CN114218174B

CN114218174B - Industrial internet data storage method, system and storage medium based on block chain

Info

Publication number: CN114218174B
Application number: CN202210155546.9A
Authority: CN
Inventors: 贾昌武; 李鸿峰; 黄筱炼; 伍康健
Original assignee: Shenzhen Xuanyu Technology Co ltd
Current assignee: Shenzhen Xuanyu Technology Co ltd
Priority date: 2022-02-21
Filing date: 2022-02-21
Publication date: 2022-05-31
Anticipated expiration: 2042-02-21
Also published as: CN114817177A; CN114896216A; CN114817178A; CN114925028A; CN114218174A

Abstract

The invention relates to the technical field of industrial internet, in particular to an industrial internet data storage method based on a block chain, which comprises the following steps: acquiring sensor data of industrial equipment, and filtering the sensor data to obtain data to be processed; sending the data to be processed to an edge server, and grouping and associating to obtain the data to be uploaded; wherein each group tag corresponds to at least one sensor; the data structure of the data to be uploaded is as follows: grouping label

，（

，

，……，

) (ii) a The method comprises the steps that an edge server receives a trained self-adaptive neural network data filtering model on a block chain node of a cloud server, and the self-adaptive neural network data filtering model is used for filtering data to be uploaded to obtain the data to be stored; and forwarding the data to be stored to a block chain node of the cloud server for associated storage based on the packet tag.

Description

Industrial Internet data storage method, system and storage medium based on block chain

Technical Field

The invention relates to the technical field of industrial internet, in particular to a block chain-based industrial internet data storage method and system, a computer-readable storage medium and electronic equipment.

Background

With the advent of the industrial internet era, there is an increasing interest in how to deal with the large amount of data generated by industrial manufacturing processes.

Meanwhile, with the development of industrial internet, big data and cloud computing are required to be used in many fields, and especially in the process of industrial production, various industrial data generated in the industry need to be processed in a big data or cloud computing mode. However, in the industrial data processing process in the related field, the data volume is huge, data is generally processed and then stored, and the current processing and storing method has the problem of low efficiency.

Disclosure of Invention

Embodiments of the present application provide a block chain-based industrial internet data storage method, system, computer-readable storage medium, and electronic device, so that data processing and storage efficiency can be improved at least to a certain extent.

According to a first aspect of embodiments of the present application, there is provided a blockchain-based industrial internet data storage method, including: acquiring sensor data of industrial equipment, and filtering the sensor data to obtain data to be processed; sending the data to be processed to an edge server, and grouping and associating to obtain data to be uploaded; wherein each group tag corresponds to at least one sensor; the data structure of the data to be uploaded is as follows: grouping label

，（

，

，……，

) Wherein i is more than or equal to 1 and less than or equal to M which is a positive integerN is the number of sensors contained in the grouping label, and n is a positive integer greater than or equal to 1; the method comprises the steps that an edge server receives a trained self-adaptive neural network data filtering model on a block chain node of a cloud server, and the self-adaptive neural network data filtering model is used for filtering data to be uploaded to obtain the data to be stored; and forwarding the data to be stored to the block chain node of the cloud server for associated storage based on the packet label.

In some embodiments of the present application, based on the foregoing scheme, the filtering the packet tag and the corresponding sensor data by using the adaptive neural network data filtering model to obtain data to be stored includes: determining a first group of sensors according to the approved load and the approved energy consumption condition of the sensors based on the preset load of the edge server; determining a second grouping of sensors according to the workload and the energy consumption of the sensors based on the residual load of the edge server; determining whether an adjustment to the group of sensors is needed based on the first and second groupings; if so, adjusting the sensor from the first grouping to the second grouping to obtain a third grouping; or adjusting the second packet to the first packet to obtain a fourth packet; if the obtained group is the third group, further processing the sensor data in each ID set in the third group; and if the obtained group is a fourth group, further processing the sensor data in each ID set in the fourth group.

In some embodiments of the present application, based on the foregoing solution, the method for block chain-based industrial internet data storage further includes: when a query instruction is received, determining whether the query instruction is an abnormal confirmation instruction; if yes, calculating the abnormal probability of the sensor grouping according to a preset algorithm, and determining abnormal grouping according to the abnormal probability of the grouping labels; and searching corresponding abnormal sensor data according to the grouping label of the abnormal grouped sensor.

