Optimized and coordinated elastic electricity transaction price data management method

Abstract

The invention discloses an optimized and coordinated elastic power transaction price data management method, relates to the technical field of data management, and solves the problems that a relatively good index mode is not set and corresponding abnormal data cannot be confirmed in time. According to the method, price data with correlation characteristics are stored in the same storage partition, specific confirmation of characteristic points is carried out on a plurality of groups of price data in the same storage partition, correlation analysis of radiation circles is carried out on the confirmed plurality of groups of characteristic points, and thus the price data with correlation characteristics can be obtained based on the confirmation process of step-by-step analysis. According to the method, the associated feature points which do not belong to the corresponding radiation circles are calibrated as abnormal points, the price data corresponding to the abnormal points are calibrated as abnormal data, and the calibrated abnormal data are displayed, so that a management party can conveniently check and evaluate the specific data abnormal condition of the price data.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to an optimized and coordinated flexible electricity trading price data management method. Background Art

[0002] Flexible electricity trading price data refers to trading price-related data in the electricity market that has a certain degree of elasticity depending on the supply and demand relationship, time changes and other factors. The price fluctuates in real time with the market supply and demand conditions. For example, during peak electricity consumption periods, demand increases significantly and flexible electricity trading prices may rise rapidly; during low periods, demand decreases and prices may fall.

[0003] The application with publication number CN107103429A discloses a method and system for managing bond holding transaction data, which relates to the field of data management technology. The main purpose is to effectively manage bond holding transaction data and reduce the risk in the transaction process. The method includes: receiving a bond holding transaction request sent by a first financial institution, the bond transaction request is used for the first financial institution to request a bond holding transaction with a second financial institution, obtaining the transaction bond price of the bond holding transaction request from the stock exchange, conducting a risk assessment on the first financial institution based on the transaction bond price, and sending a transaction reminder message based on the audit result of the risk assessment. The present invention is mainly used for the management of bond holding transaction data.

