CN117252345B - Charging method and system based on charging logic sequence - Google Patents
Charging method and system based on charging logic sequence Download PDFInfo
- Publication number
- CN117252345B CN117252345B CN202311507927.XA CN202311507927A CN117252345B CN 117252345 B CN117252345 B CN 117252345B CN 202311507927 A CN202311507927 A CN 202311507927A CN 117252345 B CN117252345 B CN 117252345B
- Authority
- CN
- China
- Prior art keywords
- charging
- data
- logic
- target
- logics
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 56
- 238000007405 data analysis Methods 0.000 claims abstract description 61
- 238000004458 analytical method Methods 0.000 claims abstract description 55
- 230000008859 change Effects 0.000 claims abstract description 39
- 238000010276 construction Methods 0.000 claims abstract description 28
- 238000012545 processing Methods 0.000 claims abstract description 13
- 238000007726 management method Methods 0.000 claims description 20
- 238000012163 sequencing technique Methods 0.000 claims description 16
- 238000000605 extraction Methods 0.000 claims description 11
- 238000004364 calculation method Methods 0.000 claims description 10
- 238000012300 Sequence Analysis Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 description 16
- 238000012098 association analyses Methods 0.000 description 8
- 238000012731 temporal analysis Methods 0.000 description 7
- 238000000700 time series analysis Methods 0.000 description 7
- 230000006399 behavior Effects 0.000 description 6
- 238000005457 optimization Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 238000010606 normalization Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 125000002015 acyclic group Chemical group 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 230000001932 seasonal effect Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000004931 aggregating effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002085 persistent effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000036962 time dependent Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
Abstract
The invention relates to the technical field of data processing, and discloses a charging method and a system based on a charging logic sequence, which are used for improving the charging accuracy based on the charging logic sequence. Comprising the following steps: redefining an operation object for a plurality of preset charging logics to obtain a plurality of redefined operation objects of each charging logic; unbinding a plurality of redefined operation objects of each charging logic in a data analysis interval to obtain a plurality of target charging logics; collecting historical operation data of management items, and extracting change parameters of the historical operation data to obtain a change parameter set; performing association relation analysis on the plurality of target charging logics to obtain an association relation set, and performing directed acyclic graph construction on the plurality of target charging logics through the association relation set to obtain a target directed acyclic graph; charging logic ordering is carried out on the target directed acyclic graph, and a logic ordering result is obtained; and updating the charging scheme of the historical operation data to obtain a target charging scheme.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a charging method and system based on a charging logic sequence.
Background
In the information age today, data management, billing and analysis have become the core tasks of numerous enterprises and organizations. With the increasing growth of large data and complex business processes, more efficient, intelligent methods are needed to manage billing logic and analyze data. The research background of the above technology stems from the increasing demand for better billing logic management, data analysis and updating of billing schemes.
In the prior art, a conventional charging system usually adopts fixed charging logic, and is difficult to cope with continuously changing service requirements and diversified operation objects. The data analysis of many billing systems is based on overall data, and it is difficult to achieve independent analysis of different operational objects, resulting in inaccuracy and inadaptability of the data analysis. The dependency between the various billing logics is often unclear and difficult to understand and manage, resulting in problems in executing the billing logics. Updating of the billing scheme is typically manual, time consuming and error prone, and does not accommodate rapidly changing business environments. I.e. the accuracy of the existing solution is lower.
Disclosure of Invention
The invention provides a charging method and a system based on a charging logic sequence, which are used for improving the charging accuracy based on the charging logic sequence.
The first aspect of the present invention provides a charging method based on a charging logic sequence, the charging method based on the charging logic sequence includes: redefining operation objects of a plurality of preset charging logics to obtain a plurality of redefined operation objects of each charging logic;
unbinding a plurality of redefined operation objects of each charging logic in a data analysis interval to obtain a plurality of target charging logics;
collecting historical operation data of a preset management item, and extracting change parameters of the historical operation data to obtain a change parameter set;
performing association relation analysis on a plurality of target charging logics to obtain an association relation set, and performing directed acyclic graph construction on the plurality of target charging logics through the association relation set to obtain a target directed acyclic graph;
charging logic ordering is carried out on the target directed acyclic graph through a preset topology ordering algorithm, and a logic ordering result is obtained;
and based on the logic sequencing result, updating the charging scheme of the historical operation data through a plurality of target charging logics to obtain a target charging scheme.
With reference to the first aspect, in a first implementation manner of the first aspect of the present invention, the redefining an operation object for a plurality of preset charging logics to obtain a plurality of redefined operation objects for each charging logic includes:
Performing operation object traversal on each charging logic to obtain an operation object set corresponding to each charging logic;
extracting parameters of operation object sets corresponding to each charging logic respectively to obtain object parameter sets of each operation object set;
and redefining the operation objects for a plurality of preset charging logics through the object parameter set of each operation object set to obtain a plurality of redefined operation objects of each charging logic.
With reference to the first aspect, in a second implementation manner of the first aspect of the present invention, the unbinding the data analysis interval is performed on the redefined operation objects of each charging logic to obtain a plurality of target charging logics, including:
performing object attribute analysis on a plurality of redefined operation objects of each charging logic to obtain an object attribute set corresponding to each charging logic;
respectively carrying out data analysis interval calculation on each charging logic to obtain a data analysis interval corresponding to each charging logic;
and based on the data analysis interval corresponding to each charging logic, unbinding the data analysis interval of a plurality of redefined operation objects of each charging logic through the object attribute set corresponding to each charging logic to obtain a plurality of target charging logics.
With reference to the first aspect, in a third implementation manner of the first aspect of the present invention, the collecting historical operation data of a preset management item, and extracting a change parameter from the historical operation data to obtain a change parameter set includes:
collecting the historical operation data, and carrying out data standardization processing on the historical operation data to obtain standardized operation data;
performing time sequence analysis on the standardized operation data to obtain time sequence data;
performing historical operation object calibration on the historical operation data through the time sequence data to obtain at least one historical operation object;
and based on at least one historical operation object, extracting the change parameters of the management item according to the historical operation data to obtain the change parameter set.
