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

Abstract

The invention provides a storage configuration method and system based on an industrial Internet of Things data center and a medium, and relates to the field of data storage and management. The method comprises the steps that an industrial Internet of Things management platform determines data uploading parameters according to data attribute information, historical calling records and historical processing records of to-be-processed data; generating cache database configuration parameters and sub-database configuration parameters; generating a data uploading instruction and sending the data uploading instruction to the industrial Internet of Things sensing network platform; generating working parameters of the second storage component, and distributing a storage space to the cache database; and generating a sub-library configuration instruction, and issuing the sub-library configuration instruction to the industrial Internet of Things sensing network platform. The industrial Internet of Things sensing network platform stores to-be-processed data to a corresponding sub-database according to the data uploading instruction, and uploads the to-be-processed data to a cache database or a processing unit; and according to the sub-database configuration instruction, generating a working parameter of the first storage component, and allocating a storage space to the sub-database. And data can be stored and managed more intelligently and efficiently.

Description

Technical Field

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

[0002] With the advent of the Industrial 4.0 era, the industrial Internet of Things has become the core driving force for the intelligent transformation of the manufacturing industry. In a production line based on the industrial Internet of Things, numerous sensors, intelligent devices and control systems are interconnected to build a highly collaborative and data-driven production environment. In such an environment, storing and managing a large amount of data is crucial for ensuring the efficient operation of the production line. However, the amount of data generated by enterprises is growing exponentially with the increase in the number of industrial devices connected to the network, and different types of data storage strategies need to be established for different production links, which poses higher requirements for the data storage system.

[0003] Therefore, it is crucial to develop a storage configuration method, system and medium based on an industrial Internet of Things data center 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 data center, which is executed by an industrial Internet of Things management platform of a storage configuration system based on an industrial Internet of Things data center. The method includes: determining data upload parameters based on the 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 industrial Internet of Things sensing network platform for the data to be processed; generating database configuration parameters based on the data upload parameters and the data acquisition frequency; the database configuration parameters including cache database configuration parameters and sub-database configuration parameters; generating a data upload instruction according to the data upload parameters and sending the data upload instruction to the industrial Internet of Things sensing network platform; generating the working parameters of a second storage component according to the cache database configuration parameters; controlling a second storage controller to allocate storage space to the cache database based on the working parameters of the second storage component; generating a sub-database configuration instruction according to the sub-database configuration parameters and sending the sub-database configuration instruction to the industrial Internet of Things sensing network platform. The industrial Internet of Things sensing network platform stores the data to be processed in the corresponding sub-database, or uploads it to the cache database, or uploads it to the processing unit according to the data upload instruction; generating the working parameters of a first storage component according to the sub-database configuration instruction; controlling a first storage controller to allocate storage space to the sub-database based on the working 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 sensing network platform. Wherein 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 sensing network platform, and an industrial Internet of Things perception control platform; the industrial Internet of Things management platform is communicatively connected to the industrial Internet of Things perception control platform based on the industrial Internet of Things sensing 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, and the storage medium 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 the 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 data upload parameters through the data attribute information, historical retrieval records, and historical processing records of the data to be processed, and determine data configuration parameters according to the data upload parameters, so as to dynamically configure the storage space sizes of the sub-databases of the industrial Internet of Things sensing 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 saturation of some databases and resource waste caused by the idleness of some databases, and can effectively improve the utilization efficiency of the storage space and relieve the storage pressure. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The present invention will be further described in an exemplary embodiment manner, and these exemplary embodiments will be described in detail through the drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where: Figure 1 is an exemplary schematic diagram of a storage configuration system based on an industrial Internet of Things data center shown in some embodiments of the present invention; Figure 2 is an exemplary flowchart of a storage configuration method based on an industrial Internet of Things data center shown in some embodiments of the present invention; Figure 3 is an exemplary schematic diagram of a data allocation model shown in some embodiments of the present invention; Figure 4 is an exemplary flowchart of determining database configuration parameters shown in some embodiments of the present invention; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, the present invention can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the figures represent the same structure or operation.

[0010] Flowcharts are used in the present invention to illustrate the operations performed by the system according to the embodiments of the present invention. It should be understood that the previous or subsequent operations do not necessarily need to be executed precisely in sequence. On the contrary, the steps can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0011] Figure 1 is an exemplary schematic diagram of a storage configuration system based on an industrial Internet of Things data center shown in some embodiments of the present invention.

[0012] In some embodiments, as Figure 1 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 sensing network platform 140, and an industrial Internet of Things perception control platform 150.

[0013] The industrial Internet of Things user platform 110 is a platform for interacting with users. In some embodiments, the industrial Internet of Things user platform 110 may include multiple users such as user 1, user 2,..., user n. For example, an industrial enterprise can correspond to one or more users. In some embodiments, the industrial Internet of Things user platform 110 is configured to receive the instruction information of the user and send the instruction information to the industrial Internet of Things service platform 120.

[0014] In some embodiments, the industrial Internet of Things 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.

[0015] The industrial Internet of Things service platform 120 is a platform for obtaining the service data of the industrial Internet of Things. In some embodiments, the industrial Internet of Things service platform 120 can interact bidirectionally with the industrial Internet of Things management platform 130. For example, after the industrial Internet of Things service platform 120 sends the instruction information of the user to the industrial Internet of Things management platform 130, the industrial Internet of Things management platform 130 uploads the execution data corresponding to the instruction information to the industrial Internet of Things service platform 120.

[0016] The Industrial Internet of Things (IIoT) management platform 130 is a platform for controlling and managing 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. Among them, the data center 131 includes a cache database, and the cache database is communicatively connected to the sub-databases of the IIoT sensing network platform 140; the processing unit 132 refers to a central processing unit for processing data to be processed; the second storage controller refers to a device that performs necessary control over the access to the memory according to certain timing rules, including the control of address signals, data signals, and various command signals, enabling the master device (the device accessing the memory) to use the storage resources on the memory according to its own requirements.

