CN112163019B

CN112163019B - Trusted electronic batch record processing method based on block chain and block chain service platform

Info

Publication number: CN112163019B
Application number: CN202011053606.3A
Authority: CN
Inventors: 童华光; 陈超; 冯泳; 李逸帆
Original assignee: Taizhou Shitongren Information Technology Co ltd
Current assignee: Taizhou Shitongren Information Technology Co ltd
Priority date: 2020-09-29
Filing date: 2020-09-29
Publication date: 2021-04-27
Anticipated expiration: 2040-09-29
Also published as: CN112163019A

Abstract

The embodiment of the application provides a credible electronic batch record processing method based on a block chain and a block chain service platform, after key data in production data are extracted, the key data are compressed to generate a plurality of block data packets arranged according to a preset rule, then the block data packets arranged according to the preset rule are respectively stored on the block chain, index address information of each block data packet is configured, subsequently, corresponding target block data packets can be searched and obtained from the block chain through the obtained index address distribution and the index address information of each block data packet, after the block data packets are spliced according to the distribution rule of the index address distribution, the spliced data compression packets are decompressed to form target electronic batch records, so that the block data packets generated according to the preset rule can reduce the probability of artificial intentional modification, and further storing the data into a block chain, and the real-time property of the tamper-proof detection can be conveniently improved by utilizing the characteristics of the blocks.

Description

Trusted electronic batch record processing method based on block chain and block chain service platform

Technical Field

The application relates to the technical field of information processing based on a block chain, in particular to a trusted electronic batch record processing method based on the block chain and a block chain service platform.

Background

The traditional handwriting batch record is suitable for almost all environments and depends on manual record, however, the authenticity of the data is not examined and is easy to be counterfeited; data cannot be recorded in time, and post writing is easily caused; the data is stored after being recorded by paper, and is easy to lose or damage and cannot be recovered; the writer has different writing, which is easy to cause confusion of recognition; too much data is inconvenient to retrieve after the data is stored for a long time.

In the related technology, the electronic batch records read production data in real time by using configuration and are stored in the database to form the electronic batch records, although the timeliness of the records is improved, the data is automatically read at fixed intervals by a system, the data can be artificially and deliberately modified, and the reliability needs to be enhanced.

Disclosure of Invention

In view of the above, an object of the present application is to provide a method for processing trusted electronic batch records based on a block chain and a block chain service platform, in which after key data in production data is extracted, the key data is compressed to generate a plurality of block data packets arranged according to a preset rule, then the plurality of block data packets arranged according to the preset rule are respectively stored in the block chain, and index address information of each block data packet is configured, when an acquisition request for a target electronic batch record is received, index address distribution of the target electronic batch record is obtained based on the acquisition request, and a corresponding target block data packet is searched and obtained from the block chain according to the index address distribution and the index address information of each block data packet, and after the target block data packets obtained by searching are spliced according to distribution rules of the index address distribution, the spliced data compressed packets are decompressed, and forming a target electronic batch record, so that the block data packets generated according to the preset rule arrangement can reduce the probability of artificial deliberate modification, and are further stored in a block chain, and whether the data is falsified or not can be conveniently judged at the first time in the follow-up process by utilizing the characteristics of the blocks.

According to a first aspect of the present application, a block chain-based trusted electronic batch record processing method is provided, which is applied to a block chain service platform, where the block chain service platform is in communication connection with a block chain service terminal, and the method includes:

storing the production data uploaded by the block chain service terminal into a target database through a configuration software platform, extracting key data in the production data, and compressing the key data to generate a plurality of block data packets arranged according to a preset rule;

respectively storing the plurality of block data packets arranged according to a preset rule to a block chain, and configuring index address information of each block data packet;

when an acquisition request aiming at a target electronic batch record is received, acquiring index address distribution of the target electronic batch record based on the acquisition request, and searching and acquiring a corresponding target block data packet from the block chain according to the index address distribution and the index address information of each block data packet;

and after splicing the target block data packets obtained by searching according to the distribution rule of the index address distribution, decompressing the spliced data compression packets to form the target electronic batch record.

In a possible implementation manner of the first aspect, after extracting key data in the production data, the step of compressing the key data and generating a plurality of block data packets arranged according to a preset rule includes:

acquiring a target production project data set corresponding to a safety analysis label in the production data as key data in the production data;

for each production item data in the target production item data set, marking at least one identification tag object, wherein each identification tag object is used for representing production record data information of a safety data identification area;

acquiring a preset arrangement rule operation component, analyzing the identification tag object by adopting the arrangement rule operation component, and acquiring an arrangement rule analysis result of the identification tag object of each production project data;

and according to the arrangement rule analysis result of the identification tag object of each production item data, carrying out corresponding arrangement operation after each production item data is blocked according to the identification tag object, and generating a plurality of block data packets arranged according to a preset rule.

In a possible implementation manner of the first aspect, the step of obtaining a target production item data set corresponding to a security resolution tag in the production data includes:

acquiring an initial production project data set corresponding to the security analysis tag in the production data, and cleaning the production project data in the initial production project data set;

marking identification information of the production project data in the initial production project data set after cleaning, associating the identification information with multidimensional information of the production project data, and determining and acquiring the target production project data set corresponding to the safety analysis tag according to the associated production project data.

