Data processing method and apparatus, networking architecture, and computer readable storage medium

Abstract

Embodiments of the present application provide a data processing method and apparatus, a networking architecture, and a computer readable storage medium. The method comprises: receiving uplink data, wherein the uplink data comprises type identification information; classifying the uplink data on the basis of the type identification information; and when the uplink data is classified as sensed object data, distributing the sensed object data to a preset local distribution device, so that the local distribution device performs data processing on the sensed object data, and sends the sensed object data having experienced the data processing to a local server. The embodiments of the present application greatly shorten the physical link, reduce latency, and improve the data transmission efficiency.

Description

Data processing method, device, network architecture and computer-readable storage medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Chinese patent application No. 202311107714.8 filed on August 29, 2023, the contents of which are incorporated herein by reference in their entirety. Technical Field

[0003] The present disclosure relates to the field of communication technology, and in particular to a data processing method, device, networking architecture, and computer-readable storage medium. Background Art

[0004] Currently, Integrated Sensing and Communication (hereinafter referred to as ISAC) technology is still in its early research stages. High-precision perception algorithms require very high computing power, and real-time perception requires high speed and low latency for both the processing and transmission of perception results. This places extremely high demands on the ISAC network architecture.

[0005] The synaesthesia fusion network architecture mainly relies on traditional wireless communication systems, as shown in Figure 1. This network architecture requires the use of base station hardware computing power, data needs to be transmitted to the public network through the core network, and a large amount of routing forwarding is required.

[0006] Summary of the Invention

[0007] The present disclosure provides a data processing method, device, networking architecture, and computer-readable storage medium.

[0008] In a first aspect, an embodiment of the present disclosure provides a data processing method, which is applied to a base station, and the method includes: receiving uplink data, the uplink data including type identification information; classifying the uplink data according to the type identification information; and when the uplink data is classified as perceived object data, diverting the perceived object data to a preset local diversion device, so as to process the perceived object data through the local diversion device, and sending the processed perceived object data to a second local server.

[0009] In a second aspect, an embodiment of the present disclosure provides a data processing method, which is applied to a local diversion device, and the method includes: receiving perceived object data diverted from a base station; performing data processing on the perceived object data; and sending the processed perceived object data to a second local server.

[0010] In a third aspect, an embodiment of the present disclosure provides a data processing device, applied to a base station, comprising: a first memory and a first processor; the first memory stores a first computer program that can be executed by the first processor, and when the first computer program is executed by the first processor, the data processing method described in the first aspect is implemented.

[0011] In a fourth aspect, an embodiment of the present disclosure provides a data processing device, applied to a local diversion device, comprising: a second memory and a second processor; the second memory stores a second computer program that can be executed by the second processor, and when the second computer program is executed by the second processor, it implements the data processing method described in the second aspect.

[0012] In the fifth aspect, an embodiment of the present disclosure provides a networking architecture, comprising: a local diversion device and at least one base station; the local diversion device comprises the data processing device described in the fourth aspect, and the base station comprises the data processing device described in the third aspect; one or more base stations among the at least one base station communicate with the perceived object; the data processing device in the local diversion device is connected to the data processing device in each base station, and is connected to a second local server.

[0013] In a sixth aspect, an embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which implements the data processing method described in the first aspect or the second aspect when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In the accompanying drawings of the embodiments of the present disclosure:

[0015] FIG1 is a schematic diagram of the structure of a traditional wireless communication system;

[0016] FIG2 is a schematic diagram of a novel networking architecture provided by an embodiment of the present disclosure;

[0017] FIG3 is a flow chart of a data processing method applied to a base station according to an embodiment of the present disclosure;

[0018] FIG4 is a flow chart of a data processing method applied to a local offload device according to an embodiment of the present disclosure;

[0019] FIG5 is a schematic diagram of a data processing method provided by an embodiment of the present disclosure;

[0020] FIG6 is a flow chart of an exemplary camera data processing method provided by an embodiment of the present disclosure;

[0021] FIG7 is a block diagram of a first data processing device applied to a base station according to an embodiment of the present disclosure;

[0022] FIG8 is a block diagram of a second data processing device applied to a local traffic distribution device according to an embodiment of the present disclosure;

[0023] FIG9 is a block diagram of a second data processing device applied to a base station according to an embodiment of the present disclosure;

[0024] FIG10 is a block diagram showing the composition of a second data processing device applied to a local traffic distribution device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the technical solution of the present disclosure, the communication perception data processing method and computer-readable storage medium provided by the embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0026] The present disclosure will be described more fully hereinafter with reference to the accompanying drawings, but the illustrated embodiments may be embodied in different forms, and the present disclosure should not be construed as limited to the embodiments set forth below. Rather, these embodiments are provided so that the present disclosure will be thorough and complete and will fully understand the scope of the present disclosure to those skilled in the art.

[0027] The accompanying drawings of the embodiments of the present disclosure are used to provide a further understanding of the embodiments of the present disclosure and constitute a part of the specification. Together with the detailed embodiments, they are used to explain the present disclosure and do not constitute a limitation of the present disclosure. The above and other features and advantages will become more apparent to those skilled in the art by describing the detailed embodiments with reference to the accompanying drawings.

[0028] The present disclosure may be described with reference to plan views and / or cross-sectional views by way of ideal schematic views of the present disclosure. Therefore, the exemplary illustrations may be modified according to manufacturing techniques and / or tolerances.

[0029] In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.

[0030] The terms used in this disclosure are only used to describe specific embodiments and are not intended to limit this disclosure. As used in this disclosure, the term "and / or" includes any and all combinations of one or more related enumerated items. As used in this disclosure, the singular forms "one" and "the" are also intended to include plural forms, unless the context clearly indicates otherwise. As used in this disclosure, the terms "including" and "made of" specify the presence of the features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. As used in this disclosure, "first", "second" and similar words do not represent any order, quantity or importance, but are only used to distinguish different components.

