Cell state sensing method and device and related equipment

Abstract

The invention provides a cell state sensing method and device and related equipment, and relates to the technical field of wireless communication, and the method comprises the steps: receiving a state sensing evaluation request for a target cell; according to the state perception evaluation request, generating a state perception evaluation result of the target cell through a pre-constructed cell state label system model and a cell state intelligent perception unit; wherein the data of the cell state label system model is the data after the state perception evaluation request of the target cell is preprocessed, and the input data of the cell state intelligent perception unit is the output data of the cell state label system model; and outputting a state perception evaluation result of the target cell. According to the invention, the accuracy of cell state sensing evaluation can be effectively improved.

Description

Background Art

[0002] The fifth generation mobile communication technology (5G), the evolution and enhanced version of 5G network (5G-Advanced, 5G-A) and the network functional architecture for the evolution of future mobile communication networks, such as NWDAF, MDAF and other network intelligent functions, all put forward higher requirements on the perception and evaluation of the status of mobile wireless network base stations. The wireless network, core network and management domain need to comprehensively perceive and evaluate the status of the cell. The current cell status perception method generally uses expert experience such as expert scoring for evaluation.

[0003] The existing cell status perception and evaluation methods have the following problems: 1. The dimensional data is insufficient and the weight standard is too subjective; 2. The traditional evaluation process is not integrated with the structure of existing and future network evolution; 3. It cannot be automatically updated according to the actual network perception and quality label evaluation needs.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The present disclosure provides a cell state perception method, apparatus and related equipment, which at least to a certain extent overcome the problem of low accuracy of cell state perception evaluation methods in related technologies.

[0006] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0007] According to one aspect of the present disclosure, a cell state perception method is provided, including: receiving a state perception evaluation request for a target cell; generating a state perception evaluation result of the target cell according to the state perception evaluation request through a pre-constructed cell state label system model and a cell state intelligent perception unit; wherein the input data of the cell state label system model is the pre-processed data of the state perception evaluation request of the target cell, and the input data of the cell state intelligent perception unit is the output data of the cell state label system model; and outputting the state perception evaluation result of the target cell.

[0008] In some exemplary embodiments of the present disclosure, based on the aforementioned scheme, according to the state perception assessment request, the state perception assessment result of the target cell is generated through a pre-built cell state label system model and a cell state intelligent perception unit, including: inputting the pre-processed data of the state perception assessment request of the target cell into the pre-built cell state label system model, and outputting the label-associated indicator data corresponding to the target cell; inputting the label-associated indicator data corresponding to the target cell into the cell state intelligent perception unit, and generating the state perception assessment result of the target cell.

[0009] In some exemplary embodiments of the present disclosure, based on the aforementioned scheme, according to the state perception assessment request, the state perception assessment result of the target cell is generated through a pre-built cell state label system model and a cell state intelligent perception unit, including: parsing the state perception assessment request to determine each level of data in the state perception assessment request; matching each level of data in the parsed state perception assessment request with a pre-set expert knowledge base to obtain a matching result; generating a corresponding level label for each level of data according to the matching result; and determining the label-associated indicator data corresponding to the target cell according to the level label corresponding to each level of data.

[0010] In some exemplary embodiments of the present disclosure, based on the aforementioned scheme, according to the state perception assessment request, the state perception assessment result of the target cell is generated through a pre-constructed cell state label system model and a cell state intelligent perception unit, including: the cell state intelligent perception unit scores and evaluates the label-related indicator data corresponding to the target cell output by the cell state label system model, and generates a state perception assessment result of the target cell.

[0011] In some exemplary embodiments of the present disclosure, based on the aforementioned scheme, the hierarchical labels include: dimension labels, indicator category labels, cell labels, sub-cell labels and sub-cell label-associated indicators, and the cell status intelligent perception unit scores and evaluates the label-associated indicator data corresponding to the target cell output by the cell status label system model, including: determining the indicator value corresponding to the dimension label of the target cell; determining the indicator value corresponding to the indicator category label of the target cell; determining the indicator value corresponding to the cell label of the target cell; determining the indicator value corresponding to the sub-cell label of the target cell; determining the indicator value corresponding to the sub-cell label-associated indicator of the target cell.

[0012] In some exemplary embodiments of the present disclosure, based on the aforementioned scheme, before generating the state perception evaluation result of the target cell according to the state perception evaluation request through a pre-constructed cell state label system model and a cell state intelligent perception unit, the method also includes: constructing the cell state label system model based on pre-set network indicators, label evaluation criteria and expert knowledge; acquiring multi-dimensional cell data as training data; and training the algorithm model library in the model training unit through the training data to obtain the cell state intelligent perception unit.

[0013] In some exemplary embodiments of the present disclosure, based on the aforementioned scheme, before generating the state perception evaluation result of the target cell according to the state perception evaluation request through a pre-built cell state label system model and a cell state intelligent perception unit, the method also includes: acquiring multi-dimensional cell data to form a data storage unit; the data storage unit generates data corresponding to the sub-cell label associated indicators according to the indicator algorithm requirements and statistical period in the cell state label system model, and loads it into the cell state label system model.

