Self-intelligent network service rule decoupling method, device, equipment, medium and product

Abstract

The invention relates to the technical field of network communication, and provides a self-intelligence network service rule decoupling method, device and equipment, a medium and a product, and the self-intelligence network service rule decoupling method comprises the steps: obtaining network data from a database; extracting existing index management rules from the database to obtain an initial index set; performing rule engine configuration according to the network data and the initial index set to obtain a new rule engine; wherein the new rule engine comprises at least one of a demand prediction rule, a quality problem identification rule, a quality problem positioning rule, a quality optimization verification rule and a quality evaluation rule; and managing the database according to the new rule engine. By means of the mode, the existing rules of the database can be extracted, a new rule engine is established, rule decoupling and flexible configuration are achieved, the current tight coupling situation of a network operation and maintenance rule algorithm and a scene is solved, and the method adapts to intelligent development of network operation and maintenance.

Description

Technical Field

[0001] The present invention relates to the field of network communication technology, and in particular to a method, device, equipment, medium and product for decoupling business rules of an intelligent network. Background Art

[0002] The business analysis process of each scenario of communication network operation and maintenance is relatively complex. Each business analysis process may contain multiple decision points, and each decision point contains multiple rules. The rules are usually embedded in the business process itself or in the internal implementation of custom Java code, which will lead to the following problems: ① It takes a long time and a lot of work to change business rules: Business rules change more frequently than the business itself, and changing and managing embedded business rules is a complex issue that is beyond the capabilities of most business analysts. Therefore, changes to business rules usually require business analysts and programmers to spend a lot of time to understand the business and IT to implement the system. Business processes are complex and change quickly, and system updates cannot respond quickly and in a timely manner.

[0003] ②Business rules are difficult to maintain: Most systems lack a central rule information database, business rules are invisible, and system users cannot obtain business rule information from the system in the first place, making it difficult to maintain business rules and promote the use of functions.

[0004] ③ Low reusability of business rules: The business rules of each system are implemented separately and implemented at the system code level. There is a problem of repeated development of the same business logic in multiple systems, and the business rules are redundant and cannot be reused. Summary of the invention

[0005] The present invention provides a self-intelligent network business rule decoupling method, device, equipment, medium and product to solve the current situation of tight coupling between network operation and maintenance rule algorithms and scenarios, and adapt to the intelligent development of network operation and maintenance.

[0006] The present invention provides a method for decoupling business rules of a self-intelligent network, comprising: obtaining network data from a database; extracting existing indicator management rules from the database to obtain an initial indicator set; configuring a rule engine according to the network data and the initial indicator set to obtain a new rule engine; wherein the new rule engine includes at least one of demand prediction rules, quality problem identification rules, quality problem location rules, quality optimization verification rules and quality assessment rules; and managing the database according to the new rule engine.

[0007] According to a self-intelligent network business rule decoupling method provided by the present invention, the new rule engine includes demand forecasting rules; the rule engine is configured according to network data and an initial indicator set to obtain a new rule engine, including: determining an initial forecasting model, an initial forecasting value output by the initial forecasting model, and an initial warning threshold of the initial forecasting value according to the network data and the initial indicator set; using a training model to configure parameters of the initial forecasting model to obtain a new forecasting model, a new forecasting value, and a new warning threshold; and determining the demand forecasting rules based on the new forecasting model, the new forecasting value, and the new warning threshold.

[0008] According to a self-intelligent network business rule decoupling method provided by the present invention, the new rule engine includes quality problem identification rules; the rule engine is configured according to network data and an initial indicator set to obtain a new rule engine, including: determining an initial problem identification model according to the network data and the initial indicator set; configuring the indicator thresholds, rules, and data cycles of the initial problem identification model to achieve quality problem identification and obtain a problem identification model; the problem identification model is used to output quality problem alarms and / or poor quality results; and quality problem identification rules are obtained according to the problem identification model.