In some embodiments of the present application, based on the foregoing solution, the finding corresponding abnormal sensor data according to the grouping labels of the sensors grouped in an abnormal manner includes: respectively according to the following formulaCarrying out hash operation on the packet label by K mutually independent hash functions: hashkey_k=Hash_k(

) (ii) a Wherein,

for said group tag, Hash_kAs a hash function with sequence number k, HashKey_kFor the hash value with the serial number of K obtained by operation, K is more than or equal to 1 and less than or equal to K, and K is an integer more than 1;

i is more than or equal to 1 and less than or equal to I which is an integer more than 1; acquiring an array corresponding to the grouping label, and judging whether the array meets the following judgment conditions: 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 Value; and if the array does not meet the judgment condition, judging that the grouping label is not inquired in a sensor database of the server.

In some embodiments of the present application, based on the foregoing solution, the method for block chain-based industrial internet data storage further includes: when no sensor data is stored in the edge server, the following assignment operation is executed for any value of m: ARRAY [ m ]]= Value _ 2; wherein M is more than or equal to 1 and less than or equal to M, M is the number of sensors in the array, and Value _2 is a preset second numerical Value; when sensor data exists in an edge server, extracting a grouping label in the sensor data; performing hash operation on the packet labels by using K mutually independent hash functions according to the following formula: HashKeyExist_k=Hash_k(

Exist); wherein,

exist is the packetLabel, HashKeyExist_kObtaining a hash value with the serial number of k for the operation; and carrying out the following assignment operation on any value of k: ARRAY [ HashKeyExist_k]=Value_1。

In some embodiments of the present application, based on the foregoing solution, the method for storing industrial internet data based on a block chain further includes: when the abnormal sensor data is determined, creating a corresponding block, and storing the abnormal sensor data on a block chain; the block chains are associated by block headers; each block includes a block header, block metadata, and block data; wherein, the block head comprises a block number, a Hash of the current block and a Hash of the previous block; the block metadata comprises block creation time, a certificate written in the program, a public key and a signature; the tile data includes abnormal sensor data.

In some embodiments of the present application, based on the foregoing solution, the method for blockchain-based industrial internet data storage further includes: when the abnormal sensor data are determined, creating a corresponding block, and storing the abnormal sensor data on a block chain; the block chains are associated by block headers; each block includes a block header, block metadata, and block data; wherein, the block head comprises a block number, a Hash of the current block and a Hash of the previous block; the block metadata comprises block creation time, a certificate written in the program, a public key and a signature; the tile data includes abnormal sensor data and normal sensor data.

According to a second aspect of embodiments of the present application, there is provided a blockchain-based industrial internet data storage system, including: the acquisition module is configured to acquire sensor data of the industrial equipment, and filter the sensor data to obtain data to be processed; the uploading module is configured to send the data to be processed to an edge server, and the data to be uploaded is obtained through grouping and association; wherein each group tag corresponds to at least one sensor; the data structure of the data to be uploaded is as follows: grouping label

，（

，

，……，

) Wherein i is more than or equal to 1 and less than or equal to M, M is a positive integer, n is the number of sensors contained in the group label, and n is a positive integer greater than or equal to 1; the filtering module is configured to receive a trained adaptive neural network data filtering model on a block chain node of the cloud server by the edge server, and filter the data to be uploaded by using the adaptive neural network data filtering model to obtain the data to be stored; the storage module is configured to forward the data to be stored to a blockchain node of a cloud server for associated storage based on a packet tag.

In some embodiments of the present application, based on the foregoing, the filtering module includes: the first determining unit is configured to determine a first group of the sensors according to the approved load and the approved energy consumption condition of the sensors based on the preset load of the edge server; a second determination unit configured to determine a second grouping of sensors according to the workload and energy consumption of the sensors based on the remaining load of the edge server; a third determining unit configured to determine whether an adjustment of the group of sensors is required based on the first and second groups; a first packet adjustment unit configured to adjust the sensor from the first packet to the second packet if yes, resulting in a third packet; or adjusting the second packet to the first packet to obtain a fourth packet; the second grouping adjusting unit is configured to further process the sensor data in each ID set in the third grouping if the third grouping is obtained; and the third grouping adjusting unit is configured to further process the sensor data in each ID set in the fourth grouping if the fourth grouping is obtained.