In a possible implementation manner of the first aspect, the step of analyzing the identification tag object by using the arrangement rule running component to obtain an arrangement rule analysis result of the identification tag object of each production item data includes:

operating the arrangement rule operating component to determine an arrangement rule matching node for each identification tag object, and determining a bookmark hierarchy to which a service of the identification tag feature of each identification tag object belongs according to the arrangement rule matching node;

constructing an arrangement rule matching node of each identification label object as a matching node distribution map according to the identification label characteristics and the bookmark hierarchy to which the service belongs;

extracting a first map distribution node of the structured identification label characteristic and a second map distribution node of the unstructured identification label characteristic in the data area corresponding to the structured identification label characteristic according to the matching node distribution map, and sequentially arranging a target matching node distribution map formed by the first map distribution node and the second map distribution node;

converting each target matching node distribution map into a distribution rule relation list of the same identification tag characteristic type, analyzing rule vector distribution level information and rule configuration distribution level information among elements of each distribution rule relation list to obtain a corresponding distribution level information set, and arranging the distribution level information sets according to a level sequence so as to construct the distribution rule relation list into an arranged distribution rule relation list;

determining hierarchical position information of bookmark hierarchies to which different identification tag feature type services belong among map distribution nodes in a distribution rule relation list after arrangement of the arrangement rule matching nodes, and distributing hierarchical positions for the arrangement rule matching nodes according to the hierarchical position information;

sequentially traversing each distribution rule object target in the arranged distribution rule relationship list, and dividing each distribution rule object target according to the incidence relationship among the distribution rule object targets to obtain a distribution rule object target hierarchy of the arrangement rule matching nodes;

respectively determining hash configuration information containing hash arrangement information of each identification tag object and interval information containing a hash arrangement interval of each identification tag object according to a distribution rule object target level of the arrangement rule matching node, wherein the hash configuration information containing the hash arrangement information of each identification tag object and the interval information containing the hash arrangement interval of each identification tag object are respectively configured with different distribution rule object target layers in a one-to-one correspondence manner;

performing index search on each permutation and combination mode information related to each identification tag object according to hash configuration information containing hash arrangement information of each identification tag object and interval information containing a hash arrangement interval of each identification tag object, and determining a permutation and combination mode corresponding to each identification tag object;

determining a permutation and combination node queue of each identification tag object according to the permutation and combination mode, extracting mode configuration information of the permutation and combination mode and extracting a mode configuration item set of the mode configuration information associated with the permutation and combination node queue;

generating a plurality of mode sub-segments according to at least two mode configuration associated items associated in the mode configuration associated item set, calculating segment offsets between all mode segments in the next mode configuration associated item and all mode segments in the previous mode configuration associated item, and obtaining a corresponding preset mode mapping relation table according to each obtained segment offset;

acquiring a mode sub-segment forming mode configuration association project diagram, wherein the mode sub-segments are matched in mode mapping relation and the segment offset between each mode segment of the two mode sub-segments is smaller than the maximum continuous segment offset of the permutation and combination mode in the segment offset according to the preset mode mapping relation table;

and when the coverage rule of the pattern configuration associated project graph is matched with the arrangement operation rule corresponding to the arrangement rule operation component, respectively determining the arrangement rule analysis result of each corresponding identification tag object based on each matched arrangement operation rule.

In one possible implementation manner of the first aspect, the step of determining a permutation-combination node queue of each identification tag object according to the permutation-combination pattern includes:

acquiring a mode information configuration stream in the arrangement and combination mode, and processing the mode information configuration stream to obtain a plurality of mode configuration running service sequences corresponding to a plurality of mode information configurations;

determining mode-related configuration running services and mode-non-related configuration running services of the mode configuration running service sequences, determining the proportion of the mode-related configuration running services in the mode configuration running services, determining the running times of the mode-related configuration running services according to the proportion, and dividing the mode-related configuration running services into a plurality of mode-related running sub-services according to the running times;

determining a locking mode signature object of each mode-related operation sub-service of each mode configuration operation service in the currently processed mode configuration operation service sequence aiming at each mode configuration operation service sequence, and generating a mode signature parameter offset variation graph of each locking mode signature object according to mode signature parameters of a plurality of mode configuration operation services contained in the current mode configuration operation service sequence of each locking mode signature object;

for each mode-related operation sub-service, determining whether the currently-processed mode-related operation sub-service contains a locking mode signature object with periodically-changed mode signature parameters according to a mode signature parameter offset change diagram of a plurality of locking mode signature objects contained in the currently-processed mode-related operation sub-service;

if the currently processed mode-related running sub-service contains a locking mode signature object with periodically changed mode signature parameters, marking the currently processed mode-related running sub-service as a selected mode-related running sub-service;

if the currently processed mode-related operation sub-service does not contain a locking mode signature object with periodically changed mode signature parameters, marking the currently processed mode-related operation sub-service as an unselected mode-related operation sub-service;

