CN111241110B - Data management method based on staff and education diagnosis and improvement platform - Google Patents
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Abstract
The invention relates to the technical field of data processing, in particular to a data management method based on a staff diagnosis and change platform, which comprises the following steps of S1, acquiring data information, and identifying the data information to obtain a data code; s2, processing the data codes to form a data sequence; s3, constructing a corresponding storage control unit; s4, generating a storage label corresponding to each data sequence to be distributed and stored; s5, carrying out label processing on the data sequence according to the storage labels, and storing the data sequence subjected to label processing into at least one corresponding data storage node in a classified manner according to the data storage characteristics. According to the invention, the data information is distributed to the geographically dispersed data storage nodes according to the model learning, so that the safety of data storage is improved, the storage pressure of the office education diagnosis and modification platform server is reduced, the data management flow is optimized, the accurate storage management of the data information is facilitated, and the overall operation efficiency of the office education diagnosis and modification platform server is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a data management method based on a staff diagnosis and improvement platform.
Background
The staff education diagnosis and modification platform is an education platform which is improved aiming at vocational education diagnosis. The teaching examination is a research activity, a bridge is erected between teaching practice and teaching theory, a good approach is provided for professional development of teachers, and through classroom observation, the teachers can obtain new development in aspects of practical knowledge, countersaving capability and the like by means of cooperative strength, so that the overall teaching quality of the teachers is improved. The office teaching and diagnosing platform is not only a platform learning process, but also a process of emotion experience and mind culture of teachers and students, and the good office teaching and diagnosing platform should fly emotion to bring enthusiasm to participate in learning activities, so that teachers have a difficult task of heart regulation, mind guiding and timely collecting and releasing.
However, in practice, it was found that: because the personal teaching and education information of the user needs to be uniformly stored through the office teaching and diagnosis modification platform server, the existing method for storing the personal teaching and education information of the user generally directly stores the personal teaching and education information of the user into the office teaching and diagnosis modification platform server, so that the office teaching and diagnosis modification platform server affects the operation efficiency due to overlarge data quantity, the efficient operation management of the data is inconvenient, and the safety of the data is far insufficient.
Disclosure of Invention
In order to overcome the above-mentioned shortcomings in the prior art at least, an object of the present application is to provide a data management method based on a staff diagnosis and modification platform, which can distribute data information to geographically dispersed data storage nodes according to model learning, so as to improve the security of data storage, reduce the storage pressure of the staff diagnosis and modification platform server, and improve the overall operation efficiency of the staff diagnosis and modification platform server.
The invention discloses a data management method based on a staff diagnosis and modification platform, which is applied to a staff diagnosis and modification platform server, wherein the staff diagnosis and modification platform server is in communication connection with at least one scattered data storage distribution node, and the method comprises the following steps:
s1, the staff teaching and modifying platform server firstly obtains data information through the data storage distribution node, and identifies the data information to obtain a data code; the specific flow of the step S1 includes: s11, the application program of the staff teaching platform server generates an acquisition signal for indicating data information acquired from the data storage distribution node in the running process; s12, the staff teaching and modifying platform server receives the acquisition signals and determines the data format of the acquisition signals according to the carrying mode of the cloud server; in step S12, the job teaching and modifying platform server determines the data format of the acquired signal according to the carrying mode of the cloud server, so as to achieve the purpose of balancing the data codes of fast responding to the acquisition speed of the acquired signal and the corresponding data quantity; s13, when the acquisition signal is determined to be responded in a first data format, the staff education modification platform server sends the acquisition signal to the data storage distribution node, and directly obtains the data information of a first data volume from the data storage distribution node; s14, when the acquired signal is determined to be responded in a second data format, the job education modification platform server sends the acquired signal to a cloud server and acquires the data information of a first data volume from the data storage distribution node according to the acquired signal by the cloud server, and the data information of a second data volume is formed by processing and expanding; and S15, the staff teaching and modifying platform server identifies the acquired data information to obtain a data code.
