CN117032996B - Power metadata management method and system - Google Patents
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Abstract
The invention belongs to the technical field of data management and power big data, and provides a power metadata management method and system, which specifically comprises the following steps: firstly, distributed power information storage environment arrangement is carried out, wherein the power information storage environment comprises a server, then operation load information is collected at the server, and metadata portraits are calculated according to the obtained operation load information; finally, the model decision is made by comparing the metadata portraits of the servers. The method has the advantages that accurate data reference is provided for the relational data model selected by the server, so that the robustness and the sustainability of the power data storage strategy are improved, the execution efficiency of power application and power data at a client or a calling end is improved, a model construction system of the power data is optimized, and the operation pressure of a processor in the server is reduced.
Description
Technical Field
The invention belongs to the technical field of data management and power big data, and particularly relates to a power metadata management method and system.
Background
Today big data technology is mature day by day, and the big data of electric power trade rises, provides the opportunity for helping electric power trade realize more high-efficient, more reliable, more intelligent energy production, transmission and consumption, in electric power market analysis direction, utilizes big data analysis technology, can predict electric power market price. By analyzing historical price trends, energy market influencing factors and the like, a model can be built to predict future power price changes, help power companies and consumers to make better power purchasing decisions, or by analyzing behaviors of all participants (power generators, power distributors, consumers and the like) in the power market, including transaction modes, competition strategies, supply capacity and the like, the power supply and demand relationship and market competition condition of the market can be known. In order to provide insight into the market, it is particularly important to help various participants better understand market conditions, predict trends, make decisions and optimize operation, achieve more efficient and stable and sustainable power market operation, build and adjust rational power big data applications.
However, the built-power analysis model is necessarily different according to different requirements, and different data structures can be used for storing different operation models in a server so as to optimize performance, memory use and calculation efficiency. Different computing models may have different data access patterns, computing requirements, and data associations, so selecting an appropriate data structure can greatly impact the performance of the server. There may be multiple designs of relational data models for the same input source data. A relational database model is a method of storing data in tables, each table containing a plurality of rows and columns, each row representing a record and each column representing an attribute. In a relational database, the same data can be stored using different table structures and relationships to accommodate different query and application requirements, and query efficiency or application efficiency of the power analysis model is of great relevance to the selection of the relational data model. However, a large amount of power data is collected on the server at any time, and once the relational data model is determined, the relational data model cannot be easily changed, so that when a new server is established, model decision needs to be made according to the application degree or the demand degree of the power analysis model, and a plurality of power analysis models often exist in the server, so that a model decision method needs to be specified according to the real demand of the data source or the application source corresponding to the server.
Disclosure of Invention
The present invention is directed to a method and a system for managing metadata of electric power, which solve one or more technical problems existing in the prior art, and at least provide a beneficial choice or creation condition.
To achieve the above object, according to an aspect of the present invention, there is provided a power metadata management method including the steps of:
s100, arranging a distributed power information storage environment, wherein the power information storage environment comprises a server;
s200, collecting operation load information in a server;
s300, calculating a metadata portrait according to the obtained operation load information;
s400, comparing metadata portraits of all servers to make model decisions.
Further, in step S100, the distributed power information storage environment is arranged, wherein the method in which the power information storage environment includes a server is: a plurality of servers exist in the power information storage environment, each server is respectively connected with a plurality of power analyzers, the power analyzers collect data in real time, and after one power analyzer collects the data, the data are uploaded to a designated server, wherein the types of the data collected by the power analyzers comprise voltage, current, power, electric energy, frequency and power factor; the server is pre-configured with a plurality of big data operation models, and each big data operation model is used as an operation end respectively, wherein the number of the operation ends is recorded as NMdl; when a user or an application program in the client sends an operation end calling request to the server, the server performs operation by combining data stored by the server through the corresponding operation end and returns the operation result to the server.
Further, in step S200, the method for collecting the operational load information at the server is as follows: setting a time period as tg and tg epsilon [6,24] hours by taking a user or a client as an operation end, and recording calling frequency NTrs of the operation end in tg in a server, wherein the calling frequency is the number of times the operation end is called; the CPU occupancy rate of the operation end obtained by the server in real time is used as the load degree of the operation end; the load degree of the operation end is used as operation load information.