[0031] Unless otherwise defined, all terms (including technical and scientific terms) used in this disclosure have the same meanings as those commonly understood by those skilled in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this disclosure, and will not be interpreted as having an idealized or overly formal meaning unless expressly defined in this disclosure.

[0032] In the present disclosure, unless otherwise defined, the terms "connect", "connected", etc. should be understood in a broad sense. For example, "connection" can be a wired connection (such as an optical fiber connection), a wireless connection, or a combination of the two.

[0033] The present disclosure is not limited to the embodiments shown in the drawings, but includes modifications of the configurations formed based on the manufacturing process. Therefore, the regions illustrated in the drawings have schematic properties, and the shapes of the regions shown in the drawings illustrate the specific shapes of the regions of the elements, but are not intended to be limiting.

[0034] With the development of 5G communication technology and the expansion of diverse business use cases, wireless mobile communication systems are no longer limited to traditional communication applications. Integrating wireless communications with related vertical industries has become a new research and application direction for wireless communication equipment manufacturers and operators. For example, combining communication systems with perception systems, based on the integration of resources and equipment, will broaden wireless communication application scenarios and create new industry value for the wireless communications industry.

[0035] Synaesthesia integration refers to the fusion of communication and perception systems. Synaesthesia integration broadens the application scenarios of traditional communication and provides many benefits.

[0036] First of all, both communication and perception require the use of radio spectrum resources, which are very scarce and valuable. If wireless signals can be used to both transmit and perceive information, spectrum resources can be saved and spectrum utilization can be improved.

[0037] Secondly, communication and perception require similar hardware (such as transceiver antennas, amplifiers, filters, etc.), which is also very expensive and complex. If the existing equipment can support both communication and perception with minimal changes, it can save hardware costs.

[0038] Secondly, both communication and perception involve similar information processing processes (such as signal encoding, modulation, demodulation, and decoding), which are extremely complex. If radar sensing capabilities could be integrated into a wireless communication base station, allowing a single device to simultaneously perform both communication and perception, achieving an environment where communication aids perception and perception aids communication, this would be highly valuable for both communication and perception applications. This is further demonstrated by the increasing adoption of applications such as low-altitude security warning, connected vehicle road monitoring, and healthcare monitoring.

[0039] In summary, the integrated fusion of tele-sensing can bring many benefits to the operation and maintenance costs and application scope of communication base stations and wireless sensing systems, broaden the application scenarios of wireless communications, and become a key technology for research in the 5G-Advanced (5G-Evolution) stage and one of the core visions of 6G.

[0040] At present, synaesthesia integration technology is still in its initial research stage. High-precision perception algorithms have high requirements for computing power, and real-time perception requires high speed and low latency for both the processing and feedback of perception results. This places extremely high demands on the network architecture of synaesthesia integration.

[0041] The current integrated synaesthesia networking architecture primarily relies on a traditional wireless communication system 100, as shown in FIG1 . Traditional wireless communication system 100 includes a first base station 101, a first core network 102, a first packet data network 103, and a first local server 104. First base station 101 is connected to first core network 102, which is in turn connected to first packet data network 103, which is in turn connected to first local server 104. First base station 101 communicates with both a first sensed object 105 and a first terminal 106 to obtain uplink data, which is then transmitted to first local server 104 via first core network 102 and first packet data network 103. The data from first sensed object 105 requires computing power from the hardware of first base station 101. Furthermore, because the data must be transmitted to the public network through first core network 102 and undergoes extensive routing and forwarding, this results in significant latency and poor user privacy and security.

[0042] The inventors noted that with local offload, data no longer needs to be transmitted through the core network to reach the public network. Instead, it is transmitted through a local gateway to the local data center and related application servers, significantly improving user privacy and security. Furthermore, local offload, as an important means of reducing latency and alleviating pressure on the core network of communication systems, can effectively address the aforementioned issues of high latency and insufficient computing power.

[0043] Therefore, in response to the above-mentioned problems, the embodiments of the present disclosure propose a new networking architecture for synaesthesia integration based on local diversion technology. By adding only one hardware single-board device to the traditional synaesthesia integrated base station, the traditional networking architecture can be optimized to solve the problems of large latency, computing power impact, and poor privacy security of perceived users in the current synaesthesia network. In addition, in the embodiments of the present disclosure, a communication perception data processing method is proposed based on the new networking architecture. This method saves the computing power of the base station by diverting the perceived object data and processing the perceived object data by a local diversion device. The local diversion device transmits the processed perceived object data to the local server without passing through the public network, thereby improving user privacy security. Moreover, the transmission is carried out through the local diversion device without a large amount of routing forwarding in the middle, which greatly shortens the physical link, reduces latency, and improves data transmission efficiency.

[0044] As shown in FIG2 , it is a schematic diagram of a novel networking architecture 200 provided in an embodiment of the present disclosure.

[0045] The networking architecture 200 provided by the embodiment of the present disclosure includes: a local offload device 201 and at least one second base station 202 (which may be referred to as a base station or a synaesthesia base station).

[0046] The local traffic distribution device 201 includes a first data processing device 2011 applied to the local traffic distribution device. The first data processing device 2011 is used to implement the data processing method applied to the local traffic distribution device according to an embodiment of the present disclosure.

[0047] Each of the at least one second base station 202 includes a data processing device 2021 applied to the base station (hereinafter referred to as the second data processing device 2021). The second data processing device 2021 is configured to implement the data processing method applied to the base station according to the embodiment of the present disclosure.

[0048] One or more second base stations 202 (e.g., first base station 101 in FIG. 2 ) of at least one second base station 202 communicate with a second sensed object 203 (hereinafter referred to as a sensed object); and the one or more second base stations 202 communicate with a second terminal 208 (hereinafter referred to as a terminal). The second sensed object 203 may include, but is not limited to, the first sensed object 105 described above. The second terminal 208 may include, but is not limited to, the first terminal 106 described above.

[0049] The first data processing device 2011 in the local offload device 201 is connected to the second data processing device 2021 in each second base station 202, and is also connected to a second local server (hereinafter referred to as a local server) 204. The local offload device 201 can be connected to the second local server 204 via an optical fiber; the second local server 204 can be a private network server.

