Power distribution cabinet operation dynamic safety assessment analysis method and system

Abstract

The invention discloses a power distribution cabinet operation dynamic safety assessment analysis method and system, and relates to the technical field of safety assessment analysis, and the power distribution cabinet operation dynamic safety assessment analysis method guarantees the accuracy and real-time performance of data through high-precision data collection and advanced monitoring software; data quality and consistency are improved through effective data preprocessing; the comprehensive safety analysis coefficient and detailed anomaly level evaluation enable anomaly detection to be more accurate and timely; and timely alarm and maintenance measures guarantee the safety and reliability of the power distribution system. Generally speaking, the method significantly improves the efficiency and effect of dynamic operation safety evaluation of the power distribution cabinet, reduces potential safety hazards, ensures stable operation of a power system, calculates the safety analysis coefficient FXZ by using a formula, and realizes comprehensive safety evaluation based on multi-source data. Compared with a traditional method, the method is remarkably improved in the aspects of accuracy, flexibility, real-time performance and early warning capability.

Description

Technical Field

[0001] The present invention relates to the technical field of safety assessment and analysis, and in particular to a method and system for dynamic safety assessment and analysis of power distribution cabinet operation. Background Art

[0002] Power system safety management is a vital part of power engineering, which involves ensuring the stable operation of the power system under various working conditions and preventing power failures from having a significant impact on society and the economy. In this field, the distribution cabinet, as a key component of the power system, is directly related to the safety and stability of the entire power system. The distribution cabinet is responsible for the distribution and management of electric energy, and its operation needs to be monitored and evaluated in real time to ensure the reliable transmission and distribution of electricity. Therefore, the research on the dynamic safety assessment method of the distribution cabinet operation is of great significance to improving the overall safety of the power system.

[0003] The existing dynamic safety assessment methods for distribution cabinet operation still lack accuracy and stability in practical applications. Packet loss or delays may occur during data transmission, and data processing is slow and complex, resulting in limited accuracy and robustness of the assessment model. As a result, real-time monitoring may result in misjudgments or omissions, which seriously affects the effect of dynamic safety assessment of distribution cabinets and reduces the safety and reliability of the power system. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides a method for dynamic safety assessment and analysis of power distribution cabinet operation, which solves the problems mentioned in the above background technology.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for dynamic safety assessment and analysis of power distribution cabinet operation, the specific steps are as follows:

[0008] S1. Data collection: by setting up sensor equipment in the power cabinet to collect various operating parameters of the power distribution cabinet in real time, and setting up monitoring software in the network transmission equipment to obtain real-time data from the network transmission equipment;

[0009] S2, data processing, preprocessing the collected multi-source data, including data cleaning, denoising and normalization, and reorganizing them into a first data set, a second data set and a third data set;

[0010] S3, data calculation, integrating and calculating the first data set, the second data set and the third data set, so as to generate a safety analysis coefficient FXZ;

[0011] S4, abnormality analysis, comparing the calculated safety analysis coefficient FXZ with a preset first threshold value Y, thereby generating a first comparison result, and determining whether the operation of the power distribution cabinet is abnormal according to the first comparison result;

[0012] S5, magnitude analysis: if the first comparison result determines that the operation of the power distribution cabinet is abnormal, the safety analysis coefficient FXZ is integrated and calculated with the first threshold value Y to generate a magnitude coefficient LJZ, and the magnitude coefficient LJZ is compared with the preset second threshold value R to generate a second comparison result, and the abnormal level of the power distribution cabinet is determined according to the second comparison result;

[0013] S6. Send an alarm to the terminal and repair the distribution cabinet.

[0014] Preferably, in step S1, real-time data collection of the power distribution cabinet includes electrical parameters and environmental parameters;

[0015] Among them, electrical parameters include: voltage value, current value and power factor;

[0016] Environmental parameters include: temperature, humidity and vibration.

[0017] Preferably, in step S1, real-time data is collected from the network transmission equipment to obtain network parameters, including bandwidth utilization, data packet loss rate, network delay value, and error rate.

