Circuit breaker actuation time prediction method, device, equipment, storage medium and program product

Abstract

The invention discloses a circuit breaker actuation time prediction method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring a plurality of historical circuit breaker actuation times respectively measured by a plurality of sensors in a circuit breaker; determining a variance of a plurality of historical circuit breaker actuation times measured by each sensor; based on the variances corresponding to the plurality of sensors, determining a weighting factor corresponding to each sensor by taking a minimum value of a preset function as a target; the preset function is constructed based on variances corresponding to the plurality of sensors and weighting factors corresponding to each sensor; the preset function is used for determining a fusion total variance corresponding to the variances; and on the basis of the weighting factor corresponding to each sensor, performing weighting processing on the target action time currently measured by each sensor, and performing fusion processing on a plurality of weighted target action time to obtain the next action time of the circuit breaker. By adopting the method, the accuracy of the predicted action time of the circuit breaker can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of circuit breakers, and in particular to a method, device, equipment, storage medium and program product for predicting the action time of a circuit breaker. Background Art

[0002] Due to differences in manufacturing processes and levels, the action time of circuit breakers is dispersed, and their long transmission chain leads to response delays and error accumulation during long-term operation. The action time of a circuit breaker refers to the time from when the circuit breaker receives a disconnect or close command to when the contacts are completely separated or in contact.

[0003] At present, there are two main methods for determining the next action time of a circuit breaker: (1) simply taking the average of previous data (offline mechanical property test or online monitoring device) as the next action time of the circuit breaker. However, this method cannot take into account the characteristics that the mechanical dispersion of the circuit breaker will change over time, resulting in a low accuracy rate of the determined action time; (2) using the external environment as a variable to predict the next action time in principle. However, this method involves too many variables and cannot take into account all influencing factors, resulting in a low accuracy rate of the determined action time.

[0004] Therefore, how to improve the accuracy of the predicted circuit breaker action time has become an urgent problem to be solved. Summary of the invention

[0005] Embodiments of the present application provide a method, apparatus, device, storage medium, and program product for predicting circuit breaker action time, which can improve the accuracy of the predicted circuit breaker action time.

[0006] In a first aspect, an embodiment of the present application provides a method for predicting a circuit breaker action time, the method comprising:

[0007] Acquire multiple historical circuit breaker action times respectively measured by multiple sensors in the circuit breaker;

[0008] Determine the variance of multiple historical circuit breaker action times measured by each sensor, and determine the weighting factor corresponding to each sensor based on the variances corresponding to the multiple sensors, with the minimum value of the preset function as the target; wherein the preset function is constructed based on the variances corresponding to the multiple sensors and the weighting factor corresponding to each sensor; the preset function is used to determine the fused total variance corresponding to the multiple variances;

[0009] Based on the weighting factors corresponding to each sensor, the target action time currently measured by each sensor is weighted, and multiple weighted target action times are fused to obtain the next action time of the circuit breaker.

[0010] In one embodiment, the plurality of historical circuit breaker action times include historical offline action times and historical online action times, the historical offline action times include the action times when the circuit breaker is in a factory test phase and / or a power outage maintenance test phase, and the historical online action times include the action times when the circuit breaker is in an energized operating state; the weighting factors corresponding to each sensor include a first weighting factor corresponding to the historical offline action time measured by each sensor, and a second weighting factor corresponding to the historical online action time; based on the weighting factors corresponding to each sensor, a target action time currently measured by each sensor is weighted, and a plurality of weighted target action times are fused to obtain the next action time of the circuit breaker, including: taking the average of N historical online action times obtained by N measurements of each sensor before the current moment as the target action time currently measured by each sensor; N is a positive integer greater than 1; based on the first weighting factor and the second weighting factor corresponding to each sensor, a target action time currently measured by each sensor is weighted to obtain a weighted target action time corresponding to each sensor; the weighted target action times corresponding to the plurality of sensors are fused to obtain the next action time of the circuit breaker.