[0014] According to another aspect of the present disclosure, a cell state perception device is also provided, including: a request receiving module, used to receive a state perception assessment request for a target cell; a perception result generating module, used to generate a state perception assessment result of the target cell according to the state perception assessment request, through a pre-built cell state label system model and a cell state intelligent perception unit; wherein the input data of the cell state label system model is the pre-processed data of the state perception assessment request of the target cell, and the input data of the cell state intelligent perception unit is the output data of the cell state label system model; a perception result output module, used to output the state perception assessment result of the target cell.

[0015] According to another aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute any one of the above-mentioned cell status perception methods by executing the executable instructions.

[0016] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, any one of the above-mentioned cell status perception methods is implemented.

[0017] According to another aspect of the present disclosure, a computer program product is also provided, including: a computer program or instructions, which implement any of the above-mentioned cell status perception methods when executed by a processor.

[0018] A cell status perception method, apparatus and related equipment provided in the embodiments of the present disclosure can more objectively complete the comprehensive perception evaluation of the target cell status through the cooperation of a pre-built cell status label system model and a cell status intelligent perception unit, so as to meet the comprehensive and coordinated subjective and objective comprehensive perception evaluation requirements of the wireless network, core network and management domain under the current and future mobile communication network functional architecture.

[0019] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0021] Figure 1 A schematic diagram showing an exemplary application system architecture of a cell state perception method in an embodiment of the present disclosure;

[0022] Figure 2 A schematic diagram of a cell state perception method in an embodiment of the present disclosure is shown;

[0023] Figure 3 A schematic diagram of a method for determining a state perception evaluation result of a cell in an embodiment of the present disclosure is shown;

[0024] Figure 4 A schematic diagram of a specific process of a cell state sensing method in an embodiment of the present disclosure is shown;

[0025] Figure 5 A schematic diagram of a system flow of a method for sensing a cell state in an embodiment of the present disclosure is shown;

[0026] Figure 6 A schematic diagram of a cell state sensing device in an embodiment of the present disclosure is shown;

[0027] Figure 7 A schematic diagram of an electronic device applying a cell status perception method in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0028] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0029] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0030] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0031] For ease of understanding, several terms involved in the present disclosure are explained below:

[0032] The evolved and enhanced version of the 5G network: usually referred to as 5G-A or 5G-Advanced, is a subsequent development stage of the fifth generation mobile communication technology (5G). 5G-A aims to further expand the capabilities of 5G to support a wider range of application scenarios and service requirements.

[0033] Network Data Analytics Function (NWDAF): It is an important component in the core network of 5G, 5G-A and future networks. It is responsible for collecting and analyzing data in the network to provide intelligent decision support for network operation and management.

[0034] Management Data Analytics Function (MDAF): In 5G, 5G-A and future network architectures, it is a functional entity responsible for collecting, processing and analyzing data within the management domain to support network management and operation and maintenance decisions.

[0035] like Figure 1As shown, the system architecture includes a terminal device 101, a network 102 and a network-side device 103; wherein the network 102 is used to provide a medium for a communication link between the terminal device 101 and the network-side device 103, and can be a wired network or a wireless network.

[0036] Optionally, the wireless network or wired network described above uses standard communication technology and / or protocol. The network is usually the Internet, but it can also be any network, including but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a dedicated network or any combination of a virtual private network). In some embodiments, the data exchanged through the network is represented by technologies and / or formats including Hyper Text Mark-up Language (HTML), Extensible Markup Language (XML), etc. In addition, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), Internet Protocol Security (IPSec) can also be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can also be used to replace or supplement the above data communication technologies.

[0037] Optionally, the terminal device in the embodiment of the present disclosure may also be referred to as UE (User Equipment). In a specific implementation, the terminal device may be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), a personal digital assistant (Personal Digital Assistant, PDA), a mobile Internet device (Mobile Internet Device, MID), a wearable device (Wearable Device) or a vehicle-mounted device, etc. It should be noted that the specific type of the terminal device is not limited in the embodiment of the present invention.

[0038] The network side device may be a base station, a relay or an access point, etc. The base station may be a base station of 5G or later versions (e.g., 5G NR NB), or a base station in other communication systems (e.g., Enhanced Node B base station, i.e., eNB base station). It should be noted that the specific type of the network side device is not limited in the embodiments of the present disclosure.

[0039] Those skilled in the art will know that Figure 1 The number of terminals, networks, and network-side devices in the example is only illustrative, and any number of terminals, networks, and network-side devices may be provided according to actual needs. The embodiments of the present disclosure are not limited to this.

[0040] Under the above system architecture, a cell status perception method is provided in an embodiment of the present disclosure, and the method can be executed by any electronic device with computing and processing capabilities.

[0041] In some embodiments, the cell status perception method provided in the embodiments of the present disclosure may be executed by a terminal device of the above-mentioned system architecture; in other embodiments, the cell status perception method provided in the embodiments of the present disclosure may be executed by a server in the above-mentioned system architecture; in other embodiments, the cell status perception method provided in the embodiments of the present disclosure may be implemented by the terminal device and the server in the above-mentioned system architecture through interaction.

