Wireless interference detection method and device, storage medium and electronic device

Abstract

The embodiment of the invention provides a wireless interference detection method and device, a storage medium and an electronic device, and the method comprises the steps: extracting tetrad information for wireless interference detection according to a wireless interference detection intention and an interference intention detection element inputted by a user; determining an atomic rule detection sequence adapted to a pre-constructed wireless interference detection knowledge graph based on the tetrad information; and performing wireless interference detection according to the atomic rule detection sequence, and outputting a wireless interference detection result. Through the method and the device, the problem that a user cannot flexibly adjust the workflow of wireless interference detection according to the wireless interference detection result and the requirement in the prior art is solved, and the effect of flexibly adjusting the wireless interference detection workflow according to the wireless interference detection result and the requirement is achieved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of communications, and in particular, to a wireless interference detection method and device, a storage medium, and an electronic device. Background Art

[0002] In telecommunications network management, interference detection of wireless cells is often performed periodically using AI or rule-based algorithms. The detection process is a predetermined workflow of the system. After detection, the system outputs analysis results. Parameters such as the time and object of initiating the interference detection task must be manually specified. At the same time, users cannot flexibly adjust the interference detection workflow based on detection results and requirements. Summary of the invention

[0003] Embodiments of the present invention provide a wireless interference detection method and device, a storage medium, and an electronic device to at least solve the problem in the related art that a user cannot flexibly adjust the workflow of wireless interference detection according to wireless interference detection results and requirements.

[0004] According to one embodiment of the present invention, a wireless interference detection method is provided, comprising: extracting four-tuple information for wireless interference detection based on wireless interference detection intention and interference intention detection elements input by a user; determining an atomic rule detection sequence adapted to a pre-constructed wireless interference detection knowledge graph based on the four-tuple information; performing wireless interference detection according to the atomic rule detection sequence, and outputting the wireless interference detection result.

[0005] According to another embodiment of the present invention, a wireless interference detection device is provided, including: an interference detection intention engine module, used to extract four-tuple information used for wireless interference detection based on the wireless interference detection intention and interference intention detection elements input by the user; an interference detection algorithm processing module, used to determine an atomic rule detection sequence adapted to a pre-built wireless interference detection knowledge graph based on the four-tuple information; an interference detection processing execution module, used to perform wireless interference detection according to the atomic rule detection sequence, and output the wireless interference detection result.

[0006] According to yet another embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any one of the above method embodiments when run.

[0007] According to yet another embodiment of the present invention, there is provided an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0008] Through the present invention, a wireless interference detection method is provided, which extracts four-tuple information for wireless interference detection according to the wireless interference detection intention and interference intention detection elements input by the user; determines the atomic rule detection sequence adapted to the pre-built wireless interference detection knowledge graph based on the four-tuple information; performs wireless interference detection according to the atomic rule detection sequence, and outputs the wireless interference detection result. Through the present invention, the problem that the user cannot flexibly adjust the workflow of wireless interference detection according to the wireless interference detection results and requirements in the related art is solved, and the effect of flexibly adjusting the wireless interference detection workflow according to the wireless interference detection results and requirements is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 It is a hardware structure block diagram of a computer terminal of a wireless interference detection method according to an embodiment of the present invention;

[0010] Figure 2 is a flow chart of a wireless interference detection method according to an embodiment of the present invention;

[0011] Figure 3 is a structural block diagram of a wireless interference detection device according to an embodiment of the present invention;

[0012] Figure 4 is a structural block diagram of a wireless interference detection device according to an embodiment of the present invention;

[0013] Figure 5 is a structural block diagram of a wireless interference detection device according to an embodiment of the present invention;

[0014] Figure 6 is a schematic diagram of the architecture of a wireless interference detection device according to a scenario embodiment of the present invention;

[0015] Figure 7 is a flow chart of a wireless interference detection method according to an embodiment of the present invention;

[0016] Figure 8 It is a schematic diagram of the process of generating an atomic rule detection sequence from a knowledge graph according to a scenario embodiment of the present invention. DETAILED DESCRIPTION

[0017] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings and in combination with the embodiments.

