CN117252636A - Electricity fee package type optimization method and system based on user - Google Patents
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Abstract
The invention relates to the technical field of power resource optimization, in particular to a user-based electric charge package type optimization method and system, which comprises the following steps: acquiring electricity consumption data of each metering point in a metering period set by a user and corresponding electricity fee settlement parameters, determining different types of electricity fee package settlement formulas and electricity consumption rules of the user, and obtaining an electricity fee package index according to the obtained electricity consumption rules and the daily electricity consumption amount of the user; and determining the proper package type of the user and the package type selected by the user through the association rule generated by the minimum confidence degree among elements in the frequent item set by taking the electric charge package settlement formulas and the electric charge package indexes of different types as candidate item sets and generating the frequent item set.
Description
Technical Field
The invention relates to the technical field of power resource optimization, in particular to a method and a system for optimizing an electric charge package type based on a user.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The electric charge package is characterized in that according to the requirements and load characteristics of users, the electric prices of different types and gears and the attached electric service are provided for the users in a packaged mode, and the supply and demand balance of an electric power system can be relieved on the basis of meeting the requirements of different users. Such as the common fixed time sharing packages, rate packages, and mixed packages. The electricity consumption rules of different types of users are different, and the packages selected by the users are not always suitable for the current electricity consumption rules, when the users switch the package types, the efficiency of electricity fee settlement is affected on one hand, and the electricity consumption cost of the users is increased on the other hand because of more parameters for electricity fee settlement in the different packages.
Disclosure of Invention
In order to solve the technical problems in the prior art, the invention provides the method and the system for optimizing the electric charge package type based on the user, which analyze the association relationship between the electric charge package and the electric user by utilizing the parameters related to the electric charge settlement, and correspond to different electric charge packages by utilizing the electric characteristics of different users, thereby omitting the step of switching package type calculation in the electric charge settlement process, accelerating the electric charge settlement speed and improving the electric charge settlement accuracy.
In order to achieve the above object, the present invention adopts the following technical embodiments:
the first aspect of the present invention provides a user-based electricity fee package type optimizing method, comprising the steps of:
acquiring electricity consumption data of each metering point in a metering period set by a user and corresponding electricity fee settlement parameters, determining different types of electricity fee package settlement formulas and electricity consumption rules of the user, and obtaining an electricity fee package index according to the obtained electricity consumption rules and the daily electricity consumption amount of the user;
and determining the proper package type of the user and the package type selected by the user through the association rule generated by the minimum confidence degree among elements in the frequent item set by taking the electric charge package settlement formulas and the electric charge package indexes of different types as candidate item sets and generating the frequent item set.
Further, obtaining the electricity consumption data of each metering point and the corresponding electricity fee settlement parameters in the metering period set by the user comprises the following steps:
acquiring the time-sharing electricity price, the daily clear electricity quantity and the fixed partial electricity quantity of each metering point of a user every day for calculation, and acquiring the calculated electricity fee of a fixed time-sharing package;
acquiring the standard electricity price, the rate class electricity price, the daily clear electricity quantity and the rate partial electricity quantity of each metering point of a user every day, and calculating to obtain the calculated electricity fee of the rate class package;
and obtaining the fixed electricity price, the rate electricity price, the daily clear electricity quantity, the fixed partial electricity quantity and the rate partial electricity quantity of each metering point of the user every day, and calculating to obtain the calculated electricity fee of the mixed package.
Further, taking the electric charge package settlement formulas and the electric charge package indexes of different types as candidate item sets and generating frequent item sets, including;
searching a frequent item set by using the candidate item set, calculating the support degree of the candidate 1 item set through multiple times of scanning, pruning the 1 item set lower than the support degree, and obtaining the frequent item set;
connecting the rest frequent item sets 1 to obtain candidate frequent 2 item sets;
repeating pruning to remove candidate frequent 2 item sets lower than the support degree to obtain a real frequent 2 item set;
and repeatedly iterating until the frequent item set k+1 cannot be found, wherein the corresponding frequent item set K is the required result.
