Airport power distribution room unattended inspection data analysis method, system, device and medium

Abstract

The application provides an airport power distribution room unmanned inspection data analysis method, system, device and medium, relates to the power distribution technical field, and the method comprises the following steps: acquiring the function type of a target power distribution room in an airport and the equipment operation data and power supply characteristic data in a preset time period; on the basis of the function type, the equipment operation data and the power supply characteristic data are combined to generate a first stability score of the target power distribution room; the first stability score is adjusted in combination with the second stability score of an associated power distribution room and the correlation index of the target power distribution room and the associated power distribution room to generate a third stability score; according to the number of takeoff and landing flights, a power supply load fluctuation index is generated, and the third stability score is adjusted according to the power supply load fluctuation index to generate a target stability score of the target power distribution room; when the target stability score is lower than a preset score, an abnormal early warning information is generated. The application has the technical effect that the accuracy of the power distribution room operation state evaluation is improved.

Description

Technical Field

[0001] This application relates to the field of power distribution technology, specifically to a method, system, equipment, and medium for unmanned inspection data analysis of airport power distribution rooms. Background Technology

[0002] With the continuous expansion of airport scale and the improvement of its intelligence level, the complexity of airport power distribution systems has increased significantly. As a key node in the airport power supply system, the operation status of the power distribution room directly affects the normal operation of the airport. In order to promptly detect potential operational hazards in the power distribution room, unmanned inspection systems are currently widely used for monitoring. However, how to scientifically analyze and evaluate the massive amounts of inspection data collected to achieve an accurate assessment of the power distribution room's operational status has become an urgent technical problem to be solved.

[0003] In existing technologies, the operational status of a power distribution room is typically assessed using fixed scoring standards based on the equipment operating parameters and power quality indicators. While this method can reflect the basic operational status of the power distribution room, it is relatively simple in terms of data analysis and processing. It only performs threshold comparisons on the collected basic data and lacks a comprehensive evaluation model, making it impossible to fully assess the overall stability of the power distribution room and resulting in inaccurate assessments of its operational status. Summary of the Invention

[0004] This application provides a data analysis method, system, equipment, and medium for unmanned inspection of airport power distribution rooms, which can improve the accuracy of power distribution room operation status assessment.

[0005] Firstly, this application provides a data analysis method for unmanned inspection of airport power distribution rooms. The method includes: acquiring the functional type of a target power distribution room in the airport, and the equipment operation data and power supply characteristic data of the target power distribution room within a preset time period; generating a first stability score for the target power distribution room based on the functional type, combined with the equipment operation data and the power supply characteristic data, wherein the first stability score is used to characterize the stable operation degree of the target power distribution room; acquiring a second stability score for associated power distribution rooms, and a correlation index between the target power distribution room and the associated power distribution rooms; wherein the associated power distribution rooms are power distribution rooms that have a power connection relationship with the target power distribution room, and the correlation index... The target power distribution room is used to characterize the degree to which it is affected by the associated power distribution room; the first stability score is adjusted by combining the second stability score and the association index to generate a third stability score for the target power distribution room; the number of flight takeoffs and landings in the power supply area corresponding to the target power distribution room within the preset time period is obtained, and a power load fluctuation index is generated based on the number of flight takeoffs and landings; the third stability score is adjusted based on the power load fluctuation index to generate a target stability score for the target power distribution room, wherein the power load fluctuation index is used to characterize the degree of impact of flight takeoffs and landings on the power supply load; when the target stability score is lower than the preset score, an abnormal warning message is generated.

[0006] By adopting the above technical solution, the basic operational status of the target power distribution room can be assessed by acquiring its functional type, equipment operation data, and power supply characteristic data, and generating a first stability score based on this data. Furthermore, by introducing a second stability score and a correlation index from related power distribution rooms, the first stability score is adjusted to obtain a third stability score, which reflects the impact of the power connection relationship between power distribution rooms on the stability of the target power distribution room. Finally, a power load fluctuation index is generated based on the number of flight takeoffs and landings, and the third stability score is adjusted accordingly to obtain the target stability score, achieving a quantitative assessment of the impact of power load fluctuations caused by flight operations. This multi-dimensional data analysis method not only considers the operational characteristics of the power distribution room itself but also combines the correlation of the power distribution system and the dynamic impact of airport business activities, thus obtaining more accurate power distribution room stability assessment results. When the target stability score is lower than the preset score, timely abnormal warning information is generated, helping maintenance personnel to quickly discover and handle potential operational hazards, improving the accuracy of the power distribution room operational status assessment.

[0007] Optionally, the equipment operating data includes equipment temperature, equipment vibration frequency, and equipment noise level; the power supply characteristic data includes voltage fluctuation and current fluctuation. Based on the functional type, and combining the equipment operating data and the power supply characteristic data, generating a first stability score for the target power distribution room includes: determining standard equipment temperature, standard equipment vibration frequency, and standard equipment noise level, as well as standard voltage fluctuation range and standard current fluctuation range, according to the functional type of the target power distribution room; calculating a first difference between the standard equipment temperature and the equipment temperature, a second difference between the standard equipment vibration frequency and the equipment vibration frequency, and a third difference between the standard equipment noise level and the equipment noise level; combining the first difference, the second difference, and the third difference to generate a first sub-stability score for the target power distribution room; and determining whether the voltage fluctuation value is within the standard voltage fluctuation range and the current fluctuation... The stability score is determined by determining whether the voltage fluctuation value is within the standard current fluctuation range. When both the voltage and current fluctuation values ​​are within the standard current fluctuation range, the first sub-stability score is used as the first stability score of the target power distribution room. When neither the voltage nor the current fluctuation value is within the standard current fluctuation range, a second sub-stability score is generated based on the degree to which the voltage and current fluctuation values ​​deviate from the standard current fluctuation range. The second sub-stability score is inversely proportional to the degree to which the voltage and current fluctuation values ​​deviate from the standard current fluctuation range. The first and second sub-stability scores are weighted according to preset weights to generate the first stability score of the target power distribution room.

[0008] By adopting the above technical solution, combining operational data such as equipment temperature, vibration frequency, and noise levels, as well as power supply characteristic data such as voltage and current fluctuations, and determining corresponding standard parameters based on the functional type of the power distribution room, the first sub-stability score is generated by calculating the difference between the actual value and the standard value. At the same time, the scoring strategy is determined based on whether the voltage and current fluctuations are within the standard range. When they exceed the standard range, a second sub-stability score is generated based on the degree of deviation. Finally, the first stability score is obtained by weighted calculation using preset weights. This achieves a quantitative assessment of the operating status of the power distribution room, making the assessment results more objective and accurate.

[0009] Optionally, generating a first sub-stability score for the target substation by combining the first difference, the second difference, and the third difference includes: normalizing the first difference, the second difference, and the third difference respectively to obtain a first normalized difference, a second normalized difference, and a third normalized difference; weighting the first normalized difference, the second normalized difference, and the third normalized difference to obtain a weighted difference; and generating a first stability score for the target substation based on the weighted difference, wherein the first sub-stability score is inversely proportional to the weighted difference.

[0010] By adopting the above technical solution, the differences between the equipment temperature, vibration frequency and noise values ​​and their standard values ​​are normalized, and a weighted difference is obtained by weighted calculation. Then, a score is generated based on the inverse relationship between the weighted difference and the first sub-stability score. This achieves unified measurement and reasonable weighting of parameters with different dimensions, making the scoring results more comparable and scientific.

[0011] Optionally, obtaining the correlation index between the target power distribution room and the associated power distribution room includes: obtaining the power connection type between the target power distribution room and the associated power distribution room, and the connection distance from the target power distribution room to the associated power distribution room; establishing a correspondence between the power connection type and the basic correlation coefficient, wherein the power connection type includes a main power supply connection and a backup power supply connection, the basic correlation coefficient of the main power supply connection is set to a first preset value, the basic correlation coefficient of the backup power supply connection is set to a second preset value, and the first preset value is greater than the second preset value; determining the corresponding basic correlation coefficient from the correspondence based on the power connection type; substituting the basic correlation coefficient and the connection distance into a first preset formula to generate the correlation index between the target power distribution room and the associated power distribution room; wherein the first preset formula is: R=K×(1-α×L / L0)^β; where R is the correlation index; K is the basic correlation coefficient; L is the connection distance between the target power distribution room and the associated power distribution room; L0 is the standard connection distance; when L is greater than L0, L is set to L0; α is the distance attenuation coefficient, and β is the nonlinear correction index.

