CN111352968B - Intelligent manufacturing element identification method based on blockchain network - Google Patents
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Abstract
The invention discloses an intelligent manufacturing element identification method based on a blockchain network. And analyzing the worker state data uploaded by the intelligent equipment in the intelligent manufacturing process in real time in a frequency domain by utilizing a highly universal Fourier transform algorithm. Through data mining, a sequence mode based on a time sequence and correlation between production procedures and projects continuously appearing in a manufacturing process are found, in a blockchain network, meaningful projects in the manufacturing process are extracted to serve as representative subjects, so that subject mining of intelligent manufacturing is realized, a side chain-based distributed consensus blockchain network is adopted for the vulnerable safety problem of intelligent equipment, a subject packaging method is used in the network, an improved blockchain distributed account book is applied to the manufacturing process, information accounts are distributed in a peer blockchain network, information is recorded and managed together, and data safety is improved.
Description
Technical Field
The invention relates to a data mining and management method, in particular to an intelligent manufacturing element identification method based on a block chain network.
Background
Intelligent manufacturing utilizes a fusion of intelligent information technology and manufacturing technology, applying standardized industrial engineering software to various aspects of society. To maintain sustainable productivity, the innovative intelligent information fusion manufacturing industry is considered a new growth engine. To meet the fourth industrial revolution, the integration of intelligent information, pattern recognition, data mining, machine learning, big data, internet of things and manufacturing technologies has innovatively changed the manufacturing industry. The intelligent information technology is combined with the intelligent manufacturing technology, and is not only in the logistics processes of production, logistics, distribution, consumption and the like, but also in the equipment process and the quality control process. For efficient development of systems, data mining analysis based on manufacturing big data and design and analysis of intelligent systems are required to be performed, and basic technical reserves such as manufacturing safety, blockchain networks, manufacturing service technologies and the like are required to be performed. The population aging and population reduction cause the production system to change, and the manufacturing industry big data decision mining process utilizes heterogeneous big data acquisition and preprocessing technology, big data distributed storage and management technology and big data analysis technology. In order to process unstructured manufacturing data into process structured data, process-based distributed integration is performed. In order to effectively process the collected unstructured big data, it is necessary to study the development of fusion techniques. In addition, in the application of blockchain technology to manufacturing and related processes in the financial field, sharing and security of the workflow transaction distributed ledger are also guaranteed. In the internet of things, intelligent manufacturing makes transparent, extensible and secure processes possible by using blockchains, and changes the network organization mode from centralized to distributed.
Blockchains are classified into public chains, hybrid chains, federated chains, and private chains. These blockchain technologies are increasingly being applied to energy, logistics, distribution, finance, medicine, automotive and public services. IBM developed a project that built a large platform on which various blockchain platforms were interconnected and integrated. Such an integrated blockchain platform may link blocks of transactions together, establish them in a distributed manner, and share transactions by blocks. The process of mining using transactions that occur in real time makes it possible to analyze big data, draw meaningful rules, and make reasonable decisions. In intelligent manufacturing, the technology can effectively save cost, improve productivity and control quality: the distribution manufacturing process comprises product turning, warehouse loading, warehouse turning, wholesale turning and organization turning. From an information flow perspective, trade transactions include manufacturing production, production practices, wholesale practices, and wholesale practices. In the element identification process, the production time can be shortened, the distribution time can be shortened, the optimization plan can be established, and the time and the cost can be saved through the redistribution of the low-efficiency process. In the manufacture and distribution of the product, the stock quantity can be reduced, thereby saving the cost, reducing the price of the product and increasing sales. Accordingly, consumer satisfaction and market share will increase.
The goal of a factory automation system is to minimize human resources and increase the efficiency of a process unit. It is therefore based on the high functionality and accurate handling capacity of automated machines and focuses on improving productivity and quality. In a more comprehensive concept, it utilizes business management models of enterprise resource planning, customer relationship management, and decision making to integrate and systemize information. These models make it possible to predict demand and respond to production and supply plans efficiently and timely. However, the system is based on a bottom-up information delivery system and generates different response periods according to the requirements. Therefore, it has a limit in meeting the rapidly changing small-lot production demands. To overcome this problem, pull-based Toyota production systems have been developed that target zero inventory, zero defects, and flexible production. At the national level, germany established an industry 4.0 for the collaboration of governments, companies and academia. In the united states, ICT enterprises based on general electric have developed collaboration. Intelligent manufacturing development is rising to government or corporate research level. In implementation, intelligent manufacturing is based on industrial robotics, requiring multiple device controllers and internet of things technology supporting data acquisition. In addition, tools such as Hadoop are used for analyzing and processing the collected data. Based on statistical analysis of the pre-processed data, further improved artificial intelligence analysis methods can be applied to control and development of the production line. The artificial intelligence with improved learning ability will help to operate a stable intelligent manufacturing apparatus, thereby greatly improving the quality and productivity of the product.
