Communication data fusion method and system based on edge computing

Abstract

The invention discloses a communication data fusion method and system based on edge calculation. The method comprises a data acquisition step, a data preprocessing step, a data fusion step, an anomaly detection step, a node feedback I step, a calculation I step, a judgment I step, a calculation II step and a node feedback II step. The invention relates to the technical field of edge computing, in particular to a communication data fusion method and system based on edge computing. The technical problem to be solved by the invention is to provide the communication data fusion method and system based on edge calculation, so that the accuracy of field information acquisition is improved, a worker can accurately judge the risk existing at a node, and the accuracy and reliability of risk assessment are improved.

Description

Technical Field

[0001] The present invention relates to the field of edge computing technology, and in particular to a communication data fusion method and system based on edge computing. Background Art

[0002] In the process of the development of smart city big data, the safety supervision of urban underground space has received more and more attention. In order to prevent dangerous events such as leakage and collapse, it is necessary to connect various sensors to underground pipelines for real-time status monitoring to ensure the normal operation of the pipelines. Since most pipelines are buried underground and are not convenient for direct observation, manholes are usually used as nodes to install various sensors.

[0003] At present, the data measured by various sensors in the node are usually uploaded directly to the cloud for calculation to obtain various on-site information such as temperature, humidity, smoke content and other indicators. Cloud computing technology is used to determine whether there are safety hazards at the node, and then the results are transmitted to the management end through the Internet and mobile network.

[0004] Regarding the above technical solution, since various sensors generate a large amount of data during operation and occupy more resources in the process of transmitting data, it is easy for some on-site information to be inaccurately obtained, resulting in the staff being unable to accurately judge the risks at the node. If the sensor is set to send a warning signal only when the measured value exceeds the upper limit, it will be difficult for the staff to understand the specific on-site information at the node, affecting the accuracy of risk assessment. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a communication data fusion method and system based on edge computing, which can improve the accuracy of on-site information acquisition, help staff to accurately judge the risks existing at the node, and improve the accuracy and reliability of risk assessment.

[0006] In the first aspect, the present invention provides a communication data fusion method based on edge computing, which adopts the following technical solutions to achieve the purpose of the invention:

[0007] The communication data fusion method based on edge computing includes the following steps:

[0008] Acquiring data: a communication device is set in each of the N nodes, and a near-field communication connection is established between the communication device and multiple sensors with different functions installed in the same node, so as to obtain the measurement data of multiple sensors in a detection cycle and the identification information of the communication device;

[0009] Data preprocessing: convert the sensor's measurement data into the field information of the corresponding node, convert the device's identification information into the IP address of the corresponding node, and store it in the database;

[0010] Data fusion: extract the field information of N nodes and group and associate them according to the preset information types stored in the database, perform weighted data fusion on the field information of each group and convert them into characteristic values, and set the characteristic upper limit of each characteristic value according to historical data;

[0011] Anomaly detection: If the characteristic value of one of the nodes exceeds the characteristic upper limit, the measurement data of the node is recorded as abnormal data;

[0012] Node feedback I: Extract the IP address and site information of the node corresponding to the abnormal data in the database and send it to the management end.

[0013] As a further limitation of the present technical solution, a calculation step I is further provided after the node feedback step I; calculation I: obtaining the emergency range radius and the number of emergency nodes according to historical data, performing actual coordinate transformation on the IP address of the abnormal data node, and obtaining the number of IP addresses with abnormal data within the emergency range radius of the actual coordinate in a detection cycle, which is recorded as the risk number;

[0014] Judgment I: If the number of risks is greater than or equal to the number of emergency nodes, the IP address and on-site information of the abnormal data are extracted and sent to the management end, otherwise they are recorded in the database.

[0015] As a further limitation of the technical solution, after the judgment step I, a calculation step II and a node feedback step II are further provided;

[0016] Calculation II: Obtain the characteristic values ​​of I nodes within the emergency range radius of the actual coordinates of the abnormal data node in a detection cycle, obtain the preset sampling interval of the detection cycle based on historical data, and calculate the similarity score α between the characteristic value of the i-th node and the characteristic value of the abnormal data node based on the detection cycle and the preset sampling interval. i ;

[0017] Node Feedback II: The edge computing device extracts the IP address and similarity score α of I nodes i , and send it to the management end.