S2, processing the data codes to form a data sequence, wherein the data sequence comprises identified data storage characteristics and storage tags, the data storage characteristics are used for identifying data types, and the storage tags are encryption tags or non-encryption tags; the specific flow of the step S2 includes: s21, splitting the data codes according to a data splitting method to form a plurality of data fragments, wherein each data fragment at least contains a plurality of data characteristic values; in step S21, splitting the data code according to a data splitting method to form a plurality of data fragments, which specifically includes: s211, firstly, determining data characteristic values in a plurality of data codes according to the data codes obtained by identifying the acquired data information by the staff teaching and modifying platform server in the step S1; s212, determining the length of the data segment according to the interval length of the data characteristic value in the data code; s213, according to the length of the data segment, based on the data arrangement format of the data codes, sequentially intercepting the data codes with the corresponding lengths to form a data segment; s214, splitting the data codes according to the determined length of the data fragments to form a plurality of data fragments; s22, adding a data storage characteristic for identifying the data type at the head end of each data segment to form a data sub-segment; s23, recombining all the data sub-fragments according to a data splitting method to form a data sub-sequence, wherein the data sub-sequence contains at least one data storage characteristic for identifying the data type; s24, adding a storage label for identifying the encryption type of the data at the tail end of the data subsequence to form a data sequence.
S3, taking the data storage characteristic as an input characteristic of a storage control unit, and taking the storage label as an output characteristic of the storage control unit to construct a corresponding storage control unit; the specific flow of the step S3 includes: s31, taking the data storage characteristics as input characteristics of a storage control unit, inputting the data storage characteristics into the storage control unit, and analyzing target storage characteristics of the data storage characteristics in a target preset data segment through the storage control unit, wherein the target storage characteristics comprise a target storage characteristic node sequence; s32, carrying out hash processing on the target storage characteristic node sequence to obtain a plurality of target storage characteristic nodes; s33, determining a plurality of first training models according to feature vectors corresponding to the target storage features, wherein the plurality of first training models are training models trained by the plurality of target storage feature nodes in the storage control unit respectively; in step S33, the storage control unit is configured to learn target storage feature nodes after hashing a plurality of target storage feature node sequences, where the plurality of target storage feature node sequences are target storage feature node sequences included in a plurality of target storage features acquired in the target preset data segment, and a training model expressed in the storage control unit by each target storage feature node after hashing; s34, sequencing the plurality of first training models according to the sequence from high priority to low priority of each first training model in the plurality of first training models to obtain a training model sequence; s35, determining a training model expressed in the storage control unit by a target storage characteristic node in the target storage characteristic nodes based on a preset similarity proportion threshold value and the training model sequence; in step S35, the preset similarity proportion threshold is used to indicate the proportion of the similar part of the target storage characteristic node sequence obtained in the target preset data segment to the target storage characteristic node sequence; s36, when the training model expressed by the target storage characteristic node in the storage control unit is matched with a preset training model, determining the target storage characteristic as a learnable target storage characteristic; when the target storage characteristic is determined to be a learnable target storage characteristic, for each first training model in the plurality of first training models, controlling the storage control unit to learn target storage characteristic nodes after hashing the plurality of target storage characteristic node sequences received in the target preset data segment according to the first training model, and generating a prediction label after training by using the training model expressed in the storage control model by each target storage characteristic node after hashing; s37, updating model parameters of the storage control unit according to the prediction tag and the storage tag.
S4, identifying data storage characteristics of the data sequences to be distributed and stored, and carrying out characteristic identification and prediction on the data storage characteristics of each data sequence to be distributed and stored according to the storage control unit to generate a storage label corresponding to each data sequence to be distributed and stored;
s5, carrying out label processing on the data sequence according to the storage labels, and storing the data sequence subjected to label processing into at least one corresponding data storage node in a classified manner according to the data storage characteristics. The specific flow of step S5 includes: s51, carrying out label processing on the data sequences according to the storage labels corresponding to the data sequences to be distributed and stored, and generating corresponding first storage fragment sequences according to the data storage characteristics; s52, calculating distribution offset parameters between each storage fragment in the first storage fragment sequence and the storage fragment corresponding to the storage fragment in the last second storage fragment sequence; s53, determining a distribution reference sequence of the first storage fragment sequence and the second storage fragment sequence on the storage fragment according to the distribution offset parameter and a distribution vector corresponding to the storage fragment in the first storage fragment sequence; s54, determining a data sequence to be distributed and stored in at least one corresponding data storage node in the first storage fragment sequence according to the distribution reference sequences of the first storage fragment sequence and other second storage fragment sequences on each storage fragment.