splicing the mode related operation sub-services with the incidence relation into candidate permutation and combination nodes according to the process incidence for the marked selected mode related operation sub-services;

determining a plurality of reference pattern signature objects of which pattern signature parameters periodically change in a plurality of pattern signature objects in the candidate permutation and combination node, and determining a signature parameter change rate of each reference pattern signature object;

weighting and calculating the signature parameter change rate of the plurality of reference pattern signature objects in the candidate permutation and combination node to obtain the signature parameter change rate of the currently processed pattern configuration operation service sequence, and screening out at least one pattern configuration operation service sequence which accords with a preset change rate according to the signature parameter change rate of each pattern configuration operation service sequence;

and taking the at least one mode configuration operation service sequence which accords with the preset change rate as a permutation and combination node queue of each identification label object.

In a possible implementation manner of the first aspect, the step of generating a plurality of block data packets arranged according to a preset rule by performing a corresponding arrangement operation after each production item data is blocked according to the identification tag object according to the analysis result of the arrangement rule of the identification tag object of each production item data includes:

according to the arrangement rule analysis result of the identification tag object of each production project data, performing corresponding arrangement operation on the block data packet obtained after each production project data is blocked according to the identification tag object, and generating a plurality of block data packets arranged according to a preset rule;

the step of respectively storing the plurality of block data packets arranged according to the preset rule to the block chain and configuring the index address information of each block data packet includes:

and respectively storing the plurality of block data packets arranged according to the preset rule to a block chain, and configuring the index address, the index service and the index label hierarchy of each block data packet as the index address information of each block data packet.

In one possible implementation of the first aspect, the method further comprises:

the method comprises the steps of issuing index address information of each block data packet to a block chain service terminal so that the block chain service terminal records an index address, an index service and an index label hierarchy of each block data packet, generating an index configuration block after detecting user operation to prompt a user to select at least one element of the index address, the index service and the index label hierarchy, and then carrying out corresponding information configuration to generate an acquisition request aiming at a target electronic batch record, wherein the acquisition request comprises index address distribution, and the index address distribution comprises configuration information of at least one element of the index address, the index service and the index label hierarchy.

In a possible implementation manner of the first aspect, the obtaining, based on the obtaining request, an index address distribution of the target electronic batch record, and searching and obtaining, according to the index address distribution and the index address information of each block data packet, a corresponding target block data packet from the block chain includes:

matching the index address distribution with the index address information of each block data packet;

and searching and obtaining a corresponding target block data packet from the block chain according to the matching result.

In one possible implementation manner of the first aspect, the step of marking at least one identification tag object for each production item data in the target production item data set includes:

according to at least one first safety data identification area identification network which is obtained in advance and corresponds to the category of the production item of each production item data, safety data identification is carried out on the production data recording characteristics of each production item data, and at least one safety data identification map is obtained respectively;

performing safety data identification area extraction on the at least one safety data identification map to obtain a safety data identification area of the production data recording characteristics, wherein the safety data identification area is used for describing a safety data identification object of each production project data;

performing security enhancement feature extraction on the at least one security data identification map to obtain security enhancement features of the production data recording features, wherein the security enhancement features are used for describing security enhancement areas of the production data recording features;

when the production data recording features comprise a recording feature set, respectively performing regression model analysis on the safety data identification region and the safety enhancement features of the recording feature set based on the safety data identification region and the safety enhancement features of the production data recording features to obtain a first regression model analysis result, performing search in the regression model analysis result of a safety enhancement reference feature database based on the regression model analysis result matched with the safety data identification region in the first regression model analysis result to obtain a first safety enhancement reference feature search result matched with the safety data identification region, and performing search in the regression model analysis result of the safety enhancement reference feature database based on the regression model analysis result matched with the safety enhancement features in the first regression model analysis result, obtaining a second security enhancement reference feature search result matched with the security enhancement feature, obtaining a same security enhancement reference feature search result in the first security enhancement reference feature search result and the second security enhancement reference feature search result, obtaining a primary security enhancement reference feature search result, obtaining the similarity between the security enhancement reference feature search result and the record feature set for each security enhancement reference feature search result included in the primary security enhancement reference feature search result, and fusing the security enhancement reference feature search results included in the primary security enhancement reference feature search result according to the similarity matched with each security enhancement reference feature search result to obtain a target security enhancement reference feature search result;

when the production data recording features comprise at least two recording feature sets, respectively carrying out regression model analysis on the safety data identification area and the safety enhancement features of the recording feature sets for each recording feature set in the production data recording features to obtain a second regression model analysis result, searching in the regression model analysis results of the safety enhancement reference feature database based on the second regression model analysis result to obtain a primary safety enhancement reference feature searching result, and fusing the primary safety enhancement reference feature searching results matched with each recording feature set to obtain the target safety enhancement reference feature searching result;

for each safety enhancement reference feature search result in the target safety enhancement reference feature search result, performing node division processing on the safety enhancement reference feature search result to obtain key feature vector nodes of the safety enhancement reference feature search result, performing safety data identification region extraction and safety enhancement feature extraction on each obtained key feature vector node, performing regression model analysis on the safety data identification region and the safety enhancement feature of each key feature vector node to obtain a regression model analysis result of each key feature vector node, then obtaining node positions of the key feature vector nodes in the safety enhancement reference feature search result, and based on identification information of the safety enhancement reference feature search result and the node positions of the key feature vector nodes, correlating the regression model analysis results of the key feature vector nodes to obtain the safety enhancement reference feature information of each production project data, wherein the regression model analysis results of the target safety enhancement reference feature search results comprise the regression model analysis results of the key feature vector nodes of each safety enhancement reference feature search result;

and marking at least one identification tag object based on the area corresponding to the safety enhancement reference characteristic information of each production project data.