Further, in step S300, the method of calculating the metadata representation from the obtained computation load information is: setting a time period as a detection period tw, wherein tw is [20,60] minutes, forming a unit load once every tw, and forming a unit load method is as follows: taking an average value of the load degrees of the operation end at all moments in a detection period as a unit load of the operation end in the detection period; setting a time period as tg, tg epsilon [6,24] hours, taking unit loads of different detection time periods in tg time periods of the same operation end as one row, taking unit loads of different operation ends of the same detection time period as one column, and constructing a matrix as a detection matrix LMX; in any detection period, defining the total length of time when the load degree of an operation end is not zero as the calling time of the operation end in the detection period;
in the LMX, the maximum value of the unit loads corresponding to different operation ends in the same detection period is recorded as Mx_ULD, the calling time of the operation end corresponding to the Mx_ULD in the detection period is recorded as ap_tg, and the unit loads except the Mx_ULD in the detection period form a sequence and are recorded as nULD_Ls; the method for calculating the jump index Ac_Amx of one detection period comprises the following steps:
；
wherein hm </SUB > is a harmonic mean function; obtaining the maximum value and the minimum value of the corresponding unit load of an operation end in the LMX in all detection time periods to be respectively recorded as MzU and MnU; the calculation method for obtaining the sub-image value sMtDt_Pt of one operation end comprises the following steps:
；
wherein j1 is an accumulation variable, and the jump index of the j1 detection period is marked as Ac_Amx j1 Nj1 is the total number of detection periods;
constructing a sequence of sub-image values of each operation terminal as metadata images MtDT_Pt, mtDT_Pt= [ sMtDt_Pt 1 ，sMtDt_Pt 2 ,…sMtDt_Pt NFD ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein the method comprises the steps ofNFD is the number of computation terminals, sMtDt_Pt NFD The sub-image value of the NFD operation terminal.
Because the metadata portrait is obtained by matching and fitting according to the load degree and the detection time period, the application degree or the demand degree of the power analysis model is effectively quantified by utilizing the two key data, however, the detection time period is fixed, which possibly leads to the acquisition of the load degree, the sensitivity of the jump index obtained in a certain detection time period is influenced, the tiny difference in each detection time period can not be effectively amplified or identified, the quantification sensitivity or the feedback efficiency is greatly reduced, and no viable technology exists at present to compensate the insufficient quantification phenomenon caused by the method, so that the invention provides a more preferable scheme for eliminating the influence of the fixed detection time period on the splitting caused by the load data acquisition:
preferably, in step S300, the method for calculating the metadata representation from the obtained computation load information is as follows:
setting a time period as tg, wherein tg is 6,24 hours, and the number of operation terminals is recorded as NFD; constructing a matrix as a time measurement matrix TMX by taking the load degrees of different operation ends at the same time as one row and taking the load degrees of the same operation end at different times as one column in the tg period; for any moment, taking the operation end with the maximum value in each load degree at the moment as the high-selection operation end at the moment;
for any operation end, if the operation end is a high-selection operation end at a corresponding time at any time, defining the time as a high-selection scale of the operation end, and then intercepting all columns corresponding to the high-selection scale from a time measurement matrix to be used as a high-selection matrix sub_TMX of the operation end; the calculation method for obtaining the cyclic tone consumption ratio of the high selection matrix corresponding to the operation end by calculation comprises the following steps: taking the difference value of the serial number value of a high-selection scale in the operation end and the serial number value of the corresponding column of the previous high-selection scale in the time measurement matrix as the sub-cyclic modulation time frequency of the high-selection scale, and recording the average value of the sub-cyclic modulation time frequencies of each high-selection scale in the operation end as the cyclic modulation time frequency sub_tg of the operation end; namely, the operation end corresponds to the cyclic timing frequency sub_tg of the high selection matrix;
the average value of the load degrees of any one operation end corresponding to all moments in the high selection matrix is obtained and used as the call uniform load PUC of the operation end in the high selection matrix; obtaining the maximum value and the minimum value in the high-selection matrix and respectively marking the maximum value and the minimum value as MzUC and MnUC; the ratio of the average value of each element in the time measurement matrix of an operation end to the average value of each element in the high selection matrix of the operation end is used as the cyclic adjustment return level ATF of the operation end, and the cyclic adjustment consumption ratio AP_CQ is calculated and obtained:
；
