Intelligent power monitoring method based on power big data

Abstract

The invention discloses an intelligent power monitoring method based on power big data, and the method comprises the steps: obtaining first operation information of power equipment and first environment information corresponding to the first operation information, the first operation information being current operation information of the power equipment, and the first environment information corresponding to the first operation information; the first environment information is current environment information of the power equipment and a first duration of the environment information; and based on the first operation information, the first environment information, the age limit information of the power equipment, the number of times of maintenance and the historical fault probability, predicting the target fault probability of the current fault of the power equipment. The technical problems that the fault information of the power equipment cannot be timely and accurately found and the repair time of the power equipment cannot be accurately predicted due to the fact that the environmental information of the power equipment and the historical information of the power equipment are not considered during intelligent monitoring of the power equipment are solved.

Description

Technical Field

[0001] The present invention relates to the field of electric power intelligent monitoring, and in particular to an electric power intelligent monitoring method based on electric power big data. Background Art

[0002] At present, the power data of power equipment is obtained in real time through a number of sensors. The power equipment is monitored according to the data obtained by the sensors to determine the operating status of the power equipment. However, in the prior art, the power equipment is only monitored by sensors, and the power equipment is repaired according to the maintenance cycle, without considering the environmental information of the power equipment and the historical information of the power equipment, resulting in the inability to timely and accurately discover the fault information of the power equipment and the inability to accurately predict the emergency repair time of the power equipment. Therefore, an intelligent power monitoring method based on power big data is proposed to solve the technical problem that the environmental information and the historical information of the power equipment are not considered when performing intelligent monitoring on the power equipment, resulting in the inability to timely and accurately discover the fault information of the power equipment and the inability to accurately predict the emergency repair time of the power equipment. Summary of the invention

[0003] The embodiment of the present invention provides an intelligent power monitoring method based on power big data, which solves the technical problem that the environmental information of the power equipment and the historical information of the power equipment are not taken into consideration when performing intelligent monitoring on the power equipment, resulting in the inability to timely and accurately discover the fault information of the power equipment and the inability to accurately predict the emergency repair time of the power equipment.

[0004] According to one aspect of an embodiment of the present invention, a method for intelligent power monitoring based on power big data is provided. The method may include: obtaining first operating information of the power equipment and first environmental information corresponding to the first operating information, wherein the first operating information is the current operating information of the power equipment, and the first environmental information is the current environmental information of the power equipment and the first duration of the environmental information; based on the first operating information, the first environmental information, the age information, the number of maintenance times and the historical failure probability of the power equipment, predicting the target failure probability of the current failure of the power equipment; based on the target failure probability and the historical maintenance information, determining the target maintenance information of the power equipment, wherein the target maintenance information of the power equipment includes the first maintenance time of the power equipment and the number information of the maintenance personnel; in response to the target maintenance information, determining the fault recovery time of the power equipment.

[0005] Optionally, before predicting the failure probability of the current failure of the power equipment based on the first operating information, the first environmental information, the age information of the power equipment, the number of maintenance times and the historical failure probability, the method also includes: obtaining historical environmental information and historical duration information of the power equipment, wherein the historical duration information is the duration of the historical environmental information; obtaining historical operating information of the power equipment in response to the historical environmental information and the historical duration information of the power equipment; extracting historical fault data from the historical operating information of the power equipment; and determining the historical failure probability of the power equipment based on the historical fault data.

[0006] Optionally, based on the historical fault data, determining the historical fault probability of the power equipment includes: according to the first formula Get the first value of the historical fault data, where C is the first value of the historical fault data, c i is the value of each historical fault data, a i is the weight of each historical fault data, i is the number of historical fault data, and the value range of i is from 1 to n; the quotient between the first value of the historical fault data and the target total value is determined as the historical failure probability of the power equipment.

[0007] Optionally, based on the first operating information, the first environmental information, the age information of the power equipment, the number of maintenance times and the historical failure probability, a target failure probability of the current failure of the power equipment is predicted, including: determining a first influencing factor of the target failure probability based on the first operating information and the first environmental information; determining a second influencing factor of the target failure probability based on the age information and the number of maintenance times of the power equipment; and obtaining the target failure probability based on the first influencing factor, the second influencing factor and the historical failure probability.