According to a second aspect of the present application, there is provided a trusted electronic batch record processing apparatus based on a blockchain, which is applied to a blockchain service platform communicatively connected to a blockchain service terminal, the apparatus including:

the extraction generation module is used for storing the production data uploaded by the block chain service terminal into a target database through a configuration software platform, extracting key data in the production data, compressing the key data and generating a plurality of block data packets arranged according to a preset rule;

the configuration module is used for respectively storing the plurality of block data packets which are arranged according to the preset rule to a block chain and configuring the index address information of each block data packet;

the searching module is used for acquiring the index address distribution of the target electronic batch record based on the acquisition request when the acquisition request aiming at the target electronic batch record is received, and searching and acquiring the corresponding target block data packet from the block chain according to the index address distribution and the index address information of each block data packet;

and the splicing decompression module is used for splicing the searched target block data packets according to the distribution rule of the index address distribution and decompressing the spliced data compression packets to form the target electronic batch record.

In a third aspect, an embodiment of the present invention further provides a block chain-based trusted electronic batch record processing system, where the block chain-based trusted electronic batch record processing system includes a block chain service platform and a block chain service terminal communicatively connected to the block chain service platform;

the blockchain service platform is configured to:

In a fourth aspect, an embodiment of the present invention further provides a blockchain service platform, where the blockchain service platform includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be communicatively connected to at least one blockchain service terminal, the machine-readable storage medium is configured to store a program, an instruction, or a code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium to perform the method for processing a trusted electronic batch record based on a blockchain in the first aspect or any one of possible implementations of the first aspect.

In a fifth aspect, an embodiment of the present invention provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed, the computer executes the method for processing a trusted electronic batch record based on a blockchain in the first aspect or any one of the possible implementations of the first aspect.

Based on any one of the aspects, the method comprises the steps of extracting key data in production data, compressing the key data to generate a plurality of block data packets arranged according to a preset rule, then a plurality of block data packets arranged according to a preset rule are respectively stored on a block chain, index address information of each block data packet is configured, subsequently, corresponding target block data packets can be searched and obtained from the block chain through the obtained index address distribution and the index address information of each block data packet, and after the splicing is carried out according to the distribution rule of the index address distribution, decompressing the spliced data compression packets to form target electronic batch records, thus reducing the probability of artificial deliberate modification by arranging the generated block data packets according to the preset rules, and further storing the data into a block chain, and the real-time property of the tamper-proof detection can be conveniently improved by utilizing the characteristics of the blocks.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

Fig. 1 is a schematic diagram illustrating an application scenario of a trusted electronic batch processing system based on a block chain according to an embodiment of the present application;

FIG. 2 is a flowchart illustrating a block chain-based trusted electronic batch record processing method according to an embodiment of the present application;

FIG. 3 is a functional block diagram of a trusted electronic batch processing device based on a blockchain according to an embodiment of the present disclosure;

fig. 4 is a schematic component diagram illustrating a blockchain service platform for performing the above-described trusted electronic batch processing method based on blockchain according to an embodiment of the present disclosure.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "device", "unit" and/or "module" as used in this specification is a method for distinguishing different components, elements, parts or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

FIG. 1 is an interactive schematic diagram of a trusted electronic batch processing system 10 based on a blockchain according to an embodiment of the present invention. The trusted electronic batch processing system 10 based on blockchain may include a blockchain service platform 100 and a blockchain service terminal 200 communicatively coupled to the blockchain service platform 100. The blockchain-based trusted electronic batch processing system 10 shown in fig. 1 is merely one possible example, and in other possible embodiments, the blockchain-based trusted electronic batch processing system 10 may also include only some of the components shown in fig. 1 or may also include other components.

In this embodiment, the blockchain service platform 100 and the blockchain service terminal 200 in the trusted electronic batch processing system 10 based on blockchain may cooperatively perform the trusted electronic batch processing method based on blockchain described in the following method embodiment, and the detailed description of the method embodiment may be referred to for the specific steps performed by the blockchain service platform 100 and the blockchain service terminal 200.

To solve the technical problem in the foregoing background art, fig. 2 is a schematic flow chart of a trusted electronic batch record processing method based on a block chain according to an embodiment of the present invention, and the trusted electronic batch record processing method based on a block chain according to the embodiment may be executed by the block chain service platform 100 shown in fig. 1, and the trusted electronic batch record processing method based on a block chain is described in detail below.