[0017] In some embodiments, the IIoT management platform 130 may read data to be processed from the data center 131. Among them, the data to be processed may include production operation data, factory monitoring data, product inspection data, etc. In some embodiments, the processing unit 132 may be used to process the start time point, end time point, etc. of the data to be processed and the cached data, as well as the usage records of the processed data results (including the call records by users and other steps / programs, including the number of calls, the initiator of each call, the call time point, etc.).

[0018] In some embodiments, the IIoT management platform 130 may include multiple sub-platforms, such as sub-platform 1, sub-platform 2,..., sub-platform n. For example, the sub-platform may include a device management platform, a quality control platform, a security monitoring platform, etc. In some embodiments, the IIoT management platform 130 may be implemented based on a processor or a server, etc.

[0019] In some embodiments, the IIoT management platform 130 may interact with the IIoT sensing network platform 140.

[0020] In some embodiments, the IIoT management platform 130 is configured to determine data upload parameters according to the data attribute information, historical retrieval records, and historical processing records of the data to be processed, and instruct the IIoT sensing network platform 140 about the storage strategy for the data to be processed.

[0021] 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 according to the data upload parameters and send it to the IIoT sensing network platform 140. Among them, the database configuration parameters include cache database configuration parameters and sub-database configuration parameters.

[0022] In some embodiments, the Industrial Internet of Things management platform 130 is further configured to generate operating parameters of the second storage component according to the cache database configuration parameters, and control the second storage controller to allocate storage space to the cache database; generate a sub-database configuration instruction according to the sub-database configuration parameters and send it to the Industrial Internet of Things sensing network platform 140.

[0023] In some embodiments, the Industrial Internet of Things sensing network platform 140 is a platform for data transmission in a storage configuration system based on the Industrial Internet of Things data center. In some embodiments, the Industrial Internet of Things sensing network platform 140 may include a first storage controller and a sub-database for storing data to be processed. Among them, the first storage controller is a device that performs necessary control on the access to the memory according to certain timing rules, including the control of address signals, data signals, and various command signals, so that the master device (the device accessing the memory) can use the storage resources on the memory according to its own requirements.

[0024] In some embodiments, the sub-database may include multiple sub-databases such as sub-database 1, sub-database 2,..., sub-database n for storing different types of data to be processed. For example, the sub-database may include a device maintenance database, a quality monitoring database, a security management database, etc. In some embodiments, the Industrial Internet of Things sensing network platform 140 may be configured with communication devices or servers, etc.

[0025] In some embodiments, the cache database of the data center 131 (located in the main memory, i.e., the first storage component) communicates with multiple sub-databases of the Industrial Internet of Things sensing network platform 140 (located in different secondary memories, i.e., the second storage component) through a wireless link (such as transmitting data to be processed uploaded by the client (the Industrial Internet of Things perception control platform 150)), where the bandwidths of different wireless links are different; the cache database and the sub-database include idle disk spaces of different sizes, and the idle disk spaces 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 a communication link.

[0026] In some embodiments, the Industrial Internet of Things sensing network platform 140 is configured to 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.

[0027] In some embodiments, the Industrial Internet of Things sensing network platform 140 is further configured to generate operating 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. Among them, the first storage component and the second storage component correspond to different memories.

[0028] In some embodiments, the Industrial Internet of Things (IIoT) sensing network platform 140 can interact with the IIoT sensing and control platform 150.

[0029] The IIoT sensing and control platform 150 is a platform for monitoring and controlling the production process. The IIoT sensing and control platform 150 includes a data acquisition device. In some embodiments, the data acquisition device may include at least one of production equipment and auxiliary devices deployed on the production line. Among them, the production equipment refers to the equipment required for the production process itself; the auxiliary devices refer to auxiliary equipment other than the production process, such as monitoring equipment, quality inspection equipment, etc.

[0030] In some embodiments, the IIoT sensing and control platform 150 is configured to collect data to be processed and upload it to the IIoT sensing network platform 140. For example, enterprise operation data such as production operation data, factory monitoring data, and product inspection data is collected and uploaded to the IIoT sensing network platform 140.

[0031] Figure 2 It is an exemplary flowchart of a storage configuration method process based on an IIoT data center according to some embodiments of the present invention. In some embodiments, the storage configuration method process based on the IIoT data center is executed by the IIoT management platform 130 of the storage configuration system based on the IIoT data center. As Figure 2 shown, the storage configuration method process based on the IIoT data center includes the following steps: Step 210, determine data upload parameters based on the data attribute information, historical retrieval records, and historical processing records of the data to be processed.

[0032] The data to be processed may include the data collected by the IIoT sensing and control platform 150. For example, one or more of production operation data, factory monitoring data, product inspection data, etc.

[0033] The data attribute information refers to the 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, etc. The data type may include the production process to which the data to be processed belongs.

[0034] In some embodiments, the data attribute information can be determined according to the data acquisition device type, collection time of the IIoT sensing and control platform 150 that uploads the data to be processed, and the storage space size occupied by the data to be processed itself.

[0035] The historical retrieval record refers to the historical record of the processing unit, cache database, etc. retrieving the data to be processed. In some embodiments, the historical retrieval record may further include information such as the data attribute information, retrieval method, and retrieval time of the retrieved data to be processed.

[0036] In some embodiments, the Industrial Internet of Things (IIoT) management platform 130 may obtain historical retrieval records from the data reading logs of each sub-database of the IIoT sensing network platform 140.

[0037] Historical processing records refer to records of the data center of the IIoT management platform 130 processing data to be processed, etc. In some embodiments, historical processing records may include the data to be processed, the start time point and end time point of processing the cached data in the cache database, etc., as well as the usage records of the processing results. Among them, the usage records of the processing results include the number of times the processing results are called by users or other computer programs and instructions, the initiating object of each call, the call time point, etc.

