Intelligent sensing data monitoring method based on distribution communication network

Abstract

The present invention relates to the technical field of communication equipment and intelligent perception network control, and specifically discloses a method for monitoring data based on intelligent perception data of a distribution communication network, which is applied to target equipment of the distribution communication network. The method comprises: real-time collection of monitoring data of preset access points of each line acquired by the target equipment of the distribution communication network for pre-processing, and storing the pre-processed monitoring data in a cloud server; initial interval sampling of device perception flow data in the cloud server according to a sampling strategy; analysis of the device perception flow data acquired after the initial interval sampling, and judging whether the performance index of the network line of the target equipment meets the safety requirements according to the analysis result; equipment operation status data analysis and adjustment of the network access point of the target equipment terminal; the present invention improves the accuracy of network data collection by data collection equipment, improves the network data transmission efficiency and the operation efficiency of the data collection system by flexibly adjusting the collection access line point.

Description

Technical Field

[0001] The present invention relates to the technical field of communication equipment and intelligent perception network control, and in particular to a method for monitoring data based on intelligent perception of a power distribution communication network. Background Art

[0002] With the development of energy Internet, large-scale access of distributed new energy, energy storage, electric vehicles and other terminals, as well as the widespread application of power electronic equipment in the power grid, has brought a great impact on the terminal communication network; the traditional terminal communication network has a relatively single communication method, and the communication bandwidth and communication delay are difficult to effectively meet the terminal business and power quality data transmission needs.

[0003] The emergence of information database systems has enabled the distribution communication network to complete the collection, transmission and data analysis of communication data by setting up intelligent sensing devices. However, the existing distribution communication network intelligent sensing only authenticates whether the data interface is legal, analyzes the line usage rate and completes the data transmission process confirmation; but in the actual use of real data acquisition equipment, there is no or little consideration of the operating status feedback of the data acquisition equipment itself controlled by the intelligent sensing network and the operating status feedback of the data monitoring port of the acquisition equipment; resulting in ignoring the location distribution setting of the hardware acquisition device interface end, as well as the reasonable frequency and interval setting of the acquisition access to obtain flow data; thus affecting the collection efficiency and data accuracy of the overall distribution communication network intelligent sensing data; thus causing the equipment status to affect the operation and efficiency of the overall network quality. Summary of the invention

[0004] The purpose of the present invention is to provide a method for monitoring data based on intelligent perception of power distribution communication network to solve the following technical problems:

[0005] How to improve the accuracy of network data collection by data collection equipment, improve network data transmission efficiency, and improve the operating efficiency of the data collection system by flexibly adjusting the collection access line points.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A method for monitoring data based on intelligent perception of a power distribution communication network is applied to a target device of the power distribution communication network, the method comprising:

[0008] Step 1: Real-time collection of monitoring data of each line access preset point acquired by the target device of the power distribution communication network for preprocessing, and storing the preprocessed monitoring data in the cloud server;

[0009] The monitoring data includes: equipment operation status data and equipment perception flow data;

[0010] Step 2: Perform initial interval sampling of the device-sensed traffic data in the cloud server according to the sampling strategy;

[0011] Step 3: Obtain the device-perceived traffic data after the initial interval sampling for analysis, and determine whether the target device network line performance indicators meet the security requirements based on the analysis results:

[0012] If it is judged that the safety requirements are not met, the sampling strategy is adjusted and the process returns to step 2;

[0013] If it meets the safety requirements, proceed to step 4;

[0014] Step 4: Analyze the equipment operation status data according to the judgment result of step 3, and adjust the network access point of the target equipment terminal.

[0015] Preferably, the sampling strategy includes:

[0016] Acquire communication traffic data parameter information of preset points according to preset sampling duration and sampling frequency;

[0017] The communication traffic data parameter information includes: the number of lines Y, the line usage frequency F Y , Line load information volume Q Y ;

[0018] The preset sampling duration and sampling frequency are adjusted according to the analysis results of step three.

