Electric equipment operation management method based on internet of things

Abstract

The application discloses an electrical equipment operation management method based on an Internet of Things, and relates to the technical field of electrical equipment.The technical scheme of the application comprises the following steps: obtaining real-time sensing data and normal operation cumulative duration of a target electrical equipment, and constructing a target state parameter trend feature map according to the real-time sensing data and the normal operation cumulative duration; selecting a to-be-tested monitoring time period according to parameter extreme values of the real-time sensing data, detecting actual state parameter features based on the to-be-tested monitoring time period, performing feature comparison on the actual state parameter features and the target state parameter trend feature map to obtain a feature comparison result, and statistically obtaining comprehensive operation state parameters of the target electrical equipment according to the feature comparison result; obtaining a target aging amount based on a core component aging rate proportion and a voltage and current fluctuation amplitude of the target electrical equipment, and obtaining an actual fault hidden danger value in combination with the comprehensive operation state parameters; and the effect is to improve the economy and effectiveness of the entire electrical equipment operation management.

Description

Technical Field

[0001] This invention relates to the field of electrical equipment technology, and more specifically, to an Internet of Things-based method for the operation and management of electrical equipment. Background Technology

[0002] Traditional management methods in the field of electrical equipment operation and management have many limitations. Collecting single parameters from electrical equipment makes it difficult to comprehensively and in real-time grasp the equipment's operating status. It fails to promptly detect parameter changes over different operating periods and struggles to capture instantaneous extreme fluctuations in parameters, leading to the failure to detect potential equipment faults in a timely manner. Repairs are often only carried out after a fault has occurred. Furthermore, traditional methods lack comprehensive quantitative analysis of core component aging and operating parameter fluctuations, relying solely on the equipment's service life to determine aging levels. This results in inaccurate aging assessments and imprecise fault prediction. It also lacks assessment of the risks associated with the coordinated operation of electrical equipment and ignores the risk of fault propagation within the equipment network. Consequently, fault handling fails to fully consider the affected scope of collaborating equipment, easily leading to incomplete fault handling or wasted resources. Summary of the Invention

[0003] In view of the shortcomings of existing technologies, the purpose of this invention is to provide an Internet of Things-based method for the operation and management of electrical equipment.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] An IoT-based method for the operation and management of electrical equipment, comprising the following steps:

[0006] Acquire real-time sensor data and cumulative normal operating time of the target electrical equipment, and construct a trend feature map of the target state parameters based on the real-time sensor data and cumulative normal operating time;

[0007] The monitoring period to be measured is selected based on the extreme values ​​of parameters in real-time sensor data. The actual state parameter characteristics are detected based on the monitoring period to be measured. The actual state parameter characteristics are compared with the trend feature map of the target state parameters to obtain the feature comparison results. The comprehensive operating state parameters of the target electrical equipment are statistically analyzed based on the feature comparison results.

[0008] The target aging amount is obtained based on the proportion of aging rate of core components of the target electrical equipment and the voltage and current fluctuation amplitude, and the actual fault risk value is obtained by combining comprehensive operating status parameters.

[0009] Extract historical sensor monitoring data between the target electrical equipment and the cooperating equipment, and extract historical anomaly correlation features and historical fault impact range comparison features from the historical sensor monitoring data;

[0010] Obtain the current data interaction characteristics between the target electrical equipment and the cooperating equipment; match the potential hazard diffusion area from the comparison characteristics based on the current data interaction characteristics; and process and analyze the cooperating equipment and the target electrical equipment within the potential hazard diffusion area to obtain the failure risk of the cooperating equipment.

[0011] Based on the actual potential fault value and fault risk level, a corresponding fault handling plan is obtained.

[0012] Preferably, the actual state parameter features are compared with the target state parameter trend feature map to obtain the feature comparison result. Based on the feature comparison result, the comprehensive operating state parameters of the target electrical equipment are statistically analyzed. Specifically, this includes the following steps:

[0013] The actual state parameter features are compared with the target state parameter trend feature map.

[0014] When the actual state parameter characteristics and the target state parameter trend characteristics of the monitoring period are consistent, the comprehensive operating state parameters of the target electrical equipment are statistically analyzed based on the target state parameter trend characteristics.

[0015] Preferably, the target aging amount is obtained based on the proportion of aging rate of the core components of the target electrical equipment and the voltage and current fluctuation amplitude, and the actual fault potential value is obtained by combining comprehensive operating status parameters. Specifically, this includes the following steps:

[0016] The component aging percentage is obtained by statistically analyzing the aging rate of the core components of the target electrical equipment.

[0017] The target fluctuation range is obtained by statistically analyzing the voltage and current fluctuation ranges of the target electrical equipment based on the trend characteristic diagram of the target state parameters.

[0018] The target aging amount of the core components of the target electrical equipment is obtained by multiplying the target fluctuation range and the component aging percentage.

[0019] The actual fault risk value of the target electrical equipment is obtained by correlating the comprehensive operating status parameters and the target aging amount.

[0020] Preferably, extracting historical anomaly correlation features and historical fault impact range comparison features from historical sensor monitoring data specifically includes the following steps:

[0021] Under the same operating conditions, historical abnormal correlation characteristics between target electrical equipment and cooperating equipment are extracted from historical sensor monitoring data;

[0022] Under the condition of historical anomaly correlation characteristics, extract the historical fault impact range of the collaborative equipment affected by the target electrical equipment from historical sensor monitoring data;

[0023] The historical anomaly correlation features and the historical fault impact range are combined to form the comparison features.

[0024] Preferably, the failure risk of the cooperating equipment is obtained by processing and analyzing the cooperating equipment and the target electrical equipment within the potential hazard diffusion area, specifically including the following steps:

[0025] The correlation ratio of collaborative equipment within the area where potential hazards are spreading is statistically analyzed.