[0018] Preferably, in step S2, the electrical parameters, environmental parameters and network parameters are reorganized into a first data set, a second data set and a third data set according to timestamps after preprocessing;

[0019] The first data set includes a voltage value, a current value, and a power factor;

[0020] The second data set includes temperature values, humidity values, and vibration values;

[0021] The third data set includes bandwidth utilization, packet loss rate, network delay value and error rate;

[0022] The voltage values ​​are recorded as DY1, DY2, DY3, ..., DYn according to the timestamps;

[0023] The current values ​​are recorded as DL1, DL2, DL3, ..., DLn according to the timestamps;

[0024] The power factors are recorded as GL1, GL2, GL3, ..., GLn according to the timestamp;

[0025] The temperature values ​​are recorded as WD1, WD2, WD3, ..., WDn according to the timestamp;

[0026] The humidity values ​​are recorded as SD1, SD2, SD3, ..., SDn according to the timestamp;

[0027] The vibration values ​​are recorded as ZD1, ZD2, ZD3, ..., ZDn according to the timestamp;

[0028] The bandwidth utilization is recorded as DK1, DK2, DK3, ..., DKn according to the timestamp;

[0029] The data packet loss rates are recorded as DB1, DB2, DB3, ..., DBn according to the timestamps;

[0030] The network delay values ​​are recorded as YC1, YC2, YC3, ..., YCn according to the timestamps;

[0031] The error rates are recorded as CW1, CW2, CW3, ..., CWn according to the timestamps.

[0032] Preferably, in step S3, the safety analysis coefficient is calculated by the following formula:

[0033]

[0034] Wherein: a1 and a2 are weight values ​​respectively, the values ​​of a1 and a2 are adjusted by the user, S1 and S2 are first stage values ​​and second stage values ​​respectively, the first stage value S1 and the second stage value S2 are obtained by integrating and calculating the first data set, the second data set and the third data set respectively.

[0035] Preferably, the first stage value S1 and the second stage value S2 are calculated and obtained by the following formulas respectively:

[0036]

[0037] Where: DYn, DLn, GLn, WDn, SDn and ZDn are the current value, voltage value, power factor value, temperature value, humidity value and vibration value at timestamp n respectively;

[0038] DYn-1, DLn-1, GLn-1, WDn-1, SDn-1 and ZDn-1 are the current value, voltage value, power factor value, temperature value, humidity value and vibration value at timestamp n-1 respectively;

[0039] DYn-2, DLn-2, ​​GLn-2, ​​WDn-2, SDn-2 and ZDn-2 are the current value, voltage value, power factor value, temperature value, humidity value and vibration value at timestamp n-2 respectively;

[0040] K is the access value obtained by integrating and calculating the third data set.

[0041] Preferably, the intervention value K is calculated by the following formula:

[0042]

[0043] Where: DKn, YCn, DBn, and CWn are the bandwidth utilization rate, network latency value, data packet loss rate, and error rate at timestamp n, respectively.

[0044] Preferably, the first comparison result is:

[0045] When FXZ ≤ Y, it means that the current power distribution cabinet is operating normally;

[0046] When FXZ > Y, it means that the current power distribution cabinet is operating abnormally.

[0047] Preferably, the second comparison result is;

[0048] When LJZ < R, it means that the current power distribution cabinet has a first-level abnormality;

[0049] When LJZ = R, it means that the current power distribution cabinet has a second-level abnormality;

[0050] When LJZ > R, it means that the current power distribution cabinet has a third-level abnormality.

[0051] This application also includes a dynamic safety assessment and analysis system for power distribution cabinet operation, including: a data acquisition unit, a data processing unit, a data calculation unit, a data analysis unit, and an alarm unit;

[0052] The data acquisition unit is used to dynamically collect data from the power distribution cabinet and network transmission equipment to obtain multi-source data;

[0053] The data processing unit preprocesses the multi-source data collected and reorganizes the preprocessed data to generate a first data set, a second data set, and a third data set;

[0054] The data calculation unit integrates and calculates the first data set, the second data set, and the third data set to generate a safety analysis coefficient FXZ;

[0055] The data analysis unit compares the safety analysis coefficient FXZ with the first threshold Y to generate a first comparison result, compares the magnitude coefficient LJZ with a preset second threshold R to generate a second comparison result, and determines the abnormality level of the power distribution cabinet according to the second comparison result;

[0056] The alarm unit sends an alarm to the terminal to repair the power distribution cabinet.