[0019] Preferably, the monitoring data preprocessing method in step 1 is:

[0020] S1. Real-time collection of location information, temperature and power system operation parameter information of the preset line access points of the target equipment of the distribution communication network;

[0021] S2, real-time collection of data packet flow information and access port information obtained by the target device terminal of the power distribution communication network;

[0022] S3: Perform data conversion and aggregation on the information collected in steps S1 and S2, and eliminate noise, anomalies, redundancy and other information in the data.

[0023] Preferably, the method for analyzing device-aware traffic data in step 3 is:

[0024] By formula Get the line stability coefficient S of the kth sampling frequency k ;

[0025] Where N is the total number of sampling frequencies under the preset sampling duration; Y 总 is the total number of lines under all sampling frequencies; Q Ykis the line load information at the kth sampling frequency; F Yk Y is the line usage frequency at the kth sampling frequency; k is the number of lines at the kth sampling frequency;

[0026] The line stability factor S k and the preset line stability coefficient threshold [S 1 , S 2 ] for comparison:

[0027] If S k <S 1 , then the network line performance index is judged to be too small and the sampling strategy is adjusted;

[0028] If k ∈[S 1 , S 2 ], then the network line performance indicators are judged to be normal;

[0029] If S k >S 2 , it is judged that the network line performance index is too large and a line safety warning is issued.

[0030] Preferably, the power system operation parameter information:

[0031] By formula Calculate the power system operating parameters P s ;

[0032] Among them, G e Generator parameters, E x is the excitation system parameter, O v is the speed regulator parameter, L e is the load parameter; μ 1 , μ 2 , μ 3 , μ 4 is the preset weight coefficient, and μ 1 , μ 2 , μ 3 , μ 4 All greater than 0; f a 、f b 、f c 、f d According to different G e 、E x , O v , L e The parameter history ratio size is the preset function of the fitting setting; G e0 is the standard generator parameter, E x0 is the standard excitation system parameter, O v0is the standard speed regulator parameter, L e0 is the standard load parameter; ΔL e It is the preset load parameter deviation value.

[0033] Preferably, the step of analyzing the equipment operation status data in step 4 is:

[0034] By formula Get the monitoring risk coefficient C of the initial interval sampling of the i-th preset point i ;

[0035] Among them, [t n , t m ] is the sampling time period, γ 1 , γ 2 is the preset weight coefficient, and γ 1 , γ 2 All greater than 0; P si is the operating parameter of the power system at the i-th preset point; T i (t) is the real-time temperature of the i-th preset point; P s0 is the standard power system operating parameter; ΔP s It is a preset power system operation parameter deviation value;

[0036] The monitoring risk factor C i With the preset threshold (C A , C B ) for comparison:

[0037] If C i ≤C A , it is determined that there is no security risk in the power supply of the target device;

[0038] If C A <C i ≤C B , then it is determined that there is a security risk in the power supply of the target device, and the network access point of the target device terminal is replaced and adjusted;

[0039] If C i >C B , it is determined that there is a safety problem with the power supply of the target device and an equipment maintenance instruction is generated.

[0040] Preferably, it also includes:

[0041] Step 6: Perform data intrusion analysis on the access node traffic data at the preset points.

[0042] Preferably, the method of performing data intrusion analysis in step 6 is:

[0043] Obtain the flow data of the monitoring points and filter the characteristic flow of the flow data based on the wavelet decomposition extraction method;

[0044] The correlation between the actual operating characteristic flow and the initial interval characteristic flow is calculated. When the correlation is lower than the preset threshold, it is determined that data intrusion exists.

[0045] Beneficial effects of the present invention:

[0046] (1) The present invention ensures that, by designing a sampling strategy, after the sampling condition device perceives the flow data and analyzes the results, the sampling scheme that does not meet the preset conditions is adjusted; by adjusting the preset sampling duration and sampling frequency, it is ensured that the sampling strategy can be flexibly and adaptively adjusted according to the size of the existing network line performance indicators, thereby improving the rational distribution of data flow acquisition in the sampling interval and improving the efficiency of accurate extraction of data information at preset points.