[0038] In some embodiments, the IIoT management platform 130 may obtain historical processing records from the data center.

[0039] In some embodiments, the data upload parameter indicates the storage strategy of the IIoT sensing network platform 140 for the data to be processed. Among them, the storage strategy may include storing the data to be processed in the corresponding sub-database, uploading it to the cache database of the data center of the IIoT management platform 130, or uploading it to the processing unit of the IIoT management platform 130, etc.

[0040] In some embodiments, the IIoT sensing network platform 140 may store or upload the data to be processed to the corresponding database or storage unit according to the data upload parameter.

[0041] In some embodiments, the IIoT management platform 130 may screen out the historical retrieval records and historical processing records of historical data to be processed with the same or similar data types as the data to be processed according to the data attribute information of the data to be processed; determine the waiting processing duration and the retrieval index according to the historical retrieval records and historical processing records; calculate the timeliness requirement degree of the data to be processed according to the waiting processing duration and the retrieval index; and determine the data upload parameter according to the timeliness requirement degree.

[0042] The waiting processing duration is the duration from when the historical data to be processed is uploaded to the sub-database of the IIoT sensing network platform 140 until the IIoT management platform 130 starts to process the historical data to be processed.

[0043] The retrieval index is used to characterize the frequency of the data to be processed being called. The retrieval index is positively correlated with the retrieval frequency in the historical retrieval records and negatively correlated with the initial retrieval time interval. The initial retrieval time interval is the duration from when the historical data to be processed is stored in the IIoT sensing network platform 140 until it is first retrieved.

[0044] The timeliness requirement degree characterizes the timeliness of the data to be processed. In some embodiments, the timeliness requirement degree can be calculated by the following formula: (1) where is a coefficient less than 0, is a coefficient greater than 0. It can be understood that the shorter the waiting processing duration, the larger the called index, and the higher the timeliness requirement degree.

[0045] In some embodiments, when the timeliness requirement degree ≥ the first threshold, the data upload parameter is to upload the data to be processed to the processing unit of the industrial Internet of Things management platform 130; when the first threshold > the timeliness requirement degree ≥ the second threshold, the data upload parameter is to upload the data to be processed to the cache database of the data center of the industrial Internet of Things management platform 130; when the timeliness requirement degree < the second threshold, the data upload parameter is to store the data to be processed in the sub-database corresponding to the data to be processed. The first threshold and the second threshold are obtained by technicians based on prior experience, and the first threshold > the second threshold.

[0046] Step 220, generate database configuration parameters based on the data upload parameter and the data collection frequency.

[0047] The data collection frequency refers to the frequency at which the data collection device collects the data to be processed.

[0048] 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 and control platform 150.

[0049] The database configuration parameter refers to the parameter for configuring the storage space size of the database. In some embodiments, the database configuration parameter includes a cache database configuration parameter and a sub-database configuration parameter. The cache database configuration parameter is used to configure the storage space size of the cache database of the data center of the industrial Internet of Things management platform 130, and the sub-database configuration parameter is used to configure the storage space size of at least one sub-database of the industrial Internet of Things sensing network platform 140.

[0050] 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 according to the data upload parameter; determine the amount of data to be stored in the future period according to the data collection frequency; calculate the future remaining space of the database for storing the data to be processed according to the amount of data to be stored in the future 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 the 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.

[0051] In some embodiments, in each preset period, the industrial Internet of Things management platform 130 may update the data acquisition frequency of the current period according to the database configuration parameters of the previous period to obtain an updated frequency; based on the updated frequency, through the industrial Internet of Things perception and control platform 150, control the data acquisition device to perform data acquisition.

[0052] The preset period refers to the period for updating the database configuration parameters. In some embodiments, the preset period is negatively correlated with the acquisition frequency of the data to be processed.

[0053] The updated frequency refers to the data acquisition frequency after the update.

[0054] In some embodiments, the industrial Internet of Things management platform 130 may query the acquisition frequency table according to the database configuration parameters of the previous period to obtain the updated frequency.

[0055] In some embodiments, the acquisition frequency table is preset by technicians. In the acquisition parameter table, the closer the storage space sizes of the cache database and the sub-databases in the database configuration parameters are to the total space size of the storage device, the lower the data acquisition frequency. It can be understood that the closer the storage space sizes of the cache database and the sub-databases are to the total space size of the storage device, the greater the storage pressure and the more likely it is to run out of storage space. Therefore, reducing the data acquisition frequency can relieve the storage pressure of the storage device.

[0056] In some embodiments of the present invention, determining the update parameter through the database configuration parameters of the previous period to adjust the data acquisition frequency of the data acquisition parameter can effectively avoid the problem of data loss caused by insufficient storage space.

[0057] Step 230, generate a data upload instruction according to the data upload parameter and send the data upload instruction to the industrial Internet of Things sensing network platform.

[0058] In some embodiments, the industrial Internet of Things management platform 130 may use the data upload parameter as the data upload instruction and send it to the industrial Internet of Things sensing network platform 140.

[0059] In some embodiments, the industrial Internet of Things sensing network platform 140 may 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.

[0060] Step 240, generate the working parameters of the second storage component according to the cache database configuration parameters.

[0061] The second storage component refers to the component in the industrial Internet of Things management platform 130 that realizes the data storage function, such as a memory, etc. In some embodiments, the first storage component and the second storage component correspond to different memories. The first storage component corresponds to the sub-database of the industrial Internet of Things sensing network platform 140, and the second storage component corresponds to the cache database of the industrial Internet of Things management platform 130. Different sub-databases are located in different first storage components. Different sub-databases and cache databases are respectively configured as the free disk space in different first storage components and second storage components for storing different types of data to be processed.