[0057] (III) Beneficial Effects

[0058] The present invention provides a method for dynamic safety assessment and analysis of power distribution cabinet operation, which has the following beneficial effects:

[0059] 1. The dynamic safety assessment and analysis method for the operation of the distribution cabinet ensures the accuracy and real-time nature of the data through high-precision data collection and advanced monitoring software; effective data preprocessing improves data quality and consistency; comprehensive safety analysis coefficients and detailed abnormality level assessments make abnormality detection more accurate and timely; timely alarms and maintenance measures ensure the safety and reliability of the distribution system. Overall, this method significantly improves the efficiency and effectiveness of the dynamic safety assessment of the operation of the distribution cabinet, reduces potential safety hazards, and ensures the stable operation of the power system.

[0060] 2. The dynamic safety assessment and analysis method for the operation of the distribution cabinet uses the formula to calculate the safety analysis coefficient FXZ, realizing a comprehensive safety assessment based on multi-source data. Compared with traditional methods, this method has significantly improved accuracy, flexibility, real-time and early warning capabilities. Through reasonable weight setting and phased calculation, the scientificity and reliability of the assessment results are ensured, providing a strong guarantee for the safe operation of the distribution cabinet and network transmission equipment, effectively reducing the risk of equipment failure, and improving the stability and operation efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a step diagram of the method of the present invention.

[0062] Figure 2 It is a flow chart of the system of the present invention. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] Example 1

[0065] See also Figure 1 , a method for dynamic safety assessment and analysis of power distribution cabinet operation, the specific steps are as follows:

[0066] S1. Data collection: by setting up sensor equipment in the power cabinet to collect various operating parameters of the power distribution cabinet in real time, and setting up monitoring software in the network transmission equipment to obtain real-time data from the network transmission equipment;

[0067] S2, data processing, preprocessing the collected multi-source data, including data cleaning, denoising and normalization, and reorganizing them into a first data set, a second data set and a third data set;

[0068] S3, data calculation, integrating and calculating the first data set, the second data set and the third data set, so as to generate a safety analysis coefficient FXZ;

[0069] S4, abnormality analysis, comparing the calculated safety analysis coefficient FXZ with a preset first threshold value Y, thereby generating a first comparison result, and determining whether the operation of the power distribution cabinet is abnormal according to the first comparison result;

[0070] S5, magnitude analysis: if the first comparison result determines that the operation of the power distribution cabinet is abnormal, the safety analysis coefficient FXZ is integrated and calculated with the first threshold value Y to generate a magnitude coefficient LJZ, and the magnitude coefficient LJZ is compared with the preset second threshold value R to generate a second comparison result, and the abnormal level of the power distribution cabinet is determined according to the second comparison result;

[0071] S6. Send an alarm to the terminal and repair the distribution cabinet.

[0072] In this embodiment: In step S1, various operating parameters of the power distribution cabinet are collected in real time by setting sensor equipment in the power distribution cabinet, and monitoring software is set in the network transmission equipment to obtain real-time data of the network transmission equipment. The accuracy and timeliness of the data can be ensured by high-precision sensors and advanced monitoring software. The beneficial effect of this step is that the operating status of the power distribution cabinet and the network transmission equipment can be fully monitored in real time, providing a reliable data basis for subsequent data processing and analysis.

[0073] In step S2, the collected multi-source data is preprocessed, including data cleaning, denoising and normalization, and reorganized into a first data set, a second data set and a third data set. Through effective data preprocessing, the quality and consistency of the data can be improved, and noise and redundant information can be eliminated. The task of this step is to convert the raw data into a usable structured data set, provide a basis for data calculation, and achieve the purpose of improving data utilization efficiency and accuracy.

[0074] In step S3, the safety analysis coefficient FXZ is generated by integrating and calculating the first data set, the second data set, and the third data set. Through the integrated calculation, the operating status of the distribution cabinet can be comprehensively analyzed and key safety indicators can be extracted. The beneficial effect of this step is to obtain a comprehensive safety analysis coefficient, which provides an important reference for subsequent abnormal analysis and magnitude analysis.

[0075] In step S4, the calculated safety analysis factor FXZ is compared with the preset first threshold value Y to generate a first comparison result, and it is determined whether the operation of the power distribution cabinet is abnormal based on the first comparison result. By comparing the threshold values, the abnormal situation in the operation of the power distribution cabinet can be quickly identified. The task of this step is to perform preliminary abnormality detection to achieve the purpose of timely discovering potential problems.

