CN117372047A

CN117372047A - Method and system for realizing data backtracking of electronic product based on LDPC error correction algorithm

Info

Publication number: CN117372047A
Application number: CN202311441477.9A
Authority: CN
Inventors: 杨磊; 黄栋科
Original assignee: Shenzhen Mick Fond Semiconductor Co ltd
Current assignee: Shenzhen Mick Fond Semiconductor Co ltd
Priority date: 2023-11-01
Filing date: 2023-11-01
Publication date: 2024-01-09
Anticipated expiration: 2043-11-01
Also published as: CN117372047B

Abstract

The invention relates to the technical field of data backtracking, and discloses a data backtracking method and a system for realizing electronic products based on an LDPC error correction algorithm, wherein the method comprises the following steps: obtaining electronic products to be traced, inquiring the corresponding Flash data, removing sensitive sources, calculating the corresponding desensitization value, extracting characteristic parameters in product configuration information, inquiring the corresponding configuration variables, positioning option variables, calculating the characteristic association degree of the characteristic parameters and the option variables, generating a corresponding configuration parameter table, identifying error record logs, stacking and tracking error paths of error codes, drawing a time sequence chart after inquiring production process records, extracting production tracing data in the time sequence chart, constructing a product data tracing model based on the configuration parameter table, the error paths and the production tracing data, constructing a corresponding sparse check matrix after inquiring LDPC codes, and carrying out data tracing on the electronic products and data tracing results. The invention aims to improve the efficiency of the backtracking of the electronic product data.

Description

Method and system for realizing data backtracking of electronic product based on LDPC error correction algorithm

Technical Field

The invention relates to the technical field of data backtracking, in particular to a data backtracking method and system for realizing electronic products based on an LDPC error correction algorithm.

Background

The data backtracking refers to a process of analyzing and processing existing data to acquire data change and history in a specific past time period, so that a user can be helped to track and know the evolution of the data, discover the rules and trends of the data, and make decisions based on the information.

At present, the data backtracking of electronic products refers to analysis and tracking of related data of the electronic products, because the electronic products involve a plurality of links and participants, and data sources are scattered, the situation of data tampering or data deletion is easy to occur, and because the collection modes and systems of various data are not uniform, comprehensive backtracking and integrated analysis are difficult to realize, and the data backtracking efficiency is easy to influence, therefore, an LDPC error correction algorithm-based data backtracking method for the electronic products is needed to be realized, so that the data backtracking efficiency of the electronic products is improved.

Disclosure of Invention

The invention provides a method and a system for realizing data backtracking of electronic products based on an LDPC error correction algorithm, which mainly aim at improving the data backtracking efficiency of the electronic products.

In order to achieve the above object, the method for implementing data backtracking of electronic products based on the LDPC error correction algorithm provided by the present invention includes:

acquiring an electronic product to be traced, inquiring flash data corresponding to the electronic product, identifying a source channel of the flash data, determining a target number source in the source channel, removing a sensitive source in the target number source, and calculating a desensitization value corresponding to the target number source;

inquiring a backtracking target corresponding to the electronic product based on the desensitization value, wherein the backtracking target comprises: product configuration information, error log, and production process record;

extracting characteristic parameters in the product configuration information, inquiring configuration variables corresponding to the characteristic parameters, positioning option variables in the configured variables, calculating characteristic association degrees of the characteristic parameters and the option variables, and generating a configuration parameter table corresponding to the product configuration information based on the characteristic association degrees;

identifying different log levels in the error log, searching an error type corresponding to the log level, positioning an error code in the error log based on the error type, and performing stack tracking on the error code to obtain an error path of the error code;

Inquiring a time source in the production process record, identifying a source sequence corresponding to the time source, time ordering the source sequence 2, drawing a time sequence diagram of the production process record based on the ordered source sequence, and extracting production backtracking data in the time sequence diagram;

and constructing a product data backtracking model based on the configuration parameter table, the error path and the production backtracking data, inquiring an LDPC code in the product data backtracking model, generating check bits corresponding to the electronic product by using the LDPC code, constructing a sparse check matrix corresponding to the electronic product based on the check bits, and carrying out data backtracking on the electronic product by using the sparse check matrix to obtain a data backtracking result of the electronic product.

