A Blockchain-Based Industrial Internet Data Storage Method and System

Abstract

The present invention relates to the technical field of industrial Internet, and specifically relates to a method for storing industrial Internet data based on blockchain, including: obtaining sensor data of industrial equipment, filtering the sensor data to obtain data to be processed; sending the data to be processed to an edge server for grouping and association to obtain data to be uploaded; the edge server receives an adaptive neural network data filtering model trained on a blockchain node of a cloud server, and uses the adaptive neural network data filtering model to filter the data to be uploaded to obtain data to be stored; forwarding the data to be stored to the blockchain node of the cloud server for associated storage based on a grouping label.

Description

[0001] This application is a divisional application of a Chinese patent application filed with the Chinese Patent Office on February 21, 2022, with the application number 202210155546.9 and the invention title "Industrial Internet Data Storage Method, System and Storage Medium Based on Blockchain". Technical Field

[0002] The present invention relates to the technical field of industrial Internet, and specifically relates to an industrial Internet data storage method, system, computer-readable storage medium and electronic device based on blockchain. Background Art

[0003] With the advent of the industrial Internet era, more and more attention has been paid to how to process the large amount of big data generated in the industrial production and manufacturing process.

[0004] At the same time, with the development of the industrial Internet, big data and cloud computing are required in many fields, especially in the process of industrial production, and various industrial data generated therein need to be processed through big data or cloud computing methods. However, in the process of industrial data processing in related fields, the data volume is huge, and generally the data is processed uniformly and then stored. The current processing and storage methods have the problem of low efficiency. Summary of the Invention

[0005] Embodiments of the present application provide an industrial Internet data storage method, system, computer-readable storage medium and electronic device based on blockchain, which can at least improve the efficiency of data processing and storage to a certain extent.

[0006] According to the first aspect of the embodiments of the present application, an industrial Internet data storage method based on blockchain is provided, including: obtaining sensor data of industrial equipment, filtering the sensor data to obtain data to be processed; sending the data to be processed to an edge server for grouping and association to obtain data to be uploaded; the edge server receives an adaptive neural network data filtering model trained on a blockchain node of a cloud server, and uses the adaptive neural network data filtering model to filter the data to be uploaded to obtain data to be stored; forwarding the data to be stored to the blockchain node of the cloud server for associated storage based on a grouping label.

[0007] In some embodiments of the present application, based on the foregoing solution, each grouping label corresponds to at least one sensor; the data structure of the data to be uploaded is: grouping label FZID i , (CGQ i1 , CGQ i2 , ……, CGQ in), where 1 ≤ i ≤ M, M is a positive integer; n is the number of sensors included in the grouping label, and n is a positive integer greater than or equal to 1.

[0008] In some embodiments of the present application, based on the foregoing solution, using the adaptive neural network data filtering model to filter the data to be uploaded to obtain the data to be stored includes: based on the preset load of the edge server, determining the first grouping of sensors according to the approved load and approved energy consumption of the sensors; based on the remaining load of the edge server, determining the second grouping of sensors according to the working load and working energy consumption of the sensors; based on the first grouping and the second grouping, determining whether it is necessary to adjust the grouping of the sensors; if so, adjusting the first grouping or the second grouping according to the actual situation to obtain the optimal grouping and data structure, and obtaining the data to be stored.

[0009] In some embodiments of the present application, based on the foregoing solution, if so, adjusting the first grouping and / or the second grouping according to the actual situation to obtain the optimal grouping and data structure, and obtaining the data to be stored includes: if so, adjusting the sensors from the first grouping to the second grouping to obtain a third grouping; if the obtained is the third grouping, further processing the sensor data in each ID set in the third grouping.

[0010] In some embodiments of the present application, based on the foregoing solution, if so, adjusting the first grouping and / or the second grouping according to the actual situation to obtain the optimal grouping and data structure includes: if so, adjusting the sensors from the second grouping to the first grouping to obtain a fourth grouping; if the obtained is the fourth grouping, further processing the sensor data in each ID set in the fourth grouping.

