Monitoring management method and system based on distribution automation

Abstract

The invention discloses a monitoring management method and system based on power distribution automation, and relates to the technical field of monitoring analysis, and the method comprises the steps: laying a monitoring device at each key node of a power distribution network, and collecting basic data and video data corresponding to each key node of the power distribution network through the monitoring devices; analyzing the basic data and the video data corresponding to each key node of the power distribution network, further determining a comprehensive evaluation coefficient corresponding to each key node, and managing each key node of the power distribution network based on the comprehensive evaluation coefficient corresponding to each key node; and estimating a change coefficient of each key node in the power distribution network in a future period of time, and performing adjustment based on the change coefficient. The method has the effect of improving the power distribution network management efficiency.

Description

Technical Field

[0001] The present application relates to the field of monitoring and analysis technology, and in particular to a monitoring and management method and system based on distribution automation. Background Art

[0002] The distribution network is one of the three major systems of the power system: power generation, transmission and distribution (sometimes also called power supply and power consumption). Power companies sell products through the distribution network - providing electricity to the majority of power users. Distribution automation refers to the use of modern electronic technology, communication technology, computer and network technology combined with power equipment to organically integrate the monitoring, protection, control, metering and work management of the distribution network under normal and accident conditions with the power supply department, improve the quality of power supply, establish a closer and more responsible relationship with users, meet the diversity of user requirements at a reasonable price, and strive to achieve the best power supply economy and more effective enterprise management. Distribution automation is a large, complex, and highly comprehensive systematic project that includes all functional data flows and controls related to the distribution system in power companies.

[0003] In the related technology, traditional power distribution management systems usually rely on manual monitoring and regular maintenance. This approach is not only inefficient, but also easily leads to untimely fault detection, thereby reducing the efficiency of power distribution network management, and there is room for improvement. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present application provides a monitoring and management method and system based on distribution automation.

[0005] In a first aspect, the present application provides a monitoring and management method based on distribution automation, comprising the following steps:

[0006] Deploy monitoring devices at key nodes of the power distribution network, and collect basic data and video data corresponding to key nodes of the power distribution network through the monitoring devices;

[0007] Analyze the basic data and video data corresponding to each key node of the distribution network, and then confirm the comprehensive evaluation coefficient corresponding to each key node, and manage each key node of the distribution network based on the comprehensive evaluation coefficient corresponding to each key node;

[0008] Estimate the coefficient of change of each key node in the distribution network in the future period of time, and make adjustments based on the coefficient of change.

[0009] Preferably, the basic data corresponding to each key node of the power distribution network includes operation data and maintenance data, the operation data includes voltage fluctuation data, harmonic distortion data, load fluctuation data and frequency deviation data, and the maintenance data includes fault type, fault frequency and average maintenance time.

[0010] Preferably, the basic data and video data corresponding to each key node of the power distribution network are analyzed, specifically including:

[0011] Acquire the operation data corresponding to each key node of the power distribution network within a preset time period, and extract the voltage fluctuation data, harmonic distortion data, load fluctuation data and frequency deviation data corresponding to each key node from the operation data;

[0012] By formula The operation evaluation coefficient αi corresponding to each key node of the distribution network is confirmed, where i represents the number corresponding to each key node of the distribution network, i=1,2,3......j, Vi, Xsi, Fbi, Pci represent the voltage fluctuation rate, harmonic distortion rate, load fluctuation rate, and frequency deviation corresponding to the i-th key node respectively, Vi′, Xsi′, Fbi′, Pci′ represent the reference voltage fluctuation rate, reference harmonic distortion rate, reference load fluctuation rate, and reference frequency deviation corresponding to the i-th key node respectively, ΔVi, ΔXsi, ΔFbi, ΔPci represent the allowable voltage fluctuation rate deviation value, allowable harmonic distortion rate deviation value, allowable load fluctuation rate deviation value, and allowable frequency deviation value corresponding to the i-th key node respectively, a1, a2, a3, and a4 represent weight coefficients, and e is a natural constant.

