Real-time data analysis system, method and equipment based on 5G message driving and medium

Abstract

The invention relates to the technical field of communication, and discloses a real-time data analysis system, method, equipment and medium based on 5G message driving, and the method comprises the following steps: firstly receiving a 5G message and collecting multivariate data, obtaining a real-time data group through the steps of analysis, extraction, retrieval and the like, then carrying out fluctuation analysis on the real-time data group, identifying a key fluctuation node, calculating a change rate, and calculating the change rate according to the change rate; in this way, real-time service scenes are restored, classified and labeled, after service channels are determined, channel quality is analyzed to calculate data throughput, channel configuration is optimized, the channel utilization rate is calculated, an analysis permission secret key is generated according to the channel utilization rate, corresponding rules are inquired, and data identifiers are extracted. And finally, a data analysis report of the 5G message is generated based on the data identifier, and a complete data processing and analysis link is formed. According to the invention, the analysis efficiency of real-time data can be improved.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and in particular to a real-time data analysis system, method, device and medium driven by 5G messages. Background Art

[0002] In today's era of rapid digital development and information explosion, real-time data analysis plays a vital role in decision-making and business optimization in various industries. With the large-scale commercialization of 5G technology, its high speed, low latency, and large connection characteristics have brought new opportunities for real-time transmission and processing of data.

[0003] In many industry fields, whether it is e-commerce precision marketing, financial risk management, industrial production process monitoring, smart city operation management, etc., traditional data analysis methods often rely on periodic data collection and offline processing modes. On the one hand, this lagging analysis method is difficult to meet the ever-changing market dynamics and cannot capture the changing trends of key business nodes in time, causing companies to miss the best decision-making opportunities; on the other hand, faced with massive and widely sourced data torrents, traditional methods have obvious shortcomings in the timeliness of data transmission and the efficiency of processing, which affects the analysis efficiency of real-time data. Therefore, a real-time data analysis method based on 5G message drive is needed to improve the analysis efficiency of real-time data. Summary of the invention

[0004] The present invention provides a real-time data analysis system, method, device and medium driven by 5G messages, the main purpose of which is to improve the analysis efficiency of real-time data.

[0005] To achieve the above object, the present invention provides a real-time data analysis method based on 5G message drive, comprising: Receive a 5G message, collect multivariate data information in the 5G message, parse a data source identifier corresponding to the multivariate data information, extract a data feature code associated with the data source identifier, and retrieve a real-time data group corresponding to the data feature code; Performing fluctuation analysis on the data items in the real-time data group to obtain a fluctuation trend sequence, identifying key fluctuation nodes in the fluctuation trend sequence, and calculating the node change rate corresponding to the key fluctuation node; Based on the change rate, restore the real-time business scene corresponding to the real-time data group, classify the real-time business scene with labels to obtain classified scene labels, query key label elements in the classified scene labels, and mark the element core points corresponding to the key label elements; Based on the element core point, determine the service channel corresponding to the real-time data group, analyze the channel quality corresponding to the service channel, calculate the data throughput corresponding to the service channel based on the channel quality, optimize the channel configuration of the real-time data group in the data transmission process based on the data throughput, and calculate the channel utilization rate corresponding to the channel configuration; Based on the channel utilization, an analysis license key corresponding to the real-time data group is generated, an analysis rule corresponding to the analysis license key is queried, a data identifier in the analysis rule is extracted, and based on the data identifier, a data analysis report corresponding to the 5G message is generated.

[0006] In order to solve the above problems, the present invention also provides a real-time data analysis system driven by 5G messages, the system comprising: A data retrieval module is used to receive a 5G message, collect multivariate data information in the 5G message, parse a data source identifier corresponding to the multivariate data information, extract a data feature code associated with the data source identifier, and retrieve a real-time data group corresponding to the data feature code; A change rate calculation module is used to perform fluctuation analysis on the data items in the real-time data group to obtain a fluctuation trend sequence, identify key fluctuation nodes in the fluctuation trend sequence, and calculate the node change rate corresponding to the key fluctuation node; A core point marking module is used to restore the real-time business scene corresponding to the real-time data group based on the change rate, classify the real-time business scene with labels, obtain classified scene labels, query key label elements in the classified scene labels, and mark the element core points corresponding to the key label elements; A channel utilization calculation module is used to determine the service channel corresponding to the real-time data group based on the element core point, analyze the channel quality corresponding to the service channel, calculate the data throughput corresponding to the service channel based on the channel quality, optimize the channel configuration of the real-time data group in the data transmission process based on the data throughput, and calculate the channel utilization corresponding to the channel configuration; A report generation module is used to generate an analysis license key corresponding to the real-time data group based on the channel utilization, query the analysis rules corresponding to the analysis license key, extract the data identifier in the analysis rule, and generate a data analysis report corresponding to the 5G message based on the data identifier.

[0007] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to implement the above-mentioned real-time data analysis based on 5G message drive.

[0008] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned real-time data analysis based on 5G message drive.

[0009] It can be seen that the present invention receives 5G messages and collects multivariate data information in the 5G messages. In the financial field, it can capture market fluctuations in real time, provide accurate basis for risk management and investment decisions, and improve the timeliness and scientificity of decisions; in industrial production, it can instantly obtain equipment operating parameters, optimize production processes, and enhance production efficiency and stability; in the construction of smart cities, it helps to quickly integrate multi-source data such as traffic and environment, realize intelligent management, and improve the overall efficiency of urban operations. Furthermore, the present invention performs fluctuation analysis on the data items in the real-time data group to obtain a fluctuation trend sequence, which can clearly understand the dynamic change rules of the data and accurately capture the fluctuations of the data. Whether it is the rise and fall of market prices, the high and low peaks of network traffic, or the fluctuations of equipment performance parameters, they can all be accurately presented, making the change trend of the data clear at a glance. Based on the change rate, the real-time business scenario corresponding to the real-time data group is restored, which can accurately understand the business dynamics. The change rate can be used to trace the transformation of the business at key nodes. For example, the change rate of sales data can show the immediate effect of promotional activities. The present invention determines the business channel corresponding to the real-time data group based on the core point of the element, and can accurately locate the data transmission path that closely matches the core needs of the business, reduce unnecessary resource consumption and data redundancy, improve transmission efficiency, and help optimize business processes. By ensuring that key data is transmitted in an efficient channel, the business response speed is enhanced. Based on the channel utilization rate, the present invention generates an analysis license key corresponding to the real-time data group, which can ensure data transmission security and compliance. The license key is accurately allocated according to the channel utilization rate. Only compliant real-time data groups can obtain transmission permissions, effectively preventing illegal data from occupying channel resources. Therefore, a real-time data analysis system, method, device and medium based on 5G message drive proposed in an embodiment of the present invention can improve the analysis efficiency of real-time data. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of an application environment for real-time data analysis based on 5G message drive provided by an embodiment of the present invention; Figure 2A schematic diagram of a process of real-time data analysis based on 5G message drive provided by an embodiment of the present invention; Figure 3 A module schematic diagram of a real-time data analysis system driven by 5G messages provided in one embodiment of the present invention; Figure 4 A schematic diagram of the internal structure of an electronic device for implementing real-time data analysis driven by 5G messages provided by an embodiment of the present invention; The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0011] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0012] The embodiment of the present invention provides a real-time data analysis method based on 5G message drive, which can be applied in Figure 1 In an application environment, wherein the client communicates with the server through a network, the server can collect and receive 5G messages through the client, collect multivariate data information in the 5G messages, parse the data source identifier corresponding to the multivariate data information, extract the data feature code associated with the data source identifier, and retrieve the real-time data group corresponding to the data feature code; perform fluctuation analysis on the data items in the real-time data group to obtain a fluctuation trend sequence, identify the key fluctuation nodes in the fluctuation trend sequence, and calculate the node change rate corresponding to the key fluctuation node; based on the change rate, restore the real-time business scene corresponding to the real-time data group, label the real-time business scene to obtain a classified scene label, and query the key label element in the classified scene label, and mark the element core point corresponding to the key label element; based on the element core point, determine the business channel corresponding to the real-time data group, analyze the channel quality corresponding to the business channel, calculate the data throughput corresponding to the business channel based on the channel quality, optimize the channel configuration of the real-time data group in the data transmission process based on the data throughput, and calculate the channel utilization corresponding to the channel configuration; Based on the channel utilization, an analysis license key corresponding to the real-time data group is generated, an analysis rule corresponding to the analysis license key is queried, a data identifier in the analysis rule is extracted, and based on the data identifier, a data analysis report corresponding to the 5G message is generated.