[0011] In some embodiments of the present application, based on the foregoing solution, it further includes: when receiving a query instruction, determining whether the query instruction is an abnormal confirmation instruction; if so, calculating the probability of sensor grouping abnormality according to a preset algorithm, and determining the abnormal grouping according to the abnormal probability of the grouping label; searching for the corresponding abnormal sensor data according to the grouping label of the sensors in the abnormal grouping.

[0012] According to the second aspect of the embodiments of the present application, there is provided an industrial Internet data storage system based on a blockchain, including: an acquisition module configured to acquire sensor data of industrial equipment, filter the sensor data to obtain data to be processed; an upload module configured to send the data to be processed to an edge server for grouping and association to obtain data to be uploaded; a filtering module configured to receive an adaptive neural network data filtering model trained on a blockchain node of a cloud server by the edge server, and use the adaptive neural network data filtering model to filter the data to be uploaded to obtain data to be stored; and a storage module configured to forward the data to be stored to the blockchain node of the cloud server for associated storage based on a grouping label.

[0013] In some embodiments of the present application, based on the foregoing solution, each grouping label corresponds to at least one sensor; the data structure of the data to be uploaded is: grouping label FZID i , (CGQ i1 , CGQ i2 , ……, CGQ in ), where 1 ≤ i ≤ M, M is a positive integer; n is the number of sensors included in the grouping label, and n is a positive integer greater than or equal to 1.

[0014] In some embodiments of the present application, based on the foregoing solution, the filtering module includes: a first determination unit configured to determine a first grouping of sensors based on a preset load of the edge server and according to the approved load and approved energy consumption of the sensors; a second determination unit configured to determine a second grouping of sensors based on the remaining load of the edge server and according to the working load and working energy consumption of the sensors; a third determination unit configured to determine whether it is necessary to adjust the group of the sensors based on the first grouping and the second grouping; and a grouping adjustment unit configured to, if so, adjust the first grouping or the second grouping according to the actual situation to obtain an optimal grouping and data structure, and obtain data to be stored.

[0015] In some embodiments of the present application, based on the foregoing solution, the grouping adjustment unit includes: a first grouping adjustment unit configured to, if so, adjust the sensors from the first grouping to the second grouping to obtain a third grouping; if the obtained is the third grouping, further process the sensor data in each ID set in the third grouping.

[0016] In some embodiments of the present application, based on the foregoing solution, the grouping adjustment unit includes: a second grouping adjustment unit configured to, if so, adjust the sensors from the second grouping to the first grouping to obtain a fourth grouping; if the obtained is the fourth grouping, further process the sensor data in each ID set in the fourth grouping.

[0017] In some embodiments of the present application, based on the foregoing solution, the blockchain-based industrial Internet data storage system further includes: a first determination module configured to determine whether the query instruction is an exception confirmation instruction when a query instruction is received; a second determination module configured to, if so, calculate the probability of sensor grouping exception according to a preset algorithm, and determine an abnormal group according to the exception probability of the grouping label; a search module configured to search for corresponding abnormal sensor data according to the grouping label of the sensors in the abnormal group.

[0018] According to the third aspect of the embodiments of the present application, there is provided a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the industrial Internet data storage method described in the first aspect is implemented.

[0019] According to the fourth aspect of the embodiments of the present application, there is provided an electronic device including: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the industrial Internet data storage method described in the first aspect.

[0020] In the blockchain-based industrial Internet data storage method provided by some embodiments of the present application, sensor data of industrial equipment is obtained, the sensor data is filtered to obtain data to be processed; the data to be processed is sent to an edge server for grouping and association to obtain data to be uploaded; the edge server receives an adaptive neural network data filtering model trained on a blockchain node of a cloud server, and uses the adaptive neural network data filtering model to filter the data to be uploaded to obtain data to be stored; the data to be stored is forwarded to the blockchain node of the cloud server for association storage based on a grouping label. This solution filters the acquired sensor data during the industrial production process and then uploads it to the edge server for grouping and association to obtain data to be uploaded; then filters the data to be uploaded through the adaptive neural network data filtering model of the edge server to obtain data to be stored; and sends the data to be stored to the blockchain node of the cloud server for association storage based on a grouping label, thereby improving the data processing efficiency and data storage efficiency based on the characteristics of the edge server, the cloud server, and the blockchain. In addition, it is also convenient for data traceability.