[0004] In the unified management process of electricity trading price data, different price data generated in different time periods are stored and managed in the same period. However, the original processing method is simply to carry out storage management, and no correlation analysis is performed on the managed price data, resulting in the inability to timely determine the corresponding abnormal data. In addition, for the indexing process of the large amount of data managed, a better indexing method is not set, resulting in a slow indexing rate, which simultaneously leads to the output rate of the indexed data. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides an optimized and coordinated flexible electricity trading price data management method, which solves the problems of not setting a better indexing method and not being able to promptly identify corresponding abnormal data.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an optimized and coordinated flexible power transaction price data management method, comprising the following steps: Step 1: Initially confirm the price data generated during the flexible trading process within the monitoring period, and initially classify the confirmed price data, and store the price data belonging to the same classification in the same storage partition, where the monitoring period is a preset period. The specific method is as follows: S11. Based on a plurality of sets of price data confirmed in a corresponding monitoring period, and preferentially based on the regional information contained in the corresponding price data, preliminarily classify the price data belonging to the same regional information to obtain a preliminarily classified price data set; S12. For the initially classified price data set: based on the time information contained in the corresponding price data, reclassify the price data belonging to the same time period to obtain a reclassified price data set, whose time period is a preset period, and the time period ∈ monitoring period, and store the confirmed groups of reclassified price data sets in the same storage partition; Step 2: Perform a preliminary analysis on the relevant price data stored in the same storage partition, perform a numerical density analysis based on the median of the interval associated with the corresponding price data, and determine and display the abnormal price data based on the specific results of the numerical density analysis. The specific sub-steps are: S21, based on the plurality of groups of price data stored in the storage partition, if the price data is single data, the value associated with the single data is used as the characteristic value of the price data; if the price data is interval data, the middle value of the interval data is determined, and the determined middle value is used as the characteristic value of the price data; S22, based on the determined sets of eigenvalues, a set of two-dimensional planes is established, and the center point of the two-dimensional plane is determined as the origin. Based on the specific numerical values ​​of the eigenvalues, a feature point is determined in the two-dimensional plane. The distance value between the feature point and the origin is consistent with the eigenvalue. Several sets of eigenvalues ​​complete the relevant confirmation of the feature points and are evenly distributed around the origin. To perform a radiation analysis session: Based on the determined origin, a set of variable radii R1 is determined to determine the radiation circle of the origin, and the area M1 of the radiation circle is confirmed. After confirming the number of characteristic points G contained in the radiation circle, P=G÷M1 is used to determine the point density P of the radiation circle. If P≤Y1, R1 is increased to P>Y1 and stopped to determine the radiation area of ​​the first set of radiation circles. At this moment, the variable radius of the radiation circle is (R1-1), where Y1 is a preset value. If P>Y1, an error signal is generated for display; Based on the determined first group of radiation circles, the second group of radiation circles is determined on the basis of the first group of radiation circles. The radius of the second group of radiation circles is greater than that of the first group of radiation circles. The area of ​​the circumferential region between the first group of radiation circles and the second group of radiation circles is confirmed and calibrated as M2. In the same way, the characteristic points inside the circumferential region are confirmed and the point density P is evaluated. If P≤Y1, R2 is increased to stop when P>Y1. The radiation area of ​​the first group of radiation circles is determined. At this moment, the variable radius of the radiation circle is (R2-1). R2 is the variable radius of the second group of radiation circles. If P is always greater than Y1, the radiation analysis process is stopped. After the second group of radiation circles is confirmed, the third group of radiation circles is determined, and the corresponding density analysis is performed, and so on, until the P generated by the corresponding radiation circles is always greater than Y1, and the radiation analysis process is stopped; S23. Based on the corresponding radiation circle determined by the radiation analysis process, the corresponding characteristic value that does not belong to the radiation circle is marked as an abnormal value, the price data associated with the abnormal value is marked as abnormal data, and the marked abnormal data is displayed; Step 3: Based on the storage sub-library of the corresponding storage interval, identify the memory usage of different processes of this storage sub-library from historical data, and select the memory usage that can be used by the cache pool. The specific method is as follows: S31. Eliminate the memory occupancy rate of the cache pool from the historical data, thereby locking the memory occupancy rate associated with other processes in this storage sub-library, average the memory occupancy rates of the same process, determine the associated average, and then sum the associated averages of multiple processes to determine the sum value ZH; S32, using ZY=1-ZH-Y2 to obtain the maximum memory occupancy rate ZY that can be used by the cache pool, where Y2 is a preset value, which is the buffer value set in advance by the system; Step 4: Based on the data extraction instruction and the determined memory usage rate of the buffer pool, a large amount of price data stored in the storage sub-database is indexed, and based on the memory usage rate of the buffer pool generated by the corresponding data during the indexing process, it is evaluated whether it can be indexed multiple times, and the indexing process of multiple times is performed based on the evaluation result. The specific sub-steps are: S41. Based on the data extraction instruction, the price data in the storage sub-library are indexed, a group of price data is preferentially selected to execute the indexing process, and the memory occupancy rate Z1 generated by the cache pool of this indexing process is recorded. The associated value GL is confirmed by using ZY÷Z1=GL, and the value Z associated with the integer digit of the associated value GL is taken. After the current indexing process is completed, the second group of indexing processes is executed, and the data capacity of the price data corresponding to the initial indexing process is calibrated as R. In the second group of indexing processes, Z price data with a capacity value of (R±Y3) are directly and synchronously indexed, where Y3 is a preset capacity fluctuation value; S42. When a group of indexing processes cannot synchronously index Z price data with a capacity value of (R±Y3), a group of price data is randomly selected again to execute the indexing process and re-execute step S41 to re-index. During re-indexing, both R and Z are changed according to the actual value changes.

[0007] Preferably, when the feature point is on a circle on the radiation circle, the feature point synchronously belongs to the radiation area of ​​the radiation circle.

