1422 results about "Block (data storage)" patented technology

The invention discloses an optical fiber data storage management system and method based on big data, and relates to the technical field of storage management. According to the optical fiber data storage management system and method based on big data, the spatial-temporal characteristics of the historical access records are extracted through the LSTM network, the data heat value is calculated in combination with the time decay function, and the thermal distribution diagram of the data blocks is generated. And based on the thermal distribution map, constructing a storage resource allocation model by using a deep reinforcement learning algorithm, and dynamically adjusting the optical path priority and copy distribution. A data prefetching strategy is optimized through the spatio-temporal joint index and the vector similarity index, and a high-frequency data cache index distribution diagram is generated. And comprehensively considering the data access delay, the index hit rate and the copy migration frequency, obtaining an optical fiber storage efficiency index, and adjusting a storage strategy according to the index. According to the method, the dynamic scheduling and utilization efficiency of data storage resources is effectively improved, the data access delay is reduced, the copy management is optimized, and the overall performance of a storage system is remarkably improved.

The invention provides a data all-flash storage optimization method and system based on cloud computing, and the method comprises the steps: receiving a storage operation request stream of a user terminal through collecting a multi-dimensional performance data set of each node of an all-flash storage cluster in real time, wherein the multi-dimensional performance data set comprises a storage space fragmentation rate, an input / output request queue depth and a network link bandwidth utilization rate; analyzing a to-be-stored data block identifier set and an operation mode label, traversing a historical access record library based on to-be-stored data block identifiers, extracting historical access time sequence characteristics and physical storage position proximity of associated data blocks, and combining the multi-dimensional performance data and the historical access time sequence characteristics to obtain a data block identifier set; according to the method, a dynamic access popularity prediction model in a preset time period is constructed, and finally, a cross-node fragmentation storage topological graph and a cache preloading strategy matrix are generated according to the proximity of the dynamic access popularity prediction model and a physical storage position, so that the optimization of all-flash storage is realized, and the data storage performance in a cloud computing environment is improved.

The invention relates to the technical field of data storage and processing, in particular to an RAID card static cache management method and device based on data popularity, and the method comprises the steps that the physical position of a data block needing to be accessed in an SSD array is acquired according to a read-write request; historical data access information of the RAID card is collected, a historical data set is generated, and a neural network model is trained by using the historical data set to obtain a cache management model; predicting and outputting data blocks which are possibly accessed in the future and the access probability of the data blocks through the cache management model; the RAID controller dynamically adjusts a cache strategy in combination with an LRU strategy according to a prediction result output by the cache management model and a read-write request of a file system, and optimizes a storage position and an updating mechanism of a data block in a cache space; and when the hit rate or the data access efficiency does not reach the preset value, adjusting the parameters of the cache strategy and updating the cache strategy. According to the method, cache resources can be more effectively distributed, the cache hit rate is improved, the cache replacement overhead is reduced, and therefore the performance of a storage system is optimized.

The invention provides a block chain-based clinical test data integrity system, and relates to the technical field of data processing, and the system comprises the following components: a multi-source heterogeneous data acquisition and preprocessing module, a multi-level encryption and under-chain data storage module, an alliance block chain network, and an alliance block chain network maintenance and verification shared distributed account book. The distributed account book only stores all digitally signed clinical test data fingerprints, metadata and operation logs, does not store original data, and is linked with the intelligent contract automatic management module, so that the clinical test data cannot be tampered in the whole life cycle from the source to archiving. Hash anchoring, digital signature and decentralized verification mechanisms of the block chain technically eradicate the possibility of unauthorized tampering of data, and ensure scientificity and reliability of clinical test results.

The invention provides an unstructured data storage optimization method and system based on information entropy and a block chain, and relates to the technical field of data storage, and the method comprises the steps: building a block chain storage network to obtain data, recognizing redundant data through dimension reduction, mapping and hyperplane construction, and removing the redundant data based on fuzzy similarity. The to-be-stored data is partitioned, the access frequency is calculated, the consistent Hash algorithm is executed to determine the storage position, the access index and the verification sequence are established, and the data integrity verification is executed, so that the data access efficiency can be improved, and the storage security can be enhanced.