[0008] Optionally, based on the first operation information and the first environment information, determining a first influencing factor of the target failure probability includes: according to the second formula A first influencing factor α1 of the target failure probability is obtained, wherein β is a parameter corresponding to the first operation information, and φ is a parameter corresponding to the first environment information.

[0009] Optionally, based on the age information and maintenance times of the power equipment, determining the second influencing factor of the target failure probability includes: according to the third formula α2=(y·Δ1) m , the second influencing factor α2 of the target failure probability is obtained, where y is the age information of the power equipment, Δ1 is the attenuation factor corresponding to the age information of the power equipment, and m is the number of maintenance times of the power equipment.

[0010] Optionally, based on the first influencing factor, the second influencing factor and the historical failure probability, obtaining the target failure probability includes: according to the fourth formula Get the target failure probability P η, where α1 is the first influencing factor, α2 is the second influencing factor, and p u is the probability of each historical failure of the power equipment, u is the number of historical failures, and the value of u ranges from 1 to μ.

[0011] Optionally, in response to the target maintenance information, determining the fault recovery time of the power equipment includes: obtaining the recovery time of the power equipment in response to the experience value of the maintenance personnel corresponding to the number information of the maintenance personnel.

[0012] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided. The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute an intelligent power monitoring method based on power big data according to an embodiment of the present invention.

[0013] According to another aspect of an embodiment of the present invention, a processor is further provided. The processor is used to run a program, wherein when the program is run, an electric power intelligent monitoring method based on electric power big data according to an embodiment of the present invention is executed.

[0014] In an embodiment of the present invention, first operating information of an electric device and first environmental information corresponding to the first operating information are obtained, wherein the first operating information is the current operating information of the electric device, and the first environmental information is the current environmental information of the electric device and the first duration of the environmental information; based on the first operating information, the first environmental information, the age information of the electric device, the number of maintenance times and the historical failure probability, the target failure probability of the current failure of the electric device is predicted; based on the target failure probability and the historical maintenance information, the target maintenance information of the electric device is determined, wherein the target maintenance information of the electric device includes the first maintenance time of the electric device and the number information of the maintenance personnel; in response to the target maintenance information, the fault recovery time of the electric device is determined, which solves the technical problem that the environmental information and the historical information of the electric device are not considered when the electric device is intelligently monitored, resulting in the inability to timely and accurately discover the fault information of the electric device and the inability to accurately predict the emergency repair time of the electric device, and achieves the technical effect that the environmental information and the historical information of the electric device are considered when the electric device is intelligently monitored, the fault information of the electric device can be discovered in a timely and accurate manner and the emergency repair time of the electric device can be accurately predicted.

[0015] Advantages of the present invention:

[0016] Compared with the existing technology, the present invention obtains the target failure probability of the current failure of the power equipment by calculating the operating information of the power equipment, the environmental information corresponding to the operating information, the age information of the power equipment, the number of maintenance times and the historical failure information. It takes into account various factors to make the calculated target failure probability of the power equipment more accurate. According to the target failure probability and the historical maintenance information, the target maintenance information of the power equipment is obtained. According to the target maintenance information, the fault recovery time of the power equipment is obtained, which further refines the fault recovery time of the power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 is a flow chart of a method for intelligent power monitoring based on power big data according to an embodiment of the present invention;

[0019] Figure 2 It is a structural block diagram of an electric power intelligent monitoring system based on electric power big data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only embodiments of a part of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] Example 1

[0023] According to an embodiment of the present invention, a method for intelligent power monitoring based on power big data is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system containing at least one set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0024] Figure 1 is a flow chart of a method for intelligent power monitoring based on power big data according to an embodiment of the present invention. Figure 1 As shown, the method may include the following steps:

[0025] Step S101, obtaining first operating information of the electric equipment and first environmental information corresponding to the first operating information, wherein the first operating information is the current operating information of the electric equipment, and the first environmental information is the current environmental information of the electric equipment and the first duration of the environmental information.

[0026] In the technical solution provided in the above step S101 of the present invention, the first operating information of the power equipment, the environmental information under the first operating information, and the first duration of the environmental information are obtained through sensors installed on the power equipment, wherein the first operating information is whether the power equipment is operating normally or abnormally, and the current environmental information may be the temperature, humidity, wind force, rainfall, snow volume, etc. of the environment in which the power equipment is located, and the first duration of the environmental information is 20 minutes when the wind force is level 5.

