A method, device, computer equipment, and storage medium for safety early warning of distribution cabinets based on a progressive early warning approach.

Abstract

This invention relates to the field of power distribution cabinet safety monitoring technology, and more particularly to a power distribution cabinet safety early warning method, device, computer equipment, and storage medium based on a progressive early warning approach. This invention divides the power distribution cabinet's operating cycle into startup, normal operation, and shutdown stages, deploying sensors to collect specific parameters at each stage. A preliminary risk index is calculated for the startup and shutdown stages using a dynamic weighted summation algorithm. Nonlinear calculations and a risk matrix are then used to calculate and fuse risk indices for different loads during the normal operation stage. A progressive factor is then calculated to correct the risk index, and finally, four risk levels—normal, attention, warning, and emergency—are defined. Early warning strategies are then formulated based on these levels. By collecting and analyzing various operating parameters at different stages of the power distribution cabinet's operation, the risk index for each stage is accurately calculated, and corresponding early warning strategies are formulated based on the risk level. This achieves comprehensive monitoring and effective early warning of the power distribution cabinet's safety status, reducing the risk of power accidents.

Description

Technical Field

[0001] This invention relates to the field of power distribution cabinet safety monitoring technology, and in particular to a power distribution cabinet safety early warning method, device, computer equipment and storage medium based on a progressive early warning approach. Background Technology

[0002] In modern power systems, switchboards serve as core equipment for power distribution and control, and their operational safety and stability are of paramount importance. With the continuous growth of electricity demand and the increasing level of industrial automation, switchboards are undertaking increasingly heavy tasks and operating in more complex and variable environments.

[0003] In actual operation, the distribution cabinet goes through different stages, including startup, normal operation, and shutdown, each facing various potential risks. During startup, sudden changes in current and voltage can cause significant stress on the electrical components within the cabinet, leading to problems such as poor contact and insulation damage. During normal operation, prolonged load operation causes the equipment to heat up, accelerating component aging; simultaneously, interference factors such as harmonics and voltage fluctuations in the power grid can also affect the normal operation of the distribution cabinet. The shutdown stage is equally important; abnormal current and voltage attenuation during shutdown may indicate potential internal faults.

[0004] Traditional methods for monitoring the safety of electrical distribution cabinets have many limitations. Some simple monitoring methods rely solely on threshold values ​​for a single parameter, such as monitoring only the current magnitude and issuing an alarm only when the current exceeds a set threshold. However, this approach cannot comprehensively consider the influence of other parameters, such as voltage deviation and temperature changes, making it prone to misjudgments or omissions. Moreover, the operating status of electrical distribution cabinets is a dynamic process, and traditional methods struggle to adapt to these changes, failing to assess potential risks in a timely and accurate manner.

[0005] Furthermore, with the advancement of intelligent development of power distribution cabinets, the requirements for the accuracy and real-time performance of their safety monitoring are becoming increasingly stringent. Existing monitoring technologies are insufficient to meet this demand, resulting in the failure to promptly detect and address some potential faults in power distribution cabinets during actual operation. This could potentially lead to serious power accidents, causing significant economic losses and safety hazards to production and daily life. Therefore, developing a more advanced and accurate safety early warning method for power distribution cabinets is urgently needed. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention proposes a method, device, computer equipment, and storage medium for safety early warning of power distribution cabinets based on a progressive early warning approach. By collecting and analyzing various operating parameters at different stages of the power distribution cabinet's operation, the risk index for each stage is accurately calculated, and corresponding early warning strategies are formulated based on the risk level. This achieves comprehensive monitoring and effective early warning of the power distribution cabinet's safety status, reducing the risk of power accidents.

[0007] The specific technical solution of the present invention is as follows:

[0008] One objective of this invention is to provide a power distribution cabinet safety early warning method based on a progressive early warning approach, comprising:

[0009] The complete operating cycle of the power distribution cabinet is divided into three stages: startup, normal operation, and shutdown. Multiple sensors are deployed in each stage to collect parameters affecting the operation of the power distribution cabinet in real time.

[0010] Based on the operating parameters, the initial risk index PSRI for the startup phase is calculated using a dynamic weighted summation algorithm.

[0011] The normal operation phase includes the light-load operation phase and the full-load operation phase. Based on the operating parameters, the risk index NRI-L for the light-load operation phase and the risk index NRI-H for the full-load operation phase are calculated. The NRI-L and NRI-H are fused according to the weights using a dynamic fusion algorithm based on a sliding time window to obtain the preliminary risk index PNRI for the normal operation phase.

[0012] Based on the operating parameters at the time of shutdown, the preliminary risk index PSHRI for the shutdown phase is calculated using a dynamic weighted summation algorithm.

[0013] Calculate the SHRI-SRI progressive factor, SRI-NRI progressive factor, and NRI-SHRI progressive factor. By calculating the progressive factors and the corresponding preliminary risk factors, obtain the risk index SRI for the start-up phase, the risk index NRI for the normal operation phase, and the risk index SHRI for the shutdown phase.

[0014] The risk levels at each stage are divided into four levels: normal, attention, warning, and emergency. A risk level-based early warning strategy is developed, and risk thresholds are set for each level. The early warning strategy is obtained by judging the risk index and risk thresholds.

[0015] According to the power distribution cabinet safety early warning method based on the progressive early warning method of this application, in one possible implementation, the operating parameters monitored and collected during the startup phase include: peak startup current, voltage fluctuation amplitude, cabinet internal temperature rise rate, initial peak vibration, and ambient temperature and humidity gradient.

[0016] During the normal operation phase, the operating parameters monitored and collected include: current harmonic content, voltage deviation, average temperature inside the cabinet, humidity value, and current transient characteristics.

[0017] During the shutdown phase, the operating parameters monitored and collected include: current decay rate, voltage drop curve, residual temperature inside the cabinet, shutdown vibration decay rate, and environmental particulate matter deposition rate.

