Video intelligent monitoring system for power distribution station

Abstract

The application relates to the technical field of intelligent monitoring, and discloses a power distribution station house video intelligent monitoring system, which is based on power distribution station house video screening target equipment pictures, analyzes equipment initial operation abnormities in combination with target equipment operation conditions, obtains future electrical load data and future environment data, carries out damage evolution of a heat damage cycle, a mechanical damage cycle and a chemical damage cycle based on the equipment initial operation abnormities, analyzes final operation abnormities of the target equipment when the target equipment is operated in the future and a time length required for reaching a critical operation abnormity value based on damage evolution results, judges whether the target equipment needs to be warned based on the final operation abnormities and the time length required for reaching the critical operation abnormity value, simulates a damage evolution process based on initial damage conditions, future electrical load and environment data, constructs a comprehensive risk index, and identifies a risk trend of the equipment when the equipment is operated in the future in advance, so that the monitoring efficiency and the accuracy of fault warning are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology, and in particular to a video intelligent monitoring system for power distribution substations. Background Technology

[0002] Condition monitoring and early warning of equipment in power distribution stations are crucial for ensuring the safe and stable operation of power systems. Existing condition monitoring technologies use various sensors or inspection images to collect and visualize key equipment parameters in real time or periodically. Threshold alarm technologies, on the other hand, set fixed safety thresholds to provide immediate alarms for exceeding limits. However, most existing technologies monitor single damage modes, while actual equipment degradation is often the result of the coupling of multiple factors such as heat, electricity, mechanics, and chemistry. They lack comprehensive damage evolution prediction capabilities and cannot reveal the dynamic evolution path of defects under future changing operating conditions, thus failing to identify potential risks in advance.

[0003] To address the aforementioned problems, this invention provides a video intelligent monitoring system for power distribution substations. Summary of the Invention

[0004] In view of this, the present invention provides a video intelligent monitoring system for power distribution rooms. Based on the initial damage situation, future electrical load and environmental data, the present invention simulates the damage evolution process, constructs a comprehensive risk index, identifies the risk trend of equipment in future operation in advance, and improves monitoring efficiency and fault early warning accuracy.

[0005] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for intelligent video monitoring of power distribution substations, comprising the following specific steps: Step 1: Filter target equipment images based on substation video, and analyze initial operational anomalies of the equipment in conjunction with the target equipment's operating conditions; Step 2: Obtain future electrical load data and future environmental data, and perform damage evolution based on the initial abnormal operation of the equipment, including thermal damage cycle, mechanical damage cycle, and chemical damage cycle. Step 3: Based on the damage evolution results, analyze the target equipment's final operational anomaly and the time required to reach the critical operational anomaly value during future operation; Step 4: Determine whether to issue an early warning for the target device based on the final operational anomaly and the time required to reach the critical operational anomaly value.

[0006] Preferably, step one includes the following specific steps: Step 11: Filter target equipment images based on the video of the substation, and obtain appearance defect data based on the target equipment images. The appearance defect data includes the degree of bolt loosening and the proportion of rust area. Step 12: Obtain the bolt hazard value based on the ratio of bolt looseness to safety bolt looseness; obtain the corrosion hazard value based on the ratio of corrosion area ratio to safety corrosion area ratio; and obtain the appearance defect value by weighted summation of bolt hazard value and corrosion hazard value. Step 13: Obtain the operating condition data of the target equipment, including operating temperature and operating current; Step 14: Obtain the temperature hazard value based on the ratio of operating temperature to safe operating temperature, obtain the current hazard value based on the ratio of operating current to safe operating current, and obtain the operating condition hazard value by weighted summation of the temperature hazard value and the current hazard value. Step 15: Obtain the initial abnormal operating value of the equipment by weighted summation of appearance defect value and operating condition hazard value.