[0021] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. Obviously, the drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0023] Figure 1 Schematically shows a flowchart of a blockchain-based industrial Internet data storage method according to an embodiment of the present application;

[0024] Figure 2 Schematically shows another flowchart of a blockchain-based industrial Internet data storage method according to an embodiment of the present application;

[0025] Figure 3 Schematically shows a schematic diagram of the sensor data format stored on the blockchain of a blockchain-based industrial Internet data storage method according to an embodiment of the present application;

[0026] Figure 4 Schematically shows another schematic diagram of the sensor data format stored on the blockchain of a blockchain-based industrial Internet data storage method according to an embodiment of the present application;

[0027] Figure 5 Schematically shows a schematic diagram of a blockchain-based industrial Internet data storage system according to an embodiment of the present application;

[0028] Figure 6 Shows a schematic diagram of the structure of a computer system of an electronic device suitable for implementing the embodiments of the present application. Detailed Embodiments

[0029] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art.

[0030] In addition, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present application. However, those skilled in the art will realize that the technical solutions of the present application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be used. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present application.

[0031] The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0032] The flowcharts shown in the drawings are only illustrative and do not necessarily include all contents and operations / steps, nor are they necessarily executed in the described order. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined, so the actual execution order may change according to the actual situation.

[0033] The implementation details of the technical solutions of the embodiments of the present application are elaborated in detail below:

[0034] Figure 1 The flowchart of the industrial Internet data storage method based on blockchain according to an embodiment of the present application is shown. The industrial Internet data storage method based on blockchain can be executed by a server, and the server includes an edge server and a cloud server. Refer to Figure 1 As shown, the industrial Internet data storage method based on blockchain at least includes step S110 to step S140, which are introduced in detail as follows:

[0035] Step S110: Obtain the sensor data of industrial equipment, and perform filtering processing on the sensor data to obtain the data to be processed.

[0036] For step S110, optionally, the industrial equipment in this embodiment includes industrial equipment such as lathes, milling machines, grinding machines, punching machines, flexible manufacturing lines, PLCs, machining centers, robots, laser machines, industrial computers, tool setting instruments, cutting machines, and welding machines. In order to monitor the working status of industrial equipment, various types of sensors are set. Obtain the data of these sensors, and perform filtering processing on the sensor data to obtain the data to be processed.

[0037] Step S120: Send the data to be processed to the edge server for grouping and association to obtain the data to be uploaded; where each grouping label corresponds to at least one sensor; the data structure of the data to be uploaded is: grouping label FZID i , (CGQ i1 , CGQ i2 , ……, CGQ in ), where 1≤i≤M, M is a positive integer; n is the number of sensors included in the grouping label, and n is a positive integer greater than or equal to 1; n is the number of sensors included in the grouping label, and n is a positive integer greater than or equal to 1.

[0038] For step S120, send the data to be processed to the edge server, group the data to be processed in the edge server, and then use grouping tags to place the sensor data in the same group under the same grouping tag. Each grouping tag corresponds to at least one sensor. For example, the grouping tag FZID 6 corresponds to the sensor data of sensor 61, sensor 62, and sensor 63. Associating multiple sensors with grouping tags facilitates the management of sensor data and the tracing of data in case of subsequent anomalies. Specifically, the data structure of the data to be uploaded can be: grouping tag FZID i , (CGQ i1 , CGQ i2 , ……, CGQ in ), where 1 ≤ i ≤ M and M is a positive integer.

[0039] Among them, after the data is sent, the data transmission can be through wired or wireless means. Among them, the above wireless connection methods can include but are not limited to 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other currently known or future-developed wireless connection methods.

[0040] Step S130: The edge server receives the trained adaptive neural network data filtering model on the blockchain node of the cloud server, and uses the adaptive neural network data filtering model to filter the data to be uploaded to obtain the data to be stored.

[0041] Reference Figure 2 , in step S130, using the adaptive neural network data filtering model to filter the grouping tags and the corresponding sensor data to obtain the data to be stored includes:

[0042] Step S1301: Based on the preset load of the edge server, determine the first group of sensors according to the approved load and approved energy consumption of the sensors.