In some embodiments of the present application, based on the foregoing solution, the block chain based industrial internet data storage system further includes: the device comprises a first determination module, a second determination module and a third determination module, wherein the first determination module is configured to determine whether a query instruction is an abnormal confirmation instruction when the query instruction is received; the second determining module is configured to calculate the probability of abnormal grouping of the sensors according to a preset algorithm and determine abnormal grouping according to the abnormal probability of the grouping labels if the abnormal grouping is determined;

a lookup module configured to lookup corresponding abnormal sensor data according to the grouping tag of the abnormally grouped sensor.

In some embodiments of the present application, based on the foregoing solution, the lookup module includes: an operation unit configured to hash the packet label using K mutually independent hash functions, respectively, according to the following equation: hashkey_k=Hash_k(

) (ii) a Wherein,

for said group tag, Hash_kAs a hash function with sequence number k, HashKey_kK is equal to or more than 1 and is equal to or less than K, and K is an integer more than 1;

i is more than or equal to 1 and less than or equal to I which is an integer more than 1; the judging unit is configured to acquire an array corresponding to the grouping label and judge whether the array meets the following judging conditions: 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 Value; a determination unit configured to determine that the group tag is not queried in a sensor database of a server if the array does not satisfy the determination condition.

In some embodiments of the present application, based on the foregoing solution, the block chain based industrial internet data storage system further includes: a first valuation module configured to determine if any sensors are not stored in the edge serverWhen data is received, the following assignment operation is executed for any value of m: ARRAY [ m ]]= Value _ 2; wherein M is more than or equal to 1 and less than or equal to 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 a packet tag in sensor data when the sensor data exists in an edge server; an operation module configured to hash the packet label using K mutually independent hash functions, respectively, according to the following formula: HashKeyExist_k=Hash_k(

Exist); wherein,

exist is the packet tag, HashKeyExist_kThe hash value with the serial number of k is obtained by operation; the first assignment module is configured to perform the following assignment operation on any value of k: ARRAY [ HashKeyExist_k]=Value_1。

In some embodiments of the present application, based on the foregoing solution, the block chain based industrial internet data storage system further includes: the system comprises a first establishing module, a second establishing module and a third establishing module, wherein the first establishing module is configured to establish a corresponding block when abnormal sensor data is determined, and store the abnormal sensor data on a block chain; wherein the block chains are associated by a block header; each block includes a block header, block metadata, and block data; wherein, the block head comprises a block number, a Hash of the current block and a Hash of the previous block; the block metadata comprises block creation time, a certificate written in the program, a public key and a signature; the tile data includes abnormal sensor data.

In some embodiments of the present application, based on the foregoing solution, the block chain based industrial internet data storage system further includes: the second establishing module is configured to establish a corresponding block when the abnormal sensor data is determined, and store the abnormal sensor data on the block chain; the block chains are associated by block headers; each block includes a block header, block metadata, and block data; wherein, the block head comprises a block number, a Hash of the current block and a Hash of the previous block; the block metadata comprises block creation time, a certificate written in the program, a public key and a signature; the tile data includes abnormal sensor data and normal sensor data.

According to a third aspect of embodiments of the present application, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the industrial internet data storage method as claimed in the first aspect.

According to a fourth aspect of embodiments of the present application, there is provided an electronic apparatus, including: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the industrial internet data storage method as described in the first aspect.

In the block chain-based industrial internet data storage method provided by some embodiments of the present application, sensor data of an industrial device is acquired, and the sensor data is filtered to obtain data to be processed; sending the data to be processed to an edge server, and grouping and associating to obtain data to be uploaded; wherein each group tag corresponds to at least one sensor; the data structure of the data to be uploaded is as follows: grouping label

，（

，

，……，

) Wherein i is more than or equal to 1 and less than or equal to M, M is a positive integer, n is the number of sensors contained in the grouping label, and n is a positive integer more than or equal to 1; edge server receiving self-adaptive neural network trained on block chain nodes of cloud serverThe data filtering model is used for filtering the data to be uploaded by using the self-adaptive neural network data filtering model to obtain the data to be stored; and forwarding the data to be stored to a block chain node of the cloud server for associated storage based on a packet label. According to the scheme, in the industrial production process, acquired sensor data are filtered and then uploaded to an edge server for grouping and association, and data to be uploaded are obtained; then, filtering the data to be uploaded through a self-adaptive neural network data filtering model of the edge server to obtain the data to be stored; and the data to be stored is sent to the block chain nodes of the cloud server for associated storage based on the grouping labels, so that the data processing efficiency and the data storage efficiency are improved based on the characteristics of the edge server, the cloud server and the block chain, and the data can be conveniently traced.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.