[0012] By adopting the above technical solution, by distinguishing the basic correlation coefficients of the main power supply connection and the backup power supply connection, and combining the connection distance between the power distribution rooms, the correlation index is calculated using a preset formula that includes a distance attenuation coefficient and a nonlinear correction index. This not only takes into account the difference in the influence weight of different power connection types, but also reflects the attenuation effect of connection distance on the degree of correlation. This makes the calculation of the correlation index more consistent with the characteristics of the actual power supply network, thereby accurately quantifying the degree of mutual influence between power distribution rooms.

[0013] Optionally, the step of adjusting the first stability score by combining the second stability score and the correlation index to generate a third stability score for the target power distribution room includes: substituting the first stability score, the second stability score, and the correlation index into a second preset formula to generate a third stability score for the target power distribution room; wherein, the second preset formula is: S3=S1-(S1-S2)×R×γ; where S3 is the third stability score; S1 is the first stability score; S2 is the second stability score; R is the correlation index; and γ is the adjustment coefficient.

[0014] By adopting the above technical solution, the third stability score is calculated by substituting the first stability score, the second stability score, and the correlation index into a preset formula containing adjustment coefficients. This formula is designed to reflect the degree of influence of the status of the associated substation on the target substation. When the stability score of the associated substation is low, the score of the target substation will be reduced according to the correlation index. This achieves accurate quantification of the correlation influence between substations and makes the scoring results more objectively reflect the overall operating status of the power distribution system.

[0015] Optionally, the step of generating a power supply load fluctuation index based on the number of flight takeoffs and landings, and adjusting the third stability score based on the power supply load fluctuation index to generate a target stability score for the target power distribution room includes: obtaining the number of standard flight takeoffs and landings in the power supply area corresponding to the target power distribution room within the preset time period; generating a power supply load fluctuation index by combining the number of standard flight takeoffs and landings and the number of flight takeoffs and landings; and substituting the third stability score and the power supply load fluctuation index into a third preset formula to generate the target stability score; wherein, the third preset formula is: S=S3×(1-δ×I); where S is the target stability score; S3 is the third stability score; I is the power supply load fluctuation index; and δ is a load sensitivity coefficient, used to characterize the degree of influence of power supply load fluctuations caused by flight takeoffs and landings on the stability of the power distribution room.

[0016] By adopting the above technical solution, a power supply load fluctuation index is generated by comparing the actual number of flight takeoffs and landings with the standard number of flight takeoffs and landings. This index, along with the third stability score, is then substituted into a preset formula containing a load sensitivity coefficient to calculate the target stability score. This achieves a quantitative assessment of the impact of flight operations on the power distribution load. When frequent flight takeoffs and landings lead to large load fluctuations, the score is adjusted accordingly using the load sensitivity coefficient, so that the final score accurately reflects the actual impact of airport business activities on the stability of the power distribution system.

[0017] Optionally, generating the power supply load fluctuation index by combining the standard flight takeoff and landing count and the flight takeoff and landing count includes: substituting the standard flight takeoff and landing count and the flight takeoff and landing count into a fourth preset formula to generate the power supply load fluctuation index; wherein, the fourth preset formula is: I=1+μ×(N / N0-1); where I is the power supply load fluctuation index; N is the flight takeoff and landing count; N0 is the standard flight takeoff and landing count; μ is the fluctuation influence factor, used to characterize the degree of influence of changes in the flight takeoff and landing count on the power supply load of the power distribution room, and the value of μ ranges from 0 to 1.

[0018] By adopting the above technical solution, the power supply load fluctuation index is calculated by substituting the ratio of the actual number of flight takeoffs and landings to the standard number of flight takeoffs and landings into a preset formula that includes a fluctuation impact factor. The value range of the fluctuation impact factor is limited to between 0 and 1. This ensures that the power supply load fluctuation index will increase accordingly when the number of flight takeoffs and landings exceeds the standard value, and also reasonably limits this growth trend through the fluctuation impact factor, making the assessment of load fluctuation more consistent with the actual power supply characteristics.

[0019] Secondly, this application provides a data analysis system for unmanned inspection of airport power distribution rooms, the system comprising: a first acquisition module, a first combination module, a second acquisition module, a second combination module, a generation module, and an early warning module; wherein,

[0020] The first acquisition module is used to acquire the functional type of the target power distribution room in the airport, and the equipment operation data and power supply characteristic data of the target power distribution room within a preset time period; the first combination module is used to generate a first stability score for the target power distribution room based on the functional type, combined with the equipment operation data and the power supply characteristic data, the first stability score being used to characterize the stable operation degree of the target power distribution room; the second acquisition module is used to acquire a second stability score for associated power distribution rooms, and a correlation index between the target power distribution room and the associated power distribution room; wherein, the associated power distribution room is a power distribution room that has a power connection relationship with the target power distribution room, and the correlation index is used to characterize the impact of the associated power distribution room on the stability of the target power distribution room. The degree of impact on the target power distribution room; the second combining module is used to combine the second stability score and the correlation index to adjust the first stability score and generate a third stability score for the target power distribution room; the generation module is used to obtain the number of flight takeoffs and landings in the power supply area corresponding to the target power distribution room within the preset time period, generate a power load fluctuation index based on the number of flight takeoffs and landings, adjust the third stability score based on the power load fluctuation index, and generate a target stability score for the target power distribution room, wherein the power load fluctuation index is used to characterize the degree of impact of flight takeoffs and landings on the power supply load; the early warning module is used to generate an abnormal early warning message when the target stability score is lower than the preset score.

[0021] Thirdly, this application provides an electronic device that adopts the following technical solution: it includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a computer program such as any of the above-described unmanned inspection data analysis methods for airport power distribution rooms.

[0022] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: storing a computer program that can be loaded by a processor and executed by any of the above-mentioned unmanned inspection data analysis methods for airport power distribution rooms.

[0023] In summary, this application includes at least one of the following beneficial technical effects:

[0024] By acquiring the functional type, equipment operation data, and power supply characteristic data of the target substation, and generating a first stability score based on this data, the basic operational status of the substation can be assessed. Furthermore, by introducing a second stability score and a correlation index from related substations, the first stability score is adjusted to obtain a third stability score, reflecting the impact of power connection relationships between substations on the stability of the target substation. Finally, a power load fluctuation index is generated based on the number of flight takeoffs and landings, and this index is used to adjust the third stability score to obtain the target stability score, achieving a quantitative assessment of the impact of power load fluctuations caused by flight operations. This multi-dimensional data analysis method not only considers the operational characteristics of the substation itself but also combines the correlation of the power distribution system and the dynamic impact of airport business activities, thus obtaining more accurate substation stability assessment results. When the target stability score is lower than the preset score, timely abnormal warning information is generated, helping maintenance personnel to quickly identify and handle potential operational hazards, improving the accuracy of the substation operational status assessment. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating a data analysis method for unmanned inspection of an airport power distribution room provided in an embodiment of this application.

[0026] Figure 2 This is a schematic diagram of the interface of the unmanned inspection data analysis platform for airport power distribution rooms provided in this application embodiment;

[0027] Figure 3 This is a schematic diagram of the equipment data recording interface of the unmanned inspection data analysis platform for airport power distribution rooms provided in this application embodiment;

[0028] Figure 4This is a schematic diagram of the equipment management interface of the airport power distribution room unmanned inspection data analysis platform provided in the embodiments of this application;

[0029] Figure 5 This is a schematic diagram of the structure of an unmanned inspection data analysis system for an airport power distribution room provided in an embodiment of this application;

[0030] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0031] Explanation of reference numerals in the attached figures: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. Detailed Implementation

[0032] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0033] In the description of the embodiments in this application, words such as "illustrative," "for example," or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "illustrative," "for example," or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of words such as "illustrative," "for example," or "for example" is intended to present the relevant concepts in a specific manner.