[0009] According to a self-intelligent network business rule decoupling method provided by the present invention, the new rule engine includes quality problem locating rules; the rule engine is configured according to network data and an initial indicator set to obtain a new rule engine, including: when a quality problem is determined according to the network data and the initial indicator set, a problem locating process is generated according to the locating process to obtain a quality problem locating rule; wherein the locating process is obtained based on the following method: adding presentation nodes to the locating process, selecting one or more indicators under each node, and using the selected indicators to configure the rule algorithm under the node; wherein the rule algorithm of each node is configured separately; branches are defined for the algorithm results, and different branches point to different next-level nodes, thereby forming a locating process.

[0010] According to a self-intelligent network business rule decoupling method provided by the present invention, the new rule engine includes quality optimization verification rules; the rule engine is configured according to network data and an initial indicator set to obtain a new rule engine, including: obtaining quality optimization verification rules according to network data and an initial indicator set; wherein the quality optimization verification rules are used to automatically associate the optimization verification rules with the processed problem work orders for processing and verification to obtain verification results.

[0011] According to a self-intelligent network business rule decoupling method provided by the present invention, the new rule engine includes quality assessment rules; the rule engine is configured according to network data and an initial indicator set to obtain a new rule engine, including: configuring various indicator evaluation systems according to the network data and the initial indicator set; determining statistical analysis of data in each dimension according to the various indicator evaluation systems, obtaining analysis results, and constructing quality assessment rules; wherein the analysis results include at least one of score ranking, quality difference indicator and indicator trend.

[0012] The present invention also provides a self-intelligent network business rule decoupling device, including: an acquisition module, used to obtain network data from a database; an extraction module, used to extract existing indicator management rules from the database to obtain an initial indicator set; a rule engine configuration module, used to configure the rule engine according to the network data and the initial indicator set to obtain a new rule engine; wherein the new rule engine includes at least one of demand prediction rules, quality problem identification rules, quality problem location rules, quality optimization verification rules and quality assessment rules; a management module, used to manage the database according to the new rule engine.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, any of the above-mentioned self-intelligent network business rule decoupling methods is implemented.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the self-intelligent network service rule decoupling method as described in any one of the above is implemented.

[0015] The present invention also provides a computer program product, including a computer program, wherein when the computer program is executed by a processor, the method for decoupling business rules of an autonomous network as described above is implemented.

[0016] The present invention provides a method, device, equipment, medium and product for decoupling business rules of self-intelligent network. The method for decoupling business rules of self-intelligent network includes: obtaining network data from a database; extracting existing indicator management rules from the database to obtain an initial indicator set; configuring a rule engine according to the network data and the initial indicator set to obtain a new rule engine; wherein the new rule engine includes at least one of demand prediction rules, quality problem identification rules, quality problem location rules, quality optimization verification rules and quality assessment rules; managing the database according to the new rule engine. Through the above method, the present invention can extract existing rules from the database and establish a new rule engine to achieve rule decoupling and flexible configuration, solve the current situation of tight coupling between network operation and maintenance rule algorithms and scenarios, and adapt to the intelligent development of network operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flowchart of a method for decoupling business rules of an intelligent network provided in an embodiment of the present invention.

[0019] Figure 2 It is an overall design diagram of the rule engine platform provided by an embodiment of the present invention.

[0020] Figure 3 It is a flowchart of indicator management provided by an embodiment of the present invention.

[0021] Figure 4 It is a structural diagram of a self-intelligent network service rule decoupling device provided in an embodiment of the present invention.

[0022] Figure 5 It is a schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] In the description of the embodiments of the present invention, it should be noted that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limitations on the embodiments of the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0025] In the description of the embodiments of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "connected" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

[0026] In the embodiments of the present invention, unless otherwise clearly specified and limited, the first feature being "above" or "below" the second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, the first feature being "above", "above" and "above" the second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. The first feature being "below", "below" and "below" the second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0027] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0028] It should be noted that all actions of acquiring signals, information or data in the present invention are performed in compliance with the corresponding data protection laws and policies of the country where the device is located and with the authorization given by the owner of the corresponding device.

[0029] The present invention provides a method for decoupling business rules of an intelligent network. Figure 1 , Figure 1 is a flow chart of a method for decoupling business rules of an intelligent network provided by an embodiment of the present invention. In this embodiment, the method for decoupling business rules of an intelligent network includes steps S110 to S140, and each step is specifically as follows: S110: Acquire network data from a database.