[0062] In some embodiments, the cache database of the industrial Internet of Things management platform 130 and one or more sub-databases of the industrial Internet of Things sensing network platform 140 are communicatively connected by wired and / or wireless means. The data transmission bandwidths of the communication connection links between the cache database and different sub-databases are different.

[0063] The working parameters of the second storage component refer to the parameters for allocating the storage space size of the cache database. In some embodiments, the industrial Internet of Things management platform 130 may use the storage space size of the cache database in the cache database configuration parameters as the working parameters of the second storage component.

[0064] Step 250, based on the working parameters of the second storage component, control the second storage controller to allocate storage space to the cache database.

[0065] In some embodiments, the second storage component may determine the space difference according to 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 to allocate storage space to the cache database.

[0066] Step 260, generate a sub-database configuration instruction according to the sub-database configuration parameters, and send the sub-database configuration instruction to the industrial Internet of Things sensing network platform.

[0067] In some embodiments, the industrial Internet of Things management platform 130 may use the sub-database configuration parameters as the sub-database configuration instruction.

[0068] In some embodiments, the industrial Internet of Things sensing network platform 140 may generate the working parameters of the first storage component according to the sub-database configuration instruction; based on the working parameters of the first storage component, control the first storage controller to allocate storage space to the sub-database.

[0069] The working parameters of the first storage component refer to the working parameters for allocating the storage space size of the sub-database. In some embodiments, the industrial Internet of Things management platform 130 may use the storage space size of at least one sub-database included in the sub-database configuration instruction as the working parameters of the first storage component.

[0070] The specific method for controlling the first storage controller to allocate storage space to the sub-database is similar to the method for controlling the second memory to allocate storage space to the cache database. For specific details, please refer to the foregoing related content.

[0071] In some embodiments of the present invention, the data upload parameters are determined based on the data attribute information, historical retrieval records, and historical processing records of the data to be processed, and the data configuration parameters are determined according to the data upload parameters, so as to dynamically configure the storage space sizes of the sub-database of the industrial Internet of Things sensing network platform 140 and the cache database of the industrial Internet of Things management platform 130 according to the data upload situation, which can avoid data loss caused by the saturation of some databases and resource waste caused by the idleness of some databases, and can effectively improve the utilization efficiency of the storage space and relieve the storage pressure.

[0072] In some embodiments, the industrial Internet of Things management platform 130 may also obtain from the first storage controller the total amount of storage space allocated or released for the task process in the sub-database within a predetermined time period, and use the sub-database with the total amount of allocated or released storage space greater than the 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.

[0073] 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.

[0074] The task process refers to the tasks or instructions executed by the industrial Internet of Things. For example, tasks such as querying data and analyzing data.

[0075] The storage space allocated for the task process refers to the storage space allocated to all task processes for executing instructions. The storage space released for the task process refers to the storage space released after the task process ends.

[0076] The size of the set threshold can be preset by a technician.

[0077] The target database refers to the sub-database for which the configuration file and structured query language need to be adjusted.

[0078] The configuration file refers to a computer file for the maximum number of connections of the sub-database from the hardware. For example, the mysql database configuration file my.ini or my.cnf file.

[0079] Structured Query Language refers to the programming language in the database configuration file that determines the maximum number of connections. For example, the max_connections programming language under the mysqld topic, etc.

[0080] In some embodiments, when the total amount of storage space allocated or released exceeds a set threshold, the industrial Internet of Things management platform 130 can modify the programming language related to determining the maximum number of connections in the configuration file so that the maximum number of connections allowed by the target sub-database increases. For example, modify the program code in the programming language that determines the maximum number of connections to modify the maximum number of connections allowed by the target sub-database.

[0081] It can be understood that when the total amount of storage space allocated or released exceeds the set threshold, it means that the frequency of reading and writing of the sub-database is higher, and the maximum number of connections of the sub-database can be increased to improve the concurrency of access.

[0082] In some embodiments of the present invention, by adjusting the maximum number of connections allowed by the sub-database according to the total amount of storage space allocated or released by the sub-database for the task process, congestion and queuing during access to the sub-database can be avoided, and the access efficiency of the sub-database can be improved.

[0083] It should be noted that the above description of the storage configuration method process based on the industrial Internet of Things data center is only for illustration and explanation, and does not limit the scope of application of the present invention. For those skilled in the art, various corrections and changes can be made to the storage configuration method process based on the industrial Internet of Things data center under the guidance of the present invention. However, these corrections and changes are still within the scope of the present invention.

[0084] Figure 3 It is an exemplary schematic diagram of the data deployment model shown in some embodiments of the present invention.

[0085] In some embodiments, the industrial Internet of Things management platform 130 can determine the uploaded analysis data through a data deployment model based on data heat, data attribute information of the data to be processed, historical retrieval records, historical processing records, the processing capacity intensity of the industrial Internet of Things management platform 130, and data collection frequency; the data deployment model is a neural network model; based on the uploaded analysis data, determine the data upload parameters.

[0086] Data heat refers to a value representing the frequency of the data to be processed being called in the future. In some embodiments, the industrial Internet of Things management platform 130 can use the average value of the frequencies of historical data to be processed of the same data type being called as the data heat of the data to be processed.

[0087] The processing capacity intensity represents data on the processing performance of the processing devices (such as processors, servers, etc.) of the industrial Internet of Things management platform 130. For example, the computing frequency of the processing device, etc. In some embodiments, the industrial Internet of Things management platform 130 may determine the processing capacity intensity based on the performance parameters of the processing device. The performance parameters are obtained from the manufacturer of the processing device.

[0088] Upload analysis data refers to analyzing data on the recommendation degree of uploading 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 numerical values between 0 and 1, and are respectively used to represent the recommendation degrees of uploading the data to be processed to the sub-database of the industrial Internet of Things sensing network platform 140, the cache database of the industrial Internet of Things management platform 130, and the processing unit of the industrial Internet of Things management platform 130. The larger the numerical value, the higher the recommendation degree.