[0034] Figure 1 This is a flowchart illustrating a data analysis method for unmanned inspection of an airport power distribution room provided in an embodiment of this application. Figure 1 As shown, the method includes S101-S106:

[0035] S101, obtain the functional type of the target power distribution room in the airport, as well as the equipment operation data and power supply characteristic data of the target power distribution room within a preset time period.

[0036] In this embodiment, the first step is to obtain basic information about the target power distribution room in the airport. Airport power distribution rooms can be categorized into different functional types based on their power supply targets and tasks. For example, there are power distribution rooms supplying power to the terminal building, runway lighting systems, and communication and navigation equipment. Different functional types of power distribution rooms have different requirements for power supply stability, and their standard values ​​for equipment operating parameters also differ. Obtaining the functional type of the target power distribution room helps in subsequently determining the corresponding standard parameter values.

[0037] Specifically, the functional type information of the target power distribution room can be obtained from the engineering drawings, design documents, or management system database of the airport power supply and distribution system. The preset time period can be set to a time interval of 1 hour, 4 hours, or 8 hours. Within this preset time period, various sensors within the power distribution room collect equipment operating data, including the temperature, vibration frequency, and noise levels of key equipment such as transformers and switchgear. Specifically, equipment temperature is collected using infrared thermometers, equipment vibration frequency using vibration sensors, and equipment noise levels using noise sensors. Simultaneously, power supply characteristic data, including voltage fluctuations and current fluctuations, are collected through power monitoring devices.

[0038] By acquiring this fundamental data, we can provide data support for subsequent assessments of the operational stability of the power distribution room. This data comprehensively reflects the operating status of various equipment within the power distribution room and the quality of power supply. For example, an abnormally high equipment temperature may indicate a potential fault, while voltage fluctuations exceeding the normal range suggest unstable power supply quality. Real-time monitoring and analysis of this data allows for the timely detection of anomalies in the power distribution room's operation, providing a basis for preventative maintenance. Compared to traditional manual inspection methods, this multi-dimensional data analysis approach provides a more comprehensive, timely, and accurate understanding of the power distribution room's operational status.

[0039] S102, based on the functional type, combined with equipment operation data and power supply characteristic data, generates the first stability score of the target power distribution room. The first stability score is used to characterize the stable operation of the target power distribution room.

[0040] After obtaining the basic information and operational data of the target power distribution room, this data needs to be analyzed and evaluated to generate a primary stability score reflecting the stable operation of the power distribution room. Since different functional types of power distribution rooms have different requirements for equipment operating status and power supply quality, it is first necessary to determine the corresponding standard parameter values ​​based on the functional type of the target power distribution room. For example, power distribution rooms supplying power to airport terminals have relatively high standard values ​​for equipment temperature due to their larger power load; while power distribution rooms supplying power to communication and navigation equipment have stricter requirements for power supply quality, and their allowable voltage fluctuation range is smaller.

[0041] The specific scoring process first calculates the differences between actual operating parameters and standard parameters. For equipment operating data, the first difference between the actual temperature and the standard temperature, the second difference between the actual vibration frequency and the standard vibration frequency, and the third difference between the actual noise value and the standard noise value are calculated respectively. To eliminate the influence of different parameter dimensions, these differences need to be normalized to obtain the first normalized difference, the second normalized difference, and the third normalized difference. The normalization process can use the maximum-minimum standardization method to map each difference to the interval between 0 and 1.

[0042] Next, considering the varying degrees of impact of different operating parameters on the stability of the power distribution room, it is necessary to perform a weighted calculation on the normalized differences. For example, abnormal equipment temperature usually indicates a more serious potential fault, so the weight of the first normalized difference can be set to be larger. The weighted difference obtained through weighted calculation is inversely proportional to the first sub-stability score; that is, the larger the weighted difference, the further the actual operating state deviates from the standard state, and the lower the corresponding first sub-stability score.

[0043] For power supply characteristic data, it is necessary to determine whether voltage and current fluctuation values ​​are within their respective standard fluctuation ranges. When both parameters are within the standard range, the first sub-stability score is directly used as the first stability score. If any parameter exceeds the standard range, a second sub-stability score needs to be calculated based on the degree of deviation. The greater the deviation, the lower the second sub-stability score, reflecting the significant impact of power supply quality on the stability of the power distribution room.

[0044] Finally, the first sub-stability score and the second sub-stability score are weighted by preset weights to obtain the final first stability score.

[0045] Based on the above embodiments, as an optional implementation, in S102, the equipment operation data includes equipment temperature, equipment vibration frequency, and equipment noise value, and the power supply characteristic data includes voltage fluctuation value and current fluctuation value. Based on the function type, and combining the equipment operation data and power supply characteristic data, the generation of the first stability score of the target power distribution room specifically includes S21-S27:

[0046] S21. Based on the functional type of the target power distribution room, determine the standard equipment temperature, standard equipment vibration frequency, standard equipment noise value, as well as the standard voltage fluctuation range and standard current fluctuation range.

[0047] S22, calculate the first difference between the standard equipment temperature and the equipment temperature, the second difference between the standard equipment vibration frequency and the equipment vibration frequency, and the third difference between the standard equipment noise value and the equipment noise value.

[0048] When assessing the stability of a power distribution room, the first step is to determine the standard values ​​for various operating parameters. Since different power distribution rooms within an airport perform different power supply tasks, their optimal operating parameters and power supply characteristics also differ. For example, a power distribution room supplying power to the terminal building, due to its larger and relatively stable load, may have a standard equipment temperature within the range of 45-50℃; while a power distribution room supplying power to communication and navigation equipment, due to its higher requirements for power quality, will have a narrower standard voltage fluctuation range, typically limited to within ±3% of the rated voltage. Therefore, it is necessary to establish a mapping relationship between the functional type of the power distribution room and the standard parameter values.

[0049] This mapping relationship can be stored in the system in the form of a configuration table. The configuration table contains parameters such as standard equipment temperature, standard equipment vibration frequency, standard equipment noise level, standard voltage fluctuation range, and standard current fluctuation range for different functional types of power distribution rooms. The determination of these standard parameter values ​​must consider the technical specifications provided by the equipment manufacturer, the actual operational experience of the airport, and the requirements of relevant industry standards. For example, for transformer equipment, the standard temperature value needs to consider factors such as ambient temperature and load rate; the setting of the standard vibration frequency needs to consider the inherent frequency characteristics of the equipment; and the determination of the standard noise level also needs to consider the noise control requirements of the environment in which the equipment is located.

[0050] After obtaining the functional type of the target power distribution room, the system retrieves the corresponding standard parameter values ​​by querying the configuration table. Subsequently, the system compares these standard values ​​with the actual collected operating data, calculating the differences for each parameter. Specifically, the first difference equals the standard equipment temperature minus the actual equipment temperature; the second difference equals the standard equipment vibration frequency minus the actual equipment vibration frequency; and the third difference equals the standard equipment noise value minus the actual equipment noise value. This method of calculating differences ensures that positive values ​​indicate actual values ​​are lower than the standard values, and negative values ​​indicate actual values ​​exceed the standard values, facilitating subsequent scoring calculations.

[0051] S23, combining the first difference, the second difference, and the third difference, generates the first sub-stability score of the target power distribution room.

[0052] Based on the above embodiments, as an optional implementation method, in S23, combining the first difference, the second difference, and the third difference to generate the first sub-stability score of the target power distribution room specifically includes S231-S233:

[0053] S231, normalize the first difference, the second difference and the third difference respectively to obtain the first normalized difference, the second normalized difference and the third normalized difference.

[0054] In the process of power distribution room stability assessment, in order to reasonably handle the differences in equipment operating parameters and generate a scientific first sub-stability score, standardized data processing methods are required. Since equipment temperature, vibration frequency, and noise level have different physical dimensions and numerical ranges, direct comparison and calculation of differences lack comparability; therefore, normalization processing is necessary first.