With reference to the first aspect, in a fourth implementation manner of the first aspect of the present invention, performing association analysis on the plurality of target charging logics to obtain an association set, and performing directed acyclic graph construction on the plurality of target charging logics through the association set to obtain a target directed acyclic graph, where the method includes:
analyzing the output data type of each target charging logic to obtain the corresponding output data type of each target charging logic;
Carrying out data association relation analysis on the output data types corresponding to each target charging logic to obtain data association relation;
based on the data association relationship, carrying out association relationship analysis on a plurality of target charging logics to obtain the association relationship set;
and constructing the directed acyclic graph for a plurality of target charging logics through the association relation set to obtain a target directed acyclic graph.
With reference to the fourth implementation manner of the first aspect, in a fifth implementation manner of the first aspect of the present invention, the performing, by using the association relation set, a directed acyclic graph construction on a plurality of target charging logics to obtain a target directed acyclic graph includes:
performing DAG node construction on the target charging logics to obtain a plurality of DAG node data;
carrying out identifier construction on each DAG node data to obtain identifier data corresponding to each DAG node data;
constructing directed edges through the association relation set and identifier data corresponding to each DAG node data to obtain a plurality of directed edges;
performing linear sorting analysis on each directed edge to obtain target linear sorting data;
And constructing a directed acyclic graph on a plurality of the directed edges through the target linear ordering data to obtain a target directed acyclic graph.
With reference to the first aspect, in a sixth implementation manner of the first aspect of the present invention, the performing, by a preset topology ordering algorithm, a charging logic ordering on the target directed acyclic graph to obtain a logic ordering result includes:
performing initial node analysis on the target directed acyclic graph through the topology ordering algorithm to obtain ordering initial nodes;
performing node traversal on the target directed acyclic graph through the ordering initial node to obtain a node traversal sequence;
performing directed edge sequencing based on the node traversing sequence to obtain a directed edge sequencing result;
and carrying out charging logic sorting according to the directed edge sorting result to obtain the logic sorting result.
The second aspect of the present invention provides a charging system based on a charging logic sequence, the charging system based on a charging logic sequence comprising:
the definition module is used for redefining operation objects of a plurality of preset charging logics to obtain a plurality of redefined operation objects of each charging logic;
the unbinding module is used for unbinding a plurality of redefined operation objects of each charging logic in a data analysis interval to obtain a plurality of target charging logics;
The extraction module is used for collecting historical operation data of a preset management item, and extracting change parameters of the historical operation data to obtain a change parameter set;
the construction module is used for carrying out association relation analysis on a plurality of target charging logics to obtain an association relation set, and carrying out directed acyclic graph construction on the plurality of target charging logics through the association relation set to obtain a target directed acyclic graph;
the ordering module is used for carrying out charging logic ordering on the target directed acyclic graph through a preset topological ordering algorithm to obtain a logic ordering result;
and the updating module is used for updating the charging scheme of the historical operation data through a plurality of target charging logics based on the logic ordering result to obtain a target charging scheme.
A third aspect of the present invention provides a charging device based on a charging logic sequence, including: a memory and at least one processor, the memory having instructions stored therein; the at least one processor invokes the instructions in the memory to cause the charging device based on charging logic order to perform the charging method based on charging logic order described above.
A fourth aspect of the invention provides a computer readable storage medium having instructions stored therein which, when run on a computer, cause the computer to perform the charging method based on a charging logic sequence as described above.
In the technical scheme provided by the invention, redefining an operation object is carried out on a plurality of preset charging logics, so as to obtain a plurality of redefined operation objects of each charging logic; unbinding a plurality of redefined operation objects of each charging logic in a data analysis interval to obtain a plurality of target charging logics; collecting historical operation data of preset management items, and extracting change parameters of the historical operation data to obtain a change parameter set; performing association relation analysis on the plurality of target charging logics to obtain an association relation set, and performing directed acyclic graph construction on the plurality of target charging logics through the association relation set to obtain a target directed acyclic graph; charging logic ordering is carried out on the target directed acyclic graph through a preset topology ordering algorithm, and a logic ordering result is obtained; based on the logic sequencing result, the historical operation data is updated by a plurality of target charging logics to obtain a target charging scheme. In the scheme of the application, through redefinition of the operation objects, different operation objects can be created for a plurality of charging logics, so that the operation objects are more flexible and adapt to different requirements. Unbinding the data analysis sections of the redefined operation objects means that the data can be analyzed more flexibly, and the data of each operation object can be independently analyzed without being affected by other operation objects. The accuracy and operability of data analysis can be improved. Multiple target charging logics can be obtained through unbinding the data analysis interval, and the logics represent independent charging requirements of different operation objects. This enables a better understanding of the billing logic of each operation object, thereby better satisfying its needs. The change parameter set of the historical operation data is extracted, so that the change trend of the behavior and performance of the operation object can be known. These parameters may be used for optimization and adjustment of billing logic to accommodate changing conditions. Through association analysis, the relationship among a plurality of target charging logics can be determined, and then a target directed acyclic graph is constructed. The dependency relationship between different charging logics can be visualized and understood, so that the charging logics can be managed more clearly. By means of a topology ordering algorithm, charging logic in the target DAG may be ordered, ensuring execution in terms of their dependencies. The logic ordering results enable the billing logic to be executed in an orderly fashion, thereby ensuring proper billing procedures and logic execution.
Drawings
FIG. 1 is a schematic diagram of an embodiment of a charging method based on a charging logic sequence in an embodiment of the present invention;
FIG. 2 is a flow chart of data analysis interval unbinding for multiple redefined operation objects of each charging logic according to an embodiment of the present invention;
FIG. 3 is a flowchart of extracting a change parameter of historical operation data according to an embodiment of the present invention;
FIG. 4 is a flowchart of performing association analysis on multiple target charging logics according to an embodiment of the present invention;
FIG. 5 is a diagram of one embodiment of a billing system based on a billing logic sequence in an embodiment of the invention;
fig. 6 is a schematic diagram of an embodiment of a charging device based on a charging logic sequence in an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a charging method and a system based on a charging logic sequence, which are used for improving the charging accuracy based on the charging logic sequence.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
For ease of understanding, the following describes a specific flow of an embodiment of the present invention, referring to fig. 1, and one embodiment of a charging method based on a charging logic sequence in the embodiment of the present invention includes:
s101, redefining an operation object for a plurality of preset charging logics to obtain a plurality of redefined operation objects of each charging logic;
it is to be understood that the implementation subject of the present invention may be a billing system based on a billing logic sequence, and may also be a terminal or a server, which is not limited herein. The embodiment of the invention is described by taking a server as an execution main body as an example.
