Data storage method and system in industrial internet environment

Abstract

The invention provides a data storage method and system in an industrial internet environment, relates to the technical field of data storage, and aims to accurately identify core data and secondary data in an industrial environment by dynamically dividing a maintenance weight and a data calling weight of industrial equipment, and adopt different storage strategies for the two types of data. For core data, higher-level protection measures and higher access speed are implemented, and meanwhile, a splitting and encrypted storage mode is adopted, so that the security of the data is remarkably enhanced, and the risk of data loss or damage is reduced; for the secondary data, a more economical storage mode, such as hard disk storage or cloud storage, is adopted, so that reasonable utilization of resources and cost saving are realized; in addition, the method has the advantages of being high in adaptability and flexibility, flexible adjustment can be conducted according to the characteristics and service requirements of different industrial devices, and the purposes of data optimization storage and efficient access are achieved.

Description

Technical Field

[0001] The present invention relates to the field of data storage technology, and in particular to a data storage method and system in an industrial Internet environment. Background Art

[0002] With the rapid development of the Industrial Internet, the scale of industrial data is growing exponentially, and the data types are becoming increasingly diverse. A large number of sensors, devices, and systems generate massive amounts of data every day, which not only needs to be stored, but also needs to be processed and analyzed in real time so that companies can make timely decisions. However, traditional storage systems often have difficulty coping with such a large amount of data, which can easily lead to storage bottlenecks. The data of the Industrial Internet usually involves the core business and confidential information of the enterprise, so data security is very important. Data leakage, tampering, and loss can cause serious losses to the enterprise.

[0003] The storage of big data is not just about simple preservation, but more importantly, how to efficiently manage and analyze this data. The selection of data cleaning, integration and analysis tools is crucial to realizing the true value of data. However, in the industrial Internet environment, the efficiency and economy of data storage and management face challenges. On the one hand, the storage system needs to have efficient data processing and analysis capabilities to support complex data mining and analysis tasks; on the other hand, the storage cost also needs to be controlled within a reasonable range to reduce the operating costs of the enterprise.

[0004] Therefore, it is necessary to provide a data storage method and system in an industrial Internet environment to solve the above technical problems. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a data storage method and system in an industrial Internet environment to solve the problems of low efficiency, low security and high storage operation cost of data storage and management in existing industrial data storage methods.

[0006] The present invention provides a data storage method in an industrial Internet environment, the storage method comprising the following steps: Obtain historical maintenance data information of industrial equipment that communicates with the industrial Internet of Things, and divide the maintenance weights of the industrial equipment according to preset weight threshold segments; According to the set cycle, periodically count the frequency of retrieval of various types of data of industrial equipment, and set the retrieval threshold section; Based on the periodic statistics of the frequency of retrieval of various types of data of industrial equipment, the retrieval weights of various types of data of industrial equipment are dynamically and periodically divided by setting the retrieval threshold segments; Quantify the retrieval weight of various types of industrial equipment data and the maintenance weight of industrial equipment into comprehensive weights, and divide the core data and secondary data of industrial equipment; Dynamic storage is performed based on the divided core data and secondary data, specifically, the divided core data is split, encrypted, stored and backed up, and the secondary data is merged and stored.

[0007] Preferably, the acquisition of historical maintenance data information of industrial equipment communicating with the industrial Internet of Things and the division of maintenance weights of the industrial equipment according to preset weight threshold segments are specifically performed as follows: Through the industrial Internet of Things platform, historical maintenance data information is collected from connected industrial equipment and cleaned, processed and sorted. The historical maintenance data information includes the number of equipment failures, maintenance time, maintenance cost and maintenance frequency; The threshold segments are set for the number of equipment failures, maintenance time, maintenance cost and maintenance frequency, and the weighted threshold segments to which the number of industrial equipment failures, maintenance time, maintenance cost and maintenance frequency belong are determined based on the acquired historical maintenance data; Through the weighted summation method, the weight threshold segments to which the number of industrial equipment failures, maintenance time, maintenance cost and maintenance frequency belong are integrated to obtain the maintenance weight of the industrial equipment.

[0008] Preferably, the specific steps of periodically counting the frequency of retrieving various types of data of industrial equipment according to a set period and setting a threshold section for retrieving data are as follows: Set a fixed statistical cycle, and at the end of each cycle, collect the frequency of various data retrieval for each industrial equipment within the statistical acquisition cycle; According to the business needs of the enterprise, the threshold segment is set. Specifically, the higher the business needs of the enterprise, the smaller the value range of the set threshold segment is. The threshold segment includes a high-frequency segment, a medium-frequency segment and a low-frequency segment.