[0011] In one embodiment, the variance includes a first variance corresponding to the historical offline action time and a second variance corresponding to the historical online action time; based on the variances corresponding to multiple sensors, with the minimum value of a preset function as a target, the weighting factor corresponding to each sensor is determined, including: based on the first variance and the second variance corresponding to each sensor, with the minimum value of the preset function as a target, the first weighting factor and the second weighting factor corresponding to each sensor are determined.

[0012] In one embodiment, the preset function is as follows:

[0013]

[0014] Among them, N represents the number of sensors; It represents the first weighting factor corresponding to the i-th sensor; represents the second weighting factor corresponding to the i-th sensor; It represents the first variance corresponding to the i-th sensor; It represents the second variance corresponding to the i-th sensor; It represents the total ensemble variance.

[0015] In one of the embodiments, based on the first weighting factor and the second weighting factor corresponding to each sensor, the target action time currently measured by each sensor is weighted to obtain the weighted target action time corresponding to each sensor, including: summing the first weighting factor and the second weighting factor corresponding to each sensor to obtain the target weighting factor corresponding to each sensor; based on the target weighting factor corresponding to each sensor, the target action time currently measured by each sensor is weighted to obtain the weighted action time corresponding to each sensor.

[0016] In one of the embodiments, the method further includes: inputting the next action time into a phase selection device corresponding to the circuit breaker, so that the phase selection device controls the circuit breaker to open or close based on the next action time.

[0017] In a second aspect, the present application provides a circuit breaker action time prediction device, the device comprising:

[0018] An acquisition module, used for acquiring a plurality of historical circuit breaker action times respectively measured by a plurality of sensors in the circuit breaker;

[0019] A determination module, used for determining the variance of a plurality of historical circuit breaker operation times measured by each sensor;

[0020] The determination module is further used to determine the weighting factor corresponding to each sensor based on the variances corresponding to the multiple sensors, with the minimum value of the preset function as the target; the preset function is constructed based on the variances corresponding to the multiple sensors and the weighting factor corresponding to each sensor; the preset function is used to determine the fusion total variance corresponding to the multiple variances;

[0021] The processing module is used to perform weighted processing on the target action time currently measured by each sensor based on the weighted factors corresponding to each sensor, and to fuse multiple weighted target action times to obtain the next action time of the circuit breaker.

[0022] In a third aspect, the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0023] Acquire multiple historical circuit breaker action times respectively measured by multiple sensors in the circuit breaker;

[0024] determining the variance of multiple historical circuit breaker operation times measured by each sensor;

[0025] Based on the variances corresponding to the multiple sensors, the weighting factor corresponding to each sensor is determined with the goal of determining the minimum value of the preset function; the preset function is constructed based on the variances corresponding to the multiple sensors and the weighting factor corresponding to each sensor; the preset function is used to determine the total fusion variance corresponding to the multiple variances;

[0026] Based on the weighting factors corresponding to each sensor, the target action time currently measured by each sensor is weighted, and multiple weighted target action times are fused to obtain the next action time of the circuit breaker.

[0027] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0028] Acquire multiple historical circuit breaker action times respectively measured by multiple sensors in the circuit breaker;

[0029] determining the variance of multiple historical circuit breaker operation times measured by each sensor;

[0030] Based on the variances corresponding to the multiple sensors, the weighting factor corresponding to each sensor is determined with the goal of determining the minimum value of the preset function; the preset function is constructed based on the variances corresponding to the multiple sensors and the weighting factor corresponding to each sensor; the preset function is used to determine the total fusion variance corresponding to the multiple variances;

[0031] Based on the weighting factors corresponding to each sensor, the target action time currently measured by each sensor is weighted, and multiple weighted target action times are fused to obtain the next action time of the circuit breaker.

[0032] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0033] Acquire multiple historical circuit breaker action times respectively measured by multiple sensors in the circuit breaker;

[0034] determining the variance of multiple historical circuit breaker operation times measured by each sensor;

[0035] Based on the variances corresponding to the multiple sensors, the weighting factor corresponding to each sensor is determined with the goal of determining the minimum value of the preset function; the preset function is constructed based on the variances corresponding to the multiple sensors and the weighting factor corresponding to each sensor; the preset function is used to determine the total fusion variance corresponding to the multiple variances;

[0036] Based on the weighting factors corresponding to each sensor, the target action time currently measured by each sensor is weighted, and multiple weighted target action times are fused to obtain the next action time of the circuit breaker.

