Data source-based power grid data compression and storage data structure and secure access algorithm thereof

Abstract

The invention discloses a data source-based power grid data compression and storage data structure and a security access algorithm thereof, and relates to the technical field of power grid data storage and security access, including data source-based data compression and structural storage, through a data compression technology, no matter how many purposes of data are, only one part of public data is stored, and no matter how many purposes the data are. And the data is stored in a database closest to an original data source, so that the data structure has remarkable advantages in data transmission. Storage and processing of the power grid data can be optimized, and the security and access efficiency of the data are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid data storage and safe access, in particular to a power grid data compression storage data structure based on a data source and a safe access algorithm thereof. Background Art

[0002] In the modern power industry, the generation and use of data is showing a rapid growth trend. Power workers generate a large amount of data in their daily work, which covers equipment operation status, fault records, maintenance logs, user power consumption information and other aspects.

[0003] However, the existing data storage structure is full of dispersion and redundancy. At present, power grid data is usually stored in a distributed, discrete, and block manner. Although this storage structure improves the availability of data to a certain extent, it also leads to a waste of data space. Since each use requires the storage of an independent copy of the data, if there are multiple uses (such as auditing, statistics, data processing, etc.), multiple copies of the same data need to be stored. This not only takes up a lot of storage space, but also increases the complexity of data processing.

[0004] At the same time, the existing data storage structure lacks a unified security management standard, resulting in chaotic data security management. Inconsistent security policies between different departments or systems make data access control and permission settings complicated and difficult to implement. This inconsistent approach not only increases the risk of data leakage, but also makes data auditing and tracking difficult.

[0005] Due to the redundancy of data storage, accessing specific data often requires searching in multiple copies, resulting in inefficient data access. When users need data, they must obtain it from multiple storage locations, increasing data transmission time and network burden. At the same time, data updating and maintenance also become more cumbersome, and updated data needs to be synchronized in all copies.

[0006] In summary, it is particularly important to construct a power grid data compression storage data structure based on the data source and its secure access algorithm, which can not only optimize the storage and processing of power grid data, but also improve the security and access efficiency of data. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a data structure for compressing and storing power grid data based on a data source and a secure access algorithm thereof, so as to optimize the storage and processing of power grid data and improve the security and access efficiency of data.

[0008] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows: A data structure for compressing and storing power grid data based on a data source includes data compression and structural storage based on the data source. Through data compression technology, no matter how many uses the data has, only one copy of the public data is stored, and it is stored in a database closest to the original data source. This data structure has significant advantages in data transmission.

[0009] Further preferably, it includes multiple primary databases, and the primary database includes compressed data from specific power workers; the compressed data is data generated by the power workers during their work, and is stored in the primary database closest to the data source after data compression processing. The data source is identified by a unique number, and the number of compressed data stored is always maintained at 0 or 1; the sum of all the primary databases constitutes a data security center, which centrally manages and protects all compressed data.

[0010] Further preferably, the data security center includes generating multi-level discrete data units based on the compressed data content: Primary discrete data unit: extract keywords from compressed data to form primary discrete data units, and process them according to the three dimensions of semantics, time and space; Secondary discrete data unit: Multiple primary discrete data units can be associated and combined to form secondary discrete data units; Central data storage unit: In the data security center, multiple secondary discrete data units can be associated and combined to form a central data unit.

[0011] A secure access algorithm based on the power grid data compression storage data structure of claim 1, comprising the following steps: Receive the user's access request, determine the identity and authority of the requesting user, and grant access rights based on the data source number. If the number of the access terminal corresponds to the data source number, access is directly allowed; If they do not correspond, additional access rights must be applied for and the visitor submits a request to the data security center. The data security center receives the visitor's access request and determines the visitor's identity and authority based on the data source number. Based on the visitor's identity and authority, the center determines whether to allow the visitor to access specific compressed data. If access is allowed, the corresponding compressed data is provided to the visitor.

[0012] Further preferably, the data security center comes with an initial security access algorithm framework, and the design of the database is based on the data source number, so each stored data naturally contains the source number. This storage structure itself provides support for the basic framework of the security algorithm. On this basic framework, access rights are further refined and modified. By aggregating the data source numbers and sending them to the access ports, the relevant ports can obtain higher levels of access rights.