[0050] The networking architecture 200 according to this embodiment may further include: a second core network 205 (hereinafter referred to as the core network) and a second packet data network 206 (hereinafter referred to as the packet data network). Each second base station 202 is connected to its corresponding second core network 205. The second core network 205 is connected to its corresponding second packet data network 206. The second packet data network 206 is connected to a third local server 207. The third local server 207 and the first local server 104 are both public network servers and may be the same or different.

[0051] The second core network 205 may include but is not limited to the above-mentioned first core network 102 , and the second packet data network 206 may include but is not limited to the above-mentioned first packet data network 103 .

[0052] As shown in Figure 2, at least one second base station 202 may include, but is not limited to, the aforementioned first base station 101, and may also include, but is not limited to, a third base station 202-1, a fourth base station 202-2, and the like. A second data processing device 2021-1 may be provided in the first base station 101, a second data processing device 2021-2 may be provided in the third base station 202-1, and a second data processing device 2021-3 may be provided in the fourth base station 202-2. The second core network 205 may include, but is not limited to, an EPC (Evolved Packet Core) and a 5G Core (5G Core).

[0053] Local traffic distribution device 201 is a hardware board, which can be dedicated to service processing. This hardware board can be deployed independently or deployed together with second base station 202 for shared network management and operation. This significantly shortens the data transmission link for the sensed object data, which is very valuable for application scenarios requiring low latency.

[0054] The local traffic diversion device 201 may further include: a synaesthesia computing power processing module 2012 .

[0055] The synaesthesia computing power processing module 2012 may be equipped with high-performance AI (artificial intelligence) computing power, and may process the massive amount of sensed object data (or synaesthesia data) collected, thereby sinking the core network user plane data processing function to the local diversion device 201, and transferring the process that originally required the consumption of base station computing power to the second local server 204 for implementation, thereby significantly reducing the pressure on the base station and not affecting the communication services of ordinary users under the base station.

[0056] The local traffic distribution device 201 may further include at least one of the following: a base station chassis backplane connection interface 2013 , an expansion module 2014 , and a third-party interface 2015 .

[0057] The base station chassis backplane connection interface 2013 is used to connect to the base station chassis backplane; the base station chassis backplane connection interface 2013 complies with the communication protocol of the second base station 202 main control board and can be plug-and-play without prior site survey.

[0058] The extension module 2014 is used for function extension.

[0059] The third-party interface 2015 may be used to connect to a third-party device other than the second base station 202 .

[0060] The configuration of the extension module 2014 and the third-party interface 2015 enables the local diversion device 201 to interact with the second local server 204 and be used for subsequent functional expansion, and on the other hand, to serve as a backup process as the data of the perceived objects increases explosively.

[0061] In some embodiments, a set of local diversion devices 201 can simultaneously support 4G public network / 4G private network / 5G public network / 5G private network services, so that only one service IP (Internet Protocol) address and one management IP address need to be added to achieve data interoperability and ultra-low energy consumption.

[0062] Compared to the traditional wireless communication system 100, the novel networking architecture 200 of the disclosed embodiment retains the upstream telepathic base station 202's data collection and transmission process, and innovatively proposes the addition of a local offload device 201, which can flexibly allocate uplink data, alleviate the computing pressure of the base station 202, and prevent the leakage of the perceived subject's personal privacy. For public network services initiated by ordinary terminals, the base station 202 directly transmits them to the transmission equipment and ultimately to the core network. For uplink data initiated by the perceived subject, the base station 202 and the local offload device 201 can directly transmit it to the second local server 204, or select, after screening, whether to transmit the data to the second local server 204 or to the public network core network. Data diverted to local diversion device 201 and transmitted to second local server 204 via local diversion device 201 does not need to pass through the core network. This effectively reduces data transmission volume, alleviates pressure on the core network, and reduces uplink data forwarding latency. Furthermore, data requiring encryption or meeting network security requirements can be diverted locally and then transmitted from local diversion device 201 to second local server 204 for encryption and authentication before being transmitted to the public core network. This reduces the possibility of the perceived object data being exposed on the public network and improves data security. Furthermore, if local diversion device 201 fails, public network services under the base station are not affected.

[0063] Based on the novel networking architecture 200 described above, the data processing method of the present embodiment is described in detail below. In FIG2 , the second base station, second core network, second packet data network, second perceived object, second terminal, second local server, and third local server are referred to as the base station, core network, packet data network, perceived object, terminal, local server, and another local server, respectively.

[0064] 3 and 5 , an embodiment of the present disclosure provides a data processing method, which is applied to a base station (eg, the second base station 202 shown in FIG. 2 ). The method includes the following steps S101 - S103 .

[0065] In step S101 , uplink data is received, where the uplink data includes type identification information.

[0066] The type identification information may include, but is not limited to, any one or more of the following: data ID (identification), source address, destination address, service type, and data packet length.

[0067] Before receiving uplink data, the method may further include: starting a perception function and a local offload function.

[0068] The base station can turn on the perception function switch and the local diversion switch, and send a perception request notification to the perceived object (e.g., the second perceived object 203 shown in Figure 2). The perceived object generates behavioral actions based on the perception request notification and generates regular behavior data. For example, the perceived object can be a camera, which changes its camera angle after receiving the perception request notification.

[0069] The sensed object can upload its data (including but not limited to the above-mentioned regular behavior data and the sensed object's collected data) to the base station as uplink data. Since not only the sensed object but also various terminals (e.g., the second terminal 208 shown in FIG2 ) access the base station, the uplink data also includes terminal communication data.

[0070] The uplink data received by the base station may be the data of the sensed object, or the terminal communication data, or both.

[0071] Terminal communication data can be sent to the core network (e.g., the second core network 205 shown in Figure 2) via the base station. If the perceived object data is also sent to the core network, it will seriously affect the base station's computing power and increase the latency of the perceived object data, affecting the real-time perception. Therefore, the disclosed embodiments consider offloading the perceived object data to a local offload device for processing. Prior to this, the base station must first classify the received uplink data and distinguish the perceived object data from the uplink data.

