Power data analysis method and system based on data center

Abstract

The invention relates to the field of power data in channels, in particular to a power data analysis method and system based on a data in channel. First power data in a data middle table is evaluated from three dimensions of a first importance degree, a second importance degree and a third importance degree, and meanwhile, the influence degree of the first importance degree, the second importance degree and the third importance degree on the total importance degree is further evaluated; the weight of the first importance degree, the weight of the second importance degree and the weight of the third importance degree are gradually adjusted by constructing an evaluation matrix, and finally the total importance degree of the first power data is calculated according to the first importance degree, the second importance degree, the third importance degree and the corresponding weight, so that the priority of the first power data in the transmission process is dynamically changed. It is ensured that data of various important degrees can be effectively transmitted; the problem that some low-importance data cannot be transmitted all the time due to the fact that different importance levels are preset for data from different sources is solved.

Description

Technical Field

[0001] The present invention relates to the field of power data middle platform, and specifically to a power data analysis method and system based on a data middle platform. Background Art

[0002] Different types of data assets are gathered in the data middle platform, and different clients obtain data through the data middle platform. At present, with the establishment of the data middle platform of the power system, a large amount of data covering grid control, power asset trading, and power energy management are concentrated in the data middle platform, which effectively guarantees the digital transformation of power-related companies.

[0003] However, due to the huge amount of data transmission in the data center, it is difficult to ensure that all data can be transmitted as soon as possible after receiving the request. The traditional method is to pre-set different importance levels for data from different sources. However, this method may cause different high-importance levels of data to be in transmission status during the data transmission process, resulting in low-importance data being unable to be transmitted at all times, making it difficult to transmit some low-importance data in a timely manner, thus posing a hidden danger to the normal operation of the enterprise. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides an electric power data analysis method and system based on a data middle platform, which solves the problem that pre-setting different importance levels for data from different sources may cause some low-importance data to never be transmitted.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A power data analysis method based on a data center, the method comprising the following steps:

[0007] S1, obtaining first power data in a transmission queue;

[0008] S2. Setting a plurality of data types, setting a first basic importance of each first power data under a different data type, and calculating a first importance of each first power data;

[0009] S3, counting the number of accesses to each first power data at any time within a certain period of time, and calculating the second importance of each first power data that is positively correlated with the number of accesses based on the number of accesses and the first importance;

[0010] S4, counting the queuing time of each first power data in the transmission queue, and calculating the third importance of the first power data being positively correlated with the queuing time;

[0011] S5. Evaluate the weights of the first importance, the second importance, and the third importance, and calculate the total importance of each first power data.

[0012] Preferably, in step S2, the following steps are specifically included:

[0013] S21. Set a number of data type indicators and establish a data type indicator set;

[0014] S22, constructing a transmission data set according to the acquired first power data in the transmission queue;

[0015] S23, setting a first basic importance of each first power data under different data types;

[0016] S24. Construct a first basic importance matrix under any data type according to the first basic importance; the expression of the first basic importance matrix is:

[0017]

[0018] in,

[0019]

[0020] In the above formula, Q n (1) represents the first basic importance matrix under the nth data type, Indicates that in the nth data type Dt n Next, the first power data d i in the transmission queue i With the jth first power data d j The relative importance of Indicates that in the nth data type Dt n The importance of the mth first power data in the transmission queue compared with the first first power data, N represents the data type Dt n The total number of m represents the total number of the first power data;

[0021] S25. Calculate the second basic importance of any first power data compared with another first power data under all data types according to the first basic importance matrix, and construct a second basic importance matrix; the expression of the second basic importance matrix is:

[0022]

[0023] in,

[0024]

[0025] In the above formula, Q(2) represents the second basic importance matrix, Qij Indicates the first power data d i With the first power data d j The second basic importance compared to the others;

[0026] S26. Calculate the third basic importance of any first power data according to the second basic importance matrix; the calculation formula of the third basic importance is:

[0027]

[0028] In the above formula, Q3(d i ) represents any first power data d i The third basic importance of m represents the total number of the first power data, Q ij Indicates the first power data d i With the first power data d j The second basic importance compared to the others;

[0029] S27, setting a first interval whose interval length is less than 1, and normalizing the third basic importance of any first power data and mapping it to the first interval to obtain the first importance of any first power data; the calculation formula of the first importance of any first power data is:

[0030]

[0031] In the above formula, Z1(d i ) represents any first power data d i The first importance of the first interval is [X,1], Q3(d i ) represents any first power data d i The third basic importance, MIN[Q3] and MAX[Q3] represent the minimum and maximum values ​​of the third basic importance.

[0032] Preferably, in step S3, the following steps are specifically included:

[0033] S31, obtaining the number of accesses to each first power data at any time within a certain period of time;

[0034] S32, calculating the basic access times of each first power data in sequence according to the access times of each first power data at any time within a certain period of time;

[0035] S33, calculating the fourth basic importance of any first power data at any time according to the basic access count;

[0036] S34, normalizing the fourth basic importance to obtain the second importance of any power data; the calculation formula of the second importance is:

[0037]

[0038] In the above formula, Z2(d i ) represents any power data d i The second importance, Z4(t n )(d i ) means that at any time t n Any first power data d i The fourth basic importance, the mapping interval of the second importance is X, 1], MIN[Z4(t n )][and MAX[Z4(t n )] are the minimum and maximum values ​​of the fourth basic importance respectively.