[0018] As a further limitation of the present technical solution, in the node feedback II step, α i The calculation model is: Among them, H is the total number of sampling time points, h is the number of sampling time points, T is the detection period, t is the preset sampling interval, Q h is the characteristic value of the hth sampling time point, Q h-1 is the characteristic value of the h-1th sampling time point, is the average value of the characteristic values ​​at H sampling time points.

[0019] As a further limitation of the technical solution, in the step of acquiring data, the sampling frequency, detection accuracy and distribution difference of the measurement data of sensors with different functions are also acquired;

[0020] The data fusion step is updated as follows: extract the field information of N nodes and group and associate them according to the preset information types stored in the database, obtain the fusion weight value by weighted summing up the sampling frequency, detection accuracy and distribution difference of the measurement data of sensors with different functions, perform weighted data fusion on the field information of each group according to the fusion weight value, and then convert it into a feature value, and set the feature upper limit of each feature value according to historical data.

[0021] In a second aspect, the present invention provides a communication data fusion system based on edge computing, which adopts the following technical solutions to achieve the purpose of the invention:

[0022] The communication data fusion system based on edge computing includes the following modules:

[0023] Communication device: The output end is connected to the input end of the acquisition module. A communication device is provided in each of the N nodes to establish a near-field communication connection with multiple sensors with different functions installed in the same node, obtain the measurement data of multiple sensors in a detection cycle and transmit it to the acquisition module;

[0024] Acquisition module: the input end is connected to the output end of the communication device, and the output end is connected to the input end of the preprocessing module, and is used to acquire the measurement data of multiple sensors and the identification information of the communication device within a detection cycle and transmit them to the preprocessing module;

[0025] Preprocessing module: The input end is connected to the output end of the acquisition module, and the output end is connected to the input end of the data fusion module, which is used to convert the measurement data of the sensor into the field information of the corresponding node, convert the identification information of the device into the IP address of the corresponding node, and store it in the database;

[0026] Data fusion module: The input end is connected to the output end of the preprocessing module, and the output end is connected to the input end of the detection module. It is used to extract the field information of N nodes and group and associate them according to the preset information types stored in the database, perform weighted data fusion on the field information of each group and convert them into feature values, and set the feature upper limit of each feature value according to historical data;

[0027] Detection module: the input end is connected to the output end of the data fusion module, and the output end is connected to the input end of the feedback module I, and is used to record the measurement data of one node as abnormal data if the characteristic value of one node exceeds the characteristic upper limit;

[0028] Feedback module I: The input end is connected to the output end of the detection module, and is used to extract the IP address and field information of the node corresponding to the abnormal data in the database and send it to the management end.

[0029] As a further limitation of the technical solution, it also includes a calculation module I;

[0030] Calculation module I: The input end is connected to the output end of the feedback module I, and the output end is connected to the input end of the judgment module I, and is used to obtain the emergency range radius and the number of emergency nodes according to historical data, transform the IP address of the abnormal data node into actual coordinates, and obtain the number of IP addresses with abnormal data within the emergency range radius of the actual coordinates in a detection cycle, which is recorded as the risk number;

[0031] Judgment module I: The input end is connected to the output end of calculation module I, and is used to extract the IP address and on-site information of abnormal data and send it to the management end if the number of risks is greater than or equal to the number of emergency nodes, otherwise it is recorded in the database.

[0032] As a further limitation of the technical solution, it also includes a calculation module II and a feedback module II;

[0033] Calculation module II: The input end is connected to the output end of the judgment module I, and the output end is connected to the input end of the feedback module II, which is used to obtain the characteristic values ​​of I nodes within the emergency range radius of the actual coordinates of the abnormal data node in a detection cycle, obtain the preset sampling interval of the detection cycle according to historical data, and calculate the similarity score α between the characteristic value of the i-th node and the characteristic value of the abnormal data node according to the detection cycle and the preset sampling interval. i ;

[0034] Feedback module II: The input end is connected to the output end of the computing module II, and is used by the edge computing device to extract the IP addresses and similarity scores α of I nodes i , and send it to the management end.