[0047] (2) The present invention determines the power supply safety of the equipment by analyzing and collecting the power system parameters of the preset points where the lines of the target equipment of the distribution communication network are accessed; in addition to excluding the direct monitoring of the equipment temperature by the sensor, it should also consider whether the power system parameters of the overall equipment meet the safety requirements, thereby reducing the hidden dangers of the hard equipment of the target equipment.

[0048] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0050] Figure 1 The figure is a schematic diagram of the steps of the method for monitoring data based on intelligent perception of power distribution communication network according to the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] See also Figure 1 As shown, the present invention is a method for monitoring data based on intelligent perception of a power distribution communication network, which is applied to a target device of the power distribution communication network. The method includes:

[0053] Step 1: Real-time collection of monitoring data of each line access preset point acquired by the target device of the power distribution communication network for preprocessing, and storing the preprocessed monitoring data in the cloud server;

[0054] Monitoring data includes: equipment operation status data and equipment perception flow data;

[0055] Step 2: Perform initial interval sampling of the device-sensed traffic data in the cloud server according to the sampling strategy;

[0056] Step 3: Obtain the device-perceived traffic data after the initial interval sampling for analysis, and determine whether the target device network line performance indicators meet the security requirements based on the analysis results:

[0057] If it is judged that the safety requirements are not met, the sampling strategy is adjusted and the process returns to step 2;

[0058] If it meets the safety requirements, proceed to step 4;

[0059] Step 4: Analyze the equipment operation status data according to the judgment result of step 3, and adjust the network access point of the target equipment terminal.

[0060] Through the above technical scheme, with the emergence of information database system, the distribution communication network can complete the collection, transmission and data analysis process of communication data by setting up intelligent sensing devices. However, the existing distribution communication network intelligent sensing only performs data interface authentication to see if it is legal, analyzes the line usage rate and completes the data transmission process confirmation; but in the actual use of real data acquisition equipment, there is no or little consideration of the operating status feedback of the data acquisition equipment itself controlled by the intelligent sensing network and the operating status feedback of the data monitoring port of the acquisition equipment; resulting in ignoring the location distribution setting of the interface end of the hardware acquisition equipment, as well as the reasonable frequency and interval setting of the acquisition access to obtain flow data; thus affecting the collection efficiency and data accuracy of the overall distribution communication network intelligent sensing data; thus causing the equipment status to affect the operation and efficiency of the overall network quality.

[0061] In order to solve the above technical problems, the present invention designs an intelligent perception data monitoring method based on a distribution communication network, which is specifically applied to the target equipment of the distribution communication network. The method mainly includes: step 1, real-time collection of monitoring data of each line access preset point obtained by the target equipment of the distribution communication network for pre-processing, and storing the pre-processed monitoring data in a cloud server; the target equipment of the distribution communication network monitors the network data information of each location node through line access and the device sensing information obtained by the sensor of the access node, pre-processes these information, and stores the integrated data in the cloud server for backup, in preparation for providing the next step of information analysis; wherein the monitoring data includes: equipment operation status data and equipment perception flow data; step 2, initial interval sampling of the equipment perception flow data in the cloud server according to the sampling strategy; step 3, analyzing the equipment perception flow data after the initial interval sampling, and judging whether the performance indicators of the network line of the target equipment meet the safety requirements according to the analysis results: if it is judged that it does not meet the safety requirements, adjusting the sampling strategy and returning to step 2; if it is judged that it meets the safety requirements, entering step 4; step 4, analyzing the equipment operation status data according to the judgment result of step 3, and adjusting the network access point of the target equipment terminal.

[0062] As an implementation mode of the present invention, the sampling strategy includes:

[0063] Acquire communication traffic data parameter information of preset points according to preset sampling duration and sampling frequency;

[0064] The communication traffic data parameter information includes: the number of lines Y, the line usage frequency F Y , Line load information volume Q Y ;

[0065] Adjust the preset sampling duration and sampling frequency according to the analysis results of step three.