[0055] The normalization process employs a maximum-minimum standardization method, mapping the first, second, and third differences to the interval [0, 1] to obtain the first, second, and third normalized differences. The normalization calculation formula is: Normalized difference = (Original difference - Minimum difference) / (Maximum difference - Minimum difference). The maximum and minimum differences can be obtained based on historical operating data statistics or determined according to equipment technical specifications. This normalization process makes the differences between different parameters comparable, laying the foundation for subsequent weighted calculations.

[0056] S232, the first normalized difference, the second normalized difference, and the third normalized difference are weighted and calculated to obtain the weighted difference.

[0057] After obtaining the normalized differences, considering the varying degrees of impact of different operating parameters on the stability of the power distribution room, a weighted calculation is required. Equipment temperature, as a key indicator reflecting equipment health, often indicates a more serious risk of failure if its abnormality is detected; therefore, the weight of the first normalized difference should be set relatively high, such as 0.5. Equipment vibration frequency reflects mechanical faults and is of slightly lower importance; the weight of the second normalized difference can be set to 0.3. Equipment noise level, as an auxiliary indicator, can be weighted at 0.2 for the third normalized difference. Through this weighting configuration, the calculated weighted differences can more accurately reflect the overall deviation of the equipment's operating status.

[0058] S233, Generate the first stability score of the target power distribution room based on the weighted difference, wherein the first sub-stability score is inversely proportional to the weighted difference.

[0059] Finally, the weighted difference needs to be converted into a first sub-stability score. Considering that a larger difference indicates a greater deviation from the standard state in actual operation, the first sub-stability score should be inversely proportional to the weighted difference. A nonlinear mapping function can be used for this conversion, such as first sub-stability score = 1 - squared weighted difference. This nonlinear conversion maintains the inverse relationship between the score and the difference, and also produces a more significant score reduction effect when the difference is large, demonstrating the serious impact of large deviations on stability.

[0060] S24, determine whether the voltage fluctuation value is within the standard voltage fluctuation range and whether the current fluctuation value is within the standard current fluctuation range.

[0061] S25, when the voltage fluctuation value is within the standard voltage fluctuation range and the current fluctuation value is within the standard current fluctuation range, the first sub-stability score is used as the first stability score of the target substation.

[0062] S26, when the voltage fluctuation value is not within the standard voltage fluctuation range and / or the current fluctuation value is not within the standard current fluctuation range, a second sub-stability score is generated based on the degree to which the voltage fluctuation value deviates from the standard voltage fluctuation range and the degree to which the current fluctuation value deviates from the standard current fluctuation range. The second sub-stability score is inversely proportional to the degree to which the voltage fluctuation value deviates from the standard voltage fluctuation range and the degree to which the current fluctuation value deviates from the standard current fluctuation range.

[0063] S27, the first sub-stability score and the second sub-stability score are weighted according to the preset weights to generate the first stability score of the target power distribution room.

[0064] When evaluating the power supply quality of a substation, voltage fluctuation and current fluctuation are two key indicators. The standard voltage fluctuation range and standard current fluctuation range are set based on the substation's functional type and power supply requirements. For example, for a substation supplying precision equipment, the standard voltage fluctuation range might be set at ±3%, while the standard current fluctuation range might be set at ±5%; for substations with ordinary loads, these standard ranges might be relatively lenient, such as a voltage fluctuation range of ±5% and a current fluctuation range of ±10%.

[0065] The system first needs to determine whether the actual voltage and current fluctuation values ​​are within their respective standard ranges. This determination uses a range comparison method, comparing the actual values ​​with the upper and lower limits of the range. When both parameters are within the standard range, it indicates that the power supply quality of the distribution room is normal. In this case, the first sub-stability score calculated earlier (based on equipment operating status) can be directly used as the final first stability score. This approach reflects an important principle: when the power supply characteristics are normal, the stability of the distribution room is mainly determined by the equipment operating status.

[0066] However, when voltage and / or current fluctuations exceed the standard range, the impact of this anomaly on the stability of the power distribution room needs to be considered. In this case, the system calculates the degree to which the parameters deviate from the standard range. Specifically, for voltage fluctuations, the distance from the standard range boundary is calculated; similarly, for current fluctuations, the distance from the standard range boundary is calculated. These distance values ​​are standardized using a third preset formula to obtain a standardized deviation index.

[0067] Based on a standardized deviation index, the system calculates the second sub-stability score using a fourth preset formula. This formula employs a non-linear mapping relationship, ensuring that the greater the deviation, the greater the score reduction. For example, an exponential decay function can be used: Score = BaseScore × exp(-k × D), where BaseScore is the base score (e.g., 100 points), k is the decay coefficient, and D is the standardized deviation. This non-linear relationship better reflects the impact of power quality anomalies on the stability of the power distribution room.

[0068] Finally, the system needs to calculate the final first stability score by weighting the first sub-stability score and the second sub-stability score. The weighting needs to consider the relative importance of equipment operating status and power supply quality to the stability of the power distribution room. For example, a fifth preset formula can be used: S1 = w1 × S11 + w2 × S12, where S1 is the first stability score, S11 is the first sub-stability score, S12 is the second sub-stability score, and w1 and w2 are the corresponding weights satisfying w1 + w2 = 1. Typically, because power supply quality has a more direct impact on the stability of the power distribution room, w2 will be slightly larger than w1.

[0069] S103, obtain the second stability score of the associated power distribution room and the correlation index between the target power distribution room and the associated power distribution room; wherein, the associated power distribution room is the power distribution room that has a power connection relationship with the target power distribution room, and the correlation index is used to characterize the degree to which the target power distribution room is affected by the associated power distribution room.

[0070] In airport power supply and distribution systems, power distribution rooms are typically interconnected, forming a network structure where each room serves as a backup or primary / backup power source. The operational status of one power distribution room is often affected by other power distribution rooms with which it is electrically connected (i.e., associated power distribution rooms). Therefore, when assessing the stability of a target power distribution room, it is necessary to consider the operational status of associated power distribution rooms and the extent of their impact.

[0071] First, it is necessary to identify the associated power distribution rooms that have a power connection with the target power distribution room by referring to the wiring diagram of the airport's power supply and distribution system. The second stability score of the associated power distribution rooms can be obtained using the same evaluation method as the first stability score of the target power distribution room. Simultaneously, it is necessary to obtain the power connection type and connection distance between the target power distribution room and the associated power distribution rooms. The power connection types mainly include two types: primary power supply connection and backup power supply connection. A primary power supply connection refers to the normal power supply path between the two power distribution rooms, while a backup power supply connection is the backup path in case of a failure in the primary power supply line.

[0072] To quantify the impact of associated substations on the target substation, a correlation index calculation model based on power connection type and connection distance was established. In this model, the correspondence between power connection type and basic correlation coefficient is first established. Considering that the impact of the main power supply connection is more direct and significant, its basic correlation coefficient is set to a first preset value (e.g., 0.8); while the impact of the backup power supply connection is relatively smaller, its basic correlation coefficient is set to a second preset value (e.g., 0.4).

[0073] Furthermore, considering the attenuation effect of power transmission distance on the degree of influence, the final correlation index is calculated using the first preset formula: R = K × (1 - α × L / L0)^β. Where R is the correlation index, K is the basic correlation coefficient, L is the actual connection distance, L0 is the standard connection distance, α is the distance attenuation coefficient (used to adjust the rate of attenuation of the correlation degree due to distance), and β is the nonlinear correction index (used to adjust the shape of the attenuation curve). When the actual connection distance L is greater than the standard connection distance L0, L = L0 is taken to avoid excessive attenuation. This calculation method, which considers the distance attenuation effect, can more accurately reflect the actual characteristics of power connection relationships changing with distance.

[0074] The correlation index calculated in this way takes into account both the differences in power connection types and the impact of connection distance, and can reasonably quantify the degree of influence of the associated substation on the target substation. For example, when the associated substation is connected to the target substation via the main power supply connection and the connection distance is short, a larger correlation index will be obtained, indicating that its influence on the target substation is greater; conversely, if it is connected via the backup power supply connection and the connection distance is long, the correlation index will be smaller, indicating that the degree of influence is limited.

