CN111241110A - Data management method based on job education diagnosis and modification 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 job education diagnosis and modification 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; and S5, performing label processing on the data sequence according to the storage label, 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 method, the data information is distributed to the data storage nodes which are geographically dispersed according to model learning, so that the data storage safety is improved, the storage pressure on the job education diagnosis platform server is reduced, the data management flow is optimized, the data information is conveniently stored and managed accurately, and the overall operation efficiency of the job education diagnosis 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 job education diagnosis and modification platform.
Background
The platform is an educational platform aiming at professional education diagnosis improvement. The teaching and education diagnosis is a research activity, a bridge is built between teaching practice and teaching theory, a good way is provided for professional development of teachers, and through classroom observation, teachers can obtain new development in the aspects of practical knowledge, anti-provincial ability and the like by means of cooperative force, so that the overall teaching quality of the teachers is improved. The platform is changed in the position of the staff education, not only is the process of platform learning, but also is the process of teacher and student's emotional experience, mental training, and good platform is changed in the position of staff education, should let everybody take the enthusiasm to participate in the study activity, and here, the teacher has a difficult task of regulating and controlling with the heart, guiding with the intention, receive and release in good time.
However, in practice, it was found that: because user's individual teaching education information need change platform server unified storage through the position of education, but the current method of saving the individual teaching education information of above-mentioned user, generally all directly store user's individual teaching education information to position of education and diagnose and change platform server in to cause position of education to change platform server because of the data bulk too big influence operating efficiency, be not convenient for carry out the high-efficient operation management of data, and the security of data is far from not enough.
Disclosure of Invention
In order to overcome the above-mentioned shortcoming in the prior art at least, the utility model aims to provide a data management method based on job education diagnosis and modification platform can distribute data information to the data storage node of geographical dispersion according to model learning to improve data storage's security, and reduce the storage pressure to job education diagnosis and modification platform server, promote job education diagnosis and modification platform server whole operating efficiency.
The invention discloses a data management method based on a job education diagnosis and modification platform, which is applied to a job education diagnosis and modification platform server, wherein the job education 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 job education diagnosis and modification platform server firstly acquires data information through the data storage distribution nodes and identifies the data information to obtain data codes; the specific process of step S1 includes: s11, the application program of the job education diagnosis platform server generates an acquisition signal for indicating to acquire data information from the data storage distribution node in the operation process; s12, the post education diagnosis and modification platform server receives the acquisition signal, and determines the data format of the acquisition signal according to the carrying mode of the cloud server; in step S12, the job education modifying platform server determines the data format of the acquired signal according to the carrying manner of the cloud server, so as to achieve the balance of fast responding to the acquisition speed of the acquired signal and the data codes of the corresponding data amount; s13, when the fact that the acquisition signal is responded in the first data format is determined, the job education modifying platform server sends the acquisition signal to the data storage distribution node, and obtains the data information of the first data volume directly from the data storage distribution node; s14, when the fact that the acquisition signal is responded in the second data format is determined, the post education diagnosis platform server sends the acquisition signal to a cloud server, and data information of the second data volume is obtained by the cloud server through obtaining a first data volume from the data storage distribution node according to the acquisition signal and processing and expanding the first data volume; and S15, the post education diagnosis 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 process of step S2 includes: s21, splitting the data code according to a data splitting method to form a plurality of data fragments, wherein each data fragment at least comprises a plurality of data characteristic values; in step S21, the data code is split according to the data splitting method to form a plurality of data fragments, which specifically includes: s211, firstly, according to the data codes obtained by identifying the acquired data information by the job education diagnosis platform server in the step S1, determining data characteristic values in a plurality of data codes; s212, determining the length of the data segment according to the interval length of the data characteristic value in the data code; s213, sequentially intercepting data codes with corresponding lengths to form a data segment according to the length of the data segment and based on the data arrangement format of the data codes; s214, splitting the data code according to the determined length of the data fragment to form a plurality of data fragments; s22, adding data storage characteristics for identifying data types at the head end of each data fragment to form data sub-fragments; s23, recombining all the data subsections according to a data splitting method to form a data subsequence, wherein the data subsequence contains at least one data storage characteristic used for identifying the data type; and S24, adding a storage label for identifying the data encryption type at the tail end of the data subsequence to form a data sequence.