[0030] S120: extracting existing indicator management rules from the database to obtain an initial indicator set.

[0031] S130: Configure the rule engine according to the network data and the initial indicator set to obtain a new rule engine.

[0032] S140: Manage the database according to the new rule engine.

[0033] The new rule engine includes at least one of demand prediction rules, quality problem identification rules, quality problem location rules, quality optimization verification rules and quality assessment rules.

[0034] The rule engine provides unified management of the entire life cycle of relevant business rules, including the editing, deployment, operation, and monitoring of reusable simple rules, decision tables, decision trees, rule flows and other components, for efficient decision management.

[0035] Exemplarily, the functions of the rule engine may include: 1) Allow users to create and edit rules using specific syntax or a graphical interface, such as a clear logical structure that is easy for non-technical personnel to understand and use.

[0036] 2) Provide functions for organizing, classifying and maintaining rules, making version control and updating of rules easier.

[0037] 3) Reasoning based on input data and defined rules, automatically making decisions and outputting corresponding results. This process includes rule matching and execution.

[0038] 4) Rules can be adjusted quickly according to changes in business needs and can respond quickly to user needs.

[0039] 5) Reduce human intervention and improve the speed and accuracy of decision-making by automating the decision-making process.

[0040] In this embodiment, a self-intelligent network refers to a network system that achieves self-optimization, self-management, and self-adjustment through advanced intelligent technologies (such as artificial intelligence, machine learning, big data analysis, etc.). A self-intelligent network not only has the connection function of a traditional network, but also can intelligently analyze and process data and dynamically adapt to environmental changes and business needs.

[0041] In combination with the core scenarios of self-intelligent networks, this embodiment builds a rule engine technology platform to achieve flexible configuration of business rules and realize a full-cycle network intelligent rule engine of rule solidification --> rule decoupling --> rule intelligence injection --> rule self-iteration in the direction of self-intelligent network evolution, which helps the digital transformation of network operation and maintenance.

[0042] Through the above method, the present invention can extract existing rules from the database and establish a new rule engine to achieve rule decoupling and flexible configuration, solve the current situation of tight coupling between network operation and maintenance rule algorithms and scenarios, and adapt to the intelligent development of network operation and maintenance.

[0043] In some embodiments, the new rule engine includes demand prediction rules; the step of configuring the rule engine according to the network data and the initial indicator set to obtain the new rule engine may specifically include: Determine the initial prediction model, the initial prediction value output by the initial prediction model, and the initial warning threshold of the initial prediction value based on network data and the initial indicator set; use the training model to configure the parameters of the initial prediction model to obtain a new prediction model, new prediction value, and a new warning threshold; determine the demand prediction rules based on the new prediction model, new prediction value, and new warning threshold.

[0044] In some embodiments, the new rule engine includes a quality problem identification rule; the step of configuring the rule engine according to the network data and the initial indicator set to obtain the new rule engine may specifically include: An initial problem identification model is determined based on network data and an initial indicator set; indicator thresholds, rules, and data cycles of the initial problem identification model are configured to achieve quality problem identification and obtain a problem identification model; the problem identification model is used to output quality problem alarms and / or poor quality results; quality problem identification rules are obtained based on the problem identification model.

[0045] In some embodiments, the new rule engine includes a quality problem location rule; the step of configuring the rule engine according to the network data and the initial indicator set to obtain the new rule engine may specifically include: When a quality problem is determined based on network data and an initial indicator set, a problem location process is generated based on the location process to obtain a quality problem location rule; wherein the location process is obtained based on the following method: adding presentation nodes to the location process, selecting one or more indicators under each node, and using the selected indicators to configure the rule algorithm under the node; wherein the rule algorithm of each node is configured separately; and branches are defined for the algorithm results, and different branches point to different next-level nodes, thereby forming a location process.