[0018] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0019] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a computer terminal of a wireless interference detection method according to an embodiment of the present invention. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the above-mentioned computer terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0020] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the wireless interference detection method in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0021] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0022] In this embodiment, a wireless interference detection method running on the above-mentioned computer terminal is provided. Figure 2 is a flow chart of a wireless interference detection method according to an embodiment of the present invention. Figure 2As shown, the process includes the following steps:

[0023] Step S202, extracting four-tuple information for wireless interference detection according to the wireless interference detection intention and interference intention detection elements input by the user;

[0024] In an exemplary embodiment, interference intention conflict detection includes at least one of the following: whether wireless interference detection resources conflict; whether wireless interference detection function scenarios conflict; whether execution time of wireless interference detection intention conflicts.

[0025] In actual implementation, the wireless interference detection intention input by the user may be in text form or voice form.

[0026] In an exemplary embodiment, the four-tuple information includes: information about the object or region of wireless interference detection, time information about wireless interference detection, action information about wireless interference detection, and information about the presentation method of the wireless interference detection result.

[0027] In an exemplary embodiment, after extracting the four-tuple information used for wireless interference detection based on the wireless interference detection intention and interference intention detection elements input by the user, it also includes: confirming the integrity and legality of the content in the four-tuple information; performing interference intention conflict detection on the four-tuple information based on the interference intention detection elements, and adjusting the four-tuple information whose detection result of the interference intention conflict detection is a conflict according to a preset conflict resolution strategy.

[0028] In the actual implementation process, interference detection intention detection is interference detection intention legitimacy detection and conflict detection. Legitimacy detection includes that the translated user intent must contain four elements; the objects contained in the four elements must exist, the detailed information must be legal, etc. Conflict detection includes resource conflicts, interference detection function scenario conflicts, interference detection intention execution time conflicts, etc. The legality and conflict detection modules will solve the problems automatically or interactively according to the specified solution strategy, and finally output the intention task without conflict, and send the task containing the user intention information to the interference detection algorithm processing center.

[0029] Step S204, determining an atomic rule detection sequence adapted to a pre-built wireless interference detection knowledge graph based on the four-tuple information;

[0030] In an exemplary embodiment, a pre-built wireless interference detection knowledge graph includes at least data entities and entity relationships between data entities, wherein the data entities include at least one of the following: supported standards for wireless interference detection, detection rules for wireless interference detection, configuration data of objects for wireless interference detection, engineering parameters of objects for wireless interference detection, performance counters or key performance indicators; and the entity relationships include at least one of the following: inclusion relationship, usage relationship, and dependency relationship.

[0031] In an exemplary embodiment, an atomic rule detection sequence adapted to a pre-built wireless interference detection knowledge graph is determined based on quadruple information, including: determining a rule sequence supported by the wireless interference detection knowledge graph based on quadruple information; obtaining an atomic rule list in the entity rules of the wireless interference detection knowledge graph according to the entity relationship in the rule sequence; and organizing the atomic rule list into an atomic rule detection sequence according to a preset strategy order.

[0032] In the actual implementation process, the interference detection algorithm processing center combines the interference detection knowledge graph to generate atomic rule detection sequences including data query, candidate cell initial screening, expert rules or AI detection, segmentation detection, etc. The algorithm processing center adapts and concretizes the objects corresponding to the rules in the knowledge graph according to the object / region and action of the command, and uses the knowledge graph to generate atomic rule detection sequences.

[0033] Step S206, performing wireless interference detection according to the atomic rule detection sequence, and outputting the wireless interference detection result.

[0034] In an exemplary embodiment, wireless interference detection is performed according to an atomic rule detection sequence, including: filling the atomic rule detection sequence according to a wireless interference detection intention to obtain an interference detection atomic rule detection workflow; and performing wireless interference detection according to the interference detection atomic rule detection workflow.

[0035] In the actual implementation process, the commands in the atomic rule detection sequence set are expanded and filled with detailed information: for each atomic rule command, according to the information input for this intention ([object] or [region], configuration data, engineering parameters or performance counters / KPI), the number of atomic rule detections and the specific information of the data entity in the rule are expanded or filled.

[0036] In an exemplary embodiment, after performing wireless interference detection according to an atomic rule detection sequence and outputting the wireless interference detection results, it also includes: evaluating the wireless interference detection results, and adjusting the entities and entity relationships of a pre-built interference detection knowledge graph according to the evaluation results, and the adjustment method includes at least one of the following: adding new entities and entity relationships, modifying entities and entity relationships, and deleting entities and entity relationships.