[0076] In step S5, when the first comparison result determines that the operation of the power distribution cabinet is abnormal, the safety analysis coefficient FXZ is integrated with the first threshold value Y to generate a magnitude coefficient LJZ, and the magnitude coefficient LJZ is compared with the preset second threshold value R to generate a second comparison result. According to the second comparison result, the abnormal level of the power distribution cabinet is determined. Through further magnitude analysis, the severity and impact range of the abnormality can be clarified. The beneficial effect of this step is to provide more detailed abnormal information to help decision makers take corresponding countermeasures.

[0077] In step S6, after determining that the distribution cabinet is operating abnormally and determining its abnormality level, an alarm is sent to the terminal and the distribution cabinet is repaired. Timely alarms and maintenance measures can prevent small problems from turning into major failures and ensure the safe operation of the distribution system. The task of this step is to implement specific response measures to reduce the risk of failures and reduce downtime.

[0078] This method ensures the accuracy and real-time nature of data through high-precision data acquisition and advanced monitoring software; effective data preprocessing improves data quality and consistency; comprehensive safety analysis coefficients and detailed abnormality level assessments make abnormality detection more accurate and timely; timely alarms and maintenance measures ensure the safety and reliability of the distribution system. Overall, this method significantly improves the efficiency and effectiveness of dynamic safety assessment of distribution cabinet operation, reduces potential safety hazards, and ensures the stable operation of the power system.

[0079] Example 2

[0080] See also Figure 1 ,In step S1, real-time data collection of the power distribution cabinet is performed, including electrical parameters and environmental parameters;

[0081] Among them, electrical parameters include: voltage value, current value and power factor;

[0082] Environmental parameters include: temperature, humidity and vibration.

[0083] In this embodiment: by collecting and discovering abnormal conditions of electrical parameters such as voltage, current, power factor, etc., the equipment is prevented from being damaged due to faults such as overload and short circuit. By monitoring temperature, humidity and vibration, it is ensured that the distribution cabinet operates under suitable environmental conditions and reduces the negative impact of environmental factors on equipment performance. By monitoring and adjusting the power factor, the use of power resources is optimized, reactive power loss is reduced, and the operating efficiency of the power system is improved. Through comprehensive parameter monitoring, potential problems can be discovered in time before a fault occurs, early warning can be given, and the risk of faults can be reduced. Through real-time monitoring and timely maintenance, small problems can be prevented from turning into major faults, equipment downtime can be reduced, maintenance costs can be reduced, and stable operation of the power system can be ensured.

[0084] Example 3

[0085] See also Figure 1 In step S1, real-time data is collected from the network transmission equipment to obtain network parameters, including bandwidth utilization, data packet loss rate, network delay value, and error rate.

[0086] In this embodiment: through high-precision data collection and advanced monitoring software, the accuracy and real-time nature of the data are guaranteed; effective data preprocessing improves data quality and consistency; comprehensive safety analysis coefficients and detailed abnormality level assessments make abnormality detection more accurate and timely; timely alarms and maintenance measures ensure the safety and reliability of the power distribution system and network transmission system. In general, this method significantly improves the efficiency and effectiveness of dynamic safety assessment of power distribution cabinet operation and monitoring of network transmission equipment, reduces potential safety hazards, and ensures the stable operation of the power system and network transmission system.

[0087] Example 4

[0088] See also Figure 1 , in step S2, the electrical parameters, environmental parameters and network parameters are reorganized into a first data set, a second data set and a third data set according to the timestamps after preprocessing;

[0089] The first data set includes a voltage value, a current value, and a power factor;

[0090] The second data set includes temperature values, humidity values, and vibration values;

[0091] The third data set includes bandwidth utilization, packet loss rate, network delay value and error rate;

[0092] The voltage values ​​are recorded as DY1, DY2, DY3, ..., DYn according to the timestamps;

[0093] The current values ​​are recorded as DL1, DL2, DL3, ..., DLn according to the timestamps;

[0094] The power factors are recorded as GL1, GL2, GL3, ..., GLn according to the timestamp;

[0095] The temperature values ​​are recorded as WD1, WD2, WD3, ..., WDn according to the timestamp;

[0096] The humidity values ​​are recorded as SD1, SD2, SD3, ..., SDn according to the timestamp;

[0097] The vibration values ​​are recorded as ZD1, ZD2, ZD3, ..., ZDn according to the timestamp;

[0098] The bandwidth utilization is recorded as DK1, DK2, DK3, ..., DKn according to the timestamp;

[0099] The data packet loss rates are recorded as DB1, DB2, DB3, ..., DBn according to the timestamps;

[0100] The network delay values ​​are recorded as YC1, YC2, YC3, ..., YCn according to the timestamps;

[0101] The error rates are recorded as CW1, CW2, CW3, ..., CWn according to the timestamps.

