RedCap industrial private network performance evaluation method and device

Abstract

The invention belongs to the technical field of mobile communication, and discloses a RedCap industrial private network performance evaluation method and device, and the overall process of the method comprises the steps: reconstructing a private network performance evaluation model according to the industrial Internet of Things service overhead characteristics; configuring a multi-protocol adaptive monitoring interface, and collecting multi-source data of network management, sensing, planning and surveying and mapping; constructing a lake-cabin integrated hierarchical model, and realizing heterogeneous fusion and classified storage of multi-modal data; performing multi-dimensional network performance comprehensive evaluation by applying an intelligent analysis engine; reports are published on line in a diversified mode, and data-driven decisions of operation and maintenance teams are supported. The method focuses on multi-protocol and multi-mode complex business characteristics in an industrial Internet of Things scene, reconstructs a performance evaluation model according to'end-network-chain 'total factor indexes, and deeply integrates advanced technologies of lake and warehouse integration, artificial intelligence and digital twinborn visualization. The industrial pain points of single industrial private network evaluation dimension and difficulty in abnormal traceability are effectively solved.

Description

Technical Field

[0001] The present invention belongs to the field of mobile communication technology, and specifically relates to a RedCap industrial private network performance evaluation method and device. Background Art

[0002] RedCap (Reduced Capability) is a lightweight version of the 5G network, designed for industrial Internet of Things (IIoT) and vertical industry applications. Compared with traditional 5G technology, RedCap reduces the complexity and cost of equipment while ensuring key performance (such as low latency and high reliability), making it more suitable for large-scale deployment in industrial scenarios. RedCap can support efficient networking and data transmission of industrial equipment by simplifying the protocol stack, optimizing hardware design, and reducing power consumption.

[0003] With the widespread application of "5G + Industrial Internet of Things" in smart manufacturing, energy management, logistics monitoring and other fields, the performance evaluation requirements of RedCap industrial private networks are developing in a more refined and intelligent direction. In the smart manufacturing scenario, the real-time data transmission and high-precision control of equipment place extremely high demands on the network's latency and reliability. For example, industrial robots, automated production lines and other equipment need to complete data interaction and command response within milliseconds. Any network delay or jitter may cause production interruption or product quality problems. Therefore, there is an urgent need for a method that can realize the automation and precision of RedCap industrial private network performance evaluation, so as to accurately capture key indicators such as network latency, packet loss rate, and jitter.

[0004] Existing technical solutions for private network performance evaluation include instrument detection, advanced network probes and network management performance statistics. Among them, the instrument detection solution is technically mature, but requires human operation and does not support automated monitoring; the advanced network probe solution supports service-aware dialing, deep packet inspection and link fault location by implanting soft probes in terminals, base stations, routers, etc., and is suitable for monitoring network transmission performance, but its implementation is complex and costly; the network management performance statistics solution collects and analyzes equipment performance data in the OMC network management system, and can monitor network operation status, locate network bottlenecks and diagnose network faults, but cannot process service-aware data on the terminal side.

[0005] Therefore, the current urgent problem to be solved is how to improve the accuracy and efficiency of performance evaluation, which not only requires the combination of artificial intelligence and big data analysis technology, but also requires the establishment of a more flexible evaluation model to adapt to the needs of the rapid development of the industrial Internet of Things. To this end, this application proposes a RedCap industrial private network performance evaluation method and device to improve or solve the above-mentioned problems. Summary of the invention

[0006] The purpose of the present invention is to provide a RedCap industrial private network performance evaluation method and device to solve the problems raised in the above background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: RedCap industrial private network performance evaluation method and device, the overall process of the method is: S101, Reconstruct the private network performance evaluation model based on the business overhead characteristics of industrial IoT; S102, configuring a multi-protocol adapted monitoring interface and collecting multi-source data of network management, perception, planning and mapping; S103, build a hierarchical model integrating lake and warehouse to realize heterogeneous fusion and classified storage of multimodal data; S104, using the intelligent analysis engine to perform multi-dimensional comprehensive network performance evaluation; S105, publish diversified online reports to support data-driven decision-making of the operation and maintenance team.

[0008] Preferably, in S101, it is necessary to first analyze the network overhead characteristics in terms of data volume, data type, transmission frequency, communication protocol, and time sensitivity for typical services in the industrial IoT scenario, and clarify the performance indicator requirements in terms of coverage, transmission, traffic, load, and reliability. Then, a private network performance evaluation model suitable for the industrial IoT scenario is constructed, and the system functional requirements of the industrial site for multi-protocol adaptation, multi-modal heterogeneous storage, multi-dimensional intelligent analysis, and diversified report publishing are met through the data flow closed loop of collection → storage → analysis → secondary storage → reporting.

[0009] Preferably, the S102 collects data based on probe sensing technology and network management performance statistics technology, and the monitoring interface includes: FTP file transfer protocol interface, through which the OMC network management is connected to the south, and statistical data of performance, resources, and wireless measurement reports are collected in real time, and transmitted in HTTP request-response message format; REST API protocol interface, through which the OMC network management is connected to the south, and statistical data of network element status and network element alarms are collected in real time, and transmitted in HTTP request-response message format; MQTT message transmission protocol interface, through which the active probe is connected to the south, and analog dial-type perception data is collected in real time, and transmitted in MQTT service-subscription message format; NFS and SMB network sharing file protocol interface, through which the network planning server and geographic mapping server are connected to the south, and global data updates are obtained, supporting multi-modal format transmission of text, pictures, tables, and 3D real-life models.

[0010] Preferably, the specific steps of S103 are: A1: First, integrate relational databases and NoSQL databases to build a dual-engine data lake storage space, and unify data governance and elastic expansion through cross-platform clients; A2 then runs a data collection script adapted for multiple protocols to achieve synchronous data collection from multiple sources, and writes the response data into the dual-engine architecture data lake in real time; A3. Then, a subject data warehouse is established through the domain dimension model to classify, integrate and store the real-time data and historical data of the private network performance evaluation, supporting efficient data query, analysis and report generation. A4, finally runs the multimodal adapted data integration script, extracts unprocessed raw data from the dual-engine architecture data lake, performs heterogeneous fusion operations, and imports the fused high-quality data into relevant subject databases.