[0037] In the actual implementation process, according to the detection result data reported by the algorithm processing center, the indicator information of the result data is analyzed and evaluated, including but not limited to the accuracy of interference type, the accuracy of unknown interference segmentation, the accuracy of interference cell location, and other indicator data, and the above indicator data is visualized to optimize the interference detection knowledge graph. The intention closed-loop module calls the interference detection knowledge graph module to provide functions such as adjustment and optimization / arrangement of entities and entity relationships, adjust existing entities and entity relationships, add and delete new knowledge graph rule entities and entity relationships, and adjust, expand and arrange the interference detection knowledge graph.

[0038] Through the above steps, the four-tuple information for wireless interference detection is extracted according to the wireless interference detection intention and interference intention detection elements input by the user; the atomic rule detection sequence adapted to the pre-built wireless interference detection knowledge graph is determined based on the four-tuple information; the wireless interference detection is performed according to the atomic rule detection sequence, and the wireless interference detection result is output. Through the present invention, the problem that the user cannot flexibly adjust the workflow of wireless interference detection according to the wireless interference detection results and requirements in the related art is solved, and the effect of flexibly adjusting the wireless interference detection workflow according to the wireless interference detection results and requirements is achieved.

[0039] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0040] In this embodiment, a wireless interference detection device is also provided, which is used to implement the above embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0041] Figure 3 is a structural block diagram of a wireless interference detection device according to an embodiment of the present invention. Figure 3As shown, the detection device 30 includes: an interference detection intention engine module 310, which is used to extract four-tuple information for wireless interference detection based on the wireless interference detection intention and interference intention detection elements input by the user; an interference detection algorithm processing module 320, which is used to determine an atomic rule detection sequence adapted to a pre-built wireless interference detection knowledge graph based on the four-tuple information; an interference detection processing execution module 330, which is used to perform wireless interference detection according to the atomic rule detection sequence and output the wireless interference detection result.

[0042] In an exemplary embodiment, the interference detection algorithm processing module 320 may include: a determination unit, used to determine the rule sequence supported by the wireless interference detection knowledge graph based on the four-tuple information; an acquisition unit, used to obtain the atomic rule list in the entity rules of the wireless interference detection knowledge graph according to the entity relationship in the rule sequence; and a sorting unit, used to organize the atomic rule list into an atomic rule detection sequence according to a preset strategy order.

[0043] In an exemplary embodiment, the interference detection processing execution module 330 may include: a filling unit, used to fill the atomic rule detection sequence according to the wireless interference detection intention to obtain an interference detection atomic rule detection workflow; a detection unit, used to perform wireless interference detection according to the interference detection atomic rule detection workflow.

[0044] Figure 4 is a structural block diagram of a wireless interference detection device according to an embodiment of the present invention. Figure 4 As shown, the detection device 40 includes Figure 3 In addition to all the modules shown, it also includes: an interference detection intention management module 410, which is used to confirm the integrity and legality of the content in the four-tuple information, and perform interference intention conflict detection on the four-tuple information based on the interference intention detection elements, so as to adjust the four-tuple information with conflicting detection results of the interference intention conflict detection according to the preset conflict resolution strategy.

[0045] Figure 5 is a structural block diagram of a wireless interference detection device according to an embodiment of the present invention. Figure 5 As shown, the detection device 50 includes Figure 4 In addition to all the modules shown, it also includes: an interference detection intention closed-loop module 510, which is used to evaluate the wireless interference detection results and adjust the entities and entity relationships of the pre-built interference detection knowledge graph according to the evaluation results. The adjustment method includes at least one of the following: adding new entities and entity relationships, modifying entities and entity relationships, and deleting entities and entity relationships.

[0046] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0047] An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0048] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0049] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0050] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0051] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0052] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0053] In order to enable those skilled in the art to better understand the technical solution of the present invention, it is described below in conjunction with specific scenario embodiments.

[0054] Scenario Example 1

[0055] The present invention starts from the perspective of intent-driven and knowledge graph, and drives the command issuance, parameter setting and result output in interference detection in an intentional manner, and automatically assembles the detection workflow for detection based on the detection knowledge graph, and also provides the adjustment and arrangement functions of the knowledge graph for users to flexibly optimize and adjust. The detection intention can instruct the system what to do and the goals to be achieved without instructing the system how to do it. Users who adopt the technical method of this application only need to reflect / indicate the goal of interference detection through the detection intention, and the system can automatically assemble interference detection instructions to form a workflow for interference detection and output the detection results, which can greatly improve management efficiency, enhance the flexibility of wireless interference detection and the ease of use of the system for users.