[0013] Preferably, the basic data and video data corresponding to each key node of the power distribution network are analyzed, and specifically also include:

[0014] Construct maintenance assessment models corresponding to key nodes in the distribution network:

[0015] βi=Typei*Gpi*Ti

[0016] The maintenance evaluation model corresponding to each key node of the distribution network is obtained by fitting historical data, where i represents the number corresponding to each key node of the distribution network, i=1,2,3...j, Typei, Gpi, Ti represent the fault type coefficient, fault frequency coefficient, and average maintenance time corresponding to the i-th key node respectively;

[0017] Confirm the maintenance data corresponding to each key node of the distribution network, and extract the fault type, fault frequency and average maintenance time corresponding to each key node from the maintenance data, and output the maintenance data corresponding to each key node to the maintenance evaluation model corresponding to each key node of the distribution network, and then confirm the maintenance evaluation coefficient βi corresponding to each key node of the distribution network.

[0018] Preferably, the basic data and video data corresponding to each key node of the power distribution network are analyzed, and specifically also include:

[0019] Acquire video data corresponding to each key node of the power distribution network, and identify the damaged position corresponding to each key node and the damaged area and damaged depth corresponding to each damaged position based on the video data;

[0020] By formula The damage assessment coefficient γi corresponding to each key node in the distribution network is identified, where i represents the number corresponding to each key node in the distribution network, i=1,2,3...j, n represents the number corresponding to each damage position, n=1,2,3...m, Sin and Hin represent the damage area and damage depth corresponding to the nth damage position in the ith key node, respectively, Si′n and Hi′n represent the reference damage area and reference damage depth corresponding to the nth damage position in the ith key node, respectively, ΔSin and ΔHin represent the allowable damage area difference and allowable damage depth difference corresponding to the nth damage position in the ith key node, respectively, and c1 and c2 represent weight coefficients.

[0021] Preferably, the comprehensive evaluation coefficient corresponding to each key node is identified, and each key node of the power distribution network is managed based on the comprehensive evaluation coefficient corresponding to each key node, specifically including:

[0022] Substitute the operation assessment coefficient αi corresponding to each key node of the distribution network, the maintenance assessment coefficient βi corresponding to each key node of the distribution network, and the damage assessment coefficient γi corresponding to each key node of the distribution network into the formula Wi=αi*ω1+βi*ω2+γi*ω3, and then confirm the comprehensive assessment coefficient Wi corresponding to each key node of the distribution network, where ω1, ω2, and ω3 represent the weight coefficients corresponding to the operation assessment coefficient, the maintenance assessment coefficient, and the damage assessment coefficient, respectively;

[0023] Compare the comprehensive evaluation coefficient Wi corresponding to each key node of the distribution network with the preset comprehensive evaluation threshold interval [W′, W″], where W′ represents the preset first comprehensive evaluation threshold and W″ represents the preset second comprehensive evaluation threshold;

[0024] If there is a comprehensive evaluation coefficient Wi < W' corresponding to a key node, there is no need to manage the key node;

[0025] If there is a key node whose corresponding comprehensive evaluation coefficient Wi is between [W′, W″], the key node needs to be managed according to the first preset management method;

[0026] If there is a comprehensive evaluation coefficient Wi>W″ corresponding to a key node, the key node needs to be managed according to the second preset management method.

[0027] Preferably, estimating the variation coefficient of each key node in the power distribution network in a future period of time and making adjustments based on the variation coefficient specifically includes:

[0028] Select a time window, identify key nodes whose comprehensive evaluation coefficients are between [W′, W″], collect the comprehensive evaluation coefficients corresponding to the key nodes in real time to form a time series, and represent the comprehensive evaluation coefficients corresponding to the key nodes by function W(t) according to the time series;

[0029] By formula Confirm the variation coefficient b corresponding to the comprehensive evaluation coefficient of the key node, where [t, t′] represents the selected time window, and W″ represents the preset second comprehensive evaluation threshold;

[0030] Compare the change coefficient b corresponding to the comprehensive evaluation coefficient of the key node with the preset change threshold b′;

[0031] If the variation coefficient b corresponding to the comprehensive evaluation coefficient of the key node is greater than b′, it is determined that the probability of failure of the key node in the future will increase, and an early warning signal is output.