[0039] Preferably, in step S32, the following steps are specifically included:

[0040] S321, calculate the initial density radius of each first power data in turn, set the initial iteration number to 1, and set the initial statistical range to all access times; the calculation formula of the initial density radius is:

[0041] R1=N max -N min

[0042] In the above formula, R1 represents the initial density radius, that is, the density radius at the first iteration, N max Indicates the maximum number of accesses to the first power data at any time within a certain period of time, N min Indicates the minimum number of accesses to the first power data at any time within a certain period of time;

[0043] S322, calculating the difference between the number of accesses to any first power data at any two moments to obtain a first number difference;

[0044] S323, sorting the first order differences to obtain the first order differences that are greater than 0 and the smallest, and marking them as the second order differences;

[0045] S324, obtaining the current number of iterations;

[0046] S325, calculating the density radius at the current number of iterations according to the current number of iterations and the initial density radius; the calculation formula of the density radius at the current number of iterations is:

[0047]

[0048] In the above formula, R c Represents the density radius under the current number of iterations, that is, the current number of iterations is c, R1 represents the initial density radius, represents the radius reduction factor;

[0049] S326, determining whether the density radius at the current iteration number is less than or equal to the second number difference, or whether the first number of access times at all times within the current statistical range is equal;

[0050] If yes, then the number of visits at all times within the current statistical range is output, and the process goes to step S329;

[0051] If not, proceed to the next step;

[0052] S327, calculating the first quantity of each first power data at any time within the statistical range according to the density radius of the first power data at the current iteration number;

[0053] S328, extract the maximum number of accesses in the first quantity, and calculate the statistical range of the next iteration according to the density radius under the current iteration number, and then return to step S324; the expression of the statistical range of the next iteration is: In the above formula, Indicates the number of visits with the largest number of first c Indicates the density radius at the current iteration number;

[0054] S329, calculating the average number of access times of the first power data at all times within the current statistical range to obtain a basic number of access times of the first power data.

[0055] Preferably, in step S4, the following steps are specifically included:

[0056] S41, obtaining the queuing time of each first power data in the transmission queue;

[0057] S42, normalize the queuing time of any first power data to obtain the third importance of each first power data; the calculation formula of the first power data is:

[0058]

[0059] In the above formula, Z3(d i ) represents any first power data d i The third importance of the third importance, the mapping interval of the third importance is [X,1], T(d i ) represents any first power data d i The queuing time in the transmission queue, MAX[T] and MAX[T] respectively represent the minimum and maximum queuing time of the first power data in the transmission queue.

[0060] Preferably, in step S5, the following steps are specifically included:

[0061] S51. Setting a number of evaluation levels and corresponding evaluation indexes;

[0062] S52, marking the first importance, the second importance, and the third importance as importance types, and determining the evaluation level and evaluation index of any importance type relative to the other importance types in turn according to the evaluation level;

[0063] S53, constructing an evaluation matrix according to the evaluation level and evaluation index of any importance type relative to the other importance types; the expression of the evaluation matrix is:

[0064]

[0065] In the above formula, B represents the evaluation matrix, B i'j' Represents the evaluation index of the i'th importance type corresponding to the j'th importance type;

[0066] S54, calculating the weight of any importance type according to the evaluation matrix; the calculation formula of the weight of any importance type is:

[0067]

[0068] In the above formula, ω i' represents the weight of the i'th importance type, n3 represents the number of importance types, B i'j' Represents the evaluation index of the i'th importance type corresponding to the j'th importance type;

[0069] S55, normalizing the weights of the importance types; the calculation formula for the weight of any importance type after normalization is:

[0070]

[0071] In the above formula, ω' i' represents the normalized weight of the i'th importance type, ω i' represents the weight of the i'th importance type before normalization, and n3 represents the number of importance types;

[0072] S56, calculating the random consistency ratio of the evaluation matrix, and determining whether the consistency ratio is less than the consistency ratio threshold;

[0073] If yes, proceed to step S57;

[0074] If not, return to step S51;

[0075] S57. Calculate the total importance of any first power data according to the normalized weight of any importance type; the calculation formula of the total importance is:

[0076]

[0077] In the above formula, Z(d i ) represents the first power data d i The total importance of ω' i' represents the normalized weight of the i'th importance type corresponding to the first power data, Z i' (d i ) represents the importance value of the i'th importance type.

