A power data analysis method and system based on a data middle platform

Abstract

The application relates to the field of power data data middle platform, in particular to a power data analysis method and system based on a data middle platform; first power data in the data middle platform is evaluated from three dimensions of a first importance degree, a second importance degree and a third importance degree, and in order to further evaluate the influence degree of the first importance degree, the second importance degree and the third importance degree on a total importance degree, weights of the first importance degree, the second importance degree and the third importance degree are gradually adjusted by constructing an evaluation matrix, so that the total importance degree of the first power data is finally calculated according to the first importance degree, the second importance degree, the third importance degree and corresponding weights, the priority of the first power data in a transmission process is dynamically changed, and it is guaranteed that data with various importance degrees can be effectively transmitted; the method of pre-setting different importance levels for data of different sources can cause the problem that some low importance degree data cannot be transmitted all the time.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of electric power data data middle platform, specifically a kind of electric power data analysis method and system based on data middle platform. BACKGROUND

[0002] In data middle platform, different types of data assets are converged, and different clients obtain data through data middle platform. At present, with the establishment of data middle platform of power system, a large amount of data related to power grid control, power asset transaction and power energy management is centralized in data middle platform, which effectively guarantees the digital transformation of power-related enterprises.

[0003] However, due to the large amount of data transmission of data middle platform, it is difficult to meet the requirement that all data can be transmitted at the first time after receiving the request. The traditional way is to set different importance levels for data from different sources in advance. However, this way may cause different high importance level data to be in transmission state all the time, resulting in low importance level data being unable to be transmitted all the time, and some low importance level data being difficult to be transmitted in time, thereby causing hidden danger to the normal operation of enterprises. SUMMARY

[0004] In view of the shortcomings of the prior art, the present application provides an electric power data analysis method and system based on data middle platform, which solves the problem that setting different importance levels for data from different sources in advance may cause some low importance level data to be unable to be transmitted all the time.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0006] An electric power data analysis method based on data middle platform, the method comprising the following steps:

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

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

[0009] S3, counting the access times of each first electric power data at any time within a certain time, and calculating a second importance of each first electric power data and access times positively related according to the access times and the first importance;

[0010] S4, counting the queuing time of each first electric power data in the transmission queue, and calculating a third importance of the first electric power data and the queuing time positively related;

[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] As preferred, in step S2, specifically comprises the following steps:

[0013] S21, set a plurality of data type indicators, and establish a data type indicator set;

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

[0015] S23, set the 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] Among them,

[0019]

[0020] In the above formula, Q n (1) represents the first basic importance matrix under the nth data type, represents the importance degree of the ith first power data d n in the transmission queue under the nth data type Dt i compared with the jth first power data d j , that is, represents the importance degree of the mth first power data in the transmission queue compared with the first first power data under the nth data type Dt n , N represents the total number of data types Dt n , and m represents the total number of 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] Among them,

[0024]

[0025] In the above formula, Q(2) represents the second basic importance matrix, Qij represents the first power data d i j second basic importance compared with the first power data d

[0026] S26, calculating 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 the third basic importance of any first power data d i , m represents the total number of the first power data, Q ij represents the second basic importance of the first power data d i compared with the first power data d j

[0029] S27, setting the first interval with interval length less than 1, and mapping the third basic importance of any first power data into the first interval after normalization, 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 the first importance of any first power data d i , the expression of the first interval is [X, 1], Q3(d i ) represents the third basic importance of any first power data d i , MIN[Q3] and MAX[Q3] represent the minimum value and the maximum value of the third basic importance.

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

[0033] S31, obtaining the access times of each first power data at any time within a certain time;

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

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

[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 the second importance degree of any power data d i , Z4(t n )(d i ) represents the fourth basic importance degree of any first power data d i at any time t n , the mapping interval of the second importance degree is [X,1], MIN[Z4(t n )] and MAX[Z4(t n )] are respectively the minimum value and the maximum value of the fourth basic importance degree.

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

[0040] S321, the initial density radius of each first power data is calculated in sequence, the initial iteration number is set to 1, and the initial statistical range is set 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, i.e. the density radius at the first iteration, N max represents the maximum access time of the first power data at any time within a certain time, and N min represents the minimum access time of the first power data at any time within a certain time.