[0032] In a second aspect, the present application provides a monitoring and management system based on distribution automation, comprising:

[0033] The data acquisition module is used to deploy monitoring devices at key nodes of the power distribution network and collect basic data and video data corresponding to key nodes of the power distribution network through the monitoring devices;

[0034] An analysis and processing module is used to analyze the basic data and video data corresponding to each key node of the power distribution network, thereby confirming the comprehensive evaluation coefficient corresponding to each key node, and managing each key node of the power distribution network based on the comprehensive evaluation coefficient corresponding to each key node;

[0035] The estimation module is used to estimate the risk coefficient of each key node in the distribution network in the future and make adjustments based on the risk coefficient.

[0036] In a third aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute any one of the above-mentioned monitoring and management methods based on distribution automation.

[0037] In summary, the present application includes at least one of the following beneficial technical effects:

[0038] 1. The present invention provides a monitoring and management method based on distribution automation, which collects and analyzes basic data and video data corresponding to each key node of the distribution network, thereby confirming the comprehensive evaluation coefficient corresponding to each key node of the distribution network, and managing each key node of the distribution network based on the comprehensive evaluation coefficient corresponding to each key node, thereby effectively reducing the occurrence of a situation in which the management efficiency of the distribution network is reduced due to untimely fault detection;

[0039] 2. By confirming the variation coefficient corresponding to the comprehensive evaluation coefficient of the key node, and comparing the variation coefficient corresponding to the comprehensive evaluation coefficient of the key node with the preset variation threshold, and confirming the probability of failure of the key node in the future based on the comparison result, and then effectively and timely early warning and prevention, it effectively reduces the occurrence of distribution network management efficiency reduced due to untimely fault detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] 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.

[0041] Figure 1 It is a flow chart of the method for monitoring and management based on distribution automation in an embodiment of the present application.

[0042] Figure 2 It is a system diagram of monitoring and management based on distribution automation according to an embodiment of the present application. DETAILED DESCRIPTION

[0043] The following is combined with Figure 1-2 This application is described in further detail.

[0044] Example 1

[0045] The embodiment of the present application discloses a monitoring and management method based on distribution automation.

[0046] Reference Figure 1 , a monitoring and management method based on distribution automation, comprising the following steps:

[0047] S1. Deploy monitoring devices at key nodes of the power distribution network, and collect basic data and video data corresponding to key nodes of the power distribution network through the monitoring devices;

[0048] S2. Analyze the basic data and video data corresponding to each key node of the distribution network, and then confirm the comprehensive evaluation coefficient corresponding to each key node, and manage each key node of the distribution network based on the comprehensive evaluation coefficient corresponding to each key node;

[0049] S3. Estimate the risk factor of each key node in the distribution network in the future period of time, and make adjustments based on the risk factor.

[0050] Specifically, in the embodiments of the present application, the key nodes of the power distribution network include but are not limited to substations, distributed power access points, load centers, key switchgear and protection devices.

[0051] Furthermore, the basic data corresponding to each key node of the distribution network includes operation data and maintenance data. The operation data includes voltage fluctuation data, harmonic distortion data, load fluctuation data and frequency deviation data. The maintenance data includes fault type, fault frequency and maintenance time.

[0052] It should be noted that the basic data and video data corresponding to each key node of the distribution network are analyzed, including:

[0053] Within a preset time period, basic data corresponding to each key node of the power distribution network is obtained, and voltage fluctuation data, harmonic distortion data, load fluctuation data and frequency deviation data corresponding to each key node are extracted from the basic data;

[0054] By formula The operation evaluation coefficient αi corresponding to each key node of the distribution network is confirmed, where i represents the number corresponding to each key node of the distribution network, i=1,2,3......j, Vi, Xsi, Fbi, Pci represent the voltage fluctuation rate, harmonic distortion rate, load fluctuation rate, and frequency deviation corresponding to the i-th key node respectively, Vi′, Xsi′, Fbi′, Pci′ represent the reference voltage fluctuation rate, reference harmonic distortion rate, reference load fluctuation rate, and reference frequency deviation corresponding to the i-th key node respectively, ΔVi, ΔXsi, ΔFbi, ΔPci represent the allowable voltage fluctuation rate deviation value, allowable harmonic distortion rate deviation value, allowable load fluctuation rate deviation value, and allowable frequency deviation value corresponding to the i-th key node respectively, a1, a2, a3, and a4 represent weight coefficients, and e is a natural constant.