Along with the development of fusion technology and the change of consumption trend, various industries pay more attention to personalized production, and product purchase involves factors such as raw materials, country of origin, production date, distribution channel and the like. With the popularity of the internet, consumers can conveniently collect product information, and their demands become more diverse. Thus, the lifecycle and release cycle of new products are shorter and shorter, and the expectations of consumers are higher and higher. Manufacturing is transitioning from existing mass custom production to personalized production using demand analysis and trend prediction. Personalized production aims at creating new service value through cooperation of different industries and providing differentiated services. The method also has the characteristics of high quality, low cost, product diversity, service practicability and the like, and provides wide product selection for consumers. There is increasing interest in applying fusion techniques to the element recognition process of personalized production mining techniques. In the element recognition process, various technologies such as Internet of things, cloud computing, artificial intelligence, big data, data mining and the like need to be carried out according to stages in the whole process from planning to sales. The element identification process is a structure related to all objects (e.g., health, raw materials, energy, components, machinery, and healthcare) related to the manufacturing industries in different fields. It also requires an environment that enables collaborative work through various connections: human to object, object to machine, robot to human, and industrial to industrial connections. This helps to maximize the flexibility of the process and enables it to cope with variations in manufacturing processes, market demands, etc. The element recognition process generates different analysis results based on the collected data. It needs to cooperate with transparent and reliable data over a large network. The blockchain-based intelligent manufacturing process records data entirely through a distributed ledger and a distributed consensus of the blockchain. With the development of fusion technology, the information amount used by consumers when selecting commodities is larger and larger, and the expected value of the consumers is higher and higher, so that the blockchain technology is provided with a mining process based on distributed consensus and distributed ledgers. The process is to prevent errors or kneading that may occur in planning, designing, producing, distributing, and selling processes by using a distributed ledger. The distributed ledger is one of the basic concepts of blockchains and is also a record of the agreement made by participants. This is a method of managing product history by applying blockchain distributed ledgers and blockchain distributed consensus to mining business processes. Distributed consistency is a method of achieving consistency among participants with respect to particular data for higher reliability. In intelligent manufacturing, a distributed ledger displays features of sequential data, which are displayed sequentially in time according to the state of a product. The condition of the product is changed continuously along with the change of the manufacturing process, and the product is manufactured into blocks and recorded in a distributed ledger. For example, a distributed ledger may provide data about products to which similar raw materials or manufacturing processes are applied through mining and associated analysis of data visualization. The distributed ledgers provided to consumers should utilize associative mining analysis and data visualization to assist the consumers in making use of actual purchases. In addition, the mining business process proposed in the intelligent manufacturing can be applied to improve the reliability and flexibility of the ledger. This allows transparent information to be provided to the consumer and improves the reliability of the mining business process. Based on the above industrial and technical features, how to apply blockchain technology to the field of intelligent manufacturing topic mining is a problem worthy of research and solution.
Disclosure of Invention
In order to solve the problems, the invention provides an intelligent manufacturing element identification method based on a blockchain network, and meanwhile, the safety of intelligent manufacturing big data analysis mining and data transmission storage is considered, so that the intelligent manufacturing level is improved.
The technical scheme of the invention comprises the following steps:
1) And performing frequency domain transformation on the time domain data acquired by the intelligent wearable equipment by adopting short-time Fourier transformation, and performing frequency domain data analysis.
2) Comparing the waveforms of the equipment in the initial normal operation and the current operation of the equipment, and grabbing abnormal production elements.
3) The intelligent equipment network is built based on the blockchain technology, so that the safety of industrial secrets such as identified manufacturing elements, employee life log data and the like is ensured.
4) Federation members (referring to miners or billboards in the federation chain) participate in creating a distributed consensus of new blocks to produce new data blocks.