[0072] In step S102, the uplink data is classified according to the type identification information.

[0073] This classification task can be completed by a pre-set diversion service in the base station. The diversion service receives uplink data, obtains the type identification information contained in the uplink data, compares the obtained type identification information with the pre-stored type identification information, and when it is determined that the obtained type identification information is consistent with the pre-stored type identification information, the uplink data can be classified as perceived object data.

[0074] In some embodiments, uplink data can be classified according to the data ID. For example, the data ID can be the ID of the corresponding perceived object. According to the data ID, it can be confirmed that the received uplink data is data sent by the perceived object, and thus the uplink data is classified as perceived object data.

[0075] In some embodiments, uplink data can be classified according to task type. For example, in a task of analyzing pedestrian flow, data sent by a camera can be determined, and the received uplink data can be confirmed to be data sent by a sensed object, and thus the uplink data can be classified as sensed object data.

[0076] In step S103, when the uplink data is classified as perceived object data, the perceived object data is diverted to a preset local diversion device (for example, the local diversion device 201 shown in Figure 2) so that the perceived object data is processed by the local diversion device and the processed perceived object data is sent to the local server.

[0077] The local server connected to the local diversion device may be a private network server, and may be the second local server 204 shown in FIG. 2 .

[0078] Before the sensed object data is diverted to the local diversion device, a user plane data transmission tunnel can be established between the local diversion device and the base station in advance for data exchange between the base station and the local diversion device. The base station can divert the classified sensed object data to the local diversion device through the user plane data transmission tunnel.

[0079] After the offload service classifies the sensed object data, it can be directly offloaded to the local offload device, or it can be preliminarily screened to select the sensed object data that meets the conditions and then offloaded to the local offload device. The sensed object data that does not meet the preset conditions can be sent to the preset core network or directly discarded.

[0080] The diversion service can divide the perceived object data into multiple groups according to preset diversion rules (for example, network slicing rules and / or PLMN (Public Land Mobile Network) rules), and detect whether each group of perceived object data meets the preset conditions.

[0081] The preset conditions may include but are not limited to any one or more of the following: the data accuracy of the perceived object data is greater than or equal to a preset accuracy threshold; the perceived object data is unencrypted data; the identity identifier of the perceived object data is consistent with the preset identity identifier; the target address of the perceived object data is consistent with the preset address; and the business type of the perceived object data is consistent with the preset business type.

[0082] In some embodiments, the diversion service can first filter out unencrypted perceived object data, and / or perceived object data with data accuracy greater than or equal to a preset accuracy threshold, and divert it to a local diversion device. By diverting the unencrypted perceived object data, the risk of personal privacy leakage caused by the unencrypted perceived object data being sent to the public network core network can be avoided; by diverting the perceived object data with data accuracy greater than or equal to a preset accuracy threshold, it can be avoided that the perceived object data with very low accuracy cannot be used after being sent to the local diversion device and its corresponding local server, thereby wasting traffic and computing power. For encrypted perceived object data, since it has been encrypted, it can be sent directly to the core network without the risk of privacy leakage; for perceived object data with data accuracy less than the preset accuracy threshold, it can be directly discarded.

[0083] The local splitting device can transmit the processed data of the sensed object to the local server connected thereto via optical fiber transmission.

[0084] In some embodiments, the offload service may divide the sensed object data into multiple groups of sensed object data according to preset offload rules. The offload rules may include but are not limited to: network slicing rules and / or PLMN (Public Land Mobile Network) rules; and each group of sensed object data may be detected to determine whether it meets the preset conditions.

[0085] If any set of perceived object data meets the preset conditions, the offload service discards the set of perceived object data or sends it to the core network. If any set of perceived object data does not meet the preset conditions, the offload service discards the set of perceived object data or sends it to the core network.

[0086] Network slicing is an on-demand networking method that allows operators to separate multiple virtual end-to-end networks on a unified infrastructure. Each network slice is logically isolated from the radio access network to the bearer network and then to the core network to adapt to a variety of applications.

[0087] A PLMN refers to a cellular mobile communications network of a specific type and type, operated by a specific operator in a specific country or region. A PLMN is a network established and operated by the government or a government-approved operator to provide land mobile communications services to the public. This network can interconnect with other communications networks, such as the PSTN (Public Switched Telephone Network), to form a regional or national communications network.

[0088] Based on network slicing technology or PLMN technology, the perceived object data can be divided into multiple networks according to different division factors (for example, destination address, service type, identity, data accuracy, encryption, etc.). The perceived object data on each network can be regarded as a group of perceived object data. Each group of perceived object data can be separately detected to see whether it meets the preset conditions. If a group of perceived object data does not meet the preset requirements, the group of perceived object data will be discarded or sent to the core network through the corresponding network. If a group of perceived object data meets the preset requirements, it can be transmitted through optical fiber to a local server connected to the local diversion device.

[0089] The embodiments of the present disclosure perform an initial screening of the perceived object data based on network slicing rules and / or PLMN rules, and process the perceived data that does not meet the preset conditions based on the network slicing rules and / or PLMN rules, so that the processing between multiple groups of data is not affected, avoiding data confusion.

[0090] In some embodiments, after classifying the uplink data according to the type identification information, the method may further include: if the uplink data is classified as terminal communication data, sending the terminal communication data to a preset core network.

[0091] Because the base station communicates not only with the sensed object but also with multiple terminals, the uplink data contains both the sensed object data and the terminal communication data. If the offload service identifies the uplink data as terminal communication data based on the type identification information, it can directly send the terminal communication data to the pre-set core network. Similarly, downlink data from the core network is also directly transmitted to the base station.

[0092] The embodiment of the present disclosure is based on the addition of a local diversion device, which can flexibly transmit the data obtained by the integrated synaesthesia (including the perceived exclusive data and the terminal communication data) to the local server (such as the second local server 204 or the third local server 207 shown in Figure 2). The diverted perceived object data does not need to pass through the public network core network, and does not need to be processed in the base station. It can increase security authentication and improve data security. At the same time, it does not affect public network consumer users. Ordinary terminal users can use conventional data transmission paths to transmit terminal communication data to the core network for communication.