wherein i1 is an accumulation variable, PUC i1 The load is the call of the ith operation end 1;
according to the corresponding cyclic adjustment consumption ratio of each operation end in each high selection matrix, each element in the time measurement matrix has the corresponding cyclic adjustment consumption ratio, and the average value of the cyclic adjustment consumption ratios corresponding to each element in the time measurement matrix is obtained and recorded as lv_AQ; if the corresponding cyclic adjustment consumption ratio of any element in the time measurement matrix is larger than lv_AQ, the element is called as a cyclic adjustment attention object; the proportion of the target of the follow-up adjustment in a computing end is taken as the proportion of the target of the follow-up adjustment RtSR of the computing end, and the follow-up adjustment consumption ratio of the target of the follow-up adjustment is constructed into a sequence and is marked as a consumption ratio sequence ls_AQ; calculating a sub-image value sMtDt_Pt of an operation end according to each circulating attention object of the operation end:
；
wherein mean is an average function, and the average value of each element in the calling sequence is calculated through the average function; exp () is an exponential function with a natural constant e as a base;
constructing a sequence as metadata image mtdt_pt, mtdt_pt= [ smtdt_pt by sub-image values of each operation terminal 1 ，sMtDt_Pt 2 ,…sMtDt_Pt NFD ]。
The beneficial effects are that: the sub-image values of the metadata image are constructed by quantifying the application degree or the demand degree of the power analysis model at each moment, so that the sensitivity of the high-demand model data is further improved by utilizing the follow-up attention object, the purpose of quantifying and amplifying the characteristics of the sub-image values is realized, meanwhile, when the follow-up consumption ratio is calculated, the follow-up time-frequency rationalization and the refinement of each adopted operation end are realized by setting constraint conditions, the defect that data acquisition possibly generates cutting is overcome, and the sensitivity or the feedback efficiency of the metadata image is improved. The application degree or the demand degree of the power analysis model is quantized through metadata portrait, precise numerical quantization data are formed for numerical feature quantization of a region of interest in the portrait, effective quantization of model demand characteristics is formed, accurate data reference is provided for a relational data model selected for a server, and therefore robustness and sustainability of a power data storage strategy are improved.
Further, in step S400, the method for making model decisions for comparing metadata portraits of respective servers is as follows: the server is provided with a plurality of relational data models, the relational data models are used as layout schemes, and each operation end is preset with a matching weight value phi of each layout scheme, wherein phi is E [0,1]; according to the metadata portraits of each server, obtaining an average value of sub-picture values of an operation end in each server as a first parameter illuminance UPT of the operation end; normalizing the first reference degree of each operation end;
the number of the operation terminals is used as NFD, a variable k is set as the serial number of the operation terminals, k is E [1, NFD]According to the first parameter illumination of the operation end and the matching weight value of each layout scheme corresponding to the operation end, calculating a decision reference value REN of one layout scheme, REN=mean { UPT k ×φ k }, UPT therein k And phi k The first parameter illumination of the kth operation end and the matching weight value of the kth operation end and the layout scheme are respectively; and selecting a layout scheme with the maximum decision reference value as a model decision result, and sending the model decision result to a client of an administrator.
Preferably, in step S400, the method for making model decisions for comparing metadata portraits of respective servers is as follows: each server sends the metadata portraits to the client of the administrator, and the administrator makes model decisions according to the metadata portraits of each server, wherein the model decisions refer to designing, selecting or setting a relational data model.
Preferably, all undefined variables in the present invention, if not explicitly defined, may be thresholds set manually.
The invention also provides a power metadata management system, which comprises: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements steps in the power metadata management method when the processor executes the computer program, the power metadata management system may be executed in a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud data center, and the like, and the executable system may include, but is not limited to, a processor, a memory, and a server cluster, and the processor executes the computer program to execute in units of the following system:
a distributed storage environment arrangement unit configured to perform distributed power information storage environment arrangement, wherein the power information storage environment includes a server;
the operation calling information acquisition unit is used for acquiring operation load information at the server;
a representation model construction unit for calculating a metadata representation based on the obtained operation load information;
and the storage model decision unit is used for comparing the metadata portraits of the servers to carry out model decision.