[0075] Based on the above embodiments, as an optional implementation method, in S103, obtaining the correlation index between the target power distribution room and the associated power distribution room specifically includes S31-S34:

[0076] S31 obtains the power connection type between the target power distribution room and the associated power distribution room, as well as the connection distance between the target power distribution room and the associated power distribution room.

[0077] S32, establish the correspondence between power connection type and basic correlation coefficient, wherein the power connection type includes main power supply connection and backup power supply connection, the basic correlation coefficient of main power supply connection is set to a first preset value, the basic correlation coefficient of backup power supply connection is set to a second preset value, and the first preset value is greater than the second preset value.

[0078] S33, Based on the power connection type, determine the corresponding basic correlation coefficient from the correspondence.

[0079] S34, Substitute the basic correlation coefficient and connection distance into the first preset formula to generate the correlation index between the target power distribution room and the associated power distribution room; wherein, the first preset formula is: R=K×(1-α×L / L0)^β; where, R is the correlation index; K is the basic correlation coefficient; L is the connection distance between the target power distribution room and the associated power distribution room; L0 is the standard connection distance; when L is greater than L0, L is taken as L0; α is the distance attenuation coefficient, and β is the nonlinear correction index.

[0080] When assessing the mutual impact between power distribution rooms, a scientific method for calculating the correlation index needs to be established. This method requires comprehensive consideration of two key factors: the type of power connection and the physical distance, as these factors directly determine the likelihood of fault propagation and mutual impact between power distribution rooms.

[0081] First, it's necessary to obtain information on the type and distance of power connections between power distribution rooms. Power connection types are mainly divided into two categories: primary power supply connections and backup power supply connections. The primary power supply connection is the main power supply channel for the power distribution room, undertaking daily power supply tasks, and its electrical connection is more robust; the backup power supply connection is a backup channel in case of a failure in the primary power supply system, and is normally in standby mode. When measuring the connection distance, the actual length of the power cable's laying path needs to be considered, rather than simply the straight-line distance.

[0082] When determining the basic correlation coefficient, it is necessary to establish a mapping relationship between the power connection type and the basic correlation coefficient. Because the main power supply connection is of higher importance, its basic correlation coefficient (first preset value) is set to a larger value, such as 0.8; while the basic correlation coefficient (second preset value) for the backup power supply connection is relatively smaller, such as 0.4. This differentiated setting reflects the different impacts of different connection types on the stability of the power distribution room.

[0083] Then, the system queries the corresponding relationship based on the actual power connection type to determine the specific basic correlation coefficient K. This basic correlation coefficient represents the maximum degree of correlation between power distribution rooms under ideal distance conditions. However, the actual degree of correlation also needs to take into account the influence of distance factors, because a longer connection distance will increase power transmission loss and fault propagation attenuation.

[0084] To accurately describe the impact of distance on the degree of association, this scheme adopts the first preset formula: R=K×(1-α×L / L0)^β. Where L is the actual connection distance, L0 is the standard connection distance (e.g., 1000 meters), and when L exceeds L0, the value of L0 is used, reflecting the upper limit characteristic of the distance's influence. α is the distance attenuation coefficient (e.g., 0.5), used to control the rate of association attenuation caused by increasing distance. β is the nonlinear correction exponent (e.g., 2), used to adjust the nonlinear characteristics of distance attenuation.

[0085] S104, combining the second stability score and the correlation index, adjusts the first stability score to generate the third stability score of the target power distribution room.

[0086] After obtaining the second stability score and correlation index of the associated substations, the first stability score of the target substation needs to be adjusted to reflect the impact of the associated substations' operating status. This adjustment is necessary because the power connection between substations means that fluctuations in the operating status of one substation may be transmitted through the power network, affecting the stability of other connected substations.

[0087] To achieve this adjustment, this embodiment uses a second preset formula: S3 = S1 - (S1 - S2) × R × γ to calculate the third stability score. Here, S3 is the final third stability score, S1 is the first stability score of the target substation, S2 is the second stability score of the associated substation, R is the correlation index, and γ is the adjustment coefficient. The adjustment coefficient γ is used to control the intensity of the correlation effect; its value can be determined based on actual operating experience and is typically set between 0 and 1.

[0088] The design philosophy of this calculation formula is that when the stability score (S2) of the associated substation is lower than the score (S1) of the target substation, it indicates that the associated substation is in a relatively unstable state, which may have a negative impact on the target substation. In this case, (S1-S2) is a positive value. By multiplying it by the correlation index R and the adjustment coefficient γ, the first stability score of the target substation is adjusted downward to obtain a lower third stability score. The magnitude of the adjustment depends on the combined effect of the score difference, the correlation index, and the adjustment coefficient.

[0089] Conversely, if the stability score of the associated substation is higher than that of the target substation, then (S1-S2) will be negative, ultimately leading to an improvement in the third stability score relative to the first stability score. This reflects the positive impact of the good operating condition of the associated substation on the target substation. The introduction of the correlation index R ensures that this impact varies with the tightness of the power connection. For example, when two substations are closely connected via the main power supply line, the correlation index is larger, and the impact is also greater; when they are connected over a long distance via a backup line, the correlation index is smaller, and the impact is correspondingly reduced.

[0090] The advantage of this scoring adjustment mechanism is that it can dynamically reflect the mutual influence between various substations in the power distribution system, making the stability assessment results more consistent with actual operating conditions. Simultaneously, by setting the adjustment coefficient γ, the weight of correlated influences can be flexibly controlled, avoiding over-adjustment that could lead to scoring distortion. This scoring method, which considers the correlation between substations, is of great significance for predicting and preventing cascading failures in the power distribution system, and helps improve the reliability and safety of the entire power supply and distribution system.

[0091] Based on the above embodiments, as an optional implementation, in S104, adjusting the first stability score by combining the second stability score and the correlation index to generate the third stability score of the target power distribution room specifically includes:

[0092] Substitute the first stability score, the second stability score, and the correlation index into the second preset formula to generate the third stability score of the target power distribution room; wherein, the second preset formula is: S3=S1-(S1-S2)×R×γ; where S3 is the third stability score; S1 is the first stability score; S2 is the second stability score; R is the correlation index; and γ is the adjustment coefficient.

[0093] In a power distribution system, there are complex interrelationships between substations. The operational status of one substation can affect other related substations through electrical connections. Therefore, when assessing the stability of a target substation, the impact of the status of related substations needs to be considered. The extent of this impact depends on two key factors: the operational status of the related substations (reflected in the second stability score) and the degree of correlation between the two substations (reflected in the correlation index).

[0094] To accurately reflect this impact in the final score of the target substation, this scheme designs a second preset formula: S3 = S1 - (S1 - S2) × R × γ. The design concept of this formula is that when the score of the associated substation is lower than that of the target substation, the score of the target substation is appropriately lowered through the association effect. Each parameter in the formula has a clear physical meaning: S1 represents the first stability score of the target substation, reflecting its own state; S2 represents the second stability score of the associated substation, reflecting its state; R is the association index calculated in the previous stage, characterizing the degree of influence between the two substations; γ is an adjustment coefficient used to control the intensity of the association effect.

[0095] This formula has several important characteristics: First, when S2 is equal to or greater than S1, it will not have a negative impact on the score of the target substation, which is consistent with the actual operating rules; second, the adjustment amount of the score is proportional to the score difference (S1-S2), that is, the worse the condition of the associated substation, the greater the negative impact on the target substation; finally, the degree of influence transmission can be flexibly controlled by the combination of the correlation index R and the adjustment coefficient γ.

[0096] The setting of the adjustment coefficient γ needs to be determined based on actual operating experience, and is usually between 0 and 1. A larger γ value will strengthen the correlation effect, while a smaller γ value will weaken the correlation effect. For example, when γ is set to 0.5, even under the maximum correlation degree (R=1), the influence of the correlated substation will not exceed 50% of the score difference. This setting can prevent over-adjustment.

[0097] The advantages of this scoring adjustment method are: first, it can quantitatively describe the correlation between power distribution rooms, making the scoring more objective and accurate; second, the design of the formula ensures the rationality and controllability of the scoring adjustment; and finally, this method has good adaptability and can be adapted to the needs of different scenarios through parameter adjustment.

