CPE (Customer Premise Equipment) communication collaborative optimization method and device for smart park

Abstract

The invention provides a CPE equipment communication collaborative optimization method and device for a smart park, and relates to the technical field of communication optimization, and the method comprises the steps: carrying out the type analysis of business operated in the park, and obtaining a business type label of the park; extracting a business data demand feature set corresponding to the park business type, and constructing a backhaul data standard model; and performing multi-source terminal service data acquisition on CPE equipment, performing division according to park service type labels, performing cross-CPE equipment relay amplification processing on service data groups according to a backhaul data standard model, and sending standardized service backhaul data to a central controller through a CPE backhaul link for communication transmission. The technical problems in the prior art that the business data processing is lack of standards and the data quality is difficult to guarantee in the communication cooperation of the CPE equipment in the smart park are solved. The technical effects of guaranteeing the accuracy and reliability of service data transmission and improving the efficiency and quality of communication cooperation of the park CPE equipment are achieved.

Description

Technical Field

[0001] This invention relates to the field of communication optimization technology, and more specifically to a method and apparatus for communication collaborative optimization of CPE devices in smart parks. Background Technology

[0002] With the rapid development of smart parks, various business systems are widely deployed within them. These systems typically rely on multiple terminal devices, which access the park network via CPEs to achieve data aggregation and transmission. However, existing CPE devices suffer from a lack of unified standards for business data processing during the data return process. Data generated by different businesses varies significantly in format, content, and importance, and the absence of standardized processing procedures leads to data corruption during transmission, storage, and analysis. This increases the complexity and error rate of data processing. Furthermore, during data acquisition, transmission, and processing, factors such as device performance differences and network interference can cause data loss, errors, and duplication, severely impacting data accuracy and integrity. These issues not only reduce data transmission stability and communication efficiency but also limit the intelligent scheduling and centralized management capabilities of the park's business systems.

[0003] Existing technologies suffer from technical problems such as a lack of standards for business data processing and difficulty in ensuring data quality in the communication and collaboration of CPE devices in smart parks, which affect communication efficiency and stable business operation. Summary of the Invention

[0004] The purpose of this application is to provide a method and apparatus for optimizing communication collaboration of CPE devices in smart parks, in order to solve the technical problems in the existing technology of lack of standards for business data processing and difficulty in ensuring data quality in the communication collaboration of CPE devices in smart parks, which affect communication efficiency and stable business operation.

[0005] In view of the above problems, this application provides a method and apparatus for CPE device communication collaboration optimization in smart parks.

[0006] The first aspect of this application provides a method for communication collaboration optimization of CPE devices in smart parks. This method includes: performing type analysis on services operating within the smart park to obtain park service type labels; extracting service data demand feature sets corresponding to each park service type, and constructing a backhaul data standard model based on the service data demand feature sets; collecting multi-source terminal service data from multiple CPE devices connected to the park network, dividing the collected multi-source service datasets according to the park service type labels to obtain multiple types of park service data groups received by each CPE device; performing cross-CPE device relay amplification processing on each type of park service data group according to the backhaul data standard model to obtain standardized service backhaul data for each CPE device; and transmitting the standardized service backhaul data to the central controller via the CPE backhaul link for communication transmission.

[0007] Optionally, the business data requirement feature set includes at least data temporal continuity requirement features, data integrity requirement features, data confidence requirement features, and data real-time requirement features; a backhaul processing dimension is defined, including a backhaul data validity dimension, a backhaul data temporal alignment dimension, a backhaul data confidence dimension, and a backhaul data redundancy dimension; a mapping relationship is established between the business data requirement feature set and the backhaul processing dimension; the business data requirement feature set is subjected to feature structuring processing to obtain a business data feature vector; backhaul constraint parameters and backhaul weight parameters are generated based on the business data feature vector; and the park business type label, the backhaul constraint parameters, and the backhaul weight parameters are encapsulated into a model to obtain a backhaul data standard model.

[0008] Optionally, the backhaul constraint parameters include a data validity threshold parameter, an upper limit parameter for allowed time series offset, a lower limit threshold parameter for confidence, and an upper limit parameter for data redundancy, all calculated based on the business data feature vector; the backhaul weight parameters include a validity decision weight, a time series alignment decision weight, a confidence guarantee weight, and a redundancy allocation weight, all calculated based on the business data feature vector.

[0009] Optionally, based on the park business type label corresponding to each type of park business data group, the backhaul constraint parameters and backhaul weight parameters of the backhaul data standard model are loaded; the backhaul constraint parameters are used to constrain and filter the similar park business data groups across CPE devices to obtain the filtered similar park business data groups; the backhaul weight parameters are used to perform collaborative amplification processing on the filtered similar park business data groups to obtain the amplified similar park business data groups; and the standardized business backhaul data of each CPE device is identified based on the amplified similar park business data groups.

[0010] Optionally, the amplified similar park business data group is re-constrained using the backhaul constraint parameters to obtain a verified similar park business data group; based on the verified similar park business data group, the standardized business backhaul data of each CPE device is identified.

[0011] Optionally, acquire similar campus business data groups from different CPE devices; perform data fusion and enhancement processing on the similar campus business data groups from different CPE devices to obtain enhanced similar campus business data groups; and identify standardized business backhaul data for each CPE device based on the enhanced similar campus business data groups.

[0012] Optionally, the collected multi-source business dataset is parsed to obtain business identification information, which includes data source terminal identification, business occurrence time identification, business function identification, and business scenario identification; a matching rule is established between park business type tags and business identification information; the collected multi-source business dataset is divided according to the matching rule to obtain multiple types of park business data groups received by each CPE device.

[0013] Optionally, historical multi-source service datasets are collected for each CPE device; park service type labels are identified for the historical multi-source service datasets of each CPE device to obtain park service type labels for each CPE device; matching rules are constructed according to the park service type labels of each CPE device and the corresponding service identification information to obtain multiple matching rules, wherein each CPE device divides the collected multi-source service dataset by calling the corresponding matching rules.

[0014] Optionally, the central controller performs a data quality assessment on the standardized business backhaul data and obtains the data quality assessment result; based on the data quality assessment result as feedback information, it updates the backhaul constraint parameters and backhaul weight parameters of the backhaul data standard model.