[0035] As a further limitation of the present technical solution, in the feedback module II, α i The calculation model is: Among them, H is the total number of sampling time points, h is the number of sampling time points, T is the detection period, t is the preset sampling interval, Q h is the characteristic value of the hth sampling time point, Q h-1 is the characteristic value of the h-1th sampling time point, is the average value of the characteristic values ​​at H sampling time points.

[0036] As a further limitation of the technical solution, the communication device is also used to obtain the sampling frequency, detection accuracy and distribution difference of the measurement data of the sensors with different functions and transmit them to the acquisition module;

[0037] The acquisition module is also used to obtain the sampling frequency, detection accuracy and distribution difference of measurement data of sensors with different functions and pass them to the preprocessing module;

[0038] The data fusion module is updated as follows: the input end is connected to the output end of the preprocessing module, and the output end is connected to the input end of the detection module. It is used to extract the field information of N nodes and group them according to the preset information types stored in the database. The fusion weight value is obtained by weighted summing up the sampling frequency, detection accuracy and distribution difference of the measurement data of sensors with different functions. The field information of each group is weightedly fused according to the fusion weight value and then converted to obtain the feature value. The feature upper limit of each feature value is set according to the historical data.

[0039] Compared with the prior art, the advantages and positive effects of the present invention are:

[0040] 1. Treat the manhole as a node to install various sensors and establish a near-field communication connection. By preprocessing the measurement data, various on-site information at the node is obtained, and the IP address corresponding to the node is associated with it and stored. Data fusion conversion is performed according to the information type preset in the database to obtain the characteristic value. The characteristic value is compared with the preset characteristic upper limit to identify and extract the abnormal data, and then the risk hazards of the pipeline are judged by the fused data. With this setting, a large amount of data generated by the operation of various sensors is preprocessed near the data source before being transmitted and further processed. Fewer resources are occupied in the process of transmitting data, which can improve the accuracy of on-site information acquisition, help staff accurately judge the risks at the node, and improve the accuracy and reliability of risk assessment.

[0041] 2. Since there is a certain correlation between the measurement data and on-site information between adjacent nodes, the emergency range radius and the number of emergency nodes preset according to the intelligent fire protection system in the database are obtained, and the number of other risks of abnormal data within the emergency range radius of the actual coordinates of the abnormal data node, that is, the number of nodes, is calculated. When the number of risks is higher than the number of emergency nodes, the IP addresses and on-site information of all abnormal data are sent, which can further analyze the correlation of the pipeline conditions around the abnormal data nodes and eliminate system faults, thereby improving the accuracy and reliability of risk assessment.

[0042] 3. Obtain the characteristic values ​​of I nodes within the emergency range radius of the actual coordinates of the abnormal data node in a detection cycle, and calculate the similarity score between the abnormal data node and other nodes based on the preset sampling interval and other data, and send the result to the management end. This setting can further perform correlation analysis on the pipeline conditions around the abnormal data node and eliminate system faults, which is beneficial for staff to accurately judge the risks at the node and improve the accuracy and reliability of risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0044] Figure 1 This is a flow chart of Embodiment 1 of the present invention;

[0045] Figure 2 This is a system diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the attached embodiment of the present invention Figure 1-Figure 2 , the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] It should be noted that the directional terms such as left, right, up, down, front and back in the embodiments of the present invention are merely relative concepts or are based on the normal use state of the product, that is, the direction of movement of the product, and should not be considered as limiting.

[0048] When a component is referred to as being “located” or “disposed on” another component, it may be on the other component or there may be an intervening component. When a component is referred to as being “connected to” another component, it may be directly connected to the other component or there may be an intervening component.

[0049] Embodiment 1: A communication data fusion method based on edge computing includes the following steps:

[0050] S1. Obtaining data: a communication device is set in each of the N nodes, and a near field communication connection is established between the communication device and multiple sensors with different functions installed in the same node, and the measurement data of multiple sensors in a detection cycle and the identification information of the communication device are obtained, and the sampling frequency, detection accuracy and distribution difference of the measurement data of sensors with different functions are obtained;

[0051] S2, data preprocessing: convert the sensor's measurement data into the field information of the corresponding node, convert the device's identification information into the IP address of the corresponding node, and store it in the database;

[0052] S3, data fusion: extract the field information of N nodes and group them according to the preset information types stored in the database. The fusion weight value is obtained by weighted summing up the sampling frequency, detection accuracy and distribution difference of the measurement data of sensors with different functions. The field information of each group is weightedly fused according to the fusion weight value and converted to obtain the feature value. The feature upper limit of each feature value is set according to the historical data.