[0026] The target monitoring range is obtained by statistically analyzing the monitoring range of the target electrical equipment.

[0027] The failure risk of the collaborative equipment is obtained by processing the comprehensive operating status parameters of the target electrical equipment, the target monitoring range, the correlation ratio, and the target fluctuation range.

[0028] Preferably, the failure risk of the coordinating equipment is obtained by processing the comprehensive operating status parameters of the target electrical equipment, the target monitoring range, the correlation ratio, and the target fluctuation amplitude. Specifically, this includes the following steps:

[0029] The unit status parameters within the unit monitoring range are obtained by processing the ratio of the comprehensive operating status parameters of the target electrical equipment to the target monitoring range;

[0030] The comprehensive collaborative risk parameter is obtained by multiplying the unit state parameter and the correlation ratio.

[0031] The collaborative risk value of the collaborative equipment is obtained by multiplying the correlation ratio and the target fluctuation range.

[0032] The failure risk of the collaborative equipment is obtained by superimposing the comprehensive collaborative risk parameters and collaborative risk values.

[0033] Preferably, a corresponding fault handling plan is obtained based on the actual fault hazard value and fault risk level, specifically including the following steps:

[0034] When the actual potential fault value is greater than or equal to the preset fault warning threshold, the first response plan shall be formulated based on the health percentage of the core components of other electrical equipment, the frequency of changes in operating conditions, and the actual potential fault value.

[0035] When the actual potential fault value is less than the preset fault warning threshold, a second handling plan is formulated based on the fault risk amount and the actual potential fault value.

[0036] Preferably, a first-response plan is formulated based on the health percentage of core components of other electrical equipment, the frequency of changes in operating conditions, and the actual potential fault value, specifically including the following steps:

[0037] Switch the remaining data acquisition nodes other than the data acquisition nodes of the collaborative device to high-frequency acquisition mode and output the control conditions of the first acquisition node.

[0038] Based on the control conditions of the first data collection node, the health status ratio of the core components of other electrical equipment and the frequency of changes in operating conditions are analyzed to obtain the hidden danger weight ratio;

[0039] Obtain the fault handling resource capacity of other electrical equipment, and multiply the fault handling resource capacity and the hidden danger weight ratio to obtain the actual handling capacity of each of the other electrical equipment;

[0040] The additional potential hazard handling amount is obtained by subtracting the actual potential hazard value from the potential hazard threshold.

[0041] Based on the control conditions of the first data collection node and the actual handling capacity, the first handling plan is obtained by handling the additional hidden dangers.

[0042] Preferably, the hazard weight ratio is obtained by analyzing the health status ratio and operating condition change frequency of the core components of other electrical equipment based on the control conditions of the first data acquisition node. This specifically includes the following steps:

[0043] Based on the control conditions of the first data acquisition node, the percentage of health status of core components in other electrical equipment is statistically analyzed to obtain the percentage of auxiliary health status, and the frequency of changes in the operating conditions of each piece of equipment in other electrical equipment during historical periods is extracted;

[0044] The proportion of auxiliary health status and the frequency of changes in operating conditions are integrated into auxiliary potential risk factors.

[0045] The ratio of each auxiliary hazard factor to the comprehensive auxiliary hazard factor is used to obtain the hazard weight ratio.

[0046] Preferably, a second remedial plan is formulated based on the fault risk level and the actual potential fault value, specifically including the following steps:

[0047] Extract the original hidden danger capacity of the collaborative equipment, and calculate the difference between the original hidden danger capacity and the fault risk to obtain the reduced hidden danger capacity.

[0048] Control the data acquisition nodes with different timing sequences for each data acquisition node, and output the control conditions for the second acquisition node;

[0049] When the control conditions of the second acquisition node are met, if the additional hidden danger handling volume is less than or equal to the actual fault hidden danger value, then there is no need to select non-cooperative equipment from other electrical equipment for continuous fault handling to obtain fault handling one.

[0050] If the additional hidden danger handling volume is greater than the actual fault hidden danger value, then non-cooperative equipment needs to be selected from other electrical equipment for continuous fault handling to obtain fault handling two;

[0051] The fault handling method one and the fault handling method two are combined to form the second handling scheme.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] This invention constructs a trend feature map of target state parameters by acquiring real-time sensor data and cumulative normal operation time. It selects the test period based on parameter extreme values ​​and compares it with actual state parameter characteristics, ensuring the timeliness and accuracy of equipment state perception. Based on the aging rate ratio of core components, voltage and current fluctuation amplitude, and comprehensive operating state parameters, it calculates the actual fault hazard value, quantifying the risk of equipment aging and operational fluctuations. This shifts equipment management from passive fault repair to proactive hazard prediction, improving the accuracy of fault hazard identification. It extracts historical anomaly correlation features and fault impact ranges to form comparison features, combines current data interaction features to match hazard diffusion areas, and calculates the fault risk of collaborative equipment. This expands the scope of equipment management from single equipment to the network of interconnected equipment, enabling early prediction of the spread and risk level of faults within the network. This provides a clear direction for fault prevention and handling of collaborative equipment, ensuring the stable operation of the entire electrical system. Based on different actual fault hazard values ​​and fault risk levels, targeted first and second handling plans are formulated. This tiered approach not only concentrates resources for strong control during high-risk situations but also optimizes resource utilization during low-risk situations, achieving precise and efficient fault handling, avoiding resource waste or shortages, and improving the economy and effectiveness of the entire electrical equipment operation and management. Attached Figure Description

[0054] Figure 1 This is a schematic diagram illustrating the Internet of Things-based electrical equipment operation management method proposed in this invention;

[0055] Figure 2 This is a schematic diagram illustrating the steps involved in obtaining actual fault hazard values ​​in the IoT-based electrical equipment operation management method proposed in this invention. Detailed Implementation

[0056] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0057] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0058] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0059] Reference Figures 1-2 As shown.