[0078] Preferably, in step S56, the following steps are specifically included:

[0079] S561, calculate the maximum eigenvalue of the evaluation matrix; the calculation formula of the maximum eigenvalue is:

[0080]

[0081] In the above formula, λ max represents the maximum eigenvalue of the evaluation matrix, B i'j' It represents the evaluation index of the i'th importance type corresponding to the j'th importance type in the evaluation matrix, and n3 represents the number of importance types;

[0082] S562, calculating the consistency of the evaluation matrix according to its maximum eigenvalue; the calculation formula of the consistency of the evaluation matrix is:

[0083]

[0084] In the above formula, CI represents the consistency of the evaluation matrix, λ max represents the maximum eigenvalue of the evaluation matrix, and n4 represents the order of the evaluation matrix;

[0085] S563. Calculate the consistency ratio according to the consistency of the evaluation matrix; the calculation formula of the consistency ratio is:

[0086]

[0087] In the above formula, CR represents the consistency ratio of the evaluation matrix, CI represents the consistency of the evaluation matrix, and RI represents the random consistency index of the evaluation matrix;

[0088] S564, determining whether the consistency ratio is less than a consistency ratio threshold;

[0089] If yes, proceed to step S57;

[0090] If not, return to step S51.

[0091] Preferably, in step S33, the calculation formula of the fourth basic importance is:

[0092]

[0093] in,

[0094]

[0095] Z4(t1)=Z1

[0096] In the above formula, Z4(t n ) means that at any time t n The fourth basic importance of any first power data, t n-1 Time is t n The previous adjacent moment of the moment, k is the attenuation coefficient, ΔZ represents the importance gradient, μ is the sign function, Z4(t1) represents the fourth basic importance of any first power data at the first moment t1, and its value is equal to Z1, which is the first importance of the corresponding first power data, For n The number of accesses to the first power data at any moment, N0 represents the basic number of accesses to the first power data.

[0097] Preferably, in step S327, the calculation formula of the first quantity is:

[0098]

[0099] in,

[0100]

[0101] In the above formula, Indicates that the first power data is within the statistical range at any time t under the current iteration number b The first number of and Respectively expressed in t a Time and t b The number of accesses to the first power data corresponding to the time, R c represents the density radius at the current iteration number, μ(·) is the sign function, and A represents the total number of moments containing the first power data.

[0102] The technical solution also provides a system for implementing the power data analysis method, the system comprising: a processor and a memory, the memory being used to store a computer program, and the computer program, when executed by the processor, implements the power data analysis method based on the data middle platform.

[0103] Compared with the prior art, the present invention provides a power data analysis method and system based on a data center, which has the following beneficial effects:

[0104] 1. The present invention evaluates the first power data in the data center from three dimensions: the first importance, the second importance and the third importance. That is, the total importance of the first power data is evaluated from three dimensions: the data type, the number of accesses and the queuing time of the first power data. At the same time, in order to further evaluate the influence of the first importance, the second importance and the third importance on the total importance, the weights of the first importance, the second importance and the third importance are gradually adjusted by constructing an evaluation matrix to meet actual needs, and finally the total importance of the first power data is calculated according to the first importance, the second importance and the third importance and the corresponding weights, so as to dynamically change the priority of the first power data in the transmission process to ensure that data of various importance levels can be effectively transmitted.

[0105] 2. The present invention constructs a first basic importance matrix by constructing a data type indicator, and constructs a second basic importance matrix of any first power data relative to another first power data through the first importance matrix, and calculates the third basic importance of each first power data according to the second basic importance matrix, and then obtains the first importance of each first power data through normalization, so as to realize the evaluation of the importance of the first power data in the data type dimension.

[0106] 3. The present invention calculates the first quantity of the first power data of the elder brother in each statistical range of different sizes in sequence by gradually reducing the density radius, and finally obtains the statistical range that meets the set conditions, and calculates the average number of access times of the first power data at all times within the current statistical range to obtain the basic access times of the first power data, so as to use the basic access times as the basis for calculating the second importance.

[0107] 4. The present invention obtains the queuing time of the first power data, normalizes the queuing time to obtain a third importance that is positively correlated with the queuing time, thereby preventing some first power data with a lower importance level from being unable to be transmitted.

[0108] 5. In order to clarify the influence of the first importance, the second importance and the third importance on the total importance, the present invention constructs an evaluation matrix and finally calculates the consistency ratio to determine whether the calculated weights of the first importance, the second importance and the third importance meet the actual accuracy requirements, thereby reconstructing the evaluation matrix to finally obtain weights that meet the requirements, thereby facilitating the calculation of a more accurate total importance. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0110] Figure 1 The figure is a flow chart of the power data analysis method of the present invention. DETAILED DESCRIPTION

[0111] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific implementation methods, so that the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0112] Those skilled in the art can understand that all or part of the steps in the following embodiments can be completed by instructing the relevant hardware through a program, so the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can be in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0113] The construction of digital asset management platform is crucial in the power market. In order to optimize data utilization, improve business efficiency and stimulate innovation potential, through the deep integration of artificial intelligence (AI) and machine learning (ML) technology, the platform will face and solve the long-standing problems such as data redundancy, inconsistency and access barriers, and realize unified management and intelligent analysis of data. It not only provides the global asset view required by decision makers, ensuring a comprehensive grasp of data distribution, quality and health status, but also simplifies the data model creation process with the help of A-assisted metadata design and management, strengthens the consistency between data instances and market standards, and thus strengthens the life cycle management of digital assets. In addition, AI-driven data exploration tools and automated data processing logic, combined with data lineage tracking and intelligent data services, greatly improve the efficiency and accuracy of data processing. The application of deep learning and natural language processing technology realizes the automatic classification and tagging of data assets, and the continuous monitoring of AI algorithms ensures the reliability and accuracy of data quality. Overall, the intelligent digital asset management platform injects powerful data insights into the power market by optimizing data utilization and intelligent analysis, promotes the optimization and upgrading of business processes, and also paves the way for the digital transformation and sustainable development of the power market.