[0053] S4, anomaly detection: if the characteristic value of one of the nodes exceeds the characteristic upper limit, the measurement data of the node is recorded as abnormal data;

[0054] S5, Node Feedback I: Extract the IP address and site information of the node corresponding to the abnormal data in the database and send it to the management end.

[0055] S6, calculation I: obtain the emergency range radius and the number of emergency nodes according to historical data, transform the IP address of the abnormal data node into actual coordinates, and obtain the number of IP addresses with abnormal data within the emergency range radius of the actual coordinates in a detection cycle, which is recorded as the risk number;

[0056] S7, judgment I: If the number of risks is greater than or equal to the number of emergency nodes, the IP address and on-site information of the abnormal data are extracted and sent to the management end, otherwise it is recorded in the database.

[0057] S8, calculation II: obtain the characteristic values ​​of I nodes within the emergency range radius of the actual coordinates of the abnormal data node in a detection cycle, obtain the preset sampling interval of the detection cycle according to historical data, and calculate the similarity score α between the characteristic value of the i-th node and the characteristic value of the abnormal data node according to the detection cycle and the preset sampling interval i ;

[0058] S9, Node Feedback II: The edge computing device extracts the IP address and similarity score α of I nodes i , and sent to the management end, α i The calculation model is: Among them, H is the total number of sampling time points, h is the number of sampling time points, T is the detection period, t is the preset sampling interval, Q h is the characteristic value of the hth sampling time point, Q h-1 is the characteristic value of the h-1th sampling time point, is the average value of the characteristic values ​​at H sampling time points.

[0059] The working principle of the communication data fusion method based on edge computing in this embodiment is:

[0060] The manhole is used as a node to install various sensors, and the sensors are connected to the communication equipment in the same node to establish a near-field communication connection, so as to obtain the measurement data of multiple sensors and the identification information of the communication equipment. There is a correlation between the measurement data provided by multiple sensors, so that various field information at the node, such as temperature and humidity, smoke content, gas monitoring data and other indicator information, is obtained by preprocessing the data, and the IP address corresponding to the node is associated with it and stored in the database connected to the near-field of the communication equipment of the node. The data is grouped and associated according to the preset information types of the database, such as distance, number, area, etc. The sampling frequency, detection accuracy and distribution difference of the measurement data of sensors with different functions are obtained, and the fusion weight value is obtained after weighted summation. The field information of each group is weighted summed according to the fusion weight value, so as to convert it into a feature value, improve the accuracy of abnormal data recognition, and compare the feature value with the preset feature upper limit to identify and extract the abnormal data, and then judge the risk of the pipeline through the fusion-processed data;

[0061] With this setting, a large amount of data generated by the operation of various sensors is pre-processed near the data source before being transmitted and further processed. Fewer resources are occupied in the process of transmitting data, which can improve the accuracy of on-site information acquisition, help staff accurately judge the risks at the nodes, and improve the accuracy and reliability of risk assessment.

[0062] There is a certain correlation between the measurement data and on-site information between adjacent nodes. The emergency range radius and the number of emergency nodes preset by the intelligent fire protection system in the database are obtained, and the number of other risk nodes with abnormal data within the emergency range radius of the actual coordinates of the abnormal data node, that is, the number of nodes, is calculated. When the risk number is higher than the number of emergency nodes, the IP addresses and on-site information of all abnormal data are sent;

[0063] The characteristic values ​​of I nodes within the emergency range radius of the actual coordinates of the abnormal data node in a detection cycle are obtained, and the similarity scores between the abnormal data node and other nodes are calculated based on the preset sampling interval and other data, and the results are sent to the management end. With this setting, the pipeline conditions around the abnormal data node can be further analyzed for correlation and system failures can be eliminated, which is beneficial for staff to accurately judge the risks at the node and improve the accuracy and reliability of risk assessment.