[0056] In a scenario embodiment of the present invention, a wireless interference detection device is provided. Figure 6 is a schematic diagram of the architecture of a wireless interference detection device according to an embodiment of the present invention. Figure 6 As shown, the device includes the following modules: interference detection intention input and result output module, interference detection intention engine module, interference detection intention management module, interference detection algorithm processing center module, interference detection processing module, interference detection knowledge graph, interference detection intention closed-loop module. In this scenario embodiment, Figure 6 The interference detection intention engine module in the embodiment may correspond to the interference detection intention engine module 310 in the above embodiment. Figure 6 The interference detection algorithm processing center module in the embodiment may correspond to the interference detection algorithm processing module 320 in the above embodiment. Figure 6 The interference detection processing module in the embodiment may correspond to the interference detection processing execution module 330 in the above embodiment. Figure 6 The interference detection intention management module in the embodiment can be the same as the interference detection intention management module 410 in the above embodiment. Figure 6 The interference detection intention closed-loop module in the embodiment can be the same as the interference detection intention closed-loop module 510 in the above embodiment. In some specific embodiments, the functional division of the above modules can be different or repeated. The above is only an example, and no specific limitation is imposed on the module division and naming of the present invention. In the actual implementation process, as long as the wireless interference detection method of the present invention can be implemented, it can be used.

[0057] In this scenario embodiment, the interference detection intention input and result output module is used to provide the user with interference detection intention input, such as accepting the following input: "Start interference detection of all SDR network element cells in subnet 8 at 6 o'clock every day, and present the detection results in a map." At the same time, the module also outputs the detection results according to the intention, such as visually presenting the location information of the interfering cell in a "map" manner.

[0058] Interference detection intent engine module: This module structures the intent information in text form into the four-tuple required by the interference detection function ([object] or [region], [time], [action], [presentation]).

[0059] For example, the interference detection intent input by the user is: "Start interference detection of all SDR network element cells in subnet 8 at 6 o'clock every day, and present the detection results on a map"; then the quaternary group output by the intent engine module is ([{Subnetwork,6},{NE,,SDR},{Cells,all}}, {6 o'clock every day}, {start}, {GIS}). As shown in Table 1, it is a specific description of the content of the quaternary group information.

[0060] Table 1 Example of the contents of the four-tuple information

[0061]

[0062] Interference detection intention management module: This module manages the user's interference detection command intention, including intention legitimacy and intention conflict detection and processing. This module detects the legitimacy of the intention input and the conflict between the intention tasks (such as resource conflicts, overlapping detection objects between concurrent tasks, or detection time period task conflicts), and solves them automatically or interactively according to the specified solution strategy, and finally outputs the conflict-free intention task, and sends the task containing the user's intention information to the interference detection algorithm processing center.

[0063] Interference detection knowledge graph: This knowledge graph reflects the knowledge entities and entity relationships related to various interference detection methods supported by the system. It allows the interference detection algorithm processing center to automatically assemble interference detection workflows based on specific intent inputs combined with this knowledge graph, and provides adjustment / optimization and orchestration functions for the intent detection closed-loop module to call to adjust / expand and optimize the knowledge graph.

[0064] The entities and attributes of the interference detection knowledge graph include but are not limited to the following information: supported formats, detection rules (including expert rules, AI, and presentation rules), configuration data, engineering parameters, performance counters or KPIs, etc.; as shown in Table 2, the relevant attributes of the interference detection entity are displayed.

[0065] Table 2 Interference detection entity and attribute examples

[0066]

[0067]

[0068] Different knowledge graph entities for interference detection also have different relationships, as shown in Table 3:

[0069] Table 3. Examples of entity relationships in the knowledge graph

[0070]

[0071] For the interference detection knowledge graph, the system provides interference detection knowledge graph adjustment / optimization and arrangement functions. The system will have a default detection knowledge graph that defines the rule entities and entity relationships required for interference detection. At the same time, the system also provides interference detection knowledge graph rule adjustment and arrangement functions for the interference detection intention closed-loop module to call to adjust / optimize / arrange entities and entity relationships.