Specifically, traversing is performed on a plurality of operation objects of preset charging logic. The system will examine each billing logic individually to determine its associated operational object. The operational object may be a particular business entity, product, service, transaction, or other transaction that requires billing. The purpose of this step is to establish an association between each billing logic and its operational object, providing a basis for subsequent analysis and redefinition. For example, the system has different billing logic such as transaction fees, asset management fees, consultation fees, etc. In this case, the operation objects include different financial products, customer accounts, transaction types, and the like. And extracting parameters from the operation object set corresponding to each charging logic. Relevant parameter information is extracted from each set of operational objects, which parameters may be used to further define billing logic. These parameters include time, amount, transaction type, customer risk level, product type, etc. The process of parameter extraction helps to better understand the characteristics of the operational objects and the requirements of the billing logic. Redefining the operation objects for a plurality of preset charging logics through the object parameter set of each operation object set. This step redefines the billing logic based on the information of the parameter set to better meet the needs of the operating object. This may include modifying charging rules, adjusting rates, introducing new charging dimensions, designing different charging policies, or adapting to specific service scenarios.
S102, unbinding a plurality of redefined operation objects of each charging logic in a data analysis interval to obtain a plurality of target charging logics;
it should be noted that, object attribute analysis is performed on a plurality of redefined operation objects of each charging logic. The characteristics, attributes and data requirements of the operation object are thoroughly analyzed. In this process, it is necessary to determine which properties are critical, such as time, place, type, quantity, quality, etc. These properties will play an important role in the unbinding of subsequent data analysis intervals. The data analysis interval for each billing logic needs to be calculated. The data analysis interval is a period of time or a range of data for aggregating and analyzing data of related operation objects. In this step, consideration is required to be given to when, where, and how the data analysis interval is defined. This may include time periods (hours, days, months), geographic locations (areas, cities), and the like. Based on the attributes of the operation object and the calculation of the data analysis interval, the attributes can be associated with the interval. This association process helps determine which attributes will function within a particular data analysis interval to better define billing logic. By unbinding different attributes from associated data analysis intervals, multiple target billing logics may be implemented. Different charging rules, policies or rates may be defined for different attributes and data analysis intervals to meet the requirements of different contexts and operational objects.
S103, acquiring historical operation data of a preset management item, and extracting change parameters of the historical operation data to obtain a change parameter set;
specifically, historical operational data related to management items is collected. This may include various operations and events related to billing logic, such as transaction records, user activities, product sales, and the like. Historical operational data is a key basis for billing logic based on such data. These data are normalized. Data normalization ensures that data is consistent, comparable, as historical operating data comes from different sources, and formats. Normalization includes processing data formats, unit conversions, date and time formats, and the like. The normalized operational data may be used to perform time series analysis. Time series analysis is a powerful tool for exploring trends, seasonal, periodic, etc. features in data. This helps to understand the dynamic nature of the historical operating data, providing important insight into the optimization of billing logic. At least one historical operational object in the historical operational data may be determined through time series analysis. The historical operating object is an entity related to billing logic and may be a product, a user, a device, etc. The identification of these operational objects is important to correlate historical operational data with billing logic. Based on the at least one historical operating object, extraction of the change parameters may begin. The historical operating data is analyzed to identify key changing parameters that will be used to optimize billing logic. The variation parameters may include price variation, demand fluctuations, user behavior, etc.
S104, carrying out association relation analysis on the plurality of target charging logics to obtain an association relation set, and carrying out directed acyclic graph construction on the plurality of target charging logics through the association relation set to obtain a target directed acyclic graph;
it should be noted that, for each target charging logic, output data type analysis is performed. This includes determining the type of output data that each billing logic will generate, e.g., reports, bills, fee summaries, etc. This helps to understand the final outcome of the billing logic. And analyzing the association relation between the output data types of each charging logic. This includes determining the manner of transfer, dependencies, and sharing elements between data. This helps to establish a data connection between the charging logics. By analyzing the data association, the association analysis can be started. This step involves determining logical relationships between billing logic, such as execution order, conditional branching, and looping. The associative analysis helps to build logical dependencies between charging logics, ensuring that they are performed in the correct order. Finally, a target directed acyclic graph can be constructed using the set of associations. This is a graphical structure in which each charging logic is represented as a node and the association is represented as a directed edge. The directed acyclic graph helps to visualize logical dependencies between charging logics, ensures that the order of execution of the charging logics is reasonable, and does not appear to be circular. This target directed acyclic graph will help manage the execution of billing logic, ensuring that they operate in the correct logical order, avoiding conflicts and errors.
And performing DAG node construction on the plurality of target charging logics. Each charging logic will be denoted as a node in the DAG. These nodes represent execution units of the charging logic and constitute the framework of the DAG. Identifier construction is performed on each DAG node data to ensure that each node has a unique identifier. This helps to distinguish between different charging logical nodes and provides accurate identification for subsequent edge construction. And constructing a directed edge through the association relation set and the identifier data corresponding to each DAG node data. Directed edges represent dependencies and execution order between charging logics. If one billing logic depends on another billing logic, a directed edge is created indicating the order of execution. Then, a linear ordering analysis is performed for each directed edge. This is a critical step to ensure that there is no loop dependency, making the DAG a directed acyclic graph. The linear ordering analysis may help determine the order of execution of the charging logic nodes, ensuring that the charging logic is executed without endless loops or conflicts. Finally, a target directed acyclic graph can be constructed from the target linear ordering data. The graph structure is composed of charging logic nodes which are connected with each other through directed edges to form an orderly execution path. This target directed acyclic graph ensures that the billing logic is executed in the correct order and that no circular dependency occurs.