The invention provides a real-time deep sea subsurface buoy monitoring system, which belongs to the technical field of deep sea measurement, and comprises a control chip, a multi-parameter sensor array, a data storage device and a power supply, high-precision monitoring is realized through the following steps: recording ocean data acquired by a multi-parameter sensor; correcting the sound wave propagation model and calculating the relative position of the subsurface buoy; constructing a three-dimensional data matrix and realizing data compression; identifying abnormal data by using a matrix disorder index; processing the time sequence data by adopting a sliding window weighted average method and eliminating jump; carrying out distributed preprocessing on the multi-source heterogeneous data and carrying out parallel processing by applying a matrix partitioning technology; accumulated errors are corrected by combining historical data and applying a Kalman filtering algorithm, and the problems of environment interference and sensor drift are solved through a deep sea environment disturbance compensation network model and a self-adaptive weighted error optimization function.

The invention relates to a health data encryption storage method and device, equipment and a storage medium, and the method comprises the steps: obtaining to-be-processed health data, carrying out the sensitivity analysis and grade division of the health data, and obtaining health data sub-blocks; performing encryption preprocessing on the health data sub-blocks, and performing group data protection according to a preset differential privacy algorithm to obtain encrypted data blocks; generating an initial master key through a hardware security module, and performing derived hierarchical encryption on the encrypted data block to obtain a data encryption key; performing fragmentation processing and regular distributed storage on the data encryption key to obtain a dynamic security key; and performing metadata separation storage on the encrypted data according to the dynamic security key, and performing data permission determination according to a preset role-based access control mechanism to obtain an encrypted isolation database. According to the invention, efficient security protection can be maintained under different application scenes and access permissions, and the risk of data leakage is reduced.

The invention relates to the technical field of block chains, and discloses a data encryption system for evidence collection and storage based on a block chain technology. According to the data encryption system for evidence collection and storage based on the block chain technology, a hash value can be generated based on a timestamp of data chaining, so that integrity of storage data is regulated and controlled, data tampering detection is realized, a user permission access range can be regulated and controlled in combination with a key distribution mechanism, sensitive data leakage is effectively prevented, and user experience is improved. The consistency of the encryption step is verified according to the intelligent contract execution logic, the multi-node synchronization error is eliminated, the error of the encrypted data storage link is verified through the Hash verification algorithm, and the consistency of the block data is ensured.

The invention discloses a data dynamic migration and storage method, device and equipment and a medium, and relates to the technical field of computers. The method comprises the steps that environment parameters of a current mechanical hard disk in a distributed cluster are collected; wherein the environment parameters comprise a vibration parameter, a temperature parameter and a noise parameter; quantifying the input and output delay performance of the current mechanical hard disk by using the environmental parameters of the current mechanical hard disk; if the input and output delay performance of the current mechanical hard disk is greater than a preset threshold value, searching a target mechanical hard disk combination in the distributed cluster; and migrating and storing the target data block of the current mechanical hard disk into the target mechanical hard disk combination based on an erasure code and a multi-copy mechanism. Through the scheme, the reliability of storing the data in the mechanical hard disk in the distributed cluster is remarkably improved.

The invention discloses a logistics data credible evidence storage and tracing method based on a block chain, and the method comprises the following steps: collecting original logistics data, and executing data standardization processing; constructing a layered Merkle mountain structure, and calculating to obtain a main root commitment of the layered Merkle mountain; dividing a layered Merkle mountain range structure, and calculating to obtain a windowed subtree root set; configuring a revision branch record structure, generating a collision pair by using chameleon hash, and generating a revision proof record; a multi-index root structure is constructed, a cross-root sharing mechanism is established, and a multi-dimensional tracing index set is generated; and based on the hierarchical Merkle mountain range main root commitment, the windowed subtree root set, the multi-dimensional traceability index set and the revision proof record, executing anchoring consistency verification, and outputting a credible evidence storage result and a traceability verification result. According to the invention, the logistics data evidence storage integrity and the multi-dimensional tracing efficiency are improved.

The invention discloses a building industry human resource management system based on a block chain technology, and aims to solve the problems that a current building industry human resource management system is slow in processing speed and difficult to meet flexible employment and rapid recruitment requirements of the building industry, so that transaction processing is delayed, sensitive information of employees may be leaked, a decentralized architecture needs multi-party coordination, and the efficiency is high. The system comprises a block chain module which is used for data storage, security and automatic execution of a consensus mechanism; and the business management module is used for carrying out precise management on complex scenes in the building industry through characteristics of decentralization, non-tampering and the like. According to the method, a decentralized distributed architecture is adopted, the data is stored on a plurality of nodes, the security of the data is ensured, each piece of data is recorded on the block chain and cannot be tampered by any person, the authenticity and integrity of the data are ensured, intermediate links can be reduced, and the management cost and risk are reduced.