[0055] Specifically, within a preset time period, the voltage fluctuation data corresponding to each key node can be monitored by a voltage detection device to form a voltage change curve, which is then compared and calculated with a preset standard voltage change curve;

[0056] The harmonic distortion rate corresponding to each key node can be calculated by measuring the current value of each harmonic through a harmonic analyzer;

[0057] The load fluctuation rate corresponding to each key node can be recorded by the load monitoring device to record the change of load power, and then construct the load curve, and analyze the maximum value, minimum value and fluctuation range of the load curve to confirm the load fluctuation rate corresponding to each key node;

[0058] The frequency deviation corresponding to each key node can be monitored in real time by a frequency meter to calculate the frequency deviation corresponding to each key node.

[0059] Specifically, by using the voltage fluctuation data, harmonic distortion data, load fluctuation data and frequency deviation data corresponding to each key node of the distribution network, the operation evaluation coefficient of each key node of the distribution network can be determined, which can provide a quantitative indicator for the distribution network and can fully reflect the operation status and performance of the node. By analyzing data such as voltage fluctuation, harmonic distortion, load fluctuation and frequency deviation, the operation evaluation coefficient can help identify potential risks and abnormal situations, thereby not only improving the accuracy of detection, but also optimizing the allocation of maintenance resources, and further supporting decision makers to make more accurate planning and adjustments to ensure the stability and reliability of the distribution network. In addition, by continuously monitoring the above data, dynamic management of the distribution network operation can be achieved, timely response to changes, and improved power quality and power supply efficiency.

[0060] It should be noted that the analysis of basic data and video data corresponding to each key node of the distribution network also includes:

[0061] Construct maintenance assessment models corresponding to key nodes in the distribution network:

[0062] βi=Typei*Gpi*Ti

[0063] The maintenance evaluation model corresponding to each key node of the distribution network is obtained by fitting historical data, where i represents the number corresponding to each key node of the distribution network, i=1,2,3...j, Typei, Gpi, Ti represent the fault type coefficient, fault frequency coefficient, and average maintenance time corresponding to the i-th key node respectively;

[0064] Confirm the maintenance data corresponding to each key node of the distribution network, and extract the fault type, fault frequency and average maintenance time corresponding to each key node from the maintenance data, and output the maintenance data corresponding to each key node to the maintenance evaluation model corresponding to each key node of the distribution network, and then confirm the maintenance evaluation coefficient βi corresponding to each key node of the distribution network.

[0065] Specifically, the fault type coefficient corresponding to the key node can be obtained from the fault type, that is, the more serious the fault type, the larger the fault type coefficient; the fault frequency coefficient can be obtained from the fault frequency, that is, the higher the fault frequency, the larger the fault frequency coefficient.

[0066] Specifically, the maintenance assessment coefficients of key nodes in the distribution network are determined. By analyzing the fault type, fault frequency, and maintenance duration, a systematic method is provided to identify and prioritize high-risk nodes to ensure efficient allocation of resources. By quantifying the above data, potential faults can be predicted and prevented more accurately, and unexpected power outages can be reduced. Secondly, the above assessment helps optimize maintenance plans, reduce long-term operating costs, extend equipment life, and improve the reliability of the overall distribution network. In addition, the maintenance assessment coefficient can also provide decision-making support for management, help formulate more targeted strategies, improve service quality, and effectively improve the stability and efficiency of the distribution network.