[0011] Preferably, the relational database in A1 adopts a distributed deployment architecture to carry the time series structured data generated by the OMC network management and active probes, while the NoSQL database adopts a distributed storage engine to carry the multimodal unstructured data generated by the network planning server and the geographic mapping server. In addition, the cross-platform client tool is based on a data weaving architecture to achieve centralized management and unified query of the relational database and the NoSQL database; The collection rules of A2 are: For the raw data collected in a lightweight file format, the data collection script parses it into structured data and maps it to the relational database; for the raw data collected in a message format, the data collection script only extracts the BODY text therein and maps it to the relational database; for the raw data collected in a text or table format, the data collection script parses and converts it into a string field, a key-value pair or an array, and maps it to the NoSQL database; for the raw data collected in a picture or three-dimensional real-scene model format, the data collection script stores it in a specified file directory and only records related metadata in the NoSQL database.

[0012] Preferably, The S104 intelligent analysis engine includes an end-to-end performance evaluation function module, a topology generation function module, a trend prediction function module, a quality difference identification function module, a bottleneck identification function module, a root cause location function module and a threshold tuning function module; The end-to-end performance evaluation function module uses a weighted average algorithm to automatically calculate the comprehensive score of service communication performance; the topology generation function module generates the optimal service path through the shortest path algorithm, and marks the key nodes and path list based on the network topology coincidence algorithm; the trend prediction function module uses time series linear analysis to predict the trend of indicator changes and identify potential degradation indicators; the quality difference identification function module combines the rule engine and clustering algorithm to screen and deeply analyze wireless cells and network element devices with abnormal performance; the bottleneck identification function module uses the rule engine to identify abnormal load flow nodes, combines the topology key node list, and marks resource utilization abnormal devices through clustering analysis; the root cause location function module integrates the quality difference and bottleneck identification results, and uses the causal reasoning engine to quickly analyze the root cause of the potential degradation indicators predicted by the trend; the threshold tuning function module dynamically adjusts the performance indicator threshold to ensure that the optimization direction is consistent with the predicted trend; The intelligent analysis engine also includes a data analysis script, which calls various functional modules of the intelligent analysis engine through the data analysis script to conduct a comprehensive and in-depth evaluation, prediction, analysis, and diagnosis of network performance, and import the evaluation results into an application-level subject database.

[0013] Preferably, the specific steps of S105 are: B1, deploy GIS server and Web server and open SIP / SDP multimedia communication protocol interface and HTTP / HTTPS communication protocol interface; B2, load the predefined modular report template and the predefined 3D visualization dashboard template; B3, run the data presentation script, call the GIS server and Web server, automatically generate modular reports and 3D visualization dashboards, and publish them online in real time through multiple channels.

[0014] Preferably, the device includes a collection module, a fusion module, an application module and a publishing module; The acquisition module connects to the OMC network management, active probes, network planning servers and geographic mapping servers through the enterprise intranet. The module realizes the acquisition synchronization and transient storage of multi-source data by running data acquisition scripts; The fusion module is used to manage and maintain the subject data warehouse of the domain dimension model. The module realizes the fusion processing and classified storage of heterogeneous data by running data integration scripts; The application module implements comprehensive and in-depth network performance evaluation by running data analysis scripts and calling intelligent analysis engines; The publishing module locally connects to the monitoring screen and the Web client, and remotely connects to the 5G message service platform and the instant messaging service platform. The module runs the data presentation script, calls the GIS server and the Web server, and realizes diversified online publishing of evaluation reports.

[0015] Preferably, the device configures the task sequence of the data acquisition script → the data integration script → the data analysis script → the data presentation script based on the system timing function of Linux Cron / Windows task planning, including the timeline of activation, termination, and abnormal exit, to achieve automatic scheduling and cyclic operation of the entire process.

[0016] Preferably, it comprises a processor and a memory in communication with the processor, the memory stores computer-executable instructions, and the processor executes the computer-executable instructions stored in the memory to implement the above method; It also includes a computer-readable storage medium and a computer program, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the above method is implemented; and when the computer program is executed by a processor, the above method is implemented.

[0017] The beneficial effects of the present invention are as follows: This invention focuses on the complex business characteristics of multiple protocols and multiple modes in the industrial Internet of Things scenario, reconstructs the performance evaluation model according to the full-factor indicators of "end-network-chain", and deeply integrates the cutting-edge technologies of lake-warehouse integration, artificial intelligence, and digital twin visualization, effectively solving the industry pain points of the single evaluation dimension of industrial private networks and the difficulty in tracing abnormalities. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a diagram of the RedCap industrial private network performance evaluation method of the present invention; Figure 2 This is the RedCap industrial private network performance evaluation model diagram of the present invention; Figure 3 It is a system framework diagram of the performance evaluation device of the present invention; Figure 4 This is an application scenario diagram of the performance evaluation device of the present invention. DETAILED DESCRIPTION

[0019] 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 only 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.

[0020] like Figures 1 to 4 As shown, the embodiment of the present invention provides a RedCap industrial private network performance evaluation method and device, and the overall process of the method is as follows: S101. Reconstruct the private network performance evaluation model based on the business overhead characteristics of industrial IoT; In this embodiment, for the overhead characteristics and carrying requirements of different types of industrial IoT services, an example of the quantitative analysis results is shown in Table 1. The data types, communication protocols, etc. involved vary greatly, and the required network bandwidth, transmission frequency, transmission delay, etc. are different.

[0021] Table 1 Quantitative analysis

[0022] In this embodiment, based on the evaluation results in Table 1, the private network performance evaluation model is reconstructed, such as Figure 2 The normalized data flow is a closed-loop architecture of collection → storage → analysis → secondary storage → reporting, as shown in Figure 2 The middle dotted line path realizes the requirements for system functions such as multi-protocol adaptation, multi-modal heterogeneous storage, multi-dimensional intelligent analysis, and diversified report publishing in the industrial Internet of Things scenario.