Optionally, the querying Flash data corresponding to the electronic product, and identifying a data channel of the Flash data includes:

identifying a product type of the electronic product;

determining a product model corresponding to the electronic product based on the product type;

inquiring Flash data of the product type;

uploading the Flash data to a preset electronic numerical control platform;

Searching a source channel corresponding to the Flash data by using a search engine in the electronic numerical control platform.

Optionally, the calculating the desensitization value corresponding to the target number source includes:

the desensitization value corresponding to the target number source is calculated by using the following formula:

wherein T represents the desensitization value corresponding to the target number source, i represents the numerical quantity corresponding to the target number source, n represents the number of the target number source, Z _i Representing the value of the ith target number source, C _i The representation is a parameter value associated with the ith said target number source.

Optionally, the positioning the option variable in the configured variables, calculating the feature association degree between the feature parameter and the option variable, including:

extracting variable characteristics in the configuration variables; based on the variable characteristics, carrying out option positioning on the configuration variables to obtain option variables corresponding to the configuration variables;

calculating the feature association degree of the feature parameter and the option variable by using the following formula:

wherein D represents the feature association degree between the feature parameter and the option variable, Y represents the feature parameter, P represents the option feature parameter corresponding to the option variable, and X represents the total number of variables corresponding to the option variable.

Optionally, the generating the configuration parameter table corresponding to the product configuration information based on the feature association degree includes:

constructing a feature association graph corresponding to the feature association degree based on the feature association degree;

extracting feature nodes in the feature association graph;

generating a configuration item corresponding to the feature association graph based on the feature node;

identifying configuration parameter values corresponding to the configuration items;

and inputting the configuration parameter values into the feature association diagram to obtain a configuration parameter table corresponding to the product configuration information.

Optionally, the locating the error code in the error log based on the error type includes:

confirming error reporting information in the error type, and identifying an identifier in the error reporting information;

inquiring an error code table corresponding to the error type based on the identifier;

and positioning error codes in the error code table.

Optionally, the performing stack tracking on the error code to obtain an error path of the error code includes:

checking an abnormal code in the error code;

enabling stack tracking of the program corresponding to the error code based on the abnormal code;

Inquiring tracking information in the stack tracking, and analyzing calling parameters in the tracking information;

positioning error points in the tracking information based on the calling parameters;

and identifying an error path corresponding to the error point.

Optionally, the identifying the number source sequence corresponding to the time number source, and time ordering the number source sequence includes:

determining the source data in the time source and identifying the timestamp corresponding to the source data;

calculating a source value in the time source based on the timestamp;

the sequence labeling is carried out on the number source values, so that a number source sequence corresponding to the time number source is obtained;

extracting a number source serial number corresponding to the number source sequence;

constructing a time tuple corresponding to the number source sequence based on the sequence number;

traversing the time tuples to achieve a time ordering of the sequence of sources.

Optionally, the calculating a value of a number source in the time number source based on the timestamp includes:

calculating a value of a source of the time source by using the following formula:

wherein S represents a source value in the time source, C represents the timestamp, E represents a source weight corresponding to the time source, T represents a source threshold corresponding to the time source, and R represents a time factor corresponding to the timestamp.

A data backtracking system for implementing an electronic product based on an LDPC error correction algorithm, the system comprising:

the desensitization value calculation module is used for acquiring an electronic product to be traced, inquiring Flash data corresponding to the electronic product, identifying a data channel of the Flash data, determining a target number source in the data channel, and calculating a desensitization value corresponding to the target number source after removing a sensitive source in the target number source;

the target inquiry module is used for inquiring a backtracking target corresponding to the electronic product based on the desensitization value, and the backtracking target comprises: product configuration information, error log, and production process record;

the association degree calculating module is used for extracting characteristic parameters in the product configuration information, inquiring configuration variables corresponding to the characteristic parameters, positioning option variables in the configured variables, calculating the characteristic association degree of the characteristic parameters and the option variables, and generating a configuration parameter table corresponding to the product configuration information based on the characteristic association degree;

the error path identification module is used for identifying different log levels in the error log, searching error types corresponding to the log levels, positioning error codes in the error log based on the error types, and carrying out stack tracking on the error codes to obtain error paths of the error codes;

The production backtracking module is used for inquiring the time source in the production process record, identifying a source sequence corresponding to the time source, time ordering the source sequence, drawing a time sequence diagram of the production process record based on the ordered source sequence, and extracting production backtracking data in the time sequence diagram;

and the backtracking result module is used for constructing a product data backtracking model based on the configuration parameter table, the error path and the production backtracking data, inquiring LDPC codes in the product data backtracking model, generating check bits corresponding to the electronic product by using the LDPC codes, constructing a sparse check matrix corresponding to the electronic product based on the check bits, and carrying out data backtracking on the electronic product by using the sparse check matrix to obtain a data backtracking result of the electronic product.