5) And constructing a transaction information account book in intelligent manufacturing element identification by adopting a distributed account book technology, and carrying out joint recording and management on transaction information by a distribution mode based on a peer-to-peer network.
6) The problem that data cannot be analyzed due to the fact that limited view data are analyzed by utilizing block chain network type transactions in a general data mining method is solved, the problem that data in a block chain network cannot be analyzed in the general data mining method is solved, and the natural defect can be overcome by the method for topic packaging.
In the step 1), the time domain data acquired by the intelligent wearable device is subjected to frequency domain transformation by adopting short-time Fourier transformation, and the reasons and the method for performing frequency domain analysis are as follows:
with the popularization of intelligent equipment, various sensor information can be obtained from users, and process monitoring, error prediction and human resource dynamic conditions are deeply analyzed. Therefore, based on dynamic situation management, production line faults can be predicted, health information of production human resources is extracted, and personalized health services are provided. In particular, the human resource hygiene service can contribute to improvement of welfare, working conditions and production efficiency. Detection sensors for manufacturing process monitoring and medical services are different and have a wide range of connections. Typical sensors include acoustic and acceleration sensors mounted on production equipment. If such a sensor is used to analyze the vibration waveform, a constant and repetitive waveform pattern is observed. Thus, equipment failures can be captured from waveform changes and some errors in large connected production equipment can be predicted to select maintenance objects and optimize operation. Also, accelerometer sensors connected to human resources can find their movements and model the classification of the working situation and the detection of the safety situation. In response to various information input from the sensor, the present invention employs a fourier transform with a high degree of versatility that is capable of analyzing the condition of the device and human body movements. And using the structured data obtained by the sensor measurement to find and analyze the motion mode and the gesture of the user. In addition, dynamic energy released during exercise, rest and movement can also be measured. The input signal of the fourier transform algorithm used for preprocessing fluctuates over time. Thus, the frequency of a particular form is easily analyzed and motion changes can be detected based on the input of the sensor. The fast fourier transform, which is typically used for frequency analysis, enables timely analysis of correlations. Therefore, in order to perform frequency analysis, it is necessary to use short-time fourier transform in practical implementation. The transformed short-time Fourier transform decomposes time in a certain unit to perform frequency domain analysis. The short-time fourier transform is shown as follows:
where ST denotes a start time in the period, ET denotes an end time of the period; t represents an independent variable time; f represents frequency; i represents the imaginary part of the imaginary number.
The step 2) is to compare the waveforms of the equipment during the initial normal operation and the current operation, grasp the abnormal production factors, and adopts the following method:
calculating an error value E of a waveform of the equipment in the initial normal operation and the current operation of the equipment by adopting the following steps:
when the frequency bins separated by the fourier transform are divided by N, i represents each frequency bin, N i Is the first normal value entered during normal operation of segment i. S is S i Representing the frequency of the i-th part of the current input. The larger the E value measured in real time, the higher the probability of observing an abnormal state. Assuming that the quality control allows a scale of G, the constant decrease of the G value is closer to the production concept based on predictive maintenance, which means that staff in operation need to adjust G, thus withstanding the actual maintenance frequency. Meanwhile, the production human resources have various motions and motions, n sections are needed to classify the frequencies, and E values are used as evaluation indexes of the similarity. The frequency of the input per unit time is compared with its past value to group according to the similarity. Accordingly, various classifications can be made, such as a fixed posture of an employee, a horizontal movement, a step movement, a rapid movement state, and the like.
The step 3) builds an intelligent equipment network based on a blockchain technology, ensures the safety of industrial secrets such as identified manufacturing elements, employee life log data and the like, and is characterized in that:
aiming at the problem of poor safety of intelligent equipment, a network based on a blockchain technology is established. Blockchains are classified into public chains, hybrid chains, federated chains, and private chains. The public chain is a fully open blockchain, and large users such as virtual coin users participate in the distributed ledger and the distributed consensus. The alliance chain is a blockchain of participation of alliance members in a distributed ledger and a distributed consensus, and is characterized by identifying users. Private blockchains are blockchains that allow a small percentage of users to participate in distributed ledgers and distributed consensus. The smart device contains enterprise and personal activity information, for which purpose the invention uses a federated blockchain with participants as management agents, only allowed users can access the data.