[0018] According to the distribution cabinet safety early warning method based on the progressive early warning approach of this application, in one possible implementation, the step of calculating the initial risk index PSRI of the startup phase using a dynamic weighted summation algorithm includes:

[0019] Determine the weights of each operating parameter during the startup phase;

[0020] Based on the weights of each operating parameter during the startup phase, the Preliminary Risk Index (PSRI) for the startup phase is calculated using the following formula:

[0021]

[0022] Among them, PSRI is the preliminary risk index for the initial phase, x ni ω is the normalized value of the i-th parameter during the startup phase. i (t) represents the weight corresponding to the i-th running parameter.

[0023] According to the distribution cabinet safety early warning method based on the progressive early warning approach of this application, in one possible implementation, the weight calculation formula for each operating parameter in the startup phase is as follows:

[0024]

[0025] Where, ω i (t) represents the weight corresponding to the i-th running parameter. Let i be the real-time rate of change of the i-th operating parameter. For the corresponding time interval, β i Let be the empirical coefficient of the i-th operating parameter.

[0026] According to the power distribution cabinet safety early warning method based on the progressive early warning method of this application, in one possible implementation, the light load operation stage refers to the operation state when the load carried by the power distribution cabinet is equal to or lower than 60% of its rated load.

[0027] The full-load operation phase refers to the operating state when the load on the distribution cabinet is higher than 60% of its rated load.

[0028] The formula for calculating the risk index NRI-L for the light-load operation phase is as follows:

[0029]

[0030] Where x1 represents the current harmonic content I h x2 represents the voltage deviation ΔV, and x3 represents the average temperature T inside the cabinet. avg x4 represents the humidity value H, and x5 represents the transient current characteristic T. tr , and These represent the coefficients of a single parameter, the coefficients of the interaction term of two parameters, and the coefficients of the interaction term of three parameters, respectively.

[0031] The formula for calculating the risk index NRI-H during the full-load operation phase is as follows:

[0032]

[0033] Where x1 represents the current harmonic content I′ h x2 represents the voltage deviation ΔV′, and x3 represents the average temperature inside the cabinet T′. avg x4 represents the humidity value H′, and x5 represents the transient current characteristic T′. tr s i It is a rating scale based on the severity of each parameter, r i It is an assessment coefficient for the probability of risk occurring.

[0034] According to the distribution cabinet safety early warning method based on the progressive early warning approach of this application, in one possible implementation, the calculation step of the preliminary risk index PNRI during normal operation includes:

[0035] Set the length T of the sliding time window. The time window slides along the operating time axis of the power distribution cabinet, and each slide is a fixed time interval Δt, where Δt... <T;

[0036] Calculate the proportion of light-load operation time P L and the percentage of full-load running time P H : And P L +P H =1, where P L P represents the percentage of time spent operating under light load. H T represents the percentage of time spent running at full load. L For the duration of light-load operation, T H The duration of full-load operation,

[0037] By using the formula: PNRI = P L ×NRI-L+P H ×NRI-H, calculate the preliminary risk index PNRI for the normal operation phase.

[0038] According to the distribution cabinet safety early warning method based on the progressive early warning approach of this application, in one possible implementation, the step of calculating the preliminary risk index PSHRI of the shutdown phase using a dynamic weighted summation algorithm includes:

[0039] Determine the weights of each operating parameter during the shutdown phase;

[0040] Based on the weights of each operating parameter during the shutdown phase, the preliminary downtime risk index (PSHRI) is calculated using the following formula:

[0041]

[0042] Wherein, PSRRI is the preliminary risk index for the shutdown phase, x′ ni Let ω′ be the normalized value of the i-th parameter during the shutdown phase. i (t) represents the weight corresponding to the i-th running parameter.

[0043] According to the distribution cabinet safety early warning method based on the progressive early warning approach of this application, in one possible implementation, the weight calculation formula for each operating parameter during the shutdown phase is as follows:

[0044]

[0045] Where, ω′ i (t) represents the weight corresponding to the i-th running parameter. Let i be the real-time rate of change of the i-th operating parameter. For the corresponding time interval, β′ i Let i be the empirical coefficients for the i operating parameters.

[0046] According to the distribution cabinet safety early warning method based on the progressive early warning approach of this application, in one possible implementation, the calculation formulas for the SHRI-SRI progressive factor, SRI-NRI progressive factor, and SRI-NRI progressive factor are as follows:

[0047]

[0048] Among them, K SHRI-SRI K is the SHRI-SRI progression factor. SRI-NRI λ1 and σ1, λ2 and σ2, and λ3 and σ3 are the adjustment coefficients corresponding to the SHRI-SRI, SRI-NRI, and NRI-SHRI progression factors, respectively.

[0049] According to the distribution cabinet safety early warning method based on the progressive early warning approach of this application, in one possible implementation, the calculation formulas for the startup phase risk index (SRI), normal operation phase risk index (NRI), and shutdown phase risk index (SHRI) are as follows:

[0050] SRI = PSRI × (1 + K) SHRI-SRI );

[0051] NRI = PNRI × (1 + K) SRI-NRI );

[0052] SHRI = PSHRI × (1 + K) NRI-SHRI ).

[0053] A second objective of this invention is to provide an apparatus for implementing a power distribution cabinet safety early warning method based on a progressive early warning approach. The apparatus includes:

[0054] Operation phase division and parameter acquisition module: This module divides the complete operation cycle of the power distribution cabinet into three phases in sequence: startup, normal operation, and shutdown. Multiple sensors are deployed in each phase to collect parameters affecting the operation of the power distribution cabinet in real time.

[0055] PSRI Calculation Module: Used to calculate the initial risk index PSRI during the startup phase based on operating parameters using a dynamic weighted summation algorithm;

[0056] PNRI calculation module: used for normal operation phase, including light load operation phase and full load operation phase. Based on the operation parameters, it calculates the risk index NRI-L for light load operation phase and the risk index NRI-H for full load operation phase. Based on the dynamic fusion algorithm of sliding time window, it fuses NRI-L and NRI-H according to weight to obtain the preliminary risk index PNRI for normal operation phase.

[0057] PSHRI Calculation Module: Used to calculate the preliminary risk index PSHRI during the shutdown phase by referencing the operating parameters at the time of shutdown and through a dynamic weighted summation algorithm;

[0058] Progressive Factor Calculation Module: Used to calculate SHRI-SRI progressive factors, SRI-NRI progressive factors, and NRI-SHRI progressive factors. Through the calculation of progressive factors and corresponding preliminary risk factors, the risk index SRI for the start-up phase, the risk index NRI for the normal operation phase, and the risk index SHRI for the shutdown phase are obtained.