[0060] The embodiments further illustrate the IoT-based electrical equipment operation management method proposed in this invention.

[0061] An IoT-based method for the operation and management of electrical equipment, comprising the following steps:

[0062] Acquire real-time sensor data and cumulative normal operating time of the target electrical equipment, and construct a trend feature map of the target state parameters based on the real-time sensor data and cumulative normal operating time;

[0063] The monitoring period to be tested is selected based on the extreme values ​​of parameters in real-time sensor data, and the actual state parameter characteristics are detected based on the monitoring period to be tested.

[0064] First, real-time sensor data of the target electrical equipment is acquired. This data includes key operating parameters such as voltage, current, and temperature, along with the cumulative duration of normal operation. For example, a transformer transmits voltage data in real time via a voltage sensor, and the operating time is accumulated from the equipment's startup. A trend characteristic graph of the target state parameters is then constructed by combining the real-time sensor data and the cumulative operating time. This graph visually reflects the changing trends of various state parameters under different operating times. For instance, as the operating time increases, the transformer temperature may gradually rise and then stabilize. Presenting this data in chart form forms the target state parameter trend characteristic graph.

[0065] The monitoring period is selected based on the extreme values ​​of parameters in the real-time sensor data. Parameter extreme values ​​refer to the key numerical points of maximum and minimum values ​​in the real-time sensor data. For example, when a significant peak appears in the real-time current data of the transformer, the period before and after this peak is selected as the monitoring period. Within this monitoring period, actual state parameter characteristics, such as voltage fluctuations and temperature changes, are detected.

[0066] The feature comparison results are obtained by comparing the actual state parameter features with the target state parameter trend feature map, and the comprehensive operating state parameters of the target electrical equipment are statistically analyzed based on the feature comparison results.

[0067] The target aging amount is obtained based on the proportion of aging rate of core components of the target electrical equipment and the voltage and current fluctuation amplitude, and the actual fault risk value is obtained by combining comprehensive operating status parameters.

[0068] Extract historical sensor monitoring data between the target electrical equipment and the cooperating equipment, and extract historical anomaly correlation features and historical fault impact range comparison features from the historical sensor monitoring data;

[0069] Obtain the current data interaction characteristics between the target electrical equipment and the cooperating equipment, and match the potential hazard spread area from the comparison characteristics based on the current data interaction characteristics;

[0070] First, the current data interaction characteristics between the target electrical equipment and the cooperating equipment are obtained. In an electrical system, the target electrical equipment and the cooperating equipment continuously interact with each other. The characteristics generated by this interaction include data interaction frequency, data interaction intensity, and data interaction timing. Data interaction frequency is the number of times data is transmitted between the target equipment and the cooperating equipment per unit time; data interaction intensity is reflected through the numerical changes in the interacting data, such as the degree of correlation between the opening and closing commands of a high-voltage circuit breaker and the current data fed back by a current transformer; data interaction timing is the time difference between the target equipment sending a command and the cooperating equipment responding. For example, a high-voltage circuit breaker sends a status query command to its associated current transformer every 3 seconds, and the current transformer feeds back current data 1 second after receiving the command. Furthermore, the amplitude of each opening and closing command is linearly correlated with the fluctuation of the current feedback data. Therefore, the interaction frequency of once every 3 seconds, the response timing of 1 second, and the linear amplitude correlation constitute the current data interaction characteristics.

[0071] The potential hazard spread area is identified by matching the comparison features based on current data interaction characteristics. The comparison features consist of historical anomaly correlation characteristics and historical fault impact ranges. Historical anomaly correlation characteristics refer to abnormal data interaction patterns between the target device and cooperating devices under similar operating conditions in the past. For example, if a high-voltage circuit breaker experienced a mechanical jamming fault, before the fault occurred, its current data interaction with a current transformer showed an abnormal pattern of halved frequency and amplitude fluctuations exceeding the normal range by 20%. The historical fault impact range is the specific area where cooperating devices are affected by the target device's fault after the occurrence of this historical anomaly correlation characteristic. Assuming that in the aforementioned mechanical jamming fault, besides the current transformer, its adjacent relay protection device also experienced false alarms due to data anomalies, then the equipment coverage area of ​​one current transformer and one relay protection device constitutes the historical fault impact range.

[0072] During the matching process, the current data interaction features need to be compared with all historical abnormal correlation features one by one, and the similarity between them needs to be calculated. The formula for calculating the similarity is: Similarity = (Number of overlapping dimensions between the current feature and historical features ÷ Total number of dimensions of historical features) × 100%. For example, if the current data interaction between the current circuit breaker and a current transformer shows a frequency reduction of half and an amplitude fluctuation exceeding the normal range by 18%, compared with the historical abnormal correlation features (frequency reduction of half and amplitude fluctuation exceeding the normal range by 20%) during the previous mechanical jamming fault, the number of overlapping dimensions is 2, and the total number of dimensions of historical features is 2. Therefore, the similarity is (2 ÷ 2) × 100% = 100%. When the similarity reaches the preset threshold of 75%, it is considered that the current data interaction feature has matched the corresponding historical abnormal correlation feature, and the impact range of the historical fault corresponding to the historical abnormal correlation feature is determined as the current hidden danger diffusion area. For example, if the similarity is 100% in the above example, then the area of ​​a current transformer and a relay protection device becomes the area where the hidden danger spreads, indicating that these coordinated devices have been affected by the potential fault of the target device, and targeted fault investigation and handling are required.

[0073] The failure risk of the collaborative equipment is obtained by analyzing the collaborative equipment and target electrical equipment within the potential hazard diffusion area.

[0074] Based on the actual potential fault value and fault risk level, a corresponding fault handling plan is obtained.

[0075] The actual state parameter features are compared with the target state parameter trend feature map to obtain the feature comparison results. Based on the feature comparison results, the comprehensive operating state parameters of the target electrical equipment are statistically analyzed, which specifically includes the following steps:

[0076] The actual state parameter features are compared with the target state parameter trend feature map.