[0072] Interference detection algorithm processing center: This module obtains the intention Figure 4 After the tuple is formed, the [object] or [area] and [action] in the four-tuple will be combined with the interference knowledge graph to obtain the supported rule sequence from the "supported format" (identifiable by the object) and the "rules" used by the "supported format" of the interference detection knowledge graph. According to the entity relationship of these rules, the ItfDtAtomicRuleList atomic rule list in the entity rule is obtained. The algorithm processing center combines these ItfDtAtomicRuleLists into an atomic rule detection sequence according to the pre-planned strategy sequence (such as the use and dependency order defined by the entity relationship), and finally selects the ItfDtAtomicRuleList according to the intention. Figure 4 After the tuple expands the atomic rule detection sequence and fills in the specific information, it generates the interference detection atomic rule detection workflow and pushes it to the interference detection execution module for execution. For the detection result data, the processing center reports it to the intention closed loop module and the result output module.

[0073] Interference detection processing module: The interference detection processing module performs interference detection storage / merging and other processing according to the acquired rule sequence, executes in sequence according to the atomic rule detection sequence, and returns the execution result data to the interference detection algorithm processing center module.

[0074] Interference detection intention closed-loop module: This module monitors / collects the detection result data of the interference detection intention execution, and the result data is reported to this intention closed-loop module by the processing center module. The interference detection intention closed-loop module analyzes the detection result data to obtain indicator data including but not limited to the accuracy of interference type, the accuracy of unknown interference segmentation, the accuracy of interference cell location, etc., and evaluates the detection effect after executing the interference detection workflow. In addition, the intention closed-loop module calls the adjustment, expansion and arrangement functions of the knowledge graph module according to the evaluation results, and adjusts / expands and optimizes the entities and entity relationships defined in the interference detection knowledge graph to obtain better detection results.

[0075] Scenario Example 2

[0076] According to the wireless interference detection device provided in the first scenario embodiment, a wireless interference detection method is provided in the second scenario embodiment. Figure 7 is a flow chart of a wireless interference detection method according to an embodiment of the present invention, such as Figure 7 As shown, the wireless interference detection method includes the following steps:

[0077] Step S701, the user inputs the interference detection intention: the user inputs the intention according to the four elements required by the interference detection intention, and voice and natural language text input are supported.

[0078] Step S702, interference detection intent translation, that is, finding four elements from the intent information input by the user, namely [object] or [region], [action], [time], and [presentation].

[0079] Step S703, interference detection intention detection: that is, interference detection intention legitimacy detection and conflict detection. Legitimacy detection includes that the translated user intention must contain four elements; the objects contained in the four elements must exist, the detailed information must be legal, etc. Conflict detection includes resource conflicts, interference detection function scenario conflicts, interference detection intention execution time conflicts, etc. The legality and conflict detection modules will solve the problem automatically or interactively according to the specified solution strategy, and finally output a conflict-free intention task, and send the task containing the user intention information to the interference detection algorithm processing center.

[0080] Step S704, sending to the interference detection algorithm processing center: sending the translated interference detection intention to the algorithm processing center.

[0081] Step S705, the interference detection algorithm processing center generates an atomic rule detection sequence: the interference detection algorithm processing center combines the interference detection knowledge graph to generate an atomic rule detection sequence including data query, candidate cell initial screening, expert rules or AI detection, segmentation detection, etc. Here, the algorithm processing center adapts and concretizes the object of the knowledge graph corresponding to the rule according to the object / region and action of the command, and uses the knowledge graph to generate an atomic rule detection sequence.

[0082] Figure 8 : is a schematic diagram of the process of generating an atomic rule detection sequence according to a knowledge graph of an embodiment of the present invention, such as Figure 8 As shown, including:

[0083] Step S7051: The algorithm processing center obtains the Figure 4 Tuple, determine the object or action of the four-tuple, match it with the supported format of the knowledge graph in the system and the ItfDtOperator of the rules used by the supported format, and obtain the matching rule ItfDtRuleID.

[0084] Step S7052: the algorithm processing center obtains the rule corresponding to the ItfDtRuleID, finds other entities that are dependent or used in the entity relationship "dependence" or "use" of the knowledge graph, and obtains the ItfDtAtomicRuleList information.

[0085] Step S7053, the algorithm processing center processes the "dependency" or "use" information of the knowledge graph one by one: find the ItfDtAtomicRuleList that depends on non-empty and the "use" entity;

[0086] Step S7054: The algorithm processing center combines the obtained ItfDtAtomicRuleList atomic rule detection commands into an atomic rule detection sequence. Here, the knowledge graph entity relationship definition is traversed in dependency order to obtain the ItfDtAtomicRuleList non-empty atomic rule detection sequence, thus obtaining an atomic rule detection sequence set.