S105, charging logic ordering is carried out on the target directed acyclic graph through a preset topology ordering algorithm, and a logic ordering result is obtained;
specifically, the topology ordering algorithm is an ordering method for the directed graph, and can be used for determining the dependency relationship between the nodes, ensuring that no circular dependency exists, and allocating a reasonable execution sequence for the nodes. The topology ordering algorithm is very useful in the case of charging logic based, as it can ensure that the charging logic is executed in the correct order to generate accurate charging results. The topology ordering algorithm performs an initial node analysis on the target directed acyclic graph. The initial nodes are those nodes that do not depend on other nodes. These nodes are typically the starting point for the charging logic and they do not have to wait for the completion of other charging logic. Once the initial node is determined, the algorithm begins node traversal of the target directed acyclic graph. Node traversal is performed in terms of dependencies between nodes to ensure that each node executes after its dependent nodes. This process determines the traversal order of the nodes. Based on the traversal ordering of the nodes, directed edge ordering may be performed. The directed edge ordering determines the order of execution of the edges to ensure that dependencies between billing logic are satisfied. This step considers the relationships between nodes, ensuring that the ordering of the edges is reasonable. And finally, according to the directed edge sequencing result, carrying out charging logic sequencing. This step determines the order of execution of each billing logic. The charging logic is executed sequentially according to the sequencing result of the charging logic, so that the logic order is ensured.
S106, based on the logic ordering result, updating the charging scheme of the historical operation data through a plurality of target charging logics to obtain a target charging scheme.
It should be noted that, the logic ordering result provides the execution sequence of the charging logic for the charging system, so as to ensure that the dependency relationship between the logics is satisfied. The historical operating data is the basis for billing plan updates, including transaction records, service usage, and price information for the user. Through analysis of the target billing logic, the historical operating data is screened, aggregated, and parameters extracted to generate a new billing scheme. The billing scheme generation phase involves fee calculation, discount application, and billing detail generation to ensure that users are accurately billed and enjoy the relevant discounts. Once the billing scheme is generated, it is applied to the relevant user or object of operation, typically including updating the user's bill and recording billing details. After the charging scheme is applied, result verification is necessary to ensure the accuracy of the charging logic. If an abnormal situation occurs, the system has an abnormal processing mechanism and notifies the related party to take corrective action. Finally, all billing operations require logging for auditing and reporting to provide transparency and history. Automation and flow optimization play an important role in the overall charging scheme update process, especially in cases where multiple target charging logics need to be executed simultaneously.
In the embodiment of the invention, redefining an operation object is carried out on a plurality of preset charging logics to obtain a plurality of redefined operation objects of each charging logic; unbinding a plurality of redefined operation objects of each charging logic in a data analysis interval to obtain a plurality of target charging logics; collecting historical operation data of preset management items, and extracting change parameters of the historical operation data to obtain a change parameter set; performing association relation analysis on the plurality of target charging logics to obtain an association relation set, and performing directed acyclic graph construction on the plurality of target charging logics through the association relation set to obtain a target directed acyclic graph; charging logic ordering is carried out on the target directed acyclic graph through a preset topology ordering algorithm, and a logic ordering result is obtained; based on the logic sequencing result, the historical operation data is updated by a plurality of target charging logics to obtain a target charging scheme. In the scheme of the application, through redefinition of the operation objects, different operation objects can be created for a plurality of charging logics, so that the operation objects are more flexible and adapt to different requirements. Unbinding the data analysis sections of the redefined operation objects means that the data can be analyzed more flexibly, and the data of each operation object can be independently analyzed without being affected by other operation objects. The accuracy and operability of data analysis can be improved. Multiple target charging logics can be obtained through unbinding the data analysis interval, and the logics represent independent charging requirements of different operation objects. This enables a better understanding of the billing logic of each operation object, thereby better satisfying its needs. The change parameter set of the historical operation data is extracted, so that the change trend of the behavior and performance of the operation object can be known. These parameters may be used for optimization and adjustment of billing logic to accommodate changing conditions. Through association analysis, the relationship among a plurality of target charging logics can be determined, and then a target directed acyclic graph is constructed. The dependency relationship between different charging logics can be visualized and understood, so that the charging logics can be managed more clearly. By means of a topology ordering algorithm, charging logic in the target DAG may be ordered, ensuring execution in terms of their dependencies. The logic ordering results enable the billing logic to be executed in an orderly fashion, thereby ensuring proper billing procedures and logic execution.
In a specific embodiment, the process of executing step S101 may specifically include the following steps:
(1) Performing operation object traversal on each charging logic to obtain an operation object set corresponding to each charging logic;
(2) Extracting parameters of operation object sets corresponding to each charging logic respectively to obtain object parameter sets of each operation object set;
(3) And redefining the operation objects for a plurality of preset charging logics through the object parameter set of each operation object set to obtain a plurality of redefined operation objects of each charging logic.
Specifically, for each preset billing logic, the system traverses related operation objects, i.e., entities such as users, services, products, etc., that need to be billed. This traversal ensures that each billing logic can overlay the associated operand for subsequent processing. Then, parameter extraction is required for the operation object set corresponding to each charging logic. This includes extracting parameters required for the billing logic from the data of the operation object. The parameters may be various information such as usage, pricing information, discount rate, etc. Parameter extraction ensures that each charging logic has the required information to charge. After the parameter extraction is completed, the system will obtain an object parameter set for each operation object set. This is an important data structure containing all the parameters required by the charging logic. The set of object parameters may be considered an associative array in which each parameter is associated with its corresponding operand. The system redefines the charging logic through the object parameter set of each operation object set. The system redefines the behavior of the billing logic using the associated set of object parameters according to the requirements of each billing logic. This includes redefinition of pricing rules, adjustment of fee calculation formulas, application of discounts, and the like. Through this process, each billing logic is assigned new operational objects and parameters that enable it to accommodate different business needs and operational objects. This helps to increase the flexibility of the billing system, enabling it to cope with changing situations.
In a specific embodiment, as shown in fig. 2, the process of executing step S102 may specifically include the following steps:
s201, performing object attribute analysis on a plurality of redefined operation objects of each charging logic to obtain an object attribute set corresponding to each charging logic;
s202, respectively carrying out data analysis interval calculation on each charging logic to obtain a data analysis interval corresponding to each charging logic;
s203, based on the data analysis interval corresponding to each charging logic, unbinding the data analysis interval of the redefined operation objects of each charging logic through the object attribute set corresponding to each charging logic to obtain a plurality of target charging logics.