The invention also discloses a device of the data management method based on the office teaching diagnosis and modification platform, which is characterized by being applied to the data management method based on the office teaching diagnosis and modification platform, comprising the following steps:
the acquisition processing module is used for acquiring data information and identifying the data information to obtain a data code; processing the data codes to form a data sequence, wherein the data sequence comprises identified data storage characteristics and a storage label, the data storage characteristics are used for identifying data types, and the storage label is an encryption label or a non-encryption label;
building a control module, wherein the control module is used for building a corresponding storage control unit by taking the data storage characteristic as an input characteristic of the storage control unit and taking the storage label as an output characteristic of the storage control unit;
the tag processing module is used for identifying the data storage characteristics of the data sequences to be distributed and stored, and carrying out characteristic identification and prediction on the data storage characteristics of each data sequence to be distributed and stored according to the storage control unit so as to generate a storage tag corresponding to each data sequence to be distributed and stored;
and the classification storage module is used for carrying out label processing on the data sequence according to the storage labels, and carrying out classification storage on the data sequence subjected to label processing into at least one corresponding data storage node according to the data storage characteristics.
Based on any one of the above aspects, the present invention constructs a corresponding storage control unit by using the data storage characteristics as input characteristics of the storage control unit and using the storage tag as output characteristics of the storage control unit, then identifies data storage characteristics of data sequences to be distributed and stored, performs characteristic identification and prediction on the data storage characteristics of each data sequence to be distributed and stored according to the storage control unit, generates a storage tag corresponding to each data sequence to be distributed and stored, performs tag processing on the data sequence according to the storage tag, and classifies and stores the data sequence processed by the tag into at least one corresponding data storage node according to the data storage characteristics. Therefore, the invention can distribute the data information to the geographically dispersed data storage nodes according to the model learning, so as to improve the safety of data storage, reduce the storage pressure of the office education diagnosis and modification platform server, optimize the data management flow, facilitate the accurate storage management of the data information and improve the overall operation efficiency of the office education diagnosis and modification platform server.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered limiting the scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a control flow chart of a data management method based on a staff diagnosis and improvement platform according to an embodiment of the present invention.
Detailed Description
The following description is provided in connection with the accompanying drawings, and the specific operation method in the method embodiment may also be applied to the device embodiment or the system embodiment. In the description of the present application, unless otherwise indicated, "at least one" includes one or more. "plurality" means two or more. For example, at least one of A, B and C, includes: a alone, B alone, a and B together, a and C together, B and C together, and A, B and C together. In the present application, "/" means or, for example, A/B may represent A or B; "and/or" herein is merely one association relationship describing associated devices, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone.
Fig. 1 is a control flow chart of a data management method based on a staff diagnosis and improvement platform according to an embodiment of the present invention, which is only a possible example. In some embodiments, the job education adaptation platform server may be a single server or may be a group of servers. The set of servers of the office consultation modification platform server may be centralized or distributed (e.g., the office consultation modification platform server may be a distributed system). In some embodiments, the job teaching platform server may be local or remote with respect to the data storage distribution node. For example, the office teaching diversion platform server may access information stored in the data storage distribution node and database, or any combination thereof, via a network. The office teaching modification platform server may be directly connected to at least one of the data storage distribution node and the database to access information and/or data stored therein. In some embodiments, the job education adaptation platform server may be implemented on a cloud platform; for example only, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud (community cloud), distributed cloud, inter-cloud (inter-cloud), multi-cloud (multi-cloud), and the like, or any combination thereof.
In some embodiments, the job education adaptation platform server may include a processor. The processor may process information and/or data related to the service request to perform one or more functions described herein. The processor may include one or more processing cores (e.g., a single core processor (S) or a multi-core processor (S)). By way of example only, the Processor may include a central processing unit (Central Processing Unit, CPU), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), special instruction set Processor (Application Specific Instruction-set Processor, ASIP), graphics processing unit (Graphics Processing Unit, GPU), physical processing unit (Physics Processing Unit, PPU), digital signal Processor (Digital Signal Processor, DSP), field programmable gate array (Field Programmable Gate Array, FPGA), programmable logic device (Programmable Logic Device, PLD), controller, microcontroller unit, reduced instruction set computer (Reduced Instruction Set Computing, RISC), microprocessor, or the like, or any combination thereof.