[0087] In step S7055, the algorithm processing center expands and fills in detailed information on the commands in the atomic rule detection sequence set through objects or "used" entities: for each atomic rule command, based on the information intended to be input this time ([object] or [region], configuration data, engineering parameters or performance counters / KPIs), the number of atomic rule detections and the specific information of the data entities in the rules are expanded or filled in.

[0088] For example, for the object {NodeB, 8} / action {start} in the intent input, the algorithm processing center will obtain all the cells under the NodeB8 (such as 1, 2, 3, a total of 3 cells), thereby expanding the number of atomic rule detections (the number of cells), and then fill in the configuration data objects {nodeb=8, cellid=1} / {nodeb=8, cellid=2} / {nodeb=8, cellid=3} of the expanded atomic rules to form a specific multiple atomic rule detection sequence.

[0089] Step S706, the algorithm processing center pushes the atomic rule detection sequence to the interference detection processing module: after the atomic rule detection sequence is generated by the algorithm processing center, the algorithm processing center pushes the atomic rule detection sequence to the interference detection processing module.

[0090] Step S707: the algorithm processing center collects the interference detection result data and reports it to the intention closed-loop module and the intention input and result output module.

[0091] Step S708, the interference detection intention input and result output module "presents" the result according to the intention visualization.

[0092] Step S709, the intention closed-loop module optimizes the knowledge graph: Based on the result data reported by the algorithm processing center, the intention closed-loop module analyzes and evaluates the indicator information of the result data, including but not limited to the accuracy of the interference type, the accuracy of the unknown interference segmentation, the accuracy of the interference cell location, and other indicator data, etc. It visualizes these indicator data and optimizes the interference detection knowledge graph. The intention closed-loop module calls the interference detection knowledge graph module to provide functions such as adjustment and optimization / arrangement of entities and entity relationships, adjusts existing entities and entity relationships, adds and deletes new knowledge graph rule entities and entity relationships, and adjusts, expands and arranges the interference detection knowledge graph.

[0093] Specifically, the interference detection knowledge graph provides the following methods or approaches (including but not limited to the following steps) to adjust, expand and arrange the knowledge graph:

[0094] (1) The system provides a knowledge graph adjustment and arrangement interface;

[0095] (2) The system presents the rule entities or data entities contained in the knowledge graph and the relationships between them in various friendly forms (such as graphics or lists, etc.), and provides an operation interface to add / delete / modify entities and adjust entity relationships; such as but not limited to the following operations: presenting operation buttons or menus such as "add", "modify", and "delete" of entities in lists or graphics, and providing the addition, deletion, and modification of entities. Presenting operation buttons or menus such as "add", "modify", and "delete" of entity relationships in lists or graphics, and providing the addition, deletion, and modification adjustment and optimization of entity relationships. Provide easy-to-use methods such as dragging and dropping connections between entities to directly add "add", "modify", and "delete" to adjust, optimize, and arrange the relationships between entities.

[0096] For example, for a preliminary screening rule in the atlas, the interference detection of the Software Defined Radio Time Division Duplex (SDR TDD) standard is only for the cell where "the value of the carrier average noise interference (counter 373414597) must be less than -120". The intention closed-loop module considers that the accuracy is insufficient based on the evaluated "cell interference location accuracy" indicator value and needs to expand the range of collected cells. It decides to adjust the value of the "carrier average noise interference (counter 373414597) value cannot be greater than -110" rule. At this time, the "SDR TDD standard preliminary screening" rule entity can be modified, and the value of its entity attribute ItfDtAtomicRuleList can be adjusted to "373414597<=-110".

[0097] For example, for the rule dependencies in the graph, the unknown segmentation rule "SDR-Unknown Segmentation Rule" depends on the result of the detection rule "Detection AI-CNN Algorithm". The intention closed-loop module believes that the detection rule "Detection AI-Clustering Algorithm" must be added between the "SDR-Unknown Segmentation Rule" and the "Detection AI-CNN Algorithm" based on the evaluated "Interference Type Accuracy" indicator value. At this time, you can drag the relationship lines between entities such as "SDR-Unknown Segmentation Rule", "Detection AI-Clustering Algorithm" and "Detection AI-CNN Algorithm" in the graph (entity relationship "dependency") to adjust (arrange) the dependency between the three. In this way, in the subsequent generation of interference detection workflows, the "Detection AI-CNN Algorithm" rule will be executed after the execution of the "Detection AI-Clustering Algorithm" rule is completed, and the "SDR-Unknown Segmentation Rule" will be executed after the "Detection AI-Clustering Algorithm" is completed, so as to optimize the interference detection workflow and improve the accuracy of interference types.