[0009] Preferably, the frequency of retrieving various types of data of industrial equipment based on periodic statistics is dynamically and periodically divided into the weights of retrieving various types of data of industrial equipment by setting the retrieving threshold segments, and the specific steps are: Assign corresponding weight values ​​to the three threshold value sections, namely, the high frequency section, the medium frequency section and the low frequency section; Based on the frequency of data retrieval of each type of industrial equipment within the statistically acquired period, the frequency of data retrieval of each type of data is compared and determined, and the specific retrieval threshold segments of the three retrieval threshold segments of the high frequency segment, the medium frequency segment and the low frequency segment are classified, and the result of the retrieval threshold segment to which each type of data belongs is obtained; Based on the obtained results of belonging various types of data to the retrieval threshold segments, weight values ​​corresponding to the retrieval threshold segments are allocated to each type of data, and the retrieval weight division results of various types of industrial equipment data are obtained.

[0010] Preferably, the retrieval weights of various types of industrial equipment data and the maintenance weights of industrial equipment are quantified into comprehensive weights, and the core data and secondary data of industrial equipment are divided, and the specific steps are: The weighted average method is used to calculate the comprehensive weight by taking the retrieval weight of various types of industrial equipment data and the maintenance weight of industrial equipment; Comparing the obtained comprehensive weight with a preset comprehensive threshold segment to obtain a comparison result, wherein the comprehensive threshold segment specifically includes a core threshold segment and a secondary threshold segment; Various types of data belonging to the core threshold segment and the secondary threshold segment in the comparison results are marked to obtain core data and secondary data.

[0011] Preferably, the dynamic storage based on the divided core data and secondary data is specifically performed by splitting the divided core data for encrypted storage and backing up, and merging the secondary data for storage, and the specific steps are: Various types of data identified as core data are split according to the structure and type of the data to obtain core data blocks, and the core data blocks obtained after the split are encrypted, wherein the encryption process adopts symmetric encryption or asymmetric encryption.

[0012] The encrypted core data blocks are stored in the primary storage device and backed up to the backup storage device at the same time; All types of data identified as secondary data will be merged and processed according to the type and purpose of the data, and stored in the hard disk or cloud storage.

[0013] A data storage system in an industrial Internet environment, the storage system comprising: A weight division module is used to obtain historical maintenance data information of industrial equipment communicating with the industrial Internet of Things, and divide the maintenance weights of the industrial equipment according to preset weight threshold segments; The data statistics module is used to periodically count the retrieval frequency of various types of data of industrial equipment according to the set cycle, and set the retrieval threshold section; The weight allocation module is used to dynamically and periodically divide the retrieval weights of various types of industrial equipment data based on the periodic statistics of the retrieval frequency of various types of industrial equipment data through the set retrieval threshold segments; The data identification module is used to quantify the retrieval weight of various types of industrial equipment data and the maintenance weight of industrial equipment into comprehensive weights, and to divide the core data and secondary data of industrial equipment; The dynamic storage module is used to perform dynamic storage based on the divided core data and secondary data, specifically splitting the divided core data for encrypted storage and backing up, and merging the secondary data for storage.

[0014] Compared with the related art, the data storage method and system in the industrial Internet environment provided by the present invention have the following beneficial effects: The present invention accurately identifies the core data and secondary data in the industrial environment by dynamically dividing the maintenance weight and data retrieval weight of industrial equipment, and adopts different storage strategies for these two types of data. For core data, a higher level of protection measures and faster access speed are implemented, and the split and encrypted storage method is adopted, which significantly enhances the security of data and reduces the risk of data loss or damage; for secondary data, a more economical storage method is adopted, such as hard disk or cloud storage, so as to achieve the rational use of resources and cost savings; in addition, the present invention also has the characteristics of strong adaptability and high flexibility, and can be flexibly adjusted according to the characteristics and business needs of different industrial equipment, achieving the goal of optimized data storage and efficient access. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a data storage method in an industrial Internet environment of the present invention.

[0016] Figure 2 This is a system block diagram of a data storage system in an industrial Internet environment of the present invention. DETAILED DESCRIPTION

[0017] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.