[0007] Preferably, step two includes the following specific steps: Step 21: Obtain future electrical load data and future environmental data based on the predicted duration. The future electrical load data is the predicted current. The predicted current is obtained by using a time series model to predict the future power demand curve based on historical load data, weather data, holiday information, etc., and converting the power into current using the following formula: ,in, For future power requirements, The rated voltage of the equipment. The power factor is the future environmental data, which includes ambient humidity and ambient relative temperature. Step 22: Calculate the cumulative thermal damage value of the target equipment based on the thermal damage evolution cycle formula, wherein the thermal damage evolution cycle formula is: ,in, This is the initial thermal damage value, which is also the temperature danger value. To predict the time step, For safe operating temperature, This refers to the service life of the equipment under safe operating temperatures. To predict the main body temperature of the equipment, the formula for predicting the main body temperature of the equipment is as follows: ,in, The ambient temperature is expressed in Kelvin (K). This is the temperature rise coefficient, with units of K / A². These are abnormal values ​​during the initial operation of the equipment. To predict the current, The activation energy is expressed in eV. is the Boltzmann constant, with a value of 8.617 × 10⁻⁶. −5 eV / K; Step 23: Calculate the cumulative mechanical damage value of the target equipment based on the mechanical damage evolution cycle formula, wherein the mechanical damage evolution cycle formula is: ,in, This is the initial mechanical damage value, which is also the current danger value. These are fatigue characteristic constants. For safe operating current, The fatigue index; Step 24: Calculate the cumulative chemical damage value of the target equipment based on the chemical damage evolution cycle formula, wherein the chemical damage evolution cycle formula is: ,in, This is the initial chemical damage value, which is also the corrosion risk value. For ambient relative humidity, The humidity impact index, The activation energy of the corrosion reaction is expressed in J / mol. This is the gas constant, with a value of 8.314 J / (mol·K). This is the standardized prediction time step.

[0008] Preferably, step three includes the following specific steps: Step 31: Obtain the damage evolution impact value of the target equipment by weighted summing of the cumulative damage values ​​of thermal damage, mechanical damage, and chemical damage. Update the abnormal operation value of the target equipment based on the equipment abnormal operation value update formula, which is: ,in, For time Changes in equipment operation abnormality update values, The impact value of damage evolution on the target equipment; Step 32: Extract the final operational anomaly value and the time required to reach the critical operational anomaly value based on the equipment operation anomaly value update formula. The formula for obtaining the final operational anomaly value is as follows: ,in, The formula for determining the time required to reach the critical operational anomaly value is as follows: ,in, The time required to reach the critical operational anomaly value This is a critical operational anomaly value.

[0009] Preferably, step four includes the following specific steps: Step 41: Obtain the comprehensive operational risk value of the target equipment based on the operational risk calculation formula, wherein the operational risk calculation formula is: ,in, The comprehensive operational risk value of the target equipment. For risk sensitivity coefficient, The duration of standard risk occurrence; Step 42: Compare the comprehensive operational risk value of the target equipment with the preset comprehensive operational risk threshold. When the comprehensive operational risk value of the target equipment is greater than or equal to the preset comprehensive operational risk threshold, issue an early warning for the target equipment.

[0010] Secondly, the present invention provides a video intelligent monitoring system for power distribution substations, comprising: The data acquisition module is used to filter target equipment images based on the video of the substation to obtain future electrical load data and future environmental data; The initial operation anomaly analysis module is used to analyze initial operation anomalies of the equipment in conjunction with the target equipment's operating conditions; The damage cycle evolution module is used to perform damage evolution based on the initial operational anomalies of the equipment, including thermal damage cycle, mechanical damage cycle, and chemical damage cycle. The final operational anomaly analysis module is used to analyze the final operational anomalies of the target equipment in future operation and the time required to reach the critical operational anomaly value based on the damage evolution results; The equipment early warning module is used to determine whether to issue an early warning for the target equipment based on the final operational anomaly and the time required to reach the critical operational anomaly value.

[0011] Thirdly, the present invention provides a storage medium comprising stored instructions, wherein, when the instructions are executed, the device containing the storage medium is controlled to perform the intelligent video monitoring method for power distribution rooms as described above.

[0012] Fourthly, the present invention provides an electronic device, including a memory and one or more instructions, wherein one or more instructions are stored in the memory and configured to be executed by one or more processors as described above for intelligent video monitoring of power distribution rooms.

[0013] Compared with the prior art, the beneficial effects of the present invention are: This invention uses video screening of target equipment images from substations to analyze initial operational anomalies, acquires future electrical load and environmental data, and performs damage evolution analysis based on initial operational anomalies, including thermal, mechanical, and chemical damage cycles. Based on the damage evolution results, it analyzes the final operational anomaly of the target equipment in future operation and the time required to reach the critical operational anomaly value. Based on the final operational anomaly and the time required to reach the critical operational anomaly value, it determines whether to issue an early warning for the target equipment. This invention simulates the damage evolution process based on initial damage conditions, future electrical load, and environmental data, constructs a comprehensive risk index, and identifies risk trends in future equipment operation in advance, improving monitoring efficiency and the accuracy of fault early warning. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of the intelligent video monitoring method for power distribution rooms provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating step one of the intelligent video monitoring method for power distribution rooms provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the intelligent video monitoring system for power distribution rooms provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of an electronic device structure provided in an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0017] In this invention, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, or apparatus. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element.