[0013] Reference Figure 2As shown, a flowchart of a real-time data analysis based on 5G message drive provided by an embodiment of the present invention is shown. In an embodiment of the present invention, the real-time data analysis based on 5G message drive includes the following steps S1-S5: S1. Receive a 5G message, collect multivariate data information in the 5G message, parse the data source identifier corresponding to the multivariate data information, extract the data feature code associated with the data source identifier, and retrieve the real-time data group corresponding to the data feature code.

[0014] The present invention receives 5G messages and collects multivariate data information in the 5G messages. In the financial field, it can capture market fluctuations in real time, provide accurate basis for risk management and investment decisions, and improve the timeliness and scientificity of decisions. In industrial production, it can instantly obtain equipment operating parameters, optimize production processes, and enhance production efficiency and stability. In the construction of smart cities, it helps to quickly integrate multi-source data such as transportation and environment, realize intelligent management, and improve the overall efficiency of urban operations.

[0015] Among them, the 5G message refers to a new message communication service based on the 5G network, which integrates the advantages of multiple communication methods. It not only supports rich media formats such as text, pictures, audio, video, etc., but also has the characteristics of strong interactivity and high intelligence. Compared with traditional SMS, 5G messages can carry more complex and larger amounts of information, achieve more efficient information transmission and interaction, and become an important carrier for many industries to achieve digital transformation and real-time data interaction. For example, in the intelligent customer service scenario, the communication between users and corporate customer service can be carried out quickly and richly through 5G messages, and companies can send users illustrated product introductions, operating guides and other information; the multivariate data information refers to data sets from different data sources with multiple data types and formats. It covers structured data (such as tabular data in a database), semi-structured data (such as data in XML and JSON formats) and unstructured data (such as images, audio, video, text files, etc.). These data can be collected from various terminal devices, sensors, business systems and Internet platforms, and contain rich business information and user behavior information. Taking the intelligent transportation system as an example, the multivariate data information may include vehicle speed, location, road conditions (camera image data), traffic light status (sensor data) and driver's operating behavior (such as braking, acceleration, etc.). Optionally, the collection of the multivariate data information in the 5G message can be achieved through data collection tools, such as Flume, Kafka Connect and other tools.

[0016] Furthermore, the present invention parses the data source identifier corresponding to the multivariate data information and extracts the data feature code associated with the data source identifier, which helps to accurately trace the data and clarify the source and channel of the data, thereby enhancing the credibility and reliability of the data, and can quickly screen and classify massive data, improve the efficiency of data processing, and make subsequent analysis work more targeted and accurate.

[0017] Among them, the data source identifier refers to specific information or mark used to uniquely determine the source of data, which can be a unique string, digital code, IP address, device number, database table name, etc. For example, in an industrial Internet of Things scenario, the data source identifier of the data collected by sensors on different production lines may be the unique device number of each sensor, so that when a large amount of data from different sensors is received, the data source identifier can be used to quickly distinguish which specific device on which production line the data is generated by; the data feature code refers to a code that is closely associated with the data source and can characterize the key features or attributes of the data generated by the data source. It can be generated based on factors such as the type, format, generation time range, and specific business meaning of the data. For example, for the sales data source of an e-commerce platform, the data feature code may include information such as product category codes (such as electronic products, clothing, etc.), sales time features (such as quarters, months, etc.), and sales area codes. Through these feature codes, specific categories of product sales data can be quickly screened out, so that specific business scenarios can be more accurately focused when performing data analysis, such as analyzing the sales trend of electronic products in a specific area in a certain quarter. Optionally, the data source identifier corresponding to the parsing of the multivariate data information can be implemented through a label classification tool, such as: Label Studio tools can be used to automatically classify data based on the existing label system to determine the data source identifier; the extraction of the data feature code associated with the data source identifier can be achieved through a hash function algorithm, such as: MD5 or SHA-256 and other hash functions, to perform hash calculations on key data fields in the data source to obtain a fixed-length hash value as the data feature code.

[0018] Furthermore, the present invention can quickly locate data sets closely related to specific features by retrieving real-time data groups corresponding to the data feature codes, greatly improving the efficiency of data query and acquisition, reducing the time cost of blindly searching in massive data, and helping to timely capture data changes associated with key business features, providing accurate and targeted basis for real-time decision-making.

[0019] Among them, the real-time data group refers to the real-time data set that is finally retrieved and accurately corresponds to the data feature code. These data not only meet the scope specified by the key identification information, but also are instant in time and can dynamically reflect the actual situation of the current business scenario. For example, in a financial transaction scenario, the real-time data group is a set of data including the current transaction amount, information of both parties to the transaction, real-time market exchange rate, etc., which is obtained instantly based on a specific transaction feature code.

[0020] As an embodiment of the present invention, the retrieval of the real-time data group corresponding to the data feature code includes: identifying key identification information corresponding to the data feature code; constructing a retrieval index framework corresponding to the key identification information; preliminarily screening the matchable data sets in the retrieval index framework; analyzing the strongly associated data blocks corresponding to the data subsets in the matchable data sets; and retrieving the real-time data group corresponding to the data feature code based on the strongly associated data blocks.

[0021] Among them, the key identification information refers to the core elements extracted from the data feature code that are representative and can guide the direction of data retrieval. It can be a specific code defined based on the business scenario. For example, in e-commerce data, the key identification information can be a product category code (such as 3C digital, beauty, etc.), a promotion activity code, or a quarterly or monthly identifier that reflects the time attribute of the data, as well as a regional sales division code, etc.; the retrieval index framework refers to a structured retrieval guidance system built based on the key identification information, which is equivalent to a "navigation map" built for data retrieval. If the key identification information includes product category and time range, the retrieval index framework will build a hierarchical directory structure around these two dimensions, classify the data indexes of the same product category in different time periods together, and set the corresponding index path to quickly locate the area where the target data can be stored; the matchable data set refers to the search results that are used to search for the target data in the search results. When the index framework performs a preliminary search, a data set that has a certain correlation with the key identification information is screened out from the massive data repository, which may include multiple data subsets. For example, when retrieving urban traffic flow data with region as the key identification information, the matchable data set is a set of traffic flow monitoring data of different sections and different time periods that is initially located by indexing within the specified geographical area; the strongly associated data block refers to a closely connected data combination that is mined based on the inherent logical relationship between the data, such as cause-effect relationship, coordinated change relationship, etc., after in-depth analysis of the data subsets in the matchable data set. Taking industrial production as an example, when monitoring the operating status of equipment, if the matchable data set contains multiple data subsets such as equipment temperature, pressure, vibration frequency, etc., through association analysis, it is found that the temperature increase is strongly correlated with the pressure increase and the vibration frequency increase. These related data subsets constitute a strongly associated data block.

[0022] Furthermore, the identification of key identification information corresponding to the data feature code can be achieved through a classification algorithm, such as support vector machine, etc.; the construction of a retrieval index framework corresponding to the key identification information can be achieved through a tree index structure method, such as B-tree, B+ tree and other tree index structures; the preliminary screening of the matching data sets in the retrieval index framework can be achieved through a Boolean retrieval model, such as: widely used in the field of information retrieval, using key identification information as search terms, and screening data sets by setting and, or, and non-logical operators to connect search terms; the analysis of strongly associated data blocks corresponding to data subsets in the matching data sets can be achieved through an association rule mining algorithm, such as Apriori and other algorithms; the retrieval of real-time data groups corresponding to the data feature codes can be achieved through a stream data processing engine, such as Apache Flink and other engines.