Step S110, storing the production data uploaded by the blockchain service terminal 200 into a target database through the configuration software platform, extracting key data in the production data, and compressing the key data to generate a plurality of block data packets arranged according to a preset rule.

Step S120, a plurality of block data packets arranged according to a preset rule are respectively stored in a block chain, and index address information of each block data packet is configured.

Step S130, when an obtaining request for the target electronic batch record is received, obtaining the index address distribution of the target electronic batch record based on the obtaining request, and searching and obtaining the corresponding target block data packet from the block chain according to the index address distribution and the index address information of each block data packet.

And step S140, after splicing the searched target block data packets according to the distribution rule of the index address distribution, decompressing the spliced data compression packets to form target electronic batch records.

In this embodiment, the configuration software platform can monitor and control the automation process and equipment, collect various information from the automation process and equipment, display the information in a more understandable manner such as graphics, transmit important information to a relevant target database in various ways, perform necessary analysis and storage on the information, send control instructions, and the like.

In this embodiment, the key data may refer to data that needs to be stored in a production data in a focused manner, that is, data that needs to be stored according to the embodiment of the present application, and may be flexibly set and selected according to business requirements.

Based on the design, the embodiment compresses the key data and generates a plurality of block data packets arranged according to the preset rule after extracting the key data in the production data, then a plurality of block data packets arranged according to a preset rule are respectively stored on a block chain, index address information of each block data packet is configured, subsequently, corresponding target block data packets can be searched and obtained from the block chain through the obtained index address distribution and the index address information of each block data packet, and after the splicing is carried out according to the distribution rule of the index address distribution, decompressing the spliced data compression packets to form target electronic batch records, thus reducing the probability of artificial deliberate modification by arranging the generated block data packets according to the preset rules, and further storing the data into a block chain, and the real-time property of the tamper-proof detection can be conveniently improved by utilizing the characteristics of the blocks.

In a possible implementation manner, for step S110, in the process of compressing the key data and generating a plurality of block data packets arranged according to a preset rule after extracting the key data in the production data, in order to improve the reliability of the rule arrangement and further reduce the probability of deliberate modification, the following exemplary sub-steps may be implemented.

And a substep S111, acquiring a target production project data set corresponding to the safety analysis label in the production data as key data in the production data.

In the substep S112, for each production item data in the target production item data set, at least one identification tag object is marked, and each identification tag object is used for representing production record data information of a safety data identification area.

And a substep S113 of obtaining a preset arrangement rule running component, analyzing the identification tag object by adopting the arrangement rule running component, and obtaining an arrangement rule analysis result of the identification tag object of each production project data.

And a substep S114 of performing corresponding arrangement operation after blocking each production item data according to the identification tag object according to the arrangement rule analysis result of the identification tag object of each production item data, and generating a plurality of block data packets arranged according to a preset rule.

For example, for the substep S111, in order to reduce data noise and reduce subsequent calculation amount, an initial production item data set corresponding to the security resolution tag in the production data may be obtained, and the cleaning process may be performed on the production item data in the initial production item data set. Then, marking the identification information of the production project data in the cleaned initial production project data set, associating the identification information with the multidimensional information of the production project data, and determining and acquiring a target production project data set corresponding to the safety analysis label according to the associated production project data.

Exemplarily, the substep S113 may be embodied by the following embodiments.

(1) And operating the arrangement rule operating component to determine an arrangement rule matching node for each identification tag object, and determining the bookmark hierarchy to which the business of the identification tag characteristics of each identification tag object belongs according to the arrangement rule matching node.

(2) And constructing the arrangement rule matching nodes of each identification label object into a matching node distribution map according to the identification label characteristics and the bookmark hierarchy to which the service belongs.

(3) And extracting a first map distribution node of the structural identification label characteristic and a second map distribution node of the unstructured identification label characteristic in the data area corresponding to the structural identification label characteristic according to the matching node distribution map, and sequentially arranging a target matching node distribution map formed by the first map distribution node and the second map distribution node.

(4) And converting each target matching node distribution map into a distribution rule relation list of the same identification tag characteristic type, analyzing rule vector distribution level information and rule configuration distribution level information among elements of each distribution rule relation list to obtain a corresponding distribution level information set, and arranging the distribution level information sets according to a level sequence so as to construct the distribution rule relation list into an arranged distribution rule relation list.

(5) Determining the hierarchical position information of the bookmark hierarchy to which different identification tag feature type services belong among map distribution nodes in a distribution rule relation list after arrangement of the arrangement rule matching nodes, and distributing the hierarchical position for the arrangement rule matching nodes according to the hierarchical position information.

(6) And traversing each distribution rule object target of the arranged distribution rule relation list in sequence, and dividing each distribution rule object target according to the incidence relation among the distribution rule object targets to obtain a distribution rule object target hierarchy of the arrangement rule matching nodes.

(7) And respectively determining hash configuration information containing the hash arrangement information of each identification tag object and interval information containing the hash arrangement interval of each identification tag object according to the distribution rule object target level of the arrangement rule matching node, wherein the hash configuration information containing the hash arrangement information of each identification tag object and the interval information containing the hash arrangement interval of each identification tag object are respectively configured with different distribution rule object target layers in a one-to-one correspondence manner.