The invention can help enterprises and regulatory authorities monitor and manage the supply chain of the electronic products by acquiring the electronic products to be traced, can prevent counterfeit products from entering the market by tracing the sources, production processes and flow directions of the electronic products, and ensures the quality and compliance of the electronic products; in addition, the invention can search the configuration variable corresponding to the characteristic parameter by extracting the characteristic parameter in the configuration information of the product, can find the configuration variable corresponding to the specific characteristic parameter rapidly and accurately, can also know the more detailed information such as the specification, the model, the version and the like of the product, so as to evaluate the quality and the applicability of the product more comprehensively. Therefore, the data backtracking method and system for the electronic product based on the LDPC error correction algorithm can improve the data backtracking efficiency of the electronic product.

Drawings

Fig. 1 is a flow chart of a method for implementing data backtracking of an electronic product based on an LDPC error correction algorithm according to an embodiment of the present invention;

FIG. 2 is a functional block diagram of a data backtracking system for implementing an electronic product based on an LDPC error correction algorithm according to an embodiment of the present invention;

fig. 3 is a schematic structural diagram of an electronic device for implementing the data backtracking method for implementing an electronic product based on the LDPC error correction algorithm according to an embodiment of the present invention.

The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

The embodiment of the application provides a data backtracking method for realizing electronic products based on an LDPC error correction algorithm. In the embodiment of the present application, the execution body of the data backtracking method for implementing an electronic product based on the LDPC error correction algorithm includes, but is not limited to, at least one of a server, a terminal, and an electronic device capable of being configured to execute the method provided in the embodiment of the present application. In other words, the data backtracking method for implementing the electronic product based on the LDPC error correction algorithm may be implemented by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution network (Content DeliveryNetwork, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

Referring to fig. 1, a flow chart of a method for implementing data backtracking of an electronic product based on an LDPC error correction algorithm according to an embodiment of the present invention is shown. In this embodiment, the method for implementing data backtracking of electronic products based on the LDPC error correction algorithm includes steps S1 to S6.

S1, acquiring an electronic product to be traced, inquiring Flash data corresponding to the electronic product, identifying a data channel of the Flash data, determining a target number source in the data channel, removing a sensitive source in the target number source, and calculating a desensitization value corresponding to the target number source.

The invention can help enterprises and administrative departments monitor and manage the supply chain of the electronic products by acquiring the electronic products to be traced, and can prevent counterfeit products from entering the market by tracing the sources, the production processes and the flow directions of the electronic products, thereby ensuring the quality and the compliance of the electronic products.

The electronic product refers to a product manufactured by using electronic technology and circuit components, such as a mobile phone, a computer, a television, a sound box and the like, and optionally, the electronic product to be traced can be obtained through database query.

Further, the invention can know the production information of the electronic product, such as manufacturer, production date and the like, and is helpful to know the technical specification and performance parameters of the product by inquiring the Flash data corresponding to the electronic product and identifying the data channel of the Flash data, thereby better selecting and comparing the product.

The Flash data refer to data stored in a Flash memory chip of the electronic product; the data channel refers to a channel or a path for information and data to flow.

As an embodiment of the present invention, the querying Flash data corresponding to the electronic product, and identifying a data channel of the Flash data, includes: identifying a product type of the electronic product; determining a product model corresponding to the electronic product based on the product type; inquiring Flash data of the product type; uploading the Flash data to a preset electronic numerical control platform; searching a source channel corresponding to the Flash data by using a search engine in the electronic numerical control platform.

Wherein, the product type refers to the classification or category of the electronic product, such as: smart phones, notebook computers, televisions, etc.; the product model refers to a specific model or a model identifier of the electronic product under a specific product type; the electronic numerical control platform refers to a platform or a system for managing and processing the electronic product data.