[0018] Embodiment 1: Figure 1 As shown, a data storage method in an industrial Internet environment includes the following steps: S1. Obtain historical maintenance data information of industrial equipment that communicates with the industrial Internet of Things, and divide the maintenance weights of the industrial equipment according to preset weight threshold segments; S2. According to the set cycle, periodically count the frequency of retrieval of various types of data of industrial equipment, and set the retrieval threshold section; S3. Based on the periodic statistics of the frequency of retrieval of various types of data of industrial equipment, the retrieval weights of various types of data of industrial equipment are dynamically and periodically divided by setting the retrieval threshold segments; S4. Quantify the retrieval weights of various types of industrial equipment data and the maintenance weights of industrial equipment into comprehensive weights, and divide the core data and secondary data of industrial equipment; S5. Perform dynamic storage based on the divided core data and secondary data, specifically, split the divided core data, encrypt and store them, and back them up, and merge and store the secondary data.

[0019] In the specific implementation process, the specific steps of step S1 are: S101. Collect historical maintenance data information from connected industrial equipment through the industrial Internet of Things platform and clean, process and organize it, where the historical maintenance data information includes the number of equipment failures, maintenance time, maintenance cost and maintenance frequency.

[0020] Specifically, the data collection interface or API of the industrial Internet of Things platform is used to collect historical maintenance data information from these devices in real time or periodically. The collected historical maintenance data information includes key indicators such as the number of equipment failures, maintenance time, maintenance cost, and maintenance frequency; then it is cleaned, including removing duplicate data, filling missing values, correcting erroneous data, etc.

[0021] S102, setting threshold segments for the number of equipment failures, maintenance time, maintenance cost, and maintenance frequency, and determining the weighted threshold segments to which the number of industrial equipment failures, maintenance time, maintenance cost, and maintenance frequency belong, respectively, based on the acquired historical maintenance data.

[0022] Specifically, threshold segments are set for key indicators such as the number of equipment failures, maintenance time, maintenance cost, and maintenance frequency in combination with business needs and industry standards. In this embodiment, for machine tool equipment, the number of failures is 0-5 times as a low-weight threshold segment, 6-10 times as a medium-weight threshold segment, and more than 10 times as a high-weight threshold segment; the maintenance time is 0-20 hours as a low-weight threshold segment, 21-40 hours as a medium-weight threshold segment, and more than 40 hours as a high-weight threshold segment; the maintenance cost is 0-5000 yuan as a low-weight threshold segment, 5001-10000 yuan as a medium-weight threshold segment, and more than 10000 yuan as a high-weight threshold segment; maintenance frequency; the maintenance frequency is 0-2 times as a low-weight threshold segment; 3-5 times as a medium-weight threshold segment, and more than 5 times as a high-weight threshold segment; it should be noted that, in this embodiment, the weight value of the low-weight threshold segment is set to 1, the weight value of the medium-weight threshold segment is set to 2, and the weight value of the high-weight threshold segment is set to 3.

[0023] S103, by means of a weighted summation method, the weight threshold segments to which the number of industrial equipment failures, maintenance time, maintenance cost, and maintenance frequency respectively belong are integrated to obtain a maintenance weight of the industrial equipment.

[0024] Specifically, indicators such as the number of equipment failures, maintenance time, maintenance cost, and maintenance frequency, each indicator has a different weight. The weight of each indicator is summed up by weight, and the result is the maintenance weight of the industrial equipment.

[0025] In this embodiment, the number of failures of the machine tool equipment obtained is 3 times, which belongs to the low weight threshold segment, the maintenance time is 13 hours, which belongs to the low weight threshold segment, the maintenance cost is 6,500 yuan, which belongs to the medium weight threshold segment, and the maintenance frequency is 4 times, which belongs to the medium weight threshold segment; therefore, according to the weight threshold segment set in step S102, the weights assigned to the number of failures, maintenance time, maintenance cost and maintenance frequency of the machine tool equipment are 1, 1, 2, and 2 respectively; then the result of the weighted sum is: 1+1+2+2=6, that is, the maintenance weight of the machine tool is 6.

[0026] In the specific implementation process, the specific steps of step S2 are: S201. Set a fixed statistical period, and at the end of each period, obtain the frequency of retrieval of various types of data for each industrial device within the period.

[0027] Specifically, according to the actual needs of the enterprise, a fixed statistical period is set, which may be daily, weekly, monthly or quarterly. Then, according to the statistical period, the industrial Internet of Things platform or data management system is used to record the retrieval of various types of data for each industrial equipment in each statistical period. It should be noted that the data retrieval frequency refers to the ratio of the number of times a certain type of data is retrieved to the total number of times in the period within the statistical period. Statistical software or scripts are used to calculate the retrieval frequency of various types of data for each industrial equipment based on the recorded data retrieval conditions.