[0114] In order to solve the problem that the existing data center pre-sets different importance levels for data from different sources, which may cause some low-importance data to be unable to be transmitted, the present invention provides a power data analysis method based on the data center, such as Figure 1 As shown, further improvements are made on the basis of the existing technology, and the importance of data is set to be automatically updated. According to the user's preset and the change of the data environment, the data importance level can be automatically updated, so as to ensure that data of different importance levels can be transmitted as much as possible. The analysis method includes the following steps:

[0115] S1. Acquire the first power data in the transmission queue. For the first power data to be transmitted simultaneously, since the transmission speed of the network link is limited, when the data transmission volume is large, it is necessary to queue up to transmit the first power data of different importance respectively.

[0116] S2. Set several data types, set the first basic importance of each first power data under different data types, and calculate the first importance of each first power data; the data type is set according to the specific situation. For example, in practice, it can be divided into several data types such as urgency, real-time, security, reliability and data category, among which urgency means data that needs to be transmitted in a short time, real-time mainly refers to data with timeliness requirements, which needs to be transmitted within a certain period of time, reliability means data with high requirements for data integrity, security refers to data that needs to take certain protection measures for transmission, such as encryption and other measures, and the data category covers a wide range, such as production data and marketing data, etc., and is specifically set according to the business situation of the enterprise. The calculation method of the first importance of each first power data is described in detail below. In step S2, it specifically includes the following steps:

[0117] S21. Set a number of data type indicators and establish a data type indicator set; the expression of the data type indicator set is: DT = {Dt1, Dt2, Dt3, Dt4, ..., Dt n}, in the above formula, DT represents the data type indicator set, Dt n Indicates the nth data type;

[0118] S22, constructing a transmission data set according to the first power data obtained in the transmission queue; the expression of the transmission data set is: D = {d1, d2, d3, d4, ..., d m}, where D represents the transmission data set, d m represents the first power data of the mth transmission queue;

[0119] S23, setting the first basic importance of each first power data under different data types; the expression of the first basic importance is:

[0120] Z1(m,n)=d m (Dt n )∈{1,2,...,k n}

[0121] In the above formula, Z1(m, n) represents the first power data d m In data type Dt n The first basic importance under k n Indicates the transmission data set for data type Dt n There exists k n Different requirements; when the data set is transmitted for data type Dt n When the requirement is the lowest, its value is 1, that is, d m (Dt n )=1, when the transmission data set is for data type Dt n When the requirement is the highest, its value is k n , that is, d m (Dt n ) = k n .

[0122] S24. Construct a first basic importance matrix under any data type according to the first basic importance; the expression of the first basic importance matrix is:

[0123]

[0124] in,

[0125]

[0126] In the above formula, Q n (1) represents the first basic importance matrix under the nth data type, Indicates that in the nth data type Dt n Next, the first power data d i in the transmission queue i With the jth first power data d j The greater the value, the more important the first power data d is. i Compared with the first power data d j The more important, the Indicates that in the nth data type Dt n The importance of the mth first power data in the transmission queue compared with the first first power data, N represents the data type Dt n The total number of m represents the total number of the first power data;

[0127] S25. Calculate the second basic importance of any first power data compared with another first power data under all data types according to the first basic importance matrix, and construct a second basic importance matrix; the expression of the second basic importance matrix is:

[0128]

[0129] in,

[0130]

[0131] In the above formula, Q(2) represents the second basic importance matrix, Q ij Indicates the first power data d i With the first power data d j The second basic importance compared to the others;

[0132] S26. Calculate the third basic importance of any first power data according to the second basic importance matrix; the calculation formula of the third basic importance is:

[0133]

[0134] In the above formula, Q3(d i ) represents any first power data d i The third basic importance of m represents the total number of the first power data, Q ij Indicates the first power data d i With the first power data d j The second basic importance compared to the others;

[0135] S27, setting a first interval whose interval length is less than 1, and normalizing the third basic importance of any first power data and mapping it to the first interval to obtain the first importance of any first power data; the calculation formula of the first importance of any first power data is:

[0136]

[0137] In the above formula, Z1(d i ) represents any first power data d i The first importance of the first interval is [X,1], Q3(d i ) represents any first power data d i The third basic importance, MIN[Q3] and MAX[Q3] represent the minimum and maximum values ​​of the third basic importance.