Further, the identification of the product type of the electronic product may be achieved by an identification tool, such as: openCV, tensorFlow, keras, etc.; the determining of the product model corresponding to the electronic product can be realized through an SQL database; the inquiring of the Flash data of the product type can be realized through a product manual; the searching of the source channel corresponding to the Flash data can be realized by utilizing a search engine in the electronic numerical control platform.

According to the invention, the target number sources in the source channel are determined so as to cover the data sources of related fields, industries or user groups, so that comprehensive and representative data samples are obtained, and the complexity and cost of data processing can be reduced and the efficiency and speed of data processing can be improved by removing the sensitive sources in the target number sources.

Wherein the target number source refers to a specific data set or data source used in the data analysis or business decision process, and can be data from different channels, departments or systems; the sensitive source refers to a data source containing sensitive information or personal privacy, such as: personal identity information, financial data, health information, social security numbers, and the like.

Further, the determining the target number source in the source channel may be implemented by a data analysis tool, such as: pandas, numPy, scikit-learn et al; the removing of the sensitive source in the target number source can be achieved through a classification algorithm, such as: naive bayes, support vector machines, random forests, etc.

As one embodiment of the present invention, the calculating the desensitization value corresponding to the target number source includes:

S2, inquiring a backtracking target corresponding to the electronic product based on the desensitization value, wherein the backtracking target comprises: product configuration information, error log, and production process log.

Based on the desensitization value, the invention inquires the backtracking target corresponding to the electronic product, thereby being beneficial to improving the accuracy and quality of the data, improving the accuracy of automatic processing and reducing the risk of misuse or leakage of the product data.

Wherein, the backtracking target refers to a target for tracking and determining the source, the flow direction and the use condition of data in the data processing process, and relates to tracking and recording the transmission path, the data operation and the use condition of the data, which comprises the following steps: product configuration information, error log, and production process log.

The trace-back target can trace the original source of specific data, each link of data processing and the final use of the data, is helpful to ensure the validity, the integrity and the credibility of the data and prevent the abuse, the misuse or the unauthorized access of the data, and can also support the work of data tracing, risk management, compliance audit and the like.

Further, the backtracking goal corresponding to the electronic product can be queried through a block chain technology.

S3, extracting characteristic parameters in the product configuration information, inquiring configuration variables corresponding to the characteristic parameters, positioning option variables in the configured variables, calculating characteristic association degrees of the characteristic parameters and the option variables, and generating a configuration parameter table corresponding to the product configuration information based on the characteristic association degrees.

According to the invention, the characteristic parameters in the product configuration information are extracted, the configuration variables corresponding to the characteristic parameters are queried, the configuration variables corresponding to the specific characteristic parameters can be quickly and accurately found, and more detailed information such as the specification, the model, the version and the like of the product can be known, so that the quality and the applicability of the product can be more comprehensively evaluated.

Wherein, the characteristic parameters refer to parameters with specific meanings or representing specific attributes in the product configuration information; the configuration variables refer to product configuration options or values which are set or adjusted according to the characteristic parameters, and optionally, the characteristic parameters can be obtained through a data mining method, such as: data mining algorithms such as cluster analysis, association rule mining, etc.; the configuration variables may be obtained by extraction by a rules engine.

Further, by locating the option variable in the configured variables and calculating the feature association degree of the feature parameter and the option variable, different product configurations can be more accurately described and distinguished, and therefore the product design and the production process can be adjusted, cost is reduced, and resource allocation is optimized.

Wherein the option variables refer to different options available for selection in products or services; the feature association degree refers to the degree of association between the feature parameter and the option variable.

As one embodiment of the present invention, the locating the option variable in the configured variables, and calculating the feature association degree between the feature parameter and the option variable includes:

The invention generates the configuration parameter table corresponding to the product configuration information based on the characteristic association degree, can quickly and accurately determine the configuration parameters according to the option variable and the characteristic association degree, improves the efficiency and the automation level of subsequent data backtracking, and provides visualized and easily understood product configuration information.

Wherein the configuration parameter table refers to a table or document containing configuration information of a product or service.

As one embodiment of the present invention, the generating, based on the feature association degree, a configuration parameter table corresponding to the product configuration information includes: constructing a feature association graph corresponding to the feature association degree based on the feature association degree; extracting feature nodes in the feature association graph; generating a configuration item corresponding to the feature association graph based on the feature node; identifying configuration parameter values corresponding to the configuration items; and inputting the configuration parameter values into the feature association diagram to obtain a configuration parameter table corresponding to the product configuration information.