[0037] The above-mentioned circuit breaker action time prediction method, device, equipment, storage medium and program product, the computer equipment can obtain multiple historical circuit breaker action times measured by multiple sensors in the circuit breaker; determine the variance of multiple historical circuit breaker action times measured by each sensor; based on the variances corresponding to the multiple sensors, determine the minimum value of the preset function as the target, and determine the weighting factor corresponding to each sensor; the preset function is constructed based on the variances corresponding to the multiple sensors and the weighting factors corresponding to each sensor; the preset function is used to determine the fused total variance corresponding to the multiple variances; based on the weighting factors corresponding to each sensor, the target action time currently measured by each sensor is weighted, and the multiple weighted target action times are fused to obtain the next action time of the circuit breaker. Using this method, the computer equipment can perform multivariate data adaptive weighted fusion processing on the action time measured by different sensors installed in the circuit breaker based on the variance extreme value theory to obtain the next action time of the circuit breaker, thereby improving the accuracy of the predicted circuit breaker action time. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 This is a schematic diagram of an application scenario of a circuit breaker action time prediction method provided in an embodiment of the present application;

[0040] Figure 2 It is a flow chart of a circuit breaker action time prediction method provided in an embodiment of the present application;

[0041] Figure 3 It is a flow chart of another circuit breaker action time prediction method provided in an embodiment of the present application;

[0042] Figure 4 It is a structural schematic diagram of a circuit breaker action time prediction device provided in an embodiment of the present application;

[0043] Figure 5 It is a structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0045] The following introduces the application scenarios of the circuit breaker action time prediction method provided in the embodiments of the present application.

[0046] See also Figure 1 , Figure 1 Schematic diagram of an application scenario of a circuit breaker action time prediction method provided in an embodiment of the present application. Figure 1 As shown, it includes a computer device 101 and a circuit breaker 102, wherein the circuit breaker 102 includes a plurality of different sensors (the figure takes sensor 1021 and sensor 1022 as examples). The computer device 101 and the circuit breaker 102 perform data transmission via a network.

[0047] Among them, the computer device 101 can obtain multiple historical circuit breaker action times measured by the sensors 1021 and 1022 in the circuit breaker 102 respectively. Then, based on the variance extreme value theory, the multiple historical circuit breaker action times measured by the sensors 1021 and 1022 in the circuit breaker 102 can be subjected to multivariate data adaptive weighted fusion processing to obtain the next action time of the circuit breaker, thereby improving the accuracy of the predicted circuit breaker action time.

[0048] Optionally, the computer device 100 may be a terminal device or a server. The terminal device mentioned here may include but is not limited to: a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart watch, a smart TV, a smart car terminal, etc. The server mentioned here may be an independent physical server, or a server cluster or a distributed system composed of multiple physical servers.

[0049] See also Figure 2 , Figure 2 1 is a flow chart of a method for predicting the action time of a circuit breaker provided in an embodiment of the present application. The method can be executed by a computer device (for example, the computer device 101 described above). Figure 2 As shown, the circuit breaker action time prediction method may include but is not limited to the following steps:

[0050] S201. Acquire multiple historical circuit breaker action times respectively measured by multiple sensors in the circuit breaker.

[0051] Among them, the circuit breaker operation time refers to the time from when the circuit breaker receives the disconnection or closing command to when the contacts are completely separated or contacted.

[0052] Optionally, the multiple sensors in the circuit breaker may include but are not limited to displacement sensors, vibration sensors, etc.

[0053] S202, determining the variance of multiple historical circuit breaker action times measured by each sensor, and based on the variances corresponding to the multiple sensors, determining the weighting factor corresponding to each sensor with the minimum value of a preset function as a target; the preset function is constructed based on the variances corresponding to the multiple sensors and the weighting factor corresponding to each sensor.

[0054] The preset function is used to determine the fused total variance corresponding to the multiple variances.