[0043] S322, the difference between the access times of any first power data at any two times is calculated to obtain a first time difference;

[0044] S323, the first time difference is sorted to obtain the smallest first time difference greater than 0, which is marked as a second time difference;

[0045] S324, the current iteration number is obtained;

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

[0047]

[0048] In the above formula, R c represents the density radius at the current iteration number, i.e. the current iteration number is c, R1 represents the initial density radius, represents the radius reduction coefficient;

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

[0050] If yes, output the access times of all time points in the current statistical range, and enter step S329;

[0051] If no, enter the next step;

[0052] S327, calculating the first number of any time point in the statistical range according to the density radius under the current iteration number of each first power data in turn;

[0053] S328, extracting the access time with the largest first number, and calculating the statistical range of the next iteration according to the density radius under the current iteration number, and then returning to step S324; the expression of the statistical range of the next iteration is: In the above formula, denotes the access time with the largest first number, R c denotes the density radius under the current iteration number;

[0054] S329, calculating the average value of the access times of all time points of the first power data in the current statistical range to obtain the basic access times of the first power data.

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

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

[0057] S42, normalizing the queuing duration 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 ) denotes the third importance of any first power data d i , the mapping interval of the third importance is [X, 1], T(d i ) denotes the queuing duration of any first power data d i in the transmission queue, and MAX[T] and MAX[T] respectively denote the minimum value and the maximum value of the queuing duration of the first power data in the transmission queue.

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

[0061] S51, set a plurality of evaluation grades and corresponding evaluation indexes;

[0062] S52, mark the first importance, the second importance and the third importance as importance types, and determine the evaluation grade and the evaluation index of any importance type relative to the remaining importance types according to the evaluation grades;

[0063] S53, construct an evaluation matrix according to the evaluation grade and the evaluation index of any importance type relative to the remaining 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, calculate 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, normalize the weight of the importance type; the calculation formula of the weight of any importance type after normalization is:

[0070]

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

[0072] S56, calculate the random consistency ratio of the evaluation matrix, and determine whether the consistency ratio is less than a consistency ratio threshold;

[0073] If yes, go to step S57;

[0074] If no, return to step S51;

[0075] S57, calculate the total importance of any first power data according to the weight of any importance type after normalization; the calculation formula of the total importance is:

[0076]

[0077] In the above formula, Z(d i ) represents the total importance of the first power data d i , 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] As preferred, 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' 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, calculate the consistency of the evaluation matrix according to the maximum eigenvalue of the evaluation matrix; 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 of the evaluation matrix 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, judge whether the consistency ratio is less than the consistency ratio threshold value;

[0089] If yes, go to step S57;

[0090] If no, return to step S51.

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

[0092]

[0093] wherein,

[0094]

[0095] Z4(t1) = Z1

[0096] In the above formula, Z4(t n ) represents the fourth basic importance of any first power data at any time t n , t n-1 is the last adjacent time of t n , 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 time t1, and the value is equal to Z1, which is the first importance of the corresponding first power data, N(t n ) is the access number of any first power data at t n , and N0 represents the basic access number of the first power data.

[0097] As a preferred, in step S327, the calculation formula of the first number is:

[0098]

[0099] wherein,

[0100]

[0101] In the above formula, Z1(t b ) represents the first number of any first power data in the statistical range at t b at the current iteration number, and respectively represent the access number of the corresponding first power data at t a and t b , R c represents the density radius at the current iteration number, μ(·) is the sign function, and A represents the total number of times containing the first power data.

[0102] The technical scheme also provides a system for implementing the power data analysis method, which comprises a processor and a memory, the memory is used to store a computer program, and the computer program is executed by the processor to realize the power data analysis method based on the data center station.

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

[0104] 1. The application evaluates the first power data in the data center from three dimensions of first importance, second importance and third importance, that is, evaluates the total importance of the first power data from three dimensions of data type, access frequency and queuing time of the first power data, respectively, and in order to further evaluate the influence degree of the first importance, the second importance and the third importance on the total importance, the weight of the first importance, the second importance and the third importance is gradually adjusted by constructing an evaluation matrix to meet the actual demand, so that the total importance of the first power data is finally calculated according to the first importance, the second importance and the third importance and the corresponding weight, to dynamically change the priority of the first power data in the transmission process, and ensure that data of various importance levels can be effectively transmitted.