[0018] Please see Figure 1 , Figure 1 This is a schematic diagram of the intelligent video monitoring method for power distribution rooms provided in an embodiment of the present invention; This invention provides a method for intelligent video monitoring of power distribution substations, comprising the following specific steps: Step 1: Filter target equipment images based on substation video, and analyze initial operational anomalies of the equipment in conjunction with the target equipment's operating conditions; Please see Figure 2 , Figure 2 This is a flowchart illustrating step one of the intelligent video monitoring method for power distribution rooms provided in an embodiment of the present invention; In this embodiment, step one includes the following specific steps: Step 11: Filter target equipment images based on the video of the substation, and obtain appearance defect data based on the target equipment images. The appearance defect data includes the degree of bolt loosening and the proportion of rust area. In this embodiment, the equipment area to be monitored is pre-defined in the video of the power distribution room. A target detection model is run within the equipment area. The target detection model identifies and locates specific equipment components, outputs their bounding boxes, and filters out unqualified images caused by focus blur or occlusion, outputting a set of images containing the target equipment. Image analysis of bolts and their connecting components is performed. When bolts are loose, visible gaps appear between the washer, nut, and base. The visible thread length of the bolt extending out of the nut is measured, and the ratio of the visible thread length of the bolt extending out of the nut to the total bolt length is calculated to obtain the degree of bolt loosening. Within the equipment component area, an image segmentation algorithm is used to identify the rusted parts, a binary mask of the rusted area is generated, the total number of pixels in the rusted mask and the total number of pixels on the surface of the equipment component are calculated, and the ratio of the total number of pixels in the rusted mask to the total number of pixels on the surface of the equipment component is calculated to obtain the rusted area percentage. Step 12: Obtain the bolt hazard value based on the ratio of bolt looseness to safety bolt looseness; obtain the corrosion hazard value based on the ratio of corrosion area percentage to safe corrosion area percentage; obtain the appearance defect value by weighted summation of bolt hazard value and corrosion hazard value; simulate different looseness levels on the experimental platform by controlling torque or gap width, measure their contact resistance temperature rise curve and vibration fatigue life; take the looseness degree corresponding to the temperature rise starting to exceed the standard or the vibration acceleration suddenly increasing as the safety bolt looseness degree; prepare standard samples, conduct wet heat cycling to obtain samples with different corrosion degrees; use image analysis method to quantify the corrosion area percentage of each sample; simultaneously test its electrical and mechanical properties; when the performance drops to the safety threshold specified by the standard, the corresponding percentage is the safe corrosion area percentage. Step 13: Obtain the operating condition data of the target equipment, including operating temperature and operating current; Step 14: Obtain the temperature hazard value based on the ratio of operating temperature to safe operating temperature, obtain the current hazard value based on the ratio of operating current to safe operating current, and obtain the operating condition hazard value by weighted summation of temperature hazard value and current hazard value. Safe operating temperature and safe operating current are obtained from the equipment technical specifications. Safe operating temperature is the maximum allowable long-term operating temperature or the allowable temperature rise limit of critical parts, and safe operating current is the rated current of the equipment. Step 15: Obtain the initial abnormal operating value of the equipment by weighted summation of appearance defect value and operating condition hazard value.

[0019] This embodiment uses intelligent video analysis to filter target device images and combines it with operational condition analysis to identify initial operational anomalies, avoiding misjudgments caused by a single data source and improving the accuracy of initial defect identification.