[0093] In some embodiments, after the perceived object data is diverted to a preset local diversion device, the method may further include: receiving the perceived object data that does not meet the preset requirements returned by the local diversion device, where the perceived object data that does not meet the preset requirements is detected before the local diversion device processes the perceived object data; and sending the perceived object data that does not meet the preset requirements to a preset core network.

[0094] In some embodiments, after receiving the diverted sensed object data, the local diversion device (e.g., the local diversion device 201 shown in FIG2 ) may further screen the sensed object data before processing the sensed object data and sending the processed sensed object data to a local server connected to the local diversion device (e.g., the second local server 204 shown in FIG2 ). That is, the local diversion device may detect whether the sensed object data meets preset requirements. The local diversion device may only process the sensed object data that meets the preset requirements and send it to the local server connected to the local diversion device. The sensed object data that does not meet the preset requirements may be directly discarded or returned to the base station, which then transmits it to another local server (e.g., the third local server 207 in FIG2 ) via the core network. The other local server to which the core network is connected via the packet data network is a public network server.

[0095] The preset requirements may include but are not limited to any one or more of the following: the data accuracy of the perceived object data is greater than or equal to a preset accuracy threshold; the perceived object data is unencrypted data; the identity identifier of the perceived object data is consistent with the preset identity identifier; the target address of the perceived object data is consistent with the preset address; and the business type of the perceived object data is consistent with the preset business type.

[0096] In some embodiments, after the perceived object data is diverted to a preset local diversion device, the method may further include: receiving perception feedback data forwarded by the local diversion device, the perception feedback data being sent by a local server connected to the local diversion device; and sending the perception feedback data to the perceived object and / or the preset core network corresponding to the perceived object data.

[0097] In some embodiments, the perception feedback data may include but is not limited to any one or more of the following: data processed by the local server on the perceived object data; control data on the perceived object sent by the local server; and downlink service data sent by the local server.

[0098] After the local server receives the perceived object data sent by the local diversion device, it can process the perceived object data, for example, encryption processing, authentication processing, etc. The perceived object data encrypted by the local server can be returned to the base station as perception feedback data and sent to the core network through the base station to avoid privacy leakage.

[0099] After the local server receives the perceived object data sent by the local diversion device, it can generate control data of the corresponding perceived object based on the behavior data or status data contained in the perceived object data, and return the control data to the base station through the local diversion device as perception feedback data, and then return it to the corresponding perceived object by the base station, so that the perceived object can be controlled through the control data, for example, adjusting the angle of the camera, etc.

[0100] After receiving the sensed object data sent by the local offload device, the local server can generate corresponding downlink service data based on the sensed object data. For example, if the current sensed object data indicates that a camera is turned on and ready, the local server can generate downlink service data for the camera based on the sensed object data. For example, it can collect pedestrian flow data within a certain period of time, or schedule multiple cameras according to preset priorities. The downlink service data is then returned as sensing feedback data to the corresponding camera via the local offload device and the base station, allowing the corresponding camera to perform the corresponding service.

[0101] The above-mentioned perception feedback data is transmitted to the base station through the local diversion device, and then transmitted downlink by the base station to the perceived object, achieving the closed-loop data processing purpose of collection-processing-feedback-adjustment.

[0102] 4 and 5 , an embodiment of the present disclosure provides a data processing method, which is applied to a local traffic diversion device. The method may include the following steps S201 - S203 .

[0103] In step S201, the sensed object data diverted by the base station is received.

[0104] After receiving the perceived object data diverted from the base station, before processing the perceived object data, the method may further include: detecting whether the perceived object data meets the preset requirements; if the perceived object data meets the preset requirements, entering the process of processing the perceived object data; if the perceived object data does not meet the preset requirements, discarding the perceived object data, or returning it to the base station, so that the returned perceived object data is sent to the preset core network through the base station.

[0105] After receiving the diverted perceived object data, the local diversion device can re-screen the perceived object data, that is, detect whether the perceived object data meets the preset requirements. The local diversion device only processes the perceived object data that meets the preset requirements and sends it to the local server connected to the local diversion device. The perceived object data that does not meet the preset requirements can be directly discarded or returned to the base station, and then transmitted to another local server through the core network by the base station.

[0106] The preset requirements may include but are not limited to any one or more of the following: the data accuracy of the perceived object data is greater than or equal to a preset accuracy threshold; the perceived object data is unencrypted data; the identity identifier of the perceived object data is consistent with the preset identity identifier; the target address of the perceived object data is consistent with the preset address; and the business type of the perceived object data is consistent with the preset business type.

[0107] In some embodiments, the local offload device can further filter out the perceived object data whose target address matches the preset address and / or whose service type matches the preset service type, process the data, and then offload it to the local offload device. This secondary screening of the target address and service type can filter out the primary perceived services, facilitating the interception of unnecessary perceived services or interfering data.

[0108] In some embodiments, detecting whether the perceived object data meets the preset requirements may include: dividing the perceived object data into multiple groups of perceived object data according to preset diversion rules, and the diversion rules may include but are not limited to: network slicing rules and / or PLMN (Public Land Mobile Network) rules; and detecting whether each group of perceived object data meets the preset requirements.

[0109] In some embodiments, when the sensed object data does not meet the preset requirements, returning the sensed object data to the base station may include: returning one or more groups of sensed object data that do not meet the preset requirements to the base station.

[0110] Based on network slicing technology or PLMN technology, the perceived object data can be divided into multiple networks according to different division factors (for example, destination address, service type, identity, data accuracy, encryption, etc.). The perceived object data on each network can be regarded as a group of perceived object data. Each group of perceived object data can be tested separately to see whether it meets the preset requirements. If a group of perceived object data does not meet the preset requirements, the group of perceived object data will be discarded or returned to the base station through the corresponding network. If a group of perceived object data meets the preset requirements, it can be transmitted to the local server through optical fiber.