[0064] Embodiment 2: A communication data fusion system based on edge computing includes the following modules:

[0065] Communication device: The output end is connected to the input end of the acquisition module. A communication device is provided in each of the N nodes to establish a near-field communication connection with multiple sensors with different functions installed in the same node, obtain the measurement data of multiple sensors in a detection cycle and transmit it to the acquisition module;

[0066] Acquisition module: the input end is connected to the output end of the communication device, and the output end is connected to the input end of the preprocessing module, and is used to acquire the measurement data of multiple sensors and the identification information of the communication device within a detection cycle and transmit them to the preprocessing module;

[0067] Preprocessing module: The input end is connected to the output end of the acquisition module, and the output end is connected to the input end of the data fusion module, which is used to convert the measurement data of the sensor into the field information of the corresponding node, convert the identification information of the device into the IP address of the corresponding node, and store it in the database;

[0068] Data fusion module: The input end is connected to the output end of the preprocessing module, and the output end is connected to the input end of the detection module. It is used to extract the field information of N nodes and group and associate them according to the preset information types stored in the database, perform weighted data fusion on the field information of each group and convert them into feature values, and set the feature upper limit of each feature value according to historical data;

[0069] Detection module: the input end is connected to the output end of the data fusion module, and the output end is connected to the input end of the feedback module I, and is used to record the measurement data of one node as abnormal data if the characteristic value of one node exceeds the characteristic upper limit;

[0070] Feedback module I: The input end is connected to the output end of the detection module, and the output end is connected to the input end of the calculation module I, which is used to extract the IP address and field information of the node corresponding to the abnormal data in the database and send it to the management end.

[0071] Calculation module I: The input end is connected to the output end of the feedback module I, and the output end is connected to the input end of the judgment module I, and is used to obtain the emergency range radius and the number of emergency nodes according to historical data, transform the IP address of the abnormal data node into actual coordinates, and obtain the number of IP addresses with abnormal data within the emergency range radius of the actual coordinates in a detection cycle, which is recorded as the risk number;

[0072] Judgment module I: The input end is connected to the output end of calculation module I, and the output end is connected to the input end of calculation module II. It is used to extract the IP address and on-site information of abnormal data and send it to the management end if the number of risks is greater than or equal to the number of emergency nodes. Otherwise, it is recorded in the database.

[0073] Calculation module II: The input end is connected to the output end of the judgment module I, and the output end is connected to the input end of the feedback module II, which is used to obtain the characteristic values ​​of I nodes within the emergency range radius of the actual coordinates of the abnormal data node in a detection cycle, obtain the preset sampling interval of the detection cycle according to historical data, and calculate the similarity score α between the characteristic value of the i-th node and the characteristic value of the abnormal data node according to the detection cycle and the preset sampling interval. i ;

[0074] Feedback module II: The input end is connected to the output end of the computing module II, and is used by the edge computing device to extract the IP addresses and similarity scores α of I nodes i , and sent to the management end, α i The calculation model is: Among them, H is the total number of sampling time points, h is the number of sampling time points, T is the detection period, t is the preset sampling interval, Q h is the characteristic value of the hth sampling time point, Q h-1 is the characteristic value of the h-1th sampling time point, is the average value of the characteristic values ​​at H sampling time points.

[0075] The working principle of the communication data fusion system based on edge computing in this embodiment is:

[0076] The sensor establishes a near-field communication connection with the communication device in the same node, and the acquisition module acquires the measurement data of multiple sensors and the identification information of the communication device. There is a correlation between the measurement data provided by the multiple sensors. The data is preprocessed by the preprocessing module to obtain various field information at the node, and the IP address corresponding to the node is associated with it and stored in the database connected to the near-field of the communication device of the node. The data fusion module groups and associates according to the information types preset in the database, such as distance, number, area, etc., and obtains the sampling frequency, detection accuracy and distribution difference of the measurement data of sensors with different functions, and obtains the fusion weight value after weighted summation, and performs weighted summation on the field information of each group according to the fusion weight value, so as to convert it into a feature value, improve the accuracy of abnormal data identification, and compare the feature value with the preset feature upper limit to identify and extract the abnormal data. The feedback module I feeds back the information related to the abnormal data to the management end, and then judges the risk hidden danger of the pipeline through the fusion processed data;