It should be noted that, for a plurality of redefined operation objects of each charging logic, the system performs object attribute analysis. This step aims at identifying key attributes of each operand. Attributes may include the identity, characteristics, status, historical data, etc. of an object. Through detailed object attribute analysis, the system obtains an object attribute set corresponding to each charging logic. The system performs data analysis interval calculations in order to determine the data analysis interval for each billing logic. The data analysis interval typically represents a range of data over a period of time for performing data calculations of the billing logic. This interval may be monthly, quarterly, annually, or any other suitable time interval, depending on the particular billing requirements. After the data analysis interval calculation, the system obtains a data analysis interval corresponding to each billing logic. Based on the data analysis interval and the object attribute set of each charging logic, the system further unbundles the data analysis interval. The purpose of this step is to unbind the billing logic from the raw data analysis interval to create a plurality of target billing logic. For example, the start and end times of the billing logic are determined. Based on the data analysis interval and the traffic demand, the system determines the start and end time points for each billing logic. This helps define the execution time range of the charging logic. Second, data is allocated. In the data analysis interval unbinding stage, the system distributes the data in the original data analysis interval according to the time range of the charging logic. This ensures that each billing logic uses only data within its specified time range. Object properties are applied. A set of object attributes will be applied to each charging logic to determine specific charging operations and parameters. These attributes may include the type of charge to be calculated, billing rules, discount information, etc. Finally, the billing logic is executed. Each charging logic is independently executed through data analysis interval unbinding, and a result of the target charging logic is generated. This process is performed independently on each billing logic to ensure accuracy of data analysis and reliability of the billing logic.
In a specific embodiment, as shown in fig. 3, the process of executing step S103 may specifically include the following steps:
s301, acquiring historical operation data, and performing data standardization processing on the historical operation data to obtain standardized operation data;
s302, carrying out time sequence analysis on the standardized operation data to obtain time sequence data;
s303, performing historical operation object calibration on the historical operation data through the time sequence data to obtain at least one historical operation object;
s304, based on at least one historical operation object, carrying out change parameter extraction on the management item according to the historical operation data to obtain a change parameter set.
It should be noted that the system collects historical operation data. Such data may include various information such as user behavior, transaction records, service usage, and the like. Historical operational data is the basis for billing and analysis because they contain records of past business activities. The data may come from different sources such as databases, log files, application records, and the like. And carrying out data standardization processing on the historical operation data. This step involves converting the data from the different sources into a consistent format for subsequent analysis and processing. Data normalization may include operations such as data cleansing, field format unification, data deduplication, etc., to ensure consistency and quality of data. Subsequently, time-series analysis is performed on the normalized operation data. Time series analysis is used to reveal trends and patterns of data over time. Such analysis may include periodic analysis, trend analysis, seasonal analysis, etc. to aid in understanding the time-dependent patterns in the historical data. By time series analysis, the system obtains time series data. These data provide time dimension information for historical operational data, enabling the system to better understand the evolution and changes of business activities. And calibrating the historical operation object by using the time sequence data. The historical operational object may be an entity associated with a business activity, such as a customer, product, region, or the like. Through time series analysis, the system determines historical data associated with a particular operand over different time periods. Finally, the system performs a change parameter extraction based on the at least one historical operating object. The system identifies and extracts parameters related to the management item based on changes in the historical operational data. These parameters may be used for billing, analysis, or prediction to help organizations better understand traffic changes and make decisions.
In a specific embodiment, as shown in fig. 4, the process of executing step S104 may specifically include the following steps:
s401, analyzing the output data type of each target charging logic to obtain the output data type corresponding to each target charging logic;
s402, carrying out data association relation analysis on the output data types corresponding to each target charging logic to obtain a data association relation;
s403, carrying out association relation analysis on a plurality of target charging logics based on the data association relation to obtain an association relation set;
s404, constructing a plurality of target charging logics through the association relation set to obtain a target directed acyclic graph.
Specifically, the output data type analysis is performed for each target billing logic. The purpose of this step is to identify the type of output data for each target billing logic, i.e., the type of data that results after execution of the billing logic. The output data types may include numbers, text, graphics, statistical reports, and the like. Analysis of these output data types helps to understand the outcome of the billing logic. And carrying out data association relation analysis. This step involves studying the relationship between the output data types of the different target billing logics. Some billing logic will generate data types that will be used by other billing logic and these relationships will need to be captured and analyzed. The data correlation analysis helps to determine dependencies and associations between different billing logics. And carrying out association relation analysis based on the data association relation. The purpose of this step is to identify and establish a relationship between different target billing logics. The association relationship may be a data dependency relationship or a logical association relationship. For example, one billing logic requires the output of another billing logic as an input, and this relationship needs to be captured. Through association analysis, the system can build a set of relationships, including the links between different billing logics. And finally, constructing a target directed acyclic graph through the association relation set. The target directed acyclic graph is a graph structure in which nodes represent different billing logics and edges represent relationships between the different billing logics. This graphical structure is directed and loop-free and clearly shows the dependencies and order between different billing logics. From this figure, the system can understand the execution order and relation between charging logics, which is very important for the management and optimization of the charging system.
In a specific embodiment, the process of executing step S404 may specifically include the following steps:
(1) Performing DAG node construction on the target charging logics to obtain a plurality of DAG node data;
(2) Carrying out identifier construction on each DAG node data to obtain identifier data corresponding to each DAG node data;
(3) Constructing directed edges through the association relation set and identifier data corresponding to each DAG node data to obtain a plurality of directed edges;
(4) Performing linear sorting analysis on each directed edge to obtain target linear sorting data;
(5) And constructing a directed acyclic graph for the plurality of directed edges through the target linear ordering data to obtain a target directed acyclic graph.
It should be noted that DAG node construction is performed on multiple target charging logics. A directed acyclic graph is a graph structure that consists of nodes and directed edges, and has no loops. In this step, the system represents each target billing logic as a node that forms part of the DAG. These nodes represent different charging logics, there being dependencies between them, but no loops are formed. Identifier construction is performed on each DAG node data. The identifier is information for uniquely identifying each node. It may include the name, ID, or other unique identifier of the billing logic. These identifiers help to distinguish between different nodes in order to better manage them in subsequent analysis. And constructing the directed edge through the association relation set and the identifier data corresponding to each DAG node data. Directed edges are connections between nodes in a DAG. These edges represent the dependency between different charging logics, with the output of one charging logic being the input of the other charging logic. Through the association set and the identifier data, the system can determine the dependency relationship between different nodes, thereby constructing the directed edge. A linear ordering analysis is performed for each directed edge. The linear ordering analysis is to determine the order of execution between different nodes. This step ensures that the billing logic is performed in the correct order to avoid problems caused by dependencies. The linear ordering may be implemented using a topology ordering algorithm, which will assign each node a linear order to ensure that dependent charging logic is performed before the pre-charging logic. And finally, constructing a directed acyclic graph for the plurality of directed edges through the target linear ordering data. Directed acyclic graphs are graph structures made up of nodes and directed edges that ensure that no loops exist and therefore do not present a deadlock problem with endless loops or billing logic. This graphical structure clearly shows the dependency and execution order between different billing logics, facilitating the management and execution of the billing logics.