[0098] The wireless interference detection method provided by the present invention can allow users to input friendly natural language when performing interference detection, that is, they only need to provide what they want to do and the goals they want to achieve, and the interference detection steps and methods can be automatically generated and effective, and continuously adjusted and optimized according to the evaluation results during the process. Specific application examples are introduced as follows:

[0099] In the specific implementation process, the user needs to perform interference detection on the cell under site 6, and the detection results are presented in the form of GIS map. The operation and maintenance user uses this system to input: start interference detection on site 6 at 6 o'clock in the morning, and the results are presented in GIS. This system recognizes the elements {NodeB:6; action: start; time: 6:00 on April 11, 2023; presentation: GIS} in the user's intention through the intention translation module. The interference detection algorithm processing center module automatically generates a series of detection rule series based on the four-element information and combined with the knowledge graph, such as querying the cell performance data, configuration data and engineering parameter data of NodeB 6, performing a preliminary screening of daily performance data to obtain candidate interference cells under the site, performing CNN algorithm detection on the performance data of the interference cell, and performing unknown cell subdivision on the cells that the convolutional neural network (CNN) algorithm fails to identify, and a series of atomic rules are pushed to the interference detection processing module for processing. After the processing module obtains the atomic rule detection sequence, it executes and outputs the final detection results (including GIS data) in sequence, and pushes the result data to the processing center, and finally transmits it to the result output module and the interference detection intention closed-loop module. The interference detection intention closed-loop collects data information for evaluation, provides a visual review of the evaluation results, and adjusts the interference detection knowledge graph.

[0100] In the specific implementation process, the user needs to perform interference detection on a fixed area, and the detection results are presented in the form of GIS maps. The operation and maintenance user uses this system to input: start the interference detection of the fixed area site at 6 o'clock in the morning, and the results are presented in GIS. This system recognizes the elements {region: a fixed area; action: start; time: 6:00 on April 11, 2023; presentation: GIS} in the user's intention through the intention translation module. The system first searches for the site NodeB:6 in the fixed area to replace the region to form a new four elements. Then, the interference detection algorithm processing center module automatically generates a series of detection rule series based on the four-element information, combined with the knowledge graph entity and entity relationship definition, such as generating the cell performance data, configuration data and engineering parameter data of querying NodeB 6, performing preliminary screening of performance data to obtain candidate interference cells under the site, performing CNN algorithm detection on the performance data of the interference cell, and performing unknown cell subdivision on the cells that the CNN algorithm failed to identify, and a series of atomic rule detection sequences, and pushes this sequence to the interference detection processing module for processing. After the processing module obtains the atomic rule detection sequence, it executes and outputs the final detection results (including Geographic Information System (GIS) data) in sequence, and pushes the detection result information to the processing center, and finally transmits it to the result output module and the interference detection intention closed loop module. The interference detection intention closed loop collects indicator information evaluation and provides a visual review of the evaluation effect for adjusting the interference detection knowledge graph.

[0101] In summary, the wireless interference detection method and device provided by the present invention use intent-driven and knowledge graph methods to achieve interference detection. It mainly includes:

[0102] 1. Use the intention method. In interference detection, users can input the object information to be managed and the specified presentation method. The system will automatically complete the corresponding interference detection and output the results in the specified way. Users can express interference detection intentions in natural language, including natural language text and voice. This core invention can support various interference detection intention inputs, greatly simplify input, and improve user usability.

[0103] 2. Automatically generate detection workflows based on the interference detection knowledge graph and intention input. The interference detection algorithm processing center will automatically generate the detection step workflow of interference detection based on the interference detection knowledge graph and the information input by intention (such as "interference detection of all sites in Shanghai Meile City at 6 o'clock in the morning, and the detection results are output in GIS", and "interference detection of all subnets at 6 o'clock in the morning, and the results are output in GIS and Table", etc.), and send it to the interference detection processing module for processing, and finally output the results as required; the interference detection knowledge graph in this method also provides adjustment and optimization of entities or entity relationships and orchestration functions for further adjustment and expansion of the knowledge graph, improvement of detection methods, and better detection results. This core invention point can meet the system's support for interference detection requirements, reduce the difficulty of using the system, enhance the flexibility of adjusting detection methods, and greatly improve network operation efficiency.