[0043] Step S1302: Based on the remaining load of the edge server, determine the second group of sensors according to the working load and working energy consumption of the sensors.

[0044] For steps S1301 and S1302, among them, pre-grouping can first determine the preliminary grouping situation of the sensors, and then determine the final grouping situation according to the actual working load and working energy consumption of the sensors. And based on the grouping tag as the identity tag of the sensor, it is grouped based on the grouping tag instead of each sensor, which greatly reduces the calculation amount.

[0045] Optionally, the relevant information of the load includes any one or more of machine load information, machine configuration information, and data volume load information. In an optional implementation, the relevant information of the load includes machine load information, machine configuration information, and data volume load information.

[0046] Step S1303: Based on the first group and the second group, determine whether it is necessary to adjust the group of the sensors.

[0047] Step S1304: If so, adjust the sensors from the first group to the second group to obtain a third group; or adjust them from the second group to the first group to obtain a fourth group.

[0048] Step S1305: If the obtained is the third group, further process the sensor data in each ID set in the third group.

[0049] Step S1306: If the obtained is the fourth group, further process the sensor data in each ID set in the fourth group.

[0050] For steps S1303 to S1306, adjust the grouping according to the actual situation to obtain the optimal grouping and data structure, and then obtain the data to be stored.

[0051] It should be noted that when training an adaptive neural network data filtering model on the blockchain node of the cloud server, the data to be stored obtained in steps S1301 to S1306 and the corresponding initial sensor data can also be used as the output and input respectively to further optimize and iterate the parameters of the adaptive neural network data filtering model.

[0052] Step S140: Forward the data to be stored to the blockchain node of the cloud server for associated storage based on the grouping label.

[0053] In step S140, the data to be stored in each edge server is forwarded to the blockchain node of the cloud server for associated storage based on the grouping label. The filtering process in the edge server can be parallel, which improves the data processing efficiency. The unified storage on the blockchain node of the cloud service is convenient for management, improves the storage efficiency, and is also convenient for data traceability.

[0054] In the blockchain-based industrial Internet data storage method provided in some embodiments of the present application, sensor data of industrial devices is obtained, the sensor data is filtered to obtain data to be processed; the data to be processed is sent to an edge server for grouping and association to obtain data to be uploaded; wherein, each grouping label corresponds to at least one sensor; the data structure of the data to be uploaded is: grouping label FZID i ,(CGQi1 , CGQ i2 , ……, CGQ in ), where 1 ≤ i ≤ M, M is a positive integer; n is the number of sensors included in the grouping label, and n is a positive integer greater than or equal to 1; the edge server receives the adaptive neural network data filtering model trained on the blockchain node of the cloud server, uses the adaptive neural network data filtering model to filter the data to be uploaded, and obtains the data to be stored; forwards the data to be stored to the blockchain node of the cloud server for associated storage based on the grouping label. This solution filters the acquired sensor data during the industrial production process and then uploads it to the edge server for grouping and association to obtain the data to be uploaded; then filters the data to be uploaded through the adaptive neural network data filtering model of the edge server to obtain the data to be stored; and sends the data to be stored to the blockchain node of the cloud server for associated storage based on the grouping label, thereby improving the data processing efficiency and data storage efficiency based on the characteristics of the edge server, cloud server, and blockchain. In addition, it is also convenient for data traceability.

[0055] In some embodiments of the present application, based on the foregoing solution, in some embodiments of the present application, based on the foregoing solution, the industrial Internet data storage method based on blockchain further includes:

[0056] Step S150: When a query instruction is received, determine whether the query instruction is an exception confirmation instruction.

[0057] Step S160: If so, calculate the probability of sensor grouping exception according to a preset algorithm, and determine the abnormal grouping according to the exception probability of the grouping label.

[0058] Among them, the preset algorithm is as follows:

[0059]

[0060] Among them, m is the serial number of the sensor node, 1 ≤ m ≤ M, M is the number of sensor nodes, and FzYcProb i is the distribution vector of the i-th group.

[0061] FzYcWtVec m is the preset weight vector corresponding to the m-th sensor node, FzYcProb i，m is the probability value of the i-th group including m sensor nodes being abnormal.