[0055] Optionally, the variance of the multiple historical circuit breaker action times measured by each sensor may also be referred to as the variance corresponding to each sensor. Optionally, the computer device may determine the variance corresponding to each sensor based on the multiple historical circuit breaker action times measured by each sensor.

[0056] In an optional implementation, the preset function may be as shown in the following formula (1).

[0057] (1)

[0058] In formula (1), N represents the number of sensors in the circuit breaker; It represents the weighting factor corresponding to the i-th sensor; It represents the variance corresponding to the i-th sensor; It represents the total ensemble variance.

[0059] In this embodiment, the computer device determines the weighting factor corresponding to each sensor based on the variances corresponding to the multiple sensors respectively, with the goal of determining the minimum value of the preset function. The computer device may determine the first objective function with the goal of determining the minimum value of the preset function; solve the first objective function based on the variances corresponding to the multiple sensors respectively and the first constraint condition to obtain the weighting factor corresponding to each sensor respectively.

[0060] Optionally, the first objective function may be as shown in the following formula (2).

[0061] (2)

[0062] In formula (2), N represents the number of sensors in the circuit breaker; It represents the weighting factor corresponding to the i-th sensor; It represents the variance corresponding to the i-th sensor; It represents the minimum total fusion variance.

[0063] Optionally, the first constraint condition may be as shown in the following formula (3).

[0064] (3)

[0065] In formula (3), N represents the number of sensors in the circuit breaker; It represents the weighting factor corresponding to the i-th sensor.

[0066] Optionally, the computer device solves the objective function based on the variances and the first constraint conditions corresponding to the multiple sensors, and the expression of the weighting factor corresponding to each sensor obtained can be shown in the following formula (4).

[0067] (4)

[0068] The physical meanings of the parameters in formula (4) can be found in the above description of the physical meanings of the parameters in formula (2), which will not be repeated here.

[0069] S203 . Based on the weighting factors corresponding to each sensor, weighted processing is performed on the target action time currently measured by each sensor, and a plurality of weighted target action times are merged to obtain the next action time of the circuit breaker.

[0070] In an optional implementation, the computer device fuses multiple weighted target action times to obtain the next action time of the circuit breaker, and the following formula (5) may be used.

[0071] (5)

[0072] In formula (5), N represents the number of sensors in the circuit breaker; It represents the weighting factor corresponding to the i-th sensor; X i It represents the target action time currently measured by the i-th sensor; It represents the i-th weighted target action time; Indicates the next action time of the circuit breaker.

[0073] In the embodiment of the present application, the computer device can obtain multiple historical circuit breaker action times measured by multiple sensors in the circuit breaker; determine the variance of multiple historical circuit breaker action times measured by each sensor; based on the variances corresponding to the multiple sensors, determine the minimum value of the preset function as the target, and determine the weighting factor corresponding to each sensor; the preset function is constructed based on the variances corresponding to the multiple sensors and the weighting factors corresponding to each sensor; the preset function is used to determine the fused total variance corresponding to the multiple variances; based on the weighting factors corresponding to each sensor, the target action time currently measured by each sensor is weighted, and the multiple weighted target action times are fused to obtain the next action time of the circuit breaker. Using this method, the computer device can perform multivariate data adaptive weighted fusion processing on the action time measured by different sensors installed in the circuit breaker based on the variance extreme value theory to obtain the next action time of the circuit breaker, thereby improving the accuracy of the predicted circuit breaker action time.

[0074] In an optional embodiment, Figure 2 In the circuit breaker action time prediction method shown, the multiple historical circuit breaker action times may include historical offline action times and historical online action times.

[0075] The historical offline action time may include but is not limited to the action time when the circuit breaker is in the factory test stage and / or the power outage maintenance test stage, and the historical online action time may include but is not limited to the action time when the circuit breaker is in the energized operating state.

[0076] In this implementation, the weighting factors corresponding to each sensor may include a first weighting factor corresponding to the historical offline action time measured by each sensor, and a second weighting factor corresponding to the historical online action time.

[0077] In this implementation, the computer device performs weighted processing on the target action time currently measured by each sensor based on the weighted factor corresponding to each sensor, and performs fusion processing on multiple weighted target action times to obtain the next action time of the circuit breaker, which may include but is not limited to the following steps:

[0078] Step 1: The average of N historical online action times obtained by each sensor from N measurements before the current moment is used as the target action time currently measured by each sensor.