[0105] 2. The application constructs a first basic importance matrix by constructing a data type index, and constructs a second basic importance matrix of any first power data relative to another first power data by 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 by normalization, to realize the evaluation of the importance degree of the first power data in the data type dimension.

[0106] 3. The application gradually reduces the density radius to sequentially calculate the first number of each first power data in each different size of statistical range, and finally obtains a statistical range meeting the set condition, and calculates the average value of the access frequency of the first power data at all time points in the current statistical range, to obtain the basic access frequency of the first power data, so that the basic access frequency is taken as the calculation basis of the second importance.

[0107] 4. The application obtains the queuing time of the first power data, normalizes the queuing time to obtain the third importance positively related to the queuing time, to prevent some first power data with low importance level from being unable to be transmitted all the time.

[0108] 5. The application constructs an evaluation matrix to finally calculate the consistency ratio to judge whether the weight of the calculated first importance, the second importance and the third importance meets the actual accuracy requirement, so as to reconstruct the evaluation matrix to finally obtain the weight meeting the requirement, to facilitate the calculation of more accurate total importance. BRIEF DESCRIPTION OF DRAWINGS

[0109] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

[0110] Figure 1 Flow chart of the power data analysis method of the present application. DETAILED DESCRIPTION

[0111] In order to make the above objectives, features and advantages of the present application more apparent, further specific embodiments of the present application will be described in detail below with reference to the accompanying drawings and specific embodiments. The implementation process of how to apply technical means to solve technical problems and achieve technical effects of the present application can be fully understood and implemented.

[0112] Those of ordinary skill in the art can understand that all or part of the steps in the following embodiment methods can be completed by programs instructing related hardware, therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt a computer program product 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 code.

[0113] In the power market, the construction of the digital asset management platform is crucial. 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 directly face and solve the long-term problems such as data redundancy, inconsistency and access barriers, and realize the unified management and intelligent analysis of data. It not only provides the overall 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 AI-assisted metadata design and management, strengthens the consistency of data instances and market standards, thereby enhancing the life cycle management of digital assets. In addition, AI-driven data exploration tools and automated data processing logic cooperate with data bloodline tracking and intelligent data services to greatly improve the efficiency and accuracy of data processing. The application of deep learning and natural language processing technology realizes the automatic classification and labeling 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 optimizes data utilization and intelligent analysis, injecting strong data insight into the power market, promoting the optimization and upgrading of business processes, and paving the way for the digital transformation and sustainable development of the power market.

[0114] In order to solve the problem that the prior art data center sets different importance levels for data from different sources, which may cause some low importance data to be unable to be transmitted, the application provides a power data analysis method based on a data center, as shown in the accompanying drawings, which is further improved on the basis of the prior art, and the importance of data is set to be automatically updated, so that the importance level of data can be automatically updated according to the preset of the user and the change of the data environment, thereby ensuring that data of different importance levels can be transmitted as much as possible. Figure 1 The analysis method comprises the following steps:

[0115] S1, acquiring first power data in a transmission queue; for the first power data being transmitted simultaneously, due to the limited transmission speed of the network link, when the data transmission amount is large, the first power data of different importance degrees needs to be queued for transmission;

[0116] S2, setting a plurality of data types, setting a first basic importance of each first power data under different data types, and calculating a first importance of each first power data; the data types are set according to specific conditions, such as in practice, the data types can be divided into urgency, real-time, security, reliability and data categories, etc., wherein the urgency represents data that needs to be transmitted within a short time, the real-time mainly refers to data that needs to be transmitted within a certain time, the reliability represents data that has high requirements for data integrity, the security refers to data that needs to be transmitted by taking certain protection measures such as encryption, and the data category covers a wide range, such as production data and marketing data, etc., which are set according to the business situation of the enterprise, and the calculation method of the first importance of each first power data is described in detail below, and in step S2, it specifically comprises the following steps:

[0117] S21, setting a plurality of data type indexes and establishing a data type index set; the expression of the data type index set is: DT={Dt1, Dt2, Dt3, Dt4,..., Dt n}, in the formula, DT represents the data type index set, Dt n represents the nth data type;

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

[0119] S23. Set 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 transmitting data sets 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 the nth data type Dt n Next, the first power data d in the transmission queue i 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 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, calculating the second basic importance degree of any first power data compared with another first power data under all data types according to the first basic importance matrix, and constructing a second basic importance matrix; the expression of the second basic importance matrix is:

[0128]

[0129] wherein,

[0130]

[0131] In the above formula, Q (2) represents the second basic importance matrix, Q ij represents the second basic importance degree of the first power data d i compared with the first power data d j .