[0062] Step S170: Search for the corresponding abnormal sensor data according to the grouping label of the sensors in the abnormal grouping.

[0063] In some embodiments of the present application, based on the foregoing solution, finding the corresponding abnormal sensor data according to the grouping label of the abnormally grouped sensors includes:

[0064] Step S1701: Use K mutually independent hash functions to perform hash operations on the grouping label respectively according to the following formula: HashKey k = Hash k (FZID i ); where FZID i is the grouping label, Hash k is the k-th hash function, HashKey k is the k-th hash value obtained by the operation, 1 ≤ k ≤ K, and K is an integer greater than 1; FZID i is the i-th grouping label, 1 ≤ i ≤ I, and I is an integer greater than 1;

[0065] Step S1702: Obtain the array corresponding to the grouping label, and determine whether the array satisfies the following judgment condition: for any value of k, the equation ARRAY[HashKey k == Value_1 holds, where ARRAY is the query array and Value_1 is a preset first numerical value;

[0066] Step S1703: If the array does not satisfy the judgment condition, it is determined that the grouping label is not found in the sensor database of the server.

[0067] In some embodiments of the present application, based on the foregoing solution, the industrial Internet data storage method based on the blockchain further includes:

[0068] Step S180: When there is no sensor data stored in the edge server, for any value of m, perform the following assignment operation: ARRAY[m]= Value_2; where 1 ≤ m ≤ M, M is the number of sensors in the array, and Value_2 is a preset second numerical value.

[0069] Step S190: When there is sensor data in the edge server, extract the grouping label in the sensor data; use K mutually independent hash functions to perform hash operations on the grouping label respectively according to the following formula: HashKeyExist k = Hash k (FZIDExist); where FZIDExist is the grouping label, and HashKeyExist k is the k-th hash value obtained by the operation; for any value of k, perform the following assignment operation: ARRAY[HashKeyExistk = Value_1。

[0070] In some embodiments of the present application, based on the foregoing solution, the blockchain-based industrial Internet data storage method further includes: when abnormal sensor data is determined, creating a corresponding block and storing the abnormal sensor data on the blockchain; the blockchain is associated through the block header; each block includes a block header, block metadata, and block data; wherein, the block header includes a block number, the current block Hash, and the Hash of the previous block; the block metadata includes the block creation time, the certificate, public key, and signature of the writing program; the block data includes abnormal sensor data. In this embodiment, the block data only includes abnormal sensor data, which further improves the storage efficiency and also improves the efficiency of searching for abnormal sensor data.

[0071] Specifically, Figure 3 It is a schematic diagram of the sensor data format stored on the blockchain of the blockchain-based industrial Internet data storage method. Among them, 3 nodes are schemed. The data formats stored on each node on the blockchain are the same and store sensor data. The data formats of sensor data on different blockchain platforms will be different, but basically they are all composed of blocks to form sensor data. Specifically, the block header includes a block number, the current block Hash (the Hash of all sensors included in the current block), and the Hash of the previous block; the block metadata includes the block creation time, the certificate, public key, and signature of the writing program, etc. The block data includes a set of abnormal sensor data (such as abnormal sensor 1, abnormal sensor 2, abnormal sensor 3, etc.), and the sensor data is written when the block is created.

[0072] In some embodiments of the present application, based on the foregoing solution, the blockchain-based industrial Internet data storage method further includes: when abnormal sensor data is determined, creating a corresponding block and storing the abnormal sensor data on the blockchain; the blockchain is associated through the block header; each block includes a block header, block metadata, and block data; wherein, the block header includes a block number, the current block Hash, and the Hash of the previous block; the block metadata includes the block creation time, the certificate, public key, and signature of the writing program; the block data includes abnormal sensor data and normal sensor data. The block data including abnormal sensor data and normal sensor data makes the sensor data more comprehensive.