(8) And performing index search on the permutation and combination mode information related to each identification tag object according to the hash configuration information containing the hash arrangement information of each identification tag object and the interval information containing the hash arrangement interval of each identification tag object, and determining the permutation and combination mode corresponding to each identification tag object.

(9) And determining a permutation and combination node queue of each identification tag object according to the permutation and combination mode, and extracting mode configuration information of the permutation and combination mode and a mode configuration item set of the mode configuration information associated with the permutation and combination node queue.

For example, a mode information configuration stream in the permutation and combination mode may be obtained, and the mode information configuration stream is processed to obtain a plurality of mode configuration running service sequences corresponding to a plurality of mode information configurations.

Then, determining mode-related configuration running services and mode-non-related configuration running services of the multiple mode configuration running service sequences, determining the proportion of the mode-related configuration running services in the mode configuration running services, determining the running times of the mode-related configuration running services according to the proportion, and dividing the mode-related configuration running services into multiple mode-related running sub-services according to the running times.

Then, for each mode configuration running service sequence, determining a locking mode signature object of each mode-related running sub-service of each mode configuration running service in the currently processed mode configuration running service sequence, and generating a mode signature parameter offset variation graph of each locking mode signature object according to mode signature parameters of a plurality of mode configuration running services included in the current mode configuration running service sequence of each locking mode signature object.

On the basis, for each mode-dependent operation sub-service, according to a mode signature parameter offset change diagram of a plurality of lock mode signature objects contained in the currently-processed mode-dependent operation sub-service, whether the currently-processed mode-dependent operation sub-service contains the lock mode signature objects with periodically-changed mode signature parameters is determined.

For example, if the currently processed mode-related operation sub-service includes a lock mode signature object with a periodically changing mode signature parameter, the currently processed mode-related operation sub-service is marked as the selected mode-related operation sub-service.

For another example, if the currently processed mode-related operation sub-service does not include a lock mode signature object whose mode signature parameter periodically changes, the currently processed mode-related operation sub-service is marked as an unselected mode-related operation sub-service.

In this way, for the marked selected multiple pattern-related operation sub-services, the pattern-related operation sub-services having the association relationship are spliced into a candidate permutation and combination node according to the process association, then multiple reference pattern signature objects with periodically changing pattern signature parameters among the multiple pattern signature objects in the candidate permutation and combination node are determined, and the signature parameter change rate of each reference pattern signature object is determined.

On the basis, the signature parameter change rate of a plurality of reference pattern signature objects in the candidate permutation and combination node is calculated through weighting, the signature parameter change rate of the currently processed pattern configuration operation service sequence is obtained, at least one pattern configuration operation service sequence which accords with the preset change rate is screened out according to the signature parameter change rate of each pattern configuration operation service sequence, and therefore the at least one pattern configuration operation service sequence which accords with the preset change rate can be used as the permutation and combination node queue of each identification label object.

(10) And according to at least two mode configuration associated items associated in the mode configuration item set, generating a plurality of mode sub-segments of the mode segments in the mode configuration associated items according to a preset mode mapping relation, calculating segment offsets between all the mode segments in the next mode configuration associated item and all the mode segments in the previous mode configuration associated item, and obtaining a corresponding preset mode mapping relation table according to each obtained segment offset.

(11) And obtaining the mode sub-segment forming mode configuration associated project diagram of which the mode mapping relation is matched and the segment deviation between each mode segment of the two mode sub-segments is smaller than the maximum continuous segment deviation of the permutation and combination mode in the segment deviation according to the preset mode mapping relation table.

(12) And when the coverage rule of the pattern configuration associated project graph is matched with the arrangement operation rule corresponding to the arrangement rule operation component, respectively determining the arrangement rule analysis result of each corresponding identification tag object based on each matched arrangement operation rule.

For example, in step S114, according to the analysis result of the arrangement rule of the identification tag object of each production item data, a corresponding arrangement operation may be performed on the block data packet obtained after each production item data is blocked according to the identification tag object, so as to generate a plurality of block data packets arranged according to a preset rule.

On the basis, in step S120, in the process of storing a plurality of block data packets arranged according to a preset rule on the block chain respectively and configuring the index address information of each block data packet, the plurality of block data packets arranged according to the preset rule may be stored on the block chain respectively and configured with the index address, the index service and the index tag hierarchy of each block data packet as the index address information of each block data packet.

On this basis, at the application level, in order to facilitate subsequent index lookup, for example, index address information of each packet may be issued to the blockchain service terminal 200, so that the blockchain service terminal 200 records an index address, an index service, and an index tag hierarchy of each packet, and after detecting a user operation, generates an index configuration block to prompt a user to select at least one element of the index address, the index service, and the index tag hierarchy, and then performs corresponding information configuration to generate an acquisition request for a target electronic batch record, where the acquisition request includes index address distribution. The index address distribution comprises an index address, index service and configuration information of at least one element in an index label hierarchy.

On this basis, for step S130, matching may be performed with the index address information of each block data packet according to the index address distribution, and then the corresponding target block data packet is searched from the block chain according to the matching result.