Fig. 1 schematically illustrates a flowchart of a blockchain based industrial internet data storage method according to an embodiment of the present application;

FIG. 2 schematically illustrates another flow diagram of a blockchain-based industrial Internet data storage method according to an embodiment of the present application;

fig. 3 schematically illustrates a sensor data format diagram stored on a blockchain of a blockchain-based industrial internet data storage method according to an embodiment of the present application;

fig. 4 schematically illustrates another schematic diagram of a sensor data format stored on a blockchain of a blockchain-based industrial internet data storage method according to an embodiment of the present application;

FIG. 5 schematically illustrates a schematic diagram of a blockchain based industrial Internet data storage system according to one embodiment of the present application;

FIG. 6 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.

Furthermore, the described features, structures, or characteristics may 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 embodiments of the application. One skilled in the relevant art will recognize, however, that the subject matter of the present application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the application.

The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.

The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.

The implementation details of the technical solution of the embodiment of the present application are set forth in detail below:

fig. 1 illustrates a flowchart of a blockchain-based industrial internet data storage method according to an embodiment of the present application, which may be performed by a server including an edge server and a cloud server. Referring to fig. 1, the method for storing industrial internet data based on a blockchain at least includes steps S110 to S140, which are described in detail as follows:

step S110: and acquiring sensor data of the industrial equipment, and filtering the sensor data to obtain data to be processed.

For step S110, optionally, the industrial equipment in this embodiment includes industrial equipment such as a lathe, a milling machine, a grinding machine, a punching machine, a flexible manufacturing line, a PLC, a machining center, a robot, a laser machine, an industrial personal computer, a tool setting gauge, a cutting machine, and a welding machine. In order to monitor the operating state of industrial equipment, various types of sensors are provided. And acquiring data of the sensors, and filtering the sensor data to obtain data to be processed.

Step S120: sending the data to be processed to an edge server, and grouping and associating to obtain data to be uploaded; wherein each group tag corresponds to at least one sensor; the data structure of the data to be uploaded is as follows: grouping label

，（

，

，……，

) Wherein i is more than or equal to 1 and less than or equal to M, M is a positive integer, and n is the groupingThe number of sensors contained in the tag, n is a positive integer greater than or equal to 1.

For step S120, the data to be processed is sent to the edge server, the data to be processed is grouped in the edge server, and then the sensor data in the same group is located under the same group tag through the group tag, where each group tag corresponds to at least one sensor. E.g. packet labels

Corresponding to sensor data of sensor 61, sensor 62 and sensor 63. The multiple sensors are associated with the grouping labels, so that the management of sensor data and the follow-up of the data if an exception occurs subsequently are facilitated. Specifically, the data structure of the data to be uploaded may be: grouping label

，（

，

，……，

) Wherein i is more than or equal to 1 and less than or equal to M, and M is a positive integer.

The data transmission after the data transmission may be in a wired or wireless manner, wherein the wireless connection manner may include, but is not limited to, a 3G/4G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a uwb (ultra wideband) connection, and other wireless connection manners known now or developed in the future.

Step S130: the edge server receives a trained self-adaptive neural network data filtering model on a block chain node of the cloud server, and the self-adaptive neural network data filtering model is used for filtering data to be uploaded to obtain the data to be stored.

Referring to fig. 2, in step S130, the adaptive neural network data filtering model is used to filter the packet labels and the corresponding sensor data, and obtain data to be stored, including:

step S1301: and determining a first group of the sensors according to the approved load and the approved energy consumption condition of the sensors based on the preset load of the edge server.

Step S1302: a second grouping of sensors is determined based on the remaining load of the edge server based on the workload and energy consumption of the sensors.