[0066] Through the above technical solution, in this embodiment, the sampling strategy is designed to ensure that the sampling scheme that does not meet the preset conditions is adjusted after the sampling condition device perceives the flow data and analyzes the results; the specific sampling strategy includes: obtaining the communication flow data parameter information of the preset point according to the preset sampling time and sampling frequency; wherein the communication flow data parameter information includes: the number of lines Y, the line usage frequency F Y , Line load information volume Q Y By adjusting the preset sampling duration and sampling frequency according to the analysis results of step three, it is ensured that the sampling strategy can be flexibly and adaptively adjusted according to the size of the existing network line performance indicators, thereby improving the rational distribution of data flow acquisition in the sampling interval and improving the efficiency of accurate extraction of preset point data information.

[0067] As an implementation mode of the present invention, the monitoring data preprocessing method in step 1 is:

[0068] S1. Real-time collection of location information, temperature and power system operation parameter information of the preset line access points of the target equipment of the distribution communication network;

[0069] S2, real-time collection of data packet flow information and access port information obtained by the target device terminal of the power distribution communication network;

[0070] S3: Perform data conversion and aggregation on the information collected in steps S1 and S2, and eliminate noise, anomalies, redundancy and other information in the data.

[0071] Through the above technical scheme, in order to improve the uniformity of data information in data preprocessing and reduce the interference of redundant and erroneous information, the steps of specific processing of the data collected by the target equipment of the distribution communication network in this embodiment include: first, real-time collection of the location information, temperature and power system operation parameter information of the preset line access points of the target equipment of the distribution communication network; obtaining real-time dynamic data by monitoring the device sensor information obtained by the sensor of the access node of the target equipment to determine the equipment operation status; then, real-time collection of data packets obtained by the terminal of the target equipment of the distribution communication network, and obtaining the network data information of each location node by acquiring the line access monitoring, mainly including data packet flow information and access port information; finally, conventional data processing is performed on the information collected in the previous two steps to meet the data analysis requirements, mainly including data conversion and aggregation, and eliminating noise, anomalies, redundancy and other information in the data.

[0072] As an implementation of the present invention, the method for performing device-aware traffic data analysis in step 3 is:

[0073] By formula Get the line stability coefficient S of the kth sampling frequency k ;

[0074] Where N is the total number of sampling frequencies under the preset sampling duration; Y 总 is the total number of lines under all sampling frequencies; Q Yk is the line load information at the kth sampling frequency; F Yk Y is the line usage frequency at the kth sampling frequency; k is the number of lines at the kth sampling frequency;

[0075] The line stability factor S k and the preset line stability coefficient threshold [S 1 , S 2 ] for comparison:

[0076] If S k<S 1 , then the network line performance index is judged to be too small and the sampling strategy is adjusted;

[0077] If S k ∈[S 1 , S 2 ], then the network line performance indicators are judged to be normal;

[0078] If S k >S 2 , it is judged that the network line performance index is too large and a line safety warning is issued.

[0079] Through the above technical solution, in order to ensure that the device perception traffic data after sampling in the preset initial interval is analyzed and whether the target device network line performance index meets the security requirements, the specific analysis method is: through the formula Get the line stability coefficient S of the kth sampling frequency k ; According to the line stability coefficient S k Determine whether the relationship between the data flow within the sampling interval and the load of each line meets the standard requirements of power operation; by detecting the line load information Q carried by the average line at the K sampling frequency Yk Can reflect the line stability coefficient S k size, ensuring accurate analysis and monitoring of relevant indicators of network line performance; by k and the preset line stability coefficient threshold [S 1 , S 2 ] to compare and analyze whether the network line performance indicators are normal, and determine if S k <S 1 , then the network line performance index is judged to be too small and the sampling strategy is adjusted; if S k ∈[S 1 , S 2 ], the network line performance index is judged to be normal; if S k >S 2 , it is judged that the network line performance index is too large and a line safety warning is issued.