[0059] Risk level classification and early warning strategy execution module: This module is used to classify the risk level of each stage into four levels: normal, attention, warning and emergency, and to formulate early warning strategies based on the risk level. It sets the risk threshold for each level and obtains the early warning strategy by judging the risk index and the risk threshold.

[0060] A third objective of this invention is to provide a computer device comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set are loaded and executed by the processor to realize a power distribution cabinet safety early warning method based on a progressive early warning approach.

[0061] The fourth objective of this invention is to provide a computer-readable storage medium storing at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to realize a power distribution cabinet safety early warning method based on a progressive early warning approach.

[0062] The beneficial effects of the technical solutions provided in this application include at least the following:

[0063] Traditional monitoring methods often overlook the differences in the different operating stages of a distribution cabinet. This method divides its complete operating cycle into startup, normal operation, and shutdown stages, deploying multiple sensors to collect specific parameters in each stage. During startup, parameters such as peak startup current are monitored; during normal operation, current harmonic content is monitored under light load and full load conditions; and during shutdown, current decay rate is monitored. Through multi-stage, detailed monitoring, the operating status of the distribution cabinet is comprehensively understood, risks are more accurately assessed, and the limitations of traditional methods are overcome.

[0064] A dynamic weighted summation algorithm is used to calculate the preliminary risk index during the startup and shutdown phases. Weights are adjusted based on the real-time rate of change of parameters, with parameters exhibiting higher rates of change receiving higher weights, thus more accurately reflecting the impact of parameters on risk. During normal operation, nonlinear calculation methods and a risk matrix combined with parameter correction coefficients are used to calculate the risk indices for light load and full load operation, respectively. A sliding time window dynamic fusion algorithm then comprehensively considers the proportion of light load and full load operating time, making the preliminary risk index for normal operation more realistic. These advanced algorithms effectively improve the accuracy of risk assessment.

[0065] The SHRI-SRI, SRI-NRI, and NRI-SHRI progressive factors are calculated to comprehensively consider the differences in operating parameters at different stages, reflecting the progressive relationship of risk at each stage. These progressive factors are used to correct the initial risk index, ensuring that the final risk index reflects the dynamic changes in the operating risk of the distribution cabinet. This optimizes the risk assessment system and provides a basis for more accurate judgment of risk trends.

[0066] Risk levels are categorized into four levels: Normal, Attention, Warning, and Emergency, with thresholds set based on historical operating data of the distribution cabinets and industry standards. Corresponding early warning strategies are developed for each level: Normal level involves continuous monitoring; Attention level requires alerts and increased inspections; Warning level triggers alarms and a comprehensive inspection to prepare for maintenance; Emergency level triggers immediate warnings and activates emergency plans. This scientific classification and targeted strategy enables timely response to risks and ensures the safe operation of the distribution cabinets. Attached Figure Description

[0067] Figure 1 This is a schematic diagram of the overall process of a power distribution cabinet safety early warning method based on a progressive early warning approach;

[0068] Figure 2 The flowchart of the S100 steps of the distribution cabinet safety early warning method based on the progressive early warning approach is shown below.

[0069] Figure 3 The flowchart of the S200 steps of the power distribution cabinet safety early warning method based on the progressive early warning approach;

[0070] Figure 4 The flowchart of the S300 step-by-step safety early warning method for distribution cabinets based on a progressive early warning approach;

[0071] Figure 5 The flowchart of the S400 safety early warning method for distribution cabinets based on a progressive early warning approach is shown below.

[0072] Figure 6 The flowchart of the S500 method for safety early warning of distribution cabinets based on a progressive early warning approach is shown below.

[0073] Figure 7 The flowchart below shows the steps of the S600 method for safety early warning of distribution cabinets based on a progressive early warning approach. Detailed Implementation

[0074] The technical solutions of the embodiments of this application 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. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0075] Example 1

[0076] Traditional methods for monitoring the safety of distribution cabinets often rely on single indicators or simple threshold judgments, making it difficult to comprehensively and accurately assess the risk status of distribution cabinets at different operational stages. This monitoring method is prone to misjudgments or omissions, failing to detect potential safety hazards in a timely manner. As a result, distribution cabinets may continue to operate in undetected faulty states, potentially leading to serious power accidents and causing huge economic losses and social impacts.

[0077] To resolve the above issues, please refer to [link / reference]. Figure 1 This illustrates a power distribution cabinet safety early warning method based on a progressive early warning approach provided by an embodiment of the present invention. The method includes:

[0078] S100: The complete operating cycle of the power distribution cabinet is divided into three stages in sequence: startup, normal operation, and shutdown. Multiple sensors are deployed in each stage to collect parameters affecting the operation of the power distribution cabinet in real time.

[0079] S200: Based on the operating parameters, the initial risk index PSRI for the startup phase is calculated using a dynamic weighted summation algorithm.

[0080] S300: The normal operation phase includes the light-load operation phase and the full-load operation phase. Based on the operating parameters, the risk index NRI-L for the light-load operation phase and the risk index NRI-H for the full-load operation phase are calculated. The NRI-L and NRI-H are fused according to their weights using a dynamic fusion algorithm based on a sliding time window to obtain the preliminary risk index PNRI for the normal operation phase.

[0081] S400: Based on the operating parameters during shutdown, the preliminary risk index PSHRI for the shutdown phase is calculated using a dynamic weighted summation algorithm.

[0082] S500: Calculate the SHRI-SRI progressive factor, SRI-NRI progressive factor, and NRI-SHRI progressive factor. By calculating the progressive factors and the corresponding preliminary risk factors, obtain the risk index SRI for the start-up phase, the risk index NRI for the normal operation phase, and the risk index SHRI for the shutdown phase.

[0083] S600: Divide the risk levels of each stage into four levels: normal, attention, warning and emergency, and formulate early warning strategies based on risk levels. Set risk thresholds for each level, and obtain early warning strategies by judging risk index and risk threshold.