[0089] The data allocation model refers to a model used to predict upload analysis data. In some embodiments, the data allocation model can be a neural network model (Neural Network, NN).

[0090] As Figure 3 shown, in some embodiments, the input of the data allocation model 370 includes the data heat 310 of the data to be processed, the data attribute information 320, the historical retrieval record 330, the historical processing record 340, the sample data collection frequency 350 of the collection device for collecting the sample data to be processed, and the processing capacity intensity 360 of the industrial Internet of Things management platform, and the output includes the upload analysis data 380. For the relevant descriptions of the data attribute information, historical retrieval record, historical processing record, and data collection frequency of the data to be processed, see Figure 2 and its related descriptions.

[0091] In some embodiments, the data allocation model can be trained based on a large number of first training samples with the first label. The first training samples include the sample data heat of the sample data to be processed, the sample data attribute information, the sample historical retrieval record, the sample historical processing record, the processing capacity intensity of the sample industrial Internet of Things management platform 130, and the sample data collection frequency.

[0092] In some embodiments, the industrial Internet of Things management platform 130 may use the data heat, data attribute information, historical retrieval record, historical processing record, processing capacity intensity of the industrial Internet of Things management platform 130, and the sample data collection frequency of the collection device for collecting the sample data to be processed in the historical data as the first training samples. 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 cache database of the processing platform in the second historical period, the first label is (0, 1, 0).

[0093] In some embodiments, the Industrial Internet of Things management platform 130 may also construct a first tag according to the timeliness requirement degree of the sample data to be processed. For example, for the sample data to be processed with a timeliness requirement degree greater than the first threshold, the first tag may be (0, 0, 1), which is used to indicate that the data to be processed is uploaded to the processing unit of the Industrial Internet of Things management platform 130; for the sample data to be processed with a timeliness requirement degree between the first threshold and the second threshold, the first tag may be (0, 1, 0), which is used to indicate that the data to be processed is uploaded to the cache database of the Industrial Internet of Things management platform 130; for the sample data to be processed with a timeliness requirement degree less than the second threshold, the first tag may be (1, 0, 0), which is used to indicate that the data to be processed is uploaded to the sub-database of the Industrial Internet of Things sensing network platform 140; for the related descriptions of the timeliness requirement degree, the first threshold, and the second threshold, see Figure 2 and its related descriptions.

[0094] In some embodiments, the Industrial Internet of Things management platform 130 may input one or more first training samples into the initial data allocation model to obtain the upload analysis data output by the initial data allocation model; based on the upload analysis data output by the initial data allocation model and the first tags corresponding to the one or more first training samples, substitute them into the formula of the predefined loss function to calculate the value of the loss function; based on the value of the loss function, update the model parameters in the initial data allocation model in reverse, and the update method of the model parameters may include the gradient descent method, etc.; when the iteration completion condition is satisfied, the model training ends, and a trained data allocation model is obtained. The iteration completion condition may include that the loss value is less than the loss threshold, the number of iterations reaches the maximum number of iterations, etc.

[0095] In some embodiments, the training stage of the data allocation model may include an initial stage and a reinforcement stage; in the initial training stage, the training sample data set is obtained based on the general data set on the cloud platform; in the reinforcement training stage, the training sample data set is obtained based on the historical data set of the target enterprise.

[0096] The initial stage refers to the stage of training the initial data allocation model for the first time to obtain the data allocation model.

[0097] The general data set includes historical data to be processed collected by the industrial Internet of Things systems in the industrial Internet of Things systems of different enterprises, as well as data such as data heat, data attribute information, historical retrieval records, historical processing records, the processing capacity intensity of the Industrial Internet of Things management platform 130, the sample data collection frequency of the collection device, and historical data upload parameters.

[0098] In some embodiments, the general dataset is stored in a cloud platform, which can be implemented based on a cloud server connecting industrial Internet of Things (IIoT) systems of different enterprises. The IIoT systems of different enterprises can upload the above data to the cloud platform regularly or irregularly.

[0099] In some embodiments, the industrial Internet of Things management platform 130 can obtain a training dataset in the initial stage based on the general dataset according to the foregoing method for determining the first training sample and its first label.

[0100] The reinforcement stage refers to the stage of performing personalized training on the data allocation model trained in the initial stage. For example, training to enhance the applicability of the data allocation model to the current enterprise.

[0101] In some embodiments, the target enterprise may include an enterprise with the same or similar enterprise type as that of the current industrial Internet of Things system. The same or similar means that the business scope, product type, etc. of the enterprise are the same or similar.

[0102] The historical dataset refers to the dataset generated when the industrial Internet of Things system of the target enterprise collects data to be processed during a historical period. In some embodiments, the historical dataset includes the data heat, data attribute information, historical retrieval records, historical processing records of the historical data to be processed collected by the industrial Internet of Things system of the target enterprise, the processing capacity intensity of the industrial Internet of Things management platform 130, the sample data collection frequency of the collection device for collecting sample data to be processed, and the data upload parameters.

[0103] In some embodiments, the industrial Internet of Things management platform 130 can obtain a training dataset in the reinforcement stage based on the historical dataset of the target enterprise according to the foregoing method for determining the first training sample and its first label.

[0104] In some embodiments, the training sample dataset includes multiple sample subsets. The labels corresponding to the training data in the same sample subset are the same; the learning rates corresponding to the training data in different sample subsets are different.

[0105] A sample subset refers to a set composed of some training sample data. For example, a set composed of some first training samples and their corresponding first labels. In some embodiments, the industrial Internet of Things management platform 130 can use the first training samples with the same first label as a sample subset.