[0042] First of all, in response to the above-mentioned problems, a method for zone status perception is provided in the embodiments of the present disclosure, which can be applied but not limited to, and can be applied to any system for perceiving and evaluating the status of base station cells in a mobile wireless network. The status perception evaluation of a specific cell is completed through the cooperation of a pre-constructed cell status label system model and a cell status intelligent perception unit, thereby improving the objectivity of the cell status perception evaluation, meeting the needs of the wireless network, core network and management domain for comprehensive, coordinated subjective and objective comprehensive perception evaluation of the cell status under the current and future mobile communication network functional architecture, and adopting a capability production and consumption model, which has good scalability.

[0043] Figure 2 A schematic diagram of a cell state sensing method in an embodiment of the present disclosure is shown, and the method includes the following steps:

[0044] S202: Receive a state awareness assessment request for a target cell.

[0045] It should be noted that the target cell in the embodiment of the present disclosure can be a cell selected in any area. In more detail, a cell, also known as a cellular cell, refers to an area covered by a base station or a part of a base station in a cellular mobile communication system, within which a mobile station can reliably communicate with the base station through a wireless channel; cell status perception refers to real-time monitoring and analysis of various operating states and performance indicators of the cell in a wireless communication network to ensure efficient operation and high-quality services of the network. Specifically, cell status perception can help network operators discover and solve problems in a timely manner, optimize network performance, and enhance user experience; a target cell status perception evaluation request refers to a request for real-time monitoring and evaluation of the current operating state and performance indicators of a specific cell (that is, the target cell) in a wireless communication network.

[0046] S204, based on the state perception assessment request, generates a state perception assessment result of the target cell through a pre-constructed cell state label system model and a cell state intelligent perception unit; wherein the data of the cell state label system model is the data pre-processed from the state perception assessment request of the target cell, and the input data of the cell state intelligent perception unit is the output data of the cell state label system model.

[0047] It should be noted that the cell status label system model in the embodiment of the present disclosure is constructed based on the network indicator system, label evaluation standards, and expert knowledge, and the cell status label system model in the embodiment of the present disclosure includes a multi-level cell portrait label system, which is divided into five levels according to the progressive relationship, namely dimension label, indicator category label, cell label, sub-cell label, and sub-cell label associated indicator.

[0048] In more detail, the dimensional labels in the embodiments of the present disclosure include major categories such as operation, resources, market, and service. For example, the operation dimension can be divided into: resource load class, call access class, call holding class, mobility management class, data traffic class, service integrity class, equipment availability, air interface performance class, cell energy consumption class, coverage class, quality class, service class, etc.; the resource dimension can be divided into: cell resource class, cell status class, cell configuration class; the market can be divided into: cell user class, cell market size class, cell value class, etc.; the service can be divided into: cell complaint class, etc.

[0049] The indicator category labels in the disclosed embodiments are divided in combination with the network indicator system and the actual cell label attribution; and the cell labels are classified and logically merged according to the similar types and categories of the sub-cell labels. For example, when the indicator category label is a resource load class label, the cell label includes the cell load; when the indicator category label is a call retention class label, the cell label includes connection reconstruction and disconnection, etc.; when the indicator category label is a mobility management class label, the cell label includes switching, etc.

[0050] In some embodiments, the sub-cell label in the embodiment of the present disclosure may be a specific sub-cell label definition type or an associated indicator. For example, the specific sub-cell label definition type is a high-load / super-busy cell, a 5G low-traffic load cell, a zero-traffic cell, etc., and the associated indicator may be an average number of radio resource control (RRC) connected users, a maximum number of RRC connected users, an average user uplink throughput, etc.; in more detail, the sub-cell label associated indicator in the embodiment of the present disclosure includes: an indicator associated with the sub-cell label definition algorithm, and for a specific sub-cell label definition type with special algorithm requirements, there may be multiple sub-label associated indicators. For example, for a high-load cell, the sub-cell label associated indicators include an average uplink physical resource block (PRB) occupancy rate of the cell, an average downlink PRB occupancy rate of the cell, etc.

[0051] In some embodiments, the cell status intelligent perception unit in the embodiments of the present disclosure can implement a comprehensive evaluation of the cell status of a certain cell through the output data of the cell status label system model.

[0052] S206: Output the state perception evaluation result of the target cell.

[0053] It should be noted that the state perception evaluation results of the target cell in the disclosed embodiments generally include detailed information in multiple aspects, which reflect the current operating status and performance indicators of the target cell. For example, the state perception evaluation results of the target cell in the disclosed embodiments may include: signal quality, load conditions, service quality, switching and connection performance of the target cell, etc. Through these detailed evaluation results, network operators can fully understand the operating status and service quality of the target cell, and take corresponding measures to optimize and manage it.

[0054] The cell state perception method provided in the embodiment of the present disclosure first receives a state perception evaluation request for a target cell; then, based on the state perception evaluation request, generates a state perception evaluation result of the target cell through a pre-constructed cell state label system model and a cell state intelligent perception unit; wherein, the input data of the cell state label system model is the data pre-processed from the state perception evaluation request of the target cell, and the input data of the cell state intelligent perception unit is the output data of the cell state label system model; finally, outputs the state perception evaluation result of the target cell. Compared with the problem of low accuracy of the cell state perception evaluation method in the related art, the embodiment of the present disclosure completes the comprehensive perception evaluation of the state of the target cell more objectively through the cooperation of the pre-constructed cell state label system model and the cell state intelligent perception unit, thereby effectively improving the accuracy of the cell state perception evaluation.