[0013] Further preferably, the basic framework of the security access algorithm itself has a multi-level structure, and the data security center is constructed based on the data source, and the data source numbering is hierarchical, so it naturally has the basic framework of the security access algorithm.

[0014] Further preferably, in addition to refining and modifying access rights by aggregating data source numbers and sending them to the access port, access rights can also be flexibly adjusted globally and extensively by adjusting the smallest unit of the data source number in the access rights.

[0015] Further preferably, the minimum unit of the data source number is constructed to correspond to the hierarchy of the data source number.

[0016] The beneficial effects of adopting the above technical solution are: In general, the present invention constructs a data source-based power grid data compression (distributed) storage data structure and its (data source parameter-based) security access algorithm (the so-called data source-based means using it as a data carrier).

[0017] Specifically, the power grid data compression storage data structure of the present invention realizes that only one copy of public data is stored regardless of the number of uses of the data and is stored in the database closest to the original data source. In addition, multiple primary databases containing compressed data generated by specific power workers during the work process and stored in the location closest to the data source after compression processing together constitute a data security center that centrally manages and protects all compressed data.

[0018] In addition, the following collaborative technologies are introduced to facilitate subsequent data processing operations oriented to the work environment: the data security center generates multi-level discrete data units based on the compressed data content, including primary discrete data units that extract keywords from the compressed data and process them according to the three dimensions of semantics, time and space, secondary discrete data units formed by associating and combining multiple primary discrete data units, and central data storage units formed by associating and combining multiple secondary discrete data units.

[0019] In terms of security access, since the design of the basic database and the security access algorithm are collaboratively constructed based on the data source number, each storage data structure corresponds to its source number, which means that the storage structure naturally has the basic framework of the security algorithm. Furthermore, on this basic framework, we can further adjust and refine the access rights. For example, if a department needs higher access rights, we can aggregate the data source numbers of the department and send them to a specific access port (the data flow and data volume of the data source number are generally only a few bytes, which greatly improves the storage, transmission and computing efficiency in the security algorithm optimization process, and can reach a level where almost no storage resources and computing power resources are consumed); thus, this port can obtain a higher level of access rights. This modification is made on top of the original security algorithm framework. Compared with the need to create the initial security access algorithm framework by yourself, it is not only extremely simple, but the security access algorithm initial screening framework also has the initial consistency with the data storage structure.

[0020] Furthermore, based on the multi-level structure of data sources and their numbers in the storage structure (for example, in the case of power grid unit 1, the data source code of department A is 1.1, and the data source code of group a under department A is 1.1.1, the code of group b is 1.1.2, and so on...), the collaboratively constructed security access algorithm framework itself also has a multi-level structure. The key point is that this also means that the basic framework for security access algorithms that comes with the above storage structure naturally has a hierarchical structure; in other words, this hierarchical structure is not only reflected in the data source itself, but also in our security access algorithm framework.

[0021] In this way, based on the natural framework and hierarchical structure of the above-mentioned security algorithm, as well as its global and micro adjustability, the security access process of the present invention can be used to construct a strict and smooth permission control mechanism. When receiving a user access request, the identity and permission of the requesting user are judged. If the access terminal number corresponds to the data source number, access can be directly allowed, otherwise additional permissions need to be applied for. After the visitor makes a request to the data security center, the center determines the visitor's identity and permission based on the data source number to decide whether to allow access to specific compressed data. The data security center has its own initial security access algorithm framework based on the data source number design so that each stored data naturally contains the source number and thus provides a basic framework for the security algorithm. At the same time, the data source number classification makes the basic framework of the security access algorithm have a multi-level structure. By aggregating the data source number and sending it to the access port, the access rights can be refined and modified. The smallest unit of the data source number in the access right can also be adjusted globally and widely flexibly. This technology, which performs well in many aspects such as data storage, structure and secure access, can greatly improve data security and access efficiency. DETAILED DESCRIPTION

[0022] The following examples describe the present invention in detail. In the description of the following examples, specific details such as specific system structures, technologies, etc. are proposed for illustration rather than for limitation, so as to thoroughly understand the present application embodiments. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, the detailed description of well-known systems, devices, circuits and methods is omitted to prevent unnecessary details from hindering the description of the present application. It should be understood that when used in the present application specification and the attached claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their collections. It should also be understood that the term "and / or" used in the present application specification and the attached claims refers to any combination of one or more of the items listed in association and all possible combinations, and includes these combinations.