[0098] In practical applications, this scoring adjustment method can effectively reflect the overall stability of the power distribution system. For example, when a critical substation experiences an anomaly, the scores of related substations will decrease accordingly. This simulation of a chain reaction helps to identify potential systemic risks early on.

[0099] S105: Obtain the number of flight takeoffs and landings in the power supply area corresponding to the target power distribution room within a preset time period. Based on the number of flight takeoffs and landings, generate a power supply load fluctuation index. Based on the power supply load fluctuation index, adjust the third stability score to generate a target stability score for the target power distribution room. The power supply load fluctuation index is used to characterize the degree of impact of flight takeoffs and landings on the power supply load.

[0100] In airport power supply and distribution systems, flight takeoffs and landings directly affect fluctuations in power load. Each flight's takeoff and landing involves the starting and stopping of electrical equipment such as lighting, communication and navigation systems, and air conditioning systems, resulting in rapid changes in power load. These load fluctuations impact the operational stability of the power distribution room; therefore, flight operation factors need to be incorporated into the stability assessment system.

[0101] First, it is necessary to determine the area to which the target power distribution room is responsible for supplying power, and obtain the actual number of flight takeoffs and landings in that area within a preset time period. Simultaneously, based on historical operational data and airport flight schedules, determine the standard number of flight takeoffs and landings in that area within the preset time period. The standard number of flight takeoffs and landings reflects the design capacity of the power distribution system and the expected load level under normal operating conditions.

[0102] To quantify the impact of flight takeoffs and landings on power supply load, the power supply load fluctuation index is calculated using the fourth preset formula: I = 1 + μ × (N / N0 - 1). Where I is the power supply load fluctuation index, N is the actual number of flight takeoffs and landings, N0 is the standard number of flight takeoffs and landings, and μ is the fluctuation impact factor. The fluctuation impact factor μ ranges from 0 to 1 and is used to characterize the impact of changes in the number of flight takeoffs and landings on the power supply load of the distribution room. A larger μ value indicates that the power supply load is more sensitive to changes in the number of flight takeoffs and landings. When the actual number of flight takeoffs and landings exceeds the standard number, the fluctuation index is greater than 1, indicating increased power supply load fluctuation; conversely, it indicates relatively small load fluctuation.

[0103] After obtaining the power supply load fluctuation index, the third stability score is adjusted using a third preset formula: S = S3 × (1 - δ × I). Where S is the final target stability score, S3 is the third stability score, I is the power supply load fluctuation index, and δ is the load sensitivity coefficient. The load sensitivity coefficient δ is used to characterize the impact of power supply load fluctuations caused by flight takeoffs and landings on the stability of the power distribution room. A larger δ value means that the stability of the power distribution room is more sensitive to load fluctuations.

[0104] The characteristic of this scoring adjustment mechanism is that when the actual number of flight takeoffs and landings significantly exceeds the standard number, a larger power load fluctuation index is obtained, resulting in a lower target stability score relative to the third stability score. This reflects the adverse impact of frequent flight activity on the stability of the power distribution system. Conversely, when the number of flight takeoffs and landings is below the standard level, the score adjustment is smaller, indicating that the operating pressure of the power distribution system is relatively low.

[0105] Based on the above implementation, as an optional implementation method, in S105, a power supply load fluctuation index is generated according to the number of flight takeoffs and landings. The third stability score is adjusted according to the power supply load fluctuation index to generate the target stability score of the target power distribution room, specifically including S51-S53:

[0106] S51, obtain the number of standard flight takeoffs and landings in the power supply area corresponding to the target power distribution room within a preset time period.

[0107] In airport power supply and distribution systems, flight takeoffs and landings directly affect the power load fluctuations in the distribution room. When flight takeoffs and landings are frequent, the starting and stopping of electrical equipment in the relevant areas are more frequent, exacerbating power load fluctuations, which can significantly impact the operational stability of the distribution room. Therefore, flight operation factors need to be taken into account when assessing the stability of the distribution room.

[0108] First, it is necessary to obtain the standard number of flight takeoffs and landings for the target power distribution room's corresponding power supply area within a preset time period. This standard number is a normal operating reference value derived from historical airport operation data and flight schedule statistics. For example, for a terminal building power distribution room, 15 flight takeoffs and landings per hour during peak hours might be used as the standard value; while for a cargo area power distribution room, the standard value may be relatively lower. The setting of the standard number of flight takeoffs and landings needs to take into account the operational characteristics of different time periods and different areas.

[0109] S52, combining the number of standard flight takeoffs and landings with the number of flight takeoffs and landings, generates a power load fluctuation index.

[0110] Based on the above embodiments, as an optional implementation method, in S52, generating the power supply load fluctuation index by combining the standard flight takeoff and landing count and the flight takeoff and landing count specifically includes:

[0111] Substituting the standard flight takeoff and landing count and the flight takeoff and landing count into the fourth preset formula, the power supply load fluctuation index is generated; where the fourth preset formula is: I=1+μ×(N / N0-1); where I is the power supply load fluctuation index; N is the flight takeoff and landing count; N0 is the standard flight takeoff and landing count; μ is the fluctuation impact factor, used to characterize the degree of impact of changes in the flight takeoff and landing count on the power supply load of the power distribution room, and the value of μ ranges from 0 to 1.

[0112] In airport power supply and distribution systems, changes in the number of flight takeoffs and landings directly affect the power load fluctuations in the distribution room. To accurately quantify this impact, a scientific method for calculating the power load fluctuation index is needed. This method requires comparing the actual number of flight takeoffs and landings with the standard number of flight takeoffs and landings, and then converting this data into a fluctuation index using a reasonable mathematical model.

[0113] This scheme uses the fourth preset formula I=1+μ×(N / N0-1) to calculate the power supply load fluctuation index. The design of this formula takes into account several key factors: First, the ratio of N / N0 reflects the degree of deviation between the actual operation and the standard state; second, the subtraction operation makes the correction term under the standard state 0; finally, the fluctuation influence factor μ controls the intensity of the influence of the deviation on the fluctuation index.

[0114] In practical applications, N represents the actual number of flight takeoffs and landings within the statistical period, and N0 represents the standard number of flight takeoffs and landings during the same period. For example, if the standard number of flight takeoffs and landings in a power distribution room's supply area during peak hours is 15, and the actual number of flight takeoffs and landings reaches 20, the ratio of N / N0 is 1.33, indicating that the operational intensity exceeds the standard level by 33%. The setting of the fluctuation impact factor μ needs to consider the characteristics of the power supply objects of the power distribution room. For example, for a power distribution room directly serving the terminal building, its μ value may be set to 0.8 because flight activities have a significant impact on its power supply load; while for a power distribution room serving auxiliary facilities, its μ value may only be 0.3 because it is relatively less affected by flight activities.

[0115] This formula has good mathematical properties: when N equals N0, meaning flight operations are in a standard state, the fluctuation index I equals 1, indicating that no load fluctuation correction is needed; when N is greater than N0, the fluctuation index is greater than 1, the degree of which depends on the excess ratio and the fluctuation impact factor; when N is less than N0, the fluctuation index is less than 1, reflecting a reduction in load fluctuation. At the same time, since the value of μ is limited to between 0 and 1, excessive amplification of the fluctuation index can be avoided.

[0116] S53, substitute the third stability score and the power supply load fluctuation index into the third preset formula to generate the target stability score; where the third preset formula is: S=S3×(1-δ×I); where S is the target stability score; S3 is the third stability score; I is the power supply load fluctuation index; δ is the load sensitivity coefficient, which is used to characterize the degree of influence of power supply load fluctuation caused by flight take-off and landing on the stability of the power distribution room.

[0117] After obtaining the standard number of flight takeoffs and landings, it is necessary to compare it with the actual number of flight takeoffs and landings to generate a power load fluctuation index. This index reflects the degree of fluctuation in power load caused by actual flight operations. For example, the ratio of the actual number of takeoffs and landings to the standard number of takeoffs and landings can be used as a basis. When the actual number of takeoffs and landings exceeds the standard number of takeoffs and landings, the fluctuation index increases with the degree of excess. This calculation method ensures that the fluctuation index can accurately reflect the intensity of load fluctuations caused by flight operations.