S3, constructing a corresponding storage control unit by taking the data storage characteristics as input characteristics of the storage control unit and taking the storage label as output characteristics of the storage control unit; the specific process of step S3 includes: s31, inputting the data storage characteristics into a storage control unit by taking the data storage characteristics as input characteristics of 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 target storage characteristic node sequences; 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 the feature vectors corresponding to the target storage features, wherein the first training models are respectively training models of the target storage feature nodes trained in the storage control unit; in step S33, the storage control unit is configured to learn a target storage feature node obtained by hashing a plurality of target storage feature node sequences and a training model expressed by each hashed target storage feature node in the storage control unit, where the 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; 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 by a target storage characteristic node in the plurality of target storage characteristic nodes in the storage control unit based on a preset similarity ratio threshold and the training model sequence; 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 obtained from the target preset data segment in the target storage characteristic node sequence; s36, when the training model expressed by the target storage feature node in the storage control unit matches a preset training model, determining the target storage feature as a learnable target storage feature; 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, according to the first training model, a target storage characteristic node obtained by hashing a plurality of target storage characteristic node sequences received in the target preset data segment and a training model expressed by each hashed target storage characteristic node in the storage control model, and generating a prediction tag after training; and S37, updating the model parameters of the storage control unit according to the prediction label and the storage label.
S4, identifying the data storage characteristics of the data sequences to be distributed and stored, and performing 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;
and S5, performing label processing on the data sequence according to the storage label, 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 process of step S5 includes: s51, performing label processing on the data sequence according to the storage label corresponding to each data sequence to be distributed and stored, and generating a corresponding first storage fragment sequence according to the data storage characteristics; s52, calculating distribution offset parameters between each memory fragment in the first memory fragment sequence and the memory fragment corresponding to the memory fragment in the last second memory fragment sequence; s53, determining distribution reference sequences of the first memory fragment sequence and the second memory fragment sequence on the memory fragments according to the distribution offset parameters and the distribution vectors corresponding to the memory fragments in the first memory fragment sequence; s54, determining a data sequence which needs to be distributed and stored to at least one corresponding data storage node in the first memory fragment sequence according to the distribution reference sequence of the first memory fragment sequence and other second memory fragment sequences on each memory fragment.
The invention also discloses a device of the data management method based on the post education diagnosis and modification platform, which is characterized in that the data management method is applied to the data management method based on the post education 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 storage tags, the data storage characteristics are used for identifying data types, and the storage tags are encryption tags or non-encryption tags;
the building control module is used for building a corresponding storage control unit by taking the data storage characteristics as input characteristics of the storage control unit and taking the storage label as output characteristics 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 performing 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 tag corresponding to each data sequence to be distributed and stored;
and the classified storage module is used for performing label processing on the data sequence according to the storage label 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.
Based on any one of the above aspects, the data storage characteristics are used as input characteristics of a storage control unit, the storage tags are used as output characteristics of the storage control unit, a corresponding storage control unit is constructed, then, the data storage characteristics of the data sequences to be distributed and stored are identified, the data storage characteristics of each data sequence to be distributed and stored are identified and predicted according to the storage control unit, a storage tag corresponding to each data sequence to be distributed and stored is generated, tag processing is performed on the data sequences according to the storage tags, and the data sequences subjected to the tag processing are classified and stored into at least one corresponding data storage node according to the data storage characteristics. Therefore, the data information can be distributed to the data storage nodes with scattered geography according to model learning, so that the data storage safety is improved, the storage pressure on the job education diagnosis and modification platform server is reduced, the data management process is optimized, the data information can be stored and managed accurately, and the overall operation efficiency of the job education diagnosis and modification platform server is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a control flowchart of a data management method based on a job education platform according to an embodiment of the present invention.