[0023] Furthermore, the collection domain is a collection of multiple monitoring data sources; between the collection domain and the storage domain, the original data is transmitted through a multi-protocol adapter interface; Furthermore, the storage domain adopts a lake-warehouse integrated architecture, where the data lake is used to temporarily store unprocessed raw data; the data warehouse is used to store the subject data that has been processed by multimodal fusion. The storage domain and the application domain transmit the subject data in real-time streams or historical batches on demand; Furthermore, the application domain is a collection of multiple evaluation function modules; the evaluation data output by the application domain is written into the data warehouse of the storage domain after standardized integration and visual conversion; Furthermore, the report domain is a collection of multiple publishing channels; the report domain reads the data warehouse of the storage domain and publishes modular reports and three-dimensional visualization dashboards driven by evaluation data; Furthermore, the template domain provides predefined modular report templates and three-dimensional visualization dashboard templates to the report domain; the template domain is linked with the acquisition domain and the application domain to newly construct or reconfigure the report template.

[0024] It should be pointed out that the above private network performance evaluation model clearly defines the core elements and basic processes of the Redcap industrial private network performance evaluation method. Based on this, the technical details of the evaluation model will be implemented in stages.

[0025] S102, configure a monitoring interface that is compatible with multiple protocols to collect multi-source data such as network management, perception, planning, and mapping; In this embodiment, through the organic combination of service-aware dialing means and network management performance statistics means, the monitoring objects are expanded to multiple levels such as service links, air interface protocols, and equipment hardware and software, as shown in Table 2, thereby enhancing the breadth and depth of private network performance evaluation.

[0026] Table 2 Performance monitoring data

[0027] In this embodiment, the monitoring requirements for geographic surveying and mapping data and network planning data are expanded, as shown in Table 3, to achieve three-dimensional visualization dashboards, automatic topology generation, etc., thereby optimizing the user experience of private network performance evaluation.

[0028] Table 3 Extended monitoring data

[0029] Furthermore, the three-dimensional real-life model is generated based on technical means such as oblique photography, laser point cloud scanning or indoor BIM (Building Information Model), and stored in the S3M (Spatial 3D Model) geospatial format, and generally complies with the technical requirements of the "Overall Implementation Plan for the Construction of Realistic Three-Dimensional China (2023-2025)" and its related supporting standards and specifications.

[0030] In this embodiment, referring to the general technical specifications of telecom operators, the statistical strategies of the OMC network management are set according to the cell granularity and the network element granularity, and statistical data sets such as performance, resources and wireless measurement reports (MRs) are transmitted through the server-client architecture of the SFTP / FTP file transfer protocol.

[0031] Furthermore, the general technical specifications of telecom operators mainly include interface specifications of 5G wireless network management systems, such as "China Telecom 5G Wireless Network Capacity Scheduling Subsystem Interface Technical Requirements I1 Interface", "YD / T 4826-20245G Digital Cellular Mobile Communication Network Wireless Operation and Maintenance Center (OMC-R) Measurement Report Technical Requirements", "YD / T 4290-20235G Network Management Technical Requirements Performance Measurement Data Requirements" and other technical documents.

[0032] Furthermore, the collection period of performance (PM) data defaults to 1 hour, the collection period of resource (NRM) data defaults to 12 hours, and the collection period of wireless measurement report (MR) defaults to 15 minutes. All are encapsulated as .CSV or .XML format files, the SFTP / FTP service port defaults to Port 22 / Port 21, and an example of the storage directory is / FTProot / NBI / Status / 5Gxxxx / .

[0033] In this embodiment, a RESTful server is run on the OMC network management, and according to the REST API convention provided by the OMC network management, a HTTP client is used to send a GET or POST method, so that statistical data sets such as network element status and network element alarm can be queried from the OMC network management.

[0034] Furthermore, REST API conventions usually include API interface name, authorization access token (TOKEN), resource URL, request method (GET method or POST method), request parameters (such as time range, paging parameters, alarm status, etc.), response parameters (data content in JSON format), etc.

[0035] Taking querying the cell status as an example, an example of the request method is GET, an example of the request address is Https: / / domain name / RestAPIdata / Devparm / GetgNBalarmlist, an example of the request parameter is shown in Table 4, and an example of the response parameter is shown in Table 5.

[0036] Table 4 GET method request parameters

[0037] Table 5 GET method response parameters

[0038] The remaining method conventions, such as querying cell alarms, network element status, network element alarms, etc., are similar to the examples in Table 4 and Table 5 and will not be repeated here.

[0039] In this embodiment, the active probe adopts a hard probe solution with a built-in RedCap module, which is deployed in the same 5G industrial base station cell as the RedCap industrial terminal and configured with the same 5G LAN group parameters in the 5G UDM. The active probe accesses the RedCap industrial private network wirelessly to establish an end-to-end transmission link for simulating dialing and carrying perception data.

[0040] Furthermore, for network terminals (such as industrial gateways, industrial routers), control terminals (such as industrial robots, programmable logic controllers PLCs, remote diagnostic units) and interactive terminals (such as HMI human-machine interfaces, augmented reality AR devices, virtual reality VR devices) at industrial sites, active probes are deployed in coordination with them in a 1:1 ratio.

[0041] Furthermore, for monitoring terminals (such as industrial cameras, smart cameras), collection terminals (such as wireless sensors for temperature, humidity, pressure, vibration, etc.) and mobile terminals (such as automatic guided vehicles AGV, autonomous mobile robots AMR) at industrial sites, active probes are deployed in coordination with them in a ratio of 1:3 or lower.

[0042] Furthermore, the active probe runs the open source iPerf3 tool, which simulates the industrial terminal behaviors of different IoT services and different 5G LAN groups through TCP / UDP traffic simulation, window adjustment, and multi-threaded parameters, implements service link performance dialing, and collects service cell wireless parameters at the same time, and packages the test results into JSON or binary format.

[0043] In this embodiment, the dialing and reporting strategies of the active probes are set according to the business granularity, and the perception-type data sets are transmitted through the MQTT message transmission protocol architecture in the publish-subscribe mode.

[0044] Furthermore, the default period for active probe dialing and reporting is 15 minutes.