[0084] The S100 divides the operation of the power distribution cabinet into three phases: startup, normal operation, and shutdown. The startup phase lasts from the moment power is connected until the internal electrical components are initialized and the equipment operates stably. The normal operation phase includes both light-load and full-load operating conditions. The shutdown phase lasts from the issuance of the shutdown command until the internal equipment completely stops working and parameters return to zero. Sensors are deployed in the power distribution cabinet to collect various operating parameters for each of the three phases.

[0085] Please refer to Figure 2 The diagram illustrates a flowchart of S100 of an exemplary distribution cabinet safety early warning method based on a progressive early warning approach, the contents of which include:

[0086] S110: The operation phase of the power distribution cabinet is divided into the startup phase, the normal operation phase, and the shutdown phase.

[0087] The startup phase is defined as the period from the moment the power distribution cabinet is connected to the circuit until all internal electrical components complete their initialization and the equipment reaches a stable operating state. During startup, parameters such as current and voltage will fluctuate significantly due to the transient responses of components like inductors and capacitors in the circuit.

[0088] The normal operation phase encompasses two operating conditions: light-load operation and full-load operation. Light-load operation refers to the operating state when the load on the distribution cabinet is less than 60% of its rated load; full-load operation is the operating state when the load reaches or approaches the rated load. Under different load conditions, the operating parameters of the distribution cabinet exhibit different patterns of change, requiring separate monitoring and evaluation.

[0089] The shutdown phase is defined as the process from when the operator issues a shutdown command, when the distribution cabinet begins to cut off the load current, until all internal electrical equipment completely stops working and relevant parameters return to zero. During shutdown, current and voltage decrease, and equipment temperature drops.

[0090] S120: Deploy sensors in the distribution cabinet to monitor and collect various operating parameters that affect the operation of the distribution cabinet during the startup, normal operation and shutdown phases.

[0091] During the startup phase, the operating parameters monitored and collected include: peak startup current, voltage fluctuation amplitude, cabinet temperature rise rate, initial peak vibration, and ambient temperature and humidity gradient.

[0092] During normal operation, the operating parameters monitored and collected include: current harmonic content, voltage deviation, average internal temperature, humidity, and current transient characteristics.

[0093] During the shutdown phase, the operating parameters monitored and collected include: current decay rate, voltage drop curve, residual temperature inside the cabinet, shutdown vibration decay rate, and environmental particulate matter deposition rate.

[0094] The S200 preprocesses the operational parameters collected during the startup phase. Weights are adjusted based on the real-time rate of change of the parameters; a higher rate of change results in a higher weight, and vice versa. Finally, a dynamic weighted summation algorithm is used to calculate the initial risk index (PSRI) for the startup phase.

[0095] Please refer to Figure 3 The diagram illustrates a flowchart of S200 of an exemplary distribution cabinet safety early warning method based on a progressive early warning approach, the contents of which include:

[0096] S210: Preprocess the operating parameters during the startup phase.

[0097] Preprocessing methods include outlier detection and data normalization.

[0098] Anomaly detection was performed on the collected data on peak starting current, voltage fluctuation amplitude, cabinet temperature rise rate, initial peak vibration, and ambient temperature and humidity gradient.

[0099] In an optional embodiment, the statistical 3σ principle is adopted, which states that if a data point deviates from the mean by more than three times the standard deviation, it is considered an outlier.

[0100] For outliers, in an optional embodiment, interpolation methods, such as linear interpolation or Lagrange interpolation, are used to handle them to ensure data continuity and accuracy.

[0101] To eliminate the influence of different parameter dimensions and orders of magnitude, the processed operating parameters are normalized.

[0102] In an optional embodiment, a min-max normalization method is used to map the data to the [0,1] interval. For the parameter x during the startup phase, the normalization formula is:

[0103]

[0104] Where, x n The normalized value of parameter x during the startup phase, x max and x min These are the minimum and maximum values ​​of this parameter during the data collection period, respectively, during the startup phase.

[0105] S220: Calculate the initial risk index PSRI for the startup phase using a dynamic weighted summation algorithm.

[0106] Determine the weights of each operating parameter during the startup phase.

[0107] In an optional embodiment, the weights of each operating parameter are adjusted according to the real-time rate of change of the parameter. The formula for calculating the weights of each operating parameter is as follows:

[0108]

[0109] Where, ω i (t) represents the weight corresponding to the i-th running parameter. Let i be the real-time rate of change of the i-th operating parameter. For the corresponding time interval, β i The empirical coefficient for the i-th operating parameter is...

[0110] The higher the real-time change rate of a parameter, the more significant its impact on the operating status of the distribution cabinet during the current startup phase, and the higher its weight should be assigned; conversely, the weight of a parameter with a lower real-time change rate should be reduced accordingly.

[0111] Based on the weights of each operating parameter during the startup phase, the Preliminary Risk Index (PSRI) for the startup phase is calculated using the following formula:

[0112]

[0113] Among them, PSRI is the preliminary risk index for the initial phase, x ni This is the normalized value of the i-th parameter during the startup phase.

[0114] The S300 subdivides the normal operation phase into light-load and full-load operation phases. During the light-load operation phase, the risk index NRI-L is calculated using a nonlinear formula considering the interaction effects between parameters. During the full-load operation phase, the risk index NRI-H is calculated based on the risk matrix and parameter correction coefficients. A sliding time window dynamic fusion algorithm is used to determine the fusion weights based on the proportion of light-load and full-load operation time, fusing NRI-L and NRI-H to obtain the preliminary risk index PNRI for the normal operation phase, reflecting the real-time risk status.

[0115] Please refer to Figure 4 The diagram illustrates a flowchart of S300 of an exemplary distribution cabinet safety early warning method based on a progressive early warning approach, the contents of which include:

[0116] S310: The normal operation phase is divided into a light-load operation phase and a full-load operation phase.

[0117] In power systems, the load on a distribution cabinet significantly affects its operating characteristics. Light load operation refers to the operating state when the load on the distribution cabinet is equal to or less than 60% of its rated load. Full load operation, on the other hand, is the operating state when the load reaches or approaches the rated load, i.e., when the load on the distribution cabinet is greater than 60% of its rated load. At this point, the components of the distribution cabinet experience greater stress, the operating parameters change more complexly, and the potential risks are higher.