[0138] S3, counting the number of accesses to each first power data at any time within a certain period of time, and calculating the second importance of each first power data that is positively correlated with the number of accesses based on the number of accesses and the first importance; in some businesses, there may be a situation where some first power data is frequently accessed and transmitted, which means that the data is relatively very important, and it is necessary to increase its transmission priority during data transmission to ensure its priority transmission, so as to ensure the rapid completion of various businesses. When the number of accesses to the data drops rapidly to the same level as other data, it needs to be quickly restored to the same level as other data to prevent its second importance from remaining high for a long time when the number of accesses drops, thereby affecting the transmission of other data. In step S3, the following steps are specifically included:

[0139] S31, obtaining the number of accesses to each first power data at any time within a certain period of time;

[0140] S32, according to the number of accesses to each first power data at any time within a certain period of time, the basic access number of each first power data is calculated in sequence; the access number of the first power data cannot be calculated as the overall average value, because a large change in the number of accesses to some first power data may cause a large reciprocating fluctuation in the average value, so it is necessary to ensure the stability of the basic access number, and be able to reflect the access number of most data as the basic access number, in step S32, specifically including the following steps:

[0141] S321, calculate the initial density radius of each first power data in turn, set the initial iteration number to 1, and set the initial statistical range to all access times; the calculation formula of the initial density radius is:

[0142] R1=N max -N min

[0143] In the above formula, R1 represents the initial density radius, that is, the density radius at the first iteration, N max Indicates the maximum number of accesses to the first power data at any time within a certain period of time, N min Indicates the minimum number of accesses to the first power data at any time within a certain period of time;

[0144] S322, calculating the difference between the number of accesses of any first power data at any two moments to obtain a first number difference; removing the absolute value of the difference between the number of accesses to obtain a positive first number difference;

[0145] S323, sorting the first order differences to obtain the first order differences that are greater than 0 and the smallest, and marking them as the second order differences;

[0146] S324, obtaining the current number of iterations; the number of iterations when this step is performed for the first time is 1, and the number of iterations increases by 1 each time this step is performed;

[0147] S325, calculating the density radius at the current number of iterations according to the current number of iterations and the initial density radius; the calculation formula of the density radius at the current number of iterations is:

[0148]

[0149] In the above formula, R c Represents the density radius under the current number of iterations, that is, the current number of iterations is c, R1 represents the initial density radius, Indicates the radius reduction factor. In actual use, it is generally Set to 0.5;

[0150] S326, determining whether the density radius at the current iteration number is less than or equal to the second number difference, or whether the first number of access times at all times within the current statistical range is equal;

[0151] If yes, then the number of visits at all times within the current statistical range is output, and the process goes to step S329;

[0152] If not, proceed to the next step;

[0153] S327, respectively calculating the first quantity of each first power data at any time within the statistical range according to the density radius at the current iteration number; the calculation formula of the first quantity is:

[0154]

[0155] in,

[0156]

[0157] In the above formula, Indicates that the first power data is within the statistical range at any time t under the current iteration number b The first number of and Respectively expressed in t a Time and t b The number of accesses to the first power data corresponding to the time, R c represents the density radius at the current iteration number, μ(·) is the sign function, and A represents the total number of moments containing the first power data;

[0158] S328, extract the maximum number of accesses in the first quantity, and calculate the statistical range of the next iteration according to the density radius under the current iteration number, and then return to step S324; the expression of the statistical range of the next iteration is: In the above formula, Indicates the number of visits with the largest number of first c Indicates the density radius at the current iteration number;

[0159] S329, calculating the average number of access times of the first power data at all times within the current statistical range to obtain a basic number of access times of the first power data.

[0160] S33, calculating the fourth basic importance of any first power data at any time according to the basic access number; the calculation formula of the fourth basic importance is:

[0161]

[0162] in,

[0163]

[0164] Z4(t1)=Z1

[0165] In the above formula, Z4(t n ) means that at any time t n The fourth basic importance of any first power data, t n-1 Time is t n The previous adjacent moment of the moment, k is the attenuation coefficient, ΔZ represents the importance gradient, μ is the sign function, Z4(t1) represents the fourth basic importance of any first power data at the first moment t1, and its value is equal to Z1, which is the first importance of the corresponding first power data, For n The number of accesses to the first power data at any time, N0 represents the basic number of accesses to the first power data;

[0166] S34, normalizing the fourth basic importance to obtain the second importance of any power data; the calculation formula of the second importance is:

[0167]

[0168] In the above formula, Z2(d i ) represents any power data d i The second importance, Z4(t n )(d i ) means that at any time t n Any first power data d iThe fourth basic importance, the mapping interval of the second importance is [X,1], MIN[Z4(t n )] and MAX[Z4(t n )] are the minimum and maximum values ​​of the fourth basic importance respectively.