[0102] In this embodiment: through real-time data collection, preprocessing and structured management of electrical parameters, environmental parameters and network parameters, comprehensive monitoring and accurate evaluation of the operating status of the distribution cabinet is achieved. Compared with traditional technical means, the improvements include high-quality data processing, time consistency management, fast integrated calculation, and real-time monitoring and decision support. These improvements effectively improve the efficiency and accuracy of dynamic safety evaluation of the distribution cabinet, reduce equipment operation risks, and ensure the stable operation of the power system and network transmission system.

[0103] Example 5

[0104] See also Figure 1 In step S3, the safety analysis coefficient is calculated by the following formula:

[0105]

[0106] Wherein: a1 and a2 are weight values ​​respectively, the values ​​of a1 and a2 are adjusted by the user, S1 and S2 are first stage values ​​and second stage values ​​respectively, the first stage value S1 and the second stage value S2 are obtained by integrating and calculating the first data set, the second data set and the third data set respectively.

[0107] In this embodiment: the formula comprehensively considers the key parameters (electrical parameters, environmental parameters, network parameters) at different stages, and comprehensively considers the influence of each data set through weighted calculation to ensure the comprehensiveness and accuracy of the evaluation results. Users can adjust the weight values ​​a1 and a2 according to actual needs to more flexibly reflect the security assessment needs in different situations.

[0108] The adjustability of the weight value enables this method to adapt to different operating environments and conditions of distribution cabinets and provide customized safety assessments. Through the phased calculation of the stage values ​​S1 and S2, different aspects of the operation of the distribution cabinet can be gradually evaluated, thereby improving the flexibility of safety monitoring.

[0109] The exponential function e in the formula can respond quickly to parameter changes, making the evaluation results more sensitive to emergencies and improving the ability of real-time monitoring and evaluation. By integrating real-time data to calculate the safety analysis coefficient FXZ, it can promptly reflect changes in the operating status of the distribution cabinet and provide a basis for quick decision-making.

[0110] Based on the integrated calculation of multi-source data, the safety analysis coefficient FXZ can comprehensively reflect the safety status of the distribution cabinet operation, improve the accuracy of abnormal detection and early warning, and ensure the influence of important parameters on the safety assessment results by reasonably setting the weight values ​​a1 and a2, thereby improving the reliability of early warning.

[0111] The safety analysis coefficient FXZ is calculated using the formula to achieve a comprehensive safety assessment based on multi-source data. Compared with traditional methods, this method has significantly improved accuracy, flexibility, real-time and early warning capabilities. Through reasonable weight setting and phased calculation, the scientificity and reliability of the evaluation results are ensured, which provides a strong guarantee for the safe operation of distribution cabinets and network transmission equipment, effectively reduces the risk of equipment failure, and improves the stability and operation efficiency of the system.

[0112] Example 6

[0113] See also Figure 1 The first stage value S1 and the second stage value S2 are calculated by the following formulas:

[0114]

[0115] Where: DYn, DLn, GLn, WDn, SDn and ZDn are the current value, voltage value, power factor value, temperature value, humidity value and vibration value at timestamp n respectively;

[0116] DYn-1, DLn-1, GLn-1, WDn-1, SDn-1 and ZDn-1 are the current value, voltage value, power factor value, temperature value, humidity value and vibration value at timestamp n-1 respectively;

[0117] DYn-2, DLn-2, ​​GLn-2, ​​WDn-2, SDn-2 and ZDn-2 are the current value, voltage value, power factor value, temperature value, humidity value and vibration value at timestamp n-2 respectively;

[0118] K is the access value obtained by integrating and calculating the third data set.

[0119] The intervention value K is calculated using the following formula:

[0120]

[0121] Where: DKn, YCn, DBn and CWn are the bandwidth utilization, network delay value, data packet loss rate and error rate at timestamp n respectively.

[0122] In this embodiment: the first stage value S1 is calculated by the change rate of voltage value, current value and power factor, which can accurately reflect the change of electrical state of the distribution cabinet and help to detect electrical anomalies in time. This calculation method can quickly identify fluctuations in electrical parameters, facilitate the prediction of potential problems, and improve the stability of the electrical system.