[0015] A second aspect of this application provides a communication collaboration optimization device for CPE devices in smart parks. The device comprises: a service type analysis component for analyzing the types of services operating within the smart park and obtaining park service type labels; a feature extraction component for extracting service data requirement feature sets corresponding to each park service type and constructing a backhaul data standard model based on the service data requirement feature sets; a data partitioning component for collecting multi-source terminal service data from multiple CPE devices connected to the park network and partitioning the collected multi-source service datasets according to the park service type labels to obtain multiple types of park service data groups received by each CPE device; a backhaul data acquisition component for performing cross-CPE device relay amplification processing on each type of park service data group according to the backhaul data standard model to obtain standardized service backhaul data for each CPE device; and a communication transmission component for transmitting the standardized service backhaul data to the central controller via the CPE backhaul link.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages: By analyzing the types of services operating within the smart park, service type labels are obtained. A service data requirement feature set corresponding to each service type is extracted, and a backhaul data standard model is constructed based on this feature set. Multi-source terminal service data is collected from multiple CPE devices connected to the park network. The collected multi-source service datasets are divided according to the service type labels, resulting in multiple types of park service data groups received by each CPE device. Each type of park service data group undergoes cross-CPE device relay amplification processing based on the backhaul data standard model to obtain standardized service backhaul data for each CPE device. This standardized service backhaul data is then transmitted to the central controller via the CPE backhaul link. Through precise analysis of smart park service types and the construction of a backhaul data standard model, along with reasonable division of multi-source service data, cross-device relay amplification, and fusion enhancement processing, high-quality standardized service backhaul data can be obtained. This data is then transmitted to the central controller via the CPE backhaul link, effectively improving the efficiency and quality of communication collaboration among park CPE devices. This ensures the accuracy, integrity, and reliability of service data transmission, providing accurate and reliable data communication support for the efficient and stable operation of the smart park.

[0017] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 A flowchart illustrating the CPE device communication collaboration optimization method for smart parks provided in this application.

[0020] Figure 2 A schematic diagram of the communication collaboration optimization device for CPE equipment in smart parks provided in this application.

[0021] Figure labeling: Service type analysis component 11, feature extraction component 12, data segmentation component 13, backhaul data acquisition component 14, communication transmission component 15. Detailed Implementation

[0022] This application provides a method and apparatus for optimizing communication collaboration among CPE devices in smart parks. It addresses the technical problems in existing technologies where the lack of standardized business data processing and difficulty in guaranteeing data quality negatively impact communication efficiency and stable business operation in CPE device communication collaboration within smart parks. The method achieves the technical effect of ensuring the accuracy and reliability of business data transmission and improving the efficiency and quality of communication collaboration among CPE devices in the park.

[0023] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0024] Example 1, as Figure 1As shown, this application provides a CPE device communication collaboration optimization method for smart parks, which includes: Analyze the types of businesses operating within the smart park and obtain park business type tags.

[0025] Specifically, by collecting relevant data on various business operations within the smart park, including business documents, operation manuals, and system logs, the process involves extracting definitions, objectives, and processes from the business documents, providing specific operational steps and specifications from the operation manuals, and recording various events and data during business operation, reflecting the actual usage of the business. The definition, objectives, and process information for each business are extracted from the business documents to clarify its functional positioning and core operational links. Combined with the descriptions of specific operational steps and specifications in the operation manuals, the execution method of the business and the key equipment and data flows involved are understood. Simultaneously, by analyzing the events and operational data recorded in the system logs, dynamic information such as the actual operating mode, data interaction characteristics, and usage frequency of the business is captured. A comprehensive comparison and summarization of static descriptions and dynamic operating characteristics is then conducted to identify the types of business operations within the smart park, such as security monitoring, energy management, environmental monitoring, and logistics distribution.

[0026] Based on the core characteristics and key attributes of each type of business, they are mapped to park business type labels. For example, security monitoring business can be labeled as real-time high-definition video monitoring, and energy management business can be labeled as multi-device data interaction and intelligent control, thus providing an accurate basis for business classification for subsequent multi-source data division and backhaul processing.

[0027] Extract the business data requirement feature set corresponding to each park business type, and construct a backhaul data standard model based on the business data requirement feature set.

[0028] Furthermore, a backhaul data standard model is constructed based on the business data requirement feature set. The method includes: the business data requirement feature set includes at least data temporal continuity requirement features, data integrity requirement features, data confidence requirement features, and data real-time requirement features; defining backhaul processing dimensions, including backhaul data validity dimension, backhaul data temporal alignment dimension, backhaul data confidence dimension, and backhaul data redundancy dimension; establishing a mapping relationship between the business data requirement feature set and the backhaul processing dimensions; performing feature structuring processing on the business data requirement feature set to obtain a business data feature vector; generating backhaul constraint parameters and backhaul weight parameters based on the business data feature vector; and encapsulating the park business type label, the backhaul constraint parameters, and the backhaul weight parameters into a model to obtain the backhaul data standard model.

[0029] Specifically, within a smart park, for each type of business label, a corresponding set of business data requirement features is extracted. This set reflects the core requirements of the business for data backhaul and includes at least the following features: data temporal continuity requirement, data integrity requirement, data confidence requirement, and data real-time requirement. The data temporal continuity requirement means that business data must remain continuous over time. For example, in security monitoring, video data must be transmitted and stored sequentially without any time jumps or interruptions, otherwise the monitoring footage will be incomplete, affecting the accurate assessment of the park's security status. The data integrity requirement ensures that business data is transmitted continuously in the correct order, without time jumps or loss. The data confidence requirement reflects the business's requirements for data accuracy and reliability, ensuring reliable data sources and accurate content. The data real-time requirement requires that business data be transmitted and processed promptly to meet the business's strict requirements for data transmission latency. Each feature can be quantified through business operation records, data flow characteristics, and operational specifications. For example, the data temporal continuity of video surveillance can be evaluated using frame rate and timestamp intervals.