[0055] In some embodiments, the disclosed embodiments generate a state perception assessment result of a target cell based on a state perception assessment request through a pre-built cell state label system model and a cell state intelligent perception unit, including: inputting the pre-processed data of the state perception assessment request of the target cell into the pre-built cell state label system model, and outputting the label-associated indicator data corresponding to the target cell; inputting the label-associated indicator data corresponding to the target cell into the cell state intelligent perception unit, and generating the state perception assessment result of the target cell. Specifically, the embodiment of the present disclosure first inputs the pre-processed data of the status perception assessment request into the model, which can not only remove noise and outliers and ensure the accuracy and reliability of the input data, but also the cell status label system model pre-constructed in the embodiment of the present disclosure has been trained and optimized, and can accurately identify and classify various states of the cell, thereby improving the accuracy of the assessment results. In more detail, the embodiment of the present disclosure can automatically process the status perception assessment request of the target cell through the pre-constructed model and intelligent perception unit, without the need for human intervention, thereby greatly improving the processing efficiency. Moreover, the cell status label system model in the embodiment of the present disclosure comprehensively considers indicators of multiple dimensions, can provide comprehensive assessment results, and can achieve automated, efficient, accurate and comprehensive assessment, thereby helping network operators to promptly discover and solve problems, optimize network performance, and enhance user experience.

[0056] In some embodiments, Figure 3 As shown, the embodiment of the present disclosure generates a state perception evaluation result of a target cell according to a state perception evaluation request through a pre-built cell state label system model and a cell state intelligent perception unit, including:

[0057] S302: Parse the state awareness assessment request to determine data at each level in the state awareness assessment request.

[0058] S304, matching each level of data in the parsed state awareness assessment request with a preset expert knowledge base to obtain a matching result.

[0059] S306: Generate a corresponding hierarchical label for each hierarchical data according to the matching result.

[0060] S308: Determine the label-related indicator data corresponding to the target cell according to the layer label corresponding to each layer data.

[0061] In some embodiments, the disclosed embodiments can structure complex data by labeling each level of data, making it clearly layered, easy to understand and process. The labeled hierarchical data can be arranged in a logical order to make the information more organized and facilitate subsequent analysis. In addition, the corresponding hierarchical labels can be used to quickly locate the data fragments corresponding to the target cell, perform accurate searching and filtering, and improve retrieval efficiency.

[0062] In some embodiments, the disclosed embodiment generates a state perception evaluation result of a target cell according to a state perception evaluation request through a pre-built cell state label system model and a cell state intelligent perception unit, including: the cell state intelligent perception unit scores and evaluates the label-related index data corresponding to the target cell output by the cell state label system model to generate a state perception evaluation result of the target cell. Specifically, the cell state intelligent perception unit in the disclosed embodiment can realize a comprehensive evaluation of the cell state of a certain cell, including using a scoring algorithm to obtain the jth index value of the i-th cell within the evaluation range: P ij =f1(x ij ). This scoring algorithm can be various types of scoring algorithms. For a certain type of sub-cell label with a determination threshold, the cell that reaches the determination threshold is directly assigned a minimum score value C1 or a maximum score value C2 (assuming the scoring interval is [C1, C2]) according to the positive type or negative type of the sub-cell label. The remaining evaluation cells score the sub-cell label-related indicators (corresponding evaluation indicators) based on the scoring algorithm.

[0063] In some embodiments, for a certain scoring algorithm, the embodiments of the present disclosure may adopt a ranking scoring method based on cell ranking. For example, the cells may be ranked according to the indicators and divided into four levels: A, B, C, and D. Assuming that there are four levels, the ranking of cells in level A is ∈ [1, N top0.2 ], the cell ranking of level B∈[N top0.2 ,N top0.6 ], the cell ranking of level C∈[N top0.6 ,N top0.8 ], the D-level cell ranking ∈[N top0.8 ,N top1 ], where N top0.2 is the number of top 20% cells, N top0.6 is the number of top 60% cells, N top0.8 is the number of top 80% cells, N top1 The number of cells that are in the top 100% is shown in Table 1. The basic scores corresponding to the four levels can be a, b, c, and d. The scores are sorted in the segments according to the sorting of the segments of each level. Finally, the two scores are accumulated. For example, the score of level B is The cell ranking is M. If the indicator is positive, the score is directly output; if it is negative, (C2-M) is output.

[0064] In some embodiments, the cell state intelligent perception unit in the embodiment of the present disclosure implements a comprehensive evaluation of the cell state of a certain cell, and also includes using a weight algorithm to calculate the weight of the sub-cell label associated index (corresponding to the evaluation index). Assuming that a certain index in the sub-cell label associated index (which may contain multiple indexes) is the jth index, its weight is W j =f2(x ij ), the weight algorithm can be any weight algorithm, for example, entropy method, principal component analysis (PCA), objective weighting method (Criteria Importance Through Intercriteria Correlation, CRITIC), etc., or artificial intelligence (AI) algorithms such as GBM (Gradient Boosting Machines), XGBoost, LightGBM, and random forest.