The invention provides a dynamic video memory allocation method and system based on data characteristics, and relates to the technical field of data storage, and the method comprises the following steps: pre-allocating a plurality of video memory blocks with different sizes in a GP U video memory to form a three-stage video memory pool; acquiring data features input by the model in real time, and establishing a multi-dimensional feature vector; constructing a time sequence prediction model based on an LSTM network, and predicting a video memory demand value by inputting a feature vector sequence subjected to continuous multiple reasoning; selecting a hierarchical distribution strategy according to the predicted video memory demand value and the scene feature matrix; constructing a video memory dependency graph, dynamically tracking the earliest release time of a video memory block, and optimizing the utilization rate of the video memory through a multiplexing strategy; monitoring a GPU video memory state index; triggering a video memory optimization strategy according to the video memory state index; through accurate prediction of video memory demands, reasonable hierarchical distribution, optimization of video memory utilization rate and coping with different scenes, the video memory management problem in deep learning model reasoning is solved, and the video memory use efficiency and the system stability are improved.

The embodiment of the invention provides a memory recovery method and device, electronic equipment and a storage medium, and relates to the technical field of data storage. Under the condition that both the file system and the storage device support the perceptual garbage collection function, the device state of the storage device is obtained in response to the starting of the perceptual garbage collection function; according to the equipment state, determining whether an execution condition for sensing a garbage collection function is met or not; and if the execution condition of the garbage collection sensing function is met, the file system is linked to perform memory collection on the storage device, so that the file system can know the data blocks moved by the storage device, the file system is prevented from moving the data blocks moved by the storage device again, and the efficiency of the file system is improved. In addition, invalid blocks in the file system and the storage device can be synchronized, the situation that invalid data blocks in the file system are moved when the storage device executes memory recovery is avoided, and therefore the service life expenditure of the storage device and the write-in amplification value of the storage device are reduced.

The invention discloses an intelligent traffic data storage method, system and device based on the Internet of Things and a medium, and relates to the technical field of electric digital data processing, and the method comprises the steps that an edge node packages received data and inserts the data into a local directed acyclic graph account book; dividing the verified data into Level-0 fragments according to a fixed duration window, and performing variable-length fragmentation based on a Rabin fingerprint algorithm; calculating Hash values of the variable-length fragments, and performing duplicate removal through a Bloom filter and a Hash index table in sequence; generating a local erasure block and a global erasure block for the deduplicated fragments, and storing complete copies of the key fragments on a plurality of storage nodes; and periodically checking a local erasure block and a complete copy, and triggering self-healing reconstruction when detecting that the fragment is lost. According to the method, the technical problems of high delay of distributed write-in consistency, high storage redundancy, complex cross-node time sequence correction, slow data recovery, poor system expansibility and the like are solved.

The invention relates to the technical field of data security, in particular to a block chain-based big data security data storage method, which comprises the following steps of acquiring a write-in request, analyzing a field path and a label, matching a structure number and state information, sorting a conflict path, compressing a number sequence, distributing a compression path, positioning a writable area and updating a path pointer. And writing data and recording index and verification information to obtain a safe chain-entering data set. According to the method, a clear mapping relation is formed through binding of field paths and label numbers, path conflicts are subjected to number rearrangement and hierarchical compression processing, the structural coherence of field distribution is enhanced, field write-in positioning is completed in combination with node capacity, position indexes and verification identifiers are generated, and unified binding of field contents, the path numbers and access positions is achieved; the writing accuracy and the structure traceability are guaranteed, and the regularity of chain segment storage and the orderliness of operation are improved.