[0111] The disclosed embodiment re-screens the perceived object data based on network slicing rules and / or PLMN rules, and returns the perceived data that does not meet the preset requirements to the base station based on the network slicing rules and / or PLMN rules, so that the processing between multiple groups of data is not affected, avoiding data confusion and data transmission confusion when returning data.

[0112] In step S202, data processing is performed on the perceived object data.

[0113] In some embodiments, data processing is performed on the perceived object data, including but not limited to any one or more of the following: data encoding, data modulation, data calculation, data prediction, and data analysis.

[0114] In addition to completing the most basic data encoding and decoding, data modulation and demodulation, and other processing operations, the local offload device can also process the massive amount of data collected from perceived objects, sinking the core network user plane data processing function to the local offload device, and transferring the process that originally required the base station's computing power to the local offload device to reduce the pressure on the base station without affecting the communication services of ordinary users under the base station. For example, the local offload device can perform data preprocessing (including but not limited to data format conversion, abnormal data cleaning, etc.) on the data collected by the camera, predict the data volume and data content of the camera data transmitted by the corresponding camera in the future based on the data volume and data content of the current camera data, and analyze the camera data to see if there is any content that does not comply with regulations.

[0115] In step S203, the processed data of the perceived object is sent to a local server.

[0116] After the local offload device processes the data of the sensed object, it can directly send it to the local server connected to the local offload device (for example, it can be sent via optical fiber). Since the sensed object data is directly transmitted through the local offload device, there is no large amount of routing forwarding in the middle, which greatly shortens the physical link, reduces latency, and improves data transmission efficiency.

[0117] In some embodiments, after sending the processed perceived object data to the local server, the method may further include: receiving perception feedback data returned by the local server; forwarding the perception feedback data to the base station, so that the base station sends it to the perceived object and / or the preset core network corresponding to the perceived object data.

[0118] In some embodiments, the perception feedback data may include but is not limited to any one or more of the following: data processed by the local server on the perceived object data; control data on the perceived object sent by the local server; and downlink service data sent by the local server.

[0119] After the local server receives the perceived object data sent by the local diversion device, it can process the perceived object data, for example, encrypt it. The perceived object data encrypted by the local server can be returned to the base station as perception feedback data and sent to the core network through the base station to avoid privacy leakage.

[0120] After the local server receives the perceived object data sent by the local diversion device, it can generate control data of the corresponding perceived object based on the behavior data or status data contained in the perceived object data, and return the control data to the base station through the local diversion device as perception feedback data, and then return it to the corresponding perceived object by the base station, so that the perceived object can be controlled through the control data, for example, adjusting the angle of the camera, etc.

[0121] After the local server receives the sensed object data sent by the local diversion device, it can generate corresponding downlink business data based on the sensed object data. For example, if the current sensed object data indicates that the camera is turned on and ready, the local server can generate downlink business data of the camera based on the sensed object data. For example, it can collect pedestrian flow data within a certain period of time, or schedule multiple set cameras according to preset priorities, and return the downlink business data to the corresponding camera through the local diversion device and the base station as perception feedback data, so that the corresponding camera can perform the corresponding business.

[0122] The above-mentioned perception feedback data is transmitted to the base station through the local diversion device, and then transmitted downlink by the base station to the perceived object, achieving the closed-loop data processing purpose of collection-processing-feedback-adjustment.

[0123] Example:

[0124] Based on the aforementioned data processing method applied to the base station and the local offload device, the following is an exemplary introduction to the method for processing camera data, taking the case where the perceived object is a camera as an example.

[0125] As shown in FIG6 , the camera data processing method may include the following steps S301 - S315 .

[0126] In step S301, the base station starts a sensing function and a local offload function.

[0127] In step S302, the base station sends a perception request notification to the camera.

[0128] In step S303, the camera takes action according to the perception request notification and generates camera data.

[0129] In step S304, the camera uploads camera data.

[0130] In step S305 , the base station receives uplink data.

[0131] In step S306, the service type of the uplink data is obtained, and the uplink data is classified according to the service type.

[0132] In step S307, it is determined that the service type of the camera data is a monitoring service, and the camera data is divided into sensed object data.

[0133] In step S308, the camera data is preliminarily screened based on data accuracy and whether it is encrypted.

[0134] In step S309, data whose data accuracy does not meet the preset conditions is discarded, and data whose data accuracy meets the preset conditions and has been encrypted is sent to the core network; data whose data accuracy meets the preset conditions but is not encrypted is sent to the local diversion device.

[0135] In step S310, the local offloading device re-screens the camera data offloaded from the base station.

[0136] In step S311, the camera data whose target address is inconsistent with the preset address is filtered out and returned to the base station, and the camera data whose target address is consistent with the preset address is processed and sent to the local server.

[0137] In step S312, the base station sends the returned camera data to the core network.

[0138] In step S313, the local server generates perception feedback data based on the received camera data and sends it to the local diversion device.

[0139] In step S314, the local offload device forwards the sensing feedback data to the base station.

[0140] In step S315, when the perception feedback data is downlink service data or control data, the base station feeds back the perception feedback data to the camera; when the perception feedback data is return data after the local server encrypts the camera data, the base station sends the perception feedback data to the core network.

[0141] In the above steps, steps S308 and S309 may not be implemented (i.e., they are optional). In other words, the base station can directly send the camera data to the local offload device, which will filter the camera data to reduce the computing pressure of the base station. Steps S310 and S311 may not be implemented. In other words, the local offload device directly sends the camera data to the local server.

[0142] The above example can achieve the following technical effects:

[0143] 1. Traffic is diverted through local diversion devices without having to pass through the core network, effectively reducing data transmission volume and alleviating the pressure on the core network.

[0144] 2. Traffic is diverted through local diversion devices without a large amount of routing forwarding in the middle, which greatly shortens the physical link, reduces the upstream data flow forwarding delay, and improves data transmission efficiency.

[0145] 3. The data of the sensed object is processed by the local offload device, saving the computing power of the base station.