[0065] In some embodiments, the embodiments of the present disclosure take the entropy method as an example: if a sub-cell label association indicator is a positive indicator, the following normalization is performed using formula (1):

[0066]

[0067] Among them, x ij ′ is the normalized value of the associated index of the jth index value of the i-th sub-cell; ij is the associated index value of the jth index value of the i-th sub-cell; 1j is the associated index value of the jth index value of the first sub-cell; x nj is the associated index value of the j-th index value of the n-th sub-cell, i is a certain cell; j is a certain index.

[0068] If the sub-cell label association indicator is a positive indicator, it is normalized as follows using formula (2):

[0069]

[0070] Where n is the total number of cells, and the i-th cell x under the j-th indicator is calculated by formula (3): ij The proportion of this indicator ij :

[0071]

[0072] The entropy value e of the jth indicator is calculated by formula (4): j :

[0073]

[0074] in, Meet e j >0.

[0075] The information entropy redundancy d is calculated by formula (5): j :

[0076] d j =1-e j (5)

[0077] The weight w of each indicator is calculated by formula (6): j :

[0078]

[0079] Among them, m is the total number of indicators.

[0080] The evaluation value of the kth sub-cell label of the ith cell is the weighted average of the index values ​​V ik :

[0081]

[0082] Among them, C k is the number of indicators associated with sub-cell label k; N is the number of sub-cell labels associated with a certain indicator.

[0083] In some embodiments, the cell label evaluation value in the embodiment of the present disclosure corresponds to the sub-cell label evaluation value V ik The cumulative summation is performed, and when calculating the total evaluation value, the interval mapping algorithm can be used to keep the total evaluation value intervals of all cells consistent.

[0084] In some embodiments, the hierarchical labels in the embodiments of the present disclosure include: dimension labels, indicator category labels, cell labels, sub-cell labels, and sub-cell label-related indicators. The cell status intelligent perception unit scores and evaluates the label-related indicator data corresponding to the target cell output by the cell status label system model, including: determining the indicator value corresponding to the dimension label of the target cell; determining the indicator value corresponding to the indicator category label of the target cell; determining the indicator value corresponding to the cell label of the target cell; determining the indicator value corresponding to the sub-cell label of the target cell; determining the indicator value corresponding to the sub-cell label of the target cell. Specifically, the embodiments of the present disclosure calculate the indicator category labels, dimension labels, and total evaluation values ​​step by step through the above formulas (1) to (7), or add a specific bias to the label value calculation process of a certain layer according to the evaluation needs. By scoring the data at each level and finally weighting and summing them, it is possible to achieve comprehensive evaluation, weight allocation, objectivity and transparency, decision support, priority sorting, dynamic adjustment, data-driven and user-friendly technical effects.

[0085] In some embodiments, before the state perception evaluation result of the target cell is generated according to the state perception evaluation request through the pre-constructed cell state label system model and the cell state intelligent perception unit, the cell state perception method in the embodiment of the present disclosure also includes: constructing a cell state label system model based on pre-set network indicators, label evaluation standards and expert knowledge; obtaining multi-dimensional cell data as training data; training the algorithm model library in the model training unit through the training data to obtain the cell state intelligent perception unit. Specifically, the algorithm model library in the embodiment of the present disclosure includes a scoring algorithm model library and a weight algorithm model library for comprehensive evaluation of the cell state, and the AI ​​algorithm model can be trained through the data storage unit data and loaded into the cell state intelligent perception unit.

[0086] In some embodiments, before the state perception evaluation result of the target cell is generated according to the state perception evaluation request through the pre-built cell state label system model and the cell state intelligent perception unit, the cell state perception method in the embodiment of the present disclosure also includes: acquiring multi-dimensional cell data to form a data storage unit; the data storage unit generates data corresponding to the sub-cell label-related indicators according to the indicator algorithm requirements and statistical period in the cell state label system model, and loads it into the cell state label system model. Specifically, the data storage unit in the embodiment of the present disclosure can complete the collection of multi-dimensional data such as evaluation operation, resources, market, and service to form a data storage unit. The data storage unit generates sub-cell label-related indicator data according to the indicator algorithm requirements and statistical period of the label system model, and loads it into the cell state label system model; and at the same time, it serves as the algorithm model library in the training data training model training unit.

[0087] In some embodiments, the system for implementing the cell status perception method in the embodiments of the present disclosure includes: a network knowledge unit, an analysis logic unit, a data storage unit, and a model training unit, wherein the analysis logic unit includes a cell status label system model and a cell status intelligent perception unit, and the analysis logic unit receives an intelligent perception evaluation request from a capability consumer, generates a cell status intelligent perception evaluation result through model algorithm operation, and returns it to the capability consumer.

[0088] In some embodiments, the network knowledge unit in the disclosed embodiments includes: intent analysis, a network big model, and an expert knowledge base, wherein the expert knowledge base includes: a vector database, a knowledge graph, etc. The network label intent includes the intention of adding, changing, and deleting information of dimension labels, indicator category labels, cell labels, sub-cell labels, and sub-cell label-related indicators. The network knowledge unit receives the network label intent, updates the labels and indicator parameters of the cell status label system model through queries of the expert knowledge base and the network big model, and writes the results into the expert knowledge base (including the vector database and the knowledge graph), and the expert knowledge base updates the cell status label system model with the parameter results.