[0077] When the actual state parameter characteristics and the target state parameter trend characteristics of the monitoring period are consistent, the comprehensive operating state parameters of the target electrical equipment are statistically analyzed based on the target state parameter trend characteristics.

[0078] First, the actual state parameter characteristics are compared with the target state parameter trend feature map. Actual state parameter characteristics refer to the specific performance of various operating parameters of the target electrical equipment during the monitoring period, such as the real-time changes in current, speed, and temperature of a motor during the monitoring period. The target state parameter trend feature map, on the other hand, is constructed based on the equipment's real-time sensor data and cumulative normal operating time. It reflects the changing trends of various parameters of the equipment under normal operating conditions over time or operating time. For example, during normal operation, the motor's current fluctuates slightly within a stable range, its speed remains constant, and its temperature rises slowly over operating time before stabilizing. These trends are integrated into the target state parameter trend feature map. During the comparison, the parameters' numerical range, changing trends, and fluctuation frequencies are compared one by one.

[0079] Determine whether the actual state parameter characteristics are consistent with the state parameter trend characteristics of the target state parameter trend feature map for the monitoring period. If they are consistent, it indicates that the equipment's operating status during the monitoring period conforms to the normal trend. At this time, the comprehensive operating status parameters of the target electrical equipment can be calculated based on the target state parameter trend feature map. The comprehensive operating status parameters are a quantitative summary of the overall operating status of the equipment, covering the equipment's operating stability and parameter compliance. For example, if the actual current fluctuation range, speed stability, and temperature rise rate of the motor are consistent with the normal trend in the trend feature map, then the comprehensive operating status parameters can be calculated by weighting these parameters: Comprehensive operating status parameters = (current compliance × weight 1 + speed stability × weight 2 + temperature change compliance × weight 3) ÷ (weight 1 + weight 2 + weight 3). Among these parameters, current compliance is the percentage of actual current within the normal current range of the trend characteristic graph; speed stability is the degree of deviation between actual speed and rated speed; and temperature change compliance is the degree of agreement between actual temperature change and normal temperature change in the trend characteristic graph. The weights are set according to the importance of each parameter to equipment operation. For example, current is crucial for motor operation, so weight 1 is set to 0.5, weight 2 to 0.3, and weight 3 to 0.2. These calculated parameters reflect the overall operating status of the equipment, providing a basis for subsequent fault hazard analysis and the development of handling plans.

[0080] The target aging amount is obtained based on the aging rate percentage of the core components of the target electrical equipment and the voltage and current fluctuation amplitude. Combined with comprehensive operating status parameters, the actual potential fault value is obtained. This process includes the following steps:

[0081] The component aging percentage is obtained by statistically analyzing the aging rate of the core components of the target electrical equipment.

[0082] The target fluctuation range is obtained by statistically analyzing the voltage and current fluctuation ranges of the target electrical equipment based on the trend characteristic diagram of the target state parameters.

[0083] The target aging amount of the core components of the target electrical equipment is obtained by multiplying the target fluctuation range and the component aging percentage.

[0084] The actual fault risk value of the target electrical equipment is obtained by correlating the comprehensive operating status parameters and the target aging amount.

[0085] First, the aging rate percentage of the core components of the target electrical equipment is calculated to obtain the component aging percentage value. Core components are those that play a crucial role in the operation of the equipment, such as the windings and bearings of a motor. The aging rate percentage refers to the ratio between the current aging level of a core component and its complete aging state. Taking a motor winding as an example, if the designed service life of the motor winding is 10,000 hours, and the current winding has been in operation for 6,000 hours, and its aging level is assessed at 60% based on tests of its insulation and conductivity performance, then the component aging percentage value for this winding is 60%.

[0086] Next, the target fluctuation range is obtained by statistically analyzing the voltage and current fluctuation ranges of the target electrical equipment based on the target state parameter trend feature graph. The target state parameter trend feature graph records the voltage and current change trends during normal operation of the equipment. The voltage and current fluctuation range refers to the degree of deviation between the actual voltage and current values ​​and the rated values ​​or the normal average values ​​in the trend feature graph within a certain period of time. For example, the voltage of a motor should be stable at 380V during normal operation. If the voltage fluctuates between 375V and 385V within a certain period of time, the voltage fluctuation range is (385-375) / 380×100%≈2.63%; if the rated current is 10A and the actual current fluctuates between 9.5A and 10.5A, the current fluctuation range is (10.5-9.5) / 10×100%=10%. The target fluctuation range is obtained by averaging the voltage fluctuation range and the current fluctuation range.

[0087] The target aging amount of the core component of the electrical equipment is obtained by multiplying the target fluctuation range and the component aging percentage. Taking a motor as an example, assuming the component aging percentage is 60% and the target fluctuation range is 6.315%, which is the average of the voltage and current fluctuation ranges, then the target aging amount = 60% × 6.315% = 3.789%. This reflects the incremental aging degree of the core component due to the combined effects of aging and voltage and current fluctuations.

[0088] The actual fault potential value of the target electrical equipment is obtained by correlating the comprehensive operating status parameters and the target aging amount. The comprehensive operating status parameters quantify the overall operating status of the equipment. First, key indicators such as current, speed, temperature, and vibration are selected, and scoring standards are set for each. Taking current as an example, if the rated current of the motor is 10A, and the normal range is 9.5A-10.5A, the score corresponding to the percentage of time the actual current is in this range is 80 points, obtained by referring to a preset table (summarized from historical data). The rated speed is 1450r / min, with normal fluctuations of ±5r / min, and the score corresponding to the percentage of time the actual speed is in this range is 85 points, obtained by referring to the preset table. The normal winding temperature is ≤85℃, and the score corresponding to the percentage of time the actual temperature is in this range is 75 points, obtained by referring to the preset table. The normal vibration amplitude is ≤0.1mm, and the score corresponding to the percentage of time the actual vibration is in this range is 80 points, obtained by referring to the preset table. Then, weights are assigned to the importance of each indicator to the motor operation: current weight 0.3, speed weight 0.25, temperature weight 0.3, and vibration weight 0.15. Finally, the weighted calculation yields the comprehensive operating status parameters as (80×0.3)+(85×0.25)+(75×0.3)+(80×0.15)=24+21.25+22.5+12=80, which directly reflects the overall operating health of the motor.