[0023] S2. Perform fluctuation analysis on the data items in the real-time data group to obtain a fluctuation trend sequence, identify key fluctuation nodes in the fluctuation trend sequence, and calculate the node change rate corresponding to the key fluctuation node.

[0024] The present invention performs fluctuation analysis on the data items in the real-time data group to obtain a fluctuation trend sequence, which can clearly understand the dynamic change rules of the data and accurately capture the fluctuations of the data. Whether it is the rise and fall of market prices, the peaks and lows of network traffic, or the fluctuations of equipment performance parameters, they can all be accurately presented, making the change trend of the data clear at a glance.

[0025] The fluctuation trend sequence refers to a series of ordered sets that are arranged in chronological order and can clearly show the trend of data fluctuations. The fluctuation amplitude indicators corresponding to each time window are connected in series according to time to form a fluctuation trend sequence, just like drawing a water level change curve for a river, which fully presents the fluctuation of data over time.

[0026] As an embodiment of the present invention, the fluctuation analysis of the data items in the real-time data group to obtain the fluctuation trend sequence includes: sorting the data items in the real-time data group to obtain sorted data items; dividing the sorted data items into their corresponding time windows; calculating the statistical characteristic values ​​corresponding to the data in the time window; analyzing the characteristic vectors corresponding to the statistical characteristic values; extracting the fluctuation amplitude index in the characteristic vector; and based on the fluctuation amplitude index, performing fluctuation analysis on the data items in the real-time data group to obtain the fluctuation trend sequence.

[0027] Among them, the sorted data item refers to an ordered data set obtained by rearranging each data item in the real-time data group according to specific rules (usually time order, data size order, etc.). For example, in a financial stock trading data group, the transaction price, transaction volume and other data of each stock are arranged in chronological order according to the transaction time. These transaction price and transaction volume data arranged in sequence are the sorted data items; the time window refers to a time period interval designated for data observation and analysis, just like when monitoring network traffic data, 5 minutes is used as a time window, and the continuous traffic data is divided into 5-minute sections, and each such 5-minute time period is a time window; the statistical characteristic value refers to a representative value obtained by performing mathematical statistical operations on the data in the same time window. Common ones include mean The median can reflect the middle position of the data and avoid the influence of extreme values. The eigenvector refers to a multidimensional vector representation constructed based on the statistical eigenvalues ​​of the time window. Taking the user access data of the e-commerce platform as an example, if the statistical eigenvalues ​​in a time window include the average user access time, the average number of visited pages, and the proportion of new user visits, these three values ​​are combined into a three-dimensional vector, which is the eigenvector. The fluctuation amplitude index refers to a key parameter used to quantitatively describe the degree of data fluctuation. When analyzing power load data, if the eigenvectors of the two time windows before and after have a large difference in the dimensional values ​​representing the load peak, this difference is the fluctuation amplitude index after processing.

[0028] Furthermore, the sorting of the data items in the real-time data group can be implemented by a sorting algorithm, such as bubble sort, quick sort and other algorithms; the division of the sorted data items into their corresponding time windows can be implemented by a window division method, such as fixed interval division method, sliding window method and other methods; the calculation of the statistical eigenvalues ​​corresponding to the data in the time window can be implemented by statistical functions in Excel, such as AVERAGE, MEDIAN, STDEV and other functions; the analysis of the eigenvectors corresponding to the statistical eigenvalues ​​can be implemented by a principal component analysis method, such as when the statistical eigenvalues ​​are large and the data dimension is high, PCA can be used to extract the main components and combine them into eigenvectors; the extraction of the fluctuation amplitude index in the eigenvector can be implemented by a fluctuation amplitude calculation method, such as first calculating the moving average of the statistical eigenvalues ​​of each dimension of the eigenvector, and then subtracting the corresponding moving average from the statistical eigenvalue of the current eigenvector, and the difference obtained is used as the fluctuation amplitude index; the fluctuation analysis of the data items in the real-time data group can be implemented by a fluctuation analysis model, such as ARIMA model, LSTM network and other models.

[0029] By identifying key fluctuation nodes in the fluctuation trend sequence, the present invention can accurately locate important turning points in complex data fluctuations. These nodes often represent key change moments in the market, business or system, such as large rise and fall points in the financial market, sudden changes in key parameters in the production process, etc.

[0030] Among them, the key fluctuation nodes refer to the fluctuation data points corresponding to specific positions or moments in the fluctuation trend sequence, which have significant characteristics, have a significant impact on the overall trend, or mark a significant change in the data. These nodes usually appear as sudden changes in data, abnormal peaks, troughs, or turning points in trends, which can reflect the key events or state changes experienced by systems, markets, business processes, etc. at specific times or conditions, such as stock price crashes in financial markets, sudden changes in parameters caused by equipment failures on industrial production lines, and sales surges in e-commerce sales data during promotional activities. Optionally, the identification of key fluctuation nodes in the fluctuation trend sequence can be achieved through threshold-based methods, such as fixed threshold methods, adaptive threshold methods, and other methods.

[0031] By calculating the node change rate corresponding to the key fluctuation nodes, the present invention can quantify the degree of change of the data at the key fluctuation nodes, and accurately understand the changing speed and amplitude of the data at these key turning points. Whether it is a sharp rise or fall in market conditions or a large fluctuation in business indicators, it can be clearly presented through specific numerical values.

[0032] The node change rate refers to an indicator used to measure the degree of change of key fluctuation nodes, which reflects the changes of key fluctuation nodes under different samples and data groupings.

[0033] As an embodiment of the present invention, the calculating the node change rate corresponding to the key fluctuation node includes: The node change rate corresponding to the key fluctuation node is calculated using the following formula: in, Indicates the node change rate corresponding to the key fluctuation node, Indicates the total number of node samples corresponding to the key fluctuation node, Indicates the number index corresponding to the node sample, Indicates The sample value of the node sample, represents the average value of node samples, Indicates the group quantity corresponding to the data grouping, represents the total number of data points in each group of data, Indicates the quantity index corresponding to the data point, Indicates The standard value of the data point.

[0034] In detail, the node samples refer to data samples used to calculate the change rate of key fluctuation nodes. These samples are related to the key fluctuation nodes, and each sample has a corresponding quantity index; the sample values ​​refer to specific numerical values ​​in the node samples, which are used to reflect the specific circumstances of the node samples when calculating the node change rate; the standard value refers to data after standardization, which is to convert the original data into a numerical value with a specific mean and standard deviation through a specific transformation.

[0035] S3, based on the change rate, restore the real-time business scene corresponding to the real-time data group, classify the real-time business scene with labels, obtain classified scene labels, query the key label elements in the classified scene labels, and mark the element core points corresponding to the key label elements, The present invention restores the real-time business scenario corresponding to the real-time data group based on the change rate, can accurately understand the business dynamics, and trace the transformation of the business at key nodes through the change rate. For example, the change rate of sales data can show the immediate effect of promotion activities.

[0036] Among them, the real-time business scenario refers to the actual business operation status restored by scenario characteristic parameters. For example, at a certain point in time, the scenario characteristic parameters such as output, cost, market share, etc. obtained through the above series of analyses comprehensively present the actual business operation status such as whether the business is in stable production, conducting promotional activities, or responding to market competition crises.

[0037] As an embodiment of the present invention, restoring the real-time business scenario corresponding to the real-time data group based on the change rate includes: analyzing the fluctuation amplitude range corresponding to the change rate; dividing the business fluctuation level corresponding to the real-time data group based on the fluctuation amplitude range; matching the business scenario type corresponding to the real-time data group based on the business fluctuation level; querying the scenario characteristic parameters corresponding to the business scenario type; and restoring the real-time business scenario corresponding to the real-time data group based on the scenario characteristic parameters.