[0106] In some embodiments, when training a data allocation model based on different subsets of samples, the industrial Internet of Things management platform 130 may set different learning rates for different subsets of samples. For example, the industrial Internet of Things management platform 130 may set the learning rate of the subset of samples with the first label (0, 0, 1) as the subset of samples with the highest learning rate, and set the learning rate of the subset of samples with the first label (1, 0, 0) as the subset of samples with the lowest learning rate.

[0107] It can be understood 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 faster. Therefore, setting the highest learning rate for the subset of samples with the first label (0, 0, 1) (i.e., uploading the data to be processed to the processing unit of the industrial Internet of Things management platform 130) can enable 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.

[0108] In some embodiments of the present invention, by setting different learning rates for different subsets of samples, 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.

[0109] In some embodiments of the present invention, training the data allocation model in the initial stage with the training data set determined by the general data set can improve the generalization of the data allocation model; on the basis of the data allocation model trained in the initial stage, performing reinforcement training with the training data set determined by the historical data set of the target enterprise can more quickly obtain a data allocation model adapted to the current enterprise, which can improve the pertinence of the model, thereby improving the accuracy of the uploaded analysis data output by the model.

[0110] In some embodiments, the industrial Internet of Things management platform 130 may use the data upload parameter with the highest value in the uploaded analysis data as the data upload parameter for the current data to be processed. Only as an example, if the uploaded analysis data is (0.23, 0.76, 0.37), then the corresponding value (0.76) uploaded to the cache database of the industrial Internet of Things management platform 130 is the highest, so the data upload parameter is to upload to the cache database of the industrial Internet of Things management platform 130.

[0111] In some embodiments, the industrial Internet of Things management platform 130 may determine the data acquisition quality based on the device operation state, device health state, and acquisition bandwidth quality; and determine the data upload parameter based on the data acquisition quality and the uploaded analysis data.

[0112] The operating state of the device refers to the operating condition of the data acquisition device. In some embodiments, the operating state of the device may include voltage data, current data, and environmental data (such as temperature, humidity, etc.) of the data acquisition device. In some embodiments, the industrial Internet of Things management platform 130 may directly read the voltage data and current data of the data acquisition device through the industrial Internet of Things sensing and control platform 150, and obtain the environmental data by using temperature sensors, humidity sensors, etc. in the same environment as the data acquisition device, so as to obtain the operating state of the device.

[0113] The device health state is used to characterize the stability of the data acquisition device. In some embodiments, the industrial Internet of Things management platform 130 may obtain the fault records, maintenance records, etc. of the data acquisition device. The lower the fault frequency of the data acquisition device in the fault record and the more the number of maintenance records, the better the device health state.

[0114] The acquisition bandwidth quality is used to characterize the quality of uploading the acquired data to be processed by the data acquisition device to the industrial Internet of Things sensing network platform 140. In some embodiments, the industrial Internet of Things management platform 130 may determine the acquisition bandwidth quality based on the packet loss rate and transmission delay when receiving the data to be processed acquired by the data acquisition device through the industrial Internet of Things sensing network platform 140. The lower the packet loss rate and the lower the transmission delay, the higher the acquisition bandwidth quality.

[0115] The data acquisition quality characterizes the quality of the data acquisition device for acquiring and uploading data.

[0116] In some embodiments, the industrial Internet of Things management platform 130 may construct an acquisition quality vector based on the device operating state, device health state, and acquisition bandwidth quality; retrieve the reference vector with the highest similarity in the quality vector database based on the acquisition quality vector; and use the reference acquisition quality corresponding to the reference vector as the data acquisition quality.

[0117] 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 industrial Internet of Things management platform 130 may construct a reference vector according to the historical device operating state, historical health state, and historical acquisition bandwidth quality corresponding to the data to be processed in the historical data, and mark the reference acquisition quality corresponding to the reference vector according to whether there are data missing or data error situations during the use of the data to be processed, so as to construct the quality vector database.

[0118] In some embodiments, the Industrial Internet of Things (IIoT) management platform 130 may determine a quality threshold based on the data upload parameter, which is the value of the recommendation degree for uploading to the processing unit of the IIoT management platform 130, in the uploaded analysis data. The quality threshold is positively correlated with the value of the recommendation degree for uploading to the processing unit of the IIoT management platform 130. Set the data upload parameter of the data to be processed with a data acquisition quality lower than the quality threshold to the corresponding sub-database in the IIoT sensing network platform 140.

[0119] In some embodiments of the present invention, by determining the data acquisition quality based on the device operation state, device health state, and acquisition bandwidth quality of the data acquisition device, and thus determining the data upload parameter, it is possible to avoid directly uploading the data to be processed with poor data acquisition quality to the IIoT management platform 130, which may cause the IIoT management platform 130 to spend a large amount of computing resources on data cleaning, and can reduce the operating pressure of the IIoT management platform 130.

[0120] In some embodiments of the present invention, by determining the uploaded analysis data through a data allocation model and determining the data upload parameter according to the upload analysis parameter, a more reasonable data upload parameter can be obtained, enabling the data to be processed that needs to be processed by the processing unit to be uploaded to the IIoT management platform 130 in a timely manner, improving the efficiency of data flow.

[0121] Figure 4 It is an exemplary flowchart of the process for determining database configuration parameters shown in some embodiments of the present invention. The process for determining database configuration parameters includes the following steps: Step 410, determine the estimated call time based on historical retrieval records and historical processing records.

[0122] The estimated call time refers to the estimated time when the data to be processed in the database is called. For example, the time point when the processing unit of the IIoT management platform 130 retrieves the data to be processed from the cache database or the sub-database of the IIoT sensing network platform 140 within a future time period.

[0123] In some embodiments, the IIoT management platform 130 may use the average value of the duration between the time points when the historical data to be processed and cached data of the same or similar data type are retrieved or processed and the time point when the historical data to be processed is collected in the historical retrieval records and historical processing records as the estimated call duration; determine the estimated call time based on the time point when the data to be processed is collected and the estimated call duration.