[0080] It should be noted that the line usage frequency F at different sampling frequencies Yk The line load information quantity Q of multiple lines with the same sampling frequency is also different. Yk The size is determined by the total number of sampling frequencies; the number of lines Y at the kth sampling frequency k greater than 0; k greater than 0; Y 总 Greater than or equal to Y k ; Generally, the number of lines in actual monitoring is Y 总 and Yk Both are greater than 1.

[0081] As an implementation mode of the present invention, the power system operation parameter information:

[0082] By formula Calculate the power system operating parameters P s ;

[0083] Among them, G e Generator parameters, E x is the excitation system parameter, O v is the speed regulator parameter, L e is the load parameter; μ 1 , μ 2 , μ 3 , μ 4 is the preset weight coefficient, and μ 1 , μ 2 , μ 3 , μ 4 All greater than 0; f a 、f b 、f c 、f d According to different G e 、E x , O v , L e The parameter history ratio size is the preset function of the fitting setting; G e0 is the standard generator parameter, E x0 is the standard excitation system parameter, O v0 is the standard speed regulator parameter, L e0 is the standard load parameter; ΔL e It is the preset load parameter deviation value.

[0084] Through the above technical solution, in this embodiment, the power supply safety of the equipment is judged by analyzing and collecting the power system parameters of the line access preset points of the target equipment of the distribution communication network; in addition to excluding the direct monitoring of the equipment temperature by the sensor, it is also necessary to consider whether the power system parameters of the overall equipment meet the safety requirements, thereby reducing the hidden dangers of the hard equipment of the target equipment; the specific power system parameters P s The calculation method is Calculate the power system operating parameters P s Among them, by combining the four major parameters of the power system, the generator parameter G e , excitation system parameters E x , speed regulator parameter O v , load parameter L eIntegrated calculations are performed to obtain comprehensive indicators of power system parameters, and these four major parameters are obtained based on corresponding sensors.

[0085] It should be noted that the preset function f a 、f d 、f c 、f d To adjust the parameters in advance based on different parameter sizes, so as to ensure that the statistical results are more in line with the reasonable reference range of the power system; standard generator parameters G e0 , standard excitation system parameters E x0 , standard speed regulator parameters O v0 , standard load parameter L e0 is the standard data value obtained based on historical experience; the preset load parameter deviation value ΔL e It is based on real-time analysis of the training network and is preset in advance based on the historical database, which will not be elaborated here.

[0086] As an implementation mode of the present invention, the steps of analyzing the equipment operation status data in step 4 are as follows:

[0087] By formula Get the monitoring risk coefficient C of the initial interval sampling of the i-th preset point i ;

[0088] Among them, [t n , t m ] is the sampling time period, γ 1 , γ 2 is the preset weight coefficient, and γ 1 , γ 2 All greater than 0; P si is the operating parameter of the power system at the i-th preset point; T i (t) is the real-time temperature of the i-th preset point; P s0 is the standard power system operating parameter; ΔP s It is a preset power system operation parameter deviation value;

[0089] The risk factor C will be monitored i With the preset threshold (C A , C B ) for comparison:

[0090] If C i ≤C A , it is determined that there is no security risk in the power supply of the target device;

[0091] If C A <C i ≤C B, then it is determined that there is a security risk in the power supply of the target device, and the network access point of the target device terminal is replaced and adjusted;

[0092] If C i >C B , it is determined that there is a safety problem with the power supply of the target device and an equipment maintenance instruction is generated.