[0008] The present invention provides an optimized and coordinated flexible power transaction price data management method. Compared with the prior art, it has the following beneficial effects: The present invention preferentially performs preliminary classification on different price data, so that price data with associated features are stored in the same storage partition, and then specifically confirms the feature points of several groups of price data in the same storage partition, and then performs association analysis of the radiation circle on the confirmed several groups of feature points, so that based on the confirmation process of step-by-step analysis, the associated feature points that do not belong to the corresponding radiation circle are marked as abnormal points, and the price data corresponding to the abnormal points are marked as abnormal data, and the marked abnormal data are displayed, so that the management party can view and evaluate the specific data abnormality of its price data; In order to ensure that the indexing rate of its database can be effectively improved, it is prioritized to identify whether its indexing process can perform multiple indexes based on the maximum proportion that the corresponding database cache pool can occupy, and execute the corresponding multiple indexing process based on the specific identification results, so as to fully improve the indexing rate and achieve better indexing processing effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 It is a schematic diagram of the arrangement of characteristic points of the present invention. DETAILED DESCRIPTION

[0010] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0011] First embodiment See also Figure 1 , the present application provides an optimized and coordinated flexible power transaction price data management method, comprising the following steps: Step 1: Initially confirm the price data generated during the flexible trading process within the monitoring period, and initially classify the confirmed price data, and store the price data of the same classification in the same storage partition. The monitoring period is a preset period, and its specific value is determined by the operator based on experience, generally 30 days or one month. Flexible trading is a related trading process in which prices can float. The final confirmed price belongs to a price range, and its specific price confirmation process is confirmed by the associated personnel themselves; Among them, the specific sub-steps of initial classification are: S11. Based on the several groups of price data confirmed in the corresponding monitoring period, the price data belonging to the same regional information are preliminarily classified based on the regional information contained in the corresponding price data (that is, different city location information is pre-calibrated in the corresponding price data) to obtain a pre-classified price data set; S12. For the initially classified price data set: based on the time information contained in the corresponding price data, the price data belonging to the same time period are reclassified to obtain a reclassified price data set, whose time period is a preset period, and the time period ∈ monitoring period, whose specific value is determined by the operator based on experience, generally 24h, and the confirmed groups of reclassified price data sets are stored in the same storage partition; Specifically, the proposed monitoring period is set to 30 days. Generally, statistics are performed from time 0 at the beginning of the corresponding month to confirm the corresponding monitoring period. The corresponding time period is 24 hours, and relevant statistics are also performed from time 0 at the beginning of the corresponding month. Since the time period will divide the monitoring period into several micro-periods, it is preferred to initially classify the data from different regions, and then reclassify the relevant data within different time periods to determine the relevant data sets. Then the relevant price data stored in the corresponding partitions are generally close in characteristics and have a greater feature correlation.