The feature association graph is a graph structure constructed according to association relations among different features; the feature nodes refer to nodes which represent different features in the feature association graph; the configuration items refer to options and value ranges contained in each feature node; the configuration parameter value refers to a specific value or a value designated for each configuration combination in the feature association diagram.

Further, the feature association graph may be obtained through an image editing tool implementation, such as: adobe Photoshop, GIMP, etc.; the feature nodes may be obtained through feature model implementation, which is built based on programming languages, such as: featureIDE, CVL and FAMA, etc.; the configuration items may be obtained in a product configuration tool implementation, such as: configit, tacton, cameleon CPQ, etc.; the configuration parameter values may be obtained by a pruning search algorithm, such as: backtracking algorithm, branch-and-bound algorithm, cut plane algorithm, etc.

S4, identifying different log levels in the error log, searching an error type corresponding to the log level, positioning an error code in the error log based on the error type, and carrying out stack tracking on the error code to obtain an error path of the error code.

According to the invention, by identifying different log levels in the error log and searching the error types corresponding to the log levels, different processing mechanisms can be set for different error types, thereby helping to more effectively manage and maintain the system and improving the efficiency of fault removal and performance optimization.

The log level refers to the importance degree or severity degree of the log information, and includes: DEBUG, INFO, WARNING, ERROR, CRITICAL; the error type refers to the error type possibly occurring in the running process of the program, and comprises the following steps: syntax errors, logic errors, configuration errors, etc.

Alternatively, the log level may be obtained by a log analysis tool implementation, such as: ELK and other tools; the error type may be obtained by a code analysis tool implementation, such as: pylint, sonarQube, ESLint, etc.

Based on the error type, the error codes in the error log are positioned, so that the specific position of the error can be quickly positioned, the problem can be accurately understood and repaired, the fault removal time is shortened, and the recognition efficiency is improved.

The error code refers to a specific numerical value or identifier returned by the system when an error or abnormality occurs in the running of the program.

As one embodiment of the present invention, the locating the error code in the error log based on the error type includes: confirming error reporting information in the error type; identifying an identifier in the error message; inquiring an error code table corresponding to the error type based on the identifier; and positioning error codes in the error code table.

Wherein, the error reporting information refers to detailed description or message of the error which is displayed in the error log; the identifier refers to a specific part which can be used for uniquely identifying the error; the error code table refers to a document or resource for recording common error codes and meanings thereof.

Further, the error reporting information may be obtained through a log record library, for example: log4j, log back, etc.; the identifier may be obtained by a code analysis tool implementation, such as: sonarQube, PMD, profiler, etc.; the error code table may be obtained through a programming interface document implementation.

The invention obtains the error path of the error code by carrying out stack tracking on the error code so as to be convenient for better knowing the execution flow of the code, finding potential logic errors, boundary condition problems or abnormal conditions, and improving and optimizing the code by analyzing the error path, thereby improving the quality and stability of software.

The stack tracking means that when the program has errors, the sequence of all functions and methods in the call stack in the running process is recorded; the error path refers to a code path or an execution flow which causes an error to occur.

As one embodiment of the present invention, the performing stack tracking on the error code to obtain an error path of the error code includes: checking an abnormal code in the error code; enabling stack tracking of the program corresponding to the error code based on the abnormal code; inquiring tracking information in the stack tracking; analyzing calling parameters in the tracking information; positioning error points in the tracking information based on the calling parameters; and identifying an error path corresponding to the error point.

Wherein, the exception code refers to an error code or an identifier which is generally provided in exception handling; the tracking information refers to a calling sequence of functions and methods displayed in the stack tracking; the calling parameter refers to an input parameter transferred when a function or a method is called; the error point refers to the specific code location located in the stack trace that caused the error.

Further, the anomaly code may be obtained by code recognition means such as: sentry, bugsnag, etc.; the tracking information may be obtained through a tracking mechanism implementation of a programming language including: java, python, C ++, etc.; the call parameters may be obtained by a target detection and recognition algorithm, such as: YOLO, fast R-CNN, SSD, etc.; the error point may be obtained by a debugger implementation.

S5, inquiring a time source in the production process record, identifying a source sequence corresponding to the time source, time ordering the source sequence, drawing a time sequence diagram of the production process record based on the ordered source sequence, and extracting production backtracking data in the time sequence diagram.