The step 3) builds an intelligent equipment network based on a blockchain technology, ensures the safety of industrial secrets such as identified manufacturing elements, employee life log data and the like, and is characterized in that:
the intelligent device continuously generates and gathers a large amount of data. Because of the structural problems of the blockchain technology itself, it is difficult to contain massive amounts of data. To overcome the structural problem, the present invention uses blockchain technology that includes side chains. The side chains take advantage of the non-operational nature of conventional blockchains, enabling the creation of data storage space in the blockchain. The method stores the data of the intelligent device in a separate database, and the data mapping occurs in side-chain transactions of the data blocks. Thus, data can be searched quickly. In the data block organization of the intelligent wearable device based on the side chains, workers form a alliance as clients and managers of distributed consensus. The data block includes a creation date, hash information of its connection block, and side chain information. In a data block, there is a set of multiple transactions on the side chain. The transaction set includes intelligent device information for the staff, mapping information for the collected data, events and their process information, and the number of distributed coherence participants.
In the step 4), the alliance members participate in creating a distributed consensus of the new block to generate a new data block, and the specific strategy is as follows:
the federation has human resources associated with production, and a distributed consensus is achieved with the consent of most federation participants. When an event occurs or in units of work progress, distributed consistency will occur that creates new blocks. If an event occurs, processing is performed according to event rules. A new block is created by distributed consensus and linked to the blockchain. Unless an event occurs until the work process completes its project, a new block is created at the endpoint through distributed consensus and then linked to the blockchain. The created blockchain and smart device data are stored in a physically distributed store in a distributed ledger to achieve consistency and data management. If an event occurs, processing according to rules: the data created before the event occurs is integrated and then a data block is created. The created data blocks are saved into a distributed database through distributed consistency.
Step 5) adopts a distributed account book technology to construct a transaction information account book in intelligent manufacturing element identification, and performs joint recording and management on transaction information by a distribution mode based on a peer-to-peer network, wherein the method comprises the following specific steps:
the blocks selected by the coherency algorithm are linked to each other to create a blockchain. Each block has two types of hash information. One is the hash of the previous chunk header and the other is the hash of the transaction. And for the transaction information account book in intelligent manufacturing, adopting a distributed account book technology, and carrying out joint recording and management on the transaction information by a distribution mode based on a peer-to-peer network. In a blockchain network, the intelligent manufacturing process determines details of data read points, P2P-based data transfer or processing, and the like. Because of its original management, the intelligent manufacturing process may suffer from a lack of effective statistics, neglecting supervision, inconsistent physical and soft data, etc. While conventional physical smart manufacturing systems have unclear information during the manufacturing process, the proposed smart manufacturing process supports end-to-end tracking based on transaction data held in multiple blockchains to prevent data loss at each step. The blockchain-based intelligent manufacturing process utilizes information exchange of real-time collected data to analyze various data related to a traceability system, various base extension infrastructures, and employee work systems. Thus, the blockchain-based distributed ledger mining technique provides innovative services in the intelligent manufacturing process.
The step 6) solves the problem that the data cannot be analyzed due to the fact that the data of the limited view are analyzed by utilizing the transactions of the blockchain network type in the general data mining method based on the theme packaging method, and specifically comprises the following steps:
data mining is the process of analyzing and exploring data to find meaningful rules and relationships from unstructured or structured data. The general data mining method can perform expression search on the intelligent manufacturing process to analyze data meaning, but it cannot obtain data analysis results in the data collection process. Particularly in the intelligent manufacturing element recognition process, there is a problem that data cannot be analyzed due to the fact that the data of the limited view is analyzed by using the transaction of the blockchain network type.
Therefore, the invention removes meaningless data variables and extracts main variables by the theme element encapsulation method of the transaction. The topic element package is a formatted statistical reasoning method for analyzing semantic environments from data, and can find potential data topics when blockchain transaction data is applied. The theme factor encapsulation process includes six steps, each presenting a different set of data for each manufacturing stage in the blockchain transaction:
step 1: collecting production element data in intelligent manufacturing process by using wearable equipment, intelligent sensors and other intelligent equipment for constructing data blocks
Step 2: and filtering and cleaning the data of the collected intelligent manufacturing production process, and checking the collected data, including checking the consistency of the data, deleting invalid values, supplementing missing values and the like.
Step 3: stem extraction is a process of removing the affix to obtain the root. For morphological roots of a word, stems need not be identical; mapping related words to the same stem gives satisfactory results even if the stem is not a valid root of the word. This step may perform data merging on synonyms in the production element.