[0038] Among them, the fluctuation amplitude range refers to the interval determined according to the change rate, which reflects the degree of data change. For example, by calculating the maximum value, minimum value and common fluctuation interval of the change rate, a range from the smallest fluctuation to the largest fluctuation is defined, which is a quantitative interval for measuring the fluctuation size of data within a certain period of time; the business fluctuation level refers to the classification of the business fluctuation degree based on the fluctuation amplitude range. For example, the fluctuation amplitude range is divided into three levels: small, medium and large. When the fluctuation amplitude is in a smaller interval, the corresponding business fluctuation level is low; when the fluctuation amplitude is in a medium interval, the business fluctuation level is medium; when the fluctuation amplitude is large, the business fluctuation level is high; the business scenario type refers to the business scenario category corresponding to different business fluctuation levels. For example, a low fluctuation level can be used for The medium volatility level may correspond to business adjustment scenarios, such as scenarios where product promotions lead to changes in sales volume; the high volatility level may correspond to business crisis or opportunity scenarios, such as scenarios where competitors launch disruptive products, leading to drastic changes in market share; the scenario characteristic parameters refer to various parameters that can describe the characteristics of business scenario types. For example, for daily stable production scenarios, the scenario characteristic parameters may include stable output, fixed production cycle, lower cost fluctuations, etc.; for product promotion scenarios, the scenario characteristic parameters may include short-term sales growth, promotion costs, market response time, etc.; for scenarios where market share changes drastically, the scenario characteristic parameters may include the market share change rate, competitor strategy parameters, and the effectiveness of their own response strategies, etc.

[0039] Furthermore, the analysis of the fluctuation amplitude range corresponding to the change rate can be achieved through a range analysis method, such as: statistical interval method, quantile method and other methods; the division of the business fluctuation level corresponding to the real-time data group can be achieved through a rule-based division method, such as: dividing the level according to business characteristics and pre-set rules; the matching of the business scenario type corresponding to the real-time data group can be achieved through a neural network model, such as: training the model with a large amount of historical data so that it can accurately output the corresponding business scenario type according to the input business fluctuation level and other characteristics; the query of the scenario feature parameters corresponding to the business scenario type can be achieved through a parameter query tool, such as: CPU-Z, Speccy and other tools; the restoration of the real-time business scenario corresponding to the real-time data group can be achieved through a visualization tool, such as: Tableau and other tools.

[0040] The present invention classifies the real-time business scenarios by labels, obtains classification scenario labels, and queries the key label elements in the classification scenario labels. It can efficiently sort out complex business scenarios, make them clear and organized through label classification, facilitate rapid positioning and understanding of various business situations, and help to accurately analyze business characteristics. Different classification scenario labels can highlight the unique attributes of the business and provide direction for targeted strategy formulation.

[0041] The classification scenario labels refer to representative identifiers assigned to business scenarios after summarizing and abstracting them according to multiple factors such as the characteristics, attributes, business process stages, and achievement of business goals of real-time business scenarios. These labels are intended to classify and manage complex business scenarios so as to more clearly identify, understand, and analyze different types of business scenario models and their characteristics. For example, in e-commerce business, there may be "daily sales scenarios", "promotional activity scenarios", "big promotion carnival scenarios", and "new product launch scenarios". Classification scene labels such as "discount intensity", "category of participating products", "promotion time range", "traffic source channel", etc. Optionally, the label classification of the real-time business scene can be achieved through machine learning classification algorithms, such as decision tree algorithms, naive Bayes algorithms, etc.; the query of the key label elements in the classification scene labels can be achieved through element query tools, such as Firefox, Chrome, etc.

[0042] The present invention can improve the accuracy of business understanding by marking the core points of the elements corresponding to the key tag elements, so that team members can quickly grasp the key points of the business, such as accurately locating the core audience and key selling points in marketing activities, which helps to optimize resource allocation and clarify the focus of investment.

[0043] Among them, the core point of the element refers to the essence condensed after coordinating the element semantics, information hierarchy and dominant characteristics, accurately summarizing the key significance, core value and hub role of key label elements to business scenarios. For "live streaming with goods", the core point of the element is to utilize the popularity of the anchor and the advantages of live streaming interaction, focus on the unique selling points of the product in a specific period of time, and realize online product sales conversion.

[0044] As an embodiment of the present invention, the marking of the element core points corresponding to the key tag elements includes: analyzing the element semantics corresponding to the key tag elements; dividing the information hierarchy corresponding to the key tag elements based on the element semantics; identifying the dominant features corresponding to the information in the information hierarchy; and marking the element core points corresponding to the key tag elements based on the dominant features.

[0045] Among them, the element semantics refers to the basic meaning carried by the key tag element, reflecting its essential meaning in the business scenario to which it belongs, and revealing the key information that the element wants to convey. It is like understanding the exact meaning of a word in a specific context. It is the cornerstone of subsequent analysis and determines the direction of the entire annotation process. For example, in "online marketing activities", "live streaming with goods" The element semantics of this key tag element; the information hierarchy refers to the hierarchical structure of various types of information derived from key tag elements according to the element semantics, according to dimensions such as the degree of information correlation, the depth of business impact, and the logical order. Taking "live streaming with goods" as an example, the first-level information can focus on the product itself, such as product features and advantages; the second level revolves around the anchor, involving the anchor's style and popularity; the third level is about the live streaming process, including the live streaming time period and duration; the dominant features refer to those prominent attributes in each information level that can influence business results, guide key business decisions, and play a key role in promoting or restricting business trends. At the product information level of "live streaming with goods", the unique selling point of the product is the dominant feature, which can directly attract the audience's interest and determine their willingness to buy. Compared with other general product descriptions, it is more influential and is the core focus of information at this level.

[0046] Furthermore, the analysis of the element semantics corresponding to the key label elements can be achieved through a word vector model, such as: Word2Vec and other models; the division of the information hierarchy corresponding to the key label elements can be achieved through a hierarchical clustering algorithm, such as: "Technical architecture upgrade" and "Data security management" can be first clustered into one category because they both involve technical levels, as a lower-level information hierarchy, and "Business process reengineering" is separately used as another higher level, thereby dividing the information hierarchy; the identification of the dominant features corresponding to the information in the information hierarchy can be achieved through a principal component analysis method, such as: it is found that equipment speed and temperature are the main factors affecting product quality, and the characteristics corresponding to these two factors that affect quality are the dominant features; the marking of the element core points corresponding to the key label elements can be achieved through a label propagation algorithm, such as: through the label propagation algorithm, starting from the identified dominant feature node, the importance label is propagated to the nodes related to it, and finally converged to the key label element node to form an element core point marking.

[0047] S4. Based on the core point of the element, determine the service channel corresponding to the real-time data group, analyze the channel quality corresponding to the service channel, calculate the data throughput corresponding to the service channel based on the channel quality, optimize the channel configuration of the real-time data group in the data transmission process based on the data throughput, and calculate the channel utilization corresponding to the channel configuration.

[0048] The present invention determines the business channel corresponding to the real-time data group based on the core point of the element, can accurately locate the data transmission path that is closely aligned with the core business requirements, reduce unnecessary resource consumption and data redundancy, improve transmission efficiency, and help optimize business processes. By ensuring that key data is transmitted in an efficient channel, the business response speed is enhanced.

[0049] The business channel refers to a channel that is used to transmit specific business data items based on the selected transmission protocol and network architecture to meet key business needs. In the live video business, the transmission path from the host end to the audience end based on the RTMP transmission protocol and CDN (content distribution network) architecture is the business channel.

[0050] As an embodiment of the present invention, determining the business channel corresponding to the real-time data group based on the element core point includes: analyzing the business critical requirements corresponding to the element core point; identifying the associated subsets in the real-time data group based on the business critical requirements; evaluating the impact weight of the associated subsets on the business process; based on the impact weight, screening the business data items in the business critical requirements; querying the transmission protocol and network architecture adapted by the business data items; and determining the business channel corresponding to the real-time data group based on the transmission protocol and the network architecture.