[0027] Step S102, predicting a target failure probability of a current failure of the power equipment based on the first operation information, the first environment information, the age information of the power equipment, the number of maintenance times and the historical failure probability.

[0028] In the technical solution provided in the above step S102 of the present invention, the failure probability of the current failure of the power equipment is calculated based on the first operating information, the first environmental information, the age information of the power equipment, the number of maintenance times and the historical failure probability, wherein the historical failure probability and the target failure probability are both specific numerical values.

[0029] Step S103, determining target maintenance information of the power equipment based on the target failure probability and the historical maintenance information, wherein the target maintenance information of the power equipment includes the first maintenance time of the power equipment and the number information of the maintenance personnel.

[0030] In the technical solution provided in the above step S103 of the present invention, the target failure probability and the historical failure probability are compared, and the historical maintenance information corresponding to the historical failure probability whose comparison result is less than the threshold is determined as the target maintenance information of the power equipment, and the maintenance information includes the first maintenance time of the power equipment and the number information of the maintenance personnel, for example, number 116.

[0031] Step S104: determining the fault recovery time of the power equipment in response to the target maintenance information.

[0032] In the technical solution provided in the above step S104 of the present invention, the fault recovery time of the power equipment is obtained according to the maintenance information. For example, the recovery time is 1 hour. This is only used as an example and is not specifically limited.

[0033] The above method of this embodiment is further introduced below.

[0034] As an optional implementation method, step S102, before predicting the failure probability of the current failure of the power equipment based on the first operation information, the first environmental information, the age information of the power equipment, the number of maintenance times and the historical failure probability, the method also includes: obtaining the historical environment information and historical duration information of the power equipment, wherein the historical duration information is the duration of the historical environment information; obtaining the historical operation information of the power equipment in response to the historical environment information and the historical duration information of the power equipment; extracting the historical fault data in the historical operation information of the power equipment; and determining the historical failure probability of the power equipment based on the historical fault data.

[0035] In this embodiment, historical environmental information of the environment in which the power equipment is located and historical duration information of the historical environmental information are obtained, and historical operation information of the power equipment is obtained based on the historical environmental information and historical duration information of the environment in which the power equipment is located, wherein the historical operation information indicates whether the operation of the power equipment is normal or abnormal. When the historical operation information of the power equipment is abnormal, historical fault data indicating that the historical operation information of the power equipment is abnormal is obtained, and based on the historical fault data, the historical failure probability of the power equipment is obtained.

[0036] As an optional embodiment, step S102, based on the historical fault data, determines the historical fault probability of the power equipment, including: according to the first formula Get the first value of the historical fault data, where C is the first value of the historical fault data, c i is the value of each historical fault data, a i is the weight of each historical fault data, i is the number of historical fault data, and the value range of i is from 1 to n; the quotient between the first value of the historical fault data and the target total value is determined as the historical failure probability of the power equipment.

[0037] In this embodiment, the value c of each historical fault data in the historical fault data is i , the weight of each historical fault data a i Substitute into the first formula The first value C of the historical fault data is obtained, wherein each historical fault data can be a voltage value, a current value, a capacitance value, etc., and the weight of each historical fault data can be 0.2, 0.3, etc. A specific analysis is performed here according to the situation of the power equipment, and the quotient between the first value of the historical fault data and the target total value is determined as the historical failure probability of the power equipment. For example, when the first value of the historical fault data is 62.35 and the target total value is 100, the historical failure probability of the power equipment is 62.35%.

[0038] As an optional implementation method, step S102 predicts the target failure probability of the current failure of the power equipment based on the first operating information, the first environmental information, the age information of the power equipment, the number of maintenance times and the historical failure probability, including: determining a first influencing factor of the target failure probability based on the first operating information and the first environmental information; determining a second influencing factor of the target failure probability based on the age information and the number of maintenance times of the power equipment; and obtaining the target failure probability based on the first influencing factor, the second influencing factor and the historical failure probability.