[0012] Step 2: Perform a preliminary analysis on the relevant price data stored in the same storage partition, perform a numerical density analysis based on the median of the interval associated with the corresponding price data, and determine and display the abnormal price data based on the specific results of the numerical density analysis. Specifically, the relevant power price data belonging to the same region generally have corresponding numerical characteristics. Since the weather and other related factors in the corresponding region are relatively consistent, the actual confirmed price data are also relatively close. If there is any abnormal price data, it can be directly confirmed; The specific sub-steps for determining abnormal price data and displaying it are: S21. Based on the several groups of price data stored in the storage partition, if the price data is single data, the value associated with the single data is used as the characteristic value of the price data; if the price data is interval data, the middle value of the interval data is determined, and the determined middle value is used as the characteristic value of the price data (the so-called single data refers to the case where the price data is 20 or 25, and the so-called interval data refers to the corresponding interval data of the price data of 20-25 or 25-30); S22, based on the determined sets of eigenvalues, a set of two-dimensional planes is established, and the center point of the two-dimensional plane is determined as the origin. Based on the specific numerical values ​​of the eigenvalues, a feature point is determined in the two-dimensional plane. The distance value between the feature point and the origin is consistent with the eigenvalue. Several sets of eigenvalues ​​complete the relevant confirmation of the feature points and are evenly distributed around the origin. To perform a radiation analysis session: Based on the determined origin, determine a set of variable radius R1 to determine the radiation circle of this origin, and confirm the area M1 of this radiation circle. After confirming the number of feature points G contained in this radiation circle, use P=G÷M1 to determine the point density P of this radiation circle. If P≤Y1, increase R1 to P>Y1 and stop, and determine the radiation area of ​​the first set of radiation circles. At this moment, the variable radius of the radiation circle is (R1-1). When the feature point is located in the circle on the radiation circle, it also belongs to the radiation area of ​​this radiation circle. Because P>Y1 is a non-standard situation, in order to make it standard, it is necessary to shorten it by one unit length to reach the critical standard state, where Y1 is a preset value, and its specific value is determined by the operator based on experience. If P>Y1, an error signal is generated for display, indicating that there is a problem with the value of the point density standard Y1, which needs to be readjusted and the radiation analysis process is re-executed; Based on the determined first group of radiation circles, the second group of radiation circles is determined on the basis of the first group of radiation circles. The radius of the second group of radiation circles is greater than that of the first group of radiation circles. The area of ​​the circumferential region between the first group of radiation circles and the second group of radiation circles is confirmed and calibrated as M2. In the same way, the characteristic points inside the circumferential region are confirmed and the point density P is evaluated. If P≤Y1, R2 is increased until P>Y1 and then stopped. The radiation area of ​​the first group of radiation circles is determined. At this moment, the variable radius of the radiation circle is (R2-1). R2 is the variable radius of the second group of radiation circles. If P is always greater than Y1 (that is, R2 is also greater than Y1 when it is 1), the radiation analysis process is stopped. After the second group of radiation circles is confirmed, the third group of radiation circles is determined, and the corresponding density analysis is performed, and so on, until the P generated by the corresponding radiation circles is always greater than Y1, and the radiation analysis process is stopped; S23. Based on the corresponding radiation circle determined by the radiation analysis process, the corresponding characteristic value that does not belong to the radiation circle is marked as an abnormal value, and the price data associated with the abnormal value is marked as abnormal data (this abnormal data has a large deviation from other price data stored in this storage interval, so it needs to be marked as abnormal and displayed), and the marked abnormal data is displayed; Example: Combination Figure 2, based on the constructed two-dimensional plane, determine its corresponding origin, based on this origin, the corresponding radiation circle can be confirmed, and then based on the determined related radiation circle, the corresponding feature points belonging to this radiation circle can be confirmed, and based on the specific circle area of ​​the corresponding radiation circle and the feature points contained, the relevant density confirmation can be carried out, and the confirmed density P is stopped when it is close to Y1 to the maximum extent, and the associated position of the corresponding radiation circle is locked, and then the relevant confirmation of the circumferential density of other areas is carried out subsequently, and the corresponding abnormal points can be determined by analogy. If all the feature points are located within the determined radiation circle, it means that there is no corresponding abnormal point. Here is to carry out the relevant confirmation of abnormal price data; After price data is generated, if there are certain price data anomalies, it can be directly determined that the reason for the data anomaly is most likely caused by malicious transactions or other factors. Therefore, such abnormal data needs to be displayed for the management to review and handle in time.

[0013] Step 3: Based on the storage sub-library of the corresponding storage interval (which can be understood as a certain disk, such as: D disk, E disk, etc.), identify the memory usage of different processes of this storage sub-library from historical data, and select the memory usage that can be used by the cache pool. The specific sub-steps for the selection are: S31. Eliminate the memory occupancy rate of the cache pool from the historical data, thereby locking the memory occupancy rate associated with other processes (that is, other processes not including the cache pool) in this storage sub-library, average the memory occupancy rates of the same process, determine the associated average, and then sum the associated averages of multiple processes to determine the sum value ZH; S32. Use ZY=1-ZH-Y2 to obtain the maximum memory occupancy rate ZY that can be used by this cache pool, where Y2 is a preset value, and its specific value is determined by the operator based on experience, and is the buffer value set in advance by the system.