[0169] S4, counting the queuing time of each first power data in the transmission queue, and calculating the third importance of the first power data which is positively correlated with the queuing time; in order to ensure that some first power data with lower importance cannot be transmitted, here, as the waiting time of the first power data of this type increases, its third importance will be gradually increased, so as to ensure that the first power data of this type can be transmitted after waiting for a certain period of time, in step S4, the following steps are specifically included:

[0170] S41, obtaining the queuing time of each first power data in the transmission queue;

[0171] S42, normalize the queuing time of any first power data to obtain the third importance of each first power data; the calculation formula of the first power data is:

[0172]

[0173] In the above formula, Z3(d i ) represents any first power data d i The third importance of the third importance, the mapping interval of the third importance is [X,1], T(d i ) represents any first power data d i The queuing time in the transmission queue, MAX[T] and MAX[T] respectively represent the minimum and maximum queuing time of the first power data in the transmission queue;

[0174] S5, evaluating the weights of the first importance, the second importance, and the third importance, and calculating the total importance of each first power data; which of the first importance, the second importance, and the third importance is more important, further evaluation and calculation are required to make the calculated total importance more realistic. In step S5, the following steps are specifically included:

[0175] S51, setting a number of evaluation levels and corresponding evaluation indexes, as shown in Table 1 below, is one method for setting a number of evaluation levels and corresponding evaluation indexes;

[0176]

[0177] S52, marking the first importance, the second importance, and the third importance as importance types, and determining the evaluation level and evaluation index of any importance type relative to the other importance types in turn according to the evaluation level;

[0178] S53, constructing an evaluation matrix according to the evaluation level and evaluation index of any importance type relative to the other importance types; the expression of the evaluation matrix is:

[0179]

[0180] In the above formula, B represents the evaluation matrix, B i'j' Represents the evaluation index of the i'th importance type corresponding to the j'th importance type;

[0181] S54, calculating the weight of any importance type according to the evaluation matrix; the calculation formula of the weight of any importance type is:

[0182]

[0183] In the above formula, ω i' represents the weight of the i'th importance type, n3 represents the number of importance types, B i'j' Represents the evaluation index of the i'th importance type corresponding to the j'th importance type;

[0184] S55, normalizing the weights of the importance types; the calculation formula for the weight of any importance type after normalization is:

[0185]

[0186] In the above formula, ω' i' represents the normalized weight of the i'th importance type, ω i' represents the weight of the i'th importance type before normalization, and n3 represents the number of importance types;

[0187] S56, calculating the random consistency ratio of the evaluation matrix, and determining whether the consistency ratio is less than the consistency ratio threshold;

[0188] If yes, proceed to step S57;

[0189] If not, return to step S51;

[0190] To further illustrate the calculation method of the random consistency ratio, in step S56, the following steps are specifically included:

[0191] S561, calculate the maximum eigenvalue of the evaluation matrix; the calculation formula of the maximum eigenvalue is:

[0192]

[0193] In the above formula, λ max represents the maximum eigenvalue of the evaluation matrix, B i'j'It represents the evaluation index of the i'th importance type corresponding to the j'th importance type in the evaluation matrix, and n3 represents the number of importance types;

[0194] S562, calculating the consistency of the evaluation matrix according to its maximum eigenvalue; the calculation formula of the consistency of the evaluation matrix is:

[0195]

[0196] In the above formula, CI represents the consistency of the evaluation matrix, λ max represents the maximum eigenvalue of the evaluation matrix, and n4 represents the order of the evaluation matrix;

[0197] S563. Calculate the consistency ratio according to the consistency of the evaluation matrix; the calculation formula of the consistency ratio is:

[0198]

[0199] In the above formula, CR represents the consistency ratio of the evaluation matrix, CI represents the consistency of the evaluation matrix, and RI represents the random consistency index of the evaluation matrix;

[0200] S564, determining whether the consistency ratio is less than a consistency ratio threshold, where the consistency ratio threshold is generally 0.1;

[0201] If yes, proceed to step S57;

[0202] If not, return to step S51.

[0203] S57. Calculate the total importance of any first power data according to the normalized weight of any importance type; the calculation formula of the total importance is:

[0204]

[0205] In the above formula, Z(d i ) represents the first power data d i The total importance of ω' i' represents the normalized weight of the i'th importance type corresponding to the first power data, Z i' (d i ) represents the importance value of the i'th importance type.

[0206] According to the data characteristics in the power data assets, the first power data in the data center is evaluated through the three dimensions of first importance, second importance and third importance, that is, the total importance of the first power data is evaluated from the three dimensions of data type, number of accesses and queuing time of the first power data respectively. At the same time, in order to further evaluate the influence of the first importance, the second importance and the third importance on the total importance, the weights of the first importance, the second importance and the third importance are gradually adjusted by constructing an evaluation matrix to meet the actual needs, and finally the total importance of the first power data is calculated according to the first importance, the second importance and the third importance and the corresponding weights, which is used as the basis for the priority during data transmission to ensure the stable transmission of various first power data.

[0207] Corresponding to the power data analysis method provided in the above embodiment, the present embodiment also provides a system for implementing the power data analysis method. Since the power data analysis system provided in the present embodiment corresponds to the power data analysis method provided in the above embodiment, the implementation method of the aforementioned power data analysis method is also applicable to the power data analysis system provided in the present embodiment and will not be described in detail in the present embodiment.