[0118] S320: Calculate the risk index NRI-L for light-load operation phase.

[0119] Operating parameters affecting the light-load operation phase include: current harmonic content I h Voltage deviation ΔV, average internal temperature T avg Humidity value H and transient current characteristics T trThe risk index NRI-L for the light-load operation phase is calculated using the operating parameters during the light-load operation phase.

[0120] In an optional embodiment, a nonlinear calculation of the risk index NRI-L for the light-load operation phase, which takes into account the interaction effects between parameters, is introduced. The calculation formula is as follows:

[0121]

[0122] Where x1 represents the current harmonic content I h x2 represents the voltage deviation ΔV, and x3 represents the average temperature T inside the cabinet. avg x4 represents the humidity value H, and x5 represents the transient current characteristic T. tr , and These represent the coefficients of a single parameter, the coefficients of the interaction term of two parameters, and the coefficients of the interaction term of three parameters, respectively.

[0123] S330: Calculate the risk index NRI-H for the full-load operation phase.

[0124] During the full-load operation phase of the distribution cabinet, the operating parameters affecting the full-load operation phase are the same as those affecting the light-load operation phase, including: current harmonic content I′. h Voltage deviation ΔV′, average internal temperature T′ avg Humidity value H′ and transient current characteristics T′ tr The risk index NRI-H for the load operation phase is calculated based on the operating parameters during the load operation phase.

[0125] In an optional embodiment, the risk index NRI-H for the full-load operation phase is calculated based on a method using the risk matrix and parameter correction coefficients, and the calculation formula is as follows:

[0126]

[0127] Where x1 represents the current harmonic content I′ h x2 represents the voltage deviation ΔV′, and x3 represents the average temperature inside the cabinet T′. avg x4 represents the humidity value H′, and x5 represents the transient current characteristic T′. tr s i It is a rating scale based on the severity of each parameter, r i It is an assessment coefficient for the probability of risk occurring.

[0128] S340: A dynamic fusion algorithm based on a sliding time window fuses NRI-L and NRI-H to calculate the preliminary risk index PNRI for the normal operation phase.

[0129] Based on the operating characteristics of the distribution cabinet and the data analysis requirements, a suitable sliding time window length T is set. The time window slides along the operating time axis of the distribution cabinet, and each slide is for a fixed time interval Δt, where Δt... <T。

[0130] Within each sliding time window, based on the proportion of light-load operation time P L and the percentage of full-load operation time P H To determine the fusion weights of NRI-L and NRI-H.

[0131] Light load running time percentage P L and the percentage of full-load running time P H The calculation method is as follows: within the sliding time window, the duration T of light-load operation is counted. L and the duration of full load operation T H ,but: And P L +P H =1.

[0132] The proportion of light-load operation time P obtained by calculation L and the percentage of full-load running time P H The NRI-L and NRI-H are fused using the following formula to obtain the preliminary risk index PNRI for the normal operation phase:

[0133] PNRI = P L ×NRI-L+P H ×NRI-H.

[0134] As the time window slides, the percentage of time spent running under light load is continuously updated (P). L and the percentage of full-load operation time P H This allows for the dynamic integration of risk indices during light and full-load operation phases, enabling PNRI to more accurately reflect the real-time risk status of the distribution cabinet during normal operation.

[0135] The S400 system collects operating parameters during the shutdown phase and adjusts the weights of each parameter based on its real-time rate of change; the higher the rate of change, the higher the weight. A dynamic weighted summation algorithm is then used to calculate the preliminary risk index (PSHRI) for the shutdown phase.

[0136] Please refer to Figure 5 The diagram illustrates a flowchart of S400 of an exemplary distribution cabinet safety early warning method based on a progressive early warning approach, the contents of which include:

[0137] S410: Preprocess the operating parameters during the shutdown phase.

[0138] During the shutdown phase of the distribution cabinet, the operating parameters obtained include: current decay rate, voltage drop curve, residual temperature inside the cabinet, shutdown vibration decay rate, and environmental particulate matter deposition rate.

[0139] Preprocess the operating parameters during the shutdown phase.

[0140] Preprocessing methods include outlier detection and data normalization.

[0141] Anomaly detection was performed on the collected current decay rate, voltage drop curve, residual temperature inside the cabinet, shutdown vibration decay rate, and environmental particulate matter deposition rate.

[0142] In an optional embodiment, the statistical 3σ principle is adopted, which states that if a data point deviates from the mean by more than three times the standard deviation, it is considered an outlier.

[0143] For outliers, in an optional embodiment, interpolation methods, such as linear interpolation or Lagrange interpolation, are used to handle them to ensure data continuity and accuracy.

[0144] To eliminate the influence of different parameter dimensions and orders of magnitude, the processed operating parameters are normalized.

[0145] In an optional embodiment, a min-max normalization method is used to map the data to the [0,1] interval. For the parameter x′, the normalization formula is:

[0146]

[0147] Where x′ is the normalized value of the shutdown phase parameter x′, x′ max and x′ min These are the minimum and maximum values ​​of this parameter during the data collection period, respectively, during the downtime phase.

[0148] S420: Calculate the preliminary risk index PSHRI for the shutdown phase using a dynamic weighted summation algorithm.

[0149] Determine the weights of each operating parameter during the shutdown phase.

[0150] In an optional embodiment, the weights of each operating parameter are adjusted according to the real-time rate of change of the parameter. The formula for calculating the weights of each operating parameter is as follows:

[0151]

[0152] Where, ω′ i (t) represents the weight corresponding to the i-th running parameter. Let i be the real-time rate of change of the i-th operating parameter. For the corresponding time interval, β′ iLet i be the empirical coefficients for the i operating parameters.

[0153] Based on the weights of each operating parameter during the shutdown phase, the preliminary downtime risk index (PSHRI) is calculated using the following formula:

[0154]

[0155] Wherein, PSRRI is the preliminary risk index for the shutdown phase, x′ ni This is the normalized value of the i-th parameter during the shutdown phase.

[0156] The S500 calculates three progressive factors: SHRI-SRI, SRI-NRI, and NRI-SHRI, to measure the degree of risk change from the last shutdown to the current startup, from startup to normal operation, and from normal operation to shutdown, respectively. These factors comprehensively consider the differences in operating parameters at the corresponding stages, and adjustment coefficients are set based on historical operating data of the distribution cabinet and expert experience. Using these progressive factors, combined with the preliminary risk indices for the startup, normal operation, and shutdown stages, the final risk indices SRI, NRI, and SHRI for each stage are calculated.

[0157] Please refer to Figure 6 The diagram illustrates a flowchart of S500 of an exemplary distribution cabinet safety early warning method based on a progressive early warning approach, the contents of which include:

[0158] S510: Calculate the SHRI-SRI progression factor, SRI-NRI progression factor, and SRI-NRI progression factor.

[0159] The SHRI-SRI progressive factor measures the degree of risk change from the previous shutdown phase to the current startup phase. It comprehensively considers the differences in various operating parameters between the previous shutdown and startup phases to reflect the progressive relationship of risk between these two key phases in the entire operating cycle of the distribution cabinet. The SRI-NRI progressive factor reflects the risk change from the startup phase to the normal operation phase. It compares relevant operating parameters between the startup and normal operation phases to analyze the progressive trend of risk between these two phases, providing a basis for accurately assessing the operational risk of the distribution cabinet. The NRI-SHRI progressive factor reflects the progressive relationship of risk from the normal operation phase to the shutdown phase. By analyzing the differences in operating parameters between these two phases, it is possible to more accurately grasp the risk change trend of the distribution cabinet in the later stages of operation, thus providing more accurate information for risk assessment and early warning. The calculation formula is:

[0160]

[0161] Among them, K SHRI-SRI K is the SHRI-SRI progression factor.SRI-NRI The SRI-NRI progression factor is defined as follows: λ1 and σ1, λ2 and σ2, and λ3 and σ3 are the adjustment coefficients corresponding to the SHRI-SRI, SRI-NRI, and NRI-SHRI progression factors, respectively. These coefficients are set based on historical operating data of the distribution cabinet and expert experience, with a value range of [0,1]. The comprehensive parameter changes during the shutdown phase are calculated by weighted summation of the changes in the operating parameters during the shutdown phase (current decay rate, voltage drop curve, residual temperature inside the cabinet, shutdown vibration decay rate, and environmental particulate matter deposition rate). The comprehensive parameter changes during the startup phase are calculated by weighted summation of the changes in the operating parameters during the startup phase (peak startup current, voltage fluctuation amplitude, cabinet temperature rise rate, initial peak vibration, and environmental temperature and humidity gradient). The comprehensive parameter changes during the normal operation phase are calculated by weighted summation of the changes in the operating parameters during the normal operation phase (current harmonic content, voltage deviation, average cabinet temperature, humidity value, and current transient characteristics).

[0162] S520: Calculate the risk index SRI for the startup phase, the risk index NRI for the normal operation phase, and the risk index SHRI for the shutdown phase.

[0163] Using the obtained SHRI-SRI, SRI-NRI, and SRI-NRI progressive factors, corresponding to the initial risk index PSRI for the startup phase, the initial risk index PNRI for the normal operation phase, and the initial risk index PSHRI for the shutdown phase, the risk index SRI for the startup phase, the risk index NRI for the normal operation phase, and the risk index SHRI for the shutdown phase are calculated respectively. The calculation formulas are as follows:

[0164] SRI = PSRI × (1 + K) SHRI-SRI );

[0165] NRI = PNRI × (1 + K) SRI-NRI );

[0166] SHRI = PSHRI × (1 + K) NRI-SHRI ).

[0167] The S600 classifies the risk levels of power distribution cabinets into four levels: Normal, Attention, Warning, and Emergency. Risk thresholds for each level are set based on historical operating data and industry standards. Corresponding early warning strategies are developed for different risk levels. The risk level is determined by comparing the risk index of each stage with the risk threshold, and the corresponding early warning strategy is automatically triggered and executed.

[0168] Please refer to Figure 7 The diagram illustrates a flowchart of S600 of an exemplary distribution cabinet safety early warning method based on a progressive early warning approach, the contents of which include:

[0169] S610: Divide the risk levels of each stage into four levels: normal, attention, warning and emergency, and set risk thresholds.

[0170] Normal Level: The risk index is at an extremely low level, indicating that the distribution cabinet is operating stably, all parameters are within safe ranges, and there is virtually no potential risk. The lower limit of the risk threshold is set to 0, and the upper limit is determined based on the distribution cabinet's historical operating data and industry standards. Within this range, the distribution cabinet equipment performs well and requires no additional attention or special measures.

[0171] Attention Level: The risk index is beginning to rise, but has not yet reached a dangerous level. At this stage, there may be some minor anomalies, but these will not immediately affect the normal operation of the distribution cabinet. The lower limit of the risk threshold is the upper limit of the normal level, and the upper limit is set based on the historical operating data of the distribution cabinet and industry standards. The Attention Level means that some noteworthy changes have occurred in the distribution cabinet, and it is necessary to begin monitoring fluctuations in relevant operating parameters.

[0172] Warning Level: The risk index has further increased, and the distribution cabinet is exhibiting significant operational abnormalities. Without intervention, this could lead to malfunctions and affect power supply stability. The lower limit of the risk threshold is the upper limit of the concern level, and the upper limit is set based on the distribution cabinet's historical operating data and industry standards. At this point, maintenance personnel need to pay close attention, promptly inspect the distribution cabinet, analyze the cause of the abnormality, and formulate and prepare to implement corresponding handling measures.

[0173] Emergency Level: The risk index has reached an extremely high level. The distribution cabinet is in a seriously dangerous state and may fail at any time, requiring immediate emergency measures. The lower limit of the risk threshold is the upper limit of the warning level; there is no upper limit. Once this level is reached, the distribution cabinet should be shut down immediately, and professional personnel should be organized for emergency repairs to prevent the fault from escalating and causing greater losses.

[0174] S620: Develop early warning strategies based on risk levels.

[0175] Normal Level Early Warning Strategy: No specific early warning information is sent, but the operating data of the distribution cabinet is continuously recorded and monitored. Operating reports are generated periodically, and the operating data is summarized and analyzed to ensure that the distribution cabinet remains in normal operating condition.

[0176] Attention-level early warning strategy: Remind maintenance personnel to monitor the operation status of the power distribution cabinet via SMS or in-system messages. Simultaneously, highlight relevant data for the power distribution cabinet in the monitoring system using a yellow indicator (or other clearly distinguishable color). It is recommended that maintenance personnel increase the frequency of power distribution cabinet inspections and focus on checking equipment components exhibiting abnormal parameters.

[0177] Warning Level Alert Strategy: In addition to SMS and system message notifications, an audible alarm should be issued to raise the awareness of maintenance personnel. The distribution cabinet should be marked in red in the monitoring system, with detailed lists of abnormal parameters and potential risk warnings. Maintenance personnel should be required to immediately conduct a comprehensive inspection of the distribution cabinet, including the equipment's appearance, wiring, and internal components. Simultaneously, technical personnel should be organized to analyze the fault, develop a repair plan, and prepare necessary repair tools and spare parts.

[0178] Emergency Warning Strategy: Immediately send emergency warning information to all relevant personnel through multiple channels. Automatically activate emergency plans, such as cutting off power to the distribution cabinet to prevent further escalation of the fault. Organize a professional repair team to quickly arrive at the scene for repairs, aiming to restore the distribution cabinet to normal operation in the shortest possible time and minimize the impact of the power outage on production and daily life.

[0179] S630: Implement early warning judgment and strategy execution.

[0180] The calculated risk indices SRI (Start-up Risk Index), NRI (Normal Risk Index), and SHRI (Shutdown Risk Index) are compared with the set risk thresholds. The risk level range for each stage is determined, thus identifying the current risk level of the distribution cabinet.

[0181] Based on the determined risk level, the corresponding early warning strategy will be automatically triggered and executed.

[0182] Example 2

[0183] According to Embodiment 1 of this application, an apparatus for a power distribution cabinet safety early warning method based on a progressive early warning approach is provided. The apparatus includes:

[0184] Operation phase division and parameter acquisition module: This module divides the complete operation cycle of the power distribution cabinet into three phases in sequence: startup, normal operation, and shutdown. Multiple sensors are deployed in each phase to collect parameters affecting the operation of the power distribution cabinet in real time.

[0185] PSRI Calculation Module: Used to calculate the initial risk index PSRI during the startup phase based on operating parameters using a dynamic weighted summation algorithm;

[0186] PNRI calculation module: used for normal operation phase, including light load operation phase and full load operation phase. Based on the operation parameters, it calculates the risk index NRI-L for light load operation phase and the risk index NRI-H for full load operation phase. Based on the dynamic fusion algorithm of sliding time window, it fuses NRI-L and NRI-H according to weight to obtain the preliminary risk index PNRI for normal operation phase.

[0187] PSHRI Calculation Module: Used to calculate the preliminary risk index PSHRI during the shutdown phase by referencing the operating parameters at the time of shutdown and through a dynamic weighted summation algorithm;

[0188] Progressive Factor Calculation Module: Used to calculate SHRI-SRI progressive factors, SRI-NRI progressive factors, and NRI-SHRI progressive factors. Through the calculation of progressive factors and corresponding preliminary risk factors, the risk index SRI for the start-up phase, the risk index NRI for the normal operation phase, and the risk index SHRI for the shutdown phase are obtained.

[0189] Risk level classification and early warning strategy execution module: This module is used to classify the risk level of each stage into four levels: normal, attention, warning and emergency, and to formulate early warning strategies based on the risk level. It sets the risk threshold for each level and obtains the early warning strategy by judging the risk index and the risk threshold.

[0190] According to Embodiment 1 of this application, a computer device is provided, the computer device including: a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, the at least one program, the code set or instruction set being loaded and executed by the processor to realize the power distribution cabinet safety early warning method based on progressive early warning mode as described in Embodiment 1.

[0191] According to Embodiment 1 of this application, a computer-readable storage medium is provided, wherein the storage medium stores at least one instruction, at least one program, code set or instruction set, wherein the at least one instruction, the at least one program, the code set or instruction set is loaded and executed by a processor to realize the power distribution cabinet safety early warning method based on progressive early warning mode as described in Embodiment 1.

[0192] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0193] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0194] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0195] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features of the invention herein.

[0196] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications or equivalent substitutions made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A power distribution cabinet safety early warning method based on a progressive early warning approach, characterized in that, include: The complete operating cycle of the power distribution cabinet is divided into three stages in sequence: startup, normal operation, and shutdown. Multiple sensors are deployed at each stage to collect parameters affecting the operation of the power distribution cabinet in real time. Based on the operating parameters, the initial risk index PSRI for the startup phase is calculated using a dynamic weighted summation algorithm. The normal operation phase includes the light-load operation phase and the full-load operation phase. Based on the operating parameters, the risk index NRI-L for the light-load operation phase and the risk index NRI-H for the full-load operation phase are calculated. The NRI-L and NRI-H are fused according to the weights using a dynamic fusion algorithm based on a sliding time window to obtain the preliminary risk index PNRI for the normal operation phase. Based on the operating parameters at the time of shutdown, the preliminary risk index PSHRI for the shutdown phase is calculated using a dynamic weighted summation algorithm. Calculate the SHRI-SRI progressive factor, SRI-NRI progressive factor, and NRI-SHRI progressive factor. By calculating the progressive factors and the corresponding preliminary risk factors, obtain the risk index SRI for the start-up phase, the risk index NRI for the normal operation phase, and the risk index SHRI for the shutdown phase. The risk levels at each stage are divided into four levels: normal, attention, warning and emergency. A risk level-based early warning strategy is formulated, and risk thresholds are set for each level. The early warning strategy is obtained by judging the risk index and risk thresholds. The three progressive factors SHRI-SRI, SRI-NRI, and NRI-SHRI respectively represent the degree of risk change from the last shutdown to the current startup, from startup to normal operation, and from normal operation to shutdown. The formulas for calculating the SHRI-SRI progression factor, SRI-NRI progression factor, and NRI-SHRI progression factor are as follows: ； ； ； in, The SHRI-SRI progression factor. The SRI-NRI progression factor, The NRI-SHRI progression factor, and , and ,as well as and These are the adjustment coefficients corresponding to the SHRI-SRI progression factor, SRI-NRI progression factor, and NRI-SHRI progression factor, respectively. The risk index SRI for the startup phase, the risk index NRI for the normal operation phase, and the risk index SHRI for the shutdown phase are calculated using the following formulas: ； ； ； PSRI is the initial risk index for the startup phase, PNRI is the initial risk index for the normal operation phase, and PSHRI is the initial risk index for the shutdown phase.