For steps S1301 and S1302, pre-grouping may determine a preliminary grouping condition of the sensors, determine a final grouping condition according to actual workload and energy consumption of the sensors, and use the grouping label as an identity label of the sensors, so that grouping is performed not on the basis of each sensor but on the basis of the grouping label, which greatly reduces the amount of calculation.

Optionally, the information related to the load includes any one or more of machine load information, machine configuration information, and data volume load information. In an alternative implementation, the information related to the load includes machine load information, machine configuration information, and data volume load information.

Step S1303: based on the first and second groupings, it is determined whether an adjustment to the group of sensors is needed.

Step S1304: if so, adjusting the sensor from the first grouping to the second grouping to obtain a third grouping; or from the second packet to the first packet, resulting in a fourth packet.

Step S1305: and if the obtained packet is the third packet, further processing the sensor data in each ID set in the third packet.

Step 1306: and if the obtained group is a fourth group, further processing the sensor data in each ID set in the fourth group.

For steps S1303 to S1306, the packet is adjusted according to the actual situation to obtain the optimal packet and data mechanism, and then the data to be stored is obtained.

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

Step S140: and forwarding the data to be stored to a block chain node of the cloud server for associated storage based on a packet label.

In step S140, the data to be stored in each edge server is forwarded to the blockchain node of the cloud server, and associated storage is performed based on the packet tag, so that filtering processing in the edge servers can be performed in parallel, the efficiency of data processing is improved, and uniform storage is performed on the blockchain nodes of the cloud server, which is convenient for management, improves the storage efficiency, and is also convenient for data traceability.

，（

，

，……，

) Wherein i is more than or equal to 1 and less than or equal to M, M is a positive integer, n is the number of sensors contained in the grouping label, and n is a positive integer more than or equal to 1; edge server receives training on blockchain node of cloud serverThe trained adaptive neural network data filtering model is used for filtering data to be uploaded to obtain data to be stored; and forwarding the data to be stored to a block chain node of the cloud server for associated storage based on a packet label. According to the scheme, in the industrial production process, acquired sensor data are filtered and then uploaded to an edge server for grouping and association, and data to be uploaded are obtained; then, filtering the data to be uploaded through a self-adaptive neural network data filtering model of the edge server to obtain the data to be stored; and the data to be stored is sent to the block chain nodes of the cloud server for associated storage based on the grouping labels, so that the data processing efficiency and the data storage efficiency are improved based on the characteristics of the edge server, the cloud server and the block chain, and the data can be conveniently traced.

In some embodiments of the present application, based on the foregoing scheme, in some embodiments of the present application, the method for block chain-based industrial internet data storage further includes:

step S150: when a query instruction is received, determining whether the query instruction is an exception confirmation instruction.

Step S160: if yes, calculating the probability of abnormal grouping of the sensors according to a preset algorithm, and determining abnormal grouping according to the abnormal probability of the grouping labels.

The preset algorithm is as follows:

wherein M is the serial number of the sensor nodes, M is the number of the sensor nodes,

is the distribution vector of the ith packet.

For the preset weight vector corresponding to the mth sensor node,

is the probability value of the i-th grouping abnormity comprising m sensor nodes.

Step S170: and searching corresponding abnormal sensor data according to the grouping labels of the sensors grouped abnormally.

In some embodiments of the present application, based on the foregoing solution, the finding corresponding abnormal sensor data according to the grouping labels of the sensors grouped in an abnormal manner includes:

step S1701: performing hash operation on the packet label by using K independent hash functions respectively according to the following formula: hashkey_k=Hash_k(

) (ii) a Wherein,

i is more than or equal to 1 and less than or equal to I which is an integer more than 1;

step S1702: acquiring an array corresponding to the grouping label, and judging whether the array meets the following judgment conditions: 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 Value;

step S1703: and if the array does not meet the judgment condition, judging that the grouping label is not inquired in a sensor database of the server.

In some embodiments of the present application, based on the foregoing solution, the method for block chain-based industrial internet data storage further includes:

step S180: when no sensor data is stored in the edge server, the following assignment operation is executed for any value of m: ARRAY [ m ] = Value _ 2; wherein M is greater than or equal to 1 and less than or equal to M, M is the number of the sensors in the array, and Value _2 is a preset second numerical Value.