Further, the association rule generated by minimum confidence between elements in the frequent item set includes:
setting a minimum support degree and a minimum confidence degree;
determining the support degree of each package and the user type through the acquired power data, comparing the support degree with the minimum support degree, wherein the packages and the user types with the support degree greater than or equal to the minimum support degree are frequent 1-item sets, and the sets are marked as L1;
self-connecting the packages in L1 with the user types to form a candidate set C2 of the frequent 2-item set; traversing all packages and user types in the C2 to obtain the support degree of each package and user type, wherein a term set with the support degree not lower than the minimum support degree is a frequent 2-term set, and the term set is marked as L2; repeating the operation until the frequent k-term set can not be found any more;
confidence levels among elements in the frequent k-term set are determined, and association rules are generated according to minimum confidence level screening.
A second aspect of the present invention provides a system for implementing the above method, comprising:
the electricity consumption data acquisition module is configured to: acquiring electricity consumption data of each metering point in a metering period set by a user and corresponding electricity fee settlement parameters, determining different types of electricity fee package settlement formulas and electricity consumption rules of the user, and obtaining an electricity fee package index according to the obtained electricity consumption rules and the daily electricity consumption amount of the user;
a package type optimization module configured to: and determining the proper package type of the user and the package type selected by the user through the association rule generated by the minimum confidence degree among elements in the frequent item set by taking the electric charge package settlement formulas and the electric charge package indexes of different types as candidate item sets and generating the frequent item set.
Further, there is also a concurrent computation module configured to: and (3) dividing all package types into modules, obtaining the results of different divided modules in the same time period, and uniformly summarizing the results of all the modules to obtain the optimized results of packages of all the types.
A third aspect of the present invention provides a computer-readable storage medium.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps in the user-based electric charge package type optimization method as described above.
A fourth aspect of the invention provides a computer device.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the user-based electricity rate package type optimization method as described above when the program is executed.
Compared with the prior art, one or more of the above technical embodiments have the following advantages:
the method has the advantages that the electricity utilization rule of the user is determined according to the electricity utilization data of the user within a period of time, the association relationship between the electricity fee package and the user is analyzed by matching with the parameters related to the electricity fee settlement, different electricity fee packages are corresponding to different electricity fee features of different users, the step of switching package type calculation in the process of the electricity fee settlement is omitted, the rate of the electricity fee settlement is accelerated, the package type suitable for the user and the package type selected by the user can be determined, and the electricity utilization cost of the user is indirectly reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a schematic diagram of a user-based electricity rate package type optimization process provided in one or more embodiments of the present invention;
FIG. 2 is a schematic diagram of a distributed computing framework provided in accordance with one or more embodiments of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
As described in the background art, there are differences in the electricity usage laws among different types of users, and the package selected by the user is not always suitable for the current electricity usage laws, when the user switches package types, because there are many parameters for electricity fee settlement in different packages, on one hand, the efficiency of electricity fee settlement is affected, and on the other hand, the electricity usage cost of the user is increased. Because different electric charge packages relate to different electric charge settlement modes, different indexes related to users are required to be acquired as calculation basis, the data are complex and various, and the data mining method is utilized in the following embodiments to mine parameters related to electric charge settlement, process the data, remove some incomplete and abnormal data, and screen applicable data through the modes of smooth aggregation, data generalization, standardization and the like. Aiming at the diversity of the electric charge package system and the diversity of users, based on the edge calculation principle, the electric charge calculation method is summarized and combed according to the rule of electric charge calculation parameter data, and a multi-line parallel electric charge test settlement method is established on the basis, so that the types of the electric charge packages are optimized, the electric charge settlement speed and the electric charge settlement efficiency of the users can be improved, more matched electric charge packages are selected, and the resources of electric metering data are well used.