Detailed Description
The present application will now be described in detail with reference to the drawings, and the specific operations in the method embodiments may also be applied to the apparatus embodiments or the system embodiments. In the description of the present application, "at least one" includes one or more unless otherwise specified. "plurality" means two or more. For example, at least one of A, B and C, comprising: a alone, B alone, a and B in combination, a and C in combination, B and C in combination, and A, B and C in combination. In this application, "/" means "or, for example, A/B may mean A or B; "and/or" herein is merely an association describing an association of devices, meaning that there may be three relationships, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone.
Fig. 1 is a control flowchart of a data management method based on an employee improvement platform according to an embodiment of the present invention, which is merely a feasible example. In some embodiments, the job referral platform server may be a single server or a group of servers. The set of servers of the job referral platform server may be centralized or distributed (e.g., the job referral platform server may be a distributed system). In some embodiments, the job referral platform server may be local or remote with respect to the data storage distribution node. For example, the job referral platform server may access information stored in a data storage distribution node and database, or any combination thereof, via a network. The job referral platform server may be directly connected to at least one of a data storage distribution node and a database to access information and/or data stored therein. In some embodiments, the job referral modification platform server may be implemented on a cloud platform; by way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud (communicuted), a distributed cloud, an inter-cloud, a multi-cloud, and the like, or any combination thereof.
In some embodiments, the job referral platform server may include a processor. The processor may process information and/or data related to the service request to perform one or more of the functions described herein. A processor may include one or more processing cores (e.g., a single-core processor (S) or a multi-core processor (S)). Merely by way of example, a Processor may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a reduced Instruction Set computer (reduced Instruction Set computer), a 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 referral platform server 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 combination thereof. Merely by way of example, 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 (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a ZigBee Network, a 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 wired or wireless network access points, such as base stations and/or network switching nodes, through which one or more components of the apparatus of the role modification platform based data management method of the role modification platform server may connect to the network to exchange data and/or information.
The aforementioned database may store data and/or instructions. In some embodiments, a database may store data assigned to data storage distribution nodes. In some embodiments, the database may store data and/or instructions for the exemplary methods described herein. In some embodiments, the database may include mass storage, removable storage, volatile Read-write Memory, or Read-Only Memory (ROM), among others, 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, tapes, and the like; volatile read-write memory may include Random Access Memory (RAM); the RAM may include Dynamic RAM (DRAM), Double data Rate Synchronous Dynamic RAM (DDR SDRAM); static RAM (SRAM), Thyristor-Based Random Access Memory (T-RAM), Zero-capacitor RAM (Zero-RAM), and the like. By way of example, ROMs may include Mask Read-Only memories (MROMs), Programmable ROMs (PROMs), Erasable Programmable ROMs (PERROMs), Electrically Erasable Programmable ROMs (EEPROMs), compact disk ROMs (CD-ROMs), digital versatile disks (ROMs), and the like. In some embodiments, the database may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, across clouds, multiple clouds, or the like, or any combination thereof.
In some embodiments, a database may be connected to a network to communicate with one or more components of an apparatus (e.g., a server, a data storage distribution node, etc.) of the job referral platform server based data management method of the job referral platform server. One or more components of the apparatus of the job referral platform server based on the data management method of the job referral 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., a server, a data storage distribution node, etc.) of the apparatus of the job referral platform-based data management method of the job referral platform server, or in some embodiments, the database may be part of the server.
In this embodiment, this server can be for the position education diagnosis change platform server, and this position education diagnosis change platform server constitutes the position education diagnosis jointly with the terminal that user on-line learning used and changes the system.