Step 4: a matrix is created that represents transaction data that may occur during the smart manufacturing process, including all elements that may occur during the smart manufacturing process, such as workers, equipment, product information, process steps, and process content.
Step 5: the intelligent manufacturing theme elements, namely the process items, are matrix-packaged into the data blocks.
Step 6: and classifying the data blocks according to the intelligent manufacturing production element stems and connecting the data blocks into block chains.
The topic element package is a formatted statistical reasoning method for analyzing semantic environments from data, and can discover potential data topics when blockchain data is applied.
The beneficial effects of the invention are as follows:
the invention can analyze and mine big data collected by intelligent equipment in intelligent manufacturing, extract meaningful items in manufacturing procedures as representative subjects, thereby realizing element mining and identification of intelligent manufacturing, and simultaneously ensuring the safety of industrial data transmission and management by applying a blockchain technology. The method can be applied to intelligent manufacturing processes such as production quality prediction, trend prediction, production monitoring, fault diagnosis, data distortion analysis and the like, and is beneficial to improving the intelligent manufacturing level.
Description of the drawings (tables)
Side-chain-based data block organization schematic in the smart device of fig. 1;
FIG. 2 is a schematic diagram of a process for creating and managing a federated chain center data block;
FIG. 3 is a schematic diagram of a block chain distributed ledger-based data transmission storage method;
FIG. 4 is a schematic diagram of an element encapsulation process for a blockchain transaction;
FIG. 5 shows the results of the validity experiments 1 and 2.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
The invention will be described in further detail with reference to the accompanying drawings and specific examples.
Specific embodiments of the invention are as follows:
example 1
An intelligent manufacturing element identification method based on a block chain network is characterized by comprising the following steps:
(1) The method for carrying out frequency domain data analysis by carrying out frequency domain data transformation on time domain data acquired by intelligent wearable equipment by adopting short-time Fourier transformation comprises the following steps:
for frequency analysis, it is necessary to use short-time fourier transform in actual implementation, and the time is decomposed by a predetermined unit in the short-time fourier transform after the transform, and frequency domain analysis is performed, and the short-time fourier transform is represented by the following formula:
where ST denotes a start time in the period, ET denotes an end time of the period; t represents an independent variable time; f represents frequency; i represents the imaginary part of the imaginary number.
(2) Comparing the waveforms of the equipment in the initial normal operation and the current operation of the equipment, and grabbing abnormal production elements, wherein the specific method comprises the following steps:
calculating an error value E of a waveform of the equipment in the initial normal operation and the current operation of the equipment by adopting the following steps:
when the frequency bins separated by the fourier transform are divided by N, i represents each frequency bin, N i Is the first normal value input during normal operation of section i, S i The greater the E value measured in real time, representing the frequency of the i-th part of the current input, the higher the probability of observing an abnormal state. The frequency of input per unit time is compared with its past value to group according to the degree of similarity, whereby various classifications can be made, such as a fixed posture of an employee, a horizontal movement, a step movement, a quick movement state, and the like.
(3) Based on the block chain technology, an intelligent equipment network is established, the safety of industrial secrets such as identified manufacturing elements, employee life log data and the like is ensured, and the network structure is specifically as follows:
as shown in FIG. 1, the present invention uses a federated technique that includes side chains. The side-chains store the data of the smart device in a separate database, with data mapping occurring in side-chain transactions of the data blocks. Thus, data can be searched quickly. And the data block organization schematic based on the side chains in the intelligent wearable equipment enables workers to form a alliance as clients and managers of distributed consensus, so that an alliance network is formed. The data block includes a creation date, hash information of its connection block, and side chain information.
(4) The federation members participate in creating a distributed consensus of new blocks to generate new data blocks, and the policies to generate new data blocks are designed to:
the alliance has human resources related to production, and the distributed consensus is achieved through the consent of most alliance participants, so that when an event occurs or takes a working process as a unit, the distributed consistency of creating a new block occurs; FIG. 2 illustrates the creation and management flow of new blocks in a coalition chain, and if an event occurs, processing according to rules: the data created before the event occurs is integrated and then a data block is created, which is saved to a distributed database through distributed consistency.