[0093] Through the above technical solution, the specific steps of analyzing the equipment operation status data in this embodiment are to analyze the sampling data related to the system operation risk of the interval of the preset point through the formula, and calculate the monitoring risk coefficient C i The analysis process includes analyzing the power system operating parameters of the preset points and the temperature conditions of the equipment during the corresponding operation process, and then counting all the data information in the corresponding sampling time period to determine the power supply security of the target equipment. The specific calculation formula is: In the sampling period [t n , t m ] the i-th preset point power system operating parameter P si The change value of To express, the real-time temperature change value of the i-th preset point is expressed by T i (t)dt is used to express the monitoring risk coefficient C i The size is consistent with the cumulative state of comprehensive interference factors in equipment operation.

[0094] By monitoring the risk factor C i With the preset threshold (C A , C B ) to compare, and then determine whether there are safety hazards in the power supply range of the target device, analyze and determine the size. i ≤C A , then it is determined that there is no safety risk in the power supply of the target device; if C A <C i ≤C B , then it is determined that there is a safety risk in the power supply of the target device, and the network access point of the target device terminal is replaced and adjusted; if C i >C B , it is determined that there is a safety problem with the power supply of the target device and an equipment maintenance instruction is generated.

[0095] It should be noted that γ 1 , γ 2 is a preset weight coefficient obtained based on historical data experience; standard power system operating parameter P s0 is the standard data value obtained based on historical experience; the preset power system operation parameter deviation value ΔP sIn order to obtain real-time analysis based on the training network and also preset in advance the historical database of the power operation department, it will not be elaborated here.

[0096] As an embodiment of the present invention, it also includes:

[0097] Step 6: Perform data intrusion analysis on the access node traffic data at the preset points.

[0098] Through the above technical solution, because the intelligent perception data of the distribution communication network needs to be monitored during the actual analysis process, the information security risk in the data exchange port process between the distribution communication network and the public network is reduced, the impact of data intrusion on the information security of the distribution communication network is reduced, and the security management requirements of the distribution communication network information are improved. This embodiment sets up data intrusion analysis on the flow data of the access node at the preset point to ensure real-time monitoring of data intrusion and reduce the harm caused by data flow signal interference to the line operation.

[0099] As an implementation mode of the present invention, the method of performing data intrusion analysis in step 6 is:

[0100] Obtain the flow data of the monitoring points and filter the characteristic flow of the flow data based on the wavelet decomposition extraction method;

[0101] The correlation between the actual operating characteristic flow and the initial interval characteristic flow is calculated. When the correlation is lower than the preset threshold, it is determined that data intrusion exists.

[0102] Through the above technical solution, the data intrusion analysis process in this embodiment is: first, the flow data of the monitoring point is obtained, and then the characteristic flow of the flow data is screened based on the wavelet decomposition extraction method; finally, by calculating the correlation between the actual operating characteristic flow and the initial interval characteristic flow, when the correlation is lower than the preset threshold, it is judged that data intrusion exists.

[0103] The judgment criteria are: judge;

[0104] Among them, I A ,I a are the characteristic traffic sets extracted according to the trusted time period and real-time monitoring respectively; I A ∩I a is the intersection of the two sets. When no data intrusion occurs, the characteristic traffic obtained by real-time monitoring should be highly consistent with the trusted period; N H () is a function that takes the number of elements in a set, then Used to indicate the correlation between real-time monitoring and characteristic traffic during a trusted period; θ int is a given threshold. When the correlation is lower than the given threshold, it is considered that there is a data intrusion problem.