[0073] Specifically, Figure 4Another schematic diagram of the sensor data format stored on the blockchain for the blockchain-based industrial Internet of Things data storage method. Among them, 3 nodes are schematically shown. The data formats stored on each node of the blockchain are the same and store sensor data. The formats of sensor data on different blockchain platforms will be different, but basically they are all composed of blocks to form sensor data. Specifically, the block header contains the block number, the Hash of the current block (the Hash of all sensors included in the current block), and the Hash of the previous block; the block metadata includes the block creation time, the certificate, public key, and signature of the writing program, etc. The block data contains a set of abnormal sensor data (such as abnormal sensor 1, abnormal sensor 2, normal sensor 3, normal sensor 4, etc.), and the sensor data is written when creating the block. A set of sensor data includes normal data and abnormal data.

[0074] Please refer to Figure 5 As shown, the following introduces an embodiment of the blockchain-based industrial Internet of Things data storage system of the present application, which can be used to execute the blockchain-based industrial Internet of Things data storage method in the embodiments of the present application. It can be understood that the system can be a computer program (including program code) running in a computer device. For example, the system is an application software; the system can be used to execute the corresponding steps in the method provided by the embodiments of the present application. For the details not disclosed in the embodiments of the device of the present application, please refer to the embodiments of the above-mentioned blockchain-based industrial Internet of Things data storage method of the present application.

[0075] Refer to Figure 5 As shown, according to an embodiment of the present application, the blockchain-based industrial Internet of Things data storage system 500 includes:

[0076] An acquisition module 510, configured to acquire sensor data of industrial equipment, perform filtering processing on the sensor data, and obtain data to be processed.

[0077] An upload module 520, configured to send the data to be processed to an edge server, perform grouping and association, and obtain data to be uploaded; wherein, each grouping label corresponds to at least one sensor; the data structure of the data to be uploaded is: grouping label FZID i , (CGQ i1 , CGQ i2 , ……, CGQ in ), where 1 ≤ i ≤ M, and M is a positive integer.

[0078] A filtering module 530, configured to receive the adaptive neural network data filtering model trained on the blockchain node of the cloud server by the edge server, and use the adaptive neural network data filtering model to perform filtering processing on the data to be uploaded to obtain data to be stored.

[0079] The storage module 540 is configured to forward the data to be stored to the blockchain node of the cloud server for associated storage based on the grouping label.

[0080] In some embodiments of the present application, based on the foregoing solution, the filtering module includes: a first determination unit configured to determine a first group of sensors based on the preset load of the edge server, according to the approved load and approved energy consumption of the sensors; a second determination unit configured to determine a second group of sensors based on the remaining load of the edge server, according to the working load and working energy consumption of the sensors; a third determination unit configured to determine whether it is necessary to adjust the group of the sensors based on the first group and the second group; a first group adjustment unit configured to, if so, adjust the sensors from the first group to the second group to obtain a third group; or adjust from the second group to the first group to obtain a fourth group; a second group adjustment unit configured to, if the obtained is the third group, further process the sensor data in each ID set in the third group; a third group adjustment unit configured to, if the obtained is the fourth group, further process the sensor data in each ID set in the fourth group.

[0081] In some embodiments of the present application, based on the foregoing solution, the industrial Internet data storage system based on the blockchain further includes: a first determination module configured to determine whether the query instruction is an abnormal confirmation instruction when receiving the query instruction; a second determination module configured to, if so, calculate the probability of sensor group abnormality according to a preset algorithm, and determine the abnormal group according to the abnormality probability of the grouping label; a search module configured to search for the corresponding abnormal sensor data according to the grouping label of the sensors in the abnormal group.

[0082] In some embodiments of the present application, based on the foregoing solution, the search module includes: an operation unit configured to perform hash operations on the grouping label respectively using K mutually independent hash functions according to the following formula: HashKey k =Hash k (FZID i ); where FZID i is the grouping label, Hash k is the hash function with the serial number k, HashKey k is the hash value with the serial number k obtained by the operation, 1≤k≤K, K is an integer greater than 1; FZID i is the grouping label with the serial number i, 1≤i≤I, I is an integer greater than 1; a judgment unit configured to obtain the array corresponding to the grouping label and judge whether the array meets the following judgment condition: for any value of k, the equation ARRAY[HashKey k== Value_1 holds for all, where ARRAY is the query array and Value_1 is a preset first numerical value; a determination unit configured to determine that the grouping tag is not found in the sensor database of the server if the array does not meet the determination condition.