Embodiment one:
the electric charge package type optimization method based on the user comprises the following steps:
acquiring the time-sharing electricity price, the daily clear electricity quantity and the fixed partial electricity quantity of each metering point of a user every day for calculation, and acquiring the calculated electricity fee of a fixed time-sharing package;
acquiring the standard electricity price, the rate class electricity price, the daily clear electricity quantity and the rate partial electricity quantity of each metering point of a user every day, and calculating to obtain the calculated electricity fee of the rate class package;
the method comprises the steps of obtaining fixed electricity price, rate electricity price, daily clear electricity quantity, fixed partial electricity quantity and rate partial electricity quantity of each metering point of a user every day, and calculating to obtain calculated electricity charge of a mixed package;
according to the obtained electric charge package type, the relation between the user and the electric charge package is mined, so that the user can quickly correspond to the related package in the complex electric charge settlement package type, the optimization of the electric charge package type is realized, and the simple and efficient electric charge settlement is performed;
by utilizing the obtained relation between the users and the electric charge packages, different package calculation tasks are divided into a plurality of small calculation modules which can be rapidly processed, so that a multi-thread calculation method for the user electric charge of the multi-user number, complex user types and diversified user electric charge packages is formed, and the rate of electric charge settlement is improved.
In this embodiment, when electricity consumption data of each metering point of a user is acquired, the data amount required by different electric fee packages is different.
In this embodiment, an electric charge settlement formula and a rule of user electricity consumption are determined according to the obtained electricity consumption data, and the electricity consumption periods of the peaks, plateaus, valleys and deep valleys are summarized, and electric charge package indexes such as catalogue electric charges, charging electric charges, transaction electric charges, basic electric charges, power adjustment electric charges and the like are calculated according to the user electricity consumption daily definition.
In this embodiment, the obtained settlement methods of the electric charges of the packages of different types are associated with different users, and association rules are established by using Apriori algorithm to obtain the association between the package types and multiple users, wherein in order to facilitate quick settlement of the electric charges of the users, the association rules algorithm is adopted to establish the relationship between the electric users and the package types, so that the settlement result is accurate and efficient.
In this embodiment, an edge calculation-based concurrent electric charge calculation method is established for different package types corresponding to different electric users, where the single calculation mode has a slower speed and low working efficiency due to the fact that the types of electric charge settlement packages and users are more, and the efficiency of electric charge settlement is greatly improved by adopting a multi-line parallel concurrent calculation method.
The method comprises the steps of establishing association rules by using an Apriori algorithm, enabling different types of electric charge packages to correspond to different electricity users, integrating different electric charge package calculation methods, and establishing an electric charge package concurrent calculation model by adopting a concurrent architecture calculation method.
In this embodiment, data such as a time-sharing electricity price, a daily electricity consumption amount, a rate electricity amount, a fixed part of electricity amount, a daily electricity consumption fee of each metering point of the user are selected, and the data are corresponding according to an electricity fee settlement formula, so that electricity consumption periods of peaks, flat, valleys and deep valleys are summarized, and a weighted electricity price of the current day or month is calculated.
The electric charge package type is as follows:
1) Fixing time sharing:。
2) Rate class:
day, month, day basis:。
real-time month benchmark:。
day-before-day basis:。
real-time day reference:。
3) Mixing:
day, month, day basis:。
real-time month benchmark:。
day-before-day basis:。
real-time day reference:。
the parameters corresponding to the electric charge settlement formula are as follows:
(1) The power transmission and distribution fee formula of the electricity user:。
(2) Capacity compensation electricity fee formula:，，，，。
(3) The calculation formula of the marketized comprehensive damage and benefit sharing electric charge comprises the following steps:。
(4) And (3) a power charge settlement formula of the excellent purchase curve:。
(5) The electricity charge calculation formula is characterized by:。
(6) Transaction electric chargeThe calculation formula is as follows:。
(7) The calculation formula of the power adjustment electricity charge comprises the following steps:。
(8) And (3) an electric charge settlement formula:。
after each package calculation formula is obtained, a model of a user package association relationship is established by using each package formula and the user type and the user electricity consumption data.
And establishing an Apriori association algorithm model, determining the association relation between the electric charge package and the user, and realizing the rapid matching of the multi-user electric package.