In detail, the embodiment of the invention discloses a data management method based on a post education diagnosis and modification platform, which is applied to a post education diagnosis and modification platform server, wherein the post education 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 job education diagnosis and modification platform server firstly acquires data information through the data storage distribution node, and identifies the data information to obtain a data code: an application program of the job education diagnosis platform server generates an acquisition signal 0101 for indicating to acquire data information from the data storage distribution node in the running process; the job education diagnosis 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 education modifying platform server determines the data format of the acquired signal according to the carrying manner of the cloud server, so as to achieve the balance of fast responding to the acquisition speed of the acquired signal and the data codes of the corresponding data amount; determining that the acquisition signal 0101 is responded in a first data format 0, and the job education modification platform server sends the acquisition signal 0101 to the data storage distribution node, and directly obtains the data information of a first data volume "i love China" from the data storage distribution node: "I love China"; the post education diagnosis 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 process of step S2 includes: s211, determining data characteristic values '01' in a plurality of data codes according to data codes 0100011001110101 obtained by identifying the acquired data information by the job education diagnosis platform server in the step S1; 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 to form 4 data fragments according to the determined length 4 of the data fragment, wherein the 4 data fragments are 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 data sub-segments are 10100, 10110, 10111 and 10101 respectively;
s23, recombining all the data subsections according to a data splitting method to form a data subsequence, wherein the data subsequence contains at least one data storage characteristic used for identifying data types, and the specific data subsequence is 10100101101011110101;
s24, adding a storage tag "1" for identifying the data encryption type at the end of the data sub-sequence 10100101101011110101 to form a data sequence 101001011010111101011.
S3, constructing a corresponding storage control unit by taking the data storage characteristic '01' as an input characteristic of the storage control unit and taking the storage label '1' as an output characteristic of the storage control unit; the specific process of 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 through 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 the feature vectors corresponding to the target storage features, wherein the first training models are respectively training models of the target storage feature nodes trained in the storage control unit; in step S33, the storage control unit is configured to learn a target storage feature node obtained by hashing a plurality of target storage feature node sequences and a training model expressed by each hashed target storage feature node in the storage control unit, where the 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; 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 by a target storage characteristic node in the plurality of target storage characteristic nodes in the storage control unit based on a preset similarity ratio threshold and the training model sequence; 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 obtained from the target preset data segment in the target storage characteristic node sequence; s36, when the training model expressed by the target storage feature node in the storage control unit matches a preset training model, determining the target storage feature as a learnable target storage feature; 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, according to the first training model, a target storage characteristic node obtained by hashing a plurality of target storage characteristic node sequences received in the target preset data segment and a training model expressed by each hashed target storage characteristic node in the storage control model, and generating a prediction tag after training; and S37, updating the model parameters of the storage control unit according to the prediction label and the storage label.
S4, identifying the data storage characteristics '01' of the data sequences to be distributed and stored, performing 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, and generating a storage label '1' corresponding to each data sequence to be distributed and stored;
s5, according to the storage label '1', performing label processing on the data sequence 0100011001110101 to obtain a new data sequence 101001011010111101011, and storing the new data sequence 101001011010111101011 after label processing into at least one corresponding data storage node in a classified manner according to the data storage characteristic '01'. The specific process of step S5 includes: s51, performing label processing on the data sequence 0100011001110101 according to the 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 the data storage characteristic '01'; s52, calculating, for each memory slice in the first memory slice sequence 101001011010111101011, that the distribution offset parameter between the memory slice and the memory slice corresponding to the memory slice in the last second memory slice sequence is 0; s53, determining a distribution reference sequence 101001011010111101011 of the first memory slice sequence and the second memory slice sequence on the memory slice according to the distribution offset parameter being 0 and the distribution vector corresponding to the memory slice in the first memory slice sequence; s54, determining a data sequence 101001011010111101011 which needs to be distributed and stored to at least one corresponding data storage node in the first memory fragment sequence according to the distribution reference sequence of the first memory fragment sequence and other second memory fragment sequences on each memory fragment.