The beneficial effects of the invention are as follows: the invention provides a power metadata management method and a system, which are used for forming precise numerical quantization data by quantizing numerical characteristics of a region of interest in an image, forming effective quantization of model demand characteristics, and providing accurate data reference for a relational data model further selected for a server, so that the robustness and the sustainability of a power data storage strategy are improved, the execution efficiency of power application and power data at a client or a calling end is improved, a model construction system of the power data is optimized, and the operation pressure of a processor in the server is reduced.
Drawings
The above and other features of the present invention will become more apparent from the detailed description of the embodiments thereof given in conjunction with the accompanying drawings, in which like reference characters designate like or similar elements, and it is apparent that the drawings in the following description are merely some examples of the present invention, and other drawings may be obtained from these drawings without inventive effort to those of ordinary skill in the art, in which:
FIG. 1 is a flow chart of a method of power metadata management;
fig. 2 is a diagram showing a structure of a power metadata management system.
Detailed Description
The conception, specific structure, and technical effects produced by the present invention will be clearly and completely described below with reference to the embodiments and the drawings to fully understand the objects, aspects, and effects of the present invention. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other.
Referring to fig. 1, which is a flowchart illustrating a power metadata management method, a power metadata management method according to an embodiment of the present invention is described below with reference to fig. 1, and includes the steps of:
s100, arranging a distributed power information storage environment, wherein the power information storage environment comprises a server;
s200, collecting operation load information in a server;
s300, calculating a metadata portrait according to the obtained operation load information;
s400, comparing metadata portraits of all servers to make model decisions.
Further, in step S100, the distributed power information storage environment is arranged, wherein the method in which the power information storage environment includes a server is: a plurality of servers exist in the power information storage environment, each server is respectively connected with a plurality of power analyzers, the power analyzers collect data in real time, and after one power analyzer collects the data, the data are uploaded to a designated server, wherein the types of the data collected by the power analyzers comprise voltage, current, power, electric energy, frequency and power factor; the server is pre-configured with a plurality of big data operation models, and each big data operation model is used as an operation end respectively, wherein the number of the operation ends is recorded as NMdl; when a user or an application program in the client sends an operation end calling request to the server, the server performs operation by combining data stored by the server through the corresponding operation end and returns the operation result to the server.
Further, in step S200, the method for collecting the operational load information at the server is as follows: setting a time period as tg and tg epsilon [6,24] hours by taking a user or a client as an operation end, and recording calling frequency NTrs of the operation end in tg in a server, wherein the calling frequency is the number of times the operation end is called; the CPU occupancy rate of the operation end obtained by the server in real time is used as the load degree of the operation end; the load degree of the operation end is used as operation load information.
Further, in step S300, the method of calculating the metadata representation from the obtained computation load information is: setting a time period as a detection period tw, wherein tw is [20,60] minutes, forming a unit load once every tw, and forming a unit load method is as follows: taking an average value of the load degrees of the operation end at all moments in a detection period as a unit load of the operation end in the detection period; setting a time period as tg, tg epsilon [6,24] hours, taking unit loads of different detection time periods in tg time periods of the same operation end as one row, taking unit loads of different operation ends of the same detection time period as one column, and constructing a matrix as a detection matrix LMX; in any detection period, defining the total length of time when the load degree of an operation end is not zero as the calling time of the operation end in the detection period;
in the LMX, the maximum value of the unit loads corresponding to different operation ends in the same detection period is recorded as Mx_ULD, the calling time of the operation end corresponding to the Mx_ULD in the detection period is recorded as ap_tg, and the unit loads except the Mx_ULD in the detection period form a sequence and are recorded as nULD_Ls; the method for calculating the jump index Ac_Amx of one detection period comprises the following steps:
；
wherein hm </SUB > is a harmonic mean function; obtaining the maximum value and the minimum value of the corresponding unit load of an operation end in the LMX in all detection time periods to be respectively recorded as MzU and MnU; the calculation method for obtaining the sub-image value sMtDt_Pt of one operation end comprises the following steps:
；
wherein j1 is an accumulation variable, and the jump index of the j1 detection period is marked as Ac_Amx j1 Nj1 is the total number of detection periods;
constructing a sequence of sub-image values of each operation terminal as metadata images MtDT_Pt, mtDT_Pt= [ sMtDt_Pt 1 ，sMtDt_Pt 2 ,…sMtDt_Pt NFD ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein NFD is the number of computation terminals, sMtDt_Pt NFD The sub-image value of the NFD operation terminal.