[0028] For example, an enterprise sets a weekly statistical cycle and needs to count the frequency of production data retrieval for each industrial equipment within a week. In a certain week, production data of a certain equipment was retrieved 100 times, and there were a total of 500 data retrieval records in that week (including retrieval of all types and all equipment). Then, the production data retrieval frequency of this equipment in that week is 100 / 500=0.2, or 20%.

[0029] S202. According to the business needs of the enterprise, set the threshold range for access. Specifically, the higher the business needs of the enterprise, the smaller the value range of the threshold range for access is set. The threshold range for access includes a high frequency range, a medium frequency range and a low frequency range.

[0030] Specifically, a retrieval threshold segment is set, and the data retrieval threshold segment is used to determine the frequency of data retrieval, thereby further determining the data retrieval weight and storage strategy. It should be noted that the setting of the retrieval threshold segment is based on the enterprise's demand for data. The higher the business demand, the smaller the value range of the set retrieval threshold segment, which means that higher requirements are placed on the accuracy and real-time performance of data retrieval.

[0031] In this embodiment, the high-frequency segment (>0.5, that is, the retrieval frequency exceeds 50%), the medium-frequency segment (0.2-0.5, that is, the retrieval frequency is between 20%-50%), and the low-frequency segment (<0.2, that is, the retrieval frequency is lower than 20%), when the retrieval frequency of the production data of the machine tool equipment exceeds 50%, the data can be considered as high-frequency data.

[0032] In the specific implementation process, the specific steps of step S3 are: S301, respectively assign corresponding weight values ​​to three threshold value segments, namely, a high frequency segment, a medium frequency segment and a low frequency segment.

[0033] Specifically, the data in the high-frequency segment usually has higher business value due to frequent access and use, so it should be assigned a higher weight. On the contrary, the data in the low-frequency segment may have lower business value due to low frequency of use, so it should be assigned a lower weight. The data in the medium-frequency segment is between the two. Therefore, in this embodiment, the high-frequency segment is assigned a weight of 0.7, the medium-frequency segment is assigned a weight of 0.2, and the low-frequency segment is assigned a weight of 0.1.

[0034] S302. Based on the statistically acquired frequency of retrieval of various types of data for each industrial equipment within the period, the frequency of retrieval of various types of data is compared and determined, and the frequency of retrieval of various types of data is classified into specific retrieval threshold segments among the three retrieval threshold segments of high frequency segment, medium frequency segment and low frequency segment, so as to obtain the result of the retrieval threshold segment to which each type of data belongs.

[0035] Specifically, based on the statistically acquired frequencies of various types of data for each industrial equipment within a period, it is compared and determined whether the frequencies of various types of data retrieval belong to the high-frequency segment, the medium-frequency segment, and the low-frequency segment.

[0036] In this embodiment, the production data retrieval frequency of a certain machine tool equipment during the statistical period is 0.6 (i.e. 60%), and the preset high-frequency segment threshold is 0.5 (i.e. 50%), the medium-frequency segment threshold is 0.2-0.5 (i.e. 20%-50%), and the low-frequency segment threshold is below 0.2 (i.e. below 20%). Therefore, the production data retrieval frequency of the machine tool equipment belongs to the high-frequency segment.

[0037] S303. Based on the obtained results of the classification of each type of data into the retrieval threshold segments, weight values ​​corresponding to the retrieval threshold segments are respectively assigned to each type of data to obtain the retrieval weight division results of each type of industrial equipment data.

[0038] Specifically, according to the threshold segment weight value assigned in step S301 and the result of the data belonging to the threshold segment obtained in step S302, specific weight values ​​are assigned to each type of data, and the assigned weight values ​​are recorded in the data management system or related documents for subsequent use.

[0039] In the specific implementation process, the specific steps of step S4 are: S401. Use the weighted average method to calculate the retrieval weights of various types of industrial equipment data and the maintenance weights of the industrial equipment into a comprehensive weight.

[0040] S402: Compare the obtained comprehensive weight with a preset comprehensive threshold segment to obtain a comparison result, wherein the comprehensive threshold segment specifically includes a core threshold segment and a secondary threshold segment.