[0023] In addition, in the description of the present application specification and the attached claims, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. The reference to "one embodiment" or "some embodiments" described in the present application specification means that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in the different parts of this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways. Example 1

[0024] In the modern electric power industry, the generation and use of data presents a rapid growth trend. Electric power workers generate a large amount of data in their daily work, which covers many aspects such as equipment operating status, fault records, maintenance logs, and user electricity consumption information. However, the existing data storage structure is full of dispersion and redundancy, and the security management is chaotic. In order to improve the storage efficiency of data, the present invention team has developed a data structure for power grid data compression storage based on the data source, which mainly includes data compression and structural storage based on the data source. Through data compression technology, no matter how many uses the data has, only one copy of the public data is stored, and it is stored in the database closest to the original data source. This data structure has significant advantages in data transmission.

[0025] More specifically, the power grid data compression storage data structure includes multiple primary databases, and the primary database includes compressed data from specific power workers; the compressed data is the data generated by the power workers during their work, and is stored in the primary database closest to the data source after data compression processing. The data source is identified by a unique number, and the number of compressed data stored always remains 0 or 1; the sum of all primary databases constitutes a data security center, which centrally manages and protects all compressed data.

[0026] The data security center includes generating multi-level discrete data units based on compressed data content: Primary discrete data unit: extract keywords from compressed data to form primary discrete data units, and process them according to the three dimensions of semantics, time and space; specifically, keyword-based retrieval and extraction can obtain discretely distributed data information. Considering the natural influence of time, different users of Netcom, the same person facing different communication objects, different groups, etc., the above keyword-based information retrieval and extraction will obtain a discretized data set; The key point here is to ensure that the information extraction based on keyword retrieval is complete, and at the same time ensure that the data of the "primary discrete data unit" has a good data hierarchy and data logical structure. To this end, the data structure of the "primary discrete data unit" is constructed as follows: The primary discrete data unit is further processed according to the three dimensions of semantics, time and space, and further constructed to form a first-level discrete data unit. In the semantic dimension, a two-layer structure is set up. The first layer is to search for the keyword itself, and the second layer is to search for the semantics associated with the current keyword. For example, if there are 10 second-layer semantic words related to the keyword, these 10 related semantic words are constructed as related semantic feature vectors, that is, a 10-dimensional feature vector is obtained; for example: analyzing other keywords related to "power outage", such as "equipment failure, weather warning, power outage, power outage maintenance, power outage plan, power outage notice, power outage recovery, power outage accident, power outage notice, power outage area", etc.; richer semantic associations can be formed, thereby expanding the space for data search and information extraction. The time dimension contains at least two time dimension parameters: a sufficiently small time parameter t1 and a sufficiently large time parameter t2. For t1, as long as the time interval of the conversation / data line in Netcom is less than t1, it is summarized as a paragraph and directly included in the primary discrete data unit. For t2, for example, if t2 is set to 24h, that is, one day, this larger time span can be used to distinguish and integrate the primary discrete data units. The simplest example is: the retrieval data spans k t2s, that is, k days, which correspond to k primary discrete data units, and are merged as a primary discrete data unit. Therefore, it can be seen that the time dimension has two data values: one is to directly generate a paragraphed data layer structure in the primary discrete data unit, and the other is to merge the primary discrete data units based on a larger time parameter, and the merged data set is used as a primary discrete data unit. The spatial dimension refers to the number of context lines. A lower limit is set, that is, when the number of lines is lower than the lower limit, the context / line expansion is performed; a two-layer spatial parameter structure is set corresponding to the semantic dimension: the first layer of core keywords, for example, sets a spatial parameter of 500 lines, and the second layer of associated semantic feature vectors sets a spatial parameter of 100 lines, with no upper limit. Secondary discrete data unit: multiple primary discrete data units can be associated and combined to form secondary discrete data units; central data storage unit: in the data security center, multiple secondary discrete data units can be associated and combined to form a central data unit. The construction of the central data storage unit can be connected to the LLM model for AI intelligent data processing or AI search, etc. Example 2