[0118] Finally, the third stability score is adjusted using the third preset formula S=S3×(1-δ×I). Here, S3 is the score considering the power distribution room's own condition and related influences, I is the power supply load fluctuation index, and δ is the load sensitivity coefficient. The load sensitivity coefficient δ needs to be set according to the function type and equipment characteristics of the power distribution room. For example, a power distribution room supplying power to equipment directly serving flight operations, such as boarding bridges, will have a larger δ value, such as 0.3; while a power distribution room supplying power to office areas may only have a δ value of 0.1.

[0119] S106. When the target stability score is lower than the preset score, an abnormal warning message is generated.

[0120] After obtaining the target stability score for the target power distribution room, an early warning mechanism needs to be established to promptly identify potential operational risks. The preset score is a stability threshold determined based on the safe operation requirements and historical operating experience of the airport's power supply and distribution system. Different preset scores can be set for power distribution rooms of different functional types. For example, power distribution rooms supplying power to critical loads such as the terminal building should have a higher preset score to ensure stricter safety management.

[0121] When the system detects that the target stability score is lower than the preset score, it indicates that the operation of the power distribution room is abnormal, and an anomaly warning message needs to be generated. The anomaly warning message should include information from multiple dimensions: First, basic information, including the power distribution room number, function type, and time of anomaly occurrence; second, anomaly cause analysis, which needs to combine various indicators from the previous scoring process to determine the main factors leading to the score reduction, such as abnormal equipment operating parameters, power supply characteristics exceeding standards, influence from related power distribution rooms, or load fluctuations caused by flight operations; finally, risk level assessment, which determines the alarm level based on the degree to which the target stability score is lower than the preset score.

[0122] To improve the effectiveness of early warnings, the system can adopt a tiered early warning mechanism. For example, a yellow warning is generated when the score is only slightly lower than the preset score, prompting maintenance personnel to pay attention; a red warning is generated when the score is significantly lower than the preset score, requiring immediate intervention. Warning information can be pushed to relevant personnel through various means such as the maintenance management platform and mobile apps to ensure timely communication.

[0123] like Figure 2 As shown, Figure 2 This is a schematic diagram of the interface of the unmanned inspection data analysis platform for airport power distribution rooms provided in this application embodiment. This platform interface demonstrates the specific implementation of the unmanned inspection data analysis method for airport power distribution rooms. The platform acquires and analyzes various data from target power distribution rooms in the airport, achieving intelligent operation and maintenance management of the power distribution rooms. The data overview at the top of the interface displays basic operational data such as a total of 250 inspections, 15 equipment fault alarms, and 120 hours of inspection time. These data originate from equipment operation data and power supply characteristic data collected by various sensors within the power distribution room. The system analysis module displays 50 automatic inspections (60% completion rate) and 2 remote inspections (43% completion rate), reflecting the implementation effect of unmanned intelligent inspection of the power distribution room. The power distribution room equipment early warning trend distribution chart on the left shows the early warning analysis results based on stability scores through a green curve. An early warning is triggered when the score falls below a preset threshold. The notification and announcement bar on the right displays various early warning information and operational status reminders. These information are automatically generated by the system after comprehensive analysis of equipment operation data, power supply characteristic data, and load fluctuations caused by flight takeoffs and landings. The platform uses data visualization to intuitively display the operating status, stability assessment results, and early warning information of the power distribution room, enabling real-time monitoring and intelligent analysis of the power distribution room's operating status and improving the operational reliability and management efficiency of the airport's power supply and distribution system.

[0124] like Figure 3 As shown, Figure 3 This is a schematic diagram of the equipment data recording interface of the unmanned inspection data analysis platform for airport power distribution rooms provided in this application embodiment. This interface is mainly used to display and manage the operational data records of various devices within the power distribution room. A search bar for device names is located at the top of the interface, allowing for quick location of data records for specific devices. The main area displays basic information for multiple devices in card format, including device name (e.g., transformer, distribution cabinet, switch cabinet, etc.), device number, and status information. Each device card has "Edit" and "Settings" buttons in the lower right corner, facilitating management and configuration of device data by maintenance personnel. This interface design allows maintenance personnel to easily view and manage the operational data of each device, providing intuitive data support for monitoring and evaluating device operational status. A pagination navigation bar is located at the bottom of the page for easy browsing of more device records.

[0125] like Figure 4 As shown, Figure 4 This is a schematic diagram of the equipment management interface of the unmanned inspection data analysis platform for airport power distribution rooms provided in this application embodiment. The interface displays the core operating data and control functions of the power distribution room: the left area displays the equipment operating status, including 271 controller measurement data points and 1649 equipment status points; the central area provides control options for equipment operation, including switch status switching and operating mode (high, medium, low) selection, with the current operating temperature at 57 degrees Celsius, and displays operating and protection status information; the right area is the intelligent analysis and processing system, displaying the current operating parameters 25 via a circular dashboard, and setting multiple control switches, including on / off options for equipment operation, temperature control, battery overload, and SMS push notification functions. This interface design enables intelligent monitoring and management of the power distribution room equipment, allowing maintenance personnel to monitor the equipment operating status in real time and perform necessary control adjustments.

[0126] Based on the above method, this application also discloses an unmanned inspection data analysis system for airport power distribution rooms, such as... Figure 5 As shown, Figure 5 This is a schematic diagram of the structure of an unmanned inspection data analysis system for an airport power distribution room provided in an embodiment of this application. The system includes: a first acquisition module, a first combination module, a second acquisition module, a second combination module, a generation module, and an early warning module; wherein,

[0127] The first acquisition module is used to acquire the functional type of the target power distribution room in the airport, and the equipment operation data and power supply characteristic data of the target power distribution room within a preset time period. The first combination module is used to generate a first stability score for the target power distribution room based on the functional type, combined with the equipment operation data and power supply characteristic data. The first stability score characterizes the degree of stable operation of the target power distribution room. The second acquisition module is used to acquire the second stability score of associated power distribution rooms, and the correlation index between the target power distribution room and the associated power distribution room. The associated power distribution room is a power distribution room that has a power connection with the target power distribution room, and the correlation index characterizes the impact of power supply on the target power distribution room. The system includes: a first stability score and a second stability index; a third stability score for the target power distribution room; a second combination module to adjust the first stability score by combining the second stability score and the correlation index; a third generation module to obtain the number of flight takeoffs and landings in the power supply area corresponding to the target power distribution room within a preset time period, generate a power load fluctuation index based on the number of flight takeoffs and landings, adjust the third stability score based on the power load fluctuation index, and generate a target stability score for the target power distribution room. The power load fluctuation index is used to characterize the degree of impact of flight takeoffs and landings on the power supply load; and an early warning module to generate an abnormal early warning message when the target stability score is lower than the preset score.

[0128] It should be noted that the system provided in the above embodiments is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0129] Please see Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 6 As shown, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0130] The communication bus 1002 is used to realize the connection and communication between these components.

[0131] The user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0132] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0133] The processor 1001 may include one or more processing cores. The processor 1001 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005. Optionally, the processor 1001 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 1001 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 1001 and may be implemented as a separate chip.

[0134] The memory 1005 may include random access memory (RAM) or read-only memory. Optionally, the memory 1005 may include a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1005 may also be at least one storage device located remotely from the aforementioned processor 1001. Figure 6 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for an unmanned inspection data analysis method for airport power distribution rooms.

[0135] exist Figure 6In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 1001 can be used to call the application program stored in the memory 1005 for an unmanned inspection data analysis method for an airport power distribution room. When executed by one or more processors, the electronic device performs one or more of the methods described in the above embodiments.

[0136] An electronic device readable storage medium stores instructions that, when executed by one or more processors, cause the electronic device to perform one or more of the methods described in the above embodiments.

[0137] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0138] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0139] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some service interfaces; indirect couplings or communication connections between devices or units may be electrical or other forms.