[0105] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A method for monitoring data based on intelligent perception of power distribution communication network, characterized in that: Applied to a target device of a power distribution communication network, the method comprises: Step 1: Real-time collection of monitoring data of each line access preset point acquired by the target device of the power distribution communication network for preprocessing, and storing the preprocessed monitoring data in the cloud server; The monitoring data includes: equipment operation status data and equipment perception flow data; Step 2: Perform initial interval sampling of the device-sensed traffic data in the cloud server according to the sampling strategy; Step 3: Obtain the device-perceived traffic data after the initial interval sampling for analysis, and determine whether the target device network line performance indicators meet the security requirements based on the analysis results: If it is judged that the safety requirements are not met, the sampling strategy is adjusted and the process returns to step 2; If it meets the safety requirements, proceed to step 4; Step 4: Analyze the device operation status data according to the judgment result of step 3, and adjust the target device terminal network access point; The method for analyzing device-aware traffic data in step 3 is as follows: By formula Get the line stability coefficient S of the kth sampling frequency k ; Where N is the total number of sampling frequencies under the preset sampling duration; Y 总 is the total number of lines under all sampling frequencies; Q Yk is the line load information at the kth sampling frequency; F Yk Y is the line usage frequency at the kth sampling frequency; k is the number of lines at the kth sampling frequency; The line stability factor S k Compare with the preset line stability coefficient threshold [S1, S2]: If S k <S1, the network line performance index is judged to be too small and the sampling strategy is adjusted; If S k ∈[S1, S2], then the network line performance index is judged to be normal; If S k >S2, the network line performance index is judged to be too large, and a line safety warning is issued; Among them, power system operation parameter information: By formula Calculate the power system operating parameters P s ; Among them, G e Generator parameters, E x is the excitation system parameter, O v is the speed regulator parameter, L e is the load parameter; μ1, μ2, μ3, μ4 are the preset weight coefficients, and μ1, μ2, μ3, μ4 are all greater than 0; f a 、f b 、f c 、f d According to different G e 、E x , O v , L e The parameter history ratio size is the preset function of the fitting setting; G e0 is the standard generator parameter, E x0 is the standard excitation system parameter, O v0 is the standard speed regulator parameter, L e0 is the standard load parameter; ΔL e is the preset load parameter deviation value; The steps for analyzing the equipment operation status data in step 4 are as follows: By formula Get the monitoring risk coefficient C of the initial interval sampling of the i-th preset point i ; Among them, [t n , t m ] is the sampling time period, γ1 and γ2 are the preset weight coefficients, and both γ1 and γ2 are greater than 0; P si is the operating parameter of the power system at the i-th preset point; T i (t) is the real-time temperature of the i-th preset point; P s0 is the standard power system operating parameter; ΔP s It is a preset power system operation parameter deviation value; The monitoring risk factor C i With the preset threshold (C A , C B ) for comparison: If C i ≤C A , it is determined that there is no security risk in the power supply of the target device; If C A <C i ≤C B , then it is determined that there is a security risk in the power supply of the target device, and the network access point of the target device terminal is replaced and adjusted; If C i >C B , it is determined that there is a safety problem with the power supply of the target device and an equipment maintenance instruction is generated.

2. The method for monitoring data based on intelligent perception of power distribution communication network according to claim 1 is characterized in that: The sampling strategy includes: Acquire communication traffic data parameter information of preset points according to preset sampling duration and sampling frequency; The communication traffic data parameter information includes: the number of lines Y, the line usage frequency F Y , Line load information volume Q Y ; The preset sampling duration and sampling frequency are adjusted according to the analysis results of step three.

3. The method for monitoring data based on intelligent perception of power distribution communication network according to claim 1 is characterized in that: The method of preprocessing the monitoring data in step 1 is: S1. Real-time collection of location information, temperature and power system operation parameter information of the preset line access points of the target equipment of the distribution communication network; S2, real-time collection of data packet flow information and access port information obtained by the target device terminal of the power distribution communication network; S3: Perform data conversion and aggregation on the information collected in steps S1 and S2, and eliminate noise, abnormalities, and redundant information in the data.

4. The method for monitoring data based on intelligent perception of power distribution communication network according to claim 1 is characterized in that: Also includes: Step 6: Perform data intrusion analysis on the access node traffic data at the preset points.

5. The method for monitoring data based on intelligent perception of power distribution communication network according to claim 4 is characterized in that: The method of performing data intrusion analysis in step 6 is: Obtain the flow data of the monitoring points and filter the characteristic flow of the flow data based on the wavelet decomposition extraction method; The correlation between the actual operating characteristic flow and the initial interval characteristic flow is calculated. When the correlation is lower than the preset threshold, it is determined that data intrusion exists.

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