[0046] In some embodiments, the new rule engine includes quality optimization verification rules; the step of configuring the rule engine according to the network data and the initial indicator set to obtain the new rule engine may specifically include: The quality optimization verification rules are obtained according to the network data and the initial indicator set; wherein the quality optimization verification rules are used to automatically associate the processed problem work orders with the optimization verification rules for processing and verification to obtain the verification results.

[0047] In some embodiments, the new rule engine includes quality assessment rules; the step of configuring the rule engine according to the network data and the initial indicator set to obtain the new rule engine may specifically include: Various indicator evaluation systems are configured according to network data and the initial indicator set; statistical analysis of data in each dimension is determined according to various indicator evaluation systems to obtain analysis results to construct quality evaluation rules; wherein the analysis results include at least one of score ranking, quality difference indicator and indicator trend.

[0048] See also Figure 2 , Figure 2 It is an overall design diagram of the rule engine platform provided by an embodiment of the present invention; Figure 3 It is a flowchart of indicator management provided by an embodiment of the present invention.

[0049] In this embodiment, the new network architecture is matched by digital transformation of operation and maintenance, and a rule engine is established to achieve rule decoupling and flexible configuration. Furthermore, AI learning is introduced to generate a diagnostic rule library, and the rule engine will automatically call the rules for root cause analysis and diagnosis, thereby accelerating the automation and intelligence of the operation and maintenance of the entire network.

[0050] exist Figure 2 In the rule engine platform of the self-intelligent network scenario shown, the technical architecture can be divided into a data collection layer, an indicator system layer, a rule engine layer, and a self-intelligent network scenario application layer.

[0051] Specifically, the data collection layer is the underlying perception of digital network operation and maintenance applications based on massive network data. The data collected by the data collection layer may include 4 / 5G DPI data, 4 / 5G PM data, wireless MR data, basic engineering parameter data, and core network OMC data, etc.

[0052] The indicator system layer is to strengthen the virtual mapping and management organization of data, knowledge, assets, etc., and provide basic resources and key tools to support the digital application of operation and maintenance. The indicator types in the indicator system layer can include perception indicators, performance indicators, resource indicators, service indicators, etc.

[0053] The rule engine layer is used for data mining analysis and value conversion to achieve intelligent optimization and decision-making in the core links. The functions of the rule engine layer can include rule design, rule executor, rule arrangement, business rule configuration, rule reading, rule verification, rule tree generation, business rule verification, etc.

[0054] The scenario application layer of the self-intelligent network can build an intelligent decision-making application map, empower more scenarios, reduce costs and increase efficiency on the operation and maintenance side, and improve quality and increase revenue on the operation side. The functions of the scenario application layer of the self-intelligent network can include business demand prediction, quality problem identification, quality problem location, quality optimization verification, batch complaint prediction and quality assessment closed loop, etc.

[0055] Indicator management serves as the basis for rule configuration and application. Indicator management is achieved through automatic extraction and manual entry. Indicators are directly linked to the backend database to support the implementation of business rules.

[0056] like Figure 3 As shown, the basic information of the indicator can be manually configured, or the standard information of the indicator can be automatically extracted, and a semi-automatic indicator set collection process can be realized, that is, automatic extraction of the indicator dimension set + manual verification. Among them, the automatically extracted indicators can include those obtained from the database after metadata extraction, dimension extraction and dimension relationship mapping.

[0057] In the rule engine layer, rule engine configuration management can be performed to flexibly configure thresholds, rules, evaluation systems, and delimitation processes for business indicators stored in the database.

[0058] The following is an example of rule engine configuration management: ①Demand forecast: It supports custom forecasting methods, which can be predicted by configuring a fixed increase or decrease range, and warning with thresholds; or through the rule engine function, the AI ​​training model can be referenced to configure the parameters of the forecasting model, and the demand forecast results can be output for comparison with historical data.

[0059] Optionally, the demand forecasting rule configuration provides a variety of forecasting model algorithms for selection. Model parameters support manual configuration. One indicator supports the configuration of multiple forecasting models. Forecasting can be selected according to actual conditions. Each forecasting model supports the configuration of warning thresholds and crash thresholds, and supports the output of warnings based on the comparison between the forecast results and the thresholds.