[0079] Wherein, N is a positive integer greater than 1. In this way, the mechanical discreteness of the circuit breaker can be avoided, thereby facilitating the improvement of the accuracy of the determined next action time of the circuit breaker.

[0080] Exemplarily, assuming that N is 5, for each sensor, the computer device may take the average of five historical online action times measured by the sensor five times before the current moment (or the five most recent times) as the target action time currently measured by the sensor.

[0081] Optionally, the N historical online action times may also be regarded as N action times obtained by N measurements after the circuit breaker switch is actuated to the current moment.

[0082] Step 2: Based on the first weighting factor and the second weighting factor corresponding to each sensor, weighted processing is performed on the target action time currently measured by each sensor to obtain the weighted target action time corresponding to each sensor.

[0083] Optionally, when the computer device performs weighted processing on the target action time currently measured by each sensor based on the first weighted factor and the second weighted factor corresponding to each sensor to obtain the weighted target action time corresponding to each sensor, the method may include: performing a sum operation on the first weighted factor and the second weighted factor corresponding to each sensor to obtain the target weighted factor corresponding to each sensor; and performing weighted processing on the target action time currently measured by each sensor based on the target weighted factor corresponding to each sensor to obtain the weighted action time corresponding to each sensor. In other words, the computer device may use the following formula (6) to determine the weighted action time corresponding to each sensor.

[0084] (6)

[0085] In formula (6), N represents the number of sensors in the circuit breaker; It represents the first weighting factor corresponding to the i-th sensor; represents the second weighting factor corresponding to the i-th sensor; X i It represents the target action time currently measured by the i-th sensor; It represents the i-th weighted target action time.

[0086] Step 3: Perform fusion processing on the weighted target action times corresponding to the multiple sensors to obtain the next action time of the circuit breaker.

[0087] Optionally, the computer device performs fusion processing on the weighted target action times corresponding to the multiple sensors to obtain the next action time of the circuit breaker, and the following formula (7) can be used.

[0088] (7)

[0089] In formula (7), It indicates the next action time of the circuit breaker; represents the i-th weighted target action time; N represents the number of sensors in the circuit breaker; It represents the first weighting factor corresponding to the i-th sensor; represents the second weighting factor corresponding to the i-th sensor; X i It represents the target action time currently measured by the i-th sensor.

[0090] In some embodiments, the variance may include but is not limited to a first variance corresponding to the historical offline action time and a second variance corresponding to the historical online action time; the computer device determines the weighting factor corresponding to each sensor based on the variances corresponding to multiple sensors to determine the minimum value of a preset function as a target, and may include: based on the first variance and the second variance corresponding to each sensor, to determine the minimum value of the preset function as a target, and determining the first weighting factor and the second weighting factor corresponding to each sensor.

[0091] Optionally, the computer device determines the first weighting factor and the second weighting factor corresponding to each sensor based on the first variance and the second variance corresponding to each sensor, with the goal of determining the minimum value of the preset function. The second objective function may be determined with the goal of determining the minimum value of the preset function; the second objective function is solved based on the first variance and the second variance corresponding to each sensor, and the second constraint to obtain the weighting factor corresponding to each sensor.

[0092] Optionally, the preset function may be as shown in the following formula (8).

[0093] (8)

[0094] In formula (8), N represents the number of sensors in the circuit breaker; It represents the first weighting factor corresponding to the i-th sensor; represents the second weighting factor corresponding to the i-th sensor; It represents the first variance corresponding to the i-th sensor; It represents the second variance corresponding to the i-th sensor; It represents the total ensemble variance.

[0095] Optionally, the second objective function may be as shown in the following formula (9).

[0096] (9)

[0097] In formula (9), N represents the number of sensors in the circuit breaker; It represents the first weighting factor corresponding to the i-th sensor; represents the second weighting factor corresponding to the i-th sensor; It represents the first variance corresponding to the i-th sensor; It represents the second variance corresponding to the i-th sensor; It represents the minimum total fusion variance.