[0089] In some embodiments, Figure 4 As shown, the embodiment of the present disclosure proposes a cell status perception method to solve the needs of comprehensive, coordinated subjective and objective comprehensive perception and evaluation of cell status by wireless networks, core networks and management domains under the current and future mobile communication network functional architecture. The specific process is as follows:

[0090] S402, constructing a cell status label system model based on the network indicator system, label evaluation criteria, and expert knowledge, wherein the cell status label system model includes: dimension labels, indicator category labels, cell labels, sub-cell labels, and sub-cell label associated indicators.

[0091] S404, analyzing the network label intent through the intent network, large model and expert knowledge base technology to form a network knowledge unit for automatically updating the cell status label system model.

[0092] S406, collect multi-dimensional data such as operation, resources, market, and services to form a data storage unit. The data storage unit generates sub-cell label associated indicator data according to the cell status label system model and loads it into the cell status label system model; at the same time, it serves as the algorithm model library in the training data training model training unit, and the algorithm model library model is loaded into the cell status intelligent perception unit.

[0093] S408, the cell status label system model and the cell status intelligent perception unit constitute an analysis logic unit. The capability consumer first analyzes the logic unit to perform an intelligent perception evaluation request, and the cell status intelligent perception unit returns the intelligent perception evaluation result.

[0094] In some embodiments, Figure 5 As shown, the system using the cell state perception method in the embodiment of the present disclosure includes a network knowledge unit, an analysis logic unit, a data storage unit, and a model training unit. The analysis logic unit includes a cell state label system model and a cell state intelligent perception unit. The analysis logic unit receives an intelligent perception evaluation request from a capability consumer, generates a cell state intelligent perception evaluation result through model algorithm operation, and returns it to the capability consumer; the data storage unit is used to complete the collection of multi-dimensional data such as evaluation operation, resources, market, and service to form a data storage unit. The data storage unit generates sub-cell label-related indicator data according to the label system model indicator algorithm requirements and statistical period, and loads it into the cell state label system model, and at the same time serves as an algorithm model library in the training model training unit as training data; the algorithm model library includes the algorithm model library for training data. The scoring algorithm model library and weight algorithm model library for comprehensive evaluation of cell status can perform AI algorithm model training through data storage unit data and load it into the cell status intelligent perception unit; the network knowledge unit includes intent analysis, network big model, and expert knowledge base, among which the expert knowledge base includes vector database, knowledge graph, etc. The network label intent includes dimension label, indicator category label, cell label, sub-cell label, sub-cell label associated indicator addition, change, and deletion information intention. The network knowledge unit receives the network label intent, updates the label and indicator parameters of the cell status label system model through query of the expert knowledge base and the network big model, and writes the results into the expert knowledge base (including vector database and knowledge graph). The expert knowledge base updates the cell status label system model with the parameter results.

[0095] In some embodiments, the disclosed embodiments propose a method for comprehensive, collaborative, subjective and objective comprehensive perception evaluation of cell status for wireless networks, core networks and management domains under the current and future mobile communication network functional architecture. In addition, the disclosed embodiments combine new technologies such as intention networks, network big models, expert knowledge bases, knowledge graphs, AI model algorithms, etc., and complete the comprehensive perception evaluation of cell status through the cooperation of cell status label system models and cell status intelligent perception units. The method and system can automatically update according to the actual perception and quality label evaluation needs of the network, and can solve the problems existing in the current technology: insufficient dimensional data and excessive subjectivity in weight standards; the evaluation process has not been integrated with the current and future network evolution structure; and it cannot be automatically updated according to the actual perception and quality label evaluation needs of the network.

[0096] Based on the same inventive concept, the present disclosure also provides a cell state perception device, such as the following embodiment. Since the principle of solving the problem in the device embodiment is similar to that in the above method embodiment, the implementation of the device embodiment can refer to the implementation of the above method embodiment, and the repeated parts will not be repeated.

[0097] Figure 6 A schematic diagram of a cell state sensing device in an embodiment of the present disclosure is shown, and the device includes:

[0098] A request receiving module 601 is used to receive a state awareness assessment request for a target cell;

[0099] The perception result generating module 602 is used to generate the state perception evaluation result of the target cell according to the state perception evaluation request through the pre-built cell state label system model and the cell state intelligent perception unit; wherein the input data of the cell state label system model is the pre-processed data of the state perception evaluation request of the target cell, and the input data of the cell state intelligent perception unit is the output data of the cell state label system model;

[0100] The perception result output module 603 is used to output the state perception evaluation result of the target cell.