[0051] Among them, the key business requirements refer to the specific requirements extracted from the core points of the elements, which play a decisive and core role in achieving business goals. They reflect the key points that the business needs to pay attention to and satisfy most at the current stage. For example, in the scenario of the "618 Big Promotion" of e-commerce, the core point of the element is "substantially increase sales in a short period of time and optimize customer shopping experience", and its key business requirements can be to quickly process massive orders, ensure smooth payment, and accurately push preferential information; the associated subset refers to a data set in the real-time data group that is closely related to the determined key business requirements. Taking the logistics and distribution business as an example, if the key business requirement is to optimize the distribution route to reduce costs, then the set of data such as vehicle location, traffic conditions, cargo weight and volume, and distribution of distribution destinations in the real-time data group is the associated subset; the impact weight refers to a quantitative indicator that measures the role of each associated subset in promoting the business process and helping to achieve the key business requirements. Continuing with the above-mentioned logistics and distribution business as an example, the vehicle location data has a significant impact on the real-time adjustment of the distribution route decision, and can have a higher weight; the cargo weight and volume data have a relatively smaller direct impact on the route adjustment, and the weight will be The business data items refer to those data elements that are selected from the associated subsets based on the impact weights and have key supporting effects on the key business needs. In the financial credit business, if the key business requirement is to accurately assess the customer's credit risk, the associated subset includes data such as customer income, assets, liabilities, consumption records, etc., and after evaluation, data items such as income stability and liability ratio; the transmission protocol refers to the agreement on a series of operational standards such as the rules, format, sequence and error correction for data transmission in the network. Common ones include the HTTP protocol for web page data transmission. In different business scenarios, the appropriate transmission protocol should be selected according to the characteristics of the business data items; the network architecture refers to the layout pattern of building the entire network system, covering the network's topological structure (such as star, bus, ring, etc.), node distribution, hierarchical division and other elements. Taking the cross-regional office of large enterprises as an example, it is to meet the key business needs of collaborative office in multiple locations and sharing of massive data.

[0052] Furthermore, the analysis of the key business requirements corresponding to the core points of the elements can be achieved through a demand analysis method, such as: further refining each sub-goal to determine key business requirements such as "real-time understanding of employee workload", "equipment utilization monitoring", "inventory warning and replenishment", etc.; the identification of the associated subsets in the real-time data group can be achieved through text mining methods, such as: cosine similarity and other methods; the evaluation of the impact weight of the associated subset on the business process can be achieved through a weight calculation method based on information entropy, such as: calculating the information entropy of each factor in the associated subset. The smaller the information entropy, the greater the amount of information provided by the factor, and the greater the impact weight on the business process may be. High; the screening of the business data items in the key business requirements can be achieved through the PCA method, such as: PCA converts these data into new principal components, and according to the variance contribution rate of each principal component (similar to the influence weight), screens out the original data items corresponding to the principal components with high variance contribution rates, such as PM2.5 and sulfur dioxide concentration data, as key business data items for evaluating air quality; the determination of the business channel corresponding to the real-time data group can be achieved through a network topology discovery tool, such as: if the business data item is video conferencing data, different paths from the conference room terminal to the server are tested, and a path with high bandwidth and low latency is selected as the business channel based on the performance results.

[0053] By analyzing the channel quality corresponding to the service channel, the present invention can ensure the smooth operation of the service, detect hidden dangers such as channel jamming, delay or packet loss in advance, and make timely optimization and adjustments to ensure that key services such as video conferencing and real-time financial transactions are not affected, and help accurately configure resources. According to the quality of the channel, network bandwidth, server computing power and other resources are reasonably allocated. Among them, the channel quality refers to the comprehensive reflection of a series of key characteristics and performance indicators presented by the service channel during the data transmission process, which covers multiple aspects, including but not limited to the stability of signal strength, ensuring that the signal will not fluctuate significantly and affect the accurate reception of data; the data transmission rate, which is directly related to the transmission efficiency of the service data, such as a high rate can enable large-capacity files to be transmitted quickly; the packet loss rate, the lower the packet loss rate, the better the integrity of the data transmission; the degree of delay, lower delay is crucial for real-time services (such as video calls, online games), and can ensure instant information interaction; and the degree of noise interference, smaller noise interference can reduce the occurrence of data transmission errors. Optionally, the analysis of the channel quality corresponding to the service channel can be achieved through network performance testing tools, such as: Iperf, PingPlotter and other tools.

[0054] Furthermore, the present invention calculates the data throughput corresponding to the service channel based on the channel quality, which helps to accurately plan the service carrying capacity, and can reasonably arrange task transmission based on this, avoid channel overload or idleness, and improve resource utilization efficiency.

[0055] Among them, the data throughput refers to a measure of the amount of data successfully transmitted through a service channel within a specific time period, which reflects the ability of the channel to effectively carry and transmit information within a unit of time, and is usually expressed in bits per second (bps), bytes per second (Bps) or other similar units. Its numerical value depends on multiple factors, including the physical characteristics of the channel, such as bandwidth. The wider the bandwidth, the higher the data throughput can theoretically be supported; the signal strength, noise level, packet loss rate and delay in the transmission process in the channel quality, etc. Optionally, the calculation of the data throughput corresponding to the service channel can be achieved by a throughput calculation method, such as: Nyquist formula, Shannon theorem and other calculation methods.

[0056] Furthermore, based on the data throughput, the present invention optimizes the channel configuration of the real-time data group in the data transmission process, which can significantly improve the transmission efficiency, ensure fast and stable data transmission, greatly speed up business processing, and help to reasonably allocate network resources. It can accurately allocate channels according to the throughput requirements of different data groups, avoid waste and imbalance of resources, and make the entire network system run more efficiently.

[0057] Among them, the channel configuration refers to a specific channel usage plan tailored for the real-time data group, including selecting high-speed, low-latency, and high-stability channels for high-priority sensitive data groups, such as allocating dedicated low-latency fiber channels for real-time imaging data in online remote surgical assistance; arranging low-speed, low-cost channels for low-priority data groups, such as using ordinary Wi-Fi channels to transmit non-emergency notification files within the enterprise. It also involves channel switching strategies, load balancing, and other multi-faceted planning.

[0058] As an embodiment of the present invention, the optimizing the channel configuration of the real-time data group in the data transmission process based on the data throughput includes: analyzing the transmission rate level corresponding to the data throughput; dividing the priority categories corresponding to the real-time data group based on the transmission rate level; evaluating the sensitivity of the data group to transmission delay under each priority level according to the priority category; querying the available channel resources corresponding to the sensitivity; and optimizing the channel configuration of the real-time data group in the data transmission process based on the available channel resources.

[0059] The transmission rate level refers to different grades divided according to the numerical value of data throughput, which directly reflects the speed of data flow in the channel. For example, data throughput can be divided into three levels: low speed (less than 10Mbps), medium speed (10Mbps-100Mbps), and high speed (greater than 100Mbps); the priority category refers to the classification of real-time data groups based on the transmission rate level, combined with factors such as the criticality of the business and real-time requirements. For example, in a financial transaction scenario, real-time transaction data has extremely high timeliness requirements. If it corresponds to the high-speed transmission rate level, it will be classified as high priority; while some background statistical data has low transmission rate requirements and belongs to At the low-speed level, it is classified as low priority accordingly; the sensitivity refers to the tolerance of data groups under each priority level to transmission delays. High-priority data groups, such as picture data in live video broadcasts, can be detected by the audience if there is a slight delay, and are extremely sensitive to transmission delays; low-priority data groups, such as regularly updated software logs, have little impact on the business even if delayed for hours or even days, and are less sensitive; the available channel resources refer to all channels available for deployment and their related attributes in the current network environment, including wired channels such as optical fiber and Ethernet, wireless channels such as Wi-Fi, 4G / 5G, their respective bandwidth, signal strength, packet loss rate, as well as channel busyness, idle time and other information.