[0020] Step 2: Obtain future electrical load data and future environmental data, and perform damage evolution based on the initial abnormal operation of the equipment, including thermal damage cycle, mechanical damage cycle, and chemical damage cycle. In this embodiment, step two includes the following specific steps: Step 21: Obtain future electrical load data and future environmental data based on the forecast duration. The future electrical load data is the predicted current. The predicted current is obtained by using a time series model to predict the future power demand curve based on historical load data, weather data, holiday information, etc., and converting the power into current using the following formula: Of which, is for future power demand. The rated voltage of the equipment. The power factor can be taken as the historical average value within the station, reflecting the inductive characteristics of the load. Future environmental data includes ambient humidity and ambient relative temperature. Step 22: Calculate the cumulative thermal damage value of the target equipment based on the thermal damage evolution cycle formula. The thermal damage evolution cycle formula is as follows: ,in, This is the initial thermal damage value, which in this embodiment is the temperature hazard value. To predict the time step, For safe operating temperature, This refers to the service life of the equipment under safe operating temperatures. To predict the main body temperature of the equipment, the formula for predicting the main body temperature of the equipment is as follows: ,in, The ambient temperature is expressed in Kelvin (K). This is the temperature rise coefficient, measured in K / A², representing the temperature rise generated per square ampere of current. It is obtained through temperature rise testing under rated operating conditions. These are abnormal values ​​during the initial operation of the equipment. To predict the current, The activation energy, measured in eV, is obtained from thermal aging tests and reflects the energy required for the thermal degradation reaction of the insulating material. Here is the Boltzmann constant, with a value of 8.617 × 10⁻⁶. −5 eV / K; Step 23: Calculate the cumulative mechanical damage value of the target equipment based on the mechanical damage evolution cycle formula. The mechanical damage evolution cycle formula is as follows: ,in, This is the initial mechanical damage value, which in this embodiment is the current hazard value. As the fatigue characteristic constant, this embodiment measures the increase in mechanical damage per unit time under the rated current of the equipment through mechanical performance testing (bolt loosening detection or connecting piece fatigue test). For safe operating current, To determine the fatigue index, this embodiment fabricates a connection specimen using the same materials and processes as the target equipment, applies a designed preload, and applies an alternating force on a fatigue testing machine to simulate electrodynamic force. The number of cycles at which the specimen fails under different force amplitudes is obtained, and an SN curve is fitted to obtain the slope. Based on the square relationship between electrodynamic force and current, the fatigue index is twice the slope. Step 24: Calculate the cumulative chemical damage value of the target equipment based on the chemical damage evolution cycle formula. The chemical damage evolution cycle formula is as follows: ,in, The initial chemical damage value is the corrosion risk value in this embodiment. For ambient relative humidity, The humidity effect index reflects the nonlinear accelerating effect of humidity on the corrosion rate. In this embodiment, multiple groups of identical material samples were prepared and placed in environments with different constant relative humidities at a constant temperature. The corrosion rate of each group of samples was measured periodically, and the data points of corrosion rate versus relative humidity were plotted on a logarithmic coordinate system. A fitted straight line was then constructed, and the slope of this straight line is the humidity effect index. The activation energy for the corrosion reaction was obtained experimentally through fitting the Arrhenius equation, and the unit is J / mol. This is the gas constant, with a value of 8.314 J / (mol·K). The standardized prediction time step is the ratio of the prediction time step to the reference time step, which can be 1 second, 1 minute, 1 hour, etc.

[0021] This embodiment considers three damage cycles simultaneously: thermal, mechanical, and chemical, covering the main failure modes of substation equipment. It achieves dynamic and coupled simulation of the entire life cycle evolution trajectory of equipment damage under multi-factor time-varying environments, ensuring the completeness and accuracy of risk assessment.

[0022] Step 3: Based on the damage evolution results, analyze the target equipment's final operational anomaly and the time required to reach the critical operational anomaly value during future operation; In this embodiment, step three includes the following specific steps: Step 31: Obtain the damage evolution impact value of the target equipment by weighted summing of the cumulative damage values ​​of thermal damage, mechanical damage, and chemical damage. Update the abnormal operation value of the target equipment based on the equipment abnormal operation value update formula. The equipment abnormal operation value update formula is as follows: ,in, For time Changes in equipment operation abnormality update values, The impact value of damage evolution on the target equipment; Step 32: Extract the final operational anomaly value and the time required to reach the critical operational anomaly value based on the equipment operation anomaly value update formula. The formula for obtaining the final operational anomaly value is: ,in, The formula for determining the time required to reach the critical outlier is as follows: ,in, The time required to reach the critical operational anomaly value To determine the critical operational anomaly value, several faulty devices are collected, historical data of the faulty devices at the critical fault point are obtained, and historical operational anomaly values ​​are calculated. The average of the historical operational anomaly values ​​of several faulty devices is taken as the critical operational anomaly value.

[0023] Step 4: Determine whether to issue an early warning for the target device based on the final operational anomaly and the time required to reach the critical operational anomaly value.