On this basis, for step S140, after the target block data packets obtained by searching are spliced according to the distribution rule of the index address distribution, the spliced data compression packets are decompressed to form the target electronic batch record, so that the whole index searching process is ended.

Further, exemplarily, the aforementioned sub-step S112 can be implemented by the following specific embodiments.

(1) And carrying out safety data identification on the production data record characteristics of each production project data according to at least one first safety data identification area identification network which is obtained in advance and corresponds to the category of the production project of each production project data, and respectively obtaining at least one safety data identification map.

(2) And performing safety data identification area extraction on at least one safety data identification map to obtain a safety data identification area of the production data recording characteristics, wherein the safety data identification area is used for describing a safety data identification object of each production project data.

(3) And performing safety enhancement feature extraction on at least one safety data identification recognition map to obtain safety enhancement features of the production data recording features.

It is worth noting, among other things, that the security-enhanced features are used to describe security-enhanced areas of production data recording features.

(4) When the production data recording features comprise a recording feature set, respectively performing regression model analysis on the safety data identification region and the safety enhancement features of the recording feature set based on the safety data identification region and the safety enhancement features of the production data recording features to obtain a first regression model analysis result, searching in the regression model analysis result of the safety enhancement reference feature database based on the regression model analysis result matched with the safety data identification region in the first regression model analysis result to obtain a first safety enhancement reference feature search result matched with the safety data identification region, searching in the regression model analysis result of the safety enhancement reference feature database based on the regression model analysis result matched with the safety enhancement features in the first regression model analysis result to obtain a second safety enhancement reference feature search result matched with the safety enhancement features, and acquiring the same safety enhancement reference feature search result in the first safety enhancement reference feature search result and the second safety enhancement reference feature search result to acquire a primary safety enhancement reference feature search result, acquiring the similarity between the safety enhancement reference feature search result and the record feature set for each safety enhancement reference feature search result included in the primary safety enhancement reference feature search result, and fusing the safety enhancement reference feature search results included in the primary safety enhancement reference feature search result according to the similarity matched with each safety enhancement reference feature search result to acquire a target safety enhancement reference feature search result.

(5) When the production data recording features comprise at least two recording feature sets, for each recording feature set in the production data recording features, respectively performing regression model analysis on the safety data identification area and the safety enhancement features of the recording feature set to obtain a second regression model analysis result, searching in the regression model analysis result of the safety enhancement reference feature database based on the second regression model analysis result to obtain a primary safety enhancement reference feature searching result, and fusing the primary safety enhancement reference feature searching results matched with each recording feature set to obtain a target safety enhancement reference feature searching result.

(6) For each safety enhancement reference feature search result in the target safety enhancement reference feature search result, performing node division processing on the safety enhancement reference feature search result to obtain key feature vector nodes of the safety enhancement reference feature search result, performing safety data identification region extraction and safety enhancement feature extraction on each obtained key feature vector node, performing regression model analysis on the safety data identification region and the safety enhancement feature of each key feature vector node to obtain a regression model analysis result of each key feature vector node, then obtaining node positions of the key feature vector nodes in the safety enhancement reference feature search result, and based on identification information of the safety enhancement reference feature search result and the node positions of the key feature vector nodes, and correlating the regression model analysis result of the key feature vector node to obtain the safety enhancement reference feature information of each production project data.

It should be noted that the regression model analysis result of the target security enhanced reference feature search result includes the regression model analysis result of the key feature vector node of each security enhanced reference feature search result.

(7) And marking at least one identification tag object based on the area corresponding to the safety enhancement reference characteristic information of each production project data.

Based on the same inventive concept, please refer to fig. 3, which illustrates a schematic diagram of functional modules of the trusted electronic batch record processing apparatus 300 based on a block chain according to an embodiment of the present application, and the embodiment can divide the functional modules of the trusted electronic batch record processing apparatus 300 based on the block chain according to the above method embodiment. For example, the functional blocks may be divided for the respective functions, or two or more functions may be integrated into one processing block. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. It should be noted that, in the embodiment of the present application, the division of the module is schematic, and is only one logic function division, and there may be another division manner in actual implementation. For example, in the case of dividing each function module according to each function, the trusted electronic batch processing device 300 based on the block chain shown in fig. 3 is only a schematic device diagram. The block chain-based trusted electronic batch processing apparatus 300 may include an extraction generation module 310, a configuration module 320, a search module 330, and a concatenation decompression module 340, where functions of the functional modules of the block chain-based trusted electronic batch processing apparatus 300 are described in detail below.

The extraction and generation module 310 is configured to store the production data uploaded by the block chain service terminal 200 into a target database through the configuration software platform, extract key data in the production data, and then compress the key data to generate a plurality of block data packets arranged according to a preset rule. It is understood that the extraction generating module 310 may be configured to perform the step S110, and for a detailed implementation of the extraction generating module 310, reference may be made to the content related to the step S110.

The configuration module 320 is configured to store a plurality of block data packets arranged according to a preset rule on a block chain, and configure index address information of each block data packet. It is understood that the configuration module 320 may be configured to perform the step S120, and for the detailed implementation of the configuration module 320, reference may be made to the above description regarding the step S120.