[0077] Since there is a certain correlation between the measurement data and the on-site information between adjacent nodes, the calculation module 1 obtains the emergency range radius and the number of emergency nodes preset in the database according to the intelligent fire protection system, and calculates the number of other risk nodes with abnormal data within the emergency range radius of the actual coordinates of the abnormal data node, that is, the number of nodes. When the risk number is higher than the number of emergency nodes, the judgment module 1 sends the IP addresses and on-site information of all abnormal data to the management end;

[0078] The calculation module II obtains the characteristic values ​​of I nodes within the emergency range radius of the actual coordinates of the abnormal data node in a detection cycle, and calculates the similarity score between the abnormal data node and other nodes according to the preset sampling interval and other data. The feedback module II sends the result to the management end, which is conducive to further correlation analysis of the pipeline conditions around the abnormal data node and troubleshooting of system failures;

[0079] With this setting, a large amount of data generated by the operation of various sensors is pre-processed near the data source before being transmitted and further processed. Fewer resources are occupied in the process of transmitting data, which can improve the accuracy of on-site information acquisition, help staff accurately judge the risks at the nodes, and improve the accuracy and reliability of risk assessment.

[0080] The above disclosure is only a specific embodiment of the present invention, but the present invention is not limited thereto, and any changes that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A communication data fusion method based on edge computing, characterized in that: The following steps are involved: Acquiring data: a communication device is set in each of the N nodes, and a near-field communication connection is established between the communication device and multiple sensors with different functions installed in the same node, so as to obtain the measurement data of multiple sensors in a detection cycle and the identification information of the communication device; Data preprocessing: convert the sensor's measurement data into the field information of the corresponding node, convert the device's identification information into the IP address of the corresponding node, and store it in the database; Data fusion: extract the field information of N nodes and group and associate them according to the preset information types stored in the database, perform weighted data fusion on the field information of each group and convert them into characteristic values, and set the characteristic upper limit of each characteristic value according to historical data; Anomaly detection: If the characteristic value of one of the nodes exceeds the characteristic upper limit, the measurement data of the node is recorded as abnormal data; Node feedback I: Extract the IP address and site information of the node corresponding to the abnormal data in the database and send it to the management end.

2. The communication data fusion method based on edge computing according to claim 1, characterized in that: After the node feedback I step, there is also a calculation I step; Calculation I: Obtain the emergency range radius and the number of emergency nodes based on historical data, transform the IP addresses of abnormal data nodes into actual coordinates, and obtain the number of IP addresses with abnormal data within the emergency range radius of the actual coordinates in a detection cycle, which is recorded as the risk number; Judgment I: If the number of risks is greater than or equal to the number of emergency nodes, the IP address and on-site information of the abnormal data are extracted and sent to the management end, otherwise it is recorded in the database.

3. The communication data fusion method based on edge computing according to claim 2 is characterized in that: After the judgment step I, there are also a calculation step II and a node feedback step II; Calculation II: Obtain the characteristic values ​​of I nodes within the emergency range radius of the actual coordinates of the abnormal data node in a detection cycle, obtain the preset sampling interval of the detection cycle based on historical data, and calculate the similarity score α between the characteristic value of the i-th node and the characteristic value of the abnormal data node based on the detection cycle and the preset sampling interval. i ; Node Feedback II: The edge computing device extracts the IP address and similarity score α of I nodes i , and send it to the management end.

4. The communication data fusion method based on edge computing according to claim 3 is characterized in that: In the node feedback II step, α i The calculation model is: Among them, H is the total number of sampling time points, h is the number of sampling time points, T is the detection period, t is the preset sampling interval, Q h is the characteristic value of the hth sampling time point, Q h-1 is the characteristic value of the h-1th sampling time point, is the average value of the characteristic values ​​at H sampling time points.

5. The communication data fusion method based on edge computing according to any one of claims 1 to 3, characterized in that: In the data acquisition step, the sampling frequency, detection accuracy and distribution difference of the measurement data of sensors with different functions are also acquired; The data fusion step is updated as follows: extract the field information of N nodes and group and associate them according to the preset information types stored in the database, obtain the fusion weight value by weighted summing up the sampling frequency, detection accuracy and distribution difference of the measurement data of sensors with different functions, perform weighted data fusion on the field information of each group according to the fusion weight value, and then convert it into a feature value, and set the feature upper limit of each feature value according to historical data.