[0101] The cell state perception device provided in the embodiment of the present disclosure receives a state perception evaluation request for a target cell through a request receiving module; generates a state perception evaluation result of the target cell through a perception result generating module according to the state perception evaluation request, through a pre-constructed cell state label system model and a cell state intelligent perception unit; wherein, the input data of the cell state label system model is the data pre-processed from the state perception evaluation request of the target cell, and the input data of the cell state intelligent perception unit is the output data of the cell state label system model; and outputs the state perception evaluation result of the target cell through a perception result output module. Compared with the problem of low accuracy of the cell state perception evaluation method in the related art, the embodiment of the present disclosure completes the comprehensive perception evaluation of the state of the target cell more objectively through the cooperation of the pre-constructed cell state label system model and the cell state intelligent perception unit, thereby effectively improving the accuracy of the cell state perception evaluation.

[0102] In some embodiments, the perception result generation module in the embodiments of the present disclosure is also used to input the pre-processed data of the state perception assessment request of the target cell into a pre-built cell state label system model, and output the label-associated indicator data corresponding to the target cell; input the label-associated indicator data corresponding to the target cell into the cell state intelligent perception unit to generate the state perception assessment result of the target cell.

[0103] In some embodiments, the perception result generation module in the embodiment of the present disclosure is also used to parse the state perception assessment request to determine each level of data in the state perception assessment request; match each level of data in the parsed state perception assessment request with a preset expert knowledge base to obtain a matching result; based on the matching result, generate a corresponding level label for each level of data; based on the level label corresponding to each level of data, determine the label-associated indicator data corresponding to the target cell.

[0104] In some embodiments, the perception result generation module in the disclosed embodiments is also used by the cell status intelligent perception unit to score and evaluate the label-related index data corresponding to the target cell output by the cell status label system model to generate a status perception evaluation result of the target cell.

[0105] In some embodiments, the hierarchical labels in the embodiments of the present disclosure include: dimension labels, indicator category labels, cell labels, sub-cell labels and sub-cell label associated indicators. The perception result generation module in the embodiments of the present disclosure is also used to determine the indicator value corresponding to the dimension label of the target cell; determine the indicator value corresponding to the indicator category label of the target cell; determine the indicator value corresponding to the cell label of the target cell; determine the indicator value corresponding to the sub-cell label of the target cell; determine the indicator value corresponding to the sub-cell label associated indicator of the target cell.

[0106] In some embodiments, the cell state perception device in the disclosed embodiment also includes: a cell state label system model construction module, which is used to construct a cell state label system model based on pre-set network indicators, label evaluation standards and expert knowledge before generating a state perception evaluation result of the target cell according to a state perception evaluation request through a pre-constructed cell state label system model and a cell state intelligent perception unit; a multi-dimensional cell data acquisition module, which is used to acquire multi-dimensional cell data as training data; and a cell state intelligent perception unit determination module, which is used to train the algorithm model library in the model training unit through the training data to obtain the cell state intelligent perception unit.

[0107] In some embodiments, the cell state perception device in the disclosed embodiments further includes: a data storage unit generation module, which is used to obtain multi-dimensional cell data to form a data storage unit before generating a state perception evaluation result of the target cell according to a state perception evaluation request through a pre-built cell state label system model and a cell state intelligent perception unit; a loading module, which is used for the data storage unit to generate data corresponding to the sub-cell label associated indicator according to the indicator algorithm requirements and statistical period in the cell state label system model, and load it into the cell state label system model.

[0108] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0109] Based on the same inventive concept, an electronic device is also provided in an embodiment of the present disclosure, the electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute any one of the above-mentioned cell state perception methods by executing the executable instructions. Since the principle of solving the problem in the electronic device embodiment is similar to that in the above-mentioned method embodiment, the implementation of the electronic device embodiment can refer to the implementation of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0110] Refer to the following Figure 7 An electronic device 700 according to this embodiment of the present disclosure is described. Figure 7 The electronic device 700 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0111] like Figure 7 As shown, the electronic device 700 is in the form of a general computing device. The components of the electronic device 700 may include but are not limited to: at least one processing unit 701, at least one storage unit 702, and a bus 703 connecting different system components (including the storage unit 702 and the processing unit 701).

[0112] The storage unit stores program codes, which can be executed by the processing unit 701, so that the processing unit 701 executes the steps described in the above “exemplary method” section of this specification according to various exemplary embodiments of the present disclosure.

[0113] In some embodiments, when the electronic device is used to control, for example, the cell state sensing method disclosed above, the processing unit 701 may perform the following steps of the above method embodiment:

[0114] Receive a state perception assessment request for a target cell; generate a state perception assessment result of the target cell according to the state perception assessment request through a pre-built cell state label system model and a cell state intelligent perception unit; wherein the input data of the cell state label system model is the pre-processed data of the state perception assessment request of the target cell, and the input data of the cell state intelligent perception unit is the output data of the cell state label system model; and output the state perception assessment result of the target cell.

[0115] The storage unit 702 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 7021 and / or a cache storage unit 7022 , and may further include a read-only storage unit (ROM) 7023 .