[0060] Optionally, the rule engine supports the configuration of different warning thresholds and crash thresholds for a single network element and multiple network elements in demand forecasting scenarios such as 5G data services, 5G voice, SMS, and home broadband.

[0061] ②Identification of quality issues: Based on the preset indicator set, the indicator thresholds, rules, and data cycles are flexibly configured through the rule engine to realize quality problem identification, output quality problem alarms and quality poor results, and connect to the work order operation system to realize problem dispatching and tracking closed loop. It supports the selection of multiple operation rules, supports fixed thresholds, dynamic thresholds, and differentiated threshold settings, and supports the implementation of quality problem identification scenarios such as 5G data services, 5G voice, SMS, and home broadband.

[0062] Optionally, you can configure different quality problem identification calculation formulas for different object types. The editor supports the configuration of combined formulas. After the quality problem identification rule formula is configured, you can choose whether to generate an alarm and whether to issue a problem order.

[0063] ③Quality problem location: Configure the problem location process for business quality indicators to achieve indicator problem location, configure the judgment rules for each node separately, select indicators, and configure indicator business logic rules. The background automatically calls the process to achieve quality problem location, present the location results, and support docking with the dispatching platform for work order dispatching.

[0064] Exemplarily, first, a presentation node is added to the positioning process, one or more indicators can be selected under each node, and the rule algorithm under the node is configured using the selected indicators. Then branches are defined for the algorithm results. Different branches point to different next-level nodes, forming a complete positioning process.

[0065] ④Quality optimization verification: Configure optimization verification rules for various alarms, quality differences, and problem location rules. By configuring rules such as work order quality inspection keywords, the processed problem work orders are automatically associated with optimization verification rules for processing and verification. It supports presenting optimization verification conclusions and drilling down to present indicator optimization verification details and indicator historical trends.

[0066] Optionally, the rule engine provides two optimization verification methods: improvement after optimization, configuring the indicator's positive improvement percentage; and reaching the target after optimization, configuring the indicator verification threshold.

[0067] ⑤Quality Assessment: Supports configuration of various indicator evaluation systems, including various dimensions within the system, indicator sets contained in dimensions, dimension and indicator weight scoring information. Indicator scoring rules support linear scoring, segmented scoring and other methods. According to the evaluation system configuration, calculate the statistical analysis of data in each dimension, including score ranking, quality difference indicators, indicator trends, etc. Quality evaluation includes single indicator scoring rule configuration and evaluation system weight configuration.

[0068] The single indicator scoring rule configuration supports configuration through the interval method (configuring the benchmark value, challenge value interval and the corresponding score), the threshold method (configuring the indicator threshold and the corresponding score), the threshold equal height method (configuring a single indicator value and the corresponding score), and the interval equal height method (configuring the value interval and the corresponding score).

[0069] The evaluation system weight configuration supports selecting indicators with configured scoring rules, and configuring their weights under each dimension of the evaluation system and the weights of each dimension. The system will calculate the score of a single indicator according to the evaluation rules, and then obtain the final total score of the indicator system according to the weight.

[0070] In some other embodiments, the self-intelligent network business rule decoupling method of the present invention can also use D3 technology to intelligently generate a decision tree.

[0071] Decision tree is a highly interpretable strategy analysis tool. Decision tree is essentially a collection of IF-THEN rules, which coincides with human decision-making behavior, making decision tree highly interpretable. Visualizing decision tree models can help us analyze and solve problems more effectively.

[0072] The rule combination of the decision tree model is presented in the form of a tree. The path from the root node to each leaf node constitutes a rule. The characteristics of the intermediate nodes on the path correspond to the conditions of the specific rule, and each leaf node represents the decision result. At the same time, this rule set has the property of being mutually exclusive and complete, that is, each instance is covered by one and only one path or one rule.

[0073] The CART algorithm (Classification And Regression Tree, CART) is used to solve classification and regression problems. The Gini coefficient is used to select features. The smaller the Gini coefficient, the lower the impurity and the better the feature. When solving classification problems, the node impurity is measured by the Gini index, and the node split evaluation is based on the Gini index reduction value; when solving regression problems, the node impurity is measured by the variance of the target feature, and the node split evaluation standard is based on the variance reduction value. It supports the processing of continuous values ​​and missing values.