[0039] In this embodiment, the first operating information and the first environmental information are calculated to obtain the first influencing factor of the target probability, the age information and the number of maintenance times of the power equipment are calculated to obtain the second influencing factor of the target failure probability, and the first influencing factor, the second influencing factor and the historical failure probability are calculated to obtain the target failure probability.

[0040] As an optional embodiment, based on the first operation information and the first environment information, determining the first influencing factor of the target failure probability includes: according to the second formula A first influencing factor α1 of the target failure probability is obtained, wherein β is a parameter corresponding to the first operation information, and φ is a parameter corresponding to the first environment information.

[0041] In this embodiment, the parameter β corresponding to the first operation information and the parameter φ corresponding to the first environment information are substituted into the second formula: In the above equation, the first influencing factor α1 of the target failure probability is obtained.

[0042] As an optional embodiment, based on the age information and maintenance times of the power equipment, determining the second influencing factor of the target failure probability includes: according to the third formula α2=(y·Δ1) m , the second influencing factor α2 of the target failure probability is obtained, where y is the age information of the power equipment, Δ1 is the attenuation factor corresponding to the age information of the power equipment, and m is the number of maintenance times of the power equipment.

[0043] In this embodiment, the age information y of the power equipment, the attenuation factor Δ1 corresponding to the age information of the power equipment, and the number of maintenance times m of the power equipment are substituted into the third formula α2=(y·Δ1): m In the above equation, the second influencing factor α2 of the target failure probability is obtained.

[0044] As an optional implementation mode, based on the first influencing factor, the second influencing factor and the historical failure probability, the target failure probability is obtained, including: according to the fourth formula Get the target failure probability P η , where α1 is the first influencing factor, α2 is the second influencing factor, and p u is the probability of each historical failure of the power equipment, u is the number of historical failures, and the value of u ranges from 1 to μ.

[0045] In this embodiment, the first influencing factor α1, the second influencing factor α2, and each historical failure probability p of the power equipment are u , the number of historical faults u, where u ranges from 1 to μ, is substituted into the fourth formula The target failure probability P is obtained η .

[0046] As an optional implementation method, step S105, in response to the target maintenance information, determines the fault recovery time of the power equipment, including: in response to the maintenance personnel's experience value corresponding to the maintenance personnel's number information, obtains the recovery time of the power equipment.

[0047] In this embodiment, when the maintenance personnel's experience value corresponding to the maintenance personnel's number information is 90%, and the maintenance personnel's emergency repair time for the same historical fault information of the power equipment is 30 minutes, the recovery time of the power equipment is 40 minutes.

[0048] In an embodiment of the present invention, first operating information of an electric power device and first environmental information corresponding to the first operating information are obtained, wherein the first operating information is the current operating information of the electric power device, and the first environmental information is the current environmental information of the electric power device and the first duration of the environmental information; based on the first operating information, the first environmental information, the age information of the electric power device, the number of maintenance times and the historical failure probability, the target failure probability of the current failure of the electric power device is predicted; based on the target failure probability and the historical maintenance information, the target maintenance information of the electric power device is determined, wherein the target maintenance information of the electric power device includes the first maintenance time of the electric power device and the number information of the maintenance personnel; in response to the target maintenance information, the fault recovery time of the electric power device is determined, which solves the technical problem that the environmental information of the electric power device and the historical information of the electric power device are not considered when performing intelligent monitoring of the electric power device, resulting in the inability to timely and accurately discover the fault information of the electric power device and the inability to accurately predict the emergency repair time of the electric power device, and achieves the technical effect of considering the environmental information of the electric power device and the historical information of the electric power device when performing intelligent monitoring of the electric power device, being able to timely and accurately discover the fault information of the electric power device and being able to accurately predict the emergency repair time of the electric power device.

[0049] Example 2

[0050] According to an embodiment of the present invention, a power intelligent monitoring system based on power big data is also provided. It should be noted that the power intelligent monitoring system based on power big data can be used to execute the power intelligent monitoring method based on power big data in Example 1.

[0051] Figure 2 is a structural block diagram of an electric power intelligent monitoring system based on electric power big data according to an embodiment of the present invention. Figure 2 As shown, an intelligent power monitoring system based on power big data may include: a first acquisition unit, a prediction unit, a first determination unit and a second determination unit.