Step S190: when sensor data exists in an edge server, extracting a grouping label in the sensor data; performing hash operation on the packet label by using K independent hash functions respectively according to the following formula: HashKeyExist_k=Hash_k(

Exist); wherein,

exist is the packet tag, HashKeyExist_kObtaining a hash value with the serial number of k for the operation; and carrying out the following assignment operation on any value of k: ARRAY [ HashKeyExist ]_k]=Value_1。

In some embodiments of the present application, based on the foregoing solution, the method for block chain-based industrial internet data storage further includes: when the abnormal sensor data is determined, creating a corresponding block, and storing the abnormal sensor data on a block chain; the block chains are associated by block headers; each block includes a block header, block metadata, and block data; wherein, the block head comprises a block number, a Hash of the current block and a Hash of the previous block; the block metadata comprises block creation time, a certificate written in the program, a public key and a signature; the tile data includes abnormal sensor data. In this embodiment, the block data only includes abnormal sensor data, which further improves the efficiency of storage and also improves the efficiency of searching for abnormal sensor data.

Specifically, fig. 3 is a schematic diagram of a sensor data format stored on a blockchain of the blockchain-based industrial internet data storage method. The 3 nodes are shown, the data format stored on each node on the block chain is the same, sensor data is stored, and the sensor data format of different block chain platforms is different, but the sensor data is basically composed of blocks. Specifically, the block header contains the block number, the Hash of the current block (the Hash of all sensors contained in the current block), the Hash of the previous block; the chunk metadata includes chunk creation time, certificate of writer, public key, signature, etc. The tile data includes a set of abnormal sensor data (e.g., abnormal sensor 1, abnormal sensor 2, abnormal sensor 3, etc.) that is written when the tile is created.

In some embodiments of the present application, based on the foregoing solution, the method for blockchain-based industrial internet data storage further includes: when the abnormal sensor data is determined, creating a corresponding block, and storing the abnormal sensor data on a block chain; the block chains are associated by block headers; each block includes a block header, block metadata, and block data; wherein, the block head comprises a block number, a Hash of the current block and a Hash of the previous block; the block metadata comprises block creation time, a certificate written in the program, a public key and a signature; the tile data includes abnormal sensor data and normal sensor data. The tile data includes abnormal sensor data and normal sensor data so that the sensor data is more comprehensive.

Specifically, fig. 4 is another schematic diagram of a sensor data format stored on a blockchain of the blockchain-based industrial internet data storage method. The 3 nodes are shown, the data format stored on each node on the block chain is the same, sensor data is stored, and the sensor data format of different block chain platforms is different, but the sensor data is basically composed of blocks. Specifically, the block header contains the block number, the Hash of the current block (the Hash of all sensors contained in the current block), the Hash of the previous block; the chunk metadata includes chunk creation time, the writer's certificate, public key, signature, etc. The tile data includes a set of abnormal sensor data (e.g., abnormal sensor 1, abnormal sensor 2, normal sensor 3, normal sensor 4, etc.) that is written when the tile is created. The set of sensor data includes normal data and abnormal data.

Referring to fig. 5, an embodiment of the block chain based industrial internet data storage system according to the present application is described below, which can be used to implement the block chain based industrial internet data storage method according to the embodiment of the present application. It will be appreciated that the system may be a computer program (including program code) running on a computer device, for example an application software; the system can be used for executing the corresponding steps in the method provided by the embodiment of the application. For details that are not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the method for storing industrial internet data based on blockchain described above in the present application.

Referring to fig. 5, a block chain based industrial internet data storage system 500 according to an embodiment of the present application includes:

the obtaining module 510 is configured to obtain sensor data of the industrial device, and filter the sensor data to obtain data to be processed.

The uploading module 520 is configured to send the data to be processed to an edge server, and perform grouping and association to obtain the data to be uploaded; wherein each group tag corresponds to at least one sensor; the data structure of the data to be uploaded is as follows: grouping label

，（

，

，……，

) Wherein i is greater than or equal to 1 and less than or equal to M, M is a positive integer, n is the number of sensors contained in the group label, and n is a positive integer greater than or equal to 1.

The filtering module 530 is configured to receive the trained adaptive neural network data filtering model on the block chain node of the cloud server by the edge server, and filter the data to be uploaded by using the adaptive neural network data filtering model to obtain the data to be stored.