[0045] Furthermore, the active probe publishes a PUBLISH message, fills in the topic path (Topic) and encapsulates the perception data into the payload (Payload), and sends it to the MQTT server (Broker). The MQTT server (Broker) pushes the message to the relevant subscribers (collectors) by querying and matching the subscription relationship.

[0046] Further, examples of topic paths are PercTest / LinkQty and PercTest / RadioCvg, and examples of payloads are shown in Tables 6 and 7. The subscriber (collector) publishes a SUBSCRIBE message, sets a topic filter using a wildcard, such as / PercTest / +, and initiates a subscription request to the MQTT server (Broker).

[0047] Table 6 Payload of Link Quality Topic

[0048] Table 7 Payload of wireless coverage topic

[0049] In this embodiment, by loading and running the NFS or SMB server on the network planning server and the geographic planning server, and enabling the Sync synchronization mode or establishing a persistent session relationship, the files of the network planning data and geographic mapping data can be automatically updated on the NFS or SMB client without manual intervention.

[0050] Furthermore, an automatic synchronization policy is set for the NFS / SMB server-client, and the default check period is 1 hour; Furthermore, the NFS (Network File System) protocol is applicable to UNIX / LINUX cluster environments. The NFS server uses the sync mode to force real-time writing, configures / etc / exports to define shared directories and permissions; the NFS client mounts the shared directory through mount -t nfs to ensure the consistency of network planning data; Furthermore, the SMB (Server Message Block) protocol is applicable to the WINDOWS environment. Persistent sessions are established on the SMB server to maintain connection stability, and cross-platform sharing rules are set through smb.conf; the SMB client can directly access the shared path, such as \\<server IP>\<shared directory>. S103. Construct a hierarchical model integrating lakes and warehouses to realize heterogeneous fusion and classified storage of multimodal data; In this embodiment, a hierarchical architecture design and a multi-topic database integration solution are used to build the best practice of lake-warehouse integration. The hierarchical architecture includes: original layer, fusion layer and topic layer. The topic database is classified according to Table 2 and Table 3, and the constellation model is used to support cross-topic joint analysis.

[0051] In this instance, in order to realize the original layer function, a dual-engine data lake architecture of "relational database + NoSQL database + cross-platform client" was designed to temporarily store unprocessed multi-source monitoring data.

[0052] Furthermore, a relational database was built using the open source PostgreSQ tool to temporarily store raw data from the OMC network management and active probes; Furthermore, the NoSQL database was built using the open source MongoDB tool to temporarily store raw data from network planning and geographic mapping; Furthermore, the cross-platform client is built using the DataGrip data lake tool, which supports processing both structured and unstructured data, such as querying, inserting, updating, and deleting operations on relational and NoSQL databases with a unified interface.

[0053] In this example, a data acquisition script was developed using the Python programming language to synchronize data acquisition of multi-protocol interfaces, dynamically parse response data, and map it to storage space.

[0054] Furthermore, the data collection script collects the raw data of performance monitoring in real time in a lightweight format (such as XML, CSV or JSON) through the SFTP / FTP protocol interface, parses it into structured data and maps it to a relational database; Furthermore, the data collection script collects the raw data of performance monitoring in real time in message format through REST API and MQTT protocol interface, extracts the BODY body or PAYLOAD payload and maps it to the relational database; Furthermore, the data collection script synchronously updates the original data in the form of text, tables, and other files through the NFS / SMB protocol interface, parses and converts it into string fields, key-value pairs, or arrays, and maps it to the NoSQL database; Furthermore, the data collection script synchronously updates the original data in the form of files such as pictures and 3D real-scene models through the NFS / SMB protocol interface, stores them in the specified local file directory, and records related metadata (such as file name, file data volume, file save address, etc.) in the NoSQL database.

[0055] In this example, in order to realize the functions of the fusion layer and the theme layer, a theme data warehouse is established based on the Microsoft SQL Server tool, and structured databases with multiple theme granularities such as business level, cell level, network element level, topology level, and application level are divided according to the domain dimension, supporting real-time stream processing of performance evaluation data and hierarchical storage of historical batches. Each theme database adopts a star model, including an independent primary key strategy, a dedicated fact table, and a dimension table system.

[0056] Furthermore, the business-level subject database uses the test case ID and active probe ID as the composite primary key, and the storage content covers multiple performance indicators such as business attributes, test cases, reliability perception, wireless perception, transmission perception, and bandwidth perception; Furthermore, the cell-level subject database uses the physical cell identifier PCI as the primary key, and the storage content covers multiple performance indicators such as cell attributes, status alarms, operation reliability, coverage performance, access performance, retention performance, mobility performance, traffic performance, load performance, etc.; Furthermore, the network element-level subject database uses the private network element ID and the 5G base station gNB ID as the composite primary key, and the storage content covers multiple performance indicators such as network element attributes, status alarms, operation reliability, transmission performance, flow performance, and load performance; Furthermore, the topology-level subject database uses network node ID, link ID and 5G LAN GID as composite primary keys, and the storage content covers multiple aspects such as topology node table, topology edge link table, 5G LAN group table, 3D real scene model metadata, etc. Furthermore, the application-level subject database uses the timestamp TimeStamp as the primary key to store the evaluation result data generated by the analysis, covering multiple aspects such as evaluation data assets, visualization charts, visualization topology, modular report templates, and three-dimensional visualization dashboard templates.

[0057] In this embodiment, a data integration script is developed using the Python programming language to implement secondary processing, heterogeneous fusion, and dimensional storage of multi-source data.