The invention relates to the technical field of storage, in particular to an SSD wear leveling method and device based on dynamic data migration and a medium. The method comprises the following steps: monitoring access frequency, recording read-write times of a logic block in real time, and identifying hotspot data; tracking a wear state, counting the erasure times of the physical flash memory block, and evaluating the wear degree of the physical flash memory block; data migration decision: determining whether to migrate data based on the access frequency and the wear frequency; preferentially migrating data with high access frequency and high wear degree according to access frequency and wear program sorting; and data migration execution: migrating the hotspot data from the high-wear area to the low-wear area, and updating the mapping relation. According to the method, the data storage position can be intelligently adjusted according to the real-time data access characteristics and the abrasion state, and therefore the service life of the SSD is more efficiently prolonged.

The invention relates to the technical field of block chains, and discloses a distributed data security storage system and method based on a block chain, and the system comprises a dynamic fragmentation encryption module which can automatically select a fragmentation strategy based on a data sensitivity level; the data sensitivity is set to be S, the data sensitivity level is determined through the following formula: # imgabs0 #, wi is the weight of the ith factor influencing the data sensitivity, fi (D) is the quantized value of the data D under the ith factor, and for low-sensitivity data (S is smaller than or equal to Slow, and Slow is a preset low-sensitivity threshold), ECIES asymmetric encryption and random fragmentation storage are adopted; and high and low sensitive data. Through the dynamic fragmentation encryption module, hierarchical storage and flexible encryption of data are realized, and storage redundancy is reduced; the cross-chain verification engine adopts zero-knowledge proof, and cross-chain verification is safely and efficiently completed; the storage node reputation system is combined with LSTM to predict the node failure probability, and reliable data storage is guaranteed; all work processes are tightly matched, and the whole process ensures data safety and efficient circulation and management.

The invention discloses a data asset secure storage method based on a block chain technology, and the method comprises the following steps: data preprocessing and fragmentation storage: carrying out the preprocessing of to-be-stored original data assets, extracting metadata information, and generating a unique hash identifier; through multi-chain collaborative storage, dynamic hierarchical encryption and cross-chain redundant backup, the security of data assets is remarkably improved, fragment storage is combined with a heterogeneous block chain network, the data storage efficiency is improved, and single-point failure and centralized attack risks are avoided; the anti-cracking capability is enhanced through dynamic encryption and distributed key management; a cross-chain synchronization mechanism ensures high availability of data, so that the data can be efficiently circulated in different block chains; the operation and maintenance cost is reduced through automatic verification and recovery driven by the intelligent contract, the auditing tracking and life cycle management functions meet the compliance requirement, and the method is suitable for data storage scenes in the high-sensitivity fields of finance, medical treatment and the like.

The invention discloses a smart home early warning method and system based on dual authentication, and relates to the technical field of smart home. Four-element data of an early warning event is collected through an edge computing gateway, the four-element data is coded and packaged into a data packet, and the data packet is transmitted to a cache region of the edge computing gateway for further processing; according to the smart home early warning system based on dual authentication, through an equipment identity dual authentication mechanism, the security of equipment access is doubly guaranteed from a physical layer and a logic layer, intrusion of unknown equipment is effectively prevented, the risk of data leakage is reduced, in the aspect of data storage, an on-chain and off-chain collaborative storage mode is adopted, and the security of equipment access is improved. The authenticity and integrity of event key information are ensured by using the non-tampering characteristic of the block chain on the chain, and the security of original data is protected through distributed storage and encryption under the chain, so that the privacy and security of user data are protected comprehensively, and the security of the data of the smart home system is ensured.

The invention provides a double-stream video conference privacy tracing method based on a block chain, and the method comprises the steps: building a tracing evidence obtaining model which comprises information uplink and tracing evidence obtaining; the information uploading comprises the following steps: in a video conference process, audio and video data and related conference information are uploaded to an interstellar file system for storage and verification after being subjected to encryption and signature processing through a threshold ring signature mechanism, and key evidence is uploaded to a block chain; the intelligent contract is used for automatically managing data storage; the traceability evidence obtaining comprises the following steps: after leaked picture information is obtained, detecting a traceability identifier in a video or a picture, and comparing the traceability identifier with information stored on a chain; a secret divulging block point is automatically tracked and positioned through an intelligent contract, and the real identity of a secret divulging party is decrypted. According to the method, tampering resistance, transparency, decentration and anonymity of chain information in the traceability evidence obtaining process are achieved, and automatic evidence obtaining and traceability operation is supported.