Preferably, in step S300, the method for calculating the metadata representation from the obtained computation load information is as follows: setting a time period as tg, wherein tg is 6,24 hours, and the number of operation terminals is recorded as NFD; constructing a matrix as a time measurement matrix TMX by taking the load degrees of different operation ends at the same time as one row and taking the load degrees of the same operation end at different times as one column in the tg period; for any moment, taking the operation end with the maximum value in each load degree at the moment as the high-selection operation end at the moment;
for any operation end, if the operation end is a high-selection operation end at a corresponding time at any time, defining the time as a high-selection scale of the operation end, and then intercepting all columns corresponding to the high-selection scale from a time measurement matrix to be used as a high-selection matrix sub_TMX of the operation end; the calculation method for obtaining the cyclic tone consumption ratio of the high selection matrix corresponding to the operation end by calculation comprises the following steps: taking the difference value of the serial number value of a high-selection scale in the operation end and the serial number value of the corresponding column of the previous high-selection scale in the time measurement matrix as the sub-cyclic modulation time frequency of the high-selection scale, and recording the average value of the sub-cyclic modulation time frequencies of each high-selection scale in the operation end as the cyclic modulation time frequency sub_tg of the operation end; namely, the operation end corresponds to the cyclic timing frequency sub_tg of the high selection matrix;
the average value of the load degrees of any one operation end corresponding to all moments in the high selection matrix is obtained and used as the call uniform load PUC of the operation end in the high selection matrix; obtaining the maximum value and the minimum value in the high-selection matrix and respectively marking the maximum value and the minimum value as MzUC and MnUC; the ratio of the average value of each element in the time measurement matrix of an operation end to the average value of each element in the high selection matrix of the operation end is used as the cyclic adjustment return level ATF of the operation end, and the cyclic adjustment consumption ratio AP_CQ is calculated and obtained:
；
wherein i1 is an accumulation variable, PUC i1 The load is the call of the ith operation end 1;
according to the corresponding cyclic adjustment consumption ratio of each operation end in each high selection matrix, each element in the time measurement matrix has the corresponding cyclic adjustment consumption ratio, and the average value of the cyclic adjustment consumption ratios corresponding to each element in the time measurement matrix is obtained and recorded as lv_AQ; if the corresponding cyclic adjustment consumption ratio of any element in the time measurement matrix is larger than lv_AQ, the element is called as a cyclic adjustment attention object; the proportion of the target of the follow-up adjustment in a computing end is taken as the proportion of the target of the follow-up adjustment RtSR of the computing end, and the follow-up adjustment consumption ratio of the target of the follow-up adjustment is constructed into a sequence and is marked as a consumption ratio sequence ls_AQ; calculating a sub-image value sMtDt_Pt of an operation end according to each circulating attention object of the operation end:
；
wherein mean is an average function, and the average value of each element in the calling sequence is calculated through the average function; exp () is an exponential function with a natural constant e as a base;
constructing a sequence as metadata image mtdt_pt, mtdt_pt= [ smtdt_pt by sub-image values of each operation terminal 1 ，sMtDt_Pt 2 ,…sMtDt_Pt NFD ]。
Further, in step S400, the method for making model decisions for comparing metadata portraits of respective servers is as follows: the server is provided with a plurality of relational data models, the relational data models are used as layout schemes, and each operation end is preset with a matching weight value phi of each layout scheme, wherein phi is E [0,1]; according to the metadata portraits of each server, obtaining an average value of sub-picture values of an operation end in each server as a first parameter illuminance UPT of the operation end; normalizing the first reference degree of each operation end;
the number of the operation terminals is used as NFD, a variable k is set as the serial number of the operation terminals, k is E [1, NFD]According to the first parameter illumination of the operation end and the matching weight value of each layout scheme corresponding to the operation end, calculating a decision reference value REN of one layout scheme, REN=mean { UPT k ×φ k }, UPT therein k And phi k The first parameter illumination of the kth operation end and the matching weight value of the kth operation end and the layout scheme are respectively; and selecting a layout scheme with the maximum decision reference value as a model decision result, and sending the model decision result to a client of an administrator.