[0041] Specifically, according to the needs of the enterprise, the comprehensive threshold segments are manually set, wherein the core threshold segment is usually set to a higher weight range, and the secondary threshold segment is set to a lower weight range. The comprehensive weight is compared with the preset comprehensive threshold segment. If the comprehensive weight of this type of data falls within the core threshold segment, then this type of data is classified as core data; if the comprehensive weight of this type of data falls within the secondary threshold segment, then this type of data is classified as secondary equipment. In this embodiment, the core threshold segment is set to 0.6 and above, and the secondary threshold segment is below 0.6.

[0042] S403 , marking various types of data belonging to the core threshold segment and the secondary threshold segment in the comparison result, to obtain core data and secondary data.

[0043] Specifically, according to the comparison result, the data of the devices belonging to the core threshold segment and the secondary threshold segment are identified, wherein specific labels or colors are used for distinction. Similarly, the device data belonging to the secondary threshold segment is also identified with labels or colors.

[0044] In the specific implementation process, the specific steps of step S5 are: S501. Split various types of data identified as core data into core data blocks according to the structure and type of the data, and encrypt the core data blocks obtained after the splitting, wherein the encryption process adopts symmetric encryption or asymmetric encryption.

[0045] Specifically, the core data needs to be split according to the structure and type of the data to obtain a series of core data blocks. It should be noted that the purpose of splitting is to decompose large data sets into smaller, easier to manage and process data blocks, which also helps to improve data access efficiency and security. After the split is completed, each core data block is encrypted, specifically using symmetric encryption or asymmetric encryption, selecting the encryption algorithm and key length, and encrypting the split core data blocks.

[0046] S502: Store the encrypted core data blocks in a primary storage device, and simultaneously back them up in a backup storage device.

[0047] In this embodiment, a symmetric encryption algorithm (such as AES) is used for the split core data blocks, and a strong password is used as the key to encrypt each data block. After the encryption is completed, these encrypted data blocks are securely stored in the enterprise's main storage device. It should be noted that the backup storage device can be a tape library, cloud storage or other reliable storage media, which can provide data recovery capabilities when the main storage device fails.

[0048] S503: various types of data identified as secondary data are merged according to the type and purpose of the data, and stored in a hard disk or cloud storage.

[0049] Specifically, the secondary data are merged and processed using data processing software or scripts according to their type and purpose, and then the merged secondary data are stored in a hard disk or cloud storage.

[0050] Embodiment 2: Figure 2 As shown, a data storage system in an industrial Internet environment applied to a data storage method in an industrial Internet environment specifically includes: A weight division module is used to obtain historical maintenance data information of industrial equipment communicating with the industrial Internet of Things, and divide the maintenance weights of the industrial equipment according to preset weight threshold segments; The data statistics module is used to periodically count the retrieval frequency of various types of data of industrial equipment according to the set cycle, and set the retrieval threshold section; The weight allocation module is used to dynamically and periodically divide the retrieval weights of various types of industrial equipment data based on the periodic statistics of the retrieval frequency of various types of industrial equipment data through the set retrieval threshold segments; The data identification module is used to quantify the retrieval weight of various types of industrial equipment data and the maintenance weight of industrial equipment into comprehensive weights, and to divide the core data and secondary data of industrial equipment; The dynamic storage module is used to perform dynamic storage based on the divided core data and secondary data, specifically splitting the divided core data for encrypted storage and backing up, and merging the secondary data for storage.

[0051] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0052] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0053] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. A data storage method in an industrial Internet environment, characterized in that: The storage method comprises the following steps: Obtain historical maintenance data information of industrial equipment that communicates with the industrial Internet of Things, and divide the maintenance weights of the industrial equipment according to preset weight threshold segments; According to the set cycle, periodically count the frequency of retrieval of various types of data of industrial equipment, and set the retrieval threshold section; Based on the periodic statistics of the frequency of retrieval of various types of data of industrial equipment, the retrieval weights of various types of data of industrial equipment are dynamically and periodically divided through the set retrieval threshold segments; Quantify the retrieval weight of various types of industrial equipment data and the maintenance weight of industrial equipment into comprehensive weights, and divide the core data and secondary data of industrial equipment; Dynamic storage is performed based on the divided core data and secondary data, specifically, the divided core data is split, encrypted, stored and backed up, and the secondary data is merged and stored.