The storage module 540 is configured to forward the data to be stored to a blockchain node of the cloud server for associated storage based on the packet tag.

In some embodiments of the present application, based on the foregoing scheme, look upThe module of looking for includes: an operation unit configured to hash the packet label using K mutually independent hash functions, respectively, according to: hashkey_k=Hash_k(

) (ii) a Wherein,

In some embodiments of the present application, based on the foregoing solution, the block chain based industrial internet data storage system further includes: the first assignment module is configured to perform the following assignment operation on any value of m when no sensor data is stored in the edge server: ARRAY [ m ]]= Value _ 2; wherein M is more than or equal to 1 and less than or equal to 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 a packet tag in sensor data when the sensor data exists in an edge server; an operation module configured to hash the packet label using K mutually independent hash functions, respectively, according to the following formula: HashKeyExist_k=Hash_k(

Exist); wherein,

exist is the packet tag, HashKeyExist_kThe hash value with the serial number of k is obtained by operation; the first assignment module is configured to perform the following assignment operation on any value of k: ARRAY [ HashKeyExist ]_k]=Value_1。

It should be noted that the computer system 600 of the electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.

As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU) 601, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 602 or a program loaded from a storage portion 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for system operation are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An Input/Output (I/O) interface 605 is also connected to bus 604.

The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output section 607 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a Network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 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, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted into the storage section 608 as necessary.

In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method illustrated by the flow chart. In such an embodiment, the computer program may 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 a Central Processing Unit (CPU) 601, various functions defined in the system of the present application are executed.

It should be noted that the computer readable media shown in the embodiments of the present application may be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with a computer program embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.

According to an aspect of the application, a computer program product or computer program is provided, comprising computer instructions, the computer instructions being 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 to cause the computer device to perform the method provided in the various alternative implementations described above.

As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method described in the above embodiments.

It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution 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 (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be 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.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. An industrial internet data storage method based on a block chain is characterized by comprising the following steps:

acquiring sensor data of industrial equipment, and filtering the sensor data to obtain data to be processed;

sending the data to be processed to an edge server, and grouping and associating to obtain data to be uploaded; wherein each packet label corresponds to at leastA sensor; the data structure of the data to be uploaded is as follows: packet label FZID_i，(CGQ_i1，CGQ_i2，……，CGQ_in) Wherein i is more than or equal to 1 and less than or equal to M, and M is a positive integer; n is the number of sensors contained in the grouping label, and n is a positive integer greater than or equal to 1;

the method comprises the steps that an edge server receives a trained self-adaptive neural network data filtering model on a block chain node of a cloud server, and the self-adaptive neural network data filtering model is used for filtering data to be uploaded to obtain the data to be stored;

forwarding the data to be stored to a block chain node of a cloud server for associated storage based on a packet label;

the method for filtering the packet tag and the corresponding sensor data by using the adaptive neural network data filtering model to obtain the data to be stored comprises the following steps:

determining a first group of sensors according to the approved load and the approved energy consumption condition of the sensors based on the preset load of the edge server;

determining a second grouping of sensors according to the workload and the energy consumption of the sensors based on the residual load of the edge server;

determining whether an adjustment to the group of sensors is needed based on the first and second groupings;

if so, adjusting the sensor from the first grouping to the second grouping to obtain a third grouping; or adjusting the second packet to the first packet to obtain a fourth packet;

if the obtained group is the third group, further processing the sensor data in each ID set in the third group; and if the obtained group is a fourth group, further processing the sensor data in each ID set in the fourth group.

2. The blockchain-based industrial internet data storage method of claim 1, further comprising:

when a query instruction is received, determining whether the query instruction is an abnormal confirmation instruction;

if yes, calculating the abnormal probability of the sensor grouping according to a preset algorithm, and determining abnormal grouping according to the abnormal probability of the grouping labels;

and searching corresponding abnormal sensor data according to the grouping labels of the sensors grouped abnormally.