[0058] Furthermore, the data integration script is based on the regular rule engine of each subject database primary key identifier. By traversing the NoSQL database storing network planning data, it dynamically parses and captures the latest value of each primary key identifier, and drives the incremental update of the primary key mapping table (PK Mapping Table); Furthermore, the data integration script extracts the values ​​of primary key identifiers such as the test case ID / active probe ID, physical cell identifier PCI, private network element ID / 5G base station gNB ID by scanning the primary key mapping table, and extracts the associated attribute data and performance data from the relational database and NoSQL database as feature indexes. After basic cleaning such as null value filling and unit normalization, as well as customized ETL processing with enhanced operations such as superimposed MR data association and business rule fusion, the data is persistently loaded into the subject databases at the business level, cell level, and network element level according to the domain dimension model; Furthermore, the data integration script extracts the values ​​of primary key identifiers including network node ID / link ID / 5GLAN GID by scanning the primary key mapping table, and extracts the associated attribute data from the NoSQL database as a feature index. After standardized ETL processing such as data cleaning, normalization, and integration, the data is persistently loaded into the subject database at the topology level.

[0059] S104. Use the intelligent analysis engine to perform multi-dimensional comprehensive network performance evaluation; In this example, in order to improve the accuracy, security and processing efficiency of private network performance evaluation, an intelligent analysis engine was developed by combining big data analysis and artificial intelligence algorithms. An example of its functional module is shown in Table 8.

[0060] Table 8 Intelligent analysis function modules

[0061] Furthermore, the end-to-end performance evaluation function module includes: extracting real-time data of performance indicators such as wireless coverage, transmission delay, transmission jitter, packet loss rate, bandwidth and reliability (such as dialing success rate) from the business-level subject database; combining predefined performance thresholds and using a weighted average algorithm to automatically calculate the comprehensive score of the communication performance of a private network service (initiated by one or more active probes); traversing the business attributes of the business-level subject database to complete all end-to-end performance evaluation tasks. The output result example is shown in Table 9.

[0062] Table 9 End-to-end performance evaluation

[0063] Furthermore, the automatic topology generation function module includes: extracting static topology node tables, topology edge link tables, and dial test cases from the topology-level and business-level subject databases; using the Dijkstra algorithm to automatically identify the optimal path of a private network service (associated with one or more probe IDs), and recording the network element device sequence related to the path; traversing the service attributes of the business-level subject database to complete the topology routing identification task of all end-to-end services, and the output result example is shown in Table 10. At the same time, an algorithm based on network topology overlap is used to identify key nodes and key edge links in the end-to-end service concentration, and the output result examples are shown in Tables 11 and 12.

[0064] Table 10 End-to-end service route identification

[0065] Table 11 End-to-end service key nodes

[0066] Table 12 End-to-end business critical path

[0067] Furthermore, the performance trend prediction function module includes: extracting historical data of performance indicators such as wireless coverage, transmission delay, transmission jitter, packet loss rate, bandwidth and reliability (such as dialing success rate) from the business-level subject database; combining predefined performance thresholds, using the ARIMA algorithm to perform linear time series analysis, predicting the indicator change trend in the next week, and identifying performance indicators that may deteriorate. The output result example is shown in Table 13.

[0068] Table 13 Trends for the next week

[0069] Furthermore, the poor quality unit identification function module includes: extracting reliability (such as operating time, interruption time), wireless coverage, user access, service retention, reselection switching, uplink and downlink traffic, user load, status alarm and other performance indicators from the cell-level subject database; extracting real-time data of reliability, transmission link, service traffic, CPU load, status alarm and other performance indicators from the network element-level subject database; combining with predefined performance thresholds, using the rule engine algorithm to quickly screen the extracted performance indicators and preliminarily identify potential problem nodes; using the K-means clustering algorithm to deeply analyze the initial screening data and identify the TOP5 communities of wireless cells and network element devices with abnormal performance. The output result examples are shown in Tables 14 and 15.

[0070] Table 14 Top 5 poor quality communities

[0071] Table 15 Top 5 network elements with poor quality

[0072] Furthermore, the resource bottleneck identification function module includes: extracting performance indicators such as cell resource utilization, uplink and downlink traffic, user load, status alarm, etc. from the cell-level subject database; extracting performance indicators such as network element resource utilization, transmission link status, service traffic, CPU load, status alarm, etc. from the network element-level subject database; combining predefined performance thresholds, using the rule engine algorithm to quickly identify potential problem nodes with abnormal load and traffic; combining the lists in Table 11 and Table 12, using the K-means clustering algorithm to conduct in-depth analysis of the initial screening data, and identifying the TOP5 communities of network element nodes with abnormal resource utilization. The output result example is shown in Table 16.

[0073] Table 16 Top 5 resource bottleneck network elements

[0074] Furthermore, the degradation root cause location function module includes: extracting real-time data and historical data of performance indicators such as coverage performance, transmission performance, status alarm, reliability, etc. from the cell-level and network element-level subject databases to build a training model for the causal reasoning engine; combining the lists in Tables 14 to 16, using the causal reasoning engine algorithm, the potential degradation indicator items identified by the trend prediction module are quickly analyzed for root cause location, which is directly related to the poor quality unit or resource bottleneck. The output result example is shown in Table 17.

[0075] Table 17 Root cause location of degradation indicators

[0076] Furthermore, the performance threshold tuning function module includes: based on the future change trend of the performance indicators output by the trend prediction module, combined with predefined tuning rules (limited adjustment range), dynamically adjusting the threshold parameters and ensuring that the optimization direction is consistent with the predicted trend. The output result example is shown in Table 18.

[0077] Table 18 Performance threshold adjustment

[0078] In this embodiment, a data analysis script is developed using the Python programming language to implement cross-topic, multi-level joint analysis by calling an intelligent analysis engine.