[0123] The second-stage value S2 is calculated by the rate of change of temperature, humidity and vibration values, which can comprehensively evaluate the environmental status of the distribution cabinet and help identify the impact of environmental factors on equipment operation. This calculation method can effectively detect abnormal changes in environmental parameters, provide comprehensive monitoring of the operating environment of the distribution cabinet, and ensure the safe operation of the equipment.

[0124] By calculating the electrical parameters and environmental parameters in stages, the efficiency and accuracy of data processing are achieved, ensuring the contribution of parameters in each stage to safety analysis. The staged calculation helps to simplify the processing of complex data and improve the efficiency and accuracy of data processing.

[0125] The introduction of the intervention value K makes the calculation formula flexible. Users can adjust parameters according to actual needs to adapt to different operating environments and conditions. This flexibility helps to improve the adaptability and accuracy of security assessment and meet diverse monitoring needs.

[0126] Example 7

[0127] See also Figure 1 , the first comparison result is:

[0128] When FXZ≤Y, it means that the current distribution cabinet is operating normally;

[0129] When FXZ>Y, it means that the current distribution cabinet operation is abnormal.

[0130] The second comparison result is:

[0131] When LJZ < R, it indicates that the current power distribution cabinet is at the first-level anomaly;

[0132] When LJZ = R, it indicates that the current power distribution cabinet is at the second-level anomaly;

[0133] When LJZ > R, it indicates that the current power distribution cabinet is at the third-level anomaly.

[0134] In this embodiment: Through the first comparison result, the abnormal operation of the power distribution cabinet can be quickly identified, ensuring the timely discovery of potential problems and preventing accidents. This real-time monitoring mechanism can effectively improve the safety of equipment operation and ensure the stable operation of the power supply system.

[0135] The second comparison result can clarify the anomaly level of the power distribution cabinet, helping the operation and maintenance personnel quickly understand the severity of the problem and take corresponding countermeasures. This hierarchical management helps improve the efficiency and accuracy of fault handling, reduces the impact of faults on the system, and the accurate warning information helps optimize the operation and maintenance strategy, reduce the maintenance cost, and improve the efficiency and effect of the operation and maintenance work. The combination of anomaly analysis and magnitude analysis can provide more accurate fault warning information, avoid false alarms and missed alarms, improve the reliability of the warning system. Through real-time monitoring and accurate warning, potential faults can be processed in a timely manner, reducing the equipment downtime and improving the stability and reliability of the system.

[0136] Please refer to Figure 2 , this application also includes a dynamic safety assessment and analysis system for the operation of a power distribution cabinet, including: a data acquisition unit, a data processing unit, a data calculation unit, a data analysis unit, and an alarm unit;

[0137] The data acquisition unit is used to dynamically collect data from the power distribution cabinet and network transmission equipment to obtain multi-source data;

[0138] The data processing unit preprocesses the multi-source data collected and re-organizes the preprocessed data to generate a first data set, a second data set, and a third data set;

[0139] The data calculation unit integrates and calculates the first data set, the second data set, and the third data set to generate a safety analysis coefficient FXZ;

[0140] The data analysis unit compares the safety analysis coefficient FXZ with a first threshold Y to generate a first comparison result, compares the magnitude coefficient LJZ with a preset second threshold R to generate a second comparison result, and determines the anomaly level of the power distribution cabinet according to the second comparison result;

[0141] The alarm unit sends an alarm to the terminal to repair the power distribution cabinet.

[0142] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for dynamic safety assessment and analysis of power distribution cabinet operation, characterized in that: The specific steps are as follows: S1. Data collection: by setting up sensor equipment in the power cabinet to collect various operating parameters of the power distribution cabinet in real time, and setting up monitoring software in the network transmission equipment to obtain real-time data from the network transmission equipment; S2, data processing, preprocessing the collected multi-source data, including data cleaning, denoising and normalization, and reorganizing them into a first data set, a second data set and a third data set; S3, data calculation, integrating and calculating the first data set, the second data set and the third data set, so as to generate a safety analysis coefficient FXZ; S4, abnormality analysis, comparing the calculated safety analysis coefficient FXZ with a preset first threshold value Y, thereby generating a first comparison result, and determining whether the operation of the power distribution cabinet is abnormal according to the first comparison result; S5, magnitude analysis: if the first comparison result determines that the operation of the power distribution cabinet is abnormal, the safety analysis coefficient FXZ is integrated and calculated with the first threshold value Y to generate a magnitude coefficient LJZ, and the magnitude coefficient LJZ is compared with the preset second threshold value R to generate a second comparison result, and the abnormal level of the power distribution cabinet is determined according to the second comparison result; S6. Send an alarm to the terminal and repair the distribution cabinet.

2. The method for dynamic safety assessment and analysis of power distribution cabinet operation according to claim 1 is characterized in that: In step S1, real-time data collection is performed on the power distribution cabinet, including electrical parameters and environmental parameters; Among them, electrical parameters include: voltage value, current value and power factor; Environmental parameters include: temperature, humidity and vibration.

3. The method for dynamic safety assessment and analysis of power distribution cabinet operation according to claim 2 is characterized in that: In step S1, real-time data is collected from the network transmission equipment to obtain network parameters, including bandwidth utilization, data packet loss rate, network delay value, and error rate.

4. The method for dynamic safety assessment and analysis of power distribution cabinet operation according to claim 3 is characterized in that: In step S2, the electrical parameters, environmental parameters and network parameters are reorganized into a first data set, a second data set and a third data set according to timestamps after preprocessing.

5. The method for dynamic safety assessment and analysis of power distribution cabinet operation according to claim 4 is characterized in that: The first data set includes a voltage value, a current value, and a power factor; The voltage values ​​are recorded as DY1, DY2, DY3, ..., DYn according to the timestamps; The current values ​​are recorded as DL1, DL2, DL3, ..., DLn according to the timestamps; The power factors are recorded as GL1, GL2, GL3, ..., GLn according to the timestamp.

6. The method for dynamic safety assessment and analysis of power distribution cabinet operation according to claim 5 is characterized in that: The second data set includes temperature values, humidity values, and vibration values; The temperature values ​​are recorded as WD1, WD2, WD3, ..., WDn according to the timestamp; The humidity values ​​are recorded as SD1, SD2, SD3, ..., SDn according to the timestamp; The vibration values ​​are recorded as ZD1, ZD2, ZD3, ..., ZDn according to the timestamps.

7. The method for dynamic safety assessment and analysis of power distribution cabinet operation according to claim 6 is characterized in that: The third data set includes bandwidth utilization, packet loss rate, network delay value and error rate; The bandwidth utilization is recorded as DK1, DK2, DK3, ..., DKn according to the timestamp; The data packet loss rates are recorded as DB1, DB2, DB3, ..., DBn according to the timestamps; The network delay values ​​are recorded as YC1, YC2, YC3, ..., YCn according to the timestamps; The error rates are recorded as CW1, CW2, CW3, ..., CWn according to the timestamps.

8. The method for dynamic safety assessment and analysis of power distribution cabinet operation according to claim 7 is characterized in that: The first comparison result is: When FXZ ≤ Y, it indicates that the current power distribution cabinet is operating normally; When FXZ > Y, it indicates that the current power distribution cabinet is operating abnormally.

9. The method for dynamic safety assessment and analysis of power distribution cabinet operation according to claim 8 is characterized in that: The second comparison result is; When LJZ < R, it indicates that the current power distribution cabinet has a first-level abnormality; When LJZ = R, it indicates that the current power distribution cabinet has a second-level abnormality; When LJZ > R, it indicates that the current power distribution cabinet has a third-level abnormality.

10. A distribution cabinet operation dynamic safety assessment and analysis system, characterized in that: The system is used to execute the dynamic safety assessment and analysis method for the operation of the power distribution cabinet described in any one of claims 1-9 above, and includes: a data acquisition unit, a data processing unit, a data calculation unit, a data analysis unit, and an alarm unit; The data acquisition unit is used to perform dynamic data acquisition on the power distribution cabinet and network transmission equipment, so as to obtain multi-source data; The data processing unit preprocesses the acquired multi-source data, and reorganizes the preprocessed data to generate a first data set, a second data set, and a third data set; The data calculation unit integrates and calculates the first data set, the second data set, and the third data set, so as to generate a safety analysis coefficient FXZ; The data analysis unit compares the safety analysis coefficient FXZ with the first threshold Y to generate a first comparison result, compares the magnitude coefficient LJZ with the preset second threshold R to generate a second comparison result, and judges the abnormality level of the power distribution cabinet according to the second comparison result; The alarm unit sends an alarm to the terminal to repair the power distribution cabinet.

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