3. The blockchain-based industrial internet data storage method of claim 2, wherein the searching for corresponding abnormal sensor data according to the grouping tags of the sensors grouped abnormally comprises:

performing hash operation on the packet label by using K independent hash functions respectively according to the following formula:

HashKey_k＝Hash_k(FZID_i)；

wherein, FZID_iFor said group label, Hash_kAs a hash function with sequence number k, HashKey_kFor the hash value with the serial number of K obtained by operation, K is more than or equal to 1 and less than or equal to K, and K is an integer more than 1; FZID_iI is greater than or equal to 1 and is less than or equal to I which is an integer greater than 1;

obtaining an array corresponding to the grouping label, and judging whether the array meets the following judgment conditions: for any value of k, the equation ARRAY [ HashKey [ ]_k]Each Value _1 holds, where ARRAY is a query ARRAY, and Value _1 is a preset first Value;

and if the array does not meet the judgment condition, judging that the grouping label is not inquired in a sensor database of the server.

4. The blockchain-based industrial internet data storage method of claim 3, further comprising:

when no sensor data is stored in the edge server, the following assignment operation is executed for any value of m:

ARRAY[m]＝Value_2

wherein M is more than or equal to 1 and less than or equal to M, M is the number of sensors in the array, and Value _2 is a preset second numerical Value;

when sensor data exists in an edge server, extracting a grouping label in the sensor data;

HashKeyExist_k＝Hash_k(FZIDExist)；

FZIDExist is the grouping label, HashKeyExist_kObtaining a hash value with the serial number of k for the operation;

and carrying out the following assignment operation on any value of k:

ARRAY[HashKeyExist_k]＝Value_1。

5. the blockchain-based industrial internet data storage method according to claim 3 or 4, further comprising:

when the abnormal sensor data is determined, creating a corresponding block, and storing the abnormal sensor data on a block chain; the block chains are associated by block headers;

each block includes a block header, block metadata, and block data; wherein, the block head comprises a block number, a Hash of the current block and a Hash of the previous block; the block metadata comprises block creation time, a certificate written in the program, a public key and a signature; the tile data includes abnormal sensor data.

6. The blockchain-based industrial internet data storage method according to claim 3 or 4, further comprising:

each block includes a block header, block metadata, and block data; wherein, the block head comprises a block number, a Hash of the current block and a Hash of the previous block; the block metadata comprises block creation time, a certificate written in the program, a public key and a signature; the tile data includes abnormal sensor data and normal sensor data.

7. An industrial internet data storage system based on a blockchain, comprising:

the acquisition module is configured to acquire sensor data of the industrial equipment and filter the sensor data to obtain data to be processed;

the uploading module is configured to send the data to be processed to an edge server, and the data to be uploaded is obtained through grouping and association; wherein each group tag corresponds to at least one sensor; the data structure of the data to be uploaded is as follows: grouping label FZID_i，(CGQ_i1，CGQ_i2，……，CGQ_in) Wherein i is more than or equal to 1 and less than or equal to M, and M is a positive integer; n is the number of sensors contained in the grouping label, and n is a positive integer greater than or equal to 1;

the filtering module is configured to receive a trained adaptive neural network data filtering model on a block chain node of the cloud server by the edge server, and filter the data to be uploaded by using the adaptive neural network data filtering model to obtain the data to be stored;

the storage module is configured to forward the data to be stored to a blockchain node of a cloud server for associated storage based on a packet tag;

wherein the filter module comprises: the first determining unit is configured to determine a first group of the sensors according to the approved load and the approved energy consumption condition of the sensors based on the preset load of the edge server; a second determination unit configured to determine a second grouping of sensors according to the workload and energy consumption of the sensors based on the remaining load of the edge server; a third determining unit configured to determine whether an adjustment of the group of sensors is required based on the first and second groups; a first packet adjustment unit configured to adjust the sensor from the first packet to the second packet if yes, resulting in a third packet; or adjusting the second packet to the first packet to obtain a fourth packet; the second grouping adjusting unit is configured to further process the sensor data in each ID set in the third grouping if the third grouping is obtained; and the third grouping adjusting unit is configured to further process the sensor data in each ID set in the fourth grouping if the fourth grouping is obtained.

8. A computer-readable storage medium on which a computer program is stored, the computer program, when being executed by a processor, implementing the industrial internet data storage method according to any one of claims 1 to 6.

9. An electronic device, comprising:

one or more processors;

a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the industrial internet data storage method of any one of claims 1 to 6.