[0140] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0141] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0142] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0143] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. An airport power distribution room unmanned inspection data analysis method, characterized in that, The method comprises: obtaining a function type of a target power distribution room in an airport, and equipment operation data and power supply characteristic data of the target power distribution room within a preset time period; generating a first stability score of the target power distribution room on the basis of the function type, in combination with the equipment operation data and the power supply characteristic data, the first stability score being used to represent a stable operation degree of the target power distribution room; obtaining a second stability score of an associated power distribution room and an association index between the target power distribution room and the associated power distribution room; wherein the associated power distribution room is a power distribution room having a power connection relationship with the target power distribution room, and the association index is used to represent a degree of influence of the target power distribution room on the associated power distribution room; adjusting the first stability score in combination with the second stability score and the association index to generate a third stability score of the target power distribution room; obtaining a number of flights taking off and landing in a power supply area corresponding to the target power distribution room within the preset time period, generating a power supply load fluctuation index according to the number of flights taking off and landing, and adjusting the third stability score according to the power supply load fluctuation index to generate a target stability score of the target power distribution room, the power supply load fluctuation index being used to represent a degree of influence of flights taking off and landing on a power supply load; generating an abnormal early warning information when the target stability score is lower than a preset score.

2. The method of claim 1, wherein the method further comprises: The equipment operation data comprises an equipment temperature, an equipment vibration frequency and an equipment noise value, the power supply characteristic data comprises a voltage fluctuation value and a current fluctuation value, and the first stability score of the target power distribution room is generated on the basis of the function type, in combination with the equipment operation data and the power supply characteristic data, comprising: determining a standard equipment temperature, a standard equipment vibration frequency and a standard equipment noise value, and a standard voltage fluctuation interval and a standard current fluctuation interval according to the function type of the target power distribution room; calculating a first difference value between the standard equipment temperature and the equipment temperature, a second difference value between the standard equipment vibration frequency and the equipment vibration frequency, and a third difference value between the standard equipment noise value and the equipment noise value; generating a first sub-stability score of the target power distribution room in combination with the first difference value, the second difference value and the third difference value; determining whether the voltage fluctuation value is within the standard voltage fluctuation interval and whether the current fluctuation value is within the standard current fluctuation interval; when the voltage fluctuation value is within the standard voltage fluctuation interval and the current fluctuation value is within the standard current fluctuation interval, taking the first sub-stability score as the first stability score of the target power distribution room; when the voltage fluctuation value is not within the standard voltage fluctuation interval and / or the current fluctuation value is not within the standard current fluctuation interval, generating a second sub-stability score according to the degree to which the voltage fluctuation value deviates from the standard voltage fluctuation interval and the degree to which the current fluctuation value deviates from the standard current fluctuation interval, wherein the second sub-stability score is inversely proportional to the degree to which the voltage fluctuation value deviates from the standard voltage fluctuation interval and the degree to which the current fluctuation value deviates from the standard current fluctuation interval; weighting the first sub-stability score and the second sub-stability score according to a preset weight to generate a first stability score of the target power distribution room.

3. The method of claim 2, wherein the method further comprises: The first sub-stability score of the target power distribution room is generated by combining the first difference value, the second difference value, and the third difference value, including: The first difference value, the second difference value, and the third difference value are normalized respectively to obtain a first normalized difference value, a second normalized difference value, and a third normalized difference value. The first normalized difference value, the second normalized difference value, and the third normalized difference value are weighted to obtain a weighted difference value. The first sub-stability score of the target power distribution room is generated according to the weighted difference value, wherein the first sub-stability score is inversely proportional to the weighted difference value.

4. The method of claim 1, wherein the method further comprises: The correlation index of the target power distribution room and the associated power distribution room is obtained, including: The power connection type between the target power distribution room and the associated power distribution room, and the connection distance from the target power distribution room to the associated power distribution room are obtained. A corresponding relationship between the power connection type and the basic correlation coefficient is established, wherein the power connection type includes main power supply connection and backup power supply connection, the basic correlation coefficient of the main power supply connection is set as a first preset value, the basic correlation coefficient of the backup power supply connection is set as a second preset value, and the first preset value is greater than the second preset value. The corresponding basic correlation coefficient is determined from the corresponding relationship according to the power connection type. The basic correlation coefficient and the connection distance are substituted into a first preset formula to generate the correlation index of the target power distribution room and the associated power distribution room, wherein The first preset formula is R=K×(1-α×L / L0)^β, wherein R is the correlation index, K is the basic correlation coefficient, L is the connection distance between the target power distribution room and the associated power distribution room, L0 is the standard connection distance, L is taken as L0 when L is greater than L0, α is the distance attenuation coefficient, and β is the nonlinear correction index.

5. The method of claim 1, wherein the method further comprises: The first stability score is adjusted to generate a third stability score of the target power distribution room by combining the second stability score and the correlation index, including: The first stability score, the second stability score, and the correlation index are substituted into a second preset formula to generate the third stability score of the target power distribution room, wherein The second preset formula is S3=S1-(S1-S2)×R×γ, wherein S3 is the third stability score, S1 is the first stability score, S2 is the second stability score, R is the correlation index, and γ is the adjustment coefficient.

6. The method of claim 1, wherein the method further comprises: The generating power supply load fluctuation index according to the flight take-off and landing times, adjusting the third stability score according to the power supply load fluctuation index, and generating the target stability score of the target power distribution room include: Obtaining the standard flight take-off and landing times of the power supply area corresponding to the target power distribution room in the preset time period; Combining the standard flight take-off and landing times and the flight take-off and landing times to generate a power supply load fluctuation index; The third preset formula is: S=S3×(1-δ×I); wherein, S is the target stability score; S3 is the third stability score; I is the power supply load fluctuation index; δ is a load sensitivity coefficient, used to represent the influence degree of the power supply load fluctuation caused by flight take-off and landing on the stability of the power distribution room.

7. The method of claim 6, wherein the method further comprises: The combining the standard flight take-off and landing times and the flight take-off and landing times to generate a power supply load fluctuation index includes: The fourth preset formula is: I=1+μ×(N / N0-1); wherein, I is the power supply load fluctuation index; N is the flight take-off and landing times; N0 is the standard flight take-off and landing times; μ is a fluctuation influence factor, used to represent the influence degree of the flight take-off and landing times change on the power supply load of the power distribution room, and the value range of μ is 0 to 1. The system includes: a first acquisition module, a first combination module, a second acquisition module, a second combination module, a generation module, and a warning module; wherein, 8. An airfield power distribution room unmanned inspection data analysis system, characterized in that, The first acquisition module is used to acquire the functional type of the target power distribution room in the airport, and the equipment operation data and power supply characteristic data of the target power distribution room in a preset time period; The first combination module is used to generate a first stability score of the target power distribution room on the basis of the functional type, in combination with the equipment operation data and the power supply characteristic data, the first stability score being used to represent the stable operation degree of the target power distribution room; The second acquisition module is used to acquire a second stability score of an associated power distribution room and an association index of the target power distribution room and the associated power distribution room; wherein, the associated power distribution room is a power distribution room having an electric connection relationship with the target power distribution room, and the association index is used to represent the degree of influence of the target power distribution room on the associated power distribution room; The second combination module is used to adjust the first stability score in combination with the second stability score and the association index, to generate a third stability score of the target power distribution room; The generation module is used to acquire the flight take-off and landing times of the power supply area corresponding to the target power distribution room in the preset time period, generate a power supply load fluctuation index according to the flight take-off and landing times, adjust the third stability score according to the power supply load fluctuation index, and generate a target stability score of the target power distribution room, the power supply load fluctuation index being used to represent the influence degree of flight take-off and landing on the power supply load; ​ The early warning module is configured to generate an abnormal early warning information when the target stability score is lower than a preset score.

9. An electronic device, comprising: An electronic device comprising a processor, a memory, a user interface, and a network interface, the memory configured to store instructions, the user interface and network interface configured to communicate with other devices, and the processor configured to execute the instructions stored in the memory to cause the electronic device to perform the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, A computer program stored in a memory and capable of being loaded by a processor and executed to perform the method of any one of claims 1-7.

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