[0060] Furthermore, the analysis of the transmission rate level corresponding to the data throughput can be achieved through a threshold division method, such as setting the threshold of the low-speed level to less than 10Mbps, the medium-speed level to 10Mbps-100Mbps, and the high-speed level to greater than 100Mbps; the division of the priority categories corresponding to the real-time data group can be achieved through a category division model, such as decision tree classification, support vector machine and other models; the evaluation of the sensitivity of the data group to transmission delay under each priority level can be achieved through a QoS method, such as requiring end-to-end delay less than 100ms to be considered highly sensitive; for low-priority file backup data groups, the delay requirement may be to complete the backup within a few hours, and the delay sensitivity is low; the query of available channel resources corresponding to the sensitivity level can be achieved through a resource query tool, such as NRMS, SDN and other tools; the optimization of the channel configuration of the real-time data group in the data transmission process can be achieved through a dynamic channel allocation algorithm, such as a greedy algorithm.

[0061] By calculating the channel utilization rate corresponding to the channel configuration, the present invention can accurately understand the utilization efficiency of network resources, provide a strong basis for business expansion, and by understanding the utilization rate, predict the carrying demand of new services for channels, plan and optimize in advance, and ensure that the service quality is not degraded.

[0062] The channel utilization rate refers to the ratio of the actual amount of data transmitted by the service channel to the theoretical maximum amount of data transmitted by the channel within a certain period of time, which is presented in the form of a percentage. This indicator directly reflects the degree to which channel resources are effectively utilized. For example, if a channel can theoretically transmit a maximum of 100GB of data within an hour, and the actual amount of data transmitted is 60GB, then its channel utilization rate is 60%. The level of channel utilization is affected by many factors, including the bandwidth of the channel, the amount of data transmitted, the frequency of transmission, the error retransmission during data transmission, and the idle time of the channel. Optionally, the calculation of the channel utilization rate corresponding to the channel configuration can be implemented by an algorithm based on traffic statistics, such as a simple proportional algorithm, a dynamic weighted algorithm, etc.

[0063] S5. Based on the channel utilization, generate an analysis license key corresponding to the real-time data group, query the analysis rule corresponding to the analysis license key, extract the data identifier in the analysis rule, and generate a data analysis report corresponding to the 5G message based on the data identifier.

[0064] The present invention generates an analysis license key corresponding to the real-time data group based on the channel utilization, which can ensure the security and compliance of data transmission, accurately allocate license keys according to the channel utilization, and only compliant real-time data groups can obtain transmission permissions, effectively preventing illegal data from occupying channel resources.

[0065] Among them, the analysis license key refers to an exclusive key that is determined to be highly compatible with the real-time data group after matching compatibility value verification and can be authorized to start the analysis process of the real-time data group. When the matching compatibility value reaches the set threshold (such as 80 points, out of 100 points), the initial key is officially confirmed as the analysis license key.

[0066] As an embodiment of the present invention, the generation of the analysis license key corresponding to the real-time data group based on the channel utilization includes: formulating a key generation rule corresponding to the real-time data group based on the channel utilization; querying the key character combination in the key generation rule; determining an initial key corresponding to the key character combination; verifying a matching compatibility value between the initial key and the real-time data group; and generating an analysis license key corresponding to the real-time data group based on the matching compatibility value.

[0067] Among them, the key generation rule refers to a set of criteria formulated based on the channel utilization status and the characteristics of the real-time data group (such as the business importance, sensitivity, transmission frequency, etc. of the data). For example, if the channel utilization is in a high-load range (such as exceeding 80%), for key data groups of real-time transactions, the key generation rule may tend to adopt high-intensity encryption algorithms and complex key construction logic, requiring the key length to be longer and contain numbers, uppercase and lowercase letters and special characters to ensure data transmission security; the key character combination refers to a specific character set form used to form the key under the definition of the key generation rule. For example, according to the above high-intensity encryption rules, the key character combination can be selected from a character pool containing 26 uppercase and lowercase English letters, 10 numbers, and 10 special characters such as "@#$%^&*"; the initial key refers to a preliminary password string generated by a specific arrangement order according to the selected key character combination. Taking the above combination as an example, "Ad3Bf#GhI9Jk" is directly determined as the initial key of a real-time data group under the corresponding rules; the matching compatibility value refers to an indicator used to quantitatively evaluate the degree of adaptation between the initial key and the real-time data group, which is calculated by comparing and analyzing the format, data type, transmission protocol and other characteristics of the data group with the encryption characteristics of the initial key. For example, if the real-time data group is transmitted in XML format, the initial key encryption algorithm has specific adaptation optimization for the XML structure.

[0068] Furthermore, the formulation of the key generation rules corresponding to the real-time data group can be implemented through a policy-based method, such as: comprehensively evaluating the criticality of the business and the channel utilization, matching corresponding policies for various real-time data groups, and thus determining the key generation rules; the querying of the key character combination in the key generation rules can be implemented through a rule engine matching query method, such as: if the rule is "10 digits containing uppercase letters, numbers, special characters and at least 2 special characters", the rule engine will perform a matching search in a predefined character combination set, and quickly locate the key character combination that meets the requirements through an efficient pattern matching algorithm; the determination of the initial key corresponding to the key character combination can be implemented through a random generation algorithm, such as: by setting a seed value to ensure a certain degree of randomness, generating an initial key such as "45678912"; the verification of the matching compatibility value of the initial key and the real-time data group can be implemented through a compatibility testing framework, such as: building a compatibility testing framework including modules such as data format parsing, transmission protocol adaptation, encryption and decryption performance testing, and using the framework to perform data verification to obtain a matching compatibility value; the generation of the analysis license key corresponding to the real-time data group can be implemented through a key generation tool, such as: Keygrip, OpenSSL and other tools.

[0069] The present invention can accurately locate required data by querying the analysis rules corresponding to the analysis license key and extracting the data identifier in the analysis rules, and can quickly find specific data associated with the license key in massive data, thereby improving data retrieval efficiency.

[0070] The analysis rule refers to a set of detailed data processing and analysis criteria pre-established for the real-time data group associated with the generated analysis license key, which covers the provisions of data extraction methods, screening conditions, conversion methods, and analysis processes. For example, in the financial data analysis scenario, for the transaction flow data group unlocked using a specific analysis license key, the analysis rule may stipulate that large transaction records within the past month are extracted in order of transaction time (screening conditions), the currency unit of the transaction amount is uniformly converted into RMB (conversion method), and the transaction frequency and amount distribution are calculated through a specific statistical model (analysis process); the data identifier refers to a label or code used to uniquely identify a specific data element or data subset in the analysis rule, which can accurately locate the data part that needs attention in a complex data set. For example, in an e-commerce sales data analysis case, according to the analysis rule, the data identifier can be "order number", "product category code", "customer region code", etc. Optionally, the query of the analysis rule corresponding to the analysis license key can be implemented by a rule query tool, such as RulerZ, FoFaX and other tools; the extraction of the data identifier in the analysis rule can be implemented by a syntax parsing tool, such as ANTLR and other tools.

[0071] Furthermore, the present invention generates a data analysis report corresponding to the 5G message based on the data identifier, which can accurately focus on key information. With the help of the data identifier, it can quickly lock the extremely valuable data fragments in the 5G message, such as user behavior, message popularity, etc., so that the report sticks to the core points.

[0072] Among them, the data analysis report refers to the written results formed after systematic organization and in-depth analysis of specific information screened and extracted by data identifiers for 5G message-related data. It covers many aspects, such as from the user dimension, including the user's reception frequency, reading time, interactive feedback (likes, comments, number of reposts), etc. of 5G messages, so as to understand user participation and preferences; from the content dimension, analyze the dissemination effect of different types of 5G messages (such as marketing, notification, and social), and which topics and formats are more popular; from the performance dimension, evaluate the success rate and delay of message sending, and reflect the quality of network transmission. Optionally, the generation of the data analysis report corresponding to the 5G message can be achieved through statistical analysis tools, such as: using the visualization function of the SPSS tool to generate bar charts and line charts to display the analysis results, and finally output a data analysis report containing detailed data, comparison charts and conclusion suggestions.