The invention discloses an intelligent valve actuator remote monitoring and fault early warning system based on the Internet of Things, which relates to the field of valve actuator monitoring and comprises a data acquisition module, a data transmission module, a data storage and management module, an analysis and fault diagnosis module, an early warning module, a remote control module, a user interaction module, a safety protection module, a log recording and auditing module, an environment perception module and an intelligent prediction module. A plurality of sensors collect data, a hybrid communication architecture and quantum encryption transmission are used, distributed storage and cloud computing are combined, deep learning and reinforcement learning are fused to diagnose faults, early warning is performed based on a dynamic threshold model, remote control is performed by using digital twinning and a block chain, an immersive interface and voice interaction are supported, and system configuration is adaptively adjusted. A multi-level security protection system is adopted, and logs are analyzed and recorded by means of a block chain and big data. The valve actuator is monitored in real time in an omnibearing mode, data are accurately collected, transmission is stable and safe, faults are accurately diagnosed, early warning is conducted in time, remote control is supported, interaction is convenient and safe, and industrial operation is guaranteed.

The invention relates to the technical field of computer data processing, and discloses a data segmentation and query processing system under a distributed architecture. The system comprises a data segmentation module which divides data blocks based on a self-adaptive partitioning algorithm; the distributed storage module is used for distributing data blocks by using a consistent Hash algorithm and dynamically adjusting the distribution of virtual nodes; the index construction module is used for generating a global index table through a multi-layer inverted index algorithm; the query optimization module is used for converting query statements by utilizing a query rewriting algorithm; and the result aggregation module is used for processing the sub-query results by adopting a parallel aggregation algorithm. The system can dynamically process data according to data characteristics, optimizes the data storage and query process, effectively improves the data processing efficiency and query performance of a distributed system, and is suitable for scenes such as big data storage and analysis.

The embodiment of the invention relates to the technical field of data storage, and discloses a cross-K8S cluster configuration change storage method and device based on a block chain, and the method comprises the steps: obtaining a transaction request message passing the compliance verification of an intelligent contract engine, and the transaction request message comprises a configuration change request; obtaining log information of the configuration change request in the transaction request message executed by the target K8S cluster, wherein the log information further comprises configuration change cluster difference information; the log information is stored in a fragmented mode based on the IPFS network, and content hash codes of the log information are obtained; and taking the transaction request message and the content hash code as complete transaction information, and storing the transaction request message and the content hash code in a transaction pool. Decentralized storage is achieved based on an IPFS network fragmentation storage mode, the problem that a centralized database or a log system is maliciously modified or historical records are deleted easily in centralized storage is solved, and auditing integrity and data storage safety are guaranteed.

The invention discloses a data storage method for an artificial intelligence learning mode, and relates to the technical field of computer data storage, and the method comprises the steps: 1, merging a multi-source perception stream into blocks in real time at the edge through monotone serial number writing, so as to provide a replayable time sequence; 2, asynchronous erasure coding is executed on the blocks, Merkel roots are calculated and written into a local cache, and dual guarantee of loss tolerance and integrity is achieved; 3, pushing slices and roots to object storage in sequence according to a network, and calling a time travel interface to solidify an incremental snapshot; 4, the cloud end monitors a snapshot hash event, a serial number chain is written through differential scanning, a gap is reconstructed through slices, an index is refreshed, and continuous consistency is kept; 5, the training process generates a Merkel proof online verification sample, and damaged data are immediately interpolated and repaired and an audit chain is recorded; and step 6, after training is finished, generating a leatherwise list and a frozen root, asynchronously cleaning redundant slices, updating a version table, and finally forming single-fingerprint traceable cost archiving.

The invention discloses a computer data storage method and system, and relates to the technical field of computer data storage.The computer data storage method comprises the steps that variant samples are generated through multi-modal feature extraction, font topology analysis and adversarial training, and fine modeling of Chinese character connection and pixel-level interference in an electric power drawing is achieved; an isolated storage pool path is dynamically generated through a tree-shaped namespace, and a physical storage address and version information can efficiently and uniquely correspond and retrieve in combination with bidirectional mapping of version identifier construction and block chain indexes; according to a confidence score obtained through weighted calculation of the topological similarity and the term library matching degree, once the confidence score is lower than a preset threshold value, the system pushes a manual recheck mark to a specified terminal through an asynchronous message queue, and meanwhile, read-only data locking is implemented to ensure that the writing process is not interrupted; the consensus management mechanism is based on an improved practical Byzantine fault-tolerant algorithm, and block chain index consistency and data traceability security are guaranteed.