[0014] Step 4: Based on the data extraction instruction and the determined memory occupancy rate of the buffer pool, a large amount of price data stored in the storage sub-database is indexed, and based on the memory occupancy rate of the buffer pool generated by the corresponding data during the indexing process, it is evaluated whether it can be indexed multiple times, and the indexing process of multiple times is performed based on the evaluation result to speed up the indexing rate of its price data; The specific sub-steps for evaluating whether multiple indexing can be performed are: S41. Based on the data extraction instruction, the price data in the storage sub-database is indexed, a group of price data is preferentially selected to execute the indexing process, and the memory occupancy rate Z1 generated by the cache pool of this indexing process is recorded. The associated value GL is confirmed by using ZY÷Z1=GL, and the value Z associated with the integer of the associated value GL is taken. After the current indexing process is completed, the second group of indexing processes is executed, and the data capacity of the price data corresponding to the initial indexing process is calibrated as R. In the second group of indexing processes, Z price data with a capacity value of (R±Y3) are directly and synchronously indexed, where Y3 is a preset capacity fluctuation value, generally 2M, which is prepared in advance by relevant operators based on experience; S42. When a group of indexing processes cannot synchronously index Z price data with a capacity value of (R±Y3), a group of price data is randomly selected again to execute the indexing process and re-execute step S41 to re-index. During re-indexing, both R and Z are changed according to the actual value changes.

[0015] The use of multiple concurrent indexes can effectively improve the original single indexing rate, and by giving priority to confirming the memory occupancy rate related to the cache pool, the multiple process Z during multiple indexing can be quickly identified, which can not only effectively guarantee the corresponding indexing rate, but also make full use of the memory occupancy rate of the cache pool, without causing the corresponding database to be overloaded, thus achieving better indexing effect.

[0016] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0017] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An optimized and coordinated flexible power transaction price data management method, characterized in that: The following steps are involved: Step 1: Initially confirm the price data generated during the flexible trading process within the monitoring period, and initially classify the confirmed price data, and store the price data belonging to the same classification in the same storage partition, wherein the monitoring period is a preset period; Step 2: Perform a preliminary analysis on the relevant price data stored in the same storage partition, perform a numerical density analysis based on the median of the interval associated with the corresponding price data, and determine and display the abnormal price data based on the specific results of the numerical density analysis; Step 3: Based on the storage sub-library of the corresponding storage interval, identify the memory usage of different processes of this storage sub-library from historical data, and select the memory usage that can be used by the cache pool; Step 4: Based on the data extraction instructions and the determined memory usage rate of the cache pool, a large amount of price data stored in the storage sub-database is indexed, and based on the memory usage rate of the cache pool generated by the corresponding data during the indexing process, it is evaluated whether multiple indexing can be performed, and the indexing process of multiple indexing is performed based on the evaluation results.

2. The optimized and coordinated flexible power transaction price data management method according to claim 1 is characterized in that: In step 1, the specific method of initially classifying the confirmed price data is as follows: S11. Based on a plurality of sets of price data confirmed in a corresponding monitoring period, and preferentially based on the regional information contained in the corresponding price data, preliminarily classify the price data belonging to the same regional information to obtain a preliminarily classified price data set; S12. For the initially classified price data set: based on the time information contained in the corresponding price data, the price data belonging to the same time period are reclassified to obtain a reclassified price data set, whose time period is a preset period, and the time period ∈ monitoring period, and the confirmed groups of reclassified price data sets are stored in the same storage partition.