[0073] It can be seen that the present invention receives 5G messages and collects multivariate data information in the 5G messages. In the financial field, it can capture market fluctuations in real time, provide accurate basis for risk management and investment decisions, and improve the timeliness and scientificity of decisions; in industrial production, it can instantly obtain equipment operating parameters, optimize production processes, and enhance production efficiency and stability; in the construction of smart cities, it helps to quickly integrate multi-source data such as traffic and environment, realize intelligent management, and improve the overall efficiency of urban operations. Furthermore, the present invention performs fluctuation analysis on the data items in the real-time data group to obtain a fluctuation trend sequence, which can clearly understand the dynamic change rules of the data and accurately capture the fluctuations of the data. Whether it is the rise and fall of market prices, the high and low peaks of network traffic, or the fluctuations of equipment performance parameters, they can all be accurately presented, making the change trend of the data clear at a glance. Based on the change rate, the real-time business scenario corresponding to the real-time data group is restored, which can accurately understand the business dynamics. The change rate can be used to trace the transformation of the business at key nodes. For example, the change rate of sales data can show the immediate effect of promotional activities. The present invention determines the business channel corresponding to the real-time data group based on the core point of the element, and can accurately locate the data transmission path that closely matches the core needs of the business, reduce unnecessary resource consumption and data redundancy, improve transmission efficiency, and help optimize business processes. By ensuring that key data is transmitted in an efficient channel, the business response speed is enhanced. Based on the channel utilization rate, the present invention generates an analysis license key corresponding to the real-time data group, which can ensure data transmission security and compliance. The license key is accurately allocated according to the channel utilization rate. Only compliant real-time data groups can obtain transmission permissions, effectively preventing illegal data from occupying channel resources. Therefore, a real-time data analysis system, method, device and medium based on 5G message drive proposed in an embodiment of the present invention can improve the analysis efficiency of real-time data.

[0074] like Figure 3 Shown is a functional module diagram of a real-time data analysis system driven by 5G messages according to the present invention.

[0075] The real-time data analysis system 200 driven by 5G messages described in the present invention can be installed in an electronic device. According to the functions implemented, the real-time data analysis system driven by 5G messages can include a data retrieval module 201, a change rate calculation module 202, a core point marking module 203, a channel utilization calculation module 204 and a report generation module 205. The module described in the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, which are stored in the memory of the electronic device.

[0076] In this embodiment, the functions of each module / unit are as follows: The data retrieval module 201 is used to receive a 5G message, collect multivariate data information in the 5G message, parse the data source identifier corresponding to the multivariate data information, extract the data feature code associated with the data source identifier, and retrieve the real-time data group corresponding to the data feature code; The change rate calculation module 202 is used to perform fluctuation analysis on the data items in the real-time data group to obtain a fluctuation trend sequence, identify key fluctuation nodes in the fluctuation trend sequence, and calculate the node change rate corresponding to the key fluctuation node; The core point marking module 203 is used to restore the real-time business scene corresponding to the real-time data group based on the change rate, classify the real-time business scene with labels, obtain classified scene labels, query key label elements in the classified scene labels, and mark the element core points corresponding to the key label elements; The channel utilization calculation module 204 is used to determine the service channel corresponding to the real-time data group based on the element core point, analyze the channel quality corresponding to the service channel, calculate the data throughput corresponding to the service channel based on the channel quality, optimize the channel configuration of the real-time data group in the data transmission process based on the data throughput, and calculate the channel utilization corresponding to the channel configuration; The report generation module 205 is used to generate an analysis license key corresponding to the real-time data group based on the channel utilization, query the analysis rules corresponding to the analysis license key, extract the data identifier in the analysis rules, and generate a data analysis report corresponding to the 5G message based on the data identifier.

[0077] Optionally, the data retrieval module 201 retrieves the real-time data group corresponding to the data feature code, including: Identify key identification information corresponding to the data feature code; Constructing a search index framework corresponding to the key identification information; Preliminarily screening the matching data sets in the search index framework; Analyze the strongly associated data blocks corresponding to the data subset in the matchable data set; Based on the strongly associated data block, a real-time data group corresponding to the data feature code is retrieved.

[0078] Accordingly, the change rate calculation module 202 performs fluctuation analysis on the data items in the real-time data group to obtain a fluctuation trend sequence, including: Sorting the data items in the real-time data group to obtain sorted data items; Dividing the sorted data items into their corresponding time windows; Calculate the statistical characteristic value corresponding to the data in the time window; Analyzing the eigenvector corresponding to the statistical eigenvalue; Extracting a fluctuation range indicator from the feature vector; Based on the fluctuation amplitude index, a fluctuation analysis is performed on the data items in the real-time data group to obtain a fluctuation trend sequence.

[0079] Optionally, the core point marking module 203 restores the real-time business scenario corresponding to the real-time data group based on the change rate, including: Analyze the fluctuation range corresponding to the change rate; Based on the fluctuation amplitude range, classify the business fluctuation level corresponding to the real-time data group; Based on the business fluctuation level, matching the business scenario type corresponding to the real-time data group; Query the scenario characteristic parameters corresponding to the business scenario type; Based on the scenario characteristic parameters, the real-time business scenario corresponding to the real-time data group is restored.

[0080] Reliably, the core point marking module 203 is specifically used to mark the core point of the element corresponding to the key tag element, including: Analyze the element semantics corresponding to the key tag element; Based on the element semantics, divide the information level corresponding to the key tag element; identifying dominant features corresponding to information in the information hierarchy; Based on the dominant feature, the element core point corresponding to the key tag element is marked.

[0081] Optionally, the channel utilization calculation module 204 is specifically configured to determine the service channel corresponding to the real-time data group based on the element core point, including: Analyze the key business requirements corresponding to the core points of the elements; Based on the business critical requirements, identifying a relevant subset of the real-time data group; Evaluate the influence weight of the associated subset on the business process; Based on the impact weights, filtering the business data items in the key business requirements; Query the transmission protocol and network architecture adapted by the business data item; Based on the transmission protocol and the network architecture, a service channel corresponding to the real-time data group is determined.

[0082] Furthermore, the channel utilization calculation module 204 is specifically used to optimize the channel configuration of the real-time data group in the data transmission process based on the data throughput, including: Analyzing the transmission rate level corresponding to the data throughput; Based on the transmission rate level, classify the priority categories corresponding to the real-time data groups; According to the priority category, evaluating the sensitivity of the data group at each priority level to transmission delay; Querying available channel resources corresponding to the sensitivity level; Based on the available channel resources, the channel configuration of the real-time data group in the data transmission process is optimized.

[0083] like Figure 4 As shown, it is a structural schematic diagram of an electronic device 1 for implementing real-time data analysis based on 5G message drive according to the present invention.

[0084] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a real-time data analysis program driven by 5G messages.

[0085] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (ControlUnit) of the electronic device 1, and uses various interfaces and lines to connect the various components of the entire electronic device 1, and executes or executes programs or modules stored in the memory 11 (for example, executing a real-time data analysis program based on 5G message drive, etc.), and calls the data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0086] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as a code of a real-time data analysis program driven by 5G messages, but also can be used to temporarily store data that has been output or is to be output.

[0087] The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0088] The communication interface 13 is used for communication between the above-mentioned electronic device 1 and other devices, including a network interface and an employee interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices 1. The employee interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the employee interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual employee interface.

[0089] Figure 4 Only the electronic device 1 having components is shown, and those skilled in the art can understand that Figure 4The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0090] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0091] It should be understood that the embodiment is for illustrative purposes only and the scope of the patent invention is not limited to this structure.

[0092] The real-time data analysis program based on 5G message drive stored in the memory 11 of the electronic device 1 is a combination of multiple computer programs. When running in the processor 10, it can achieve: Receive a 5G message, collect multivariate data information in the 5G message, parse a data source identifier corresponding to the multivariate data information, extract a data feature code associated with the data source identifier, and retrieve a real-time data group corresponding to the data feature code; Performing fluctuation analysis on the data items in the real-time data group to obtain a fluctuation trend sequence, identifying key fluctuation nodes in the fluctuation trend sequence, and calculating the node change rate corresponding to the key fluctuation node; Based on the change rate, restore the real-time business scene corresponding to the real-time data group, classify the real-time business scene with labels to obtain classified scene labels, query key label elements in the classified scene labels, and mark the element core points corresponding to the key label elements; Based on the element core point, determine the service channel corresponding to the real-time data group, analyze the channel quality corresponding to the service channel, calculate the data throughput corresponding to the service channel based on the channel quality, optimize the channel configuration of the real-time data group in the data transmission process based on the data throughput, and calculate the channel utilization rate corresponding to the channel configuration; Based on the channel utilization, an analysis license key corresponding to the real-time data group is generated, an analysis rule corresponding to the analysis license key is queried, a data identifier in the analysis rule is extracted, and based on the data identifier, a data analysis report corresponding to the 5G message is generated.

[0093] Specifically, the specific implementation method of the processor 10 for the above computer program can refer to Figure 1 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0094] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0095] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of the electronic device 1, the computer program can implement: Receive a 5G message, collect multivariate data information in the 5G message, parse a data source identifier corresponding to the multivariate data information, extract a data feature code associated with the data source identifier, and retrieve a real-time data group corresponding to the data feature code; Performing fluctuation analysis on the data items in the real-time data group to obtain a fluctuation trend sequence, identifying key fluctuation nodes in the fluctuation trend sequence, and calculating the node change rate corresponding to the key fluctuation node; Based on the change rate, restore the real-time business scene corresponding to the real-time data group, classify the real-time business scene with labels to obtain classified scene labels, query key label elements in the classified scene labels, and mark the element core points corresponding to the key label elements; Based on the element core point, determine the service channel corresponding to the real-time data group, analyze the channel quality corresponding to the service channel, calculate the data throughput corresponding to the service channel based on the channel quality, optimize the channel configuration of the real-time data group in the data transmission process based on the data throughput, and calculate the channel utilization rate corresponding to the channel configuration; Based on the channel utilization, an analysis license key corresponding to the real-time data group is generated, an analysis rule corresponding to the analysis license key is queried, a data identifier in the analysis rule is extracted, and based on the data identifier, a data analysis report corresponding to the 5G message is generated.

[0096] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0097] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0098] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0099] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0100] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0101] The embodiments of the present invention can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0102] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The second and other words are used to indicate names, but not to indicate any particular order.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

[0104] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0105] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit it. If software tools or components other than those of the company appear in the application embodiments, they are only used for illustrative purposes and do not represent actual use. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A real-time data analysis method based on 5G message drive, characterized in that: The method comprises: Receive a 5G message, collect multivariate data information in the 5G message, parse a data source identifier corresponding to the multivariate data information, extract a data feature code associated with the data source identifier, and retrieve a real-time data group corresponding to the data feature code; Performing fluctuation analysis on the data items in the real-time data group to obtain a fluctuation trend sequence, identifying key fluctuation nodes in the fluctuation trend sequence, and calculating the node change rate corresponding to the key fluctuation node; Based on the change rate, restore the real-time business scene corresponding to the real-time data group, classify the real-time business scene with labels to obtain classified scene labels, query key label elements in the classified scene labels, and mark the element core points corresponding to the key label elements; Based on the element core point, determine the service channel corresponding to the real-time data group, analyze the channel quality corresponding to the service channel, calculate the data throughput corresponding to the service channel based on the channel quality, optimize the channel configuration of the real-time data group in the data transmission process based on the data throughput, and calculate the channel utilization rate corresponding to the channel configuration; Based on the channel utilization, an analysis license key corresponding to the real-time data group is generated, an analysis rule corresponding to the analysis license key is queried, a data identifier in the analysis rule is extracted, and based on the data identifier, a data analysis report corresponding to the 5G message is generated.

2. A real-time data analysis method based on 5G message drive as claimed in claim 1, characterized in that: The retrieving the real-time data group corresponding to the data feature code includes: Identify key identification information corresponding to the data feature code; Constructing a search index framework corresponding to the key identification information; Preliminarily screening the matching data sets in the search index framework; Analyze the strongly associated data blocks corresponding to the data subset in the matchable data set; Based on the strongly associated data block, a real-time data group corresponding to the data feature code is retrieved.

3. A real-time data analysis method based on 5G message drive as claimed in claim 1, characterized in that: The performing fluctuation analysis on the data items in the real-time data group to obtain a fluctuation trend sequence includes: Sorting the data items in the real-time data group to obtain sorted data items; Dividing the sorted data items into their corresponding time windows; Calculate the statistical characteristic value corresponding to the data in the time window; Analyzing the eigenvector corresponding to the statistical eigenvalue; Extracting a fluctuation range indicator from the feature vector; Based on the fluctuation amplitude index, a fluctuation analysis is performed on the data items in the real-time data group to obtain a fluctuation trend sequence.

4. A real-time data analysis method based on 5G message drive as claimed in claim 1, characterized in that: The restoring the real-time business scenario corresponding to the real-time data group based on the change rate includes: Analyze the fluctuation range corresponding to the change rate; Based on the fluctuation amplitude range, classify the business fluctuation level corresponding to the real-time data group; Based on the business fluctuation level, matching the business scenario type corresponding to the real-time data group; Query the scenario characteristic parameters corresponding to the business scenario type; Based on the scenario characteristic parameters, the real-time business scenario corresponding to the real-time data group is restored.

5. A real-time data analysis method based on 5G message drive as claimed in claim 1, characterized in that: The marking of the core point of the element corresponding to the key tag element includes: Analyze the element semantics corresponding to the key tag element; Based on the element semantics, divide the information level corresponding to the key tag element; identifying dominant features corresponding to information in the information hierarchy; Based on the dominant feature, the element core point corresponding to the key tag element is marked.

6. A real-time data analysis method based on 5G message drive as claimed in claim 1, characterized in that: The determining, based on the element core point, a service channel corresponding to the real-time data group includes: Analyze the key business requirements corresponding to the core points of the elements; Based on the business-critical requirements, identifying a relevant subset of the real-time data set; Evaluate the influence weight of the associated subset on the business process; Based on the impact weights, filtering the business data items in the key business requirements; Query the transmission protocol and network architecture adapted by the business data item; Based on the transmission protocol and the network architecture, a service channel corresponding to the real-time data group is determined.

7. A real-time data analysis method based on 5G message drive as claimed in claim 1, characterized in that: The optimizing the channel configuration of the real-time data group in the data transmission process based on the data throughput includes: Analyzing the transmission rate level corresponding to the data throughput; Based on the transmission rate level, classify the priority categories corresponding to the real-time data groups; According to the priority category, evaluating the sensitivity of the data group at each priority level to transmission delay; Querying available channel resources corresponding to the sensitivity level; Based on the available channel resources, the channel configuration of the real-time data group in the data transmission process is optimized.

8. A real-time data analysis system based on 5G message drive, characterized in that: The system comprises: A data retrieval module is used to receive a 5G message, collect multivariate data information in the 5G message, parse a data source identifier corresponding to the multivariate data information, extract a data feature code associated with the data source identifier, and retrieve a real-time data group corresponding to the data feature code; A change rate calculation module is used to perform fluctuation analysis on the data items in the real-time data group to obtain a fluctuation trend sequence, identify key fluctuation nodes in the fluctuation trend sequence, and calculate the node change rate corresponding to the key fluctuation node; A core point marking module is used to restore the real-time business scene corresponding to the real-time data group based on the change rate, classify the real-time business scene with labels, obtain classified scene labels, query key label elements in the classified scene labels, and mark the element core points corresponding to the key label elements; A channel utilization calculation module is used to determine the service channel corresponding to the real-time data group based on the element core point, analyze the channel quality corresponding to the service channel, calculate the data throughput corresponding to the service channel based on the channel quality, optimize the channel configuration of the real-time data group in the data transmission process based on the data throughput, and calculate the channel utilization corresponding to the channel configuration; A report generation module is used to generate an analysis license key corresponding to the real-time data group based on the channel utilization, query the analysis rules corresponding to the analysis license key, extract the data identifier in the analysis rule, and generate a data analysis report corresponding to the 5G message based on the data identifier.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a real-time data analysis method based on 5G message driving as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, it implements a real-time data analysis method based on 5G message drive as described in any one of claims 1 to 7.

Images