In a specific embodiment, the process of executing step S105 may specifically include the following steps:
(1) Analyzing initial nodes of the target directed acyclic graph through a topology ordering algorithm to obtain ordering initial nodes;
(2) Performing node traversal on the target directed acyclic graph through the ordering initial nodes to obtain a node traversal sequence;
(3) Conducting directed edge sequencing based on the node traversing sequence to obtain a directed edge sequencing result;
(4) And carrying out charging logic sorting according to the directed edge sorting result to obtain the logic sorting result.
Specifically, the initial node analysis is performed on the target directed acyclic graph through a topological sorting algorithm. Topological ordering is an ordering algorithm for directed acyclic graphs that determines the order of execution between nodes. In this step, the system finds nodes in the graph that have no degree of invasiveness (i.e., no nodes that depend on other nodes), which are considered initial nodes. These initial nodes are the starting point for the execution of the charging logic. Node traversal is performed on the target directed acyclic graph using the ordered initial nodes. Node traversal is to access each node in turn in the correct order of execution. Starting from the initial node, each node is accessed step by step according to the topological order, so that no condition of violating the dependency relationship is ensured. The directed edge ordering is based on the node traversal order. In this step, the system orders the directed edges in order of execution to ensure that the dependencies between charging logics are satisfied. This can be achieved by recording the order of the directed edges during traversal. And finally, carrying out charging logic sorting according to the directed edge sorting result. The result of the ordering of the directed edges may be used to determine the order of execution of the different billing logics. The billing logic ordering ensures that each billing logic executes after the billing logic it depends on to satisfy the dependency. In this way, the system is able to get the order of execution of the billing logic.
By the above steps, complex billing logic can be efficiently managed and executed, ensuring that they are executed in the correct order. Through the topological sorting algorithm, the system can clearly know the dependency relationship between charging logics, thereby ensuring the accuracy and efficiency of the charging system.
The foregoing describes a charging method based on a charging logic sequence in the embodiment of the present invention, and the following describes a charging system based on a charging logic sequence in the embodiment of the present invention, referring to fig. 5, an embodiment of the charging system based on a charging logic sequence in the embodiment of the present invention includes:
a definition module 501, configured to redefine an operation object for a plurality of preset charging logics, so as to obtain a plurality of redefined operation objects for each charging logic;
the unbinding module 502 is configured to unbinding a plurality of redefined operation objects of each charging logic in a data analysis interval to obtain a plurality of target charging logics;
the extracting module 503 is configured to collect historical operation data of a preset management item, and extract a change parameter from the historical operation data to obtain a change parameter set;
the construction module 504 is configured to perform association analysis on a plurality of target charging logics to obtain an association set, and perform directed acyclic graph construction on a plurality of target charging logics through the association set to obtain a target directed acyclic graph;
The ordering module 505 is configured to perform charging logic ordering on the target directed acyclic graph through a preset topology ordering algorithm, so as to obtain a logic ordering result;
and an updating module 506, configured to update the charging scheme for the historical operating data through a plurality of target charging logics based on the logic ordering result, so as to obtain a target charging scheme.
Redefining an operation object for a plurality of preset charging logics through the cooperative cooperation of the components, so as to obtain a plurality of redefined operation objects of each charging logic; unbinding a plurality of redefined operation objects of each charging logic in a data analysis interval to obtain a plurality of target charging logics; collecting historical operation data of preset management items, and extracting change parameters of the historical operation data to obtain a change parameter set; performing association relation analysis on the plurality of target charging logics to obtain an association relation set, and performing directed acyclic graph construction on the plurality of target charging logics through the association relation set to obtain a target directed acyclic graph; charging logic ordering is carried out on the target directed acyclic graph through a preset topology ordering algorithm, and a logic ordering result is obtained; based on the logic sequencing result, the historical operation data is updated by a plurality of target charging logics to obtain a target charging scheme. In the scheme of the application, through redefinition of the operation objects, different operation objects can be created for a plurality of charging logics, so that the operation objects are more flexible and adapt to different requirements. Unbinding the data analysis sections of the redefined operation objects means that the data can be analyzed more flexibly, and the data of each operation object can be independently analyzed without being affected by other operation objects. The accuracy and operability of data analysis can be improved. Multiple target charging logics can be obtained through unbinding the data analysis interval, and the logics represent independent charging requirements of different operation objects. This enables a better understanding of the billing logic of each operation object, thereby better satisfying its needs. The change parameter set of the historical operation data is extracted, so that the change trend of the behavior and performance of the operation object can be known. These parameters may be used for optimization and adjustment of billing logic to accommodate changing conditions. Through association analysis, the relationship among a plurality of target charging logics can be determined, and then a target directed acyclic graph is constructed. The dependency relationship between different charging logics can be visualized and understood, so that the charging logics can be managed more clearly. By means of a topology ordering algorithm, charging logic in the target DAG may be ordered, ensuring execution in terms of their dependencies. The logic ordering results enable the billing logic to be executed in an orderly fashion, thereby ensuring proper billing procedures and logic execution.
Fig. 5 above describes the charging system based on the charging logic sequence in the embodiment of the present invention in detail from the point of view of the modularized functional entity, and the charging device based on the charging logic sequence in the embodiment of the present invention is described in detail from the point of view of hardware processing.
Fig. 6 is a schematic structural diagram of a billing device based on a billing logic sequence according to an embodiment of the present invention, where the billing device 600 based on the billing logic sequence may have a relatively large difference due to different configurations or performances, and may include one or more processors (central processing units, CPU) 610 (e.g., one or more processors) and a memory 620, and one or more storage media 630 (e.g., one or more mass storage devices) storing application programs 633 or data 632. Wherein the memory 620 and the storage medium 630 may be transitory or persistent storage. The program stored on the storage medium 630 may include one or more modules (not shown), each of which may include a series of instruction operations in the charging device 600 based on the charging logic order. Still further, the processor 610 may be configured to communicate with the storage medium 630 to execute a series of instruction operations in the storage medium 630 on the billing device 600 based on the billing logic sequence.