[0027] The security access algorithm of the power grid data compression storage data structure based on the data source grants access rights through the data source number. If the number of the access terminal corresponds to the data source number, access is directly allowed; if not, additional access rights need to be applied for; the visitor makes a request to the data security center, and the data security center reviews the request and assigns access rights according to the data source number; the visitor makes a request to the data security center, and the data security center reviews the request and assigns access rights according to the data source number. The system automatically assigns specific permissions to different ports, and the permission assignment is based on the data source parameters; in a single access event or when the access rights need to be expanded, the visitor needs to request permission from the data security center, and the permission issuance is based on the data source number, which specifically includes the following steps: receiving the user's access request and judging the identity and permissions of the requesting user; deciding whether to allow the user to access specific compressed data based on the user's identity and permissions; if access is allowed, providing the corresponding compressed data to the requesting user. Before the visitor accesses specific data, the visitor is required to apply for access rights from the data security center, and the review is conducted based on the sensitivity of the data and the user's permissions. The security access algorithm specifically grants access rights through the data source number. If the number of the access end corresponds to the data source number, access is allowed directly; if not, additional access rights must be applied for. The visitor submits a request to the data security center, which receives the visitor's access request and determines the visitor's identity and authority based on the data source number; based on the visitor's identity and authority, it determines whether the visitor is allowed to access specific compressed data; if access is allowed, the corresponding compressed data is provided to the visitor.

[0028] The design of the database is based on the data source number, which means that each piece of stored data has a unique source number so that the storage structure naturally has the basic framework of the security algorithm.

[0029] On this basic framework, we can further adjust and refine access rights. For example, if a department needs higher access rights, we can aggregate the data source numbers of the department and send them to a specific access port (the data flow and data volume of the data source number are generally only a few bytes, which greatly improves the storage, transmission and computing efficiency in the security algorithm optimization process, and can reach a level where storage resources and computing power resources are almost not consumed); thus, this port can obtain a higher level of access rights. This modification is made on top of the original security algorithm framework. Compared with the need to create the initial security access algorithm framework by yourself, it is not only extremely simple, but the security access algorithm initial screening framework also has initial consistency with the data storage structure.

[0030] This security access algorithm framework itself has a multi-level structure. Taking power grid unit 1 as an example, the data source code of department A is 1.1, and the data source code of group a under department A is 1.1.1, the code of group b is 1.1.2, and so on, until all groups in department A have their own numbers (1.1.n). Similarly, the data source code of department B is 1.2, and the codes of the groups below are 1.2.1, 1.2.2, and so on.

[0031] In this way, the data source numbering is hierarchical. The key point is that this also means that the basic framework for the security access algorithm that is naturally provided by the above storage structure also naturally has a hierarchical structure. In other words, this hierarchical structure is not only reflected in the data source itself, but also in our security access algorithm framework.

[0032] Specifically, a specific, low-level and ready-made example is that in the basic framework of the security access algorithm that comes with the storage structure, different visitors have hierarchical initial access rights based on their hierarchical numbers. For example, setting the minimum unit of access rights to individuals means that the initial access level of an individual port is the compressed data corresponding to their individual source number. Of course, the minimum unit of access rights can also be set to a group, department or even a unit. For example, when set to a group, an individual can at least access the compressed data corresponding to the data source number of his or her group - this also means that a potential functional architecture of this data structure and its security algorithm is: in addition to manually modifying and adding or deleting access rights to different ports by assigning / deleting data source numbers, access rights can also be flexibly adjusted globally and extensively by adjusting the minimum unit of the data source number in the access rights. Among them, the smallest unit of this data source number can generally be directly set to correspond to the multi-level structure of the security access algorithm framework itself. Specifically, referring to the example of the above-mentioned power grid unit 1, the data source code of department A is 1.1, and the data source code of group a under department A is 1.1.1..., then the smallest unit of the above-mentioned data source number corresponds to the leftmost 1 in the data source code 1.1.1, which means that the minimum access unit is a certain power grid unit. Conversely, if the smallest unit of the data source number corresponds to the rightmost 1 in the data source code 1.1.1, it means that the smallest access unit is a specific group in a department under a certain power grid unit; and so on.