Preferably, in step S400, the method for making model decisions for comparing metadata portraits of respective servers is as follows: each server sends the metadata portraits to the client of the administrator, and the administrator makes model decisions according to the metadata portraits of each server, wherein the model decisions refer to designing or setting a relational data model.
An embodiment of the present invention provides a power metadata management system, as shown in fig. 2, which is a structure diagram of the power metadata management system of the present invention, where the power metadata management system of the embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, the processor implementing the steps in one of the power metadata management system embodiments described above when the computer program is executed.
The system comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in units of the following system:
a distributed storage environment arrangement unit configured to perform distributed power information storage environment arrangement, wherein the power information storage environment includes a server;
the operation calling information acquisition unit is used for acquiring operation load information at the server;
a representation model construction unit for calculating a metadata representation based on the obtained operation load information;
and the storage model decision unit is used for comparing the metadata portraits of the servers to carry out model decision.
The power metadata management system can be operated in computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The power metadata management system may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that the examples are merely examples of one power metadata management system and are not limiting of one power metadata management system, and may include more or fewer components than examples, or may combine certain components, or different components, e.g., the one power metadata management system may further include input and output devices, network access devices, buses, etc.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center of the power metadata management system operation system, and connects various parts of the entire power metadata management system operation system using various interfaces and lines.
The memory may be used to store the computer program and/or module, and the processor may implement various functions of the power metadata management system by running or executing the computer program and/or module stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Although the present invention has been described in considerable detail and with particularity with respect to several described embodiments, it is not intended to be limited to any such detail or embodiment or any particular embodiment so as to effectively cover the intended scope of the invention. Furthermore, the foregoing description of the invention has been presented in its embodiments contemplated by the inventors for the purpose of providing a useful description, and for the purposes of providing a non-essential modification of the invention that may not be presently contemplated, may represent an equivalent modification of the invention.
Claims (7)
1. A method of power metadata management, the method comprising the steps of:
s100, arranging a distributed power information storage environment, wherein the power information storage environment comprises a server;
s200, collecting operation load information in a server;
s300, calculating a metadata portrait according to the obtained operation load information;
s400, comparing metadata portraits of all servers to make model decisions;
in step S300, the method for calculating the metadata representation from the obtained computation load information includes: setting a time period as tg, and recording the number of operation terminals as NFD; constructing a matrix as a time measurement matrix TMX by taking the load degrees of different operation ends at the same time as one row and taking the load degrees of the same operation end at different times as one column in the tg period; for any moment, taking the operation end with the maximum value in each load degree at the moment as the high-selection operation end at the moment;
for any operation end, if the operation end is a high-selection operation end at a corresponding moment at any moment, defining the moment as a high-selection scale of the operation end, and then intercepting all columns corresponding to the high-selection scale from a time measurement matrix to be used as a high-selection matrix of the operation end; the calculation method for obtaining the cyclic tone consumption ratio of the high selection matrix corresponding to the operation end by calculation comprises the following steps: taking the difference value of the serial number value of a high-selection scale in the operation end and the serial number value of the corresponding column of the previous high-selection scale in the time measurement matrix as the sub-cyclic modulation time frequency of the high-selection scale, and recording the average value of the sub-cyclic modulation time frequencies of each high-selection scale in the operation end as the cyclic modulation time frequency of the operation end;
the average value of the load degrees of any one operation end corresponding to all moments in the high selection matrix is obtained and used as the call average load of the operation end in the high selection matrix; the ratio of the average value of each element in the time measurement matrix of an operation end to the average value of each element in the high selection matrix of the operation end is used as the cyclic adjustment return level of the operation end, and the cyclic adjustment consumption ratio is obtained according to the cyclic adjustment return level and the cyclic adjustment time-frequency calculation:
according to the corresponding cyclic adjustment consumption ratio of each operation end in each high selection matrix, each element in the time measurement matrix has the corresponding cyclic adjustment consumption ratio, and the average value of the cyclic adjustment consumption ratios corresponding to each element in the time measurement matrix is obtained and recorded as lv_AQ; if the corresponding cyclic adjustment consumption ratio of any element in the time measurement matrix is larger than lv_AQ, the element is called as a cyclic adjustment attention object; the proportion of the circulating and regulating objects of interest in one operation end is taken as the proportion of interest in the operation end, and the circulating and regulating consumption ratio of the circulating and regulating objects of interest is constructed into a sequence and recorded as a consumption ratio sequence; calculating sub-image values of the target objects of interest by combining all the cyclic adjustment of the operation terminals, and combining the sub-image values of all the operation terminals to construct a sequence serving as a metadata image;
in step S400, the method for comparing metadata portraits of each server to make a model decision is as follows: each server sends the metadata portraits to the client of the administrator, and the administrator makes model decisions according to the metadata portraits of each server, wherein the model decisions refer to designing or setting a relational data model.