The network may be used for the exchange of information and/or data. In some embodiments, one or more components (e.g., servers, data storage distribution nodes, and databases) in the apparatus of the job education adaptation platform server job education adaptation platform-based data management method may send information and/or data to other components. In some embodiments, the network may be any type of wired or wireless network, or a combination thereof. By way of example only, the network may include a wired network, a wireless network, a fiber optic network, a telecommunications network, an intranet, the Internet, a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN), a wireless local area network (Wireless Local Area Networks, WLAN), a metropolitan area network (Metropolitan Area Network, MAN), a wide area network (Wide Area Network, WAN), a public switched telephone network (Public Switched Telephone Network, PSTN), a Bluetooth network, a ZigBee network, a near field communication (Near Field Communication, NFC) network, or the like, or any combination thereof. In some embodiments, the network may include one or more network access points. For example, the network may include a wired or wireless network access point, such as a base station and/or a network switching node, through which one or more components in an apparatus of the office consultation adaptation platform server office consultation adaptation platform based data management method may connect to the network to exchange data and/or information.
The aforementioned database may store data and/or instructions. In some embodiments, the database may store data assigned to data storage distribution nodes. In some embodiments, the database may store data and/or instructions of the exemplary methods described in the present application. In some embodiments, the database may include mass storage, removable storage, volatile Read-write Memory, or Read-Only Memory (ROM), or the like, or any combination thereof. By way of example, mass storage may include magnetic disks, optical disks, solid state drives, and the like; removable memory may include flash drives, floppy disks, optical disks, memory cards, zip disks, magnetic tape, and the like; the volatile read-write memory may include random access memory (Random Access Memory, RAM); the RAM may include dynamic RAM (Dynamic Random Access Memory, DRAM), double data Rate Synchronous dynamic RAM (DDR SDRAM); static Random-Access Memory (SRAM), thyristor RAM (T-RAM) and Zero-capacitor RAM (Zero-RAM), etc. By way of example, ROM may include Mask Read-Only Memory (MROM), programmable ROM (Programmable Read-Only Memory, PROM), erasable programmable ROM (Programmable Erasable Read-Only Memory, PEROM), electrically erasable programmable ROM (Electrically Erasable Programmable Read Only Memory, EEPROM), compact disk ROM (CD-ROM), digital versatile disk ROM, and the like. In some embodiments, the database may be implemented on a cloud platform. For example only, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, cross-cloud, multi-cloud, or other similar, or the like, or any combination thereof.
In some embodiments, the database may be connected to a network to communicate with one or more components in a device (e.g., server, data storage distribution node, etc.) of the job education adaptation platform server that is based on the job education adaptation platform data management method. One or more components in the apparatus of the office consultation adaptation platform server based data management method of the office consultation adaptation platform may access data or instructions stored in a database via a network. In some embodiments, the database may be directly connected to one or more components (e.g., servers, data storage distribution nodes, etc., or in some embodiments, the database may be part of a server) in an apparatus of the office consultation adaptation platform server based data management method.
In this embodiment, the server may be a staff modification platform server, and the staff modification platform server and a terminal used by the user for online learning together form a staff modification system.
In detail, the embodiment of the invention discloses a data management method based on a staff diagnosis and improvement platform, which is applied to a staff diagnosis and improvement platform server, wherein the staff diagnosis and improvement platform server is in communication connection with at least one scattered data storage distribution node, and the method comprises the following steps:
S1, the staff teaching and modifying platform server firstly obtains data information through the data storage distribution node, and identifies the data information to obtain a data code: the application of the office teaching adaptation platform server generating, during operation, an acquisition signal 0101 for instructing acquisition of data information from the data storage distribution node; the staff education modification platform server receives the acquisition signal 0101 and determines a first data format 0 of the acquisition signal according to a carrying mode 0 of a cloud server; in step S12, the job teaching and modifying platform server determines the data format of the acquired signal according to the carrying mode of the cloud server, so as to achieve the purpose of balancing the data codes of fast responding to the acquisition speed of the acquired signal and the corresponding data quantity; determining that said acquisition signal 0101 is responded to in a first data format 0, said job education adaptation platform server transmitting said acquisition signal 0101 to said data storage distribution node and obtaining said data information of a first data volume "i love china" directly from said data storage distribution node: "I love China"; the staff teaching and modifying platform server identifies the acquired data information "i love china" to obtain a data code 0100011001110101.