The Apriori algorithm mainly uses candidate item sets to find frequent item sets, and the basic idea is that the support degree of candidate 1 item sets is calculated through multiple scans of a database, and then pruning is carried out to remove 1 item sets lower than the support degree, so that the frequent item sets are obtained. And then connecting the rest frequent item sets 1 to obtain candidate frequent 2 item sets, pruning before repeating to remove the candidate frequent 2 item sets lower than the support degree, and obtaining the real frequent 2 item sets. This is repeated until a frequent item set k+1 cannot be found. At this time, the corresponding frequent K term set is the output result of the algorithm.
(1) Association rules, assuming that the association rules are implications of an x= > Y, wherein X, Y is a proper subset of a transaction database D (i.e. the user electricity data and package type database), and X is called the premise of the rules and Y is called the result of the rules. The association rules reflect the rules of occurrence of the items in Y when the items in X occur in a transaction. In this embodiment, the transaction database D in which the user electricity and electricity fee packages are input is the user electricity, X is the subset of D, Y is the type of electricity fee packages, and the association relationship of the Apriori algorithm is explored by implications between X and Y, and the change rule between X and Y.
(2) Support, the support of the association rule refers to the ratio of the number of transactions containing X and Y to the number of all transactions in the transaction database, and reflects the frequency of simultaneous occurrence of the items of the transactions contained in X and Y in the transaction database, which is recorded asThe method comprises the following steps: />。
(3) Confidence, confidence of association rule, is the ratio of the number of transactions containing X and Y in the transaction set to the number of transactions containing X, recorded asThe confidence reflects the conditional probability of Y occurring in a transaction containing X. Namely: />。
(4) Degree of lift, which is the ratio of the probability of containing X at the same time under the condition of containing Y, to the probability of X as a whole, namely:。
the degree of lifting indicates the degree of association between X and Y, if the degree of lifting is greater than 1, it indicates that X and Y are valid strong association rules, and if the degree of lifting is less than 1, it indicates that X and Y are invalid strong association rules. However, if X and Y are independent, there areBecause of->。
(5) Frequent item set, if item set U exists, andthe support of the item set U on the object database T is the percentage of the objects containing U in the T, namely：/>。
Wherein,representing the number of elements in the collection. For item set I, all item sets in the transactional database T that meet the minimum support specified by the user, i.e., non-empty subsets of I that are not less than the minimum support threshold, are referred to as frequent item sets or large item sets.
The selection of frequent item sets in a data set typically requires custom evaluation criteria, and most often support, or a combination of support and confidence.
Frequent item sets are generated using Apriori algorithm, and association rules are generated by comparison with minimum confidence levels. If the user fits in the package of X (unified time-sharing) type tags, the user must select the package of Y (time-sharing price class) type tags, as in association with the rule x→y. And setting the minimum degree of action, and returning only the association rules higher than the minimum degree of action.
The association rule mining model based on the Apriori algorithm comprises the following steps:
step 1: setting a minimum support degree and a minimum confidence degree;
step 2: and calculating the support degree of each user type and the corresponding package according to the acquired power data. Comparing the set with the minimum support degree, wherein all packages and user types with the support degree greater than or equal to the minimum support degree are called frequent 1-item sets, and the sets are marked as L1;
step 3: scanning L1, and performing self-connection on the packages in the L1 and the user types to form a candidate set C2 of the frequent 2-item set;
step 4: traversing all packages and user types in the C2, calculating the support degree of each package and user type, wherein a term set with the support degree not lower than the minimum support degree is a frequent 2-term set, and the term set is marked as L2;
step 5: repeating Step3, step4 until no more frequent k-term sets can be found;
step 6: and calculating the confidence coefficient between the user type and the package formula in the frequent k-term set, and screening and generating an association rule according to the minimum confidence coefficient.
The association relation model is built, the obtained electric charge settlement formula, the user type and the user data characteristics are input into the model, and after the model is trained, association rules between the electric charge package and the electricity user are formed.
And constructing a multi-user-electric charge package association pre-analysis method, and providing support for an electric charge calculation method forming a concurrent calculation architecture.
And finally, constructing a concurrent computing architecture, dividing all package types into modules, respectively computing different modules in synchronous time, and finally, uniformly summarizing computing results of the modules to obtain settlement results obtained by computing packages of various types.