[0024] In this embodiment, step four includes the following specific steps: Step 41: Obtain the comprehensive operational risk value of the target equipment based on the operational risk calculation formula. The operational risk calculation formula is as follows: ,in, The comprehensive operational risk value of the target equipment. For risk sensitivity coefficient, To determine the standard risk occurrence time, this embodiment obtains the average value by statistically analyzing the warning time of historical faults, i.e., the time from warning to fault occurrence. Step 42: Compare the comprehensive operational risk value of the target equipment with the preset comprehensive operational risk threshold. When the comprehensive operational risk value of the target equipment is greater than or equal to the preset comprehensive operational risk threshold, issue an early warning for the target equipment.

[0025] The steps for obtaining all weights, risk sensitivity coefficients, and preset comprehensive operational risk thresholds in this embodiment are as follows: acquiring images of several historical devices, historical electrical load data, historical environmental data, and historical records of whether faults occurred; calculating the historical final operational anomalies and the time required to reach the critical operational anomaly value of the historical devices; obtaining the judgment result of whether to issue an early warning; inputting the judgment result and historical records into a pre-trained fitting software to output the set of data with the highest judgment accuracy as the values ​​of weights, risk sensitivity coefficients, and preset comprehensive operational risk thresholds in this embodiment.

[0026] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of the intelligent video monitoring system for power distribution rooms provided in an embodiment of the present invention; This invention also provides a video intelligent monitoring system for power distribution substations, including: The data acquisition module is used to filter target equipment images based on the video of the substation to obtain future electrical load data and future environmental data; The initial operation anomaly analysis module is used to analyze initial operation anomalies of the equipment in conjunction with the target equipment's operating conditions; The damage cycle evolution module is used to perform damage evolution based on the initial operational anomalies of the equipment, including thermal damage cycle, mechanical damage cycle, and chemical damage cycle. The final operational anomaly analysis module is used to analyze the final operational anomalies of the target equipment in future operation and the time required to reach the critical operational anomaly value based on the damage evolution results; The equipment early warning module is used to determine whether to issue an early warning for the target equipment based on the final operational anomaly and the time required to reach the critical operational anomaly value.

[0027] This invention also provides a storage medium, which includes stored instructions, wherein when the instructions are executed, the device where the storage medium is located is controlled to perform the intelligent video monitoring method for power distribution rooms as described above.

[0028] Please see Figure 4 , Figure 4 A schematic diagram of an electronic device structure provided in an embodiment of the present invention; This invention also provides an electronic device, specifically including a memory and one or more instructions, wherein one or more instructions are stored in the memory and configured to be executed by one or more processors to perform the following operations: Based on the video of the substation, images of target equipment are selected, and the initial operational anomalies of the equipment are analyzed in conjunction with the operating conditions of the target equipment. Acquire future electrical load data and future environmental data, and perform damage evolution based on initial equipment operation anomalies, including thermal damage cycle, mechanical damage cycle, and chemical damage cycle. Based on the damage evolution results, analyze the target equipment's final operational anomaly and the time required to reach the critical operational anomaly value during future operation; Whether to issue an early warning for the target device is determined based on the final operational anomaly and the time required to reach the critical operational anomaly value.

[0029] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, they generate in whole or in part the flow or function according to the embodiments of the present invention. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more sets of available media. The available media can be a magnetic medium, an optical medium, or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0030] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple. For relevant parts, refer to the description of the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0031] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0032] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art. The general principles defined in this invention may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for intelligent video monitoring of power distribution substations, characterized in that, The specific steps include the following: Step 1: Filter target equipment images based on substation video, and analyze initial operational anomalies of the equipment in conjunction with the target equipment's operating conditions; Step 2: Obtain future electrical load data and future environmental data, and perform damage evolution based on the initial abnormal operation of the equipment, including thermal damage cycle, mechanical damage cycle, and chemical damage cycle. Step 3: Based on the damage evolution results, analyze the target equipment's final operational anomaly and the time required to reach the critical operational anomaly value during future operation; Step 4: Determine whether to issue an early warning for the target device based on the final operational anomaly and the time required to reach the critical operational anomaly value.

2. The intelligent video monitoring method for power distribution substations according to claim 1, characterized in that, Step one includes the following specific steps: Step 11: Filter target equipment images based on the video of the substation, and obtain appearance defect data based on the target equipment images. The appearance defect data includes the degree of bolt loosening and the proportion of rust area. Step 12: Obtain the bolt hazard value based on the ratio of bolt looseness to safety bolt looseness; obtain the corrosion hazard value based on the ratio of corrosion area ratio to safety corrosion area ratio; and obtain the appearance defect value by weighted summation of bolt hazard value and corrosion hazard value. Step 13: Obtain the operating condition data of the target equipment, including operating temperature and operating current; Step 14: Obtain the temperature hazard value based on the ratio of operating temperature to safe operating temperature, obtain the current hazard value based on the ratio of operating current to safe operating current, and obtain the operating condition hazard value by weighted summation of the temperature hazard value and the current hazard value. Step 15: Obtain the initial abnormal operating value of the equipment by weighted summation of appearance defect value and operating condition hazard value.