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

[0133]

[0134] In the above formula, Q3(d i ) represents the third basic importance degree of any first power data d i , m represents the total number of the first power data, Q ij represents the second basic importance degree of the first power data d i compared with the first power data d j .

[0135] S27, setting a first interval with an interval length less than 1, and mapping the third basic importance degree of any first power data into the first interval after normalization to obtain the first importance degree of any first power data; the calculation formula of the first importance degree of any first power data is:

[0136]

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

[0138] S3, count the access times of each first power data at any time within a certain time, and calculate the second importance of each first power data positively correlated with the access times according to the access times and the first importance; in some businesses, there may be some first power data that is frequently accessed and transmitted, which means that the data is relatively important, and the priority of the data transmission needs to be improved to ensure its priority transmission, so as to ensure the rapid completion of each business. When the access times of the data decrease 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 being high for a long time when the access times decrease, thereby affecting the transmission of other data. In step S3, the following steps are included:

[0139] S31, obtain the access times of each first power data at any time within a certain time;

[0140] S32, calculate the basic access times of each first power data according to the access times of each first power data at any time within a certain time; the access times of the first power data cannot be calculated by the average value of the whole, because the large fluctuation of the average value may be caused by the large fluctuation of the access times of some first power data, so the stability of the basic access times needs to be ensured, and the access times of most data need to be reflected to serve as the basic access times. In step S32, the following steps are included:

[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, i.e. the density radius at the first iteration, N max represents the maximum access times of the first power data at any time within a certain time, N min represents the minimum access times of the first power data at any time within a certain time;

[0144] S322, calculate the difference between the access times of any first power data at any two times to obtain the first time difference; take the absolute value of the difference between the access times to obtain the first time difference with a positive value;

[0145] S323, sort the first time difference to obtain the smallest first time difference greater than 0, and mark it as the second time difference;

[0146] S324, obtain the current iteration number; the iteration number is 1 when passing through this step for the first time, and the iteration number is increased by 1 after each passing through;

[0147] S325, calculate the density radius under the current iteration number according to the current iteration number and the initial density radius; the calculation formula of the density radius under the current iteration number is:

[0148]

[0149] In the above formula, R c represents the density radius under the current iteration number, that is, the current iteration number is c, R1 represents the initial density radius, represents the radius reduction coefficient, and in actual use, generally, is set to 0.5;

[0150] S326, judge whether the density radius under the current iteration number is less than or equal to the second number difference or whether the first number of the access number of all time points in the current statistical range is equal;

[0151] If yes, output the access number of all time points in the current statistical range, and enter step S329;

[0152] If no, enter the next step;

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

[0154]

[0155] Wherein,

[0156]

[0157] In the above formula, represents the first number of the first power data at any time point t b in the statistical range under the current iteration number, and respectively represent the access number of the corresponding first power data at t a and t b , R c represents the density radius under the current iteration number, μ(·) is a symbol function, and A represents the total number of time points containing the first power data;

[0158] S328. Extract the first largest number of access times, 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 first largest number of visits, R c Indicates the density radius at the current iteration number;

[0159] S329: Calculate 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.

[0160] S33. Calculate the fourth basic importance of any first power data at any time according to the basic access count; 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 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 t 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;

[0166] S34. Normalize 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 degree, the mapping interval of the second importance degree is [X, 1], MIN[Z4(t n )] and MAX[Z4(t n )] are the minimum value and the maximum value of the fourth basic importance degree respectively.