[0074] The above embodiment provides a method for decoupling business rules of an autonomous network, which implements rule decoupling configuration for common scenarios of a communication autonomous network based on a rule engine, and can have the following beneficial effects: 1) Low-code development of business processes to shorten the business function implementation cycle: Low-code development helps innovative businesses go online quickly. By building a SQL-based "low-code development" model, complex business rules can be quickly implemented without coding through page configuration, and the rule engine separates complex business logic from the business code, so the difficulty of implementing business logic can be significantly reduced; at the same time, with the good business rule designer provided by the rule engine, the changeable business rules become maintainable and easy to maintain.

[0075] 2) Intelligent rule matching in the rule base to improve the rule reuse rate: After defining the rule file according to business needs, all the rule files involved can be packaged to generate a rule base. The system reads the configuration module to obtain information about various configurations in the computer; the business module automatically identifies and calculates the web application based on the read configuration information.

[0076] 3) Multi-scenario business rule orchestration to support business needs in an agile manner: Call the rule engine tool to provide eight types of business rule design tools, including rule sets, decision tables, cross-decision tables (decision matrices), decision trees, score cards, complex score cards, and rule flows, to meet the needs of complex business rule design from all angles. Expand based on open source component code, introduce Java rule engine technology, perform personalized transformation, and create multi-scenario business rule orchestration capabilities such as self-intelligent networks, alarm monitoring, quality difference analysis, quality assessment, and network element health.

[0077] 4) Improve system stability and reduce development costs: Reduce unnecessary system updates. Separate the business decision logic of business decision makers and the technical decisions of application developers, effectively improve the maintainability of the code that implements complex logic, and manage the business rules of each platform and each thematic application based on the rule engine, which changes the dilemma of repeated resource construction and repeated application development, greatly improves R&D efficiency, and effectively reduces human development costs.

[0078] On the other hand, the present invention also provides a self-intelligent network business rule decoupling device. The self-intelligent network business rule decoupling device provided by the present invention is described below. The self-intelligent network business rule decoupling device described below and the self-intelligent network business rule decoupling method described above can be referenced to each other.

[0079] See also Figure 4 , Figure 4 4 is a schematic diagram of the structure of the self-intelligent network service rule decoupling device provided by an embodiment of the present invention. In this embodiment, the self-intelligent network service rule decoupling device may include an acquisition module 410, an extraction module 420, a rule engine configuration module 430 and a management module 440.

[0080] The acquisition module 410 is used to acquire network data from a database.

[0081] The extraction module 420 is used to extract existing indicator management rules from the database to obtain an initial indicator set.

[0082] The rule engine configuration module 430 is used to configure the rule engine according to the network data and the initial indicator set to obtain a new rule engine; wherein the new rule engine includes at least one of demand prediction rules, quality problem identification rules, quality problem location rules, quality optimization verification rules and quality assessment rules.

[0083] The management module 440 is used to manage the database according to the new rule engine.

[0084] In some embodiments, the new rule engine includes demand forecasting rules; the rule engine configuration module 430 can be specifically used to: Determine the initial prediction model, the initial prediction value output by the initial prediction model, and the initial warning threshold of the initial prediction value based on network data and the initial indicator set; use the training model to configure the parameters of the initial prediction model to obtain a new prediction model, new prediction value, and a new warning threshold; determine the demand prediction rules based on the new prediction model, new prediction value, and new warning threshold.

[0085] In some embodiments, the new rule engine includes quality problem identification rules; the rule engine configuration module 430 can be specifically used to: An initial problem identification model is determined based on network data and an initial indicator set; indicator thresholds, rules, and data cycles of the initial problem identification model are configured to achieve quality problem identification and obtain a problem identification model; the problem identification model is used to output quality problem alarms and / or poor quality results; quality problem identification rules are obtained based on the problem identification model.