Since the algorithm under a single thread has the risk of being slower in running speed and possibly even causing downtime of a machine when facing a large amount of data processing, distributed computation is added to improve the running speed sum of the algorithm.
Through the efficient distributed computing framework recommended by the correlation rule, the generated correlation rule splits the whole variable of the electricity consumption data into a plurality of mutually non-overlapping partitions according to the correlation of the generated correlation rule, and the original serial computing problem is equivalently converted into a parallel problem, so that a plurality of sub-computing servers can be utilized for solving simultaneously, and the computing efficiency is improved.
The framework comprises two modules of frequent pattern mining and association rule policy, and an existing electric charge calculation method is seamlessly fused by utilizing the efficient distributed calculation framework. The structure is referred to in fig. 2.
Compared with a single thread, the method improves the accuracy and the efficiency of the user electric charge settlement, enables the system resources to be well used, responds to each step of execution of the electric charge settlement algorithm in as little time as possible, greatly improves the overall operation efficiency of the process, and achieves efficient settlement of the user electric charge.
And adopting data related to daily and real-time electric charge settlement, utilizing the parameters related to electric charge settlement, namely the basic data of daily clear electric quantity, fixed partial electric quantity, rate partial electric quantity, fixed electric charge, rate partial electric charge and the like, and combing by combining an electric charge settlement formula.
In order to solve the problem of complex multiple users in the spot market, a method for setting up a concurrent calculation architecture is provided according to a package of an electric charge settlement formula.
In order to further accelerate the rate of electric charge settlement and improve the accuracy, an association model is established by using an Apriori algorithm, the association relation between the electric charge packages and the electric users is analyzed, and different electric charge packages are corresponding to different electric charge features of different users, so that the step of calculating the type of packages is switched in the electric charge settlement process is omitted, the rate of electric charge settlement is accelerated, and the accuracy of electric charge settlement is improved.
The past step-type electric charge calculation method is abandoned, an intelligent electric charge accounting mode is selected, the transaction scale of the electric market is enlarged, the spot market is further standardized and started, and the construction of a unified, open and competitive ordered electric market system is further promoted.
Embodiment two:
an electricity fee package type optimizing system based on a user, comprising:
the electricity consumption data acquisition module is configured to: acquiring electricity consumption data of each metering point in a metering period set by a user and corresponding electricity fee settlement parameters, determining different types of electricity fee package settlement formulas and electricity consumption rules of the user, and obtaining an electricity fee package index according to the obtained electricity consumption rules and the daily electricity consumption amount of the user;
a package type optimization module configured to: and determining the proper package type of the user and the package type selected by the user through the association rule generated by the minimum confidence degree among elements in the frequent item set by taking the electric charge package settlement formulas and the electric charge package indexes of different types as candidate item sets and generating the frequent item set.
Embodiment III:
the present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps in the user-based electric charge package type optimizing method according to the above embodiment.
Embodiment four:
the present embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps in the method for optimizing an electric charge package type based on a user according to the above embodiment when executing the program.
The steps or modules in the second to fourth embodiments correspond to the first embodiment, and the detailed description of the first embodiment may be referred to in the related description section of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media including one or more sets of instructions; it should also be understood to include any medium capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any one of the methods of the present invention.
The steps or networks involved in the above embodiments two to four correspond to the embodiment one, and the detailed description of the embodiment one can be referred to in the relevant description section of the embodiment one. The term "computer-readable storage medium" should be taken to include a single medium or multiple media including one or more sets of instructions; it should also be understood to include any medium capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any one of the methods of the present invention.
Claims (10)
1. The electric charge package type optimization method based on the user is characterized by comprising the following steps of:
acquiring electricity consumption data of each metering point in a metering period set by a user and corresponding electricity fee settlement parameters, determining different types of electricity fee package settlement formulas and electricity consumption rules of the user, and obtaining an electricity fee package index according to the obtained electricity consumption rules and the daily electricity consumption amount of the user;
and determining the proper package type of the user and the package type selected by the user through the association rule generated by the minimum confidence degree among elements in the frequent item set by taking the electric charge package settlement formulas and the electric charge package indexes of different types as candidate item sets and generating the frequent item set.