[0098] Optionally, the second constraint condition may be as shown in the following formula (10).

[0099] (10)

[0100] In formula (10), N represents the number of sensors in the circuit breaker; It represents the first weighting factor corresponding to the i-th sensor; It represents the second weighting factor corresponding to the i-th sensor.

[0101] Optionally, the computer device solves the second objective function based on the first variance and the second variance corresponding to each sensor and the second constraint condition, and the obtained expressions of the first weighting factor and the second weighting factor corresponding to each sensor can be shown in the following formula (11).

[0102] (11)

[0103] The physical meanings of the parameters in formula (11) can be found in the above description of the physical meanings of the parameters in formula (8), which will not be repeated here.

[0104] See also Figure 3 , Figure 3 Schematic diagram of the overall process of a circuit breaker action time prediction method provided by an embodiment of the present application. Figure 3 As shown, the circuit breaker action time prediction method may include but is not limited to the following steps:

[0105] S301. Acquire multiple historical offline action times and multiple historical online action times respectively measured by multiple sensors in a circuit breaker.

[0106] Among them, multiple historical offline action times may include but are not limited to the action time when the circuit breaker is in the factory test stage and / or the power outage maintenance test stage; multiple historical online action times may include but are not limited to the action time when the circuit breaker is in the energized operating state.

[0107] S302: Determine a first variance corresponding to a plurality of historical offline action times measured by each sensor, and a second variance corresponding to a plurality of historical online action times measured by each sensor.

[0108] S303 : Based on the first variance and the second variance corresponding to each sensor, with the goal of determining the minimum value of the preset function, determine the first weighting factor and the second weighting factor corresponding to each sensor, respectively.

[0109] The first weighting factor is a weighting factor corresponding to the historical offline action time; and the second weighting factor is a weighting factor corresponding to the historical online action time.

[0110] In an optional implementation, the computer device may use the above formulas (8) to (11) to determine the first weighting factor and the second weighting factor corresponding to each sensor.

[0111] S304. Taking the average of N historical online action times obtained by N measurements of each sensor before the current moment as the target action time currently measured by each sensor; N is a positive integer greater than 1.

[0112] S305 . Based on the first weighting factor and the second weighting factor respectively corresponding to each sensor, weighted processing is performed on the target action time currently measured by each sensor to obtain the weighted target action time corresponding to each sensor.

[0113] In an optional implementation, the computer device may use the above formula (6) to determine the weighted target action time corresponding to each sensor.

[0114] S306: Perform fusion processing on the weighted target action times corresponding to the multiple sensors to obtain the next action time of the circuit breaker.

[0115] In an optional implementation, the computer device may use the above formula (7) to determine the next action time of the circuit breaker.

[0116] S307: Input the next action time into the phase selection device corresponding to the circuit breaker, so that the phase selection device controls the circuit breaker to open or close based on the next action time.

[0117] In an embodiment of the present application, the computer device can perform multivariate data adaptive weighted fusion processing on the offline action time and the online action time measured by different sensors installed in the circuit breaker based on the variance extreme value theory to obtain a more accurate next action time of the circuit breaker. Thereafter, the more accurate next action time is input into the phase selection device corresponding to the circuit breaker, so that the phase selection device can control the opening or closing of the circuit breaker based on the more accurate next action time, thereby improving the accuracy of circuit breaker control.

[0118] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0119] Based on the same inventive concept, the embodiment of the present application also provides a circuit breaker action time prediction device for implementing the circuit breaker action time prediction method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more circuit breaker action time prediction device embodiments provided below can refer to the limitations of the circuit breaker action time prediction method above, and will not be repeated here.