[0146] 4. Traffic is diverted through local diversion devices without having to pass through the core network, reducing the possibility of the perceived object data being exposed on the public network and improving data security.

[0147] 5. According to the networking architecture of this example, the data of the sensed object can be transmitted by the local diversion device to the local server connected to it for encryption, authentication and other operations through local diversion, and then transmitted to the public network core network, which can add additional security authentication and local cloud storage to improve data security.

[0148] 6. The local diversion device does not need to be connected to the core network. Operators, government and enterprises can sell local diversion services, the local diversion device, and the local diversion device's synaesthesia computing power processing module, expansion module, etc. in a retail manner to improve economic benefits.

[0149] 7. A packaged 2B (business-to-business) charging model can be implemented without the need for traditional traffic billing.

[0150] 8. Resource requirements are lightweight, and there is no need to apply for additional resources, avoiding lengthy approval processes. Local wireless networks can meet the requirements. It supports 5G / 4G / NSA (non-independent networking) / SA (independent networking), wireless local operation and maintenance, wholesale and retail, and is suitable for rapid promotion.

[0151] As shown in Figure 7, an embodiment of the present disclosure further provides a data processing device 700. The data processing device 700 is applied to a base station and may include: a first receiving module 701, configured to receive uplink data, the uplink data including type identification information; a classification module 702, configured to classify the uplink data according to the type identification information; and a diversion module 703, configured to, when the uplink data is classified as sensed object data, divert the sensed object data to a preset local diversion device, so that the local diversion device processes the sensed object data, and sends the processed sensed object data to a local server.

[0152] In some embodiments, the communication perception data processing device also includes: a return data sending module 704; after the diversion module 703 diverts the perceived object data to a preset local diversion device, the return data sending module 704 is configured to: receive the perceived object data that does not meet the preset requirements returned by the local diversion device, and the perceived object data that does not meet the preset requirements is detected before the local diversion device processes the perceived object data; and send the perceived object data that does not meet the preset requirements to the preset core network.

[0153] In some embodiments, the communication perception data processing device further includes: a perception feedback data sending module 705.

[0154] The perception feedback data sending module 705 is configured to: after the diversion module 703 diverts the perceived object data to a preset local diversion device, receive the perception feedback data forwarded by the local diversion device; and send the perception feedback data to the perceived object corresponding to the perceived object data and / or the preset core network, wherein the perception feedback data is sent by the local server.

[0155] In some embodiments, the communication awareness data processing device further includes: an uplink data sending module 706.

[0156] The uplink data sending module 706 is configured to classify the uplink data according to the type identification information after the classification module 702 has classified the uplink data, and

[0157] When the uplink data is classified as terminal communication data, the terminal communication data is sent to a preset core network.

[0158] In some embodiments, the communication awareness data processing device further includes: a starting module 707 .

[0159] The starting module 707 is configured to start the sensing function and the local offload function before the first receiving module receives the uplink data.

[0160] Any embodiment of the aforementioned data processing method applied to a base station is applicable to the data processing device 700 and will not be described in detail here.

[0161] As shown in Figure 8, an embodiment of the present disclosure also provides a data processing device 800, which is applied to a local diversion device and may include: a second receiving module 801, configured to receive the perceived object data diverted from the base station; a processing module 802, configured to perform data processing on the perceived object data; and a third sending module 803, configured to send the processed perceived object data to a second local server.

[0162] In some embodiments, the processing module 802 performs data processing on the perceived object data, including any one or more of the following: data encoding, data modulation, data calculation, data prediction, and data analysis.

[0163] In some embodiments, the data processing device 800 further includes: a detection module 803 .

[0164] The detection module 803 is configured to: before the processing module processes the perceived object data, detect whether the perceived object data meets the preset requirements; if the perceived object data meets the preset requirements, enter the process of data processing of the perceived object data; if the perceived object data does not meet the preset requirements, discard the perceived object data, or return it to the base station, so that the returned perceived object data can be sent to the preset core network through the base station.

[0165] In some embodiments, the detection module 803 detects whether the perceived object data meets the preset requirements, including: dividing the perceived object data into multiple groups of perceived object data according to preset diversion rules, and separately detecting whether each group of perceived object data meets the preset requirements, wherein the diversion rules may include: network slicing rules and / or public land mobile network PLMN rules.

[0166] In some embodiments, the detection module 803 returns the sensed object data to the base station when the sensed object data does not meet the preset requirements, including: returning one or more groups of sensed object data that do not meet the preset requirements to the base station.

[0167] In some embodiments, the data processing device 800 further includes a forwarding module 804. The forwarding module 804 is configured to: after the third sending module 803 sends the processed sensed object data to the local server, receive the sensed feedback data returned by the local server; and forward the sensed feedback data to a base station, so that the base station sends the sensed object corresponding to the sensed object data and / or a preset core network.

[0168] Any embodiment of the aforementioned data processing method applied to the local offload device is applicable to the embodiment of the data processing device 800 and will not be described in detail here.

[0169] As shown in Figure 9, an embodiment of the present disclosure further provides a data processing device 900, which is applied to a base station. The data processing device 900 includes a first memory 901 and a first processor 902. The first memory 901 stores a first computer program executable by the first processor 902. When the first computer program is executed by the first processor 902, it implements a data processing method applied to the base station. The data processing device 900 may also include one or more first I / O (input / output) interfaces 903 connected between the first processor 902 and the first memory 901.

[0170] As shown in Figure 10, an embodiment of the present disclosure further provides a data processing device 1000 for use in a local data distribution device. The data processing device 1000 includes a second memory 1001 and a second processor 1002. The second memory 1001 stores a second computer program executable by the second processor 1002. When the second computer program is executed by the second processor 1002, it implements a data processing method for use in the local data distribution device. The data processing device 1000 may also include one or more second I / O (input / output) interfaces 1003 connected between the second processor 1002 and the second memory 1001.

[0171] The first processor and the second processor are both devices with data processing capabilities, including but not limited to a central processing unit (CPU); the first memory and the second memory are both devices with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read-write interface) is connected between the processor and the memory (for example, the first I / O interface is connected between the first processor and the first memory, and the second I / O interface is connected between the second processor and the second memory), which can realize information interaction between the memory and the processor, including but not limited to a data bus (Bus), etc.

[0172] An embodiment of the present disclosure also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements the communication perception data processing method.

[0173] Those skilled in the art will appreciate that all or some of the steps, systems, and functional modules / units in the apparatus disclosed above may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0174] In hardware implementations, the division between functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation.

[0175] Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit (CPU), a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As is well known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory (FLASH) or other disk storage; compact disc (CD-ROM), digital versatile disc (DVD) or other optical disc storage; magnetic cassettes, tapes, disk storage or other magnetic storage; any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0176] The present disclosure has disclosed example embodiments, and although specific terms are employed, they are used and should be interpreted only in a general illustrative sense and not for purposes of limitation. In some instances, it will be apparent to those skilled in the art that, unless otherwise expressly indicated, features, characteristics, and / or elements described in conjunction with a particular embodiment may be used alone or in combination with features, characteristics, and / or elements described in conjunction with other embodiments. Therefore, it will be understood by those skilled in the art that various changes in form and detail may be made without departing from the scope of the present disclosure as set forth in the appended claims.

Claims

1. A data processing method, applied to a base station, the method comprising: receiving uplink data, wherein the uplink data includes type identification information; Classifying the uplink data according to the type identification information; In the case where the uplink data is classified as perceived object data, the perceived object data is diverted to a preset local diversion device, so that the perceived object data is processed by the local diversion device, and the processed perceived object data is sent to a local server.

2. The data processing method according to claim 1, wherein: After the sensed object data is diverted to a preset local diversion device, the method further includes: receiving the sensed object data that does not meet the preset requirements and is returned by the local diversion device, wherein the sensed object data that does not meet the preset requirements is detected before the local diversion device performs data processing on the sensed object data; The sensed object data that does not meet the preset requirements is sent to a preset core network.

3. The data processing method according to claim 2, wherein: The preset requirements include any one or more of the following: The data accuracy of the sensed object data is greater than or equal to a preset accuracy threshold; The sensed object data is unencrypted data; The identity identifier of the sensed object data is consistent with the preset identity identifier; The target address of the sensed object data is consistent with the preset address; as well as, The business type of the sensed object data is consistent with the preset business type.

4. The data processing method according to claim 1, wherein: After the sensed object data is diverted to a preset local diversion device, the method further includes: receiving the perception feedback data forwarded by the local traffic distribution device, wherein the perception feedback data is sent by the local server; The perception feedback data is sent to the perceived object corresponding to the perceived object data and / or a preset core network.

5. The data processing method according to claim 4, wherein: The perception feedback data includes any one or more of the following: Data after the local server processes the perceived object data; The control data of the sensed object sent by the local server, and Downlink service data sent by the local server.

6. The data processing method according to claim 1, wherein: After classifying the uplink data according to the type identification information, the method further includes: In a case where the uplink data is classified as terminal communication data, the terminal communication data is sent to a preset core network.

7. A data processing method, wherein: Applied to a local shunt device, the method comprises: Receiving the sensed object data diverted from the base station; Performing data processing on the sensed object data; The processed data of the sensed object is sent to a local server.

8. The data processing method according to claim 7, wherein: Before processing the perceived object data, the method further includes: Detecting whether the perceived object data meets preset requirements; When the sensed object data meets the preset requirements, a process of processing the sensed object data is entered; In the case that the sensed object data does not meet the preset requirement, the sensed object data is discarded or returned to the base station, so that the returned sensed object data is sent to a preset core network through the base station.

9. The data processing method according to claim 8, wherein: The detecting whether the sensed object data meets preset requirements includes: Dividing the sensed object data into multiple groups of sensed object data according to a preset diversion rule, wherein the diversion rule includes: a network slicing rule and / or a public land mobile network PLMN rule; Each group of the sensed object data is detected separately to see whether it meets the preset requirements.

10. The data processing method according to claim 9, wherein: When the sensed object data does not meet the preset requirement, returning the sensed object data to the base station includes: returning one or more groups of the sensed object data that do not meet the preset requirement to the base station.

11. The data processing method according to claim 7, wherein: After sending the processed data of the sensed object to the local server, the method further includes: Receiving the perception feedback data returned by the local server; The perception feedback data is forwarded to a base station, so that the base station sends it to a perceived object corresponding to the perceived object data and / or a preset core network.

12. A data processing device, wherein: Applied to a base station, comprising: a first memory and a first processor; the first memory stores a first computer program that can be executed by the first processor, and when the first computer program is executed by the first processor, the data processing method as described in any one of claims 1-6 is implemented.

13. A data processing device, wherein: Applied to a local diversion device, comprising: a second memory and a second processor; the second memory stores a second computer program that can be executed by the second processor, and when the second computer program is executed by the second processor, it implements the data processing method as described in any one of claims 7-11.

14. A networking architecture, comprising: A local distribution device and at least one base station; the local distribution device comprises the data processing device according to claim 13, and the base station comprises the data processing device according to claim 12; One or more of the at least one base station communicates with the sensed object; The data processing device in the local distribution device is connected to the data processing device in each base station and is connected to a local server.

15. The networking architecture according to claim 14, further comprising: Core network and packet data network; Each base station is respectively connected to the core network corresponding to itself; The core network is connected to the corresponding packet data network; The packet data network is connected to another local server.

16. The networking architecture according to claim 14, wherein: The local traffic distribution device further includes: a base station frame backplane connection interface, an expansion module and / or a third-party interface; The base station frame backplane connection interface is configured to connect to the base station frame backplane; The expansion module is configured to perform functional expansion; The third-party interface is configured to connect to a third-party device.

17. The networking architecture according to claim 14, wherein: The local traffic distribution device is deployed separately or together with the base station.

18. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the data processing method according to any one of claims 1 to 6 or claims 7 to 11.

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