[0030] After clarifying the characteristics of business data requirements, processing dimensions related to backhaul processing are defined. These dimensions include backhaul data validity, backhaul data time-series alignment, backhaul data confidence, and backhaul data redundancy. Backhaul data validity measures whether the data meets business requirements and can be quantified using error rate and packet loss rate. During the data backhaul process, each data item is validated, such as for data format, field integrity, and checksum. The number of erroneous data items is counted to obtain the error rate. Simultaneously, the data transmission link is monitored to record the number of lost or unsuccessfully transmitted data packets, and the packet loss rate is calculated. The validity score can be mapped to a standardized value based on a set threshold to constrain subsequent processing. Backhaul data time-series alignment measures the degree of synchronization of multi-source data in the time dimension. This can be calculated using timestamp deviation. By recording the timestamps of data collected from different CPE devices for the same business event, the deviation value between timestamps of similar events is calculated, such as the maximum or average deviation, and compared with the upper limit of allowed time-series offset. The smaller the deviation, the higher the degree of time-series alignment, which can be used to guide cross-device collaborative processing and data fusion. Backhaul data confidence measures the reliability and accuracy of data, which can be achieved through data source verification and historical accuracy statistics. Verification involves checking the legitimacy of the data source, such as whether the source terminal is registered and whether the data is signed or encrypted. Then, historical data is statistically analyzed to calculate the accuracy or anomaly rate of that source in past backhauls, mapping this to a confidence score. Backhaul data redundancy measures the amount of duplicate or redundant data, aiming to reduce network load. This is achieved by deduplicating data from similar business events, for example, by identifying duplicate records using business identification information, event ID, time window, and data source. The proportion of duplicate data to the total data volume is calculated and used as a redundancy dimension indicator. This redundancy dimension can be used to control the adoption and weighting of duplicate data during collaborative amplification, ensuring efficient utilization of the backhaul link. A mapping relationship is established between the set of business data requirement features and backhaul processing dimensions. This is achieved by defining quantifiable metrics for each business data requirement feature and using a rule base to match each feature with one or more backhaul processing dimensions. For example, data time-series continuity requirements are mapped to the backhaul data time-series alignment dimension, data integrity requirements to the backhaul data validity dimension, data confidence requirements to the backhaul data confidence dimension, and data real-time requirements can be mapped to either the time-series alignment dimension or the redundancy control dimension. This mapping relationship enables an operable transformation of business requirements into backhaul processing dimensions, providing a foundation for subsequent quantification and optimization.

[0031] The business data requirement characteristics are structured, and quantifiable indicators are set for each characteristic. For example, the data time series continuity requirement is represented by the allowable time deviation range, the data integrity requirement by the packet loss rate threshold, the data confidence requirement by the reliability score, and the data real-time requirement by the maximum transmission delay. These indicators are combined in a unified order to form a quantifiable business data feature vector. Backhaul constraint parameters and backhaul weight parameters are generated based on this feature vector. The backhaul constraint parameters impose restrictions on the backhaul data, and the backhaul weight parameters measure the importance of different backhaul processing dimensions. Finally, the park business type labels, backhaul constraint parameters, and backhaul weight parameters are uniformly encapsulated to form a standard backhaul data model for each type of business. The backhaul data standard model is similar to an intelligent rule base. It can automatically call the corresponding constraint parameters and weight parameters according to different business types to accurately process backhaul data and realize standardized, business-driven data backhaul management. Backhaul data refers to the data set that is transmitted from the data collection terminal back to the data processing center or related business systems during the operation of various businesses in the smart park. It is used to support business decision-making, monitoring, analysis and other operations, and needs to be accurately processed according to the characteristics of business data requirements and backhaul processing dimensions to meet the core business requirements.

[0032] By establishing a link between business data demand characteristics and data backhaul processing dimensions, and by generating operable constraints and weight parameters through quantifying business characteristics, business-driven backhaul data standardization is achieved. This enables each type of business to be precisely optimized according to its own timing, integrity, confidence, and real-time requirements when processing data across CPEs, ensuring high quality and high reliability of data backhaul. At the same time, it provides a unified standardized model foundation for subsequent scaling, fusion, and central controller feedback optimization.

[0033] Furthermore, the method for generating backhaul constraint parameters and backhaul weight parameters based on the business data feature vector includes: the backhaul constraint parameters include a data validity threshold parameter, an allowed time series offset upper limit parameter, a confidence level lower limit threshold parameter, and a data redundancy upper limit parameter calculated based on the business data feature vector; the backhaul weight parameters include a validity decision weight, a time series alignment decision weight, a confidence level guarantee weight, and a redundancy allocation weight calculated based on the business data feature vector.

[0034] Specifically, backhaul constraint parameters are generated based on the quantitative indicators in the business data feature vector. These parameters include a data validity threshold parameter, an upper limit parameter for allowed time-series offset, a lower limit threshold parameter for confidence, and an upper limit parameter for data redundancy. The data validity threshold parameter is calculated using indicators reflecting data integrity and error rate in the business data feature vector. For example, the average historical packet loss rate and error rate are combined with the acceptable business threshold to obtain the maximum allowable error rate. The upper limit parameter for allowed time-series offset is calculated based on the data's time-series continuity characteristics and real-time indicators to determine the maximum deviation range of multi-source data in the time dimension. The lower limit threshold parameter for confidence is obtained from the confidence score in the business data feature vector and historical data reliability statistics, setting a threshold for eliminating low-confidence data. The upper limit parameter for data redundancy is calculated by analyzing redundancy characteristics and the proportion of historical duplicate data, used to limit the maximum proportion of duplicate or redundant data in the backhaul link, thereby ensuring efficient utilization of network resources.

[0035] Backhaul weight parameters include validity decision weight, time-series alignment decision weight, confidence guarantee weight, and redundancy allocation weight. These parameters measure the relative importance of different processing dimensions in backhaul optimization. They are calculated based on the sensitivity of each indicator in the business data feature vector and business priority. Specifically, the validity decision weight is assigned a high weight based on the importance of data integrity and error rate characteristics to ensure the validity of core business data is prioritized. The time-series alignment decision weight is determined based on time-series continuity and real-time indicators to prioritize time synchronization in multi-source data collaborative processing. The confidence guarantee weight is allocated based on confidence characteristics and historical data reliability statistics to ensure that high-confidence data dominates the amplification or fusion process. The redundancy allocation weight is calculated based on redundancy characteristics and business tolerance for redundancy to balance the adoption and deduplication of duplicate data, thereby optimizing data quality and network load during collaborative processing.

[0036] By generating operable backhaul control parameters and weights, backhaul data processing can strictly follow business requirements, perform collaborative optimization among multiple CPE devices, and ensure that key indicators such as data timing, integrity, confidence, and real-time performance are guaranteed, providing a standardized, controllable, and high-quality processing foundation for data backhaul in smart parks.

[0037] Multiple CPE devices connected to the campus network are subjected to multi-source terminal service data collection. The collected multi-source service datasets are divided according to the campus service type labels to obtain multiple types of campus service data groups received by each CPE device.

[0038] Furthermore, the collected multi-source business dataset is divided according to the park business type label to obtain multiple types of park business data groups received by each CPE device. The method includes: parsing the collected multi-source business dataset to obtain business identification information, which includes data source terminal identifier, business occurrence time identifier, business function identifier, and business scenario identifier; establishing matching rules between park business type labels and business identification information; and dividing the collected multi-source business dataset according to the matching rules to obtain multiple types of park business data groups received by each CPE device.

[0039] Specifically, CPE devices, or Customer Premises Equipment, are deployed at the user end and are capable of aggregating, converting, and transmitting data generated by various types of terminal devices, such as sensors, smart meters, and surveillance cameras, to the campus network. They are crucial hardware infrastructure for realizing the collection and transmission of multi-source terminal business data within the campus. The process involves collecting multi-source terminal business data from multiple CPE devices connected to the campus network. Through CPE device interfaces, such as APIs and data interfaces, business data generated by various sensors, monitoring equipment, and smart meters is acquired in real time. This business data includes various forms of multi-source data such as temperature, humidity, video streams, sensor data, and energy consumption data. The collection process must ensure data integrity and timeliness, while also handling data format unification and conversion to ensure that different types of data can be aggregated and uploaded to the campus network for unified processing. The collected multi-source business data is then integrated to form a multi-source business dataset, which records the actual operation of services at different times and on different terminals.

[0040] After data collection, the multi-source business dataset is parsed to extract business identification information for each data entry. This includes the data source terminal identifier, business occurrence time identifier, business function identifier, and business scenario identifier. The data source terminal identifier indicates which CPE device the data originated from. Device information identifying the data source is extracted from the data packet or data stream, such as the CPE device ID or terminal device serial number, ensuring traceability to the specific terminal device. The business occurrence time identifier records the time of the business event. The timestamp of each data entry indicates the time the data was generated. The business function identifier distinguishes the business type. Based on the data content and protocol, the business type identifier is extracted to determine the type of business the data belongs to, such as security monitoring or energy management. The business scenario identifier identifies the specific business scenario. Based on the geographical location of the device deployment or the scenario attributes of the business, the specific scenario to which the corresponding business data belongs is extracted, such as a conference room, road, or warehouse scenario.

[0041] To classify and identify different business data, matching rules are established between park business type tags and business identification information. Business identification information is extracted from each data point within the multi-source business dataset, and matched against a rule base using a matching rule engine to obtain matching results. Based on the matching results, each piece of business data is mapped to a corresponding business type tag; for example, video surveillance data is mapped to the real-time high-definition video surveillance category, and energy sensor data is mapped to the multi-device data interaction and intelligent control category. According to the source terminal identifier of each data point, each matched data point is assigned to its corresponding CPE device. This ensures that the data group received by each CPE device contains multiple types of business data that the device is responsible for. The collected multi-source business data is divided into multiple park business data groups received by each CPE device, ensuring that similar business data are aggregated together, facilitating subsequent cross-CPE collaborative processing and backhaul data standardization.

[0042] By dividing multi-source business data according to business type labels, clear multi-category business data groups are generated for each CPE device. This not only ensures the accurate matching of business data in subsequent cross-CPE collaborative amplification, fusion, and standardization processing, but also enables the data of each type of business to be independently optimized according to its own time sequence, integrity, confidence level, and real-time requirements, providing a basic guarantee for the efficiency and reliability of data backhaul in smart parks.

[0043] Furthermore, a matching rule is established between park business type labels and business identification information. The method includes: collecting historical multi-source business datasets for each CPE device; identifying park business type labels for each CPE device's historical multi-source business datasets to obtain park business type labels for each CPE device; constructing matching rules according to the park business type labels and corresponding business identification information for each CPE device to obtain multiple matching rules. Each CPE device divides the collected multi-source business dataset by calling the corresponding matching rule.

[0044] Specifically, by accessing the local cache, log storage, or periodically uploaded business records of each CPE device, historical multi-source business datasets are collected for each CPE device. These datasets include business data generated by different terminal devices within the smart park, such as surveillance cameras, sensors, and smart meters, recording the actual operation of various services at different times, in different scenarios, and on different terminal devices. By parsing and identifying the historical multi-source business datasets of each CPE device, a park business type label is obtained for each device. This label is a general identifier of the essential characteristics of the business. Based on the park business type label and corresponding business identification information of each CPE device, matching rules are constructed for each device, resulting in multiple matching rules forming a rule base. The matching rules for each CPE device are customized based on its business type label, device identification, scenario, and other information to ensure that the rules can accurately identify and classify different types of data. For example, for security monitoring services, the matching rules include identifying video stream data, monitoring device ID, video storage path, etc.; for energy management services, the matching rules involve device interaction data, sensor type, energy consumption data, etc. The rule engine automatically parses and matches each piece of business data, comparing the data source and business type identifier with the predefined rule base to determine the business category and attributes of each piece of data. Each CPE device automatically calls the matching rule according to its own business type label, thereby accurately dividing the collected multi-source business dataset and ensuring that the data can be classified, stored and processed according to different business needs.

[0045] By identifying service type tags from historical data of CPE devices and building customized matching rules based on different devices and service requirements, it is ensured that the service data received by each CPE device can be efficiently classified and processed according to service requirements, thereby improving the accuracy and efficiency of communication collaboration optimization processing of CPE devices in smart parks.

[0046] Based on the backhaul data standard model, cross-CPE device relay amplification processing is performed on each type of park business data group to obtain standardized business backhaul data for each CPE device.

[0047] Furthermore, based on the backhaul data standard model, cross-CPE device relay amplification processing is performed on each type of park business data group. The method includes: loading the backhaul constraint parameters and backhaul weight parameters of the backhaul data standard model according to the park business type label corresponding to each type of park business data group; using the backhaul constraint parameters to constrain and filter the similar park business data groups across CPE devices to obtain the filtered similar park business data groups; using the backhaul weight parameters to perform collaborative amplification processing on the filtered similar park business data groups to obtain the amplified similar park business data groups; and identifying the standardized business backhaul data of each CPE device based on the amplified similar park business data groups.

[0048] Specifically, for each type of park business data group, based on the park business type tag (e.g., security monitoring, energy management), a search and matching process is performed in a pre-established backhaul data standard model library. Using the business type tag as the index key, the corresponding backhaul data standard model is queried, and backhaul constraint parameters and backhaul weight parameters for that business type are loaded from the corresponding backhaul data standard model. The backhaul constraint parameters define the basic conditions that the data must meet, while the backhaul weight parameters guide the priority allocation of each processing dimension during subsequent multi-dimensional processing. In this way, different business types can automatically match different backhaul processing strategies, achieving business-driven parameter loading.

[0049] Using backhaul constraint parameters, similar campus business data groups across CPE devices are constrained and filtered. Each data item is checked to see if it meets the conditions set in the backhaul constraint parameters. The data validity threshold is determined by whether the data error rate and packet loss rate are within allowable ranges. If the data error rate and packet loss rate are within allowable ranges, the current business data group meets the data validity threshold. Simultaneously, the timestamp deviation is compared with the upper limit of the allowable time offset parameter to determine if it exceeds the limit, and with the lower limit of the confidence threshold parameter to determine the reliability of the data source. Furthermore, the data redundancy upper limit parameter is compared to determine if there is excessive duplicate data. Data that does not meet any constraint condition is marked as invalid and removed, thus obtaining a filtered set of similar campus business data that is of acceptable quality and has a consistent structure.

[0050] The filtered data sets of similar park operations undergo further collaborative amplification processing using backhaul weight parameters. Based on validity decision weights, time-series alignment decision weights, confidence guarantee weights, and redundancy allocation weights, data from different CPE devices are assigned different processing priorities and levels of participation. For example, data with high confidence levels receives higher weight in fusion and compensation processing; businesses with higher time-series alignment weights are prioritized for time correction; and redundancy allocation weights are used to control the merging ratio of duplicate data. Through multi-CPE collaborative computing, data fusion, and enhancement processing, a more complete, time-series stable, and reliable amplified data set of similar park operations is generated. After amplification, based on the data source identifier and processing results, similar park business data groups are reverse-mapped and split to identify the standardized business backhaul data corresponding to each CPE device. Specifically, according to the CPE device identifier, the collaboratively amplified similar park business data content is allocated back to the backhaul data units of each CPE device, and the data format, timestamp, integrity identifier, etc. are uniformly standardized to ensure that the data finally returned by each CPE device is consistent in structure, timing, and quality, forming standardized business backhaul data. Standardized business backhaul data can be uniformly scheduled and centrally processed within the park, enabling multiple CPE devices located in different physical locations to truly achieve collaborative backhaul around the same type of business data.

[0051] For example, in a smart park, one type of business is security monitoring, tagged as real-time high-definition video surveillance. In the backhaul data standard model library, querying the model corresponding to this tag yields the following parameters: Data validity threshold parameters: error rate ≤ 0.5%, packet loss rate ≤ 0.5%; Allowable time series offset upper limit parameters: maximum timestamp deviation ≤ 100ms; Confidence lower limit threshold parameters: confidence ≥ 0.95, i.e., data source reliability is at least 95%; Data redundancy upper limit parameters: duplicate frames ≤ 5%; Backhaul weight parameters: validity decision weight: 0.4, time series alignment decision weight: 0.3, confidence guarantee weight: 0.2, redundancy allocation weight: 0.1. Before executing data processing, these parameters are automatically loaded according to the business type tag to ensure that the data processing strategy adapts to different business types. Collected video frame data of the same type includes ID, CPE device, timestamp (ms), error rate, packet loss rate, confidence level, and duplicate frame ratio. The video frame data of the same type collected across three CPE devices are [V001, CPE1, 1000, 0.3%, 0.2%, 0.98, 2%], [V002, CPE2, 1005, 0.6%, 0.4%, 0.96, 1%], and [V003, CPE3, 1010, 0.4%, 0.3%, 0.94, 6%]. Constraints are checked: V001 meets all constraints and is retained; V002 has an error rate of 0.6% > 0.5%, exceeding the validity threshold and is removed; V003 has a confidence level of 0.94 < 0.95 and a duplicate frame ratio of 6% > 5% and is removed. The filtered group of similar business data includes V001, thus ensuring data quality and structural consistency. Then, based on the backhaul weight parameters, collaborative amplification processing is performed. A timing alignment weight of 0.3 guides fine-tuning of video frames with adjacent timestamps, correcting frame times to the standard timeline. A confidence guarantee weight of 0.2 ensures priority retention of data from CPE1. A redundancy allocation weight of 0.1 controls the merging of duplicate frames; for example, if a frame is repeatedly captured by multiple CPEs, only the optimal frame is retained. A validity weight of 0.4 ensures that the processed data still meets the error rate and packet loss rate thresholds. After collaborative processing, the generated amplified service data group contains video data with complete frames, consistent timing, and high confidence. Based on the data source CPE device, the amplified service data is distributed back to each CPE device. CPE1 transmits a standardized video frame V001. The standardized service backhaul data supports unified scheduling and centralized processing by the park's central controller, enabling collaborative backhaul of the same type of service data across multiple CPE devices.

[0052] During the cross-CPE device relay amplification process, filtering and amplification based on constraint parameters and weight parameters can ensure that the processed data meets business requirements, improve the accuracy, reliability and real-time performance of business data, and thus improve the standardization and efficient backhaul of various business data in the park. By uniformly analyzing and processing the data collected by different CPE devices, cross-CPE device relay amplification and collaborative work are realized, improving the overall performance of the park network and meeting the diverse business needs of the smart park.