[0089] The correlation calculation uses the following formula: Actual fault risk value = (1 - Comprehensive operating status parameter / 100) × Target aging amount × 100. Therefore, the actual fault risk value is (1 - 80 / 100) × 3.789% × 100 = 20% × 3.789% × 100 = 7.578%. The higher this value, the greater the potential for equipment failure, which facilitates the development of corresponding fault handling plans based on this value.

[0090] Extracting historical anomaly correlation features and historical fault impact range comparison features from historical sensor monitoring data, specifically including the following steps:

[0091] Under the same operating conditions, historical abnormal correlation characteristics between target electrical equipment and cooperating equipment are extracted from historical sensor monitoring data;

[0092] Under the condition of historical anomaly correlation characteristics, extract the historical fault impact range of the collaborative equipment affected by the target electrical equipment from historical sensor monitoring data;

[0093] Among them, the historical anomaly correlation features and the impact range of historical faults are combined to form the comparison features.

[0094] Taking a motor and its associated relay protection devices and instrument transformers as an example, the historical abnormal correlation characteristics between the target electrical equipment and its associated devices are first extracted from historical sensor monitoring data under the same operating conditions. For example, when the motor is operating at full load, historical data may show abnormal increases in motor current, delays in the relay protection device's action signal, and voltage fluctuations in the instrument transformer exceeding the normal range. These abnormal linkage patterns between the devices represent historical abnormal correlation characteristics.

[0095] Under the condition of historical anomaly correlation characteristics, the historical fault impact range of the coordinated equipment affected by the target electrical equipment is extracted from historical sensor monitoring data. Assuming that under the condition of full load operation of motor and abnormal current, in addition to the abnormality of relay protection device and transformer, the adjacent circuit breaker also malfunctions due to data disorder. Then the equipment coverage area of ​​relay protection device, transformer and circuit breaker is the historical fault impact range.

[0096] Among them, historical anomaly correlation characteristics and historical fault impact range are combined to form comparison features. The formula for calculating the anomaly correlation degree is: Anomaly correlation degree = (Number of collaborative devices with anomalies / Total number of collaborative devices participating in data interaction) × 100%. For example, if there are 3 collaborative devices participating in data interaction, namely relay protection devices, instrument transformers, and circuit breakers, and 3 of them have anomalies, then the anomaly correlation degree = (3 / 3) × 100% = 100%. In this way, the anomaly correlation patterns and fault impact ranges between devices in history are integrated to obtain comparison features. The comparison features are used for subsequent matching with current data interaction features, thereby predicting the area of ​​potential hazard spread.

[0097] Based on the analysis of the collaborative equipment and target electrical equipment within the potential hazard diffusion area, the failure risk of the collaborative equipment is obtained, specifically including the following steps:

[0098] The correlation ratio of collaborative equipment within the area where potential hazards are spreading is statistically analyzed.

[0099] The target monitoring range is obtained by statistically analyzing the monitoring range of the target electrical equipment.

[0100] The failure risk of the collaborative equipment is obtained by processing the comprehensive operating status parameters of the target electrical equipment, the target monitoring range, the correlation ratio, and the target fluctuation range.

[0101] Taking the electric motor and its cooperating equipment as an example, the correlation ratio of the cooperating equipment within the hazard diffusion area is first calculated. The correlation ratio reflects the closeness of data interaction between the cooperating equipment and the target equipment, and is calculated by the frequency of historical data interaction and data overlap between the cooperating equipment and the target equipment. Assuming there are 3 cooperating devices within the hazard diffusion area, and 2 of them reach the high correlation standard in terms of historical data interaction frequency and overlap with the electric motor, then the correlation ratio is (2 / 3) × 100% ≈ 66.67%.

[0102] The target monitoring range is obtained by statistically analyzing the monitoring range of the target electrical equipment. The target monitoring range refers to the physical area or number of devices that the target equipment can monitor. For example, if a motor's sensor can monitor its own operating data and that of three surrounding cooperating devices, then the target monitoring range is [missing information].

[0103] The failure risk of the cooperating equipment is obtained by processing the comprehensive operating status parameters, target monitoring range, correlation ratio, and target fluctuation amplitude of the target electrical equipment. If the comprehensive operating status parameter is 80 and the target fluctuation amplitude is 6.315%, then the failure risk is calculated as follows: Failure Risk = (Comprehensive Operating Status Parameter / 100) × Target Monitoring Range × Correlation Ratio × Target Fluctuation Amplitude × 100. Therefore, the failure risk is approximately (80 / 100) × 4 × 66.67% × 6.315% × 100 ≈ 80 × 0.04 × 0.6667 × 6.315 ≈ 13.18. A higher value indicates a greater failure risk for the cooperating equipment, providing a quantitative basis for subsequent failure handling.

[0104] The failure risk of the coordinating equipment is obtained by processing the comprehensive operating status parameters of the target electrical equipment, the target monitoring range, the correlation ratio, and the target fluctuation amplitude. This process includes the following steps:

[0105] The unit status parameters within the unit monitoring range are obtained by processing the ratio of the comprehensive operating status parameters of the target electrical equipment to the target monitoring range;

[0106] The comprehensive collaborative risk parameter is obtained by multiplying the unit state parameter and the correlation ratio.