[0104] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A wireless interference detection method, characterized in that: include: Extracting four-tuple information for wireless interference detection according to the wireless interference detection intention and interference intention detection elements input by the user; Determine an atomic rule detection sequence adapted to a pre-constructed wireless interference detection knowledge graph based on the four-tuple information; Wireless interference detection is performed according to the atomic rule detection sequence, and a wireless interference detection result is output.

2. The method according to claim 1, characterized in that After extracting the quadruple information for performing wireless interference detection according to the wireless interference detection intention and the interference intention detection element input by the user, the method further includes: Confirming the integrity and legality of the contents of the four-tuple information; Based on the interference intention detection element, interference intention conflict detection is performed on the four-tuple information, and according to a preset conflict resolution strategy, the four-tuple information with a detection result of the interference intention conflict detection as a conflict is adjusted.

3. The method according to claim 2, characterized in that The interference intention conflict detection includes at least one of the following: Whether wireless interference detection resources conflict; Whether the wireless interference detection function scenarios conflict; Whether the execution time of the wireless interference detection intent conflicts.

4. The method according to claim 1, characterized in that: The four-tuple information includes: The object or area information of wireless interference detection, the time information of wireless interference detection, the action information of wireless interference detection, and the presentation method information of wireless interference detection results.

5. The method according to claim 1, characterized in that The pre-built wireless interference detection knowledge graph includes at least data entities and entity relationships between the data entities, wherein: The data entity includes at least one of the following: a supported standard for wireless interference detection, a detection rule for wireless interference detection, configuration data of an object for wireless interference detection, an engineering parameter of an object for wireless interference detection, a performance counter or a key performance indicator; The entity relationship includes at least one of the following: a containment relationship, a usage relationship, and a dependency relationship.

6. The method according to claim 1, characterized in that Based on the four-tuple information, an atomic rule detection sequence adapted to a pre-built wireless interference detection knowledge graph is determined, including: Determine a rule sequence supported by the wireless interference detection knowledge graph based on the four-tuple information; According to the entity relationship in the rule sequence, obtain a list of atomic rules in the entity rules of the wireless interference detection knowledge graph; The atomic rule list is organized into the atomic rule detection sequence according to a preset strategy order.

7. The method according to claim 1, characterized in that Performing wireless interference detection according to the atomic rule detection sequence includes: Filling the atomic rule detection sequence according to the wireless interference detection intention to obtain an interference detection atomic rule detection workflow; The wireless interference detection is performed according to the interference detection atomic rule detection workflow.

8. The method according to claim 1, characterized in that After performing wireless interference detection according to the atomic rule detection sequence and outputting the wireless interference detection result, the method further includes: The wireless interference detection results are evaluated, and the entities and entity relationships of the pre-built interference detection knowledge graph are adjusted according to the evaluation results, wherein the adjustment method includes at least one of the following: adding the entities and entity relationships, modifying the entities and entity relationships, and deleting the entities and entity relationships.

9. A wireless interference detection device, characterized in that: include: An interference detection intention engine module, used to extract four-tuple information for wireless interference detection based on the wireless interference detection intention and interference intention detection elements input by the user; An interference detection algorithm processing module, used to determine an atomic rule detection sequence adapted to a pre-built wireless interference detection knowledge graph based on the four-tuple information; The interference detection processing execution module is used to perform wireless interference detection according to the atomic rule detection sequence and output the wireless interference detection result.

10. The device according to claim 9, characterized in that Also includes: The interference detection intention management module is used to confirm the integrity and legality of the content in the four-tuple information, and perform interference intention conflict detection on the four-tuple information based on the interference intention detection elements, so as to adjust the four-tuple information whose detection result of the interference intention conflict detection is a conflict according to a preset conflict resolution strategy.

11. The device according to claim 9, characterized in that Also includes: The interference detection intention closed-loop module is used to evaluate the wireless interference detection results and adjust the entities and entity relationships of the pre-built interference detection knowledge graph according to the evaluation results, and the adjustment method includes at least one of the following: adding the entities and entity relationships, modifying the entities and entity relationships, and deleting the entities and entity relationships.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program implements the method described in any one of claims 1 to 8 when executed by a processor.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method described in any one of claims 1 to 8 is implemented.

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