The invention can help trace the root of the quality problem by inquiring the time number source in the production process record and generating the quality problem in a specific time period, so that the cause possibly causing the problem can be found out by analyzing the time number source, and corresponding measures are taken for adjustment.

Wherein the time number source refers to time-related data recorded in the production process record, such as: the actual production time, the planned production time, the task completion time and the like, and optionally, the querying of the time number source in the production process record can be realized through a time tracking tool, such as: toggl, rescueTime, etc.

According to the invention, through identifying the number source sequence corresponding to the time number source, the trend and the mode of the data can be better observed, the event sequence, the observation trend and the mode, the abnormality and the mutation can be conveniently determined, the data in the number source sequence is further analyzed and predicted, optionally, the time sequence is carried out on the number source sequence, a basis is provided for the further analysis and the prediction of the data, the data value of each time point can be better displayed, and the comparison and the search of the data are convenient, for example: the statistical indexes such as the minimum value, the maximum value, the median and the like can be quickly found.

Wherein the source sequence refers to a time-dependent data sequence extracted from the production process record, and the source sequence may be ordered according to time so as to better understand and analyze the trend of the data over time.

As one embodiment of the present invention, the identifying the number source sequence corresponding to the time number source, and time ordering the number source sequence includes: determining source data in the time source; identifying a timestamp corresponding to the number source data; calculating a source value in the time source based on the timestamp; the sequence labeling is carried out on the number source values, so that a number source sequence corresponding to the time number source is obtained; extracting a number source serial number corresponding to the number source sequence; constructing a time tuple corresponding to the number source sequence based on the sequence number; traversing the time tuples to achieve a time ordering of the sequence of sources.

Wherein, the number source data refers to collected numerical information related to time, such as: temperature, sales, yield, etc.; the time stamp refers to a mark for recording the occurrence time of data, such as: date, time of day, or other form of time representation; the digital source value refers to digital data corresponding to the time stamp; the number source serial number refers to the sequence of the time stamp and is used for sequencing or reconstructing the number source sequence; the time tuple refers to a tuple list comprising the timestamp and a corresponding value.

Further, the determining the number source data in the time number source may be implemented by a data processing tool, such as: numPy, pandas; the identifying the timestamp corresponding to the number source data may be implemented by a time processing library, for example: python, datetime; the calculation of the source value in the time source can be realized by the following formula; the sequence labeling of the digital source values can be realized by a time sequence analysis method; the extraction of the number source serial numbers corresponding to the number source sequences can be realized through an extraction tool; the construction of the time tuples corresponding to the number source sequences may be performed by tuple construction tools, such as: a repetition, list.

As one embodiment of the present invention, the calculating, based on the time stamp, a source value in the time source includes:

The invention draws the time sequence diagram corresponding to the production process record based on the ordered number source sequence, can display the production data in time sequence, clearly displays the change trend and mode in the production process, and provides a basis for helping to analyze the production data, find abnormality and monitor the process.

The time sequence chart refers to that time is taken as a horizontal axis, corresponding data values are taken as a vertical axis, and a change trend of the data is presented through continuous data points or lines, and optionally, the drawing of the time sequence chart corresponding to the production process record can be realized through drawing tools, such as: matplotlib, plotly and Plotly.

Further, the invention extracts the production backtracking data in the time sequence diagram, can obtain the trend part, seasonal part and random fluctuation part of each time point, and can analyze the production backtracking data to know the overall trend, seasonal change and abnormal condition of the production quantity of the electronic products.

Wherein, the production backtracking data refers to tracking and recording the whole process data from raw material purchase to manufacture, assembly and delivery of the electronic product, and comprises the following steps: raw material information, production process data, traceability information and the like, and optionally, the extraction of the production traceability data corresponding to the electronic product can be realized through an ARIMA model or a Holt-windows model.

S6, constructing a product data backtracking model based on the configuration parameter table, the error path and the production backtracking data, inquiring LDPC codes in the product data backtracking model, generating check bits corresponding to the electronic product by using the LDPC codes, constructing a sparse check matrix corresponding to the electronic product based on the check bits, and carrying out data backtracking on the electronic product by using the sparse check matrix to obtain a data backtracking result of the electronic product.