[0083] In some embodiments of the present application, based on the foregoing solution, the industrial Internet data storage system based on the blockchain further includes: a first assignment module configured to, when no sensor data is stored in the edge server, perform the following assignment operation for any value of m: ARRAY[m] = Value_2; where 1 ≤ m ≤ M, M is the number of sensors in the array, and Value_2 is a preset second numerical value; an extraction module configured to extract the grouping tag in the sensor data when there is sensor data in the edge server; an operation module configured to perform hash operations on the grouping tag using K mutually independent hash functions according to the following formula: HashKeyExist k = Hash k (FZID Exist); where FZID Exist is the grouping tag and HashKeyExist k is the hash value with the serial number k obtained by the operation; a first assignment module configured to perform the following assignment operation for any value of k: ARRAY[HashKeyExist k = Value_1.

[0084] In some embodiments of the present application, based on the foregoing solution, the industrial Internet data storage system based on the blockchain further includes: a first creation module configured to create a corresponding block when abnormal sensor data is determined and store the abnormal sensor data on the blockchain; where the blockchain is associated through a block header; each block includes a block header, block metadata, and block data; where the block header includes a block number, the current block Hash, and the Hash of the previous block; the block metadata includes the block creation time, the certificate, public key, and signature of the writing program; the block data includes abnormal sensor data.

[0085] In some embodiments of the present application, based on the foregoing solution, the industrial Internet data storage system based on the blockchain further includes: a second creation module configured to create a corresponding block when abnormal sensor data is determined and store the abnormal sensor data on the blockchain; the blockchain is associated through a block header; each block includes a block header, block metadata, and block data; where the block header includes a block number, the current block Hash, and the Hash of the previous block; the block metadata includes the block creation time, the certificate, public key, and signature of the writing program; the block data includes abnormal sensor data and normal sensor data.

[0086] Figure 6 The figure shows a schematic structural diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application.

[0087] It should be noted that Figure 6 The computer system 600 of the shown electronic device is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0088] As Figure 6 shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 602 or the program loaded from the storage section 608 into the random access memory (RAM) 603, such as executing the methods described in the above embodiments. In the RAM 603, various programs and data required for system operations are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 606. The input / output (I / O) interface 605 is also connected to the bus 606.

[0089] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. The drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed so that the computer program read from it can be installed into the storage section 608 as needed.

[0090] In particular, according to an embodiment of the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present application includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, various functions defined in the system of the present application are executed.

[0091] It should be noted that the computer-readable medium shown in the embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. And in the present application, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable computer program is carried. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The computer program included on the computer-readable medium can be transmitted by any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0092] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or part of the code includes one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, as well as the combination of blocks in the block diagram or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0093] The units involved in the embodiments described in the present application can be implemented in software or in hardware, and the described units can also be provided in a processor. Among them, the names of these units do not, in some cases, constitute a limitation on the unit itself.

[0094] According to one aspect of the present application, there is provided a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the above various alternative implementation manners.

[0095] As another aspect, the present application further provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the one or more programs are executed by an electronic device, the electronic device implements the methods described in the above embodiments.

[0096] It should be noted that although several modules or units of the device for performing actions are mentioned in the above detailed description, such a division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of the two or more modules or units described above may be embodied in one module or unit. Conversely, the features and functions of one module or unit described above may be further divided and embodied by multiple modules or units.

[0097] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (such as a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0098] After considering the specification and practicing the embodiments disclosed herein, those skilled in the art will readily conceive of other embodiments of the present application. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include common general knowledge or conventional technical means in the technical field not disclosed in the present application.