[0107] The collaborative risk value of the collaborative equipment is obtained by multiplying the correlation ratio and the target fluctuation range.

[0108] The failure risk of the collaborative equipment is obtained by superimposing the comprehensive collaborative risk parameters and collaborative risk values.

[0109] The unit status parameter within a unit monitoring range is obtained by comparing the comprehensive operating status parameter of the target electrical equipment with the target monitoring range. Taking a motor as an example, if the comprehensive operating status parameter is 80 and the target monitoring range is 4, then the unit status parameter is 80 ÷ 4 = 20. This parameter reflects the operating status performance of the target equipment within the unit monitoring range.

[0110] The comprehensive collaborative risk parameter is obtained by multiplying the unit status parameter and the correlation ratio. Assuming the correlation ratio of collaborative equipment within the hazard diffusion area is 66.67%, then the comprehensive collaborative risk parameter = 20 × 66.67% ≈ 13.33. This parameter reflects the degree of risk associated between collaborative equipment and the target equipment within the unit's monitoring range.

[0111] The collaborative risk value of the collaborative equipment is obtained by multiplying the correlation ratio and the target fluctuation range. If the target fluctuation range is 6.315%, then the collaborative risk value is approximately 66.67% × 6.315% ≈ 4.21%. This value reflects the magnitude of the risk faced by the collaborative equipment due to the combined effects of correlation and voltage / current fluctuations.

[0112] The failure risk level of the collaborative equipment is obtained by superimposing the comprehensive collaborative risk parameters and collaborative risk values. That is, failure risk level = 13.33 + 4.21% × 100 ≈ 13.33 + 4.21 = 17.54. Here, the percentage of the collaborative risk value is converted into a numerical value for superposition to ensure unit consistency. This failure risk level comprehensively considers factors such as equipment operating status, monitoring range, correlation degree, and fluctuation range. The higher the value, the greater the failure risk of the collaborative equipment, providing an accurate quantitative basis for subsequent failure handling plans.

[0113] Based on the actual potential fault value and fault risk level, a corresponding fault handling plan is derived, which specifically includes the following steps:

[0114] When the actual fault risk value is greater than or equal to the preset fault warning threshold, the first disposal plan is formulated based on the health ratio of the core components of other electrical equipment, the frequency of changes in operating conditions, and the actual fault risk value. Other electrical equipment refers to equipment other than the target electrical equipment and cooperating equipment.

[0115] Switch the remaining data acquisition nodes other than the data acquisition nodes of the collaborative device to high-frequency acquisition mode and output the control conditions of the first acquisition node.

[0116] Based on the control conditions of the first data collection node, the health status ratio of the core components of other electrical equipment and the frequency of changes in operating conditions are analyzed to obtain the hidden danger weight ratio;

[0117] Based on the control conditions of the first data acquisition node, the percentage of health status of core components in other electrical equipment is statistically analyzed to obtain the percentage of auxiliary health status, and the frequency of changes in the operating conditions of each piece of equipment in other electrical equipment during historical periods is extracted;

[0118] The proportion of auxiliary health status and the frequency of changes in operating conditions are integrated into auxiliary potential risk factors.

[0119] The ratio of each auxiliary hazard factor to the comprehensive auxiliary hazard factor is used to obtain the hazard weight ratio.

[0120] Obtain the fault handling resource capacity of other electrical equipment, and multiply the fault handling resource capacity and the hidden danger weight ratio to obtain the actual handling capacity of each of the other electrical equipment;

[0121] The additional potential hazard handling amount is obtained by subtracting the actual potential hazard value from the potential hazard threshold.

[0122] Based on the control conditions of the first data collection node and the actual handling capacity, the first handling plan is obtained by handling the additional hidden dangers.

[0123] Taking electric motors, relay protection devices, and backup transformers as examples, the target equipment is the electric motor, the cooperating equipment is the relay protection device, and other electrical equipment is the backup transformer. When the actual fault risk value of the electric motor is greater than or equal to the preset fault warning threshold, assuming the fault warning threshold is 10%, the process of formulating the first disposal plan is initiated.

[0124] Switch the remaining data acquisition nodes of the collaborative device, excluding the data acquisition nodes themselves, to high-frequency acquisition mode and output the control conditions for the first acquisition node. For example, if the collaborative device originally has 5 data acquisition nodes, 2 of which are used for self-monitoring, and the remaining 3 are switched to high-frequency acquisition to enhance the acquisition of real-time data from other devices.

[0125] Based on the control conditions of the first data collection node, the health percentage and operating condition change frequency of the core components of other electrical equipment are obtained, thus yielding the hazard weight ratio. The health percentage of the core components in other electrical equipment is then statistically analyzed to obtain the auxiliary health percentage; for example, the health percentage of the core components of the standby transformer is 85%. The operating condition change frequency of each piece of equipment in other electrical equipment over historical periods is extracted; for example, the historical operating condition change frequency of the standby transformer is 2 times / hour. The auxiliary health percentage and operating condition change frequency are integrated into an auxiliary hazard factor; the auxiliary hazard factor for the standby transformer is 85% × 2 = 1.7. The hazard weight ratio is obtained by calculating the ratio of each auxiliary hazard factor to the comprehensive auxiliary hazard factor. Assuming there are 3 other pieces of equipment with auxiliary hazard factors of 1.7, 2, and 1.4 respectively, the comprehensive auxiliary hazard factor is 5.1. Therefore, the hazard weight ratio of the standby transformer is 1.7 ÷ 5.1 ≈ 33.33%.

[0126] Obtain the fault handling resource capacity of other electrical equipment, and multiply the fault handling resource capacity by the hazard weight ratio to get the actual handling capacity of each of the other electrical equipment. For example, if the fault handling resource capacity of the standby transformer is 100 units, its actual handling capacity is 100 × 33.33% ≈ 33.33 units.