The invention relates to the technical field of embedded data storage, and discloses a method and a system for prolonging the service life of a FLASH simulation EEPROM (Electrically Erasable Programmable Read-Only Memory) in a single chip microcomputer, and the method comprises the following steps: firstly, initializing the FLASH, dividing the FLASH into a main data area, a mapping table area, a backup area and a transaction log area, selecting a physical block through a dynamic wear leveling algorithm when data is written, and writing the data into a database; the method comprises the following steps of: starting a backup area or a transaction log area, safely updating a mapping table by using a transaction mechanism, performing cyclic redundancy check when reading data, if the cyclic redundancy check fails, starting cascade recovery from the backup area or the transaction log area, and finally, updating a transaction log to record operation so as to form a closed-loop management process. According to the invention, through a set of dynamic wear leveling and data classification management mechanism, global and uniform distribution of the write load in the storage array is realized, the physical block with the least erasing times is selected during writing, the access frequency of the data can be identified, and cold data which is not changed frequently can be actively migrated.

The invention discloses a software development result traceability analysis system based on version control, and relates to the technical field of intelligent software analysis. According to the method, the non-tampering property of code submission records is ensured through a block chain technology, a credible basis is provided for traceability, the code semantic analysis module generates a data set containing semantic association in combination with a clustering algorithm and weighted calculation, and the function influence positioning module constructs a function association graph by applying a graph neural network algorithm, so that the traceability is improved. An influence path of code change on system functions is automatically identified, the time cost of complex project function influence positioning is remarkably reduced, an evolution path generation module analyzes and tracks a code evolution path in combination with a time sequence, and a core module identification module clusters key nodes and positions a core function module. And the time-tracing source data storage module generates a visual relation graph and a queried data set, so that the whole-process management from code submission to result tracing is realized.

The technical scheme can be applied to the field of financial science and technology / medical health. The invention discloses a mercuric chloride chip-based large model reasoning operator optimization method, device and equipment, and the method comprises the steps: carrying out the blocking processing of original input data according to the parallel calculation capability of a mercuric chloride chip, and generating the blocking data of an adaptive chip calculation unit; optimizing a memory access path of a matrix multiplication operator by combining a memory hierarchical structure and a calculation core type of a mercuric chloride chip based on the block data, and adjusting a sliding step length and a filling mode of convolution operation; a plurality of operators continuously executed in the large model are fused into a composite operator, the data storage process of the composite operator is optimized, and collaborative execution is achieved by dynamically allocating computing resources; and integrating and decoding block calculation results after collaborative optimization, and adjusting an input data block strategy and operator execution parameters through a verification feedback mechanism to form closed-loop optimization. According to the technical scheme, the reasoning efficiency of a large model can be improved.

The invention relates to a data processing method and system of an NAND FLASH storage array, computer equipment and a storage medium. The method comprises the following steps: receiving a data writing or reading instruction; and analyzing the instruction through the system-on-chip, and distributing the data to a target module in the plurality of NAND FLASH modules connected in parallel according to a preset strategy. And writing the data into the target module or reading the data from the target module through the control and communication line. And executing data management operation through the system-on-chip, specifically, writing data into different NAND FLASH modules in a dispersed manner by adopting a dynamic wear leveling algorithm so as to balance the erasing times of each module. And detecting and marking bad blocks in the NAND FLASH module in real time, and avoiding the bad blocks in calling operation after data storage. By the adoption of the method, the storage performance, reliability and expandability of satellite-borne storage can be comprehensively improved, and the method is suitable for task scenes with high reliability requirements.

The invention discloses a state monitoring method of a solid state disk, electronic equipment and a storage medium, and relates to the technical field of data storage, the method comprises the steps of integrating multi-source data such as temperature, erasing times and average read-write bit error rate, comprehensively evaluating the health state of each block storage in the solid state disk through a weighted summation mode, and obtaining the state of each block storage in the solid state disk. The limitations of isolated analysis and single analysis dimension of each parameter in a related monitoring mechanism are broken through, and the problem that the health state of the solid state disk is difficult to accurately evaluate and predict due to the fact that cross-layer data cannot be effectively integrated and analyzed in related technologies is solved.