[0120] See also Figure 4 , Figure 4 Schematic diagram of the structure of a circuit breaker action time prediction device provided in an embodiment of the present application. Figure 4 As shown, the circuit breaker action time prediction device may include but is not limited to:

[0121] An acquisition module 401 is used to acquire a plurality of historical circuit breaker action times respectively measured by a plurality of sensors in the circuit breaker;

[0122] The determination module 402 is used to determine the variance of multiple historical circuit breaker action times measured by each sensor, and determine the weighting factor corresponding to each sensor based on the variances corresponding to the multiple sensors, with the minimum value of the preset function as the target; wherein the preset function is constructed based on the variances corresponding to the multiple sensors and the weighting factor corresponding to each sensor; the preset function is used to determine the fused total variance corresponding to the multiple variances;

[0123] The processing module 403 is used to perform weighted processing on the target action time currently measured by each sensor based on the weighted factor corresponding to each sensor, and to fuse multiple weighted target action times to obtain the next action time of the circuit breaker.

[0124] In one embodiment, the plurality of historical circuit breaker action times include historical offline action times and historical online action times, the historical offline action times include the action times when the circuit breaker is in a factory test phase and / or a power outage maintenance test phase, and the historical online action times include the action times when the circuit breaker is in a live operation state; the weighting factors corresponding to each sensor include a first weighting factor corresponding to the historical offline action time measured by each sensor, and a second weighting factor corresponding to the historical online action time; the processing module 403 is used to perform weighted processing on the target action time currently measured by each sensor based on the weighting factors corresponding to each sensor, and When a plurality of weighted target action times are fused to obtain the next action time of the circuit breaker, it is specifically used to: take the average of N historical online action times obtained by N measurements of each sensor before the current moment as the target action time currently measured by each sensor; N is a positive integer greater than 1; based on the first weighting factor and the second weighting factor corresponding to each sensor, the target action time currently measured by each sensor is weighted to obtain the weighted target action time corresponding to each sensor; the weighted target action times corresponding to multiple sensors are fused to obtain the next action time of the circuit breaker.

[0125] In one embodiment, the variance includes a first variance corresponding to the historical offline action time and a second variance corresponding to the historical online action time; when the determination module 402 is used to determine the weighting factor corresponding to each sensor based on the variances corresponding to multiple sensors, with the goal of determining the minimum value of a preset function, the determination module 402 is specifically used to: determine the first weighting factor and the second weighting factor corresponding to each sensor based on the first variance and the second variance corresponding to each sensor, with the goal of determining the minimum value of the preset function.

[0126] In one embodiment, the preset function is as follows:

[0127]

[0128] Among them, N represents the number of sensors; It represents the first weighting factor corresponding to the i-th sensor; represents the second weighting factor corresponding to the i-th sensor; It represents the first variance corresponding to the i-th sensor; It represents the second variance corresponding to the i-th sensor; It represents the total ensemble variance.

[0129] In one embodiment, the processing module 403 is used to weight the target action time currently measured by each sensor based on the first weighting factor and the second weighting factor corresponding to each sensor to obtain the weighted target action time corresponding to each sensor. It is specifically used to: sum the first weighting factor and the second weighting factor corresponding to each sensor to obtain the target weighting factor corresponding to each sensor; based on the target weighting factor corresponding to each sensor, weight the target action time currently measured by each sensor to obtain the weighted action time corresponding to each sensor.

[0130] In one embodiment, the processing module 403 is further used to: input the next action time into a phase selection device corresponding to the circuit breaker, so that the phase selection device performs opening or closing control on the circuit breaker based on the next action time.

[0131] Each module in the circuit breaker action time prediction device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a terminal device in the form of hardware, or can be stored in a memory in the terminal device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0132] In an exemplary embodiment, the present application provides a computer device, which may be a terminal device, and its internal structure diagram may be as follows: Figure 5As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a circuit breaker action time prediction method is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0133] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0134] In an exemplary embodiment, the present application provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned circuit breaker action time prediction method when executing the computer program.

[0135] In an exemplary embodiment, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the above-mentioned circuit breaker action time prediction method are implemented.

[0136] In an exemplary embodiment, the present application provides a computer program product, including a computer program, which implements the steps in the above-mentioned circuit breaker action time prediction method when executed by a processor.