[0169] S4, statistics of the queuing time length of each first power data in the transmission queue, and calculating the third importance degree of the first power data and the queuing time length positively correlated; in order to ensure that some first power data with low importance degree cannot be transmitted all the time, here the third importance degree of the first power data of this type is gradually increased with the increase of the waiting time length, so as to ensure that the first power data of this type can complete transmission after waiting for a certain time length, in step S4, the following steps are specifically included:

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

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

[0172]

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

[0174] S5, evaluating the weight of the first importance degree, the second importance degree and the third importance degree, and calculating the total importance degree of each first power data; the importance degree of which of the first importance degree, the second importance degree and the third importance degree is higher needs to be further evaluated and calculated, so that the calculated total importance degree is more in line with the actual situation, in step S5, the following steps are specifically included:

[0175] S51, setting a plurality of evaluation levels and corresponding evaluation indexes, as shown in Table 1, which is one of the setting methods of a plurality of evaluation levels and corresponding evaluation indexes;

[0176]

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

[0178] S53, constructing an evaluation matrix according to the evaluation level and the evaluation index of any importance type relative to the rest of the 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 weight of the importance type; the calculation formula of the weight of any importance type after normalization is:

[0185]

[0186] In the above formula, ω' i' represents the weight of the i'th importance type after normalization, ω i' represents the weight of the i'th importance type before normalization, 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, go to step S57;

[0189] If no, return to step S51;

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

[0191] S561, calculating 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'represents the evaluation index of the ith importance type corresponding to the jth importance type in the evaluation matrix, and n3 represents the number of importance types;

[0194] S562, the consistency of the evaluation matrix is calculated according to the maximum eigenvalue of the evaluation matrix; 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, the consistency ratio of the evaluation matrix is calculated 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, it is judged whether the consistency ratio is less than the consistency ratio threshold value, and the consistency ratio threshold value is generally 0.1;

[0201] If yes, go to step S57;

[0202] If no, return to step S51.

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

[0204]

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

[0206] According to the data characteristics in the power data assets, the first power data in the data middle station is evaluated through three dimensions of 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 of the data type, the access frequency and the queuing time of the first power data, and in order to further evaluate the influence degree of the first importance, the second importance and the third importance on the total importance, the evaluation matrix is constructed to gradually adjust the weights of the first importance, the second importance and the third importance, so as to meet the actual demand, and finally the total importance of the first power data is calculated according to the first importance, the second importance, the third importance and the corresponding weights, so as to serve as the basis of the priority in data transmission and ensure the stable transmission of various first power data.

[0207] Corresponding to the power data analysis method provided by the above-mentioned embodiment, the present embodiment also provides a system for implementing the power data analysis method. Since the power data analysis system provided by the present embodiment corresponds to the power data analysis method provided by the above-mentioned embodiment, the implementation modes of the foregoing power data analysis method are also applicable to the power data analysis system provided by the present embodiment, which will not be described in detail in the present embodiment.

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

[0209] The above embodiments have been described in detail, and the principles and implementation modes of the present application have been described by applying specific examples. The above embodiment is only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed, and the above description should not be understood as the limitation of the present application.