[0099] It should be understood that the present application is not limited to the precise structures already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A method for storing industrial Internet data based on blockchain, characterized in that, it includes: Obtain the sensor data of industrial equipment, filter the sensor data to obtain data to be processed; Send the data to be processed to the edge server for grouping and association to obtain data to be uploaded; The edge server receives the self-adaptive neural network data filtering model trained on the blockchain node of the cloud server, and uses the self-adaptive neural network data filtering model to filter the data to be uploaded to obtain data to be stored; Forward the data to be stored to the blockchain node of the cloud server for associated storage based on the grouping label; The step of using the self-adaptive neural network data filtering model to filter the data to be uploaded to obtain data to be stored includes: Based on the preset load of the edge server, determine the first group of sensors according to the approved load and approved energy consumption of the sensors; Based on the remaining load of the edge server, determine the second group of sensors according to the working load and working energy consumption of the sensors; Based on the first group and the second group, determine whether it is necessary to adjust the group of sensors; If so, adjust the first group or the second group according to the actual situation to obtain the optimal grouping and data structure, and obtain the data to be stored; When receiving a query instruction, determine whether the query instruction is an abnormal confirmation instruction; Wherein, the preset algorithm is as follows: Among them, m is the serial number of the sensor node, 1 ≤ m ≤ M, where M is the number of sensor nodes, and FzYcProb i is the distribution vector of the i-th group; FzYcWtVec m is the preset weight vector corresponding to the m-th sensor node, FzYcProb i,m is the probability value that the i-th group including m sensor nodes is abnormal; Search for the corresponding abnormal sensor data according to the grouping label of the sensors in the abnormal group.

2. The method for storing industrial Internet data based on blockchain according to claim 1, characterized in that, Each grouping label corresponds to at least one sensor; the data structure of the data to be uploaded is: grouping label FZID i , (CGQ i1 , CGQ i2 , ……, CGQ in ), where 1 ≤ i ≤ M, M is a positive integer; n is the number of sensors included in the grouping label, and n is a positive integer greater than or equal to 1.

3. The method for storing industrial Internet data based on blockchain according to claim 1, characterized in that, The step of if so, adjusting the first group and / or the second group according to the actual situation to obtain the optimal grouping and data structure, and obtaining the data to be stored includes: If so, adjust the sensors from the first group to the second group to obtain a third group; If the obtained is the third group, further process the sensor data in each ID set in the third group.

4. The method for storing industrial Internet data based on blockchain according to claim 1, characterized in that, The step of if so, adjusting the first group and / or the second group according to the actual situation to obtain the optimal grouping and data structure includes: If so, adjust the sensors from the second group to the first group to obtain a fourth group; If the obtained is the fourth group, further process the sensor data in each ID set in the fourth group.

5. An industrial Internet data storage system based on blockchain, characterized in that, it includes: An acquisition module configured to acquire the sensor data of industrial equipment, filter the sensor data to obtain data to be processed; An upload module configured to send the data to be processed to the edge server for grouping and association to obtain data to be uploaded; A filtering module, configured to receive, by an edge server, an adaptive neural network data filtering model trained on a blockchain node of a cloud server, and use the adaptive neural network data filtering model to filter the data to be uploaded to obtain data to be stored; A storage module, configured to forward the data to be stored to a blockchain node of a cloud server for associated storage based on a grouping label; Wherein, the step of using the adaptive neural network data filtering model to filter the data to be uploaded to obtain data to be stored includes: Based on a preset load of the edge server, determine a first group of sensors according to the approved load and approved energy consumption of the sensors; Based on the remaining load of the edge server, determine a second group of sensors according to the working load and working energy consumption of the sensors; Based on the first group and the second group, determine whether it is necessary to adjust the group of sensors; If so, adjust the first group or the second group according to the actual situation to obtain an optimal grouping and data structure, and obtain data to be stored; When a query instruction is received, determine whether the query instruction is an abnormal confirmation instruction; Wherein, the preset algorithm is as follows: where m is the serial number of the sensor node, 1 ≤ m ≤ M, M is the number of sensor nodes, and FzYcProb i is the distribution vector of the i-th group; FzYcWtVec m is the preset weight vector corresponding to the m-th sensor node, FzYCProb i,m is the probability value of the i-th group including m sensor nodes being abnormal; Search for corresponding abnormal sensor data according to the grouping label of the sensors in the abnormal group.

6. A computer-readable storage medium, on which a computer program is stored, Characterized in that, When the computer program is executed by a processor, it implements the industrial Internet data storage method according to any one of claims 1 to 4.

7. An electronic device, Characterized in that, Comprising: One or more processors; A storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the industrial Internet data storage method according to any one of claims 1 to 4.

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