The invention constructs the product data backtracking model based on the configuration parameter table, the error path and the production backtracking data, can trace back the production, sales and supply links of the electronic product, clearly knows the source and the flow direction of the product, and is beneficial to improving the traceability efficiency of the electronic product.

The product data backtracking model refers to a systematic method and tool for tracking, recording and managing data related to a production process, a sales process and a supply process of a product, and optionally, the product data backtracking model can be implemented through a model construction tool, such as: scikit-learn, tensorFlow, pyTorch, etc.

According to the invention, through inquiring the LDPC code in the product data backtracking model and utilizing the LDPC code to generate the check bit corresponding to the electronic product, the problems of loss, damage or tampering and the like in the data transmission process can be found in time, the data transmission efficiency is improved, the transmission time and the resource consumption are reduced, and the data integrity is ensured.

The LDPC code refers to an error correction code generated by an LDPC programming algorithm; the check bit refers to an extra bit generated when the LDPC code is used for verifying whether the received data is correct; alternatively, the LDPC code may be obtained by what LDPC programming algorithm generation; the check bits may be obtained by an error correction code algorithm, such as: reed-Solomon codes, BCH codes, etc.

According to the invention, the sparse check matrix corresponding to the electronic product is constructed based on the check bit, so that the problems of loss, damage or tampering in data transmission can be detected and corrected, the reliability of the data is improved, and check and error correction operations are performed when needed.

Wherein the check bits refer to extra bits generated when an error correction code (e.g., an LDPC code) is used; the sparse check matrix refers to a check matrix adopted when a check code is used; alternatively, the check bits may be obtained by CRC validation generation; the sparse check matrix can be obtained through construction of a Tanner graph.

According to the invention, the data backtracking result of the electronic product is obtained by utilizing the sparse check matrix, so that the production process, raw material sources and supply chain information of the electronic product can be accurately tracked, the traceability reliability of the product is ensured, and low-quality or unknown raw materials are avoided.

The data backtracking refers to a process of tracking and tracing the source, the flow direction and the change process of the data, and optionally, the data backtracking of the electronic product can be realized by using the sparse check matrix.

Fig. 2 is a functional block diagram of a data backtracking system for implementing an electronic product based on an LDPC error correction algorithm according to an embodiment of the present invention.

The data backtracking system 100 for realizing the electronic product based on the LDPC error correction algorithm can be installed in electronic equipment. Depending on the implemented functions, the data backtracking system 100 for implementing an electronic product based on the LDPC error correction algorithm may include a desensitization value calculation module 101, a target query module 102, a relevance calculation module 103, an error path identification module 104, a production backtracking module 105, and a backtracking result module 106. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.

In the present embodiment, the functions concerning the respective modules/units are as follows:

the desensitization value calculation module 101 is configured to obtain an electronic product to be traced, query Flash data corresponding to the electronic product, identify a data channel of the Flash data, determine a target number source in the data channel, remove a sensitive source in the target number source, and calculate a desensitization value corresponding to the target number source;

The target query module 102 is configured to query a backtracking target corresponding to the electronic product based on the desensitization value, where the backtracking target includes: product configuration information, error log, and production process record;

the association degree calculating module 103 is configured to extract a feature parameter in the product configuration information, query a configuration variable corresponding to the feature parameter, locate an option variable in the configured variable, calculate a feature association degree between the feature parameter and the option variable, and generate a configuration parameter table corresponding to the product configuration information based on the feature association degree;

the error path identifying module 104 is configured to identify different log levels in the error log, search an error type corresponding to the log level, locate an error code in the error log based on the error type, and perform stack tracking on the error code to obtain an error path of the error code;

the production backtracking module 105 is configured to query a time source in the production process record, identify a source sequence corresponding to the time source, time-sequence the source sequence, draw a time sequence diagram of the production process record based on the sequenced source sequence, and extract production backtracking data in the time sequence diagram;

The backtracking result module 106 is configured to construct a product data backtracking model based on the configuration parameter table, the error path and the production backtracking data, query an LDPC code in the product data backtracking model, generate check bits corresponding to the electronic product using the LDPC code, construct a sparse check matrix corresponding to the electronic product based on the check bits, and perform data backtracking on the electronic product using the sparse check matrix to obtain a data backtracking result of the electronic product.