Claims

1. A power data analysis method based on a data center, characterized in that: The method comprises the following steps: S1. Acquire 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 item at any time within a certain period of time, and calculating a second importance of each first power data item that is positively correlated with the number of accesses based on the number of accesses and the first importance; 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 count of each first power data item in sequence according to the access count of each first power data item at any time within a certain period of time; S33. Calculate the fourth basic importance of any first power data at any time according to the basic access count. The calculation formula of the fourth basic importance is: ； in, ； ； In the above formula, Indicates that at any moment The fourth basic importance of any first power data, Time is The previous adjacent moment of the moment, is the attenuation coefficient, represents the importance gradient, is a symbolic function, Indicates that at the first moment The fourth basic importance of any first power data at the time of , is the first importance of the corresponding first power data, For The number of accesses to any first power data at any moment, Indicates the basic access count of the first power data; S34. Normalize 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, Indicates any power data The second most important Indicates that at any moment At that time, any first power data The fourth basic importance of the second importance is , and are the minimum and maximum values ​​of the fourth basic importance respectively; S4. Counting the queuing time of each first power data in the transmission queue, and calculating a third importance that is positively correlated between the first power data and 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 several 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, represents the first basic importance matrix under the nth data type, Indicates the nth data type Next, the first power data of the i-th transmission queue is With the jth first power data The relative importance, Indicates the nth data type Next, the first The importance of the first power data compared with the first first power data, Represents data type 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, represents the second basic importance matrix, Indicates the first power data With First Power Data 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, Indicates any first power data The third basic importance, represents the total amount of the first power data, Indicates the first power data With First Power Data The second basic importance compared to the others; S27. Set a first interval with an interval length less than 1, and normalize the third basic importance of any first power data and map it to the first interval to obtain the first importance of any first power data; the calculation formula for the first importance of any first power data is: ； In the above formula, Indicates any first power data The first importance of the first interval is expressed as , Indicates any first power data The third basic importance, and Indicates 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 S32, the following steps are specifically included: S321. Calculate the initial density radius of each first power data in sequence, set the initial number of iterations to 1, and set the initial statistical range to all access times; the calculation formula for the initial density radius is: ； In the above formula, represents the initial density radius, that is, the density radius at the first iteration, Indicates the maximum number of accesses to the first power data at any time within a certain period of time. 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 smallest first order difference that is greater than 0, and marking it as the second order difference; S324. Obtain the current number of iterations; S325. Calculate the density radius at the current number of iterations according to the current number of iterations and the initial density radius. The calculation formula for the density radius at the current number of iterations is: ； In the above formula, Indicates the density radius under the current number of iterations, that is, the current number of iterations is c times, represents the initial density radius, represents the radius reduction factor; S326: Determine 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 at the current iteration number; S328. Extract the first largest number of access times, 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 first largest number of visits, Indicates the density radius at the current iteration number; S329: Calculate 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.

4. 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, Indicates any first power data The third importance of the third importance, the mapping interval of the third importance is , Indicates any first power data The length of time it has been in the transmission queue, and They respectively represent the minimum and maximum queuing time of the first power data in the transmission queue.

5. The power data analysis method according to claim 1, characterized in that: In step S5, the following steps are specifically included: S51. Set several 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 sequence according to the evaluation levels; S53. Construct an evaluation matrix based on 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, represents the evaluation matrix, Indicates the The importance type corresponds to Evaluation index of each importance type; S54. Calculate the weight of any importance type according to the evaluation matrix. The calculation formula for the weight of any importance type is: ； In the above formula, Indicates the The weight of each importance type, Indicates the number of importance types, Indicates the The importance type corresponds to Evaluation index of each importance type; S55. Normalize the weights of the importance types. The calculation formula for the weight of any importance type after normalization is: ； In the above formula, Represents the normalized The weight of each importance type, represents the first The weight of each importance type, Indicates 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 for the total importance is: ； In the above formula, Indicates the first power data The total importance of Indicates the first power data corresponding to the The normalized weight of the importance type, Indicates the Importance value of each importance type.

6. The power data analysis method according to claim 5, characterized in that: In step S56, the following steps are specifically included: S561. Calculate the maximum eigenvalue of the evaluation matrix. The calculation formula for the maximum eigenvalue is: ； In the above formula, represents the maximum eigenvalue of the evaluation matrix, Indicates the first The importance type corresponds to The evaluation index of the importance type, Indicates the number of importance types; S562. Calculate the consistency of the evaluation matrix based on its maximum eigenvalue; the calculation formula for the consistency of the evaluation matrix is: ； In the above formula, represents the consistency of the evaluation matrix, represents the maximum eigenvalue of the evaluation matrix, represents the order of the evaluation matrix; S563. Calculate the consistency ratio based on the consistency of the evaluation matrix. The calculation formula for the consistency ratio is: ； In the above formula, represents the consistency ratio of the evaluation matrix, represents the consistency of the evaluation matrix, represents the random consistency index of the evaluation matrix; S564: Determine whether the consistency ratio is less than the consistency ratio threshold; If yes, proceed to step S57; If not, return to step S51.

7. The power data analysis method according to claim 3, 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 under the current number of iterations The first number of and Respectively expressed in Moment and The number of accesses to the first power data corresponding to the time, Indicates the density radius under the current number of iterations, is a sign function, and A represents the total number of moments containing the first power data.

8. A power data analysis system based on a data center, 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 7.

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