[0067] It should be noted that the analysis of basic data and video data corresponding to each key node of the distribution network also includes:

[0068] Acquire video data corresponding to each key node of the power distribution network, and identify the damaged position corresponding to each key node and the damaged area and damaged depth corresponding to each damaged position based on the video data;

[0069] By formula The damage assessment coefficient γi corresponding to each key node in the distribution network is identified, where i represents the number corresponding to each key node in the distribution network, i=1,2,3...j, n represents the number corresponding to each damage position, n=1,2,3...m, Sin and Hin represent the damage area and damage depth corresponding to the nth damage position in the ith key node, respectively, Si′n and Hi′n represent the reference damage area and reference damage depth corresponding to the nth damage position in the ith key node, respectively, ΔSin and ΔHin represent the allowable damage area difference and allowable damage depth difference corresponding to the nth damage position in the ith key node, respectively, and c1 and c2 represent weight coefficients.

[0070] Specifically, by analyzing the damage location, damage area, and damage depth of each key node to determine the damage assessment coefficient, the maintenance and management efficiency of the distribution network can be significantly improved. The above assessment method provides accurate quantification of the degree of damage, helping engineers quickly identify and prioritize severely damaged areas. Secondly, it helps to optimize the allocation of maintenance resources, ensure timely repairs where they are most needed, reduce maintenance time and maintenance costs. In addition, the damage assessment coefficient can also provide data support for long-term planning, help predict equipment life and formulate preventive maintenance strategies, thereby improving the overall reliability and safety of the network.

[0071] Furthermore, the comprehensive evaluation coefficient corresponding to each key node is identified, and the key nodes of the distribution network are managed based on the comprehensive evaluation coefficient corresponding to each key node, specifically including:

[0072] Substitute the operation assessment coefficient αi corresponding to each key node of the distribution network, the maintenance assessment coefficient βi corresponding to each key node of the distribution network, and the damage assessment coefficient γi corresponding to each key node of the distribution network into the formula Wi=αi*ω1+βi*ω2+γi*ω3, and then confirm the comprehensive assessment coefficient Wi corresponding to each key node of the distribution network, where ω1, ω2, and ω3 represent the weight coefficients corresponding to the operation assessment coefficient, the maintenance assessment coefficient, and the damage assessment coefficient, respectively;

[0073] Compare the comprehensive evaluation coefficient Wi corresponding to each key node of the distribution network with the preset comprehensive evaluation threshold interval [W′, W″], where W′ represents the preset first comprehensive evaluation threshold and W″ represents the preset second comprehensive evaluation threshold;

[0074] If there is a comprehensive evaluation coefficient Wi < W' corresponding to a key node, there is no need to manage the key node;

[0075] If there is a key node whose corresponding comprehensive evaluation coefficient Wi is between [W′, W″], the key node needs to be managed according to the first preset management method;

[0076] If there is a comprehensive evaluation coefficient Wi>W″ corresponding to a key node, the key node needs to be managed according to the second preset management method.

[0077] Specifically, in an embodiment of the present application, the first preset management method is set to monitor and warn key nodes, and perform planned maintenance on the key nodes; the second preset management method is set to issue emergency alarms to key nodes, and perform troubleshooting and repair on the key nodes.

[0078] It should be noted that estimating the variation coefficient of each key node in the power distribution network in the future and making adjustments based on the variation coefficient specifically include:

[0079] Select a time window, identify key nodes whose comprehensive evaluation coefficients are between [W′, W″], collect the comprehensive evaluation coefficients corresponding to the key nodes in real time to form a time series, and represent the comprehensive evaluation coefficients corresponding to the key nodes by function W(t) according to the time series;

[0080] By formula Confirm the variation coefficient b corresponding to the comprehensive evaluation coefficient of the key node, where [t, t′] represents the selected time window, and W″ represents the preset second comprehensive evaluation threshold;

[0081] Compare the change coefficient b corresponding to the comprehensive evaluation coefficient of the key node with the preset change threshold b′;

[0082] If the variation coefficient b corresponding to the comprehensive evaluation coefficient of the key node is greater than b′, it is determined that the probability of failure of the key node in the future will increase, and an early warning signal is output.