[0116] The storage unit 702 may also include a program / utility 7024 having a set (at least one) of program modules 7025, such program modules 7025 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0117] Bus 703 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0118] The electronic device 700 may also communicate with one or more external devices 704 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 700, and / or may communicate with any device that enables the electronic device 700 to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication may be performed through an input / output (I / O) interface 705. In addition, the electronic device 700 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 706. As shown, the network adapter 706 communicates with other modules of the electronic device 700 through a bus 703. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0119] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0120] Based on the same inventive concept, a computer-readable storage medium is also provided in an embodiment of the present disclosure, on which a computer program is stored, and when the computer program is executed by a processor, any of the above-mentioned cell state perception methods is implemented. Since the principle of solving the problem in the computer-readable storage medium embodiment is similar to that in the above-mentioned method embodiment, the implementation of the computer-readable storage medium embodiment can refer to the implementation of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0121] More specific examples of computer-readable storage media in the present disclosure may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0122] In the present disclosure, a computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, wherein a readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A readable signal medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0123] Alternatively, the program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0124] In a specific implementation, the program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0125] Based on the same inventive concept, a computer program product is also provided in an embodiment of the present disclosure, including a computer program product, including: a computer program or an instruction, and when the computer program or the instruction is executed by a processor, the cell state perception method of any one of the above method embodiments is implemented. Since the principle of solving the problem in the computer program product embodiment is similar to that in the above method embodiment, the implementation of the computer program product embodiment can refer to the implementation of the above method embodiment, and the repeated parts will not be repeated.

[0126] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0127] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0128] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0129] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A cell state perception method, characterized in that: include: Receiving a state awareness assessment request for a target cell; According to the state perception evaluation request, the state perception evaluation result of the target cell is generated through a pre-constructed cell state label system model and a cell state intelligent perception unit; wherein the input data of the cell state label system model is the pre-processed data of the state perception evaluation request of the target cell, and the input data of the cell state intelligent perception unit is the output data of the cell state label system model; Output the state perception evaluation result of the target cell.

2. The cell state sensing method according to claim 1, characterized in that: According to the state perception evaluation request, a state perception evaluation result of the target cell is generated through a pre-constructed cell state label system model and a cell state intelligent perception unit, including: Input the preprocessed data of the state perception assessment request of the target cell into a pre-built cell state label system model, and output the label association indicator data corresponding to the target cell; The label-associated index data corresponding to the target cell is input into the cell state intelligent perception unit to generate a state perception evaluation result of the target cell.

3. The cell status sensing method according to claim 1, characterized in that: According to the state perception evaluation request, a state perception evaluation result of the target cell is generated through a pre-constructed cell state label system model and a cell state intelligent perception unit, including: Parsing the state awareness assessment request to determine data at each level in the state awareness assessment request; Matching the data of each level in the parsed state perception assessment request with a preset expert knowledge base to obtain a matching result; According to the matching results, a corresponding level label is generated for each level of data; According to the hierarchical label corresponding to each hierarchical data, the label-associated indicator data corresponding to the target cell is determined.

4. The cell status sensing method according to claim 3, characterized in that: According to the state perception evaluation request, a state perception evaluation result of the target cell is generated through a pre-constructed cell state label system model and a cell state intelligent perception unit, including: The cell state intelligent perception unit scores and evaluates the label-related index data corresponding to the target cell output by the cell state label system model, and generates a state perception evaluation result of the target cell.

5. The cell state sensing method according to claim 4, characterized in that: The hierarchical labels include: dimension labels, indicator category labels, cell labels, sub-cell labels, and sub-cell label-related indicators. The cell state intelligent perception unit scores and evaluates the label-related indicator data corresponding to the target cell output by the cell state label system model, including: Determine the index value corresponding to the dimension label of the target cell; Determining an indicator value corresponding to the indicator category label of the target cell; Determine an index value corresponding to the cell label of the target cell; Determine an index value corresponding to the sub-cell label of the target cell; Determine an index value corresponding to the sub-cell label association index of the target cell.

6. The cell status sensing method according to claim 1, characterized in that: Before generating the state perception evaluation result of the target cell according to the state perception evaluation request by using a pre-built cell state label system model and a cell state intelligent perception unit, the method further includes: Constructing the cell status label system model based on pre-set network indicators, label evaluation criteria and expert knowledge; Acquire multi-dimensional cell data as training data; The cell status intelligent perception unit is obtained by training the algorithm model library in the model training unit through training data.

7. The cell status sensing method according to claim 1, characterized in that: Before generating the state perception evaluation result of the target cell according to the state perception evaluation request by using a pre-built cell state label system model and a cell state intelligent perception unit, the method further includes: Acquire multi-dimensional cell data to form a data storage unit; The data storage unit generates data corresponding to the sub-cell label associated index according to the index algorithm requirements and statistical period in the cell status label system model, and loads the data into the cell status label system model.

8. A cell status sensing device, characterized in that: include: A request receiving module, used to receive a state perception assessment request for a target cell; A perception result generating module, used to generate the state perception evaluation result of the target cell according to the state perception evaluation request through a pre-built cell state label system model and a cell state intelligent perception unit; wherein the input data of the cell state label system model is the pre-processed data of the state perception evaluation request of the target cell, and the input data of the cell state intelligent perception unit is the output data of the cell state label system model; The perception result output module is used to output the state perception evaluation result of the target cell.

9. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; The processor is configured to execute the cell status perception method described in any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the cell status perception method described in any one of claims 1 to 7 is implemented.

11. A computer program product comprising: A computer program or instruction, characterized in that when the computer program or instruction is executed by a processor, it implements the cell status perception method described in any one of claims 1 to 7.

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