2. The method for optimizing an electric charge package type based on a user according to claim 1, wherein the step of acquiring the electricity data of each metering point and the corresponding electric charge settlement parameters in the metering period set by the user comprises the steps of:
and acquiring the time-sharing electricity price, the daily clear electricity quantity and the fixed partial electricity quantity of each metering point of the user every day for calculation, and acquiring the calculated electricity fee of the fixed time-sharing package.
3. The user-based electric charge package type optimization method of claim 1, wherein the step of obtaining the electricity data of each metering point and the corresponding electric charge settlement parameters in the metering period set by the user, further comprises:
and obtaining the standard electricity price, the rate class electricity price, the daily clear electricity quantity and the rate partial electricity quantity of each metering point of the user every day for calculation, and obtaining the calculated electricity fee of the rate class package.
4. The user-based electric charge package type optimization method of claim 1, wherein the step of obtaining the electricity data of each metering point and the corresponding electric charge settlement parameters in the metering period set by the user, further comprises:
and obtaining the fixed electricity price, the rate electricity price, the daily clear electricity quantity, the fixed partial electricity quantity and the rate partial electricity quantity of each metering point of the user every day, and calculating to obtain the calculated electricity fee of the mixed package.
5. The user-based electric charge package type optimizing method of claim 1, wherein the electric charge package settlement formulas and electric charge package indexes of different types are used as candidate item sets and frequent item sets are generated, comprising;
searching a frequent item set by using the candidate item set, calculating the support degree of the candidate 1 item set through multiple times of scanning, pruning the 1 item set lower than the support degree, and obtaining the frequent item set;
and connecting the rest frequent item sets 1 to obtain candidate frequent 2 item sets.
6. The user-based electric power rate package type optimizing method as claimed in claim 5, wherein the electric power rate package settlement formulas and electric power rate package indexes of different types are used as candidate item sets and frequent item sets are generated, further comprising;
repeating pruning to remove candidate frequent 2 item sets lower than the support degree to obtain a real frequent 2 item set;
and repeatedly iterating until the frequent item set k+1 cannot be found, wherein the corresponding frequent item set K is the required result.
7. The user-based electric charge package type optimization method of claim 1, wherein the association rule generated by the minimum confidence between the elements in the frequent item set comprises:
setting a minimum support degree and a minimum confidence degree;
and determining the support degree of each package and the user type through the acquired power data, comparing the support degree with the minimum support degree, wherein the packages and the user types with the support degree greater than or equal to the minimum support degree are frequent 1-item sets, and the sets are marked as L1.
8. The user-based electric power rate package type optimization method of claim 7, wherein the association rule generated by minimum confidence between elements in the frequent item set further comprises:
self-connecting the packages in L1 with the user types to form a candidate set C2 of the frequent 2-item set; traversing all packages and user types in the C2 to obtain the support degree of each package and user type, wherein a term set with the support degree not lower than the minimum support degree is a frequent 2-term set, and the term set is marked as L2; repeating the operation until the frequent k-term set can not be found any more;
confidence levels among elements in the frequent k-term set are determined, and association rules are generated according to minimum confidence level screening.
9. An electric charge package type optimizing system based on a user, characterized by comprising:
the electricity consumption data acquisition module is configured to: acquiring electricity consumption data of each metering point in a metering period set by a user and corresponding electricity fee settlement parameters, determining different types of electricity fee package settlement formulas and electricity consumption rules of the user, and obtaining an electricity fee package index according to the obtained electricity consumption rules and the daily electricity consumption amount of the user;
a package type optimization module configured to: and determining the proper package type of the user and the package type selected by the user through the association rule generated by the minimum confidence degree among elements in the frequent item set by taking the electric charge package settlement formulas and the electric charge package indexes of different types as candidate item sets and generating the frequent item set.
10. The user-based power rate package type optimization system of claim 9, further comprising a concurrent computation module configured to: and (3) dividing all package types into modules, obtaining the results of different divided modules in the same time period, and uniformly summarizing the results of all the modules to obtain the optimized results of packages of all the types.
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