2. According to the data storage method in the industrial Internet environment of claim 1, it is characterized in that: The specific steps of obtaining historical maintenance data information of industrial equipment communicating with the industrial Internet of Things and dividing the maintenance weights of the industrial equipment according to preset weight threshold segments are as follows: Through the industrial Internet of Things platform, historical maintenance data information is collected from connected industrial equipment and cleaned, processed and sorted. The historical maintenance data information includes the number of equipment failures, maintenance time, maintenance cost and maintenance frequency; The threshold segments are set for the number of equipment failures, maintenance time, maintenance cost and maintenance frequency, and the weighted threshold segments to which the number of industrial equipment failures, maintenance time, maintenance cost and maintenance frequency belong are determined based on the acquired historical maintenance data; Through the weighted summation method, the weight threshold segments to which the number of industrial equipment failures, maintenance time, maintenance cost and maintenance frequency belong are integrated to obtain the maintenance weight of the industrial equipment.

3. According to the data storage method in an industrial Internet environment of claim 1, it is characterized in that: The specific steps of periodically counting the frequency of retrieving various data of industrial equipment according to the set cycle and setting the retrieving threshold section are as follows: Set a fixed statistical cycle, and at the end of each cycle, collect the frequency of various data retrieval for each industrial equipment within the statistical acquisition cycle; According to the business needs of the enterprise, the threshold segment is set. Specifically, the higher the business needs of the enterprise, the smaller the value range of the set threshold segment is. The threshold segment includes a high-frequency segment, a medium-frequency segment and a low-frequency segment.

4. According to a data storage method in an industrial Internet environment according to claim 1, it is characterized in that: The frequency of retrieving various types of data of industrial equipment based on periodic statistics is dynamically and periodically divided into the weights of retrieving various types of data of industrial equipment by setting the retrieving threshold segments, and the specific steps are as follows: Assign corresponding weight values ​​to the three threshold value sections, namely, the high frequency section, the medium frequency section and the low frequency section; Based on the frequency of data retrieval of each type of industrial equipment within the statistically acquired period, the frequency of data retrieval of each type of data is compared and determined, and the specific retrieval threshold segments of the three retrieval threshold segments of the high frequency segment, the medium frequency segment and the low frequency segment are classified, and the result of the retrieval threshold segment to which each type of data belongs is obtained; Based on the obtained results of belonging various types of data to the retrieval threshold segments, weight values ​​corresponding to the retrieval threshold segments are allocated to each type of data, and the retrieval weight division results of various types of industrial equipment data are obtained.

5. The data storage method in an industrial Internet environment according to claim 1 is characterized in that: The specific steps of quantifying the retrieval weights of various types of industrial equipment data and the maintenance weights of industrial equipment into comprehensive weights and dividing the core data and secondary data of industrial equipment are as follows: The weighted average method is used to calculate the comprehensive weight by taking the retrieval weight of various types of industrial equipment data and the maintenance weight of industrial equipment; Comparing the obtained comprehensive weight with a preset comprehensive threshold segment to obtain a comparison result, wherein the comprehensive threshold segment specifically includes a core threshold segment and a secondary threshold segment; Various types of data belonging to the core threshold segment and the secondary threshold segment in the comparison results are marked to obtain core data and secondary data.

6. The data storage method in an industrial Internet environment according to claim 1 is characterized in that: The dynamic storage based on the divided core data and secondary data is specifically performed by splitting the divided core data for encrypted storage and backing up, and merging the secondary data for storage, and the specific steps are: Splitting various types of data identified as core data into core data blocks according to the structure and type of the data, and encrypting the core data blocks obtained after the splitting, wherein the encryption process adopts symmetric encryption or asymmetric encryption; The encrypted core data blocks are stored in the primary storage device and backed up to the backup storage device at the same time; All types of data identified as secondary data will be merged and processed according to the type and purpose of the data, and stored in the hard disk or cloud storage.

7. A data storage system in an industrial Internet environment, applied to a data storage method in an industrial Internet environment according to any one of claims 1 to 6, characterized in that: The storage system comprises: A weight division module is used to obtain historical maintenance data information of industrial equipment communicating with the industrial Internet of Things, and divide the maintenance weights of the industrial equipment according to preset weight threshold segments; The data statistics module is used to periodically count the retrieval frequency of various types of data of industrial equipment according to the set cycle, and set the retrieval threshold section; The weight allocation module is used to dynamically and periodically divide the retrieval weights of various types of industrial equipment data based on the periodic statistical retrieval frequency of various types of industrial equipment data through the set retrieval threshold segments; The data identification module is used to quantify the retrieval weight of various types of industrial equipment data and the maintenance weight of industrial equipment into comprehensive weights, and to divide the core data and secondary data of industrial equipment; The dynamic storage module is used to perform dynamic storage based on the divided core data and secondary data, specifically splitting the divided core data for encrypted storage and backing up, and merging the secondary data for storage.

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