[0127] The additional hazard handling amount is obtained by subtracting the actual fault hazard value from the fault warning threshold. If the actual fault hazard value is 12%, then the additional hazard handling amount is 12%-10%=2%.

[0128] Based on the control conditions of the first data acquisition node and the actual handling capacity, the first handling plan is obtained by handling the additional hidden dangers. That is, according to the real-time data collected by high frequency, 33.33 units of handling resources of other equipment such as backup transformers are allocated to deal with the 2% of additional hidden dangers in a targeted manner. For example, emergency maintenance is carried out on the power circuit associated with the motor, and the capacity of the backup transformer is used to share the load, thereby completing the fault handling.

[0129] When the actual potential fault value is less than the preset fault warning threshold, a second handling plan is formulated based on the fault risk amount and the actual potential fault value.

[0130] Extract the original hidden danger capacity of the collaborative equipment, and calculate the difference between the original hidden danger capacity and the fault risk to obtain the reduced hidden danger capacity.

[0131] Control the data acquisition nodes with different timing sequences for each data acquisition node, and output the control conditions for the second acquisition node;

[0132] When the control conditions of the second acquisition node are met, if the additional hidden danger handling volume is less than or equal to the actual fault hidden danger value, then there is no need to select non-cooperative equipment from other electrical equipment for continuous fault handling to obtain fault handling one.

[0133] If the additional hidden danger handling volume is greater than the actual fault hidden danger value, then non-cooperative equipment needs to be selected from other electrical equipment for continuous fault handling to obtain fault handling two;

[0134] Among them, fault handling one and fault handling two are combined to form the second handling plan.

[0135] The original hidden danger capacity of the collaborative equipment is extracted, and the difference between the original hidden danger capacity and the failure risk is calculated to obtain the reduced hidden danger capacity. Assuming the original hidden danger capacity of the collaborative equipment is 20 and the failure risk is 17.54, then the reduced hidden danger capacity is 20 - 17.54 = 2.46. The reduced hidden danger capacity reflects the remaining hidden danger capacity of the collaborative equipment after assuming the failure risk.

[0136] Different time-series data acquisition nodes are controlled to output the control conditions for the second acquisition node. For example, different acquisition time intervals are set for different data acquisition nodes of motors and collaborative equipment, with some nodes acquiring data every 10 seconds and others every 20 seconds, thereby achieving differentiated data monitoring and understanding the equipment status.

[0137] When the control conditions of the second data acquisition node are met, the relationship between the additional hidden danger handling quantity and the actual fault hidden danger value is determined. The additional hidden danger handling quantity is the difference between the actual fault hidden danger value and the fault warning threshold. Since the actual fault hidden danger value is less than the fault warning threshold, it is a negative value. Here, the absolute value is taken as the additional quantity that needs to be handled. Assuming the actual fault hidden danger value is 7.578%, the absolute value of the additional hidden danger handling quantity is 10% - 7.578% = 2.422%. If the additional hidden danger handling quantity of 2.422% is less than or equal to the actual fault hidden danger value of 7.578%, then there is no need to select non-cooperative equipment from other electrical equipment for continuous fault handling, thus obtaining fault handling method one, such as only optimizing and adjusting the operating parameters of the motor itself and strengthening the status monitoring of existing cooperative equipment; if the additional hidden danger handling quantity is greater than the actual fault hidden danger value, then non-cooperative equipment from other electrical equipment needs to be selected for continuous fault handling, thus obtaining fault handling method two, such as calling the backup power equipment in the system to share the load of the motor and reduce its operating pressure.

[0138] The first and second fault handling methods are combined to form the second handling plan. This hierarchical handling method can make efficient use of existing resources when the potential danger is small, and can also reasonably mobilize external resources when necessary to handle potential faults.

[0139] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0140] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for operating and managing electrical equipment based on the Internet of Things, characterized in that, The method includes the following steps: Acquire real-time sensor data and cumulative normal operating time of the target electrical equipment, and construct a trend feature map of the target state parameters based on the real-time sensor data and cumulative normal operating time; The monitoring period to be measured is selected based on the extreme values ​​of parameters in real-time sensor data. The actual state parameter characteristics are detected based on the monitoring period to be measured. The actual state parameter characteristics are compared with the target state parameter trend feature map to obtain the feature comparison results. The comprehensive operating state parameters of the target electrical equipment are statistically analyzed based on the feature comparison results. The target aging amount is obtained based on the proportion of aging rate of core components of the target electrical equipment and the voltage and current fluctuation amplitude, and the actual fault risk value is obtained by combining comprehensive operating status parameters. Extract historical sensor monitoring data between the target electrical equipment and the cooperating equipment, and extract historical anomaly correlation features and historical fault impact range comparison features from the historical sensor monitoring data; Obtain the current data interaction characteristics between the target electrical equipment and the cooperating equipment; match the potential hazard diffusion area from the comparison characteristics based on the current data interaction characteristics; and process and analyze the cooperating equipment and the target electrical equipment within the potential hazard diffusion area to obtain the failure risk of the cooperating equipment. Based on the actual potential fault value and fault risk level, a corresponding fault handling plan is obtained.

2. The method for operating and managing electrical equipment based on the Internet of Things according to claim 1, characterized in that, The actual state parameter features are compared with the target state parameter trend feature map to obtain the feature comparison results. Based on the feature comparison results, the comprehensive operating state parameters of the target electrical equipment are statistically analyzed, which specifically includes the following steps: The actual state parameter features are compared with the target state parameter trend feature map. When the actual state parameter characteristics and the target state parameter trend characteristics of the monitoring period are consistent, the comprehensive operating state parameters of the target electrical equipment are statistically analyzed based on the target state parameter trend characteristics.