S2, processing the data code 0100011001110101 to form a data sequence: the specific flow of the step S2 includes: s211, firstly, identifying the acquired data information according to the staff teaching and modifying platform server in the step S1 to obtain a data code 0100011001110101, and determining data characteristic values '01' in a plurality of data codes; s212, determining the length 4 of the data segment according to the interval length 2 of the data characteristic value '01' in the data code; s213, according to the length 4 of the data segment, based on the data arrangement format of the data code 0100011001110101, sequentially intercepting the data code 0100011001110101 with the corresponding length 4 from left to right to form a data segment 0110; s214, splitting the data code 0100011001110101 according to the determined length 4 of the data segment to form 4 data segments, namely 0100, 0110, 0111 and 0101 respectively;
s22, adding a data storage characteristic 1 for identifying the data type at the head end of each data segment to form data sub-segments, wherein the specific data sub-segments are 10100, 10110, 10111 and 10101 respectively;
S23, recombining all the data sub-fragments according to a data splitting method to form a data sub-sequence, wherein the data sub-sequence contains at least one data storage characteristic for identifying the data type, and the specific data sub-sequence is 10100101101011110101;
and S24, adding a storage tag '1' for identifying the encryption type of the data at the tail end of the data subsequence 10100101101011110101 to form a data sequence 101001011010111101011.
S3, taking the data storage characteristic '01' as an input characteristic of a storage control unit, and taking the storage label '1' as an output characteristic of the storage control unit to construct a corresponding storage control unit; the specific flow of the step S3 includes: s31, taking the data storage characteristic '01' as an input characteristic of a storage control unit, inputting the data storage characteristic '01' into the storage control unit, and analyzing a target storage characteristic of the data storage characteristic '01' in a target preset data segment by the storage control unit, wherein the target storage characteristic comprises a target storage characteristic node sequence; s32, carrying out hash processing on the target storage characteristic node sequence to obtain a plurality of target storage characteristic nodes; s33, determining a plurality of first training models according to feature vectors corresponding to the target storage features, wherein the plurality of first training models are training models trained by the plurality of target storage feature nodes in the storage control unit respectively; in step S33, the storage control unit is configured to learn target storage feature nodes after hashing a plurality of target storage feature node sequences, where the plurality of target storage feature node sequences are target storage feature node sequences included in a plurality of target storage features acquired in the target preset data segment, and a training model expressed in the storage control unit by each target storage feature node after hashing; s34, sequencing the plurality of first training models according to the sequence from high priority to low priority of each first training model in the plurality of first training models to obtain a training model sequence; s35, determining a training model expressed in the storage control unit by a target storage characteristic node in the target storage characteristic nodes based on a preset similarity proportion threshold value and the training model sequence; in step S35, the preset similarity proportion threshold is used to indicate the proportion of the similar part of the target storage characteristic node sequence obtained in the target preset data segment to the target storage characteristic node sequence; s36, when the training model expressed by the target storage characteristic node in the storage control unit is matched with a preset training model, determining the target storage characteristic as a learnable target storage characteristic; when the target storage characteristic is determined to be a learnable target storage characteristic, for each first training model in the plurality of first training models, controlling the storage control unit to learn target storage characteristic nodes after hashing the plurality of target storage characteristic node sequences received in the target preset data segment according to the first training model, and generating a prediction label after training by using the training model expressed in the storage control model by each target storage characteristic node after hashing; s37, updating model parameters of the storage control unit according to the prediction tag and the storage tag.