[0052] The first acquisition unit is used to acquire first operating information of the power equipment and first environmental information corresponding to the first operating information, wherein the first operating information is the current operating information of the power equipment, and the first environmental information is the current environmental information of the power equipment and a first duration of the environmental information.

[0053] The prediction unit is used to predict the target failure probability of the current failure of the power equipment based on the first operation information, the first environment information, the age information of the power equipment, the number of maintenance times and the historical failure probability.

[0054] The first determination unit is used to determine target maintenance information of the power equipment based on the target failure probability and the historical maintenance information, wherein the target maintenance information of the power equipment includes the first maintenance time of the power equipment and the number information of the maintenance personnel.

[0055] The second determining unit is used to determine the fault recovery time of the power equipment in response to the target maintenance information.

[0056] Optionally, before predicting the failure probability of the current failure of the power equipment based on the first operating information, the first environmental information, the age information of the power equipment, the number of maintenance times and the historical failure probability, the system also includes: a second acquisition unit, used to acquire the historical environmental information and historical duration information of the power equipment, wherein the historical duration information is the duration of the historical environmental information; an acquisition unit, used to obtain the historical operating information of the power equipment in response to the historical environmental information and the historical duration information of the power equipment; an extraction unit, used to extract historical fault data from the historical operating information of the power equipment; and a third determination unit, used to determine the historical failure probability of the power equipment based on the historical fault data.

[0057] Optionally, the third determining unit may further include: a first obtaining module for obtaining, according to the first formula Get the first value of the historical fault data, where C is the first value of the historical fault data, c i is the value of each historical fault data, a i is the weight of each historical fault data, i is the number of historical fault data, and the value range of i is from 1 to n; the first determination module is used to determine the quotient between the first value of the historical fault data and the target total value as the historical failure probability of the power equipment.

[0058] Optionally, the prediction unit may also include: a second determination module, used to determine the first influencing factor of the target failure probability based on the first operating information and the first environmental information; a third determination module, used to determine the second influencing factor of the target failure probability based on the age information and maintenance times of the power equipment; a second obtaining module, used to obtain the target failure probability based on the first influencing factor, the second influencing factor and the historical failure probability.

[0059] Optionally, the second determining module may further include: a first obtaining submodule for determining A first influencing factor α1 of the target failure probability is obtained, wherein β is a parameter corresponding to the first operation information, and φ is a parameter corresponding to the first environment information.

[0060] Optionally, the third determining module may further include: a second obtaining submodule, configured to obtain the value of α2=(y·Δ1) according to the third formula: m , the second influencing factor α2 of the target failure probability is obtained, where y is the age information of the power equipment, Δ1 is the attenuation factor corresponding to the age information of the power equipment, and m is the number of maintenance times of the power equipment.

[0061] Optionally, the second obtaining module may further include: a third obtaining submodule for obtaining according to the fourth formula Get the target failure probability P η , where α1 is the first influencing factor, α2 is the second influencing factor, and p u is the probability of each historical failure of the power equipment, u is the number of historical failures, and the value of u ranges from 1 to μ.

[0062] Optionally, the second determining unit may further include: a third obtaining module, configured to obtain the recovery time of the power equipment in response to the experience value of the maintenance personnel corresponding to the number information of the maintenance personnel.

[0063] In this embodiment, a first acquisition unit is used to acquire first operating information of the electric power equipment and first environmental information corresponding to the first operating information, wherein the first operating information is the current operating information of the electric power equipment, and the first environmental information is the current environmental information of the electric power equipment and the first time when the environmental information lasts; a prediction unit is used to predict a target failure probability of the current failure of the electric power equipment based on the first operating information, the first environmental information, the age information of the electric power equipment, the number of maintenance times and the historical failure probability; a first determination unit is used to determine the target maintenance information of the electric power equipment based on the target failure probability and the historical maintenance information, wherein the target maintenance information of the electric power equipment includes the first maintenance time of the electric power equipment and the number information of the maintenance personnel; a second determination unit is used to determine the fault recovery time of the electric power equipment in response to the target maintenance information, thereby solving the technical problem that the environmental information and the historical information of the electric power equipment are not considered when the electric power equipment is intelligently monitored, resulting in the inability to timely and accurately discover the fault information of the electric power equipment and the inability to accurately predict the emergency repair time of the electric power equipment, thereby achieving the technical effect that the environmental information and the historical information of the electric power equipment are considered when the electric power equipment is intelligently monitored, the fault information of the electric power equipment can be timely and accurately discovered and the emergency repair time of the electric power equipment can be accurately predicted.