[0079] Furthermore, the data analysis script calls various functional modules of the intelligent analysis engine to obtain various evaluation data shown in Tables 9 to 18, and writes them into the evaluation data asset table of the application-level subject database; Among them, the evaluation data asset table is built based on predefined modular report templates to store standardized and structured real-time evaluation data and historical evaluation data; Further, the data analysis script extracts the current evaluation data shown in Table 9, Table 13, Table 17, and Table 18, dynamically generates the classification labels, value axis ranges, metadata, and color coding rules required for the visualization chart, and writes them into the visualization chart information table of the application-level theme database; Among them, the visualization chart information table is built based on the predefined 3D visualization dashboard template, which stores visualization properties (such as dynamic parameter configuration, interactive rule set), content data (including original indicators and aggregated results) and rendering configuration (such as lighting model, layer transparency) of 3D time series diagrams, heat maps, scatter plots, cross tables, pivot tables, etc. It supports real-time mapping to 3D visualization dashboards through spatial coordinate systems, realizing dynamic data superposition and interactive analysis across levels; Furthermore, the data analysis script converts the current evaluation data shown in Tables 11 to 12 and Tables 14 to 16 into topological relationship mappings and writes them into the visualization topology information table of the application-level subject database; Among them, the visual topology information table is built based on a predefined 3D visualization dashboard template, storing visual attributes such as color gradient, brightness threshold, and alarm icon, and is used for enhanced prompts such as key routes, node failures, and link congestion. It supports mapping to a 3D visualization dashboard through a spatial coordinate system, and supports cross-level topology status overlay presentation. S105. Publish diversified reports online to support data-driven decision-making by the operation and maintenance team.

[0080] In this embodiment, with the help of information technologies such as Web servers and GIS servers, the current evaluation data is injected into modular report templates and three-dimensional visualization dashboard templates respectively, driving the content updates of Web online reports (dynamic web pages), local offline copies, 5G rich media messages, instant messaging messages, etc., as well as driving the real-time rendering of visualization charts and visualization topologies on the monitoring large-screen interface. Evaluation reports are published to the operation and maintenance team through multiple channels and in a diversified manner, supporting data-driven decision-making for intelligent operation and maintenance.

[0081] Furthermore, 5G rich media messages and instant messaging messages are operated by third parties. The third-party 5G messaging service platform uses the SIP / SDP multimedia communication protocol interface, and the transmission format follows the GSMA RCS UP specification; the instant messaging service platform uses the HTTP / HTTPS communication protocol interface, and the transmission format complies with lightweight specifications such as JSON or XML.

[0082] In this embodiment, the Web server is deployed based on the combined architecture of Nginx+Tomcat open source tools, supporting HTTP / HTTPS protocol response, supporting structured data interaction such as JSON / XML, etc. Among them, the Nginx tool processes static requests and load balancing, and the Tomcat tool carries dynamic business logic.

[0083] Furthermore, the Web server implements the following functions in parallel through processing: using a dynamic template engine to build an adaptive WEB dynamic web page report; using a PDF generation library plug-in to convert dynamic web page content into a PDF local document; using DOM parsing and data extraction technology to serialize the web page report content into a standard JSON text transmitted over the HTTP protocol; using the GSMA RCS UP specification message converter to map the JSON text into an XML format that complies with the 5G rich media standard.

[0084] In this embodiment, the GIS server is constructed based on the combined architecture of SuperMap iServer + SuperMap iClient3D commercial tools, wherein the SuperMap iServer tool processes the database interface and backend service logic, and the SuperMap iClient3D tool realizes the frontend display, interaction and visualization.

[0085] Furthermore, the GIS server implements the following functions: efficiently load and render three-dimensional real-life models in the S3M (Spatial 3D Model) format through the REST-Data service and the three-dimensional scene service; support the conversion and mapping of dynamic coordinate systems through a custom-developed SDK API to dynamically overlay visualization layers on the S3M model; transmit three-dimensional visualization data through the WebGL interface to realize complex three-dimensional scene display, interaction, and analysis.

[0086] In this embodiment, a data presentation script is written using a combined architecture of Python+React open source tools, wherein Python is used to develop a backend API, and open source React is used to develop a frontend interaction.

[0087] Furthermore, the data presentation script extracts 3D model metadata, visualization chart configuration information, and visualization topology configuration information from the topology-level theme database and the application-level theme database by calling the GIS server, and dynamically generates a 3D visualization dashboard driven by evaluation data, which users can experience online through the monitoring screen and Web client. Furthermore, the data presentation script extracts the evaluation data asset table from the application-level subject database by calling the Web server, and modularly assembles it to generate a dynamic web page version of the Web online report, which users can browse online through the Web client. At the same time, the data presentation script generates a local offline copy in PDF format to ensure the security, traceability and compliance requirements of the evaluation data.

[0088] Furthermore, the data presentation script extracts key data, charts and the URL of the web server from the web online report by calling the web server, converts it into a data format that complies with the GSMA RCS UP specification, and sends it to the 5G message service platform in real time. Users can receive and view it online through terminal applications such as Google Messages, RcsChat APP, and Apple iOS Messages.

[0089] Furthermore, the data presentation script extracts key data, charts and Web server URLs from the Web online report by calling the Web server program, converts them into data formats such as JSON or XML, and sends them to the instant messaging service platform in real time. Users can receive and view them online through the instant messaging APP.

[0090] A performance evaluation device provided in the second embodiment of the present application is described below. The performance evaluation device described below and the RedCap industrial private network performance evaluation method described above can be referenced to each other.

[0091] In a possible embodiment, a system framework of a performance evaluation device is as follows: Figure 3 As shown, it includes: acquisition module, fusion module, application module and publishing module.

[0092] In this embodiment, the collection module includes: a multi-protocol collection interface, a native data lake, and a data collection script.

[0093] Furthermore, the multi-protocol acquisition interface supports multiple communication protocols such as SFTP / FTP, RESTful API, MQTT, NFS / SMB, etc., meeting the access requirements of multi-source mixed data such as operation and maintenance (performance statistics / status alarm), business (resource configuration / network planning), perception (probe measurement / wireless measurement report), and geography (geographic surveying and mapping); Furthermore, the native data lake uses a dual-engine architecture of relational and NoSQL to provide temporary storage space for structured, semi-structured, and unstructured data, and configures client tools to provide unified query and operation and maintenance management capabilities; Furthermore, the acquisition module runs the data acquisition script to achieve the synchronous acquisition, preliminary screening and temporary storage of multi-source heterogeneous data.

[0094] In this embodiment, the fusion module includes: a subject data warehouse and a data integration script.