(5) The transaction information account book in the intelligent manufacturing element identification is constructed by adopting a distributed account book technology, and transaction information is jointly recorded and managed by a distribution mode based on a peer-to-peer network, and the method comprises the following specific steps:
by using the intelligent manufacturing data transmission and storage method based on the blockchain distributed ledger shown in fig. 3, the transaction information ledger in intelligent manufacturing adopts the distributed ledger technology, and the transaction information is jointly recorded and managed by a distribution mode based on a peer-to-peer network.
(6) The method solves the problem that the data cannot be analyzed due to the fact that the transaction of the block chain network type is utilized to analyze the data of the limited view in the general data mining method. The topic element package is a formatted statistical reasoning method for analyzing semantic environments from data, and can find potential data topics when blockchain transaction data is applied. FIG. 4 illustrates the element encapsulation process of a blockchain transaction. The theme factor encapsulation process includes six steps, each presenting a different set of data for each manufacturing stage in the blockchain transaction.
Example 2:
and selecting a certain textile production line to perform the effect test of the invention. The test environment comprises 7 textile workers (respectively marked as W1-W7), each person wears a happy 5S bracelet and is used for sensing the heart rate (reflecting the health condition and the working strength) and the step number (reflecting the health condition and the working strength) of the textile workers, and each person distributes a smart phone for event and transaction confirmation, wherein the model of the smart phone is Redmi Note8 Pro, and the memory is 6GB.7 textile workers play the roles of workers and simultaneously play the roles of administrators and participate in the system consensus process. And 3, installing flatness test sensors at the product outlets of the production line, detecting the quality of the produced cloth, and warning the system when detecting flaws.
Based on the production system, the effectiveness and the safety of the invention are respectively tested, and the following experiments are carried out:
validity experiment 1: worker condition monitoring
In the normal working process, the worker W2 is enabled to remove the intelligent bracelet at the time t 1. The system prompts that the worker has abnormal conditions, namely stopping moving, and the heart rate is 0.
Validity experiment 2: product quality monitoring
In the normal working process, the product quality of the production line 2 is deliberately destroyed at the moment t2, so that the cloth is uneven, and a worker W3 is designated for processing and transaction confirmation.
Validity experiment 3: production efficiency improvement statistics
The production conditions of the textile production line in the traditional production mode (without carrying the system of the invention) are compared with those of the production mode carrying the intelligent manufacturing element identification method system.
Safety experiment 1: false transaction validation
A worker is designated to handle a flaw in the production process, but the worker performs transaction confirmation on the smartphone terminal without performing a related inspection process.
Safety experiment 2: malicious tampering and deletion of data
Assuming that a worker smart phone terminal is illegally occupied or invaded, an illegal molecule tries to tamper with and delete system data.
The results of the validity experiments 1 and 2 are shown in fig. 5. At time t1, the system detects that the heart rate of the worker W2 is remarkably abnormal, the heart rate is reduced to 0, and an alarm of abnormal working state of the worker W2 can be sent out. At time t2, the system detects that the worker W3 is handling the abnormal problem of the product, the body load is increased, the heart rate is increased, and the information uploaded by the flatness test sensor installed at the product outlet can be used for verifying that the corresponding production line is in question and the worker W3 is handling the product. The results of the effectiveness test 3 are shown in Table 1. In the new mode, the total production amount is slightly increased, and the defective rate is obviously reduced, because the quality abnormality can be timely found and processed, the fault error production condition is reduced, and the defective rate is reduced. In addition, the worker can optimize own production behavior according to system prompt, the working accuracy is improved, invalid movement is reduced, and the working strength is obviously reduced.
TABLE 1 effectiveness test 3 results
TABLE 1
1. Is the sum of three production lines; 2. is the average value of three production lines; 3. is an average value of the movement amount of 7 workers for 8 hours.
The experimental results of security experiment 1 were: since the abnormality is not processed, a flatness test sensor installed at the outlet of the production line continuously uploads abnormal alarm data of the product, and a certain false transaction confirmation cannot obtain system consensus and does not record a system account book. The system sends out the abnormal alarm of the working state of the staff, and continuously alarms the abnormal quality of the product until other workers stop the abnormal quality alarm after smoothly handling the product.
The safety test 2 has the following test results: the system data is not successfully tampered and deleted, because the invention adopts the intelligent manufacturing data transmission and storage method based on the block chain distributed account book, all the system data is not stored in a single terminal, and the system data cannot be changed under the condition that the system consensus is not acquired.
The foregoing detailed description is provided to illustrate the present invention and not to limit the invention, and any modifications and changes made to the present invention within the spirit of the present invention and the scope of the appended claims fall within the scope of the present invention.