[0208] The system includes a processor and a memory, the memory is used to store a computer program, and when the computer program is executed by the processor, an electric power data analysis method based on a data middle station is implemented.

[0209] The above implementation methods have been described in detail. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A power data analysis method based on a data center, characterized in that: The method comprises the following steps: S1, obtaining first power data in a transmission queue; S2. Setting a plurality of data types, setting a first basic importance of each first power data under a different data type, and calculating a first importance of each first power data; S3, counting the number of accesses to each first power data at any time within a certain period of time, and calculating the second importance of each first power data that is positively correlated with the number of accesses based on the number of accesses and the first importance; S4, counting the queuing time of each first power data in the transmission queue, and calculating the third importance of the first power data being positively correlated with the queuing time; S5. Evaluate the weights of the first importance, the second importance, and the third importance, and calculate the total importance of each first power data.

2. The power data analysis method according to claim 1, characterized in that: In step S2, the following steps are specifically included: S21. Set a number of data type indicators and establish a data type indicator set; S22, constructing a transmission data set according to the acquired first power data in the transmission queue; S23, setting a first basic importance of each first power data under different data types; S24, constructing a first basic importance matrix under any data type according to the first basic importance; The expression of the first basic importance matrix is: in, In the above formula, Q n (1) represents the first basic importance matrix under the nth data type, Indicates that in the nth data type Dt n Next, the first power data d i in the transmission queue i and the jth first power data d j The relative importance of Indicates that in the nth data type Dt n The importance of the mth first power data in the transmission queue compared with the first first power data, N represents the data type Dt n The total number of m represents the total number of the first power data; S25. Calculate the second basic importance of any first power data compared with another first power data under all data types according to the first basic importance matrix, and construct a second basic importance matrix; the expression of the second basic importance matrix is: in, In the above formula, Q(2) represents the second basic importance matrix, Q ij Indicates the first power data d i With the first power data d j The second basic importance compared to the others; S26. Calculate the third basic importance of any first power data according to the second basic importance matrix; the calculation formula of the third basic importance is: In the above formula, Q3(d i ) represents any first power data d i The third basic importance of m represents the total number of the first power data, Q ij Indicates the first power data d i With the first power data d j The second basic importance compared to the others; S27, setting a first interval whose interval length is less than 1, and normalizing the third basic importance of any first power data and mapping it to the first interval to obtain the first importance of any first power data; the calculation formula of the first importance of any first power data is: In the above formula, Z1(d i ) represents any first power data d i The first importance of the first interval is [X,1], Q3(d i ) represents any first power data d i The third basic importance, MIN[Q3] and MAX[Q3] represent the minimum and maximum values ​​of the third basic importance.

3. The power data analysis method according to claim 1, characterized in that: In step S3, the following steps are specifically included: S31, obtaining the number of accesses to each first power data at any time within a certain period of time; S32, calculating the basic access times of each first power data in sequence according to the access times of each first power data at any time within a certain period of time; S33, calculating the fourth basic importance of any first power data at any time according to the basic access count; S34, normalizing the fourth basic importance to obtain the second importance of any power data; the calculation formula of the second importance is: In the above formula, Z2(d i ) represents any power data d i The second importance, Z4(t n )(d i ) means that at any time t n Any first power data d i The fourth basic importance, the mapping interval of the second importance is [X, 1], MIN[Z4(t n )] and MAX[Z4(t n )] are the minimum and maximum values ​​of the fourth basic importance respectively.

4. The power data analysis method according to claim 3, characterized in that: In step S32, the following steps are specifically included: S321, calculate the initial density radius of each first power data in turn, set the initial iteration number to 1, and set the initial statistical range to all access times; the calculation formula of the initial density radius is: R1=N max -N min In the above formula, R1 represents the initial density radius, that is, the density radius at the first iteration, N max Indicates the maximum number of accesses to the first power data at any time within a certain period of time, N min Indicates the minimum number of accesses to the first power data at any time within a certain period of time; S322, calculating the difference between the number of accesses to any first power data at any two moments to obtain a first number difference; S323, sorting the first order differences to obtain the first order differences that are greater than 0 and the smallest, and marking them as the second order differences; S324, obtaining the current number of iterations; S325, calculating the density radius at the current number of iterations according to the current number of iterations and the initial density radius; the calculation formula of the density radius at the current number of iterations is: In the above formula, R c Represents the density radius under the current number of iterations, that is, the current number of iterations is c, R1 represents the initial density radius, represents the radius reduction factor; S326, determining whether the density radius at the current iteration number is less than or equal to the second number difference, or whether the first number of access times at all times within the current statistical range is equal; If yes, then the number of visits at all times within the current statistical range is output, and the process goes to step S329; If not, proceed to the next step; S327, calculating the first quantity of each first power data at any time within the statistical range according to the density radius of the first power data at the current iteration number; S328, extract the maximum number of accesses in the first quantity, and calculate the statistical range of the next iteration according to the density radius under the current iteration number, and then return to step S324; the expression of the statistical range of the next iteration is: In the above formula, Indicates the number of visits with the largest number of first c Indicates the density radius at the current iteration number; S329, calculating the average number of accesses to the first power data at all times within the current statistical range to obtain a basic number of accesses to the first power data.