[0095] Furthermore, the subject data warehouse adopts a domain dimension model, including structured databases at multiple subject granularities such as business level, cell level, network element level, topology level, and application level; Furthermore, the business-level subject database is used to store business-based perception data, covering business attributes, dial test cases, reliability perception, wireless perception, transmission perception, bandwidth perception, and other aspects; Furthermore, the cell-level subject database is used to store statistical data by cell, covering cell attributes, status alarms, operational reliability, coverage performance, access performance, retention performance, mobility performance, flow performance, load performance, and other aspects; Furthermore, the network element-level subject database is used to store statistical data by network element, covering network element attributes, status alarms, operation reliability, transmission performance, flow performance, load performance and other aspects; Furthermore, the topological-level subject database is used to store global planning and geographic data, including topological node tables, topological edge link tables, 5G LAN group tables, 3D real-life model metadata, and other aspects; Furthermore, the application-level subject database is used to store the evaluation results generated by intelligent analysis, covering multiple aspects such as evaluation data assets, visual chart information, visual topology information, modular report templates, and three-dimensional visual dashboard templates; Furthermore, the fusion module runs the data integration script, implements the heterogeneous fusion operation of multimodal data, and imports the fused high-quality data into the relevant subject database.

[0096] In this embodiment, the application module includes: an intelligent analysis engine and a data analysis script.

[0097] Furthermore, the intelligent analysis engine includes multiple functional modules such as end-to-end performance evaluation, topology generation, trend prediction, quality difference identification, bottleneck identification, root cause location, and threshold tuning; Furthermore, the application module runs the data analysis script, calls the various functional modules of the intelligent analysis engine, conducts a comprehensive and in-depth network performance evaluation, and imports the evaluation results into the application-level subject database.

[0098] In this embodiment, the publishing module includes: a GIS server, a Web server, a global distribution interface, and a data presentation script.

[0099] Furthermore, the GIS server is used for efficient loading and accelerated rendering of 3D real-scene models. Through the conversion and mapping of dynamic coordinate systems, the spatial registration and overlay analysis of visualization layers are realized, and dynamic rendering of 3D visualization dashboards driven by evaluation data injection is supported. Furthermore, the Web server is used to process HTTP / HTTPS protocol responses, handle structured data interactions such as JSON / XML, and support dynamic updates of modular reports driven by evaluation data injection; Furthermore, the global distribution interface supports multiple communication protocol interfaces such as SIP / SDP, HTTP / HTTPS, and supports multiple publishing methods such as Web online reports, local offline copies, monitoring large screens, 5G messages, and instant messaging; Furthermore, the publishing module runs the data presentation script, calls the GIS server and the Web server, and realizes diversified online publishing of evaluation reports.

[0100] In this embodiment, based on Linux Cron or Windows task planning, the automatic scheduling and loop operation strategy of the whole process is set according to the task sequence of data collection script → data integration script → data analysis script → data presentation script.

[0101] In a possible embodiment, an application scenario of a performance evaluation device, such as Figure 4 shown.

[0102] In this embodiment, the RedCap industrial private network consists of RedCap industrial terminals, 5G industrial base stations, 5G backhaul bearers, 5GC core networks, OMC network management, MEC servers, and industrial IoT consoles.

[0103] Furthermore, the RedCap industrial terminal accesses the 5GC core network through the 5G industrial base station, and terminates the 5G control plane messages and signaling here. A 5G data plane channel is established between the RedCap industrial terminal and the industrial IoT console, and between the RedCap industrial terminals in the same 5G LAN group, to achieve high-speed transmission of industrial IoT data; Furthermore, active probes are deployed near the monitored RedCap industrial terminals and connected to the same 5G industrial base station to ensure that their wireless air interface coverage quality is highly consistent with that of the industrial terminals.

[0104] In this embodiment, the performance evaluation device is connected to the OMC network management, MEC server, network planning server, geographic mapping server, etc. through the enterprise intranet to collect and process multi-source data.

[0105] Furthermore, the OMC network management opens a southbound interface for monitoring private network elements such as 5G industrial base stations and 5G backhaul bearers; Furthermore, the MEC server provides IP packet parsing and diversion functions, and forwards the MQTT message data sent by the active probe directly to the performance evaluation device without passing through the 5GC core network.

[0106] In this embodiment, the performance evaluation device locally connects to the monitoring large screen and the Web client through the LAN port, and remotely connects to the instant messaging service platform and the 5G message service platform through the WAN port, for diversified online publishing of evaluation reports.

[0107] Furthermore, the instant messaging service platform and 5G message service platform adopt a cloud architecture and are built on the Internet infrastructure layer.

[0108] The present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided in an embodiment of the aforementioned application.

[0109] The present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the method provided in one embodiment of the aforementioned application.

[0110] The present application also proposes a computer program product, including a computer program, which implements the method provided in the first embodiment of the aforementioned application when executed by a processor.

[0111] It is worth noting that any process or method description in the flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and that the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0112] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0113] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0114] A person skilled in the art may understand that all or part of the steps in the above-mentioned embodiment method may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0115] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0116] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

[0117] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0118] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. RedCap industrial private network performance evaluation method, characterized by: The overall process of this method is: S101, Reconstruct the private network performance evaluation model based on the business overhead characteristics of industrial IoT; S102, configuring a multi-protocol adapted monitoring interface and collecting multi-source data of network management, perception, planning and mapping; S103, build a hierarchical model integrating lake and warehouse to realize heterogeneous fusion and classified storage of multimodal data; S104, using the intelligent analysis engine to perform multi-dimensional comprehensive network performance evaluation; S105, publish diversified online reports to support data-driven decision-making of the operation and maintenance team.

2. The RedCap industrial private network performance evaluation method according to claim 1, characterized in that: In S101, it is necessary to first analyze the network overhead characteristics in terms of data volume, data type, transmission frequency, communication protocol, and time sensitivity for typical services in the industrial IoT scenario, and clarify the performance indicator requirements in terms of coverage, transmission, traffic, load, and reliability. Then, a private network performance evaluation model suitable for the industrial IoT scenario is constructed. Through the data flow closed loop of collection → storage → analysis → secondary storage → reporting, the system function requirements of the industrial site for multi-protocol adaptation, multi-modal heterogeneous storage, multi-dimensional intelligent analysis, and diversified report publishing are met.