Claims (5)
1. An intelligent manufacturing element identifying method based on a block chain network is characterized by comprising the following steps:
(1) Performing frequency domain transformation on the time domain data acquired by the intelligent wearable equipment by adopting short-time Fourier transformation, and performing frequency domain data analysis;
(2) Comparing the waveforms of the equipment in the initial normal operation and the current operation of the equipment, and grabbing abnormal production elements;
(3) To ensure the security of industrial confidential data, an intelligent device network is built based on a alliance chain and a side chain technology, intelligent manufacturing data and data mapping are stored in a side chain, and hash information and side chain information of a data block are stored in the alliance chain;
(4) The coalition members participate in creating a distributed consensus of the new block to produce a new data block;
(5) Constructing a transaction information account book in intelligent manufacturing element identification by adopting a distributed account book technology, and carrying out joint recording and management on transaction information by a distribution mode based on a peer-to-peer network;
(6) Analyzing limited view data generated by block chain network type transactions based on a theme packaging method; the method for encapsulating the theme elements of the transaction removes meaningless data variables and extracts main variables, and comprises the following specific steps:
6.1, collecting production element data in the intelligent manufacturing process by using intelligent equipment such as wearable equipment, intelligent sensors and the like for constructing a data block;
6.2, filtering and cleaning the collected data in the intelligent manufacturing production process, and checking the collected data, including checking the consistency of the data, deleting invalid values and supplementing missing values;
6.3, extracting word stems, and merging data of synonyms in the production elements;
6.4 creating a [ procedure item ] matrix comprising all elements that may occur in the smart manufacturing process, such as workers, equipment, product information, process steps and process content;
6.5, packaging the intelligent manufacturing theme elements, namely the process items, into a data block;
and 6.6, classifying the data blocks according to the word stems of the intelligent manufacturing production elements and connecting the data blocks into a blockchain.
2. The blockchain network-based intelligent manufacturing element identification method of claim 1, wherein: the method for carrying out frequency domain data analysis by carrying out frequency domain data transformation on the time domain data acquired by the intelligent wearable equipment by adopting short-time Fourier transformation in the step (1) comprises the following steps:
in practical implementation, frequency analysis is performed by using short-time fourier transform, time is decomposed by a certain unit by the transformed short-time fourier transform, frequency domain analysis is performed, and the short-time fourier transform is represented by the following formula:
where ST denotes a start time in the period, ET denotes an end time of the period; t represents an independent variable time; f represents frequency; i represents the imaginary part of the imaginary number.
3. The blockchain network-based intelligent manufacturing element identification method of claim 1, wherein: the step (2) is to compare the waveforms of the equipment during the initial normal operation and the current operation of the equipment, and grasp abnormal production elements, and the specific method is as follows:
calculating an error value E of a waveform of the equipment in the initial normal operation and the current operation of the equipment by adopting the following steps:
when the frequency bins separated by the fourier transform are divided by N, i represents each frequency bin, N i Is the first normal value input during normal operation of section i, S i The greater the E value measured in real time, the higher the probability of observing an abnormal state, representing the frequency of the i-th part of the current input, and the frequency of the input per unit time is compared with its past value to group according to the similarity.
4. The blockchain network-based intelligent manufacturing element identification method of claim 1, wherein: in the step (3), an intelligent equipment network is built based on a blockchain technology, so that the safety of industrial secrets such as identified manufacturing elements, employee life log data and the like is ensured, and the intelligent equipment network is specifically as follows:
the side chains store the data of the intelligent equipment in a single database, the data mapping occurs in side chain transactions of data blocks, the data blocks based on the side chains in the intelligent wearing equipment are organized and indicated, workers serve as clients and managers of distributed consensus to form a alliance network, and the data blocks comprise creation date, hash information of connecting blocks and side chain information.
5. The blockchain network-based intelligent manufacturing element identification method of claim 1, wherein: the strategy of the step (4) that the alliance member participates in creating the distributed consensus of the new block to generate the new data block is designed as follows:
the alliance has human resources related to production, and a distributed consensus is achieved through the consent of most alliance participants, namely, data created before an event occurs are integrated, then data blocks are created, the created data blocks are stored into a distributed database through distributed consistency, and when the event occurs or a working process is taken as a unit, the distributed consistency of creating a new block occurs.
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