2. The power metadata management method according to claim 1, wherein in step S100, the distributed power information storage environment is arranged, and wherein the method in which the power information storage environment includes a server is: a plurality of servers exist in the power information storage environment, each server is respectively connected with a plurality of power analyzers, the power analyzers collect data in real time, and after one power analyzer collects the data, the data are uploaded to a designated server, wherein the types of the data collected by the power analyzers comprise voltage, current, power, electric energy, frequency and power factor; the server is pre-configured with a plurality of big data operation models, and each big data operation model is used as an operation end respectively, wherein the number of the operation ends is recorded as NMdl; when a user or an application program in the client sends an operation end calling request to the server, the server performs operation by combining data stored by the server through the corresponding operation end and returns the operation result to the server.
3. The method for managing power metadata according to claim 1, wherein in step S200, the method for collecting the operational load information at the server is: setting a time period as tg and tg epsilon [6,24] hours by taking a user or a client as an operation end, and recording calling frequency NTrs of the operation end in tg in a server, wherein the calling frequency is the number of times the operation end is called; the CPU occupancy rate of the operation end obtained by the server in real time is used as the load degree of the operation end; the load degree of the operation end is used as operation load information.
4. The method according to claim 1, wherein in step S300, the method for calculating the metadata representation from the obtained computation load information is: setting a time period as a detection period, wherein every other detection period, the average value of the load degrees in the detection period is used as the unit load in the detection period; setting a time period as tg, constructing a matrix to be marked as LMX by taking unit loads of different detection time periods and different operation ends in tg as rows and columns of the matrix respectively, and defining the length of time with the load degree of the operation end not being zero as the calling time of the operation end;
in the LMX, the maximum value of the unit loads corresponding to different operation ends in the same detection period is recorded as Mx_ULD, the calling time of the operation end corresponding to the Mx_ULD in the detection period is recorded as ap_tg, and the unit loads except the Mx_ULD in the detection period form a sequence and are recorded as nULD_Ls; calculating and obtaining a jump index Ac_Amx of a detection period according to the nULD_ls; and obtaining the maximum value and the minimum value of the unit load of one operation end in all detection periods in the LMX, calculating the sub-image values of the operation end by combining the jump indexes of the operation end, and combining the sub-image values of the operation ends to construct a sequence serving as a metadata image.
5. The method for power metadata management according to claim 1, wherein in step S400, the method for making model decisions for comparing metadata portraits of respective servers is as follows: the server is provided with a plurality of relational data models, the relational data models are used as layout schemes, and each operation end is preset with a matching weight value phi of each layout scheme, wherein phi is E [0,1]; according to the metadata portraits of each server, obtaining an average value of sub-picture values of an operation end in each server as a first parameter illuminance UPT of the operation end; normalizing the first reference degree of each operation end;
the number of the operation terminals is used as NFD, a variable k is set as the serial number of the operation terminals, k is E [1, NFD]According to the first parameter illumination of the operation end and the matching weight value of each layout scheme corresponding to the operation end, calculating a decision reference value REN of one layout scheme, REN=mean { UPT k ×φ k }, UPT therein k And phi k The first parameter illumination of the kth operation end and the matching weight value of the kth operation end and the layout scheme are respectively; and selecting a layout scheme with the maximum decision reference value as a model decision result, and sending the model decision result to a client of an administrator.
6. The method for power metadata management according to claim 1, wherein in step S400, the method for making model decisions for comparing metadata portraits of respective servers is as follows: each server sends the metadata portraits to the client of the administrator, and the administrator makes model decisions according to the metadata portraits of each server, wherein the model decisions refer to designing or setting a relational data model.
