecting a sub-database whose total amount of storage space is greater than a set threshold as a target sub-database; The configuration file and structured query language of the target sub-database are modified to modify the maximum number of connections allowed by the target sub-database.

2. The method according to claim 1, characterized in that The method further comprises: Calculate the timeliness requirement of the data to be processed based on the waiting processing time and the call index; In response to the timeliness requirement being greater than a first threshold, uploading the to-be-processed data to a processing unit of the industrial Internet of Things management platform; In response to the timeliness requirement being greater than a second threshold and less than the first threshold, uploading the to-be-processed data to a cache database of the industrial Internet of Things management platform; In response to the timeliness requirement being less than the second threshold, uploading the to-be-processed data to a sub-database of the industrial Internet of Things sensor network platform; Among them, the waiting processing time refers to the time from when the data to be processed is uploaded to the sub-database to when the industrial Internet of Things management platform starts processing the data to be processed, the call index represents the frequency with which the data to be processed is called, and the timeliness requirement represents the timeliness of the data to be processed. The timeliness requirement is positively correlated with the call index and negatively correlated with the waiting processing time, and the first threshold is greater than the second threshold.

3. The method according to claim 1, characterized in that The method further comprises: The preset period refers to a period for updating the database configuration parameters, and the preset period is negatively correlated with the collection frequency of the data to be processed.

4. The storage configuration system based on the industrial Internet of Things data center is characterized by: The system includes an industrial Internet of Things management platform, an industrial Internet of Things sensor network platform and an industrial Internet of Things perception control platform; the industrial Internet of Things management platform is connected to the industrial Internet of Things perception control platform based on the industrial Internet of Things sensor network platform; The industrial Internet of Things management platform is configured to execute the method according to claim 1.

5. The system according to claim 4, characterized in that The industrial Internet of Things management platform is also configured to: Calculate the timeliness requirement of the data to be processed based on the waiting processing time and the call index; In response to the timeliness requirement being greater than a first threshold, uploading the to-be-processed data to a processing unit of the industrial Internet of Things management platform; In response to the timeliness requirement being greater than a second threshold and less than the first threshold, uploading the to-be-processed data to a cache database of the industrial Internet of Things management platform; In response to the timeliness requirement being less than the second threshold, uploading the to-be-processed data to a sub-database of the industrial Internet of Things sensor network platform; Among them, the waiting processing time refers to the time from when the data to be processed is uploaded to the sub-database to when the industrial Internet of Things management platform starts processing the data to be processed, the call index represents the frequency with which the data to be processed is called, and the timeliness requirement represents the timeliness of the data to be processed. The timeliness requirement is positively correlated with the call index and negatively correlated with the waiting processing time, and the first threshold is greater than the second threshold.

6. The system according to claim 4, characterized in that: The preset period refers to a period for updating the database configuration parameters, and the preset period is negatively correlated with the collection frequency of the data to be processed.

7. A computer-readable storage medium, characterized in that The storage medium stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer executes the storage configuration method based on the industrial Internet of Things data center according to any one of claims 1 to 3.

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