Method for monitoring power equipment based on power gap operating system

Abstract

The invention relates to a method for monitoring power equipment based on a power gap operating system, and the method comprises the following steps: controlling all edge nodes to collect the operation data of the power equipment based on the power gap operating system, and carrying out the preprocessing of the operation data of the power equipment in the edge nodes; performing operation state evaluation on the power equipment in each edge node according to the preprocessed operation data of the power equipment; and the edge node performs anomaly detection according to the operation state evaluation to obtain an anomaly score of the power equipment, and if the anomaly score reaches a preset threshold value, it is considered that the equipment has a fault, and alarm information is returned to the power gap operating system.

Description

Technical Field

[0001] The present invention relates to a method for monitoring electric power equipment based on the electric power Hongmeng operating system, which belongs to the technical field of electric power monitoring. Background Art

[0002] In the power system, real-time monitoring, fault detection and predictive maintenance of equipment are crucial. Traditional power monitoring systems usually rely on centralized architectures to transmit all data to central servers for storage and processing. However, centralized monitoring systems have problems such as high data latency, heavy load on central nodes, and poor real-time performance. In addition, with the development of smart grids, the types, quantity, and distribution of equipment in power systems are becoming increasingly complex, making it difficult for traditional centralized methods to meet large-scale, multi-point monitoring needs.

[0003] In recent years, technologies such as the Internet of Things (IoT), edge computing, and artificial intelligence (AI) have been gradually applied to power monitoring systems. IoT enables interconnection between devices, edge computing relieves the burden on central servers, and AI is used for device anomaly detection and fault prediction. However, these technologies still face problems such as insufficient standardization, difficulty in device coordination, limited real-time performance, and poor compatibility when integrated, making it difficult to form a unified and efficient solution.

[0004] Traditional centralized monitoring systems require all data to be centralized at a central node for processing and analysis, which increases data transmission delays and affects the real-time performance of the system. In addition, failure of the central node will cause the system to crash, reducing the reliability and stability of the system. Summary of the invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the present invention proposes a method for monitoring power equipment based on the power Hongmeng operating system.

[0006] The technical solution of the present invention is as follows:

[0007] A method for monitoring power equipment based on the power Hongmeng operating system includes the following steps:

[0008] Based on the power Hongmeng operating system, all its edge nodes are controlled to collect power equipment operation data, and the power equipment operation data is pre-processed in the edge nodes;

[0009] In each edge node, the operation status of the power equipment is evaluated according to the preprocessed operation data of the power equipment;

[0010] The edge node performs anomaly detection based on the operating status assessment to obtain the anomaly score of the power equipment. If the anomaly score reaches the preset threshold, the equipment is considered to have failed and the alarm information is returned to the power Hongmeng operating system.

[0011] As a preferred embodiment of the present invention, the preprocessing steps of the power equipment operation data are:

[0012] The Kalman filter algorithm is used to optimize the operation data of power equipment, as shown in the following formula:

[0013] X t+1 =F·X t-1 +K t (X t -H·X t-1 )

[0014] Where: X t+1 represents the predicted power equipment operation data at time t+1; F represents the state transfer matrix; K t represents the Kalman gain at time t; X t-1 represents the power equipment operation data at time t-1; H represents the observation matrix; X t Represents the power equipment operation data at time t

[0015] The predicted power equipment operation data at time t+1 is compared with the actual power equipment operation data at time t+1. If the difference is within the threshold, the data is considered to be normal, otherwise the data is removed.

[0016] As a preferred embodiment of the present invention, the data in the edge node is returned to the power Hongmeng operating system through the edge device self-organizing network, as shown in the following formula:

[0017]

[0018] Where: T s represents the transmission time; M represents the number of edge nodes; L m Indicates the data size transmitted by the mth edge node; B m represents the transmission bandwidth of the mth edge node; Δt represents the transmission time limit.

[0019] As a preferred embodiment of the present invention, the operating status evaluation step of the power equipment is:

[0020] The time domain multivariate regression model is constructed to evaluate the operating status of each power equipment based on the preprocessed power equipment operation data, as shown in the following formula:

[0021]

[0022] Where: S(t) represents the operating status evaluation value of the power equipment at time t; γ represents the bias term; N represents the total number of data samples; α n represents the regression coefficient of the nth data sample; β j represents the regression coefficient of the jth data sample; Xn,t represents the nth data sample at time t; X j,t Represents the j-th data sample at time t.

[0023] As a preferred embodiment of the present invention, anomaly detection is performed according to the operating status evaluation value of each power device, as shown in the following formula:

[0024]

[0025] Where: d(S(t)) represents the abnormal score of the power equipment at time t; μ represents the mean value of the historical power equipment operating status assessment.

[0026] As a preferred embodiment of the present invention, the future state of the power equipment is predicted by a recursive neural network based on the operating data of the power equipment, as shown in the following formula:

[0027] h t =tanh(W xh X t +W hh h t-1 +b h )

[0028] Y t+1 =W hy h t +b y

[0029] Where: Y t+1 represents the predicted value of the future state of the power equipment at time t+1; h t represents the hidden layer state of the recurrent neural network at time t; W xh Represents the weight matrix of power equipment operation data; W hh represents the hidden layer weight matrix of the recurrent neural network at time t; W hy represents the hidden layer weight matrix of the recursive neural network at time t-1; b h 、b y All are bias terms.

[0030] As a preferred embodiment of the present invention, the recursive neural network parameters are adjusted by feedback optimization, as shown in the following formula:

[0031]

[0032] Where: θ t+1 represents the recurrent neural network parameters at time t+1; θ t represents the recursive neural network parameters at time t; η represents the learning rate; L represents the loss function.

[0033] On the other hand, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in any embodiment of the present invention is implemented.

[0034] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0035] The present invention has the following beneficial effects:

[0036] 1. The distributed architecture of the Hongmeng operating system of the present invention enables each device to become an independent monitoring node, realizing local data processing and anomaly detection. Distributed computing power avoids data transmission delays and central node overload problems, while improving the fault tolerance of the system. Central node failure will no longer cause the entire system to fail.

[0037] 2. The present invention uses the edge computing function of Hongmeng to enable the device to perform data preprocessing and status evaluation locally, and realize real-time collaboration among multiple devices through distributed algorithms. Through multi-node collaboration, the system can quickly detect and handle sudden failure events, greatly improving the response speed of monitoring and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0041] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0042] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0043] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0044] Embodiment 1:

[0045] See also Figure 1 , a method for monitoring power equipment based on the power Hongmeng operating system, comprising the following steps:

[0046] Based on the power Hongmeng operating system, all edge nodes are controlled to collect power equipment operation data, where the power equipment operation data includes equipment current, equipment voltage and equipment temperature, and the power equipment operation data is pre-processed in the edge nodes;

[0047] In each edge node, the operation status of the power equipment is evaluated according to the preprocessed operation data of the power equipment;

[0048] The edge node performs anomaly detection based on the operating status assessment to obtain the anomaly score of the power equipment. If the anomaly score reaches the preset threshold, the equipment is considered to have failed and the alarm information is returned to the power Hongmeng operating system.

[0049] As a preferred implementation of this embodiment, the preprocessing steps of the power equipment operation data are:

[0050] The Kalman filter algorithm is used to optimize the operation data of power equipment, as shown in the following formula:

[0051] X t+1 =F·X t-1 +K t (X t -H·X t-1 )

[0052] Where: X t+1 represents the predicted power equipment operation data at time t+1; F represents the state transfer matrix; K t represents the Kalman gain at time t; X t-1 represents the power equipment operation data at time t-1; H represents the observation matrix; X t Represents the power equipment operation data at time t

[0053] The predicted power equipment operation data at time t+1 is compared with the actual power equipment operation data at time t+1. If the difference is within the threshold, the data is considered to be normal, otherwise the data is removed.

[0054] As a preferred implementation of this embodiment, the data in the edge node is returned to the power Hongmeng operating system through the edge device self-organizing network, as shown in the following formula:

[0055]

[0056] Where: T s represents the transmission time; M represents the number of edge nodes; L m Indicates the data size transmitted by the mth edge node; B m represents the transmission bandwidth of the mth edge node; Δt represents the transmission time limit.

[0057] As a preferred implementation of this embodiment, the operating status evaluation steps of the power equipment are:

[0058] The time domain multivariate regression model is constructed to evaluate the operating status of each power equipment based on the preprocessed power equipment operation data, as shown in the following formula:

[0059]

[0060] Where: S(t) represents the operating status evaluation value of the power equipment at time t; γ represents the bias term; N represents the total number of data samples; α n represents the regression coefficient of the nth data sample; β j represents the regression coefficient of the jth data sample; X n,t represents the nth data sample at time t; X j,t Represents the j-th data sample at time t.

[0061] As a preferred implementation of this embodiment, anomaly detection is performed according to the operating status evaluation value of each power device, as shown in the following formula:

[0062]

[0063] Where: d(S(t)) represents the abnormal score of the power equipment at time t; μ represents the mean value of the historical power equipment operating status assessment.

[0064] As a preferred implementation of this embodiment, the future state of the power equipment is predicted through a recursive neural network based on the operating data of the power equipment, as shown in the following formula:

[0065] h t =tanh(W xh Xt +W hh h t-1 +b h )

[0066] Y t+1 =W hy h t +b y

[0067] Where: Y t+1 represents the predicted value of the future state of the power equipment at time t+1; h t represents the hidden layer state of the recurrent neural network at time t; W xh Represents the weight matrix of power equipment operation data; W hh represents the hidden layer weight matrix of the recurrent neural network at time t; W hy represents the hidden layer weight matrix of the recursive neural network at time t-1; b h 、b y All are bias terms.

[0068] As a preferred implementation of this embodiment, the recursive neural network parameters are adjusted through feedback optimization, as shown in the following formula:

[0069]

[0070] Where: θ t+1 represents the recurrent neural network parameters at time t+1; θ t represents the recursive neural network parameters at time t; η represents the learning rate; L represents the loss function.

[0071] Embodiment 2:

[0072] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present invention when executing the program.

[0073] Embodiment three:

[0074] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in any embodiment of the present invention is implemented.

[0075] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0076] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0077] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0078] In several embodiments provided in the present application, if any function 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 application, or the part that contributes to the prior art or the 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, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0079] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for monitoring power equipment based on the power Hongmeng operating system, characterized in that: The following steps are involved: Based on the power Hongmeng operating system, all its edge nodes are controlled to collect power equipment operation data, and the power equipment operation data is pre-processed in the edge nodes; In each edge node, the operation status of the power equipment is evaluated according to the preprocessed operation data of the power equipment; The edge node performs anomaly detection based on the operating status assessment to obtain the anomaly score of the power equipment. If the anomaly score reaches the preset threshold, the equipment is considered to have failed and the alarm information is returned to the power Hongmeng operating system.

2. According to claim 1, a method for monitoring power equipment based on the power Hongmeng operating system is characterized in that: The preprocessing steps of the power equipment operation data are as follows: The Kalman filter algorithm is used to optimize the operation data of power equipment, as shown in the following formula: X t+1 =F·X t-1 +K t (X t -H·X t-1 ) Where: X t+1 represents the predicted power equipment operation data at time t+1; F represents the state transfer matrix; K t represents the Kalman gain at time t; X t-1 represents the power equipment operation data at time t-1; H represents the observation matrix; X t The power equipment operation data at time t represents the power equipment operation data predicted at time t+1 and the power equipment operation data actually at time t+1 are compared. If the difference is within the threshold, the data is considered normal, otherwise the data is removed.

3. According to claim 1, a method for monitoring power equipment based on the power Hongmeng operating system is characterized in that: The data in the edge node is returned to the power Hongmeng operating system through the edge device self-organizing network, as shown in the following formula: Where: T s represents the transmission time; M represents the number of edge nodes; L m Indicates the data size transmitted by the mth edge node; B m represents the transmission bandwidth of the mth edge node; Δt represents the transmission time limit.

4. According to claim 1, a method for monitoring power equipment based on the power Hongmeng operating system is characterized in that: The operating status evaluation steps of the power equipment are as follows: The time domain multivariate regression model is constructed to evaluate the operating status of each power equipment based on the preprocessed power equipment operation data, as shown in the following formula: Where: S(t) represents the operating status evaluation value of the power equipment at time t; γ represents the bias term; N represents the total number of data samples; α n represents the regression coefficient of the nth data sample; β j represents the regression coefficient of the jth data sample; X n,t represents the nth data sample at time t; X j,t Represents the j-th data sample at time t.

5. According to claim 2, a method for monitoring power equipment based on the power Hongmeng operating system is characterized in that: Anomaly detection is performed based on the operating status evaluation value of each power device, as shown in the following formula: Where: d(S(t)) represents the abnormal score of the power equipment at time t; μ represents the mean value of the historical power equipment operating status assessment.

6. According to claim 1, a method for monitoring power equipment based on the power Hongmeng operating system is characterized in that: Based on the operating data of the power equipment, the future state of the power equipment is predicted through a recursive neural network, as shown in the following formula: h t =tanh(W xh X t +W hh h t-1 +b h ) Y t+1 =W hy h t +b y Where: Y t+1 represents the predicted value of the future state of the power equipment at time t+1; h t represents the hidden layer state of the recurrent neural network at time t; W xh Represents the weight matrix of power equipment operation data; W hh represents the hidden layer weight matrix of the recurrent neural network at time t; W hy represents the hidden layer weight matrix of the recursive neural network at time t-1; b h 、b y All are bias terms.

7. According to claim 6, a method for monitoring power equipment based on the power Hongmeng operating system is characterized in that: The recurrent neural network parameters are adjusted through feedback optimization, as shown in the following formula: Where: θ t+1 represents the recurrent neural network parameters at time t+1; θ t represents the recursive neural network parameters at time t; η represents the learning rate; L represents the loss function.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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