7. A power metadata management system, the power metadata management system comprising: a processor, a memory and a computer program stored in the memory and executable on the processor, the processor implementing the steps in a power metadata management method according to any one of claims 1 to 6 when the computer program is executed, the power metadata management system being run in a computing device of a desktop computer, a notebook computer, a palm computer and a cloud data center.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101697526A (en) * | 2009-10-10 | 2010-04-21 | 中国科学技术大学 | Method and system for load balancing of metadata management in distributed file system |
| CN103116306A (en) * | 2013-02-05 | 2013-05-22 | 浙江大学 | Automatic stepping type ordered time interval dividing method |
| CN104881424A (en) * | 2015-03-13 | 2015-09-02 | 国家电网公司 | Regular expression-based acquisition, storage and analysis method of power big data |
| CN111694636A (en) * | 2020-05-11 | 2020-09-22 | 国网江苏省电力有限公司南京供电分公司 | Electric power Internet of things container migration method oriented to edge network load balancing |
| CN113608876A (en) * | 2021-08-12 | 2021-11-05 | 中国科学技术大学 | Distributed file system metadata load balancing method based on load type perception |
| WO2022193569A1 (en) * | 2021-03-15 | 2022-09-22 | 南京邮电大学 | Method and system for optimizing turbine of thermal power unit on basis of sparse big data mining |
| CN115543608A (en) * | 2022-09-06 | 2022-12-30 | 广西电网有限责任公司 | Method and device for rapidly accounting reference load of ordered power utilization users |
| CN116131256A (en) * | 2023-01-04 | 2023-05-16 | 中国电力科学研究院有限公司 | Method and system for calculating openable capacity of power distribution network based on equipment synchronization rate |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2350770A4 (en) * | 2008-10-21 | 2012-09-05 | Raritan Americas Inc | Methods of achieving cognizant power management |
| KR101942647B1 (en) * | 2017-04-04 | 2019-01-25 | 한국전력공사 | Apparatus for asset management of power transformers |
| US11237866B2 (en) * | 2017-05-04 | 2022-02-01 | Salesforce.Com, Inc. | Systems, methods, and apparatuses for implementing a scheduler and workload manager with scheduling redundancy and site fault isolation |
-
2023
- 2023-10-09 CN CN202311293424.7A patent/CN117032996B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101697526A (en) * | 2009-10-10 | 2010-04-21 | 中国科学技术大学 | Method and system for load balancing of metadata management in distributed file system |
| CN103116306A (en) * | 2013-02-05 | 2013-05-22 | 浙江大学 | Automatic stepping type ordered time interval dividing method |
| CN104881424A (en) * | 2015-03-13 | 2015-09-02 | 国家电网公司 | Regular expression-based acquisition, storage and analysis method of power big data |
| CN111694636A (en) * | 2020-05-11 | 2020-09-22 | 国网江苏省电力有限公司南京供电分公司 | Electric power Internet of things container migration method oriented to edge network load balancing |
| WO2022193569A1 (en) * | 2021-03-15 | 2022-09-22 | 南京邮电大学 | Method and system for optimizing turbine of thermal power unit on basis of sparse big data mining |
| CN113608876A (en) * | 2021-08-12 | 2021-11-05 | 中国科学技术大学 | Distributed file system metadata load balancing method based on load type perception |
| CN115543608A (en) * | 2022-09-06 | 2022-12-30 | 广西电网有限责任公司 | Method and device for rapidly accounting reference load of ordered power utilization users |
| CN116131256A (en) * | 2023-01-04 | 2023-05-16 | 中国电力科学研究院有限公司 | Method and system for calculating openable capacity of power distribution network based on equipment synchronization rate |
Non-Patent Citations (1)
| Title |
|---|
| 风电场输出功率的多时段联合概率密度预测;杨明;朱思萌;韩学山;王洪涛;;电力系统自动化(10);全文 * |
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Denomination of invention: A method and system for managing power metadata Granted publication date: 20231222 Pledgee: Bank of Beijing Limited by Share Ltd. Changsha branch Pledgor: Hunan Zhongqingneng Technology Co.,Ltd. Registration number: Y2024980028185 |