[0083] Example 2

[0084] The embodiment of the present application also discloses a monitoring and management system based on distribution automation.

[0085] Reference Figure 2 , a monitoring and management system based on distribution automation, comprising:

[0086] The data acquisition module is used to deploy monitoring devices at key nodes of the power distribution network and collect basic data and video data corresponding to key nodes of the power distribution network through the monitoring devices;

[0087] An analysis and processing module is used to analyze the basic data and video data corresponding to each key node of the power distribution network, thereby confirming the comprehensive evaluation coefficient corresponding to each key node, and managing each key node of the power distribution network based on the comprehensive evaluation coefficient corresponding to each key node;

[0088] The estimation module is used to estimate the risk coefficient of each key node in the distribution network in the future and make adjustments based on the risk coefficient.

[0089] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art 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, they should all fall within the protection scope of the present invention.

[0090] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0091] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well.

Claims

1. A monitoring and management method based on distribution automation, characterized in that: The following steps are involved: Deploy monitoring devices at key nodes of the power distribution network, and collect basic data and video data corresponding to key nodes of the power distribution network through the monitoring devices; Analyze the basic data and video data corresponding to each key node of the distribution network, and then confirm the comprehensive evaluation coefficient corresponding to each key node, and manage each key node of the distribution network based on the comprehensive evaluation coefficient corresponding to each key node; Estimate the coefficient of change of each key node in the distribution network in the future period of time, and make adjustments based on the coefficient of change.

2. A monitoring and management method based on distribution automation according to claim 1, characterized in that: The basic data corresponding to each key node of the power distribution network includes operation data and maintenance data. The operation data includes voltage fluctuation data, harmonic distortion data, load fluctuation data and frequency deviation data. The maintenance data includes fault type, fault frequency and average maintenance time.

3. A monitoring and management method based on distribution automation according to claim 2, characterized in that: Analyze the basic data and video data corresponding to each key node of the distribution network, including: Acquire the operation data corresponding to each key node of the power distribution network within a preset time period, and extract the voltage fluctuation data, harmonic distortion data, load fluctuation data and frequency deviation data corresponding to each key node from the operation data; By formula The operation evaluation coefficient αi corresponding to each key node of the distribution network is confirmed, where i represents the number corresponding to each key node of the distribution network, i=1,2,3......j, Vi, Xsi, Fbi, Pci represent the voltage fluctuation rate, harmonic distortion rate, load fluctuation rate, and frequency deviation corresponding to the i-th key node respectively, Vi′, Xsi′, Fbi′, Pci′ represent the reference voltage fluctuation rate, reference harmonic distortion rate, reference load fluctuation rate, and reference frequency deviation corresponding to the i-th key node respectively, ΔVi, ΔXsi, ΔFbi, ΔPci represent the allowable voltage fluctuation rate deviation value, allowable harmonic distortion rate deviation value, allowable load fluctuation rate deviation value, and allowable frequency deviation value corresponding to the i-th key node respectively, a1, a2, a3, and a4 represent weight coefficients, and e is a natural constant.

4. A monitoring and management method based on distribution automation according to claim 2, characterized in that: Analyze the basic data and video data corresponding to each key node of the distribution network, including: Construct maintenance assessment models corresponding to key nodes in the distribution network: βi=Typei*Gpi*Ti The maintenance evaluation model corresponding to each key node of the distribution network is obtained by fitting historical data, where i represents the number corresponding to each key node of the distribution network, i=1,2,3...j, Typei, Gpi, Ti represent the fault type coefficient, fault frequency coefficient, and average maintenance time corresponding to the i-th key node respectively; Confirm the maintenance data corresponding to each key node of the distribution network, and extract the fault type, fault frequency and average maintenance time corresponding to each key node from the maintenance data, and output the maintenance data corresponding to each key node to the maintenance evaluation model corresponding to each key node of the distribution network, and then confirm the maintenance evaluation coefficient βi corresponding to each key node of the distribution network.