[0137] It should be noted that the data involved in this application (including but not limited to current data, processed data, usage information of processed data, unstructured data, data sets corresponding to current environmental information, etc.) are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0138] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0139] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0140] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A circuit breaker action time prediction method, characterized in that: The method comprises: Acquire multiple historical circuit breaker action times respectively measured by multiple sensors in the circuit breaker; Determine the variance of the plurality of historical circuit breaker action times measured by each of the sensors, and determine the weighting factor corresponding to each of the sensors based on the variances corresponding to the plurality of sensors, with the goal of determining the minimum value of a preset function; wherein the preset function is constructed based on the variances corresponding to the plurality of sensors and the weighting factor corresponding to each of the sensors; the preset function is used to determine the fused total variance corresponding to the plurality of variances; Based on the weighting factors corresponding to each of the sensors, the target action time currently measured by each of the sensors is weighted, and a plurality of weighted target action times are fused to obtain the next action time of the circuit breaker.

2. The method according to claim 1, characterized in that The plurality of historical circuit breaker action times include historical offline action times and historical online action times, wherein the historical offline action times include action times when the circuit breaker is in a factory test phase and / or a power outage maintenance test phase, and the historical online action times include action times when the circuit breaker is in an energized operating state; The weighting factors corresponding to each of the sensors include a first weighting factor corresponding to the historical offline action time measured by each of the sensors, and a second weighting factor corresponding to the historical online action time; The step of performing weighted processing on the target action time currently measured by each sensor based on the weighted factor corresponding to each sensor, and fusing multiple weighted target action times to obtain the next action time of the circuit breaker includes: The average of N historical online action times obtained by each sensor from N measurements before the current moment is used as the target action time currently measured by each sensor; N is a positive integer greater than 1; Based on the first weighting factor and the second weighting factor respectively corresponding to each sensor, weighted processing is performed on the target action time currently measured by each sensor to obtain a weighted target action time corresponding to each sensor; The weighted target action times corresponding to the plurality of sensors are fused to obtain the next action time of the circuit breaker.

3. The method according to claim 2, characterized in that The variance includes a first variance corresponding to the historical offline action time and a second variance corresponding to the historical online action time; the weighting factor corresponding to each sensor is determined based on the variances corresponding to the plurality of sensors, with the minimum value of a preset function as a target, and includes: Based on the first variance and the second variance corresponding to each of the sensors, with the goal of determining a minimum value of a preset function, the first weighting factor and the second weighting factor corresponding to each of the sensors are determined.

4. The method according to claim 3, characterized in that The preset function is as follows: Wherein, N represents the number of the sensors; represents the first weighting factor corresponding to the i-th sensor; represents the second weighting factor corresponding to the i-th sensor; represents the first variance corresponding to the i-th sensor; represents the second variance corresponding to the i-th sensor; It represents the total ensemble variance.

5. The method according to claim 2, characterized in that: The step of performing weighted processing on the target action time currently measured by each sensor based on the first weighted factor and the second weighted factor respectively corresponding to each sensor to obtain the weighted target action time corresponding to each sensor includes: Performing a sum operation on the first weighting factor and the second weighting factor respectively corresponding to each of the sensors to obtain a target weighting factor respectively corresponding to each of the sensors; Based on the target weighting factor corresponding to each of the sensors, the target action time currently measured by each of the sensors is weighted to obtain the weighted action time corresponding to each of the sensors.

6. The method according to claim 1, characterized in that The method further comprises: The next action time is input into a phase selection device corresponding to the circuit breaker, so that the phase selection device controls the opening or closing of the circuit breaker based on the next action time.

7. A circuit breaker action time prediction device, characterized in that: The device comprises: An acquisition module, used for acquiring a plurality of historical circuit breaker action times respectively measured by a plurality of sensors in the circuit breaker; A determination module, configured to determine a variance of a plurality of historical circuit breaker action times measured by each of the sensors; The determination module is further used to determine the weighting factor corresponding to each of the sensors based on the variances corresponding to the multiple sensors, with the minimum value of the preset function as the target; the preset function is constructed based on the variances corresponding to the multiple sensors and the weighting factor corresponding to each of the sensors; the preset function is used to determine the fusion total variance corresponding to the multiple variances; The processing module is used to perform weighted processing on the target action time currently measured by each sensor based on the weighted factor corresponding to each sensor, and to fuse multiple weighted target action times to obtain the next action time of the circuit breaker.

8. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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