[0086] In some embodiments, the new rule engine includes quality problem location rules; the rule engine configuration module 430 can be specifically used to: When a quality problem is determined based on network data and an initial indicator set, a problem location process is generated based on the location process to obtain a quality problem location rule; wherein the location process is obtained based on the following method: adding presentation nodes to the location process, selecting one or more indicators under each node, and using the selected indicators to configure the rule algorithm under the node; wherein the rule algorithm of each node is configured separately; and branches are defined for the algorithm results, and different branches point to different next-level nodes, thereby forming a location process.

[0087] In some embodiments, the new rule engine includes quality optimization verification rules; the rule engine configuration module 430 can be specifically used to: The quality optimization verification rules are obtained according to the network data and the initial indicator set; wherein the quality optimization verification rules are used to automatically associate the processed problem work orders with the optimization verification rules for processing and verification to obtain the verification results.

[0088] In some embodiments, the new rule engine includes quality assessment rules; the rule engine configuration module 430 can be specifically used to: Various indicator evaluation systems are configured according to network data and the initial indicator set; statistical analysis of data in each dimension is determined according to various indicator evaluation systems to obtain analysis results to construct quality evaluation rules; wherein the analysis results include at least one of score ranking, quality difference indicator and indicator trend.

[0089] As mentioned above, an embodiment of the present invention provides a self-intelligent network business rule decoupling device, including: an acquisition module, used to acquire network data from a database; an extraction module, used to extract existing indicator management rules from the database to obtain an initial indicator set; a rule engine configuration module, used to configure the rule engine according to the network data and the initial indicator set to obtain a new rule engine; wherein the new rule engine includes at least one of demand forecasting rules, quality problem identification rules, quality problem location rules, quality optimization verification rules and quality assessment rules; a management module, used to manage the database according to the new rule engine. Through the above method, the embodiment of the present invention can extract existing rules from the database and establish a new rule engine, realize rule decoupling and flexible configuration, solve the current situation of tight coupling between network operation and maintenance rule algorithms and scenarios, and adapt to the intelligent development of network operation and maintenance.

[0090] On the other hand, an embodiment of the present invention further provides an electronic device, see Figure 5 , Figure 5 is a schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention, such as Figure 5 As shown, the electronic device may include: the electronic device may include a memory 520, a processor 510, and a computer program stored in the memory 520 and executable on the processor 510. When the processor 510 executes the program, the self-intelligent network service rule decoupling method provided by the above methods is implemented.

[0091] Optionally, the electronic device may further include a communication bus 530 and a communication interface (CommunicationsInterface) 540, wherein the processor 510, the communication interface 540, and the memory 520 communicate with each other through the communication bus 530. The processor 510 may call a computer program in the memory 520 to execute the self-intelligent network service rule decoupling method, which may include: Acquire network data from a database; extract existing indicator management rules from the database to obtain an initial indicator set; configure a rule engine based on the network data and the initial indicator set to obtain a new rule engine; wherein the new rule engine includes at least one of demand prediction rules, quality problem identification rules, quality problem location rules, quality optimization verification rules and quality assessment rules; manage the database based on the new rule engine.

[0092] In addition, the logic instructions in the above-mentioned memory 520 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0093] On the other hand, the present invention also provides a computer program product, which includes a computer program, and the computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the self-intelligent network business rule decoupling method provided by the above methods. Its steps and principles have been introduced in detail in the above methods and will not be repeated here.

[0094] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the self-intelligent network business rule decoupling method provided by the above methods. Its steps and principles have been introduced in detail in the above methods and will not be repeated here.

[0095] Non-transitory computer-readable storage media can be any available media or data storage devices that can be accessed by the processor, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO)), optical storage (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NANDFLASH), solid-state drives (SSDs)), etc.

[0096] In summary, the present invention provides a method, device, equipment, medium and product for decoupling business rules of self-intelligent networks. The method for decoupling business rules of self-intelligent networks includes: obtaining network data from a database; extracting existing indicator management rules from a database to obtain an initial indicator set; configuring a rule engine according to the network data and the initial indicator set to obtain a new rule engine; wherein the new rule engine includes at least one of demand prediction rules, quality problem identification rules, quality problem location rules, quality optimization verification rules and quality assessment rules; managing the database according to the new rule engine. Through the above method, the present invention can extract existing rules from a database and establish a new rule engine to achieve rule decoupling and flexible configuration, solve the current situation of tight coupling between network operation and maintenance rule algorithms and scenarios, and adapt to the intelligent development of network operation and maintenance.