The embodiment of the invention also discloses a device of the data management method based on the post education diagnosis and modification platform, which is applied to the data management method based on the post education 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 storage tags, the data storage characteristics are used for identifying data types, and the storage tags are encryption tags or non-encryption tags;
the building control module is used for building a corresponding storage control unit by taking the data storage characteristics as input characteristics of the storage control unit and taking the storage label as output characteristics 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 performing 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 tag corresponding to each data sequence to be distributed and stored;
and the classified storage module is used for performing label processing on the data sequence according to the storage label 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.
Based on any one of the above aspects, the data storage characteristics are used as input characteristics of a storage control unit, the storage tags are used as output characteristics of the storage control unit, a corresponding storage control unit is constructed, then, the data storage characteristics of the data sequences to be distributed and stored are identified, the data storage characteristics of each data sequence to be distributed and stored are identified and predicted according to the storage control unit, a storage tag corresponding to each data sequence to be distributed and stored is generated, tag processing is performed on the data sequences according to the storage tags, and the data sequences subjected to the tag processing are classified and stored into at least one corresponding data storage node according to the data storage characteristics. Therefore, the data information can be distributed to the data storage nodes with scattered geography according to model learning, so that the data storage safety is improved, the storage pressure on the job education diagnosis and modification platform server is reduced, the data management process is optimized, the data information can be stored and managed accurately, and the overall operation efficiency of the job education diagnosis and modification platform server is improved.
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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 changes and modifications may be made in 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 of the present application and their equivalents, the present application is also intended to encompass such modifications and variations.
Claims (10)
1. A data management method based on a job education diagnosis and modification platform is applied to a job education diagnosis and modification platform server which is in communication connection with at least one scattered data storage distribution node, and the method comprises the following steps:
s1, the job education diagnosis and modification platform server firstly acquires data information through the data storage distribution nodes and identifies the data information to obtain data codes;
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, constructing a corresponding storage control unit by taking the data storage characteristics as input characteristics of the storage control unit and taking the storage label as output characteristics of the storage control unit;
s4, identifying the data storage characteristics of the data sequences to be distributed and stored, and performing 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;
and S5, performing label processing on the data sequence according to the storage label, 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.
2. The data management method based on the post office consultation platform according to claim 1, wherein the specific process of the step S1 includes:
s11, the application program of the job education diagnosis platform server generates an acquisition signal for indicating to acquire data information from the data storage distribution node in the operation process;
s12, the post education diagnosis and modification platform server receives the acquisition signal, and determines the data format of the acquisition signal according to the carrying mode of the cloud server;
s13, when the fact that the acquisition signal is responded in the first data format is determined, the job education modifying platform server sends the acquisition signal to the data storage distribution node, and obtains the data information of the first data volume directly from the data storage distribution node;
s14, when the fact that the acquisition signal is responded in the second data format is determined, the post education diagnosis platform server sends the acquisition signal to a cloud server, and data information of the second data volume is obtained by the cloud server through obtaining a first data volume from the data storage distribution node according to the acquisition signal and processing and expanding the first data volume;
and S15, the post education diagnosis platform server identifies the acquired data information to obtain a data code.
3. The method for data management based on the vocational education modifying platform of claim 1, wherein in step S12, the vocational education modifying platform server determines the data format of the acquired signal according to the carrying manner of the cloud server, so as to achieve the balance of fast response to the acquisition speed of the acquired signal and the data codes of the corresponding data amount.
4. The data management method based on the post office consultation platform according to claim 1, wherein the specific process of the step S2 includes:
s21, splitting the data code according to a data splitting method to form a plurality of data fragments, wherein each data fragment at least comprises a plurality of data characteristic values;
s22, adding data storage characteristics for identifying data types at the head end of each data fragment to form data sub-fragments;
s23, recombining all the data subsections according to a data splitting method to form a data subsequence, wherein the data subsequence contains at least one data storage characteristic used for identifying the data type;
and S24, adding a storage label for identifying the data encryption type at the tail end of the data subsequence to form a data sequence.