6. The communication data fusion system based on edge computing is characterized by: Includes the following modules: Communication device: The output end is connected to the input end of the acquisition module. A communication device is provided in each of the N nodes to establish a near-field communication connection with multiple sensors with different functions installed in the same node, obtain the measurement data of multiple sensors in a detection cycle and transmit it to the acquisition module; Acquisition module: the input end is connected to the output end of the communication device, and the output end is connected to the input end of the preprocessing module, and is used to acquire the measurement data of multiple sensors and the identification information of the communication device within a detection cycle and transmit them to the preprocessing module; Preprocessing module: The input end is connected to the output end of the acquisition module, and the output end is connected to the input end of the data fusion module, which is used to convert the measurement data of the sensor into the field information of the corresponding node, convert the identification information of the device into the IP address of the corresponding node, and store it in the database; Data fusion module: The input end is connected to the output end of the preprocessing module, and the output end is connected to the input end of the detection module. It is used to extract the field information of N nodes and group and associate them according to the preset information types stored in the database, perform weighted data fusion on the field information of each group and convert them into feature values, and set the feature upper limit of each feature value according to historical data; Detection module: the input end is connected to the output end of the data fusion module, and the output end is connected to the input end of the feedback module I, and is used to record the measurement data of one node as abnormal data if the characteristic value of one node exceeds the characteristic upper limit; Feedback module I: The input end is connected to the output end of the detection module, and is used to extract the IP address and field information of the node corresponding to the abnormal data in the database and send it to the management end.

7. The communication data fusion system based on edge computing according to claim 6, characterized in that: Also includes a computing module I; Calculation module I: The input end is connected to the output end of the feedback module I, and the output end is connected to the input end of the judgment module I, and is used to obtain the emergency range radius and the number of emergency nodes according to historical data, transform the IP address of the abnormal data node into actual coordinates, and obtain the number of IP addresses with abnormal data within the emergency range radius of the actual coordinates in a detection cycle, which is recorded as the risk number; Judgment module I: The input end is connected to the output end of calculation module I, and is used to extract the IP address and on-site information of abnormal data and send it to the management end if the number of risks is greater than or equal to the number of emergency nodes, otherwise it is recorded in the database.

8. The communication data fusion system based on edge computing according to claim 7 is characterized in that: Also includes a calculation module II and a feedback module II; Calculation module II: The input end is connected to the output end of the judgment module I, and the output end is connected to the input end of the feedback module II, which is used to obtain the characteristic values ​​of I nodes within the emergency range radius of the actual coordinates of the abnormal data node in a detection cycle, obtain the preset sampling interval of the detection cycle according to historical data, and calculate the similarity score α between the characteristic value of the i-th node and the characteristic value of the abnormal data node according to the detection cycle and the preset sampling interval. i ; Feedback module II: The input end is connected to the output end of the computing module II, and is used by the edge computing device to extract the IP addresses and similarity scores α of I nodes i , and send it to the management end.

9. The communication data fusion system based on edge computing according to claim 8, characterized in that: In feedback module II, α i The calculation model is: Among them, H is the total number of sampling time points, h is the number of sampling time points, T is the detection period, t is the preset sampling interval, Q h is the characteristic value of the hth sampling time point, Q h-1 is the characteristic value of the h-1th sampling time point, is the average value of the characteristic values ​​at H sampling time points.

10. The communication data fusion system based on edge computing according to any one of claims 6 to 9, characterized in that: The communication device is also used to obtain the sampling frequency, detection accuracy and distribution difference of measurement data of sensors with different functions and transmit them to the acquisition module; The acquisition module is also used to obtain the sampling frequency, detection accuracy and distribution difference of measurement data of sensors with different functions and pass them to the preprocessing module; The data fusion module is updated as follows: the input end is connected to the output end of the preprocessing module, and the output end is connected to the input end of the detection module. It is used to extract the field information of N nodes and group them according to the preset information types stored in the database. The fusion weight value is obtained by weighted summing up the sampling frequency, detection accuracy and distribution difference of the measurement data of sensors with different functions. The field information of each group is weightedly fused according to the fusion weight value and then converted to obtain the feature value. The feature upper limit of each feature value is set according to the historical data.

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