5. A monitoring and management method based on distribution automation according to claim 2, characterized in that: Analyze the basic data and video data corresponding to each key node of the distribution network, including: Acquire video data corresponding to each key node of the power distribution network, and identify the damaged position corresponding to each key node and the damaged area and damaged depth corresponding to each damaged position based on the video data; By formula The damage assessment coefficient γi corresponding to each key node in the distribution network is identified, where i represents the number corresponding to each key node in the distribution network, i=1,2,3...j, n represents the number corresponding to each damage position, n=1,2,3...m, Sin and Hin represent the damage area and damage depth corresponding to the nth damage position in the ith key node, respectively, Si′n and Hi′n represent the reference damage area and reference damage depth corresponding to the nth damage position in the ith key node, respectively, ΔSin and ΔHin represent the allowable damage area difference and allowable damage depth difference corresponding to the nth damage position in the ith key node, respectively, and c1 and c2 represent weight coefficients.

6. A monitoring and management method based on distribution automation according to claim 5, characterized in that: Identify the comprehensive evaluation coefficients corresponding to each key node, and manage the key nodes of the distribution network based on the comprehensive evaluation coefficients corresponding to each key node, including: Substitute the operation assessment coefficient αi corresponding to each key node of the distribution network, the maintenance assessment coefficient βi corresponding to each key node of the distribution network, and the damage assessment coefficient γi corresponding to each key node of the distribution network into the formula Wi=αi*ω1+βi*ω2+γi*ω3, and then confirm the comprehensive assessment coefficient Wi corresponding to each key node of the distribution network, where ω1, ω2, and ω3 represent the weight coefficients corresponding to the operation assessment coefficient, the maintenance assessment coefficient, and the damage assessment coefficient, respectively; Compare the comprehensive evaluation coefficient Wi corresponding to each key node of the distribution network with the preset comprehensive evaluation threshold interval [W′, W″], where W′ represents the preset first comprehensive evaluation threshold and W″ represents the preset second comprehensive evaluation threshold; If there is a comprehensive evaluation coefficient Wi < W' corresponding to a key node, there is no need to manage the key node; If there is a key node whose corresponding comprehensive evaluation coefficient Wi is between [W′, W″], the key node needs to be managed according to the first preset management method; If there is a comprehensive evaluation coefficient Wi>W″ corresponding to a key node, the key node needs to be managed according to the second preset management method.

7. A monitoring and management method based on distribution automation according to claim 6, characterized in that: Estimate the change coefficient of each key node in the distribution network in the future period of time, and make adjustments based on the change coefficient, specifically including: Select a time window, identify key nodes whose comprehensive evaluation coefficients are between [W′, W″], collect the comprehensive evaluation coefficients corresponding to the key nodes in real time to form a time series, and represent the comprehensive evaluation coefficients corresponding to the key nodes by function W(t) according to the time series; By formula Confirm the variation coefficient b corresponding to the comprehensive evaluation coefficient of the key node, where [t, t′] represents the selected time window and W″ represents the preset second comprehensive evaluation threshold; Compare the change coefficient b corresponding to the comprehensive evaluation coefficient of the key node with the preset change threshold b′; If the variation coefficient b corresponding to the comprehensive evaluation coefficient of the key node is greater than b′, it is determined that the probability of failure of the key node in the future will increase, and an early warning signal is output.

8. A monitoring and management system based on distribution automation, applied to a monitoring and management method based on distribution automation as described in any one of claims 1 to 7, characterized in that: include: The data acquisition module is used to deploy monitoring devices at key nodes of the power distribution network and collect basic data and video data corresponding to key nodes of the power distribution network through the monitoring devices; An analysis and processing module is used to analyze the basic data and video data corresponding to each key node of the power distribution network, thereby confirming the comprehensive evaluation coefficient corresponding to each key node, and managing each key node of the power distribution network based on the comprehensive evaluation coefficient corresponding to each key node; The estimation module is used to estimate the risk coefficient of each key node in the distribution network in the future and make adjustments based on the risk coefficient.

9. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute a monitoring and management method based on distribution automation as described in any one of claims 1 to 7.

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