2. The distribution cabinet safety early warning method based on a progressive early warning approach according to claim 1, characterized in that, During the startup phase, the operating parameters monitored and collected include: peak startup current, voltage fluctuation amplitude, cabinet temperature rise rate, initial peak vibration, and ambient temperature and humidity gradient. During the normal operation phase, the operating parameters monitored and collected include: current harmonic content, voltage deviation, average temperature inside the cabinet, humidity value, and current transient characteristics. During the shutdown phase, the operating parameters monitored and collected include: current decay rate, voltage drop curve, residual temperature inside the cabinet, shutdown vibration decay rate, and environmental particulate matter deposition rate.

3. The distribution cabinet safety early warning method based on a progressive early warning approach according to claim 2, characterized in that, The steps for calculating the initial risk index PSRI for the startup phase using a dynamic weighted summation algorithm include: Determine the weights of each operating parameter during the startup phase; Based on the weights of each operating parameter during the startup phase, the Preliminary Risk Index (PSRI) for the startup phase is calculated using the following formula: ； in, This is a preliminary risk index for the initial phase. For the start-up phase The normalized values ​​of the parameters For the first The weights corresponding to each operating parameter; The weighting formula for each operating parameter during the startup phase is as follows: ； in, For the first The weights corresponding to each running parameter For the first Real-time rate of change of each operating parameter For the corresponding time interval, For the first The empirical coefficients of each operating parameter.

4. The distribution cabinet safety early warning method based on a progressive early warning approach according to claim 2, characterized in that, The light-load operation phase refers to the operating state when the load on the distribution cabinet is equal to or lower than 60% of its rated load. The full-load operation phase refers to the operating state when the load on the distribution cabinet is higher than 60% of its rated load. The formula for calculating the risk index NRI-L for the light-load operation phase is as follows: ； in, Represents the harmonic content of the current. , Represents voltage deviation , Represents the average temperature inside the cabinet. , Represents humidity value , Representative current transient characteristics , , and These represent the coefficients of a single parameter, the coefficients of the interaction term of two parameters, and the coefficients of the interaction term of three parameters, respectively. The formula for calculating the risk index NRI-H during the full-load operation phase is as follows: ； in, Represents the harmonic content of the current. , Represents voltage deviation , Represents the average temperature inside the cabinet. , Represents humidity value , Representative current transient characteristics , It is a rating system based on the severity of each parameter. It is an assessment coefficient for the probability of risk occurring; The calculation steps for the preliminary risk index PNRI during the normal operation phase include: Set the length of the sliding time window The time window slides along the operating time axis of the power distribution cabinet, sliding for a fixed time interval each time. ,in ; Calculate the percentage of light-load operation time and percentage of full-load operation time : , ,and ,in, The percentage of time spent operating under light load. This represents the percentage of time spent running at full load. For the duration of light-load operation, The duration of full-load operation, By using the formula: Calculate the preliminary risk index PNRI for the normal operation phase.

5. The distribution cabinet safety early warning method based on a progressive early warning approach according to claim 2, characterized in that, The steps for calculating the preliminary risk index (PSHRI) for the shutdown phase using a dynamic weighted summation algorithm include: Determine the weights of each operating parameter during the shutdown phase; Based on the weights of each operating parameter during the shutdown phase, the preliminary downtime risk index (PSHRI) is calculated using the following formula: ； in, This is a preliminary risk index for the shutdown phase. For the shutdown phase The normalized values ​​of the parameters For the first The weights corresponding to each operating parameter; The weighting formula for each operating parameter during the shutdown phase is as follows: ； in, For the first The weights corresponding to each running parameter For the first Real-time rate of change of each operating parameter For the corresponding time interval, for The empirical coefficients of each operating parameter.

6. The apparatus for a distribution cabinet safety early warning method based on a progressive early warning approach according to any one of claims 1-5, characterized in that, The device includes: Operation phase division and parameter acquisition module: used to divide the complete operation cycle of the power distribution cabinet into the startup phase, normal operation phase, and shutdown phase in sequence; Multiple sensors are deployed at each stage to collect parameters affecting the operation of the power distribution cabinet in real time. PSRI Calculation Module: Used to calculate the initial risk index PSRI during the startup phase based on operating parameters using a dynamic weighted summation algorithm; PNRI calculation module: used for normal operation phase, including light load operation phase and full load operation phase. Based on the operation parameters, it calculates the risk index NRI-L for light load operation phase and the risk index NRI-H for full load operation phase. Based on the dynamic fusion algorithm of sliding time window, it fuses NRI-L and NRI-H according to weight to obtain the preliminary risk index PNRI for normal operation phase. PSHRI Calculation Module: Used to calculate the preliminary risk index PSHRI during the shutdown phase by referencing the operating parameters at the time of shutdown and through a dynamic weighted summation algorithm; Progressive Factor Calculation Module: Used to calculate SHRI-SRI progressive factors, SRI-NRI progressive factors, and NRI-SHRI progressive factors. Through the calculation of progressive factors and corresponding preliminary risk factors, the risk index SRI for the start-up phase, the risk index NRI for the normal operation phase, and the risk index SHRI for the shutdown phase are obtained. Risk level classification and early warning strategy execution module: This module is used to classify the risk level of each stage into four levels: normal, attention, warning and emergency, and to formulate early warning strategies based on the risk level. It sets the risk threshold for each level and obtains the early warning strategy by judging the risk index and the risk threshold.

7. A computer device, characterized in that, The computer device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the power distribution cabinet safety early warning method based on a progressive early warning approach as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or instruction set is loaded and executed by a processor to implement the power distribution cabinet safety early warning method based on a progressive early warning approach as described in any one of claims 1 to 5.

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