5. The power data analysis method according to claim 1, characterized in that: In step S4, the following steps are specifically included: S41, obtaining the queuing time of each first power data in the transmission queue; S42, normalize the queuing time of any first power data to obtain the third importance of each first power data; the calculation formula of the first power data is: In the above formula, Z3(d i ) represents any first power data d i The third importance of the third importance, the mapping interval of the third importance is [X,1], T(d i ) represents any first power data d i The queuing time in the transmission queue, MAX[T] and MAX[T] respectively represent the minimum and maximum queuing time of the first power data in the transmission queue.

6. The power data analysis method according to claim 1, characterized in that: In step S5, the following steps are specifically included: S51. Setting a number of evaluation levels and corresponding evaluation indexes; S52, marking the first importance, the second importance, and the third importance as importance types, and determining the evaluation level and evaluation index of any importance type relative to the other importance types in turn according to the evaluation level; S53, constructing an evaluation matrix according to the evaluation level and evaluation index of any importance type relative to the other importance types; the expression of the evaluation matrix is: In the above formula, B represents the evaluation matrix, B i'j' Represents the evaluation index of the i'th importance type corresponding to the j'th importance type; S54, calculating the weight of any importance type according to the evaluation matrix; the calculation formula of the weight of any importance type is: In the above formula, ω i' represents the weight of the i'th importance type, n3 represents the number of importance types, B i'j' Represents the evaluation index of the i'th importance type corresponding to the j'th importance type; S55, normalizing the weights of the importance types; the calculation formula for the weight of any importance type after normalization is: In the above formula, ω' i' represents the normalized weight of the i'th importance type, ω i' represents the weight of the i'th importance type before normalization, and n3 represents the number of importance types; S56, calculating the random consistency ratio of the evaluation matrix, and determining whether the consistency ratio is less than the consistency ratio threshold; If yes, proceed to step S57; If not, return to step S51; S57. Calculate the total importance of any first power data according to the normalized weight of any importance type; the calculation formula of the total importance is: In the above formula, Z(d i ) represents the first power data d i The total importance of ω' i' represents the normalized weight of the i'th importance type corresponding to the first power data, Z i' (d i ) represents the importance value of the i'th importance type.

7. The power data analysis method according to claim 6, characterized in that: In step S56, the following steps are specifically included: S561, calculate the maximum eigenvalue of the evaluation matrix; the calculation formula of the maximum eigenvalue is: In the above formula, λ max represents the maximum eigenvalue of the evaluation matrix, B i'j' It represents the evaluation index of the i'th importance type corresponding to the j'th importance type in the evaluation matrix, and n3 represents the number of importance types; S562, calculating the consistency of the evaluation matrix according to its maximum eigenvalue; the calculation formula of the consistency of the evaluation matrix is: In the above formula, CI represents the consistency of the evaluation matrix, λ max represents the maximum eigenvalue of the evaluation matrix, and n4 represents the order of the evaluation matrix; S563. Calculate the consistency ratio according to the consistency of the evaluation matrix; the calculation formula of the consistency ratio is: In the above formula, CR represents the consistency ratio of the evaluation matrix, CI represents the consistency of the evaluation matrix, and RI represents the random consistency index of the evaluation matrix; S564, determining whether the consistency ratio is less than a consistency ratio threshold; If yes, proceed to step S57; If not, return to step S51.

8. The power data analysis method according to claim 3, characterized in that: In step S33, the calculation formula of the fourth basic importance is: in, Z4(t1)=Z1 In the above formula, Z4(t n ) means that at any time t n The fourth basic importance of any first power data, t n-1 Time is t n The previous adjacent moment of the moment, k is the attenuation coefficient, ΔZ represents the importance gradient, μ is the sign function, Z4(t1) represents the fourth basic importance of any first power data at the first moment t1, and its value is equal to Z1, which is the first importance of the corresponding first power data, For n The number of accesses to the first power data at any moment, N0 represents the basic number of accesses to the first power data.

9. The power data analysis method according to claim 4, characterized in that: In step S327, the calculation formula of the first quantity is: in, In the above formula, Indicates that the first power data is within the statistical range at any time t under the current iteration number b The first number of and Respectively expressed in t a Time and t b The number of accesses to the first power data corresponding to the time, R c represents the density radius at the current iteration number, μ(·) is the sign function, and A represents the total number of moments containing the first power data.

10. An analysis system for implementing the power data analysis method according to any one of claims 1 to 9, characterized in that: It includes a processor and a memory, the memory is used to store a computer program, and when the computer program is executed by the processor, it implements the power data analysis method based on the data middle platform as described in any one of claims 1 to 9.

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