3. The optimized and coordinated flexible power transaction price data management method according to claim 1 is characterized in that: In step 2, the specific sub-steps of determining abnormal price data and displaying it are: S21, based on the plurality of groups of price data stored in the storage partition, if the price data is single data, the value associated with the single data is used as the characteristic value of the price data; if the price data is interval data, the middle value of the interval data is determined, and the determined middle value is used as the characteristic value of the price data; S22, based on the determined sets of eigenvalues, a set of two-dimensional planes is established, and the center point of the two-dimensional plane is determined as the origin. Based on the specific numerical values ​​of the eigenvalues, a feature point is determined in the two-dimensional plane. The distance value between the feature point and the origin is consistent with the eigenvalue. Several sets of eigenvalues ​​complete the relevant confirmation of the feature points and are evenly distributed around the origin. To perform a radiation analysis session: Based on the determined origin, a set of variable radii R1 is determined to determine the radiation circle of the origin, and the area M1 of the radiation circle is confirmed. After confirming the number of characteristic points G contained in the radiation circle, P=G÷M1 is used to determine the point density P of the radiation circle. If P≤Y1, R1 is increased to P>Y1 and stopped to determine the radiation area of ​​the first set of radiation circles. At this moment, the variable radius of the radiation circle is (R1-1), where Y1 is a preset value. If P>Y1, an error signal is generated for display; Based on the determined first group of radiation circles, the second group of radiation circles is determined on the basis of the first group of radiation circles. The radius of the second group of radiation circles is greater than that of the first group of radiation circles. The area of ​​the circumferential region between the first group of radiation circles and the second group of radiation circles is confirmed and calibrated as M2. In the same way, the characteristic points inside the circumferential region are confirmed and the point density P is evaluated. If P≤Y1, R2 is increased to stop when P>Y1. The radiation area of ​​the first group of radiation circles is determined. At this moment, the variable radius of the radiation circle is (R2-1). R2 is the variable radius of the second group of radiation circles. If P is always greater than Y1, the radiation analysis process is stopped. After the second group of radiation circles is confirmed, the third group of radiation circles is determined, and the corresponding density analysis is performed, and so on, until the P generated by the corresponding radiation circles is always greater than Y1, and the radiation analysis process is stopped; S23. Based on the corresponding radiation circle determined by the radiation analysis process, the corresponding characteristic values ​​that do not belong to the radiation circle are marked as abnormal values, the price data associated with the abnormal value is marked as abnormal data, and the marked abnormal data is displayed.

4. The optimized and coordinated flexible power transaction price data management method according to claim 3 is characterized in that: When the feature point is located on a circle on the radiation circle, the feature point synchronously belongs to the radiation area of ​​the radiation circle.

5. The optimized and coordinated flexible power transaction price data management method according to claim 1 is characterized in that: In step 3, the specific method of selecting the memory usage rate that can be used by the cache pool is: S31. Eliminate the memory occupancy rate of the cache pool from the historical data, thereby locking the memory occupancy rate associated with other processes in this storage sub-library, average the memory occupancy rates of the same process, determine the associated average, and then sum the associated averages of multiple processes to determine the sum value ZH; S32. Use ZY=1-ZH-Y2 to obtain the maximum memory occupancy rate ZY that can be used by this cache pool, where Y2 is a preset value, which is the buffer value set in advance by the system.

6. The optimized and coordinated flexible power transaction price data management method according to claim 1 is characterized in that: In step 4, the specific sub-steps for evaluating whether multiple indexing can be performed are: S41. Based on the data extraction instruction, the price data in the storage sub-library are indexed, a group of price data is preferentially selected to execute the indexing process, and the memory occupancy rate Z1 generated by the cache pool of this indexing process is recorded. The associated value GL is confirmed by using ZY÷Z1=GL, and the value Z associated with the integer digit of the associated value GL is taken. After the current indexing process is completed, the second group of indexing processes is executed, and the data capacity of the price data corresponding to the initial indexing process is calibrated as R. In the second group of indexing processes, Z price data with a capacity value of (R±Y3) are directly and synchronously indexed, where Y3 is a preset capacity fluctuation value; S42. When a group of indexing processes cannot synchronously index Z price data with a capacity value of (R±Y3), a group of price data is randomly selected again to execute the indexing process and re-execute step S41 to re-index. During re-indexing, both R and Z are changed according to the actual value changes.

Images