5. The data management method based on the vocational education platform-modifying platform as claimed in claim 1, wherein in step S21, the data code is split into a plurality of data fragments according to a data splitting method, which includes:
s211, firstly, according to the data codes obtained by identifying the acquired data information by the job education diagnosis platform server in the step S1, determining data characteristic values in a plurality of data codes;
s212, determining the length of the data segment according to the interval length of the data characteristic value in the data code;
s213, sequentially intercepting data codes with corresponding lengths to form a data segment according to the length of the data segment and based on the data arrangement format of the data codes;
s214, splitting the data code according to the determined length of the data fragment to form a plurality of data fragments.
6. The data management method based on the post office consultation platform according to claim 1, wherein the specific process of the step S3 includes:
s31, inputting the data storage characteristics into a storage control unit by taking the data storage characteristics as input characteristics of 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 target storage characteristic node sequences;
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 the feature vectors corresponding to the target storage features, wherein the first training models are respectively training models of the target storage feature nodes trained in the storage control unit;
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 by a target storage characteristic node in the plurality of target storage characteristic nodes in the storage control unit based on a preset similarity ratio threshold and the training model sequence;
s36, when the training model expressed by the target storage feature node in the storage control unit matches a preset training model, determining the target storage feature as a learnable target storage feature; 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, according to the first training model, a target storage characteristic node obtained by hashing a plurality of target storage characteristic node sequences received in the target preset data segment and a training model expressed by each hashed target storage characteristic node in the storage control model, and generating a prediction tag after training;
and S37, updating the model parameters of the storage control unit according to the prediction label and the storage label.
7. The data management method based on the vocational education modifying platform according to claim 6, wherein in step S33, the storage control unit is configured to learn a target storage feature node obtained by hashing a plurality of target storage feature node sequences and a training model expressed in the storage control unit for each hashed target storage feature node, wherein the target storage feature node sequences are target storage feature node sequences included in a plurality of target storage features obtained in the target preset data segment.
8. The method for data management based on job education modifying platform according to claim 7, wherein in step S35, the preset similarity ratio threshold is used to indicate a ratio of the target storage feature node sequence to a similar portion of the target storage feature node sequence obtained in the target preset data segment in the target storage feature node sequence.
9. The data management method based on the post office consultation platform according to claim 1, wherein the specific process of the step S5 includes:
s51, performing label processing on the data sequence according to the storage label corresponding to each data sequence to be distributed and stored, and generating a corresponding first storage fragment sequence according to the data storage characteristics;
s52, calculating distribution offset parameters between each memory fragment in the first memory fragment sequence and the memory fragment corresponding to the memory fragment in the last second memory fragment sequence;
s53, determining distribution reference sequences of the first memory fragment sequence and the second memory fragment sequence on the memory fragments according to the distribution offset parameters and the distribution vectors corresponding to the memory fragments in the first memory fragment sequence;
s54, determining a data sequence which needs to be distributed and stored to at least one corresponding data storage node in the first memory fragment sequence according to the distribution reference sequence of the first memory fragment sequence and other second memory fragment sequences on each memory fragment.
10. The device for the data management method based on the platform for change of job education and diagnosis is applied to the data management method based on the platform for change of job education and diagnosis 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 storage tags, the data storage characteristics are used for identifying data types, and the storage tags are encryption tags or non-encryption tags;
the building control module is used for building a corresponding storage control unit by taking the data storage characteristics as input characteristics of the storage control unit and taking the storage label as output characteristics 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 performing 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 tag corresponding to each data sequence to be distributed and stored;
and the classified storage module is used for performing label processing on the data sequence according to the storage label 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.
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