[0053] Furthermore, after obtaining the amplified similar park business data group, the method also includes: re-performing constraint filtering on the amplified similar park business data group using the backhaul constraint parameters to obtain the verified similar park business data group; and identifying the standardized business backhaul data of each CPE device based on the verified similar park business data group.

[0054] Specifically, the backhaul constraint parameters for the corresponding business type in the backhaul data standard model are invoked to perform a secondary screening on the amplified similar park business data group. The backhaul constraint parameters include data validity threshold parameters, allowed time series offset upper limit parameters, confidence level lower limit threshold parameters, and data redundancy upper limit parameters. During the verification process, each business data in the amplified similar park business data group is checked to see if it meets these constraints. Data that does not meet the conditions is removed or marked as invalid, thus obtaining the verified similar park business data group.

[0055] Based on the CPE device source identifier of each data item in the verified similar park business data group, the data is split and mapped back to the corresponding CPE device, generating standardized business return data for each CPE device. The data format, timestamp, and integrity status are uniformly standardized to ensure that the data returned by different CPE devices are consistent in structure, timing, and quality.

[0056] By performing secondary quality checks on the amplified service data, it is ensured that the data generated by cross-CPE collaborative processing fully meets the preset constraints, further improving the reliability and consistency of service data, and generating high-quality, uniformly standardized service backhaul data for each CPE device, thereby improving the overall performance and service operation efficiency of the campus network.

[0057] Furthermore, after obtaining the amplified similar park business data group, the method also includes: obtaining similar park business data groups from different CPE devices; performing data fusion enhancement processing on the similar park business data groups from different CPE devices to obtain the enhanced similar park business data group; and identifying the standardized business backhaul data of each CPE device based on the enhanced similar park business data group.

[0058] Specifically, this involves acquiring similar campus business data sets from different CPE devices. These data sets record the collection results of the same type of business at different physical locations. Data fusion and enhancement processing is then performed on these data sets from different CPE devices. The fusion process includes multi-source data alignment, information completion, and weighted synthesis. During fusion enhancement, the data from different CPE devices is first synchronized in time through multi-source data alignment. The timestamp information of each business data item is read, and the data collected across CPEs is aligned according to a predefined time granularity, such as milliseconds or seconds. Interpolation or time correction algorithms are used to adjust for deviations, ensuring that all business data accurately match on a unified time axis. For example, if the timestamp of the temperature data collected by CPE1 is 1000ms and that of CPE2 is 1003ms, then linear interpolation is used to adjust the data of CPE2 to 1000ms, thus ensuring time sequence consistency during subsequent fusion. Then, information completion is performed, filling in missing or erroneous data with redundant or high-confidence data. If data from a certain CPE device is lost or has low confidence, data from other CPE devices at the corresponding time point is used to complete the data. For multi-frame video or multiple sensor samplings, the data with the highest confidence is selected as a reference to repair lost or abnormal data, thereby enhancing data integrity and reliability.

[0059] Finally, weighted fusion is performed, assigning processing priorities and fusion impact to data from different CPE devices based on the backhaul weight parameters. During the weighted fusion process, weights are first assigned to each piece of similar business data from different CPE devices, and then the contribution of each piece of data to the fusion result is calculated based on the backhaul weight parameters. Let the set of similar data at a certain point in time be D={d1,d2…dn}, and the corresponding CPE devices be CPE1, CPE2…CPEn. The fusion weight Wi of each data point can be calculated by the formula: Wi=α×Vi+β×Ti+γ×Ci+δ×Ri, where α, β, γ, and δ are the coefficients of the validity decision weight, time sequence alignment decision weight, confidence guarantee weight, and redundancy allocation weight, respectively, and their sum is 1. Vi is the validity score of the i-th data point, calculated based on indicators such as data error rate and packet loss rate, ranging from 0 to 1. The closer to 1, the more valid the data. Ti is the time sequence alignment score of the i-th data point, calculated based on the deviation between the data timestamp and the standard time axis. The smaller the deviation, the higher the score. Ci is the confidence score of the i-th data point, reflecting the reliability of the data source and the accuracy of the content. The larger the value, the higher the credibility. Ri is the redundancy score of the i-th data point, indicating the degree of duplication or redundancy between this data point and other data. After normalization processing, a high redundancy score results in a low score, thereby reducing its influence in the fusion result. By weighting and summarizing the validity, temporal alignment, confidence level, and redundancy of data from different CPE devices, the final influence of each data point in the fusion result is obtained, thereby achieving optimized fusion of cross-CPE collaborative data. Through fusion enhancement processing, the enhanced group of similar campus business data is optimized in terms of data integrity, temporal consistency, and confidence level.

[0060] Based on the enhanced similar campus business data groups, the data is split and mapped according to the CPE device source identifier of each data entry. By reading the source CPE identifier of each data entry, the enhanced similar campus business data is categorized according to the source device. The dataset corresponding to each CPE device is then uniformly formatted, including timestamp standardization, data type unification, and integrity status marking, ensuring consistent data structure, accurate timing, and reliable content across different CPE devices. Through splitting and mapping, each CPE device obtains its corresponding standardized business backhaul data, which can be directly used for backhaul to the central controller, achieving unified and high-quality transmission of multi-CPE collaborative backhaul.

[0061] By enhancing the fusion of multiple CPE data, the integrity, reliability, and consistency of similar business data are further improved. High-quality, standardized business backhaul data is generated for each CPE device, ensuring the accuracy of CPE collaborative backhaul. This provides a more solid foundation for centralized data management, unified scheduling, and intelligent analysis within the smart park, thereby maximizing the effect of CPE device communication collaboration optimization and business collaboration.

[0062] The standardized service backhaul data is sent to the central controller for communication transmission via the CPE backhaul link.

[0063] Furthermore, the standardized service backhaul data is transmitted to the central controller via the CPE backhaul link for communication transmission. The method includes: the central controller performs a data quality assessment on the standardized service backhaul data and obtains the data quality assessment result; and updates the backhaul constraint parameters and backhaul weight parameters of the backhaul data standard model based on the data quality assessment result as feedback information.

[0064] Specifically, each CPE device sends standardized business backhaul data, which has been filtered, merged, and standardized, to the central controller via its CPE backhaul link. The CPE backhaul link refers to the communication channel connecting the CPE device and the central control system of the park, supporting multiple data transmission protocols and ensuring the stability and real-time performance of business data during transmission. After receiving the standardized business backhaul data, the central controller performs a data quality assessment on the standardized business backhaul data. The assessment includes key indicators such as data integrity, timing accuracy, confidence level, and redundancy, and obtains the data quality assessment results.

[0065] The evaluation results are used as feedback information. Based on indicators such as completeness, timing, confidence, and redundancy in the evaluation results, the backhaul constraint parameters and backhaul weight parameters in the backhaul data standard model for the corresponding service type are updated and optimized. For example, if the frame loss rate of a certain type of service remains high, the data validity threshold is increased to require the next round of CPE equipment to transmit more complete data. The backhaul weight parameters are adjusted according to the performance of each dimension in the data quality evaluation. For example, if timing deviations are frequent, the timing alignment weight is increased to enhance the importance of time alignment during fusion processing. The updated backhaul data standard model will be distributed or applied to the next round of service data backhaul, making the CPE equipment more consistent with the optimized constraint and weight strategy during collection, filtering, fusion, and standardization processing, thus achieving closed-loop optimization.

[0066] By uniformly sending data from CPE devices to the central controller and adjusting based on data quality feedback, problems in the data backhaul process can be detected and corrected in real time, improving the accuracy and stability of data transmission. Simultaneously, dynamically updating the constraint and weight parameters in the backhaul data standard model continuously optimizes the data processing strategy across CPE devices, effectively improving the efficiency and quality of communication and collaboration among CPE devices in the smart park. This ensures the accuracy, integrity, and real-time performance of business data transmission, providing solid data communication support for the efficient and stable operation of the smart park.

[0067] Example 2, based on the same inventive concept as the CPE device communication collaboration optimization method for smart parks in the previous examples, such as... Figure 2 As shown, this application provides a CPE device communication collaboration optimization device for smart parks, wherein the CPE device communication collaboration optimization device for smart parks includes: The service type analysis component 11 is used to analyze the types of services operating within the smart park and obtain park service type labels; the feature extraction component 12 is used to extract the service data requirement feature set corresponding to each park service type and construct a backhaul data standard model based on the service data requirement feature set; the data partitioning component 13 is used to collect multi-source terminal service data from multiple CPE devices connected to the park network, and partition the collected multi-source service dataset according to the park service type label to obtain multiple types of park service data groups received by each CPE device; the backhaul data acquisition component 14 is used to perform cross-CPE device relay amplification processing on each type of park service data group according to the backhaul data standard model to obtain standardized service backhaul data for each CPE device; and the communication transmission component 15 is used to send the standardized service backhaul data to the central controller for communication transmission via the CPE backhaul link.

[0068] Furthermore, the feature extraction component 12 is also used for: defining a backhaul processing dimension, which includes at least a backhaul data validity dimension, a backhaul data time sequence alignment dimension, a backhaul data confidence dimension, and a backhaul data redundancy dimension; establishing a mapping relationship between the business data requirement feature set and the backhaul processing dimension; performing feature structuring processing on the business data requirement feature set to obtain a business data feature vector; generating backhaul constraint parameters and backhaul weight parameters based on the business data feature vector; and encapsulating the park business type label, the backhaul constraint parameters, and the backhaul weight parameters into a model to obtain a backhaul data standard model.

[0069] Furthermore, the feature extraction component 12 is also used for: the backhaul constraint parameters include a data validity threshold parameter, an allowed time series offset upper limit parameter, a confidence lower limit threshold parameter, and a data redundancy upper limit parameter calculated based on the business data feature vector; the backhaul weight parameters include a validity decision weight, a time series alignment decision weight, a confidence guarantee weight, and a redundancy allocation weight calculated based on the business data feature vector.

[0070] Furthermore, the backhaul data acquisition component 14 is also used to: load the backhaul constraint parameters and backhaul weight parameters of the backhaul data standard model according to the park business type label corresponding to each type of park business data group; perform constraint filtering on the same type of park business data groups across CPE devices using the backhaul constraint parameters to obtain the filtered same type of park business data groups; perform collaborative amplification processing on the filtered same type of park business data groups using the backhaul weight parameters to obtain the amplified same type of park business data groups; and identify the standardized business backhaul data of each CPE device based on the amplified same type of park business data groups.

[0071] Furthermore, the backhaul data acquisition component 14 is also used to: re-perform constraint filtering on the amplified similar park business data group with the backhaul constraint parameters to obtain the verified similar park business data group; and identify the standardized business backhaul data of each CPE device based on the verified similar park business data group.

[0072] Furthermore, the backhaul data acquisition component 14 is also used to: acquire similar campus business data groups from different CPE devices; perform data fusion enhancement processing on the similar campus business data groups from different CPE devices to obtain enhanced similar campus business data groups; and identify standardized business backhaul data for each CPE device based on the enhanced similar campus business data groups.

[0073] Furthermore, the data segmentation component 13 is also used to: parse the collected multi-source business dataset to obtain business identification information, the business identification information including data source terminal identification, business occurrence time identification, business function identification and business scenario identification; establish matching rules between park business type tags and business identification information; and segment the collected multi-source business dataset according to the matching rules to obtain multiple types of park business data groups received by each CPE device.

[0074] Furthermore, the data segmentation component 13 is also used to: collect historical multi-source service datasets for each CPE device; identify park service type labels for the historical multi-source service datasets of each CPE device to obtain park service type labels for each CPE device; construct matching rules according to the park service type labels of each CPE device and the corresponding service identification information to obtain multiple matching rules, wherein each CPE device segments the collected multi-source service dataset by calling the corresponding matching rules.

[0075] Furthermore, the communication transmission component 15 is also used for: the central controller to perform data quality assessment on the standardized service backhaul data and obtain data quality assessment results; and to update the backhaul constraint parameters and backhaul weight parameters of the backhaul data standard model based on the data quality assessment results as feedback information.

[0076] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The communication collaboration optimization method and specific examples for CPE devices in smart parks described in the foregoing embodiment 1 are also applicable to the communication collaboration optimization device for CPE devices in smart parks in this embodiment. Through the foregoing detailed description of the communication collaboration optimization method for CPE devices in smart parks, those skilled in the art can clearly understand the communication collaboration optimization device for CPE devices in smart parks in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0077] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0078] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for optimizing communication collaboration of CPE devices in smart parks, characterized in that, The method includes: Analyze the types of businesses operating within the smart park and obtain park business type tags; Extract the business data requirement feature set corresponding to each park business type, and construct a backhaul data standard model based on the business data feature set; Multi-source terminal service data is collected from multiple CPE devices connected to the campus network. The collected multi-source service dataset is divided according to the campus service type label to obtain multiple types of campus service data groups received by each CPE device. Based on the backhaul data standard model, cross-CPE device relay amplification processing is performed on each type of park business data group to obtain standardized business backhaul data for each CPE device. The standardized service backhaul data is sent to the central controller for communication transmission via the CPE backhaul link.

2. The CPE device communication collaboration optimization method for smart parks as described in claim 1, characterized in that, The method for constructing a standard model for backhaul data based on the aforementioned business data requirement feature set includes: The business data requirement feature set includes at least the data time-series continuity requirement feature, data integrity requirement feature, data confidence requirement feature, and data real-time requirement feature; Define the backhaul processing dimensions, which include backhaul data validity dimension, backhaul data time sequence alignment dimension, backhaul data confidence dimension, and backhaul data redundancy dimension. Establish a mapping relationship between the business data requirement feature set and the backhaul processing dimension; The business data requirement feature set is subjected to feature structuring processing to obtain the business data feature vector; Backhaul constraint parameters and backhaul weight parameters are generated based on the business data feature vector. The backhaul data standard model is obtained by encapsulating the park business type label, the backhaul constraint parameters, and the backhaul weight parameters into a model.

3. The CPE device communication collaboration optimization method for smart parks as described in claim 2, characterized in that, The method for generating backhaul constraint parameters and backhaul weight parameters based on the business data feature vector includes: The backhaul constraint parameters include a data validity threshold parameter, an upper limit parameter for allowed time offset, a lower limit threshold parameter for confidence, and an upper limit parameter for data redundancy, all calculated based on the feature vector of the business data. The backhaul weight parameters include validity decision weight, time alignment decision weight, confidence guarantee weight, and redundancy allocation weight, all calculated based on the business data feature vector.

4. The CPE device communication collaboration optimization method for smart parks as described in claim 1, characterized in that, Based on the aforementioned backhaul data standard model, cross-CPE device relay amplification processing is performed on each type of park business data group, including the following methods: Based on the park business type label corresponding to each type of park business data group, load the backhaul constraint parameters and backhaul weight parameters of the backhaul data standard model; The backhaul constraint parameters are used to constrain and filter similar park business data groups across CPE devices to obtain the filtered similar park business data groups. The filtered similar park business data groups are collaboratively amplified using the backhaul weight parameters to obtain amplified similar park business data groups. Identify standardized business return data for each CPE device based on the magnified similar park business data group.

5. The CPE device communication collaboration optimization method for smart parks as described in claim 4, characterized in that, After obtaining the enlarged data set of similar park business data, the method also includes: The amplified similar park business data group is re-filtered using the return constraint parameters to obtain the verified similar park business data group. Based on the verified business data groups of similar parks, identify the standardized business backhaul data for each CPE device.

6. The CPE device communication collaboration optimization method for smart parks as described in claim 4, characterized in that, After obtaining the enlarged data set of similar park business data, the method also includes: Acquire similar campus business data groups from different CPE devices; Data fusion and enhancement processing is performed on similar campus business data groups from different CPE devices to obtain enhanced similar campus business data groups; Identify standardized business backhaul data for each CPE device based on the enhanced cluster of similar campus business data sets.

7. The CPE device communication collaboration optimization method for smart parks as described in claim 1, characterized in that, The collected multi-source service dataset is divided according to the park service type labels to obtain multiple types of park service data groups received by each CPE device. The method includes: The collected multi-source business dataset is parsed to obtain business identification information, which includes data source terminal identifier, business occurrence time identifier, business function identifier, and business scenario identifier. Establish matching rules between park business type labels and business identification information; The collected multi-source service dataset is divided according to the matching rules to obtain multiple types of campus service data groups received by each CPE device.

8. The CPE device communication collaboration optimization method for smart parks as described in claim 7, characterized in that, The method includes: Collect historical multi-source service datasets for each CPE device; The park service type label is identified by performing park service type label on the historical multi-source service dataset of each CPE device. Matching rules are constructed according to the park business type label and corresponding business identification information of each CPE device, resulting in multiple matching rules. Each CPE device divides the collected multi-source business dataset by calling the corresponding matching rule.

9. The CPE device communication collaboration optimization method for smart parks as described in claim 2, characterized in that, The standardized service backhaul data is transmitted to the central controller via the CPE backhaul link for communication transmission, including the following methods: The central controller performs a data quality assessment on the standardized business backhaul data and obtains the data quality assessment results. The return constraint parameters and return weight parameters of the return data standard model are updated based on the data quality assessment results as feedback information.

10. A CPE device communication collaboration optimization device for smart parks, characterized in that, The step of implementing the CPE device communication collaboration optimization method for smart parks according to any one of claims 1 to 9, wherein the CPE device communication collaboration optimization device for smart parks comprises: The business type analysis component is used to analyze the types of businesses operating within the smart park and obtain park business type tags; A feature extraction component is used to extract the business data requirement feature set corresponding to each park business type, and to construct a backhaul data standard model based on the business data feature set. The data segmentation component is used to collect multi-source terminal service data from multiple CPE devices connected to the campus network, and to segment the collected multi-source service dataset according to the campus service type label to obtain multiple types of campus service data groups received by each CPE device. The backhaul data acquisition component is used to perform cross-CPE device relay amplification processing on each type of park business data group according to the backhaul data standard model, and to acquire standardized business backhaul data of each CPE device. The communication transmission component is used to send the standardized service backhaul data to the central controller via the CPE backhaul link for communication transmission.

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