3. The intelligent video monitoring method for substations according to claim 2, characterized in that, Step two includes the following specific steps: Step 21: Obtain future electrical load data and future environmental data based on the predicted duration. The future electrical load data is the predicted current, and the future environmental data includes ambient humidity and ambient relative temperature. Step 22: Calculate the cumulative thermal damage value of the target equipment based on the thermal damage evolution cycle formula, wherein the thermal damage evolution cycle formula is: ,in, This is the initial thermal damage value. To predict the time step, For safe operating temperature, This refers to the service life of the equipment under safe operating temperatures. To predict the main body temperature of the equipment, the formula for predicting the main body temperature of the equipment is as follows: ,in, For ambient temperature, The coefficient of temperature rise. Here, is the initial abnormal value of the equipment, is the predicted current, is the activation energy, and is the Boltzmann constant; Step 23: Calculate the cumulative mechanical damage value of the target equipment based on the mechanical damage evolution cycle formula, wherein the mechanical damage evolution cycle formula is: , where, This is the initial mechanical damage value. These are fatigue characteristic constants. For safe operating current, The fatigue index; Step 24: Calculate the cumulative chemical damage value of the target equipment based on the chemical damage evolution cycle formula, wherein the chemical damage evolution cycle formula is: Where is the initial chemical damage value, Relative humidity is the ambient humidity, and the humidity effect index is the humidity index. is the activation energy of the corrosion reaction, is the gas constant, and is the standardized prediction time step.

4. The intelligent video monitoring method for substations according to claim 3, characterized in that, Step three includes the following specific steps: Step 31: Obtain the damage evolution impact value of the target equipment by weighted summing of the cumulative damage values ​​of thermal damage, mechanical damage, and chemical damage. Update the abnormal operation value of the target equipment based on the equipment abnormal operation value update formula, which is: ,in, For time Changes in equipment operation abnormality update values, The impact value of damage evolution on the target equipment; Step 32: Extract the final operational anomaly value and the time required to reach the critical operational anomaly value based on the equipment operation anomaly value update formula. The formula for obtaining the final operational anomaly value is as follows: ,in, The formula for determining the time required to reach the critical operational anomaly value is as follows: ,in, The time required to reach the critical operational anomaly value This is a critical operational anomaly value.

5. The intelligent video monitoring method for substations according to claim 4, characterized in that, Step four includes the following specific steps: Step 41: Obtain the comprehensive operational risk value of the target equipment based on the operational risk calculation formula, wherein the operational risk calculation formula is: ,in, The comprehensive operational risk value of the target equipment. For risk sensitivity coefficient, The duration of standard risk occurrence; Step 42: Compare the comprehensive operational risk value of the target equipment with the preset comprehensive operational risk threshold. When the comprehensive operational risk value of the target equipment is greater than or equal to the preset comprehensive operational risk threshold, issue an early warning for the target equipment.

6. A video intelligent monitoring system for substations, used to implement the video intelligent monitoring method for substations as described in any one of claims 1-5, characterized in that, include: The data acquisition module is used to filter target equipment images based on the video of the substation to obtain future electrical load data and future environmental data; The initial operation anomaly analysis module is used to analyze initial operation anomalies of the equipment in conjunction with the target equipment's operating conditions; The damage cycle evolution module is used to perform damage evolution based on the initial operational anomalies of the equipment, including thermal damage cycle, mechanical damage cycle, and chemical damage cycle. The final operational anomaly analysis module is used to analyze the final operational anomalies of the target equipment in future operation and the time required to reach the critical operational anomaly value based on the damage evolution results; The equipment early warning module is used to determine whether to issue an early warning for the target equipment based on the final operational anomaly and the time required to reach the critical operational anomaly value.

7. A storage medium, characterized in that, The storage medium includes stored instructions, wherein, when the instructions are executed, the device containing the storage medium is controlled to perform the intelligent video monitoring method for power distribution rooms as described in any one of claims 1-5.

8. An electronic device, characterized in that, It includes a memory and one or more instructions, wherein one or more instructions are stored in the memory and configured to be executed by one or more processors as described in any one of claims 1-5.

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