[0124] In some embodiments, since the data to be processed may be called multiple times, the industrial Internet of Things management platform 130 may determine the first estimated call time of the data to be processed according to the time when multiple historical data to be processed are first called or processed; determine the second estimated call time of the current data to be processed as the time when the multiple historical data to be processed are called or processed for the second time; and so on; thereby obtaining multiple estimated call times of the data to be processed.

[0125] Step 420, determine the space release rate based on the estimated call time.

[0126] The space release rate refers to the rate of cleaning up old data in the database. For example, the amount of old data cleaned per unit time in the next preset period. For the relevant description of the preset period, see Figure 2 And its related description.

[0127] In some embodiments, the industrial Internet of Things management platform 130 takes the data to be processed with the maximum number of calls in the next preset period as the data to be processed that needs to be cleaned up in the next period, where the maximum number can be preset by technicians; determines the amount of data to be processed that needs to be cleaned up in the next preset period according to the amount of data to be processed that needs to be cleaned up; and calculates the space release rate according to the amount of data to be processed that needs to be cleaned up and the duration of the next preset period.

[0128] In some embodiments, the industrial Internet of Things management platform 130 may also determine the data call threshold based on the data attribute information, the current database type, and the processing capacity intensity; and determine the space release rate based on the data call threshold and the estimated call time.

[0129] The current database type refers to the type of the database where the data to be processed is located. For example, a cache database, a sharded database, etc.

[0130] The data call threshold refers to the maximum number of times the data to be processed is called. In some embodiments, when the number of times the data to be processed in the database (such as a cache database, a sharded database, etc.) is called reaches the data call threshold, the database will clean up the data to be processed.

[0131] In some embodiments, the industrial Internet of Things management platform 130 may construct a call feature vector according to the data attribute information, the current database type, and the processing capacity intensity; retrieve the reference call vector with the highest similarity in the call vector database according to the call feature vector; and take the reference call threshold corresponding to the reference call vector as the data call threshold. For the relevant description of the data attribute information, see Figure 2 And its related description, for the relevant description of the processing capacity intensity, see Figure 3 And its related description.

[0132] The call vector database includes a large number of reference call vectors and their corresponding reference call thresholds. In some embodiments, the industrial Internet of Things management platform 130 may construct reference call vectors based on the historical data attribute information, historical database type, and historical processing capacity intensity corresponding to the historical data to be processed, and use the actual number of times called corresponding to the historical data to be processed as the reference call threshold corresponding to the reference call vector, thereby constructing a call vector database.

[0133] In some embodiments, the industrial Internet of Things management platform 130 may use the data to be processed whose call times reach the data call threshold within the next preset period as the data to be processed that needs to be cleared, and determine the space release rate according to the foregoing method.

[0134] In some embodiments of the present invention, by determining the data call threshold of the database based on the data attribute information, the current database type, and the processing capacity intensity and using it to determine the space release rate, a more reasonable space release rate can be obtained.

[0135] Step 430, determine database configuration parameters based on the data upload parameter, space release rate, and data collection frequency.

[0136] In some embodiments, if the data upload parameter is to upload to the sub-database of the industrial Internet of Things sensing network platform 140, the industrial Internet of Things management platform 130 may calculate the data storage rate of the sub-database according to the data collection frequency corresponding to all the data to be processed that needs to be stored in the sub-database; according to the current remaining storage space, data storage rate, and space release rate of the sub-database, calculate whether there is a moment when the remaining storage space of the sub-database is less than 0 within the next preset period. If not, it means that the storage space is sufficient within the next preset period and there is no need to adjust the sub-database configuration parameters of the sub-database; if so, it means that the storage space is insufficient within the next preset period, then calculate the total data volume according to the current data volume, data storage rate, and space release rate of the sub-database, and use the storage space size required for the total data volume as the sub-database configuration parameter. For example, there is currently 3 GB of data in the sub-database, the data storage rate within the next preset period is 0.4 GB / hour, the space release rate is 0.2 GB / 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 , and then configure the sub-database to 7.8 GB.

[0137] In some embodiments, the database configuration parameters further 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, user enterprise type, historical retrieval records, historical processing records, device health status, and device operation status corresponding to the cache data in the cache database; and update the update configuration parameters of the second storage component according to the cache update parameters.

[0138] The cache update parameters refer to the relevant parameters for reconfiguring the storage space of the cache database. In some embodiments, the 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.

[0139] Cache data refers to the data stored in the cache database. For example, the data to be processed and / or its processing results stored in the cache database, etc.

[0140] 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 himself.

[0141] In some embodiments, the industrial Internet of Things management platform 130 can input the 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 the cache update parameters. Among them, the update parameter determination model can be a Recurrent Neural Network (RNN) model. For the relevant content of the data attribute information, historical retrieval records, and historical processing records, see Figure 2 and its related descriptions. For the relevant content of the device health status and device operation status, see Figure 3 and its related descriptions.

[0142] In some embodiments, the update parameter determination model can be trained based on a large number of second training samples with a second label. The second training samples may include the sample data attribute information, sample user enterprise type, sample historical retrieval records, sample historical processing records, sample device health status, and sample device operation status of the sample data to be processed in the sample cache database. The second label can be the actual cache update parameters corresponding to the sample cache database.

[0143] In some embodiments, the Industrial Internet of Things management platform 130 may construct a second training sample based on the historical data attribute information, historical user enterprise types, historical retrieval records, historical processing records, historical device health status, and historical device operating status corresponding to the cached data in different historical periods in the historical data. The validity period of the historical cached data corresponding to the second training sample is used as the update period, and the amount of data in the cache database when the update period is reached is used as the update amount, thereby obtaining a second label. The validity period refers to the duration from when the historical cached data is stored in the cache database to when it is deleted. The training process of the update parameter determination model is similar to Figure 3 the training process of the data allocation model in Figure 3 and its related descriptions.