Billing device 600 based on a billing logic sequence may also include one or more power sources 640, one or more wired or wireless network interfaces 650, one or more input/output interfaces 660, and/or one or more operating systems 631, such as Windows Serves, macOSX, unix, linux, freeBSD, and the like. It will be appreciated by those skilled in the art that the billing device structure based on billing logic sequence shown in fig. 6 does not constitute a limitation of billing devices based on billing logic sequence, and may include more or fewer components than shown, or may combine certain components, or may be arranged in different components.
The invention also provides a charging device based on the charging logic sequence, which comprises a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to execute the steps of the charging method based on the charging logic sequence in the above embodiments.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, and may also be a volatile computer readable storage medium, where instructions are stored in the computer readable storage medium, which when executed on a computer, cause the computer to perform the steps of the charging method based on a charging logic order.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The integrated units, if implemented in the form of software functional units and sold or passed as separate products, may be stored in a computer readable storage medium. Based on the understanding that the technical solution of the present invention may be embodied in essence or in a part contributing to the prior art or in whole or in part in the form of a software product stored in a storage medium, comprising instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (randomacce Smemory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (4)
1. The charging method based on the charging logic sequence is characterized by comprising the following steps:
redefining operation objects of a plurality of preset charging logics to obtain a plurality of redefined operation objects of each charging logic; the method specifically comprises the following steps: performing operation object traversal on each charging logic to obtain an operation object set corresponding to each charging logic; extracting parameters of operation object sets corresponding to each charging logic respectively to obtain object parameter sets of each operation object set; redefining the operation objects for a plurality of preset charging logics through the object parameter set of each operation object set to obtain a plurality of redefined operation objects of each charging logic;
Unbinding a plurality of redefined operation objects of each charging logic in a data analysis interval to obtain a plurality of target charging logics; the method specifically comprises the following steps: performing object attribute analysis on a plurality of redefined operation objects of each charging logic to obtain an object attribute set corresponding to each charging logic; respectively carrying out data analysis interval calculation on each charging logic to obtain a data analysis interval corresponding to each charging logic; based on the data analysis interval corresponding to each charging logic, unbinding the data analysis interval of a plurality of redefined operation objects of each charging logic through the object attribute set corresponding to each charging logic to obtain a plurality of target charging logics;
collecting historical operation data of a preset management item, and extracting change parameters of the historical operation data to obtain a change parameter set; the method specifically comprises the following steps: collecting the historical operation data, and carrying out data standardization processing on the historical operation data to obtain standardized operation data; performing time sequence analysis on the standardized operation data to obtain time sequence data; performing historical operation object calibration on the historical operation data through the time sequence data to obtain at least one historical operation object; based on at least one historical operation object, carrying out change parameter extraction on the management item according to the historical operation data to obtain the change parameter set;
Performing association relation analysis on a plurality of target charging logics to obtain an association relation set, and performing directed acyclic graph construction on the plurality of target charging logics through the association relation set to obtain a target directed acyclic graph; the method specifically comprises the following steps: analyzing the output data type of each target charging logic to obtain the corresponding output data type of each target charging logic; carrying out data association relation analysis on the output data types corresponding to each target charging logic to obtain data association relation; based on the data association relationship, carrying out association relationship analysis on a plurality of target charging logics to obtain the association relationship set; building a plurality of target charging logics through the association relation set to obtain a target directed acyclic graph; wherein, obtain the target directed acyclic graph, include: performing DAG node construction on the target charging logics to obtain a plurality of DAG node data; carrying out identifier construction on each DAG node data to obtain identifier data corresponding to each DAG node data; constructing directed edges through the association relation set and identifier data corresponding to each DAG node data to obtain a plurality of directed edges; performing linear sorting analysis on each directed edge to obtain target linear sorting data; constructing a directed acyclic graph on a plurality of directed edges through the target linear ordering data to obtain a target directed acyclic graph;
Charging logic ordering is carried out on the target directed acyclic graph through a preset topology ordering algorithm, and a logic ordering result is obtained; the method specifically comprises the following steps: performing initial node analysis on the target directed acyclic graph through the topology ordering algorithm to obtain ordering initial nodes; performing node traversal on the target directed acyclic graph through the ordering initial node to obtain a node traversal sequence; performing directed edge sequencing based on the node traversing sequence to obtain a directed edge sequencing result; charging logic sorting is carried out according to the directed edge sorting result, and the logic sorting result is obtained;
and based on the logic sequencing result, updating the charging scheme of the historical operation data through a plurality of target charging logics to obtain a target charging scheme.