[0097] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0098] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for decoupling business rules of an intelligent network, characterized in that: include: Get network data from the database; Extracting existing indicator management rules from the database to obtain an initial indicator set; A rule engine is configured according to the network data and the initial indicator set to obtain a new rule engine; wherein the new rule engine includes at least one of a demand prediction rule, a quality problem identification rule, a quality problem location rule, a quality optimization verification rule, and a quality assessment rule; The database is managed according to the new rule engine.

2. The method for decoupling business rules of an intelligent network according to claim 1, characterized in that: The new rule engine includes demand prediction rules; the rule engine is configured according to the network data and the initial indicator set to obtain a new rule engine, including: Determine an initial prediction model, an initial prediction value output by the initial prediction model, and an initial warning threshold of the initial prediction value according to the network data and the initial indicator set; Using the training model to configure parameters of the initial prediction model to obtain a new prediction model, a new prediction value, and a new warning threshold; The demand forecasting rule is determined based on the new forecasting model, the new forecasting value and the new warning threshold.

3. The method for decoupling business rules of an intelligent network according to claim 1, characterized in that: The new rule engine includes quality problem identification rules; the rule engine is configured according to the network data and the initial indicator set to obtain a new rule engine, including: Determining an initial problem identification model based on the network data and the initial indicator set; The indicator thresholds, rules, and data cycles of the initial problem identification model are configured to achieve quality problem identification and obtain a problem identification model; the problem identification model is used to output quality problem alarms and / or poor quality results; The quality problem identification rule is obtained according to the problem identification model.

4. The method for decoupling business rules of an intelligent network according to claim 1, characterized in that: The new rule engine includes quality problem location rules; the rule engine is configured according to the network data and the initial indicator set to obtain a new rule engine, including: When a quality problem is determined according to the network data and the initial indicator set, a problem location process is generated according to the location process to obtain the quality problem location rule; The positioning process is based on the following method: Add presentation nodes to the positioning process, select one or more indicators under each node, and use the selected indicators to configure the rule algorithm under the node; the rule algorithm of each node is configured separately; Define branches for the algorithm results, and different branches point to different next-level nodes, thus forming a positioning process.

5. The method for decoupling business rules of an intelligent network according to claim 1, characterized in that: The new rule engine includes quality optimization verification rules; the rule engine is configured according to the network data and the initial indicator set to obtain a new rule engine, including: The quality optimization verification rule is obtained according to the network data and the initial indicator set; wherein the quality optimization verification rule is used to automatically associate the processed problem work order with the optimization verification rule for processing and verification to obtain a verification result.

6. The method for decoupling business rules of an intelligent network according to claim 1, characterized in that: The new rule engine includes quality assessment rules; the rule engine is configured according to the network data and the initial indicator set to obtain a new rule engine, including: Configuring various indicator evaluation systems according to the network data and the initial indicator set; According to the various indicator evaluation systems, statistical analysis of data in each dimension is determined to obtain analysis results to construct the quality evaluation rules; wherein the analysis results include at least one of score ranking, quality difference indicator and indicator trend.

7. A self-intelligent network service rule decoupling device, characterized in that: include: The acquisition module is used to obtain network data from the database; An extraction module, used to extract existing indicator management rules from the database to obtain an initial indicator set; A rule engine configuration module, configured to configure the rule engine according to the network data and the initial indicator set to obtain a new rule engine; wherein the new rule engine includes at least one of a demand prediction rule, a quality problem identification rule, a quality problem location rule, a quality optimization verification rule, and a quality assessment rule; A management module is used to manage the database according to the new rule engine.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for decoupling business rules of an autonomous network as claimed in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for decoupling business rules of an autonomous network as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for decoupling business rules of an autonomous network as claimed in any one of claims 1 to 6 is implemented.

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