S4, identifying the data storage characteristics '01' of the data sequences to be distributed and stored, and carrying out characteristic identification and prediction on the data storage characteristics '01' of each data sequence 0100011001110101 to be distributed and stored according to the storage control unit to generate a storage tag '1' corresponding to each data sequence to be distributed and stored;
s5, according to the stored tag '1', the new data sequence 101001011010111101011 is obtained by performing tag processing on the data sequence 0100011001110101, and the new data sequence 101001011010111101011 after tag processing is classified and stored into at least one corresponding data storage node according to the data storage characteristic '01'. The specific flow of step S5 includes: s51, performing label processing on the data sequence 0100011001110101 according to a storage label "1" corresponding to each data sequence 0100011001110101 to be distributed and stored, and generating a corresponding first storage fragment sequence 101001011010111101011 according to a data storage characteristic "01"; s52, calculating a distribution offset parameter between each storage fragment in the first storage fragment sequence 101001011010111101011 and the storage fragment corresponding to the storage fragment in the last second storage fragment sequence to be 0; s53, determining a distribution reference sequence 101001011010111101011 of the first storage fragment sequence and the second storage fragment sequence on the storage fragment according to the distribution offset parameter being 0 and a distribution vector corresponding to the storage fragment in the first storage fragment sequence; s54, determining a data sequence 101001011010111101011 to be distributed and stored in at least one corresponding data storage node in the first storage fragment sequence according to the distribution reference sequences of the first storage fragment sequence and other second storage fragment sequences on each storage fragment.
The embodiment of the invention also discloses a device of the data management method based on the office teaching diagnosis and modification platform, which is applied to the data management method based on the office teaching diagnosis and modification platform, and comprises the following steps:
the acquisition processing module is used for acquiring data information and identifying the data information to obtain a data code; processing the data codes to form a data sequence, wherein the data sequence comprises identified data storage characteristics and a storage label, the data storage characteristics are used for identifying data types, and the storage label is an encryption label or a non-encryption label;
building a control module, wherein the control module is used for building a corresponding storage control unit by taking the data storage characteristic as an input characteristic of the storage control unit and taking the storage label as an output characteristic of the storage control unit;
the tag processing module is used for identifying the data storage characteristics of the data sequences to be distributed and stored, and carrying out characteristic identification and prediction on the data storage characteristics of each data sequence to be distributed and stored according to the storage control unit so as to generate a storage tag corresponding to each data sequence to be distributed and stored;
and the classification storage module is used for carrying out label processing on the data sequence according to the storage labels, and carrying out classification storage on the data sequence subjected to label processing into at least one corresponding data storage node according to the data storage characteristics.
Based on any one of the above aspects, the present invention constructs a corresponding storage control unit by using the data storage characteristics as input characteristics of the storage control unit and using the storage tag as output characteristics of the storage control unit, then identifies data storage characteristics of data sequences to be distributed and stored, performs characteristic identification and prediction on the data storage characteristics of each data sequence to be distributed and stored according to the storage control unit, generates a storage tag corresponding to each data sequence to be distributed and stored, performs tag processing on the data sequence according to the storage tag, and classifies and stores the data sequence processed by the tag into at least one corresponding data storage node according to the data storage characteristics. Therefore, the invention can distribute the data information to the geographically dispersed data storage nodes according to the model learning, so as to improve the safety of data storage, reduce the storage pressure of the office education diagnosis and modification platform server, optimize the data management flow, facilitate the accurate storage management of the data information and improve the overall operation efficiency of the office education diagnosis and modification platform server.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made to the embodiments of the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the embodiments of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to encompass such modifications and variations.
Claims (9)
1. A data management method based on a staff modification platform, applied to a staff modification platform server, the staff modification platform server being communicatively connected to at least one decentralized data storage distribution node, the method comprising:
s1, the staff teaching and modifying platform server firstly obtains data information through the data storage distribution node, and identifies the data information to obtain a data code;
s2, processing the data codes to form a data sequence, wherein the data sequence comprises identified data storage characteristics and storage tags, the data storage characteristics are used for identifying data types, and the storage tags are encryption tags or non-encryption tags;
s3, taking the data storage characteristic as an input characteristic of a storage control unit, and taking the storage label as an output characteristic of the storage control unit to construct a corresponding storage control unit;
s4, identifying data storage characteristics of the data sequences to be distributed and stored, and carrying out characteristic identification and prediction on the data storage characteristics of each data sequence to be distributed and stored according to the storage control unit to generate a storage label corresponding to each data sequence to be distributed and stored;
S5, carrying out label processing on the data sequence according to the storage labels, and storing the data sequence subjected to label processing into at least one corresponding data storage node in a classified manner according to the data storage characteristics;
the specific flow of the step S3 includes:
s31, taking the data storage characteristics as input characteristics of a storage control unit, inputting the data storage characteristics into the storage control unit, and analyzing target storage characteristics of the data storage characteristics in a target preset data segment through the storage control unit, wherein the target storage characteristics comprise a target storage characteristic node sequence;
s32, carrying out hash processing on the target storage characteristic node sequence to obtain a plurality of target storage characteristic nodes;
s33, determining a plurality of first training models according to feature vectors corresponding to the target storage features, wherein the plurality of first training models are training models trained by the plurality of target storage feature nodes in the storage control unit respectively;
s34, sequencing the plurality of first training models according to the sequence from high priority to low priority of each first training model in the plurality of first training models to obtain a training model sequence;
S35, determining a training model expressed in the storage control unit by a target storage characteristic node in the target storage characteristic nodes based on a preset similarity proportion threshold value and the training model sequence;
s36, when the training model expressed by the target storage characteristic node in the storage control unit is matched with a preset training model, determining the target storage characteristic as a learnable target storage characteristic; when the target storage characteristic is determined to be a learnable target storage characteristic, for each first training model in the plurality of first training models, controlling the storage control unit to learn target storage characteristic nodes after hashing the plurality of target storage characteristic node sequences received in the target preset data segment according to the first training model, and generating a prediction label after training by using the training model expressed in the storage control model by each target storage characteristic node after hashing;
s37, updating model parameters of the storage control unit according to the prediction tag and the storage tag.
2. The data management method based on the office teaching and modifying platform according to claim 1, wherein the specific flow of step S1 includes:
S11, the application program of the staff teaching platform server generates an acquisition signal for indicating data information acquired from the data storage distribution node in the running process;
s12, the staff teaching and modifying platform server receives the acquisition signals and determines the data format of the acquisition signals according to the carrying mode of the cloud server;
s13, when the acquisition signal is determined to be responded in a first data format, the staff education modification platform server sends the acquisition signal to the data storage distribution node, and directly obtains the data information of a first data volume from the data storage distribution node;
s14, when the acquired signal is determined to be responded in a second data format, the job education modification platform server sends the acquired signal to a cloud server and acquires the data information of a first data volume from the data storage distribution node according to the acquired signal by the cloud server, and the data information of a second data volume is formed by processing and expanding;
and S15, the staff teaching and modifying platform server identifies the acquired data information to obtain a data code.
3. The data management method based on the office consultation and modification platform according to claim 1, wherein in step S12, the office consultation and modification platform server determines the data format of the acquired signal according to the carrying mode of the cloud server, so as to achieve the purpose of balancing the acquiring speed of quickly responding to the acquired signal and the data codes of the corresponding data quantity.
4. The data management method based on the office teaching and modifying platform according to claim 1, wherein the specific flow of step S2 includes:
s21, splitting the data codes according to a data splitting method to form a plurality of data fragments, wherein each data fragment at least contains a plurality of data characteristic values;
s22, adding a data storage characteristic for identifying the data type at the head end of each data segment to form a data sub-segment;
s23, recombining all the data sub-fragments according to a data splitting method to form a data sub-sequence, wherein the data sub-sequence contains at least one data storage characteristic for identifying the data type;
s24, adding a storage label for identifying the encryption type of the data at the tail end of the data subsequence to form a data sequence.
5. The data management method based on the office teaching and modifying platform according to claim 1, wherein in step S21, the data code is split according to a data splitting method to form a plurality of data fragments, which specifically includes:
s211, firstly, determining data characteristic values in a plurality of data codes according to the data codes obtained by identifying the acquired data information by the staff teaching and modifying platform server in the step S1;
S212, determining the length of the data segment according to the interval length of the data characteristic value in the data code;
s213, according to the length of the data segment, based on the data arrangement format of the data codes, sequentially intercepting the data codes with the corresponding lengths to form a data segment;
s214, splitting the data codes according to the determined length of the data fragments to form a plurality of data fragments.
6. The data management method based on the office teaching and modifying platform according to claim 5, wherein in step S33, the storage control unit is configured to learn target storage feature nodes after hashing a plurality of target storage feature node sequences, and a training model expressed in the storage control unit by each target storage feature node after hashing, where the plurality of target storage feature node sequences are target storage feature node sequences included in a plurality of target storage features acquired in the target preset data segment.
7. The method for managing data based on a post-office teaching modification platform according to claim 6, wherein in step S35, the preset similarity ratio threshold is used to indicate a ratio of the target storage characteristic node sequence to a similar portion of the target storage characteristic node sequence acquired in a target preset data segment in the target storage characteristic node sequence.
8. The data management method based on the office teaching and modifying platform according to claim 1, wherein the specific flow of step S5 includes:
s51, carrying out label processing on the data sequences according to the storage labels corresponding to the data sequences to be distributed and stored, and generating corresponding first storage fragment sequences according to the data storage characteristics;
s52, calculating distribution offset parameters between each storage fragment in the first storage fragment sequence and the storage fragment corresponding to the storage fragment in the last second storage fragment sequence;
s53, determining a distribution reference sequence of the first storage fragment sequence and the second storage fragment sequence on the storage fragment according to the distribution offset parameter and a distribution vector corresponding to the storage fragment in the first storage fragment sequence;
s54, determining a data sequence to be distributed and stored in at least one corresponding data storage node in the first storage fragment sequence according to the distribution reference sequences of the first storage fragment sequence and other second storage fragment sequences on each storage fragment.
9. The device of the data management method based on the office teaching diagnosis and modification platform is characterized in that the device is applied to the data management method based on the office teaching diagnosis and modification platform as claimed in claim 1, and comprises the following steps:
The acquisition processing module is used for acquiring data information and identifying the data information to obtain a data code; processing the data codes to form a data sequence, wherein the data sequence comprises identified data storage characteristics and a storage label, the data storage characteristics are used for identifying data types, and the storage label is an encryption label or a non-encryption label;
building a control module, wherein the control module is used for taking the data storage characteristic as an input characteristic of a storage control unit, taking the storage label as an output characteristic of the storage control unit, and building a corresponding storage control unit;
the tag processing module is used for identifying the data storage characteristics of the data sequences to be distributed and stored, and carrying out characteristic identification and prediction on the data storage characteristics of each data sequence to be distributed and stored according to the storage control unit so as to generate a storage tag corresponding to each data sequence to be distributed and stored;
the classification storage module is used for carrying out label processing on the data sequence according to the storage labels, and carrying out classification storage on the data sequence subjected to label processing into at least one corresponding data storage node according to the data storage characteristics;
The building control module is configured to use the data storage characteristic as an input characteristic of a storage control unit, and use the storage tag as an output characteristic of the storage control unit, so as to build a corresponding storage control unit, where the building control module includes:
the data storage characteristics are used as input characteristics of a storage control unit, the data storage characteristics are input into the storage control unit, target storage characteristics of the data storage characteristics in a target preset data segment are analyzed through the storage control unit, and the target storage characteristics comprise a target storage characteristic node sequence;
performing hash processing on the target storage characteristic node sequence to obtain a plurality of target storage characteristic nodes;
determining a plurality of first training models according to feature vectors corresponding to the target storage features, wherein the plurality of first training models are training models trained by the plurality of target storage feature nodes in the storage control unit respectively;
sequencing the plurality of first training models according to the sequence from high priority to low priority of each first training model in the plurality of first training models to obtain a training model sequence;
Determining a training model expressed by a target storage characteristic node in the storage control unit in the plurality of target storage characteristic nodes based on a preset similarity proportion threshold value and the training model sequence;
when the training model expressed by the target storage characteristic node in the storage control unit is matched with a preset training model, determining the target storage characteristic as a learnable target storage characteristic; when the target storage characteristic is determined to be a learnable target storage characteristic, for each first training model in the plurality of first training models, controlling the storage control unit to learn target storage characteristic nodes after hashing the plurality of target storage characteristic node sequences received in the target preset data segment according to the first training model, and generating a prediction label after training by using the training model expressed in the storage control model by each target storage characteristic node after hashing;
and updating model parameters of the storage control unit according to the prediction tag and the storage tag.
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