The searching module 330 is configured to, when an obtaining request for a target electronic batch record is received, obtain index address distribution of the target electronic batch record based on the obtaining request, and search and obtain a corresponding target block data packet from the block chain according to the index address distribution and the index address information of each block data packet. It is understood that the search module 330 can be used to perform the step S130, and for the detailed implementation of the search module 330, reference can be made to the contents related to the step S130.

And the splicing decompression module 340 is configured to decompress the spliced data compression packets after splicing the target block data packets obtained by searching according to the distribution rule of the index address distribution, so as to form the target electronic batch record. It is understood that the splicing decompression module 340 can be used to perform the step S140, and for the detailed implementation of the splicing decompression module 340, reference can be made to the above-mentioned contents related to the step S140.

It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules may all be implemented in software invoked by a processing element. Or may be implemented entirely in hardware. And part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the extraction generation module 310 may be a separate processing element, or may be integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and a processing element of the apparatus calls and executes the functions of the extraction generation module 310. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.

For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when some of the above modules are implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can call program code. As another example, these modules may be integrated together, implemented in the form of a system-on-a-chip (SOC).

Fig. 4 is a schematic diagram illustrating a hardware structure of the blockchain service platform 100 for implementing the control device according to an embodiment of the present invention, and as shown in fig. 4, the blockchain service platform 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.

In a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120 (for example, the extraction generation module 310, the configuration module 320, the lookup module 330, and the concatenation decompression module 340 included in the block chain-based trusted electronic batch processing apparatus 300 shown in fig. 3), so that the processor 110 may execute the block chain-based trusted electronic batch processing method according to the above method embodiment, where the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected via the bus 130, and the processor 110 may be configured to control transceiving actions of the transceiver 140, so as to transceive data with the aforementioned block chain service terminal 200.

For a specific implementation process of the processor 110, reference may be made to the above-mentioned various method embodiments executed by the blockchain service platform 100, which implement principles and technical effects are similar, and this embodiment is not described herein again.

In the embodiment shown in fig. 4, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

The machine-readable storage medium 120 may comprise high-speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.

The bus 130 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus 130 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.

In addition, an embodiment of the present invention further provides a readable storage medium, where the readable storage medium stores computer-executable instructions, and when a processor executes the computer-executable instructions, the above trusted electronic batch processing method based on a block chain is implemented.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.

Also, the description uses specific words to describe embodiments of the description. Such as "one possible implementation," "one possible example," and/or "exemplary" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "one possible implementation," "one possible example," and/or "exemplary" in various places throughout this specification are not necessarily referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present description may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of this specification may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or block chain service platform. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, while the system components described above may be implemented by subscribing to activatable objects, they may also be implemented by software-only solutions, such as installing the described system on an existing blockchain service platform or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A credible electronic batch record processing method based on a block chain is characterized by being applied to a block chain service platform, wherein the block chain service platform is in communication connection with a block chain service terminal, and the method comprises the following steps:

splicing the searched target block data packets according to the distribution rule of the index address distribution, decompressing the spliced data compression packets to form the target electronic batch record;

after extracting the key data in the production data, compressing the key data and generating a plurality of block data packets arranged according to a preset rule, the method comprises the following steps:

according to the arrangement rule analysis result of the identification tag object of each production project data, carrying out corresponding arrangement operation after each production project data is blocked according to the identification tag object, and generating a plurality of block data packets arranged according to a preset rule;

the step of generating a plurality of block data packets arranged according to a preset rule by performing corresponding arrangement operation after each production project data is blocked according to the identification tag object according to the arrangement rule analysis result of the identification tag object of each production project data includes:

2. The method according to claim 1, wherein the step of obtaining a target production item dataset corresponding to a security resolution tag in the production data comprises:

3. The method according to claim 1, wherein the step of obtaining the analysis result of the arrangement rule of the identification tag object of each production item data by analyzing the identification tag object with the arrangement rule running component comprises:

4. The method of claim 3, wherein the step of determining a permutation-combination node queue for each identification tag object according to the permutation-combination pattern comprises:

5. The method of blockchain-based trusted electronic batch processing according to claim 1, further comprising:

6. The method according to claim 5, wherein the step of obtaining the index address distribution of the target electronic batch record based on the obtaining request, and searching and obtaining the corresponding target block data packet from the block chain according to the index address distribution and the index address information of each block data packet comprises:

7. The method of claim 1, wherein the step of tagging at least one identification tag object for each production item data in the target production item data set comprises:

8. A blockchain service platform, comprising a processor, a machine-readable storage medium, and a network interface, wherein the machine-readable storage medium, the network interface, and the processor are connected via a bus system, the network interface is configured to be communicatively connected to at least one blockchain-based trusted electronic batch processing system, the machine-readable storage medium is configured to store a program, a command, or a code, and the processor is configured to execute the program, the command, or the code in the machine-readable storage medium to perform the method for processing blockchain-based trusted electronic batch records according to any one of claims 1 to 7.