[0144] In some embodiments, the cache update parameter is also related to data acquisition quality, acquisition bandwidth quality, and data storage rate.

[0145] 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 may calculate the data storage rate based on the data acquisition frequency of the data stored in the cache database and the amount of data acquired each time. For example, if the data acquisition device acquires 50 MB of data each time and the data acquisition frequency is 3 times per hour, the data storage rate can be calculated as .

[0146] In some embodiments, the update period in the cache update parameter is positively correlated with data acquisition quality and acquisition bandwidth quality, and negatively correlated with the data storage rate. For example, the Industrial Internet of Things management platform 130 may adjust based on data acquisition quality, acquisition bandwidth quality, and data storage rate on the basis of the previously determined cache update parameter to obtain the adjusted cache update parameter. For the relevant content of data acquisition quality and acquisition bandwidth quality, see Figure 3 and its related descriptions.

[0147] It can be understood that for cached data with high data acquisition quality and acquisition bandwidth quality, the Industrial Internet of Things management platform 130 may call it multiple times, so the update period can be extended; for cached data with a high data storage rate, it may fill up the storage space of the cache database faster, so the update period can be shortened to avoid data loss caused by insufficient cache database.

[0148] In some embodiments of the present invention, by adjusting the cache update parameter through data acquisition quality, acquisition bandwidth quality, and data storage rate, the adjustment requirements of the cache database can be combined with the actual needs, and data with better quality can be retained while ensuring sufficient storage space in the cache database.

[0149] In some embodiments, the Industrial Internet of Things management platform 130 may send 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 the relevant description of the second storage component, reference may be made to Figure 2 the relevant description in step 240.

[0150] In some embodiments of the present invention, by determining cache update parameters based on the data attribute information corresponding to the cached data, the user enterprise type, historical retrieval records, historical processing records, device health status, and device operating status, more reasonable cache update parameters can be obtained. In some embodiments of the present invention, by determining the estimated call time of the data to be processed based on historical data retrieval records and historical data processing records, and further determining the space release rate, data configuration parameters can be obtained, and the storage space of the database can be flexibly adjusted according to the storage, call, and deletion of data, so as to ensure the normal operation of different databases and avoid data loss.

[0151] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to the present invention. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to the present invention. Such modifications, improvements, and corrections are proposed in the present invention, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of the present invention.

[0152] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences, the use of numbers and letters, or the use of other names in the present invention is not used to limit the order of the processes and methods of the present invention. Although some currently useful exemplary embodiments of the invention have been discussed through various examples in the above disclosure, it should be understood that such details are only for illustrative purposes, 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 conform to the essence 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 through software solutions, such as installing the described system on existing servers or mobile devices.

[0153] Similarly, it should be noted that, in order to simplify the expression of the present invention disclosure and thus help the understanding of one or more exemplary embodiments of the invention, in the previous description of the exemplary embodiments of the present invention, multiple features are sometimes merged into one embodiment, drawing, or description thereof. However, this disclosure method does not mean that the features required by the object of the present invention are more than those mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0154] For each patent, patent application, patent application publication, and other materials cited in this invention, such as articles, books, specifications, publications, documents, etc., their entire contents are hereby incorporated by reference into this invention. This excludes application history files that are inconsistent with or conflict with the content of this invention, as well as files that limit the broadest scope of the claims of this invention (currently or subsequently appended to this invention). It should be noted that if there are any inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the attached materials of this invention and the content described in this invention, the descriptions, definitions, and / or uses of terms in this invention shall prevail.

[0155] Finally, it should be understood that the embodiments described in this invention are only used to illustrate the principles of the embodiments of this invention. Other variations may also fall within the scope of this invention. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this invention may be considered to be consistent with the teachings of this invention. Accordingly, the embodiments of this invention are not limited to the embodiments explicitly presented and described in this invention.

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 data attribute information, historical retrieval records, and historical processing records of the data to be processed; Generate database configuration parameters based on the data upload parameters and data collection frequency; Generate a data upload instruction according to the data upload parameter and send the data upload instruction to the industrial Internet of Things sensor network platform; Generating working 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.

2. The method according to claim 1, characterized in that The determining of data upload parameters based on the data attribute information, historical retrieval records, and historical processing records of the data to be processed includes: Based on the data heat, the data attribute information of the data to be processed, 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; Based on the uploaded analysis data, data upload parameters are determined.

3. The method according to claim 1, characterized in that: The generating of database configuration parameters based on the data upload parameters and the data collection frequency includes: Determining an estimated call time based on the historical call record and the historical processing record; Determining a space release rate based on the estimated call time; The database configuration parameters are determined based on the data upload parameters, the space release rate, and the data collection frequency.

4. The method according to claim 3, characterized in that The database configuration parameters also include cache update parameters of the cache database; The method further comprises: 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 According to the cache update parameter, the update configuration parameter of the second storage component is updated.

5. 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.

6. The system according to claim 5, characterized in that The industrial Internet of Things management platform is also configured to: Based on the data heat, the data attribute information of the data to be processed, 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; Based on the uploaded analysis data, data upload parameters are determined.

7. The system according to claim 5, characterized in that The industrial Internet of Things management platform is also configured to: Determining an estimated call time based on the historical call record and the historical processing record; Determining a space release rate based on the estimated call time; The database configuration parameters are determined based on the data upload parameters, the space release rate, and the data collection frequency.

8. The system according to claim 7, characterized in that The database configuration parameters also include cache update parameters of the cache database; The industrial Internet of Things management platform is also configured to: Determine the cache update parameter based on the data attribute information corresponding to the cache 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; as well as, According to the cache update parameter, the update configuration parameter of the second storage component is updated.

9. The system according to claim 8, characterized in that The cache update parameters are also related to data acquisition quality, acquisition bandwidth quality, and data storage rate.

10. 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 as described in any one of claims 1 to 4.

Images