2. A billing system based on a billing logic sequence, the billing system based on a billing logic sequence comprising:
the definition module is used for redefining operation objects of a plurality of preset charging logics to obtain a plurality of redefined operation objects of each charging logic; the method specifically comprises the following steps: performing operation object traversal on each charging logic to obtain an operation object set corresponding to each charging logic; extracting parameters of operation object sets corresponding to each charging logic respectively to obtain object parameter sets of each operation object set; redefining the operation objects for a plurality of preset charging logics through the object parameter set of each operation object set to obtain a plurality of redefined operation objects of each charging logic;
The unbinding module is used for unbinding a plurality of redefined operation objects of each charging logic in a data analysis interval to obtain a plurality of target charging logics; the method specifically comprises the following steps: performing object attribute analysis on a plurality of redefined operation objects of each charging logic to obtain an object attribute set corresponding to each charging logic; respectively carrying out data analysis interval calculation on each charging logic to obtain a data analysis interval corresponding to each charging logic; based on the data analysis interval corresponding to each charging logic, unbinding the data analysis interval of a plurality of redefined operation objects of each charging logic through the object attribute set corresponding to each charging logic to obtain a plurality of target charging logics;
the extraction module is used for collecting historical operation data of a preset management item, and extracting change parameters of the historical operation data to obtain a change parameter set; the method specifically comprises the following steps: collecting the historical operation data, and carrying out data standardization processing on the historical operation data to obtain standardized operation data; performing time sequence analysis on the standardized operation data to obtain time sequence data; performing historical operation object calibration on the historical operation data through the time sequence data to obtain at least one historical operation object; based on at least one historical operation object, carrying out change parameter extraction on the management item according to the historical operation data to obtain the change parameter set;
The construction module is used for carrying out association relation analysis on a plurality of target charging logics to obtain an association relation set, and carrying out directed acyclic graph construction on the plurality of target charging logics through the association relation set to obtain a target directed acyclic graph; the method specifically comprises the following steps: analyzing the output data type of each target charging logic to obtain the corresponding output data type of each target charging logic; carrying out data association relation analysis on the output data types corresponding to each target charging logic to obtain data association relation; based on the data association relationship, carrying out association relationship analysis on a plurality of target charging logics to obtain the association relationship set; building a plurality of target charging logics through the association relation set to obtain a target directed acyclic graph; wherein, obtain the target directed acyclic graph, include: performing DAG node construction on the target charging logics to obtain a plurality of DAG node data; carrying out identifier construction on each DAG node data to obtain identifier data corresponding to each DAG node data; constructing directed edges through the association relation set and identifier data corresponding to each DAG node data to obtain a plurality of directed edges; performing linear sorting analysis on each directed edge to obtain target linear sorting data; constructing a directed acyclic graph on a plurality of directed edges through the target linear ordering data to obtain a target directed acyclic graph;
The ordering module is used for carrying out charging logic ordering on the target directed acyclic graph through a preset topological ordering algorithm to obtain a logic ordering result; the method specifically comprises the following steps: performing initial node analysis on the target directed acyclic graph through the topology ordering algorithm to obtain ordering initial nodes; performing node traversal on the target directed acyclic graph through the ordering initial node to obtain a node traversal sequence; performing directed edge sequencing based on the node traversing sequence to obtain a directed edge sequencing result; charging logic sorting is carried out according to the directed edge sorting result, and the logic sorting result is obtained;
and the updating module is used for updating the charging scheme of the historical operation data through a plurality of target charging logics based on the logic ordering result to obtain a target charging scheme.
3. A billing apparatus based on a billing logic sequence, the billing apparatus based on a billing logic sequence comprising: a memory and at least one processor, the memory having instructions stored therein;
the at least one processor invokes the instructions in the memory to cause the charging logic order based charging device to perform the charging logic order based charging method of claim 1.
4. A computer readable storage medium having instructions stored thereon, which when executed by a processor implement the charging method based on a charging logic order of claim 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311507927.XA CN117252345B (en) | 2023-11-14 | 2023-11-14 | Charging method and system based on charging logic sequence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311507927.XA CN117252345B (en) | 2023-11-14 | 2023-11-14 | Charging method and system based on charging logic sequence |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117252345A CN117252345A (en) | 2023-12-19 |
| CN117252345B true CN117252345B (en) | 2024-02-23 |
Family
ID=89135402
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311507927.XA Active CN117252345B (en) | 2023-11-14 | 2023-11-14 | Charging method and system based on charging logic sequence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117252345B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107292464A (en) * | 2016-03-30 | 2017-10-24 | 北京矩道优达网络科技有限公司 | Automation assessment method and system based on directed acyclic graph and topological sorting |
| CN115775137A (en) * | 2022-12-02 | 2023-03-10 | 用友网络科技股份有限公司 | Logic table generation method, logic table generation device, and readable storage medium |
| CN116468436A (en) * | 2023-03-28 | 2023-07-21 | 百融至信(北京)科技有限公司 | Charging rule generation method and device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11823260B2 (en) * | 2018-04-03 | 2023-11-21 | Scratch Platform, Llc | Methods and systems for order-sensitive computations in loan accounting |
-
2023
- 2023-11-14 CN CN202311507927.XA patent/CN117252345B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107292464A (en) * | 2016-03-30 | 2017-10-24 | 北京矩道优达网络科技有限公司 | Automation assessment method and system based on directed acyclic graph and topological sorting |
| CN115775137A (en) * | 2022-12-02 | 2023-03-10 | 用友网络科技股份有限公司 | Logic table generation method, logic table generation device, and readable storage medium |
| CN116468436A (en) * | 2023-03-28 | 2023-07-21 | 百融至信(北京)科技有限公司 | Charging rule generation method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117252345A (en) | 2023-12-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8014983B2 (en) | Computer-implemented system and method for storing data analysis models | |
| Măruşter et al. | Redesigning business processes: a methodology based on simulation and process mining techniques | |
| US7251589B1 (en) | Computer-implemented system and method for generating forecasts | |
| US20170124486A1 (en) | Methods and systems for predictive engine evaluation and replay of engine performance | |
| US8751436B2 (en) | Analyzing data quality | |
| US8340995B2 (en) | Method and system of using artifacts to identify elements of a component business model | |
| US20070299741A1 (en) | Method and Apparatus Upgrade Assistance Using Critical Historical Product Information | |
| KR20060061759A (en) | Automatic validation and calibration of transaction-based performance models | |
| US10346862B2 (en) | Migration system to migrate users to target services | |
| US6816839B1 (en) | Demand planning for configure-to-order and building blocks-based market environment | |
| CN108280541A (en) | Customer service strategies formulating method, device based on random forest and decision tree | |
| US20230409014A1 (en) | Industrial Bottleneck Detection and Management Method and System | |
| WO2011159984A1 (en) | Accounting for data dependencies in process models, analysis, and management | |
| Alquthami et al. | Analytics framework for optimal smart meters data processing | |
| Dunke et al. | A data-driven methodology for the automated configuration of online algorithms | |
| Sangeeta et al. | New failure rate model for iterative software development life cycle process | |
| CN117252345B (en) | Charging method and system based on charging logic sequence | |
| US20060025981A1 (en) | Automatic configuration of transaction-based performance models | |
| JP2003288476A (en) | Line capacity integrated evaluation/management operation system for production line and line capacity integrated evaluation/management operation method for the production line | |
| US20230259994A1 (en) | Reference-based software application blueprint creation | |
| Müller et al. | Capacity management as a service for enterprise standard software | |
| US7610241B1 (en) | Method and apparatus for evaluating optimal access providers for long haul communication providers | |
| CN117435582B (en) | Method and device for capturing and processing ERP temporary data | |
| Cheng | An Approach of QoS Evaluation for Web Services Design with Optimized Avoidance of SLA Violations | |
| US20230385820A1 (en) | Methods and Systems for Predicting Cash Flow |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |