A data processing method and system based on Internet of Things (IoT) communication

Abstract

This invention discloses a data processing method and system based on Internet of Things (IoT) communication, relating to the field of communication data processing technology. It includes analyzing the communication topology to determine the communication line information between the data acquisition end and the data processing end, thereby assessing the impact or interference on each communication line and providing a basis for subsequent communication transmission quality assessment. The method evaluates communication transmission quality and multi-source data quality based on communication line information and the matching relationship between multi-source data and communication data, providing a basis for subsequent data fusion and thus improving the reliability of data processing. It assigns a combination weight to each type of multi-source data based on the communication transmission quality and multi-source data quality of the multi-source data, and performs multi-source data fusion based on the multi-source data association graph and the combination weights to generate a fused information set. This fused information set indirectly reflects value information and assists in subsequent value information analysis. This reduces data redundancy and ensures the safe and stable operation of the IoT.

Description

Technical Field

[0001] This invention relates to the field of communication data processing technology, and in particular to a data processing method and system based on Internet of Things (IoT) communication. Background Technology

[0002] Data processing in IoT communication involves the widespread deployment of IoT devices and the real-time generation and transmission of data. With the rapid development of IoT technology, various smart devices such as sensors and RFID tags are being deployed in large numbers, capable of collecting and transmitting data in real time. This data is characterized by its temporal sequence, structure, sparsity, massive volume, diversity, and real-time nature, placing high demands on data processing systems.

[0003] The purpose of IoT communication data processing is to efficiently and accurately process and analyze this data to extract valuable information and knowledge. Through technologies such as data cleaning, data storage, data fusion, and data mining, IoT data can be preprocessed, stored, integrated, and analyzed. This provides support for enterprise decision-making, optimizes operational efficiency, creates new business models, and promotes the in-depth development of digital transformation. Furthermore, data processing can help achieve goals such as remote equipment monitoring, fault prediction, and efficient resource utilization, thereby enhancing enterprise competitiveness.

[0004] In existing technologies, extracting valuable information from multi-source data may require referencing multi-source data from multiple data acquisition terminals for joint processing and analysis. The large number and variety of data categories lead to data redundancy, and there is no data that can directly represent the relationship between multi-source data, resulting in poor data processing performance and low adaptability and reliability.

[0005] Therefore, how to improve the processing effect, adaptability, and reliability is a technical problem that needs to be solved. Summary of the Invention

[0006] The purpose of this invention is to address the problems of poor data processing performance, low adaptability, and low reliability in existing technologies using IoT communication. Therefore, this invention proposes a data processing method based on IoT communication, comprising:

[0007] The communication network of the target IoT is obtained, the data acquisition end and the data processing end are determined in the communication topology of the target IoT, the communication topology is established based on the data acquisition end and the data processing end, and the communication topology is analyzed to determine the communication line information between the data acquisition end and the data processing end.

[0008] Multi-source data is acquired through a data acquisition terminal, which transmits the multi-source data to the data processing terminal. At the same time, network monitoring tools are used to capture communication data during the data transmission process. The multi-source data and communication data are matched to obtain the matching relationship between multi-source data and communication data.

[0009] The quality of communication transmission and multi-source data is evaluated based on the matching relationship between communication line information and multi-source data. The association rules between data collection terminals are mined, and multi-source data are matched according to the association rules to generate a multi-source data association graph.

[0010] The combination weights of each type of multi-source data are assigned based on the communication transmission quality and the quality of the multi-source data. Multi-source data are then fused according to the multi-source data association graph and the combination weights to generate a fused information set, which helps with subsequent data analysis.

[0011] In some embodiments of this application, the communication topology is analyzed to determine the communication line information between the data acquisition end and the data processing end, including:

[0012] The communication topology is transformed into a circuit diagram in graph theory. In the circuit diagram, one type of node represents the data acquisition end or the data processing end, and the edge in the circuit diagram represents the communication link between the data acquisition end and the data processing end. The length of the edge is quantified according to the communication link of wired transmission and wireless transmission.

[0013] Collect interference source information in the actual environment corresponding to the communication topology, mark the interference sources at the corresponding positions in the diagram, determine the interference intensity of the interference source based on the interference source information, the distance between the interference source and the data acquisition terminal, and the distance between the interference source and the communication link, treat the interference source as a type II node in the line diagram, and mark the interference intensity of each type II node.

[0014] The impact on each edge in the circuit diagram is determined based on the edge length, communication link status, and interference intensity of the second-class nodes. The impact and communication link status are then used as the communication line information of the edge.

[0015] In some embodiments of this application, the impact on each edge of the circuit diagram is determined based on the edge length, communication link conditions, and interference intensity of the second-type nodes, including:

[0016] The affected range of an edge is determined by the length of the edge in the circuit diagram and the communication link status. Within the affected range of the edge, the second-class nodes are statistically analyzed, and the interference intensity of each second-class node is allocated to obtain the interference intensity of each second-class node to the edge. Then, the impact on each edge is determined by combining the length of the edge in the circuit diagram.

[0017] ;

[0018] in, The first one in the route diagram The amount of influence on the edge. The first one in the route diagram The length of the strip, This is the initial impact amount. Indicates the number of lines in the circuit diagram. The initial influence value obtained by mapping the length of the edge. The first one in the route diagram The number of type II nodes within the affected range of the edge. For the first The influence weight of each type II node For the first in the circuit diagram The first edge The interference intensity of each type II node The first one in the route diagram Preset constants corresponding to the edges.

[0019] In some embodiments of this application, multi-source data is matched with communication data to obtain a multi-source data-communication data matching relationship, including:

[0020] The time window is set according to the length of the edge in the route diagram and the communication link. The corresponding communication data is searched in this time window according to the timestamp of the multi-source data and other matching features to obtain the matching relationship between multi-source data and communication data. Other matching features include one or more of the following: packet size feature, IP address feature, protocol type feature and data content feature.

[0021] In some embodiments of this application, communication transmission quality and multi-source data quality are evaluated based on communication line information and the matching relationship between multi-source data and communication data, including:

[0022] Confirm the attribute information of the multi-source data, and combine the impact, communication link status, and attribute information of the multi-source data to set a reasonable range of communication data to match the multi-source data;

[0023] Compare the actual value and reasonable range of the same type of communication data. When the actual value is within the reasonable range, the deviation value of the communication data is 0. Otherwise, the deviation value of the communication data is the difference between the actual value and the reasonable range. The endpoint that is close to the actual value in the reasonable range is taken as the proximity endpoint. The difference between the actual value and the reasonable range is the difference between the actual value and the proximity endpoint.

[0024] The deviation values ​​of each type of communication data are integrated, and the integrated deviation values ​​are used to describe the quality of communication transmission.

[0025] Perform multi-dimensional analysis and inspection of multi-source data to determine the quality of the multi-source data.

[0026] In some embodiments of this application, the association rules between data acquisition terminals are mined, including...

[0027] The system acquires multi-source data previously generated by the data collection terminal, performs data preprocessing on the multi-source data, analyzes the scale characteristics of the multi-source data at each data collection terminal to determine the flow of the multi-source data at each data collection terminal, sets the parameters of the mining algorithm based on the flow of the multi-source data at the data collection terminal, and performs association rule mining on the preprocessed multi-source data to obtain association rules.

[0028] In some embodiments of this application, the combination weight of each type of multi-source data is assigned based on the communication transmission quality and the quality of the multi-source data, including:

[0029] Assess the encryption security of each type of multi-source data at the data acquisition end and during transmission, and define the verification value for each type of multi-source data based on encryption security, communication transmission quality of multi-source data, and multi-source data quality;

[0030] ;

[0031] in, For the first The verification value of multi-source data, , The first The conversion factor corresponding to the communication transmission quality of multi-source data and the conversion factor corresponding to the quality of multi-source data. , The first Communication transmission quality and multi-source data quality for multi-source data. For the first Encryption security of multi-source data For the first Preset constants corresponding to multi-source data;

[0032] The verification values ​​of all multi-source data are normalized, and then the combined weight of each type of multi-source data is assigned based on the verification values ​​of the multi-source data.

[0033] In some embodiments of this application, multi-source data fusion is performed based on a multi-source data association graph and the combined weights of the multi-source data to generate a fused information set, including:

[0034] Based on the multi-source data association diagram, the related multi-source data are weighted and summed according to their respective combination weights to obtain the representative value. The fused information set contains the related multi-source data and the representative value.

[0035] Correspondingly, this application also provides a data processing system based on Internet of Things (IoT) communication, including:

[0036] The line module is used to acquire the communication network of the target IoT, determine the data acquisition end and the data processing end in the communication topology of the target IoT, establish the communication topology based on the data acquisition end and the data processing end, and analyze the communication topology to determine the communication line information between the data acquisition end and the data processing end.

[0037] The matching module is used to acquire multi-source data through the data acquisition terminal. The data acquisition terminal transmits multi-source data to the data processing terminal. At the same time, network monitoring tools are used to capture communication data during the data transmission process. The multi-source data and communication data are matched to obtain the matching relationship between multi-source data and communication data.

[0038] The association module is used to evaluate the communication transmission quality and multi-source data quality based on communication line information and the matching relationship between multi-source data and communication data, mine the association rules between data acquisition terminals, match multi-source data according to the association rules, and generate a multi-source data association graph.

[0039] The analysis module is used to assign a combination weight to each type of multi-source data based on the communication transmission quality and the quality of the multi-source data. It then performs multi-source data fusion based on the multi-source data association graph and the combination weights to generate a fused information set, thereby aiding in subsequent data analysis.

[0040] Compared with the prior art, the beneficial effects of this invention are as follows:

[0041] 1. Analyze the communication topology to determine the communication line information between the data acquisition end and the data processing end, so as to assess the impact or interference on each communication line and provide a basis for subsequent communication transmission quality assessment.

[0042] 2. The quality of communication transmission and multi-source data is evaluated based on communication line information and the matching relationship between multi-source data and communication data. This provides a basis for subsequent data fusion, thereby improving the reliability of data processing. The combination weights for each type of multi-source data are assigned based on the communication transmission quality and multi-source data quality. Multi-source data fusion is performed according to the multi-source data association diagram and the combination weights. Multi-source data is adaptively combined according to the combination weights to generate a fused information set. This fused information set indirectly reflects valuable information and assists in subsequent value information analysis. This reduces data redundancy, improves data processing efficiency, and ensures the safe and stable operation of the Internet of Things (IoT). Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating a data processing method based on Internet of Things (IoT) communication proposed in this invention.

[0044] Figure 2 This is a schematic diagram of the structure of a data processing system based on Internet of Things (IoT) communication proposed in this invention. Detailed Implementation

[0045] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0046] Reference Figure 1 A data processing method based on Internet of Things (IoT) communication includes the following steps:

[0047] Step S101: Obtain the communication network of the target IoT, determine the data acquisition end and data processing end in the communication topology of the target IoT, establish the communication topology based on the data acquisition end and data processing end, and analyze the communication topology to determine the communication line information between the data acquisition end and the data processing end.

[0048] In this embodiment, all data acquisition terminals (such as sensors, smart devices, etc.) and data processing terminals (such as data centers, cloud servers, etc.) are identified. Based on the obtained network topology, a communication topology model is established using graph theory, where nodes represent data acquisition terminals and data processing terminals, and edges represent communication links.

[0049] In some embodiments of this application, the communication topology is analyzed to determine the communication line information between the data acquisition end and the data processing end, including:

[0050] The communication topology is transformed into a circuit diagram in graph theory. In the circuit diagram, one type of node represents the data acquisition end or the data processing end, and the edge in the circuit diagram represents the communication link between the data acquisition end and the data processing end. The length of the edge is quantified according to the communication link of wired transmission and wireless transmission.

[0051] Collect interference source information in the actual environment corresponding to the communication topology, mark the interference sources at the corresponding positions in the diagram, determine the interference intensity of the interference source based on the interference source information, the distance between the interference source and the data acquisition terminal, and the distance between the interference source and the communication link, treat the interference source as a type II node in the line diagram, and mark the interference intensity of each type II node.

[0052] The impact on each edge in the circuit diagram is determined based on the edge length, communication link status, and interference intensity of the second-class nodes. The impact and communication link status are then used as the communication line information of the edge.

[0053] In this embodiment, the communication link status includes whether it is wired or wireless, and the specific type of the line (such as fiber optic, network cable, Wi-Fi, Bluetooth, etc.). Edge quantization involves recording the actual length of wired lines and the coverage area or signal strength of wireless lines.

[0054] In this embodiment, the interference source information refers to the specific information (power, frequency, etc.) of electromagnetic devices that can affect communication signals. The interference intensity is determined by comprehensively considering the interference source information, the distance between the interference source and the data acquisition terminal, and the distance between the interference source and the communication link. Generally, the closer the interference source is to the line, the greater the impact of its generated electromagnetic field on the line, which may lead to a greater amount of interference. This is because the strength of the electromagnetic field attenuates with increasing distance, so a closer distance usually means a higher level of interference. The acquisition terminal is the device responsible for capturing and recording changes in the line's state. If the interference source is close to the acquisition terminal, the acquired signal may contain more interference components, which may affect the accuracy and reliability of the data.

[0055] It is understandable that the amount of impact on each edge refers to the degree of interference affecting the communication line.

[0056] In some embodiments of this application, the impact on each edge of the circuit diagram is determined based on the edge length, communication link conditions, and interference intensity of the second-type nodes, including:

[0057] The affected range of an edge is determined by the length of the edge in the circuit diagram and the communication link status. Within the affected range of the edge, the second-class nodes are statistically analyzed, and the interference intensity of each second-class node is allocated to obtain the interference intensity of each second-class node to the edge. Then, the impact on each edge is determined by combining the length of the edge in the circuit diagram.

[0058] ;

[0059] in, The first one in the route diagram The amount of influence on the edge. The first one in the route diagram The length of the strip, This is the initial impact amount. Indicates the number of lines in the circuit diagram. The initial influence value obtained by mapping the length of the edge. The first one in the route diagram The number of type II nodes within the affected range of the edge. For the first The influence weight of each type II node For the first in the circuit diagram The first edge The interference intensity of each type II node The first one in the route diagram Preset constants corresponding to the edges.

[0060] In this embodiment, for each edge, a preliminary affected range can be determined based on its length (which reflects the impact of signal attenuation, etc.) and communication link type (such as optical fiber, copper cable, wireless communication, etc.) and protocol (such as TCP / IP, HTTP, Wi-Fi, etc.), which have different transmission characteristics and anti-interference capabilities. The interference intensity of each type II node is allocated to obtain the interference intensity of each type II node for the edge, thus specifying the interference intensity of the interference source, i.e., the specific interference intensity of the interference source on a certain edge (communication transmission route).

[0061] In this embodiment, the lengths of different sides represent different degrees of signal attenuation, so the length corresponds to an initial influence amount. This indicates a correction to the initial impact of the interference source on that side.

[0062] Step S102: Acquire multi-source data through the data acquisition terminal. The data acquisition terminal transmits the multi-source data to the data processing terminal. At the same time, use network monitoring tools to capture communication data during the data transmission process. Match the multi-source data with the communication data to obtain the matching relationship between the multi-source data and the communication data.

[0063] In this embodiment, a data acquisition program is deployed at the data acquisition end to collect multi-source data according to a preset sampling frequency and data format. A network monitoring tool (such as Wireshark, tcpdump, etc.) is deployed in the communication network to capture communication data packets during data transmission. The captured communication data packets are timestamped with the multi-source data to ensure that each data packet corresponds to a specific data source.

[0064] It is understandable that the matching relationship between multi-source data and communication data mentioned here means that, for example, data acquisition terminal 1 acquires multi-source data 1, and the communication parameters generated by multi-source data 1 during transmission are communication data 1. Multi-source data 1 and communication data 1 are matched accordingly.

[0065] In some embodiments of this application, multi-source data is matched with communication data to obtain a multi-source data-communication data matching relationship, including:

[0066] The time window is set according to the length of the edge in the route diagram and the communication link. The corresponding communication data is searched in this time window according to the timestamp of the multi-source data and other matching features to obtain the matching relationship between multi-source data and communication data. Other matching features include one or more of the following: packet size feature, IP address feature, protocol type feature and data content feature.

[0067] In this embodiment, to improve matching accuracy, since there may be slight time differences between the acquisition of communication data and multi-source data (e.g., due to network latency, data processing delays, etc.), matching cannot rely solely on precise timestamps. Instead, a reasonable time window should be set, allowing timestamps to fluctuate within a certain range. For example, a time window of ±1 second or ±500 milliseconds can be set, assuming that data records within this time window are relevant. In addition to timestamps, other characteristic information can be used to further confirm the matching relationship between communication data and multi-source data. This characteristic information may include packet size, source IP address, destination IP address, protocol type, data content, etc.

[0068] Data packet size matching:

[0069] If both the communication data and the multi-source data contain information about the packet size, the packet size can be used as a matching criterion. However, it should be noted that packet size can be affected by various factors (such as data compression and encryption), so matching relationships cannot be determined solely based on packet size.

[0070] IP address matching:

[0071] If both the communication data and the multi-source data contain information about the source or destination IP addresses, the IP address can be used as a matching criterion. Especially when the multi-source data contains metadata related to a specific IP address (such as device ID, user ID, etc.), IP address matching can significantly improve the accuracy of the match.

[0072] Protocol type matching:

[0073] Different communication protocols may have different data packet formats and transmission characteristics. Therefore, if both the communication data and multi-source data contain protocol type information, the protocol type can be used as a matching criterion. This helps to exclude data records that are irrelevant to a specific protocol.

[0074] Step S103: Evaluate the communication transmission quality and multi-source data quality based on the communication line information and the matching relationship between multi-source data and communication data, mine the association rules between data acquisition terminals, match multi-source data according to the association rules, and generate a multi-source data association graph.

[0075] In this embodiment, the quality of multi-source data and communication transmission is assessed to facilitate subsequent data fusion, and related data are correlated to generate a multi-source data correlation graph.

[0076] In some embodiments of this application, communication transmission quality and multi-source data quality are evaluated based on communication line information and the matching relationship between multi-source data and communication data, including:

[0077] Confirm the attribute information of the multi-source data, and combine the impact, communication link status, and attribute information of the multi-source data to set a reasonable range of communication data to match the multi-source data;

[0078] Compare the actual value and reasonable range of the same type of communication data. When the actual value is within the reasonable range, the deviation value of the communication data is 0. Otherwise, the deviation value of the communication data is the difference between the actual value and the reasonable range. The endpoint that is close to the actual value in the reasonable range is taken as the proximity endpoint. The difference between the actual value and the reasonable range is the difference between the actual value and the proximity endpoint.

[0079] The deviation values ​​of each type of communication data are integrated, and the integrated deviation values ​​are used to describe the quality of communication transmission.

[0080] Perform multi-dimensional analysis and inspection of multi-source data to determine the quality of the multi-source data.

[0081] In this embodiment, communication data includes transmission delay, packet loss rate, bandwidth utilization, etc. The attribute information of multi-source data refers to attributes that can affect the communication data, such as data volume, format, and type. These affect the setting of a reasonable range for communication data. Multi-dimensional analysis (completeness, accuracy, and timeliness, etc.) and checks are performed on the multi-source data to determine its quality. Data integrity assessment: checking whether data is lost or damaged during transmission. Verifying whether the data is received according to the expected format and structure. Data accuracy assessment: comparing the data from the data source and the receiving end to ensure that the data has not been tampered with during transmission. Verifying data integrity using methods such as checksums or hash values. Data timeliness assessment: analyzing the time difference between data generation and reception to ensure that the data arrives within a reasonable time. Assessing whether the data meets real-time requirements.

[0082] In some embodiments of this application, the association rules between data acquisition terminals are mined, including...

[0083] The system acquires multi-source data previously generated by the data collection terminal, performs data preprocessing on the multi-source data, analyzes the scale characteristics of the multi-source data at each data collection terminal to determine the flow of the multi-source data at each data collection terminal, sets the parameters of the mining algorithm based on the flow of the multi-source data at the data collection terminal, and performs association rule mining on the preprocessed multi-source data to obtain association rules.

[0084] In this embodiment, scale characteristics include data volume, data update frequency, etc., and liquidity (average data change in general) is determined. The parameters (minimum support, minimum confidence, etc.) of the mining algorithm (common algorithms include Apriori algorithm, FP-Growth algorithm, etc.) are set through liquidity.

[0085] Understandably, the specific goal of the association rules here is to achieve further analysis, such as device status detection and prediction in the Internet of Things (IoT), resource optimization and scheduling in the IoT, and anomaly detection.

[0086] Step S104: Assign a combination weight to each type of multi-source data based on the communication transmission quality and multi-source data quality. Perform multi-source data fusion based on the multi-source data association graph and the combination weight of multi-source data to generate a fused information set, thereby assisting in subsequent data analysis.

[0087] In this embodiment, a weighting factor is determined based on the combined quality of multi-source data and communication transmission quality to perform multi-source data fusion, which aids in subsequent data analysis (equipment status monitoring and prediction, resource optimization and scheduling, anomaly detection and identification, etc.). The fused information set is then used as input for subsequent data analysis, mining, and decision support.

[0088] In some embodiments of this application, the combination weight of each type of multi-source data is assigned based on the communication transmission quality and the quality of the multi-source data, including:

[0089] Assess the encryption security of each type of multi-source data at the data acquisition end and during transmission, and define the verification value for each type of multi-source data based on encryption security, communication transmission quality of multi-source data, and multi-source data quality;

[0090] ;

[0091] in, For the first The verification value of multi-source data, , The first The conversion factor corresponding to the communication transmission quality of multi-source data and the conversion factor corresponding to the quality of multi-source data. , The first Communication transmission quality and multi-source data quality for multi-source data. For the first Encryption security of multi-source data For the first Preset constants corresponding to multi-source data;

[0092] The verification values ​​of all multi-source data are normalized, and then the combined weight of each type of multi-source data is assigned based on the verification values ​​of the multi-source data.

[0093] In this embodiment, different calibration values ​​correspond to different combination weights, and the encryption of data during data collection and transmission is also a way to describe quality. This indicates that encryption security is used to improve the combination of communication transmission quality and multi-source data quality.

[0094] In some embodiments of this application, multi-source data fusion is performed based on a multi-source data association graph and the combined weights of the multi-source data to generate a fused information set, including:

[0095] Based on the multi-source data association diagram, the related multi-source data are weighted and summed according to their respective combination weights to obtain the representative value. The fused information set contains the related multi-source data and the representative value.

[0096] In this embodiment, the weighted sum can be the features of each multi-source data after extraction. The representative value represents the contribution of the fused information set to the subsequent analysis. The fused information set includes related multi-source data (which can be data after combining features extracted from each multi-source data) and the representative value.

[0097] Compared with the prior art, the beneficial effects of this invention are as follows:

[0098] 1. Analyze the communication topology to determine the communication line information between the data acquisition end and the data processing end, so as to assess the impact or interference on each communication line and provide a basis for subsequent communication transmission quality assessment.

[0099] 2. The quality of communication transmission and multi-source data is evaluated based on communication line information and the matching relationship between multi-source data and communication data. This provides a basis for subsequent data fusion, thereby improving the reliability of data processing. The combination weights for each type of multi-source data are assigned based on the communication transmission quality and multi-source data quality. Multi-source data fusion is performed according to the multi-source data association diagram and the combination weights. Multi-source data is adaptively combined according to the combination weights to generate a fused information set. This fused information set indirectly reflects valuable information and assists in subsequent value information analysis. This reduces data redundancy, improves data processing efficiency, and ensures the safe and stable operation of the Internet of Things (IoT).

[0100] Improving data accuracy: By integrating data from multiple sources and processing it through fusion algorithms, fusion datasets can eliminate redundancy and errors, thereby improving data accuracy. This increased accuracy helps subsequent data analysis yield more reliable conclusions.

[0101] Enhanced data integrity: Because the fused information set contains integrated information from all multi-source data, it provides a more comprehensive view of the data. This integrity enables data analysts to gain a more complete understanding of the data, thereby uncovering more potential patterns and trends.

[0102] Enhancing decision support capabilities: High-quality data from integrated information sets provide a solid foundation for decision-making. Whether it's business or technology decisions, deeper analysis and prediction can be conducted based on integrated information sets, leading to more informed decisions.

[0103] Through the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented in hardware or by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0104] Correspondingly, this application also provides a data processing system based on Internet of Things (IoT) communication, such as... Figure 2 As shown, including,

[0105] The line module is used to acquire the communication network of the target IoT, determine the data acquisition end and the data processing end in the communication topology of the target IoT, establish the communication topology based on the data acquisition end and the data processing end, and analyze the communication topology to determine the communication line information between the data acquisition end and the data processing end.

[0106] The matching module is used to acquire multi-source data through the data acquisition terminal. The data acquisition terminal transmits multi-source data to the data processing terminal. At the same time, network monitoring tools are used to capture communication data during the data transmission process. The multi-source data and communication data are matched to obtain the matching relationship between multi-source data and communication data.

[0107] The association module is used to evaluate the communication transmission quality and multi-source data quality based on communication line information and the matching relationship between multi-source data and communication data, mine the association rules between data acquisition terminals, match multi-source data according to the association rules, and generate a multi-source data association graph.

[0108] The analysis module is used to assign a combination weight to each type of multi-source data based on the communication transmission quality and the quality of the multi-source data. It then performs multi-source data fusion based on the multi-source data association graph and the combination weights to generate a fused information set, thereby aiding in subsequent data analysis.

[0109] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.

[0110] Those skilled in the art will understand that the modules in the system of the implementation scenario can be distributed throughout the system of the implementation scenario as described, or they can be modified to reside in one or more systems different from this implementation scenario. The modules of the above-mentioned implementation scenario can be merged into one module, or they can be further divided into multiple sub-modules.

[0111] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A data processing method based on Internet of Things (IoT) communication, characterized in that, include, The communication network of the target IoT is obtained, the data acquisition end and the data processing end are determined in the communication topology of the target IoT, the communication topology is established based on the data acquisition end and the data processing end, and the communication topology is analyzed to determine the communication line information between the data acquisition end and the data processing end. Multi-source data is acquired through a data acquisition terminal, which transmits the multi-source data to the data processing terminal. At the same time, network monitoring tools are used to capture communication data during the data transmission process. The multi-source data and communication data are matched to obtain the matching relationship between multi-source data and communication data. The quality of communication transmission and multi-source data is evaluated based on the matching relationship between communication line information and multi-source data. The association rules between data collection terminals are mined, and multi-source data are matched according to the association rules to generate a multi-source data association graph. The combination weights of each type of multi-source data are assigned based on the communication transmission quality and the quality of the multi-source data. Multi-source data are then fused according to the multi-source data association graph and the combination weights to generate a fused information set, which helps with subsequent data analysis. in, Analyze the communication topology to determine the communication line information between the data acquisition end and the data processing end, including, The communication topology is transformed into a circuit diagram in graph theory. In the circuit diagram, one type of node represents the data acquisition end or the data processing end, and the edge in the circuit diagram represents the communication link between the data acquisition end and the data processing end. The length of the edge is quantified according to the communication link of wired transmission and wireless transmission. Collect interference source information in the actual environment corresponding to the communication topology, mark the interference sources at the corresponding positions in the diagram, determine the interference intensity of the interference source based on the interference source information, the distance between the interference source and the data acquisition terminal, and the distance between the interference source and the communication link, treat the interference source as a type II node in the line diagram, and mark the interference intensity of each type II node. The impact on each edge in the circuit diagram is determined based on the edge length, communication link status, and interference intensity of the second type of nodes. The impact and communication link status are used as the communication line information of the edge. The impact on each edge of the circuit diagram is determined based on the edge length, communication link conditions, and interference intensity of the second-class nodes. The affected range of an edge is determined by the length of the edge in the circuit diagram and the communication link status. Within the affected range of the edge, the second-class nodes are statistically analyzed, and the interference intensity of each second-class node is allocated to obtain the interference intensity of each second-class node to the edge. Then, the impact on each edge is determined by combining the length of the edge in the circuit diagram. ； in, The first one in the route diagram The amount of influence on the edge. The first one in the route diagram The length of the strip, This is the initial impact amount. Indicates the number of lines in the circuit diagram. The initial influence quantity obtained by mapping the length of the edge. The first one in the route diagram The number of type II nodes within the affected range of the edge. For the first The influence weight of each type II node For the first in the circuit diagram The first edge The interference intensity of each type II node The first one in the route diagram Preset constants corresponding to the edges; Mining the correlation rules between data collection terminals, including, The system acquires multi-source data previously generated by the data collection terminal, performs data preprocessing on the multi-source data, analyzes the scale characteristics of the multi-source data at each data collection terminal to determine the flow of the multi-source data at each data collection terminal, sets the parameters of the mining algorithm based on the flow of the multi-source data at the data collection terminal, and performs association rule mining on the preprocessed multi-source data to obtain association rules.

2. The data processing method based on Internet of Things communication according to claim 1, characterized in that, Matching multi-source data with communication data yields the matching relationships between multi-source data and communication data, including: The time window is set according to the length of the edge in the route diagram and the communication link. The corresponding communication data is searched in this time window according to the timestamp of the multi-source data and other matching features to obtain the matching relationship between multi-source data and communication data. Other matching features include one or more of the following: packet size feature, IP address feature, protocol type feature and data content feature.

3. The data processing method based on Internet of Things communication according to claim 1, characterized in that, The quality of communication transmission and the quality of multi-source data are evaluated based on communication line information and the matching relationship between multi-source data and communication data, including: Confirm the attribute information of the multi-source data, and combine the impact, communication link status, and attribute information of the multi-source data to set a reasonable range of communication data to match the multi-source data; Compare the actual value and reasonable range of the same type of communication data. When the actual value is within the reasonable range, the deviation value of the communication data is 0. Otherwise, the deviation value of the communication data is the difference between the actual value and the reasonable range. The endpoint that is close to the actual value in the reasonable range is taken as the proximity endpoint. The difference between the actual value and the reasonable range is the difference between the actual value and the proximity endpoint. The deviation values ​​of each type of communication data are integrated, and the integrated deviation values ​​are used to describe the quality of communication transmission. Perform multi-dimensional analysis and inspection of multi-source data to determine the quality of the multi-source data.

4. The data processing method based on Internet of Things communication according to claim 3, characterized in that, The weighting of each type of multi-source data is assigned based on the communication transmission quality and the quality of the multi-source data itself. include, Assess the encryption security of each type of multi-source data at the data acquisition end and during transmission, and define the verification value for each type of multi-source data based on encryption security, communication transmission quality of multi-source data, and multi-source data quality; ； in, For the first The verification value of multi-source data, , The first The conversion factor corresponding to the communication transmission quality of multi-source data and the conversion factor corresponding to the quality of multi-source data. , The first Communication transmission quality and multi-source data quality for multi-source data. For the first Encryption security of multi-source data For the first Preset constants corresponding to multi-source data; The verification values ​​of all multi-source data are normalized, and then the combined weight of each type of multi-source data is assigned based on the verification values ​​of the multi-source data.

5. The data processing method based on Internet of Things communication according to claim 4, characterized in that, Multi-source data is fused based on the multi-source data association diagram and the combined weights of the multi-source data to generate a fused information set, including: Based on the multi-source data association diagram, the related multi-source data are weighted and summed according to their respective combination weights to obtain the representative value. The fused information set contains the related multi-source data and the representative value.

6. A data processing system based on Internet of Things (IoT) communication, characterized in that, The system is used to implement the data processing method based on Internet of Things (IoT) communication as described in any one of claims 1-5, and the system comprises: The line module is used to acquire the communication network of the target IoT, determine the data acquisition end and the data processing end in the communication topology of the target IoT, establish the communication topology based on the data acquisition end and the data processing end, and analyze the communication topology to determine the communication line information between the data acquisition end and the data processing end. The matching module is used to acquire multi-source data through the data acquisition terminal. The data acquisition terminal transmits multi-source data to the data processing terminal. At the same time, network monitoring tools are used to capture communication data during the data transmission process. The multi-source data and communication data are matched to obtain the matching relationship between multi-source data and communication data. The association module is used to evaluate the communication transmission quality and multi-source data quality based on communication line information and the matching relationship between multi-source data and communication data, mine the association rules between data acquisition terminals, match multi-source data according to the association rules, and generate a multi-source data association graph. The analysis module is used to assign a combination weight to each type of multi-source data based on the communication transmission quality and the quality of the multi-source data. It then performs multi-source data fusion based on the multi-source data association graph and the combination weights to generate a fused information set, thereby aiding in subsequent data analysis.

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