[0033] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0034] From the above embodiments, it can be seen that, in general, the present invention adopts a compressed storage structure to store data in a database closest to the original data source, regardless of how many uses the data has, which can effectively reduce the delay of data transmission, improve the response speed of data access, significantly reduce redundant storage of data, reduce storage costs, and optimize the efficiency of storage resource utilization; grant access rights through data source numbers, when the number of the access terminal matches the data source number, the user can directly access the relevant data; if they do not match, the user needs to apply for additional access rights, making permission management simple and efficient; on the basis of the basic framework, it allows the access rights to be refined and adjusted. For example, if a department needs higher access rights, it can be achieved by aggregating the data source numbers and sending them to a specific access port, with flexible permission adjustment capabilities; the minimum unit of access rights can be set as needed, such as individuals, groups or departments, allowing organizations to flexibly adjust permissions globally according to specific needs.

[0035] In various embodiments, the hardware implementation of the technology can directly adopt existing intelligent devices, including but not limited to industrial computers, PCs, smart phones, handheld stand-alone computers, floor-standing stand-alone computers, etc. Its input device preferably adopts a screen keyboard, its data storage and calculation module adopts existing memory, calculator, controller, its internal communication module adopts existing communication ports and protocols, and its remote communication adopts existing GPRS network, World Wide Internet, etc.

[0036] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0037] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0038] Each functional unit in each embodiment of the present invention may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit. If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and may also instruct the relevant hardware to complete it through a computer program. The computer program may be stored in a computer-readable storage medium, and the computer program may implement the steps of the above-mentioned method embodiments when executed by the processor. . Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electrical carrier signals and telecommunications signals.

[0039] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the scope of protection of the claims of the present invention.

Claims

1. A data structure for compressing and storing power grid data based on a data source, characterized in that: It includes data compression and structured storage based on the data source. Through data compression technology, only one copy of public data is stored regardless of how many uses the data has, and it is stored in the database closest to the original data source. This data structure has significant advantages in data transmission.

2. A data structure for compressing and storing power grid data based on a data source according to claim 1, characterized in that: It includes multiple primary databases, each of which includes compressed data from specific power workers. The compressed data is data generated by the power workers during their work, and is stored in the primary database closest to the data source after data compression. The data source is identified by a unique number, and the number of compressed data stored is always kept at 0 or 1. The sum of all the primary databases constitutes a data security center to centrally manage and protect all compressed data.

3. The data structure for compressing and storing power grid data based on a data source according to claim 1, characterized in that: The data security center includes generating multi-level discrete data units based on compressed data content: Primary discrete data unit: extract keywords from compressed data to form primary discrete data units, and process them according to the three dimensions of semantics, time and space; Secondary discrete data unit: Multiple primary discrete data units can be associated and combined to form secondary discrete data units; Central data storage unit: In the data security center, multiple secondary discrete data units can be associated and combined to form a central data unit.

4. A secure access algorithm based on the power grid data compression storage data structure of claim 1, characterized in that: The following steps are involved: Receive the user's access request, determine the identity and authority of the requesting user, and grant access rights based on the data source number. If the number of the access terminal corresponds to the data source number, access is directly allowed; If they do not correspond, additional access rights must be applied for. The visitor submits a request to the data security center. The data security center receives the visitor's access request and determines the visitor's identity and authority based on the data source number. Based on the visitor's identity and authority, the center decides whether to allow the visitor to access specific compressed data. If access is allowed, the corresponding compressed data is provided to the visitor.

5. The secure access algorithm for the data structure of power grid data compression storage based on the data source according to claim 4 is characterized in that: The data security center comes with an initial security access algorithm framework. The database is designed based on the data source number, so each stored data naturally contains the source number. This storage structure itself provides support for the basic framework of the security algorithm. On this basic framework, access rights are further refined and modified. By aggregating the data source numbers and sending them to the access ports, the relevant ports can obtain higher levels of access rights.

6. The secure access algorithm for the data structure of power grid data compression storage based on the data source according to claim 4 is characterized in that: The basic framework of the security access algorithm itself has a multi-level structure. The data security center is built based on the data source, and the data source numbering is hierarchical, so it naturally has the basic framework of the security access algorithm.

7. A secure access algorithm for a data structure of power grid data compression storage based on a data source according to claim 4 or 5, characterized in that: In addition to refining and modifying access rights by aggregating data source numbers and sending them to access ports, access rights can also be flexibly adjusted globally and extensively by adjusting the smallest unit of the data source number in the access rights.

8. The secure access algorithm for the data structure of power grid data compression storage based on the data source according to claim 7 is characterized in that: The minimum unit of the data source number is constructed to correspond to the hierarchy of the data source number.