3. The RedCap industrial private network performance evaluation method according to claim 1, characterized in that: The S102 collects data based on probe sensing technology and network management performance statistics technology, and the monitoring interface includes: FTP file transfer protocol interface, through which the OMC network management is connected to the south, real-time statistical data of performance, resources, and wireless measurement reports are collected and transmitted in HTTP request-response message format; REST API protocol interface, through which the OMC network management system is connected to the south, and statistical data on network element status and network element alarms are collected in real time, and transmitted in the HTTP request-response message format; MQTT message transmission protocol interface, through which the active probe is connected to the south, and analog dial-up sensing data is collected in real time, and transmitted in the MQTT service-subscription message format; NFS and SMB network sharing file protocol interface, through which the network planning server and geographic mapping server are connected to the south, and global data updates are obtained, supporting multi-modal format transmission of text, pictures, tables and 3D real-life models.

4. The RedCap industrial private network performance evaluation method according to claim 1, characterized in that: The specific steps of S103 are: A1: First, integrate relational databases and NoSQL databases to build a dual-engine data lake storage space, and unify data governance and elastic expansion through cross-platform clients; A2 then runs a data collection script adapted for multiple protocols to achieve synchronous data collection from multiple sources, and writes the response data into the dual-engine architecture data lake in real time; A3. Then, a subject data warehouse is established through the domain dimension model to classify, integrate and store the real-time data and historical data of the private network performance evaluation, supporting efficient data query, analysis and report generation. A4, finally runs the multimodal adapted data integration script, extracts unprocessed raw data from the dual-engine architecture data lake, performs heterogeneous fusion operations, and imports the fused high-quality data into relevant subject databases.

5. The RedCap industrial private network performance evaluation method according to claim 4 is characterized in that: The relational database in A1 adopts a distributed deployment architecture to carry the time series structured data generated by the OMC network management and active probes, while the NoSQL database adopts a distributed storage engine to carry the multi-modal unstructured data generated by the network planning server and the geographic mapping server. In addition, the cross-platform client tool is based on the data weaving architecture to achieve centralized management and unified query of the relational database and the NoSQL database; The collection rules of A2 are: For the raw data collected in a lightweight file format, the data collection script parses it into structured data and maps it to the relational database; for the raw data collected in a message format, the data collection script only extracts the BODY text therein and maps it to the relational database; for the raw data collected in a text or table format, the data collection script parses and converts it into string fields, key-value pairs and arrays, and maps it to the NoSQL database; for the raw data collected in a picture or three-dimensional real-scene model format, the data collection script stores it in a specified file directory and only records related metadata in the NoSQL database.

6. The RedCap industrial private network performance evaluation method according to claim 1, characterized in that: The S104 intelligent analysis engine includes an end-to-end performance evaluation function module, a topology generation function module, a trend prediction function module, a quality difference identification function module, a bottleneck identification function module, a root cause location function module and a threshold tuning function module; The end-to-end performance evaluation function module uses a weighted average algorithm to automatically calculate the comprehensive score of service communication performance; the topology generation function module generates the optimal service path through the shortest path algorithm, and marks the key nodes and path list based on the network topology coincidence algorithm; the trend prediction function module uses time series linear analysis to predict the trend of indicator changes and identify potential degradation indicators; the quality difference identification function module combines the rule engine and clustering algorithm to screen and deeply analyze the wireless cells and network element devices with abnormal performance; the bottleneck identification function module uses the rule engine to identify abnormal load and flow nodes, combines the topology key node list, and marks the abnormal resource utilization devices through clustering analysis; The root cause location function module integrates the quality difference and bottleneck identification results, and uses the causal reasoning engine to quickly analyze the root cause of the potential degradation indicators of the trend prediction; the threshold tuning function module dynamically adjusts the performance indicator threshold to ensure that the optimization direction is consistent with the predicted trend; The intelligent analysis engine also includes a data analysis script, which calls various functional modules of the intelligent analysis engine through the data analysis script to conduct a comprehensive and in-depth evaluation, prediction, analysis, and diagnosis of network performance, and import the evaluation results into an application-level subject database.

7. The RedCap industrial private network performance evaluation method according to claim 1, characterized in that: The specific steps of S105 are: B1, deploy GIS server and Web server and open SIP / SDP multimedia communication protocol interface and HTTP / HTTPS communication protocol interface; B2, load the predefined modular report template and the predefined 3D visualization dashboard template; B3, run the data presentation script, call the GIS server and Web server, automatically generate modular reports and 3D visualization dashboards, and publish them online in real time through multiple channels.

8. RedCap industrial network performance evaluation device, characterized by: The device includes a collection module, a fusion module, an application module and a publishing module; The acquisition module connects to the OMC network management, active probes, network planning servers and geographic mapping servers through the enterprise intranet. The module realizes the acquisition synchronization and transient storage of multi-source data by running data acquisition scripts; The fusion module is used to manage and maintain the subject data warehouse of the domain dimension model. The module realizes the fusion processing and classified storage of heterogeneous data by running data integration scripts; The application module implements comprehensive and in-depth network performance evaluation by running data analysis scripts and calling intelligent analysis engines; The publishing module locally connects to the monitoring screen and the Web client, and remotely connects to the 5G message service platform and the instant messaging service platform. The module runs the data presentation script, calls the GIS server and the Web server, and realizes diversified online publishing of evaluation reports.

9. The RedCap industrial private network performance evaluation device according to claim 8, characterized in that: The device is based on the system timing function of Linux Cron / Windows task planning, and configures the task sequence of the data acquisition script → the data integration script → the data analysis script → the data presentation script, including the timeline of activation, termination, and abnormal exit, to realize automatic scheduling and cyclic operation of the whole process.

10. RedCap is an electronic device for industrial network performance evaluation, which is characterized by: A method comprising: comprising a processor and a memory in communication with the processor, wherein the memory stores computer-executable instructions, and wherein the processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7; It also includes a computer-readable storage medium and a computer program, wherein the computer-readable storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, the method as claimed in any one of claims 1 to claim 7 is implemented, and when the computer program is executed by a processor, the method as claimed in any one of claims 1 to claim 7 is implemented.

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