[0064] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0065] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0066] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0067] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0068] In addition, each functional unit in each embodiment of the present invention may be integrated into a first processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0069] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0070] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for intelligent power monitoring based on power big data, characterized in that: include: Acquire first operating information of an electric device and first environmental information corresponding to the first operating information, wherein the first operating information is current operating information of the electric device, and the first environmental information is current environmental information of the electric device and a first duration of the environmental information; Predicting a target failure probability of a current failure of the power equipment based on the first operation information, the first environment information, the age information, the number of maintenance times, and the historical failure probability of the power equipment; Based on the target failure probability and the historical maintenance information, determine the target maintenance information of the electric equipment, wherein the target maintenance information of the electric equipment includes the first maintenance time of the electric equipment and the number information of the maintenance personnel; In response to the target maintenance information, a fault recovery time of the electric power equipment is determined.

2. According to claim 1, a method for intelligent power monitoring based on power big data is characterized in that: Before predicting the failure probability of a current failure of the power equipment based on the first operation information, the first environment information, the age information, the number of maintenance times and the historical failure probability of the power equipment, the method further includes: Acquire historical environment information and historical duration information of the electric power equipment, wherein the historical duration information is the duration of the historical environment information; In response to the historical environment information and the historical duration information of the electric power equipment, obtaining the historical operation information of the electric power equipment; Extracting historical fault data from historical operation information of the electric power equipment; Based on the historical failure data, a historical failure probability of the power equipment is determined.

3. The method for intelligent power monitoring based on power big data according to claim 2 is characterized in that: The determining, based on the historical fault data, the historical fault probability of the power equipment comprises: According to the first formula Get the first value of the historical fault data, where C is the first value of the historical fault data, c i is the value of each historical fault data, a i is the weight of each historical fault data, i is the number of historical fault data, and the value range of i is from 1 to n; The quotient between the first value of the historical fault data and the target total value is determined as the historical failure probability of the power equipment.

4. The method for intelligent power monitoring based on power big data according to claim 1 is characterized in that: The predicting a target failure probability of a current failure of the power equipment based on the first operation information, the first environment information, the age information, the number of maintenance times and the historical failure probability of the power equipment includes: Determining a first influencing factor of the target failure probability based on the first operating information and the first environmental information; Determining a second influencing factor of the target failure probability based on the age information and maintenance times of the power equipment; A target failure probability is obtained based on the first influencing factor, the second influencing factor, and the historical failure probability.

5. The method for intelligent power monitoring based on power big data according to claim 4 is characterized in that: The determining, based on the first operation information and the first environment information, a first influencing factor of the target failure probability includes: According to the second formula A first influencing factor α1 of the target failure probability is obtained, wherein β is a parameter corresponding to the first operating information, and φ is a parameter corresponding to the first environmental information.

6. The method for intelligent power monitoring based on power big data according to claim 4 is characterized in that: The determining of the second influencing factor of the target failure probability based on the age information and the number of maintenance times of the electric power equipment includes: According to the third formula α2=(y·Δ1) m , obtain the second influencing factor α2 of the target failure probability, where y is the age information of the power equipment, Δ1 is the attenuation factor corresponding to the age information of the power equipment, and m is the number of maintenance times of the power equipment.

7. The method for intelligent power monitoring based on power big data according to claim 4 is characterized in that: The obtaining a target failure probability based on the first influencing factor, the second influencing factor and the historical failure probability includes: According to the fourth formula Get the target failure probability P η , where α1 is the first influencing factor, α2 is the second influencing factor, and p u is the probability of each historical failure of the power equipment, u is the number of times the historical failure occurred, and the value of u is 1~μ.

8. The method for intelligent power monitoring based on power big data according to claim 1 is characterized in that: The step of determining the fault recovery time of the electric power equipment in response to the target maintenance information includes: In response to the experience value of the maintenance personnel corresponding to the number information of the maintenance personnel, the recovery time of the power equipment is obtained.

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