3. The method for operating and managing electrical equipment based on the Internet of Things according to claim 1, characterized in that, The target aging amount is obtained based on the aging rate percentage of the core components of the target electrical equipment and the voltage and current fluctuation amplitude. Combined with comprehensive operating status parameters, the actual potential fault value is obtained. This process includes the following steps: The component aging percentage is obtained by statistically analyzing the aging rate of the core components of the target electrical equipment. The target fluctuation range is obtained by statistically analyzing the voltage and current fluctuation range of the target electrical equipment based on the trend characteristic diagram of the target state parameters. The target aging amount of the core component of the target electrical equipment is obtained by multiplying the target fluctuation range and the component aging percentage. The actual fault risk value of the target electrical equipment is obtained by correlating the comprehensive operating status parameters and the target aging amount.

4. The method for operating and managing electrical equipment based on the Internet of Things according to claim 1, characterized in that, Extracting historical anomaly correlation features and historical fault impact range comparison features from historical sensor monitoring data, specifically including the following steps: Under the same operating conditions, historical abnormal correlation characteristics between target electrical equipment and cooperating equipment are extracted from historical sensor monitoring data; Under the condition of historical anomaly correlation characteristics, extract the historical fault impact range of the collaborative equipment affected by the target electrical equipment from historical sensor monitoring data; The historical anomaly correlation features and the historical fault impact range are combined to form the comparison features.

5. The method for operating and managing electrical equipment based on the Internet of Things according to claim 3, characterized in that, Based on the analysis of the collaborative equipment and target electrical equipment within the potential hazard diffusion area, the failure risk of the collaborative equipment is obtained, specifically including the following steps: The correlation ratio of collaborative equipment within the area where potential hazards are spreading is statistically analyzed. The target monitoring range is obtained by statistically analyzing the monitoring range of the target electrical equipment. The failure risk of the collaborative equipment is obtained by processing the comprehensive operating status parameters of the target electrical equipment, the target monitoring range, the correlation ratio, and the target fluctuation range.

6. The method for operating and managing electrical equipment based on the Internet of Things according to claim 5, characterized in that, The failure risk of the coordinating equipment is obtained by processing the comprehensive operating status parameters of the target electrical equipment, the target monitoring range, the correlation ratio, and the target fluctuation amplitude. This process includes the following steps: The unit status parameters within the unit monitoring range are obtained by processing the ratio of the comprehensive operating status parameters of the target electrical equipment to the target monitoring range; The comprehensive collaborative risk parameter is obtained by multiplying the unit state parameter and the correlation ratio. The collaborative risk value of the collaborative equipment is obtained by multiplying the correlation ratio and the target fluctuation range. The failure risk of the collaborative equipment is obtained by superimposing the comprehensive collaborative risk parameters and collaborative risk values.

7. The method for operating and managing electrical equipment based on the Internet of Things according to claim 1, characterized in that, Based on the actual potential fault value and fault risk level, a corresponding fault handling plan is derived, which specifically includes the following steps: When the actual potential fault value is greater than or equal to the preset fault warning threshold, the first response plan shall be formulated based on the health percentage of the core components of other electrical equipment, the frequency of changes in operating conditions, and the actual potential fault value. When the actual potential fault value is less than the preset fault warning threshold, a second handling plan is formulated based on the fault risk amount and the actual potential fault value.

8. The method for operating and managing electrical equipment based on the Internet of Things according to claim 7, characterized in that, Based on the health status of core components of other electrical equipment, the frequency of changes in operating conditions, and the actual potential for faults, a first-line response plan is formulated, which includes the following steps: Switch the remaining data acquisition nodes other than the data acquisition nodes of the collaborative device to high-frequency acquisition mode and output the control conditions of the first acquisition node. Based on the control conditions of the first data collection node, the health status ratio of the core components of other electrical equipment and the frequency of changes in operating conditions are analyzed to obtain the hidden danger weight ratio; Obtain the fault handling resource capacity of other electrical equipment, and multiply the fault handling resource capacity and the hidden danger weight ratio to obtain the actual handling capacity of each of the other electrical equipment; The additional potential hazard handling amount is obtained by subtracting the actual potential hazard value from the potential hazard threshold. Based on the control conditions of the first data collection node and the actual handling capacity, the first handling plan is obtained by handling the additional hidden dangers.

9. The method for operating and managing electrical equipment based on the Internet of Things according to claim 8, characterized in that, Based on the control conditions of the first data acquisition node, the health status ratio of the core components of other electrical equipment and the frequency of changes in operating conditions are analyzed to obtain the hidden danger weight ratio. The specific steps include: Based on the control conditions of the first data acquisition node, the percentage of health status of core components in other electrical equipment is statistically analyzed to obtain the percentage of auxiliary health status, and the frequency of changes in the operating conditions of each piece of equipment in other electrical equipment during historical periods is extracted; The proportion of auxiliary health status and the frequency of changes in operating conditions are integrated into auxiliary potential risk factors. The ratio of each auxiliary hazard factor to the comprehensive auxiliary hazard factor is used to obtain the hazard weight ratio.

10. The method for operating and managing electrical equipment based on the Internet of Things according to claim 9, characterized in that, A second response plan will be developed based on the level of fault risk and the actual potential fault value, specifically including the following steps: Extract the original hidden danger capacity of the collaborative equipment, and calculate the difference between the original hidden danger capacity and the fault risk to obtain the reduced hidden danger capacity. Control the data acquisition nodes with different timing sequences for each data acquisition node, and output the control conditions for the second acquisition node; When the control conditions of the second acquisition node are met, if the additional hidden danger handling volume is less than or equal to the actual fault hidden danger value, then there is no need to select non-cooperative equipment from other electrical equipment for continuous fault handling to obtain fault handling one. If the additional hidden danger handling volume is greater than the actual fault hidden danger value, then non-cooperative equipment needs to be selected from other electrical equipment for continuous fault handling to obtain fault handling two; The fault handling method one and the fault handling method two are combined to form the second handling scheme.

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