In detail, each module in the data backtracking system 100 for implementing an electronic product based on the LDPC error correction algorithm in the embodiment of the present application adopts the same technical means as the method for implementing the data backtracking method for implementing the electronic product based on the LDPC error correction algorithm described in fig. 1, and can produce the same technical effects, which are not described herein again.

Fig. 3 is a schematic structural diagram of an electronic device 1 for implementing a data backtracking method for implementing an electronic product based on an LDPC error correction algorithm according to an embodiment of the present invention.

The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as a data backtracking method program for implementing an electronic product based on an LDPC error correction algorithm.

The processor 10 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing Unit, CPU), a microprocessor, a digital processing chip, a graphics processor, a combination of various control chips, and so on. The processor 10 is a Control Unit (Control Unit) of the electronic device 1, connects respective parts of the entire electronic device using various interfaces and lines, executes or executes programs or modules stored in the memory 11 (for example, executes a data trace back method program for implementing an electronic product based on an LDPC error correction algorithm, etc.), and invokes data stored in the memory 11 to perform various functions of the electronic device and process data.

The memory 11 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used to store not only application software installed in an electronic device and various data, such as codes for implementing a data backtracking method program of an electronic product based on an LDPC error correction algorithm, but also temporarily store data that has been output or is to be output.

The communication bus 12 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.

The communication interface 13 is used for communication between the electronic device 1 and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.

Fig. 3 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.

For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.

It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.

The data backtracking method program stored in the memory 11 of the electronic device 1 and implemented on the basis of the LDPC error correction algorithm is a combination of a plurality of instructions, and when running in the processor 10, it can be implemented:

In particular, the specific implementation method of the above instructions by the processor 10 may refer to the description of the relevant steps in the corresponding embodiment of the drawings, which is not repeated herein.

Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM).

The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:

In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.

The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.

The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.

Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms first, second, etc. are used to denote a name, but not any particular order.

Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. The method for realizing the data backtracking of the electronic product based on the LDPC error correction algorithm is characterized by comprising the following steps:

Acquiring an electronic product to be traced, inquiring Flash data corresponding to the electronic product, identifying a data channel of the Flash data, determining a target number source in the data channel, removing a sensitive source in the target number source, and calculating a desensitization value corresponding to the target number source;

inquiring a time source in the production process record, identifying a source sequence corresponding to the time source, time ordering the source sequence, drawing a time sequence diagram of the production process record based on the ordered source sequence, and extracting production backtracking data in the time sequence diagram;

2. The method for implementing data backtracking of electronic products based on LDPC error correction algorithm according to claim 1, wherein said querying Flash data corresponding to said electronic products, identifying data channels of said Flash data, comprises:

identifying a product type of the electronic product;

inquiring Flash data of the product type;

uploading the Flash data to a preset electronic numerical control platform;

3. The method for implementing data backtracking of electronic products based on the LDPC error correction algorithm as claimed in claim 1, wherein the calculating the desensitization value corresponding to the target number source includes:

4. The method for implementing data backtracking of electronic products based on the LDPC error correction algorithm as claimed in claim 1, wherein the locating the option variable in the configured variables, calculating the feature association degree of the feature parameter and the option variable, includes:

5. The method for implementing data backtracking of electronic products based on the LDPC error correction algorithm as claimed in claim 4, wherein the generating the configuration parameter table corresponding to the product configuration information based on the feature association degree includes:

extracting feature nodes in the feature association graph;

6. The method for implementing data backtracking of electronic products based on the LDPC error correction algorithm as claimed in claim 1, wherein the locating the error code in the error log based on the error type comprises:

and positioning error codes in the error code table.

7. The method for implementing data backtracking of electronic products based on the LDPC error correction algorithm as claimed in claim 1, wherein the performing stack tracking on the error code to obtain an error path of the error code comprises:

checking an abnormal code in the error code;

and identifying an error path corresponding to the error point.

8. The method for implementing data backtracking of electronic products based on the LDPC error correction algorithm as claimed in claim 1, wherein the identifying the number source sequence corresponding to the time number source, and time ordering the number source sequence, includes:

calculating a source value in the time source based on the timestamp;

9. The method for implementing data backtracking of electronic products based on the LDPC error correction algorithm as claimed in claim 8, wherein the calculating the source value in the time source based on the time stamp comprises:

10. A data backtracking system for implementing an electronic product based on an LDPC error correction algorithm, the system comprising: