Energy-saving power supply intelligent control method and device based on sine wave tracking mechanism

Abstract

The invention relates to an energy-saving power supply intelligent control method and device based on a sine wave tracking mechanism, and belongs to the technical field of power grids, and the method comprises the steps: constructing an electric equipment graph structure based on the structure data of electric equipment at a load end, and obtaining a first sine wave signal corresponding to a target node according to the electric equipment graph structure; based on a sine wave tracking mechanism, filtering the first sine wave signal to obtain a second sine wave signal; inputting related parameters of the second sine wave signal into a pre-constructed energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of the target node; fitting the energy-saving power supply evaluation results of the plurality of target nodes to generate a target fitting result; and on the basis of the target fitting result, predicting power consumption data in a target range formed by a plurality of target nodes so as to determine an energy-saving power supply adjustment scheme. According to the invention, the power consumption prediction efficiency and accuracy can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of power grid technology, and in particular to an energy-saving power supply intelligent control method and device based on a sine wave tracking mechanism. Background Art

[0002] With the complexity and diversification of load-side electrical equipment, many current energy-saving power supply solutions require real-time measurement and monitoring of power consumption and load quality of loads with different purposes, and then take some targeted energy-saving measures, such as manual control and timing control of high-energy-consuming loads to reduce power loss. However, this solution has poor testing efficiency and accuracy for load-side power usage, resulting in the inability to adjust and control the power supply in a timely and accurate manner to achieve the purpose of energy saving. In addition, the above energy-saving measures almost all rely on manual operation, and the additional manual input leads to poor economic efficiency of energy-saving behavior.

[0003] Therefore, there is an urgent need to propose an energy-saving power supply intelligent control method and device based on a sine wave tracking mechanism that can improve the efficiency and accuracy of power consumption prediction. Summary of the invention

[0004] Based on this, it is necessary to provide an energy-saving power supply intelligent control method and device based on a sine wave tracking mechanism, which can improve the efficiency and accuracy of energy-saving power supply intelligent control based on a sine wave tracking mechanism, in order to address the above technical problems.

[0005] On the one hand, a method for intelligent control of energy-saving power supply based on a sine wave tracking mechanism is provided, the method comprising:

[0006] Based on the structural data of the load-side electrical equipment, construct an electrical equipment graph structure, and obtain a first sinusoidal wave signal corresponding to the target node according to the electrical equipment graph structure;

[0007] Based on the sine wave tracking mechanism, filtering the first sine wave signal to obtain a second sine wave signal;

[0008] Inputting relevant parameters of the second sinusoidal wave signal into a pre-built energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of the target node;

[0009] Fitting the energy-saving power supply evaluation results of multiple target nodes to generate target fitting results;

[0010] Based on the target fitting result, the power consumption data within the target range composed of multiple target nodes is predicted to determine an energy-saving power supply adjustment plan.

[0011] Optionally, based on the structural data of the load-side electrical equipment, constructing the electrical equipment graph structure includes:

[0012] Acquire structural data of the load-end electrical equipment, wherein the structural data of the load-end electrical equipment at least includes location distribution of the electrical equipment in the target area, electrical equipment identification, connection relationship between the electrical equipment and the power supply node, and / or connection relationship between each electrical equipment;

[0013] Determine the nodes and edges of the electric equipment graph structure based on the location distribution and various connection relationships of the electric equipment in the target area;

[0014] The electric device graph structure is constructed according to the nodes and edges, and the electric device identifier is mapped to a corresponding node of the electric device graph structure.

[0015] Optionally, based on a sine wave tracking mechanism, filtering the first sine wave signal to obtain a second sine wave signal includes:

[0016] Based on the first sinusoidal wave signal, a system state equation is constructed, including:

[0017] x(k)=A*x(k-1)+w(k-1)

[0018] And the observation equations, including:

[0019] z(k)=H*x(k)+v(k)

[0020] Among them, x(k) represents the system state value, A represents the state transfer matrix, w(k-1) represents the noise, z(k) represents the observation value, H represents the observation matrix, and v(k) represents the observation noise;

[0021] The first sinusoidal wave signal is estimated and corrected based on the system state equation, observation equation and Kalman filter algorithm to generate a second sinusoidal wave signal.

[0022] Optionally, inputting relevant parameters of the second sinusoidal wave signal into a pre-built energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of the target node includes:

[0023] Acquire a second sinusoidal wave signal to obtain a sinusoidal wave signal graph within a target time period;

[0024] Based on the sine wave signal graph, obtaining relevant parameters of the second sine wave signal, the relevant parameters at least including: amplitude, frequency and phase;

[0025] Normalizing the relevant parameters to obtain a target data set;

[0026] The target data set is input into a pre-built energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of the target node.

[0027] Optionally, the method for constructing the energy-saving power supply evaluation model includes:

[0028] Constructing an initial energy-saving power supply evaluation model, the initial energy-saving power supply evaluation model includes:

[0029]

[0030] y i =F(U i )=1 / [1+exp(-U i )]

[0031] Among them, ω ij represents the weight between power supply node i and power consumption node j, k represents the number of nodes, U i Represents the model input value, y i Indicates the energy-saving power supply evaluation value, F(U i ) indicates about U i The evaluation function, θ j represents the amplitude, O j represents frequency, f j Indicates the fundamental frequency, h j Indicates phase;

[0032] The initial energy-saving power supply evaluation model is trained and tested using a training set and a test set generated by historical parameter information;

[0033] In response to detecting that the test result meets the preset standard, the target energy-saving power supply evaluation model is output to complete the construction of the energy-saving power supply evaluation model.

[0034] Optionally, the energy-saving power supply evaluation results of multiple target nodes are fitted to generate target fitting results including:

[0035] Obtain energy-saving power supply evaluation results of multiple target nodes within a target range;

[0036] The energy-saving power supply evaluation results of the multiple target nodes are fitted using a fitting function to generate a target fitting result, wherein the fitting function includes:

[0037]

[0038] Among them, X k represents the stable value in the node sequence, K t Represents the periodic correction factor, E t Represents the linear value, y i =y 1 ,y 2 ,...,y k represents the energy-saving power supply evaluation result of the node, R srepresents the fitting value, and t represents the time period;

[0039] The fitting value is defined as the target fitting result.

[0040] Optionally, based on the target fitting result, predicting the power consumption data within the target range composed of multiple target nodes to determine the energy-saving power supply adjustment plan includes:

[0041] In response to detecting that the target fitting result is greater than a preset threshold, selecting a preset number of nodes within the target range and sending a message notification;

[0042] In response to not detecting the response information of the message notification within the preset time period, the power supply to the electric device corresponding to the selected node is stopped.

[0043] On the other hand, an energy-saving power supply intelligent control device based on a sine wave tracking mechanism is provided, the device comprising:

[0044] A first sinusoidal wave signal acquisition module, configured to construct an electric device graph structure based on the structural data of the load-side electric device, and acquire a first sinusoidal wave signal corresponding to a target node according to the electric device graph structure;

[0045] A second sinusoidal wave signal acquisition module, configured to filter the first sinusoidal wave signal based on a sinusoidal wave tracking mechanism to obtain a second sinusoidal wave signal;

[0046] An energy-saving power supply evaluation result acquisition module, used for inputting relevant parameters of the second sinusoidal wave signal into a pre-built energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of a target node;

[0047] A fitting module, used for fitting the energy-saving power supply evaluation results of multiple target nodes to generate target fitting results;

[0048] The adjustment module is used to predict the power consumption data within the target range composed of multiple target nodes based on the target fitting result to determine an energy-saving power supply adjustment plan.

[0049] In another aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented:

[0050] Based on the structural data of the load-side electrical equipment, construct an electrical equipment graph structure, and obtain a first sinusoidal wave signal corresponding to the target node according to the electrical equipment graph structure;

[0051] Based on the sine wave tracking mechanism, filtering the first sine wave signal to obtain a second sine wave signal;

[0052] Inputting relevant parameters of the second sinusoidal wave signal into a pre-built energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of the target node;

[0053] Fitting the energy-saving power supply evaluation results of multiple target nodes to generate target fitting results;

[0054] Based on the target fitting result, the power consumption data within the target range composed of multiple target nodes is predicted to determine an energy-saving power supply adjustment plan.

[0055] In another aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0056] Based on the structural data of the load-side electrical equipment, construct an electrical equipment graph structure, and obtain a first sinusoidal wave signal corresponding to the target node according to the electrical equipment graph structure;

[0057] Based on the sine wave tracking mechanism, filtering the first sine wave signal to obtain a second sine wave signal;

[0058] Inputting relevant parameters of the second sinusoidal wave signal into a pre-built energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of the target node;

[0059] Fitting the energy-saving power supply evaluation results of multiple target nodes to generate target fitting results;

[0060] Based on the target fitting result, the power consumption data within the target range composed of multiple target nodes is predicted to determine an energy-saving power supply adjustment plan.

[0061] The above-mentioned energy-saving power supply intelligent control method and device based on the sinusoidal wave tracking mechanism, the method includes: constructing an electrical equipment graph structure based on the structural data of the load-end electrical equipment, and obtaining a first sinusoidal wave signal corresponding to the target node according to the electrical equipment graph structure; filtering the first sinusoidal wave signal based on the sinusoidal wave tracking mechanism to obtain a second sinusoidal wave signal; inputting the relevant parameters of the second sinusoidal wave signal into a pre-constructed energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of the target node; fitting the energy-saving power supply evaluation results of multiple target nodes to generate a target fitting result; based on the target fitting result, predicting the power consumption data within the target range composed of multiple target nodes to determine the energy-saving power supply adjustment plan. The present application can effectively improve the efficiency and accuracy of power consumption prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1This is an application environment diagram of an energy-saving power supply intelligent control method based on a sine wave tracking mechanism in one embodiment;

[0063] Figure 2 It is a flowchart of an energy-saving power supply intelligent control method based on a sine wave tracking mechanism in one embodiment;

[0064] Figure 3 It is a structural block diagram of an energy-saving power supply intelligent control device based on a sine wave tracking mechanism in one embodiment;

[0065] Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0067] It should be understood that in the description of the present application, unless the context clearly requires otherwise, words such as "include", "comprises", and the like throughout the specification should be interpreted as inclusive rather than exclusive or exhaustive; that is, the meaning of "including but not limited to".

[0068] It should also be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, "plurality" means two or more.

[0069] It should be noted that the terms "S1", "S2", etc. are only used for the purpose of describing the steps, and do not specifically refer to the order or sequence, nor are they used to limit the present application. They are only for the convenience of describing the method of the present application, and cannot be understood as indicating the order of the steps. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present application.

[0070] The energy-saving power supply intelligent control method based on the sine wave tracking mechanism provided in this application can be applied to Figure 1In the application environment shown, the terminal 102 communicates with the data processing platform set on the server 104 through the network, wherein the terminal 102 can be but not limited to various personal computers, laptops, smart phones, tablet computers and portable wearable devices, and the server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0071] In one embodiment, Figure 2 As shown, an energy-saving power supply intelligent control method based on a sine wave tracking mechanism is provided. Figure 1 The terminal in is used as an example to illustrate, including the following steps:

[0072] S1: constructing an electric device graph structure based on the structural data of the load-side electric device, and acquiring a first sinusoidal wave signal corresponding to a target node according to the electric device graph structure.

[0073] It should be noted that the structural data of the load-end electrical equipment at least includes the location distribution of the electrical equipment in the target area, the electrical equipment identification, the connection relationship between the electrical equipment and the power supply node and / or the connection relationship between each electrical equipment. The graph structure refers to a network structure generated within a certain area based on the connection relationship and location distribution of the electrical equipment. The electrical equipment identification refers to a unique identifier used to mark and distinguish different electrical equipment.

[0074] In some specific implementations, constructing the electrical equipment graph structure based on the structural data of the load-side electrical equipment includes:

[0075] Acquiring structural data of the load-end electrical equipment;

[0076] Based on the location distribution and various connection relationships of the electric devices in the target area, determine the nodes and edges of the electric device graph structure, that is, the electric devices are nodes, and the connection relationships between the electric devices and the power supply nodes are edges;

[0077] According to the nodes and edges, the electric equipment graph structure is constructed, and the electric equipment identifier is mapped to the corresponding node of the electric equipment graph structure, that is, each electric equipment node and power supply node are connected based on the above connection relationship, and the corresponding node is marked with the electric equipment identifier.

[0078] S2: Based on the sine wave tracking mechanism, the first sine wave signal is filtered to obtain a second sine wave signal.

[0079] It should be noted that the sine wave tracking mechanism refers to filtering the acquired sine signal to obtain a more accurate denoised sine wave, and the second sine wave signal is the denoised sine wave.

[0080] In some specific implementations, based on a sine wave tracking mechanism, filtering the first sine wave signal to obtain a second sine wave signal includes:

[0081] Based on the first sinusoidal wave signal, a system state equation is constructed, including:

[0082] x(k)=A*x(k-1)+w(k-1)

[0083] And the observation equations, including:

[0084] z(k)=H*x(k)+v(k)

[0085] Among them, x(k) represents the system state value, A represents the state transfer matrix, w(k-1) represents the noise, z(k) represents the observation value, H represents the observation matrix, and v(k) represents the observation noise;

[0086] The first sinusoidal wave signal is estimated and corrected based on the system state equation, observation equation and Kalman filter algorithm to generate a second sinusoidal wave signal.

[0087] Specifically, Kalman filtering is a recursive filtering algorithm that filters and predicts signals by estimating the system state and correcting the observed values. When processing sinusoidal signals, the amplitude, frequency, and phase of the sinusoidal signal can be used as the system state, and estimated and corrected by Kalman filtering to obtain accurate signal estimation values. When using Kalman filtering to process sinusoidal signals, it is first necessary to establish the system state equation and observation equation, that is, the above-mentioned state equation and observation equation. In actual application scenarios, observation values ​​can be obtained by sampling and measurement, and processed by the Kalman filtering algorithm. The Kalman filtering algorithm The method mainly includes two steps of prediction and correction. In the prediction step, according to the state equation and observation equation of the system, the value of the current state and the error covariance matrix are estimated to obtain the predicted value of the state at the next moment and the predicted error covariance matrix. In the correction step, according to the difference between the observed value and the predicted value, the estimated value and the error covariance matrix are updated to obtain the corrected state estimated value and error covariance matrix. By processing sinusoidal signals through Kalman filtering, noise interference can be effectively suppressed and the required signal information can be extracted. Kalman filtering has good adaptability and robustness and is suitable for various complex signal processing tasks. In practical applications, the parameters and algorithms of Kalman filtering can be adjusted according to the specific signal characteristics and system requirements to achieve the expected filtering effect. The specific calculation and adjustment methods are not repeated here.

[0088] S3: Inputting relevant parameters of the second sinusoidal wave signal into a pre-built energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of the target node.

[0089] It should be noted that the relevant parameters of the second sinusoidal wave signal at least include: amplitude, frequency and phase.

[0090] In some specific implementations, inputting the relevant parameters of the second sinusoidal wave signal into a pre-built energy-saving power supply evaluation model to obtain the energy-saving power supply evaluation result of the target node includes:

[0091] Acquire the second sine wave signal to obtain a sine wave signal graph within a target time period, that is, a sine wave signal graph generated based on the rectangular coordinate system axis. The time range of the target time period can be set according to actual needs;

[0092] Based on the sine wave signal graph, obtaining relevant parameters of the second sine wave signal, the relevant parameters;

[0093] The relevant parameters are normalized to obtain a target data set, wherein the normalization method is a common method and will not be described in detail here;

[0094] The target data set is input into a pre-built energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of the target node.

[0095] In some specific implementations, the method for constructing the energy-saving power supply evaluation model includes:

[0096] Constructing an initial energy-saving power supply evaluation model, the initial energy-saving power supply evaluation model includes:

[0097]

[0098] y i =F(U i )=1 / [1+exp(-U i )]

[0099] Among them, ω ij represents the weight between power supply node i and power consumption node j, k represents the number of nodes, U i Represents the model input value, y i Indicates the energy-saving power supply evaluation value, F(U i ) indicates about U i The evaluation function, θ j represents the amplitude, O j represents frequency, f j Indicates the fundamental frequency, h j Indicates phase;

[0100] The initial energy-saving power supply evaluation model is trained and tested using a training set and a test set generated by the historical parameter information; wherein the training set and the test set can be generated by dividing the historical parameter information by a preset ratio, and the preset ratio can be set according to actual needs, such as 8:2;

[0101] In response to detecting that the test results meet the preset standards, the target energy-saving power supply evaluation model is output to complete the construction of the energy-saving power supply evaluation model, wherein the preset standard may refer to the evaluation accuracy reaching the standard or the number of training times reaching the set number of times.

[0102] The energy-saving power supply evaluation value is defined as the energy-saving power supply evaluation result of the target node.

[0103] S4: Fit the energy-saving power supply evaluation results of multiple target nodes to generate a target fitting result.

[0104] It should be noted that this step specifically includes:

[0105] Obtain energy-saving power supply evaluation results of multiple target nodes within a target range;

[0106] The energy-saving power supply evaluation results of the multiple target nodes are fitted using a fitting function to generate a target fitting result, wherein the fitting function includes:

[0107]

[0108] Among them, X k represents the stable value in the node sequence, K t Represents the periodic correction factor, E t Represents the linear value, y i =y 1 ,y 2 ,...,y k represents the energy-saving power supply evaluation result of the node, R s represents the fitting value, and t represents the time period;

[0109] The fitting value is defined as the target fitting result.

[0110] S5: Based on the target fitting result, predict the power consumption data within the target range composed of multiple target nodes to determine an energy-saving power supply adjustment plan.

[0111] It should be noted that this step specifically includes:

[0112] In response to detecting that the target fitting result is greater than a preset threshold, selecting a preset number of nodes within the target range and sending a message notification;

[0113] In response to not detecting the response information of the message notification within the preset time period, the power supply to the electric device corresponding to the selected node is stopped.

[0114] Among them, the preset threshold and the preset number can be set according to actual needs. The preset number is generally an integer multiple of the fitting value. The message notification sent indicates that the power supply to the electrical equipment needs to be stopped. If there is no response to the message notification within a period of time, such as 2 minutes, there is no manual intervention to determine that the selected equipment that is about to be powered off needs to continue to be powered on, then the power supply to the electrical equipment corresponding to the selected node will be stopped.

[0115] In the above-mentioned energy-saving power supply intelligent control method based on the sinusoidal wave tracking mechanism, the method includes: constructing an electrical equipment graph structure based on the structural data of the load-end electrical equipment, and obtaining a first sinusoidal wave signal corresponding to the target node according to the electrical equipment graph structure; filtering the first sinusoidal wave signal based on the sinusoidal wave tracking mechanism to obtain a second sinusoidal wave signal; inputting relevant parameters of the second sinusoidal wave signal into a pre-constructed energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of the target node; fitting the energy-saving power supply evaluation results of multiple target nodes to generate a target fitting result; based on the target fitting result, predicting the power consumption data within the target range composed of multiple target nodes to determine the energy-saving power supply adjustment plan. The present application can effectively improve the efficiency and accuracy of power consumption prediction.

[0116] It should be understood that although Figure 2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0117] In one embodiment, Figure 3 As shown, an energy-saving power supply intelligent control device based on a sine wave tracking mechanism is provided, comprising: a first sine wave signal acquisition module, a second sine wave signal acquisition module, an energy-saving power supply evaluation result acquisition module, a fitting module and an adjustment module, wherein:

[0118] A first sinusoidal wave signal acquisition module, configured to construct an electric device graph structure based on the structural data of the load-side electric device, and acquire a first sinusoidal wave signal corresponding to a target node according to the electric device graph structure;

[0119] A second sinusoidal wave signal acquisition module, configured to filter the first sinusoidal wave signal based on a sinusoidal wave tracking mechanism to obtain a second sinusoidal wave signal;

[0120] An energy-saving power supply evaluation result acquisition module, used for inputting relevant parameters of the second sinusoidal wave signal into a pre-built energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of a target node;

[0121] A fitting module, used for fitting the energy-saving power supply evaluation results of multiple target nodes to generate target fitting results;

[0122] The adjustment module is used to predict the power consumption data within the target range composed of multiple target nodes based on the target fitting result to determine an energy-saving power supply adjustment plan.

[0123] As a preferred implementation manner, in the embodiment of the present invention, the first sinusoidal wave signal acquisition module is specifically used for:

[0124] Acquire structural data of the load-end electrical equipment, wherein the structural data of the load-end electrical equipment at least includes location distribution of the electrical equipment in the target area, electrical equipment identification, connection relationship between the electrical equipment and the power supply node, and / or connection relationship between each electrical equipment;

[0125] Determine the nodes and edges of the electric equipment graph structure based on the location distribution and various connection relationships of the electric equipment in the target area;

[0126] The electric device graph structure is constructed according to the nodes and edges, and the electric device identifier is mapped to a corresponding node of the electric device graph structure.

[0127] As a preferred implementation, in the embodiment of the present invention, the second sinusoidal wave signal acquisition module is specifically used for:

[0128] Based on the first sinusoidal wave signal, a system state equation is constructed, including:

[0129] x(k)=A*x(k-1)+w(k-1)

[0130] And the observation equations, including:

[0131] z(k)=H*x(k)+v(k)

[0132] Among them, x(k) represents the system state value, A represents the state transfer matrix, w(k-1) represents the noise, z(k) represents the observation value, H represents the observation matrix, and v(k) represents the observation noise;

[0133] The first sinusoidal wave signal is estimated and corrected based on the system state equation, observation equation and Kalman filter algorithm to generate a second sinusoidal wave signal.

[0134] As a preferred implementation, in an embodiment of the present invention, the energy-saving power supply evaluation result acquisition module is specifically used to:

[0135] Acquire a second sinusoidal wave signal to obtain a sinusoidal wave signal graph within a target time period;

[0136] Based on the sine wave signal graph, obtaining relevant parameters of the second sine wave signal, the relevant parameters at least including: amplitude, frequency and phase;

[0137] Normalizing the relevant parameters to obtain a target data set;

[0138] The target data set is input into a pre-built energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of the target node.

[0139] As a preferred implementation, in the embodiment of the present invention, the energy-saving power supply evaluation result acquisition module is further used to:

[0140] Constructing an initial energy-saving power supply evaluation model, the initial energy-saving power supply evaluation model includes:

[0141]

[0142] y i =f(U i )=1 / [1+exp(-U i )]

[0143] Among them, ω ij represents the weight between power supply node i and power consumption node j, k represents the number of nodes, U i Represents the model input value, y i Indicates the energy-saving power supply evaluation value, F(U i ) indicates about U i The evaluation function, θ j represents the amplitude, O j represents frequency, f j Indicates the fundamental frequency, h j Indicates phase;

[0144] The initial energy-saving power supply evaluation model is trained and tested using a training set and a test set generated by historical parameter information;

[0145] In response to detecting that the test result meets the preset standard, the target energy-saving power supply evaluation model is output to complete the construction of the energy-saving power supply evaluation model.

[0146] As a preferred implementation, in the embodiment of the present invention, the fitting module is specifically used for:

[0147] Obtain energy-saving power supply evaluation results of multiple target nodes within a target range;

[0148] The energy-saving power supply evaluation results of the multiple target nodes are fitted using a fitting function to generate a target fitting result, wherein the fitting function includes:

[0149]

[0150] Among them, X k represents the stable value in the node sequence, K t Represents the periodic correction factor, E t Represents the linear value, y i =y 1 ,y 2 ,...,y k represents the energy-saving power supply evaluation result of the node, R s represents the fitting value, and t represents the time period;

[0151] The fitting value is defined as the target fitting result.

[0152] As a preferred implementation, in the embodiment of the present invention, the adjustment module is specifically used for:

[0153] In response to detecting that the target fitting result is greater than a preset threshold, selecting a preset number of nodes within the target range and sending a message notification;

[0154] In response to not detecting the response information of the message notification within the preset time period, the power supply to the electric device corresponding to the selected node is stopped.

[0155] For the specific definition of the energy-saving power supply intelligent control device based on the sine wave tracking mechanism, please refer to the definition of the energy-saving power supply intelligent control method based on the sine wave tracking mechanism in the above text, which will not be repeated here. Each module in the above-mentioned energy-saving power supply intelligent control device based on the sine wave tracking mechanism can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0156] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, 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 network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an energy-saving power supply intelligent control method based on a sine wave tracking mechanism is implemented. The display screen of the computer device 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 covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0157] Those skilled in the art will understand that Figure 4 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.

[0158] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0159] S1: constructing an electric device graph structure based on the structural data of the load-side electric device, and acquiring a first sinusoidal wave signal corresponding to a target node according to the electric device graph structure;

[0160] S2: Based on the sine wave tracking mechanism, filtering the first sine wave signal to obtain a second sine wave signal;

[0161] S3: Inputting relevant parameters of the second sinusoidal wave signal into a pre-built energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of the target node;

[0162] S4: Fitting the energy-saving power supply evaluation results of multiple target nodes to generate a target fitting result;

[0163] S5: Based on the target fitting result, predict the power consumption data within the target range composed of multiple target nodes to determine an energy-saving power supply adjustment plan.

[0164] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0165] Acquire structural data of the load-end electrical equipment, wherein the structural data of the load-end electrical equipment at least includes location distribution of the electrical equipment in the target area, electrical equipment identification, connection relationship between the electrical equipment and the power supply node, and / or connection relationship between each electrical equipment;

[0166] Determine the nodes and edges of the electric equipment graph structure based on the location distribution and various connection relationships of the electric equipment in the target area;

[0167] The electric device graph structure is constructed according to the nodes and edges, and the electric device identifier is mapped to a corresponding node of the electric device graph structure.

[0168] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0169] Based on the first sinusoidal wave signal, a system state equation is constructed, including:

[0170] x(k)=A*x(k-1)+w(k-1)

[0171] And the observation equations, including:

[0172] z(k)=H*x(k)+v(k)

[0173] Among them, x(k) represents the system state value, A represents the state transfer matrix, w(k-1) represents the noise, z(k) represents the observation value, H represents the observation matrix, and v(k) represents the observation noise;

[0174] The first sinusoidal wave signal is estimated and corrected based on the system state equation, observation equation and Kalman filter algorithm to generate a second sinusoidal wave signal.

[0175] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0176] Acquire a second sinusoidal wave signal to obtain a sinusoidal wave signal graph within a target time period;

[0177] Based on the sine wave signal graph, obtaining relevant parameters of the second sine wave signal, the relevant parameters at least including: amplitude, frequency and phase;

[0178] Normalizing the relevant parameters to obtain a target data set;

[0179] The target data set is input into a pre-built energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of the target node.

[0180] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0181] Constructing an initial energy-saving power supply evaluation model, the initial energy-saving power supply evaluation model includes:

[0182]

[0183] y i =F(U i )=1 / [1+exp(-U i )]

[0184] Among them, ω ij represents the weight between power supply node i and power consumption node j, k represents the number of nodes, U i Represents the model input value, y i Indicates the energy-saving power supply evaluation value, F(U i ) indicates about U i The evaluation function, θ j represents the amplitude, O j represents frequency, f j Indicates the fundamental frequency, h j Indicates phase;

[0185] The initial energy-saving power supply evaluation model is trained and tested using a training set and a test set generated by historical parameter information;

[0186] In response to detecting that the test result meets the preset standard, the target energy-saving power supply evaluation model is output to complete the construction of the energy-saving power supply evaluation model.

[0187] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0188] Obtain energy-saving power supply evaluation results of multiple target nodes within a target range;

[0189] The energy-saving power supply evaluation results of the multiple target nodes are fitted using a fitting function to generate a target fitting result, wherein the fitting function includes:

[0190]

[0191] Among them, X k represents the stable value in the node sequence, K t Represents the periodic correction factor, E t Represents the linear value, y i =y 1 ,y 2 ,...,y k represents the energy-saving power supply evaluation result of the node, R srepresents the fitting value, and t represents the time period;

[0192] The fitting value is defined as the target fitting result.

[0193] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0194] In response to detecting that the target fitting result is greater than a preset threshold, selecting a preset number of nodes within the target range and sending a message notification;

[0195] In response to not detecting the response information of the message notification within the preset time period, the power supply to the electric device corresponding to the selected node is stopped.

[0196] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0197] S1: constructing an electric device graph structure based on the structural data of the load-side electric device, and acquiring a first sinusoidal wave signal corresponding to a target node according to the electric device graph structure;

[0198] S2: Based on the sine wave tracking mechanism, filtering the first sine wave signal to obtain a second sine wave signal;

[0199] S3: Inputting relevant parameters of the second sinusoidal wave signal into a pre-built energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of the target node;

[0200] S4: Fitting the energy-saving power supply evaluation results of multiple target nodes to generate a target fitting result;

[0201] S5: Based on the target fitting result, predict the power consumption data within the target range composed of multiple target nodes to determine an energy-saving power supply adjustment plan.

[0202] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0203] Acquire structural data of the load-end electrical equipment, wherein the structural data of the load-end electrical equipment at least includes location distribution of the electrical equipment in the target area, electrical equipment identification, connection relationship between the electrical equipment and the power supply node, and / or connection relationship between each electrical equipment;

[0204] Determine the nodes and edges of the electric equipment graph structure based on the location distribution and various connection relationships of the electric equipment in the target area;

[0205] The electric device graph structure is constructed according to the nodes and edges, and the electric device identifier is mapped to a corresponding node of the electric device graph structure.

[0206] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0207] Based on the first sinusoidal wave signal, a system state equation is constructed, including:

[0208] x(k)=A*x(k-1)+w(k-1)

[0209] And the observation equations, including:

[0210] z(k)=H*x(k)+v(k)

[0211] Among them, x(k) represents the system state value, A represents the state transfer matrix, w(k-1) represents the noise, z(k) represents the observation value, H represents the observation matrix, and v(k) represents the observation noise;

[0212] The first sinusoidal wave signal is estimated and corrected based on the system state equation, observation equation and Kalman filter algorithm to generate a second sinusoidal wave signal.

[0213] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0214] Acquire a second sinusoidal wave signal to obtain a sinusoidal wave signal graph within a target time period;

[0215] Based on the sine wave signal graph, obtaining relevant parameters of the second sine wave signal, the relevant parameters at least including: amplitude, frequency and phase;

[0216] Normalizing the relevant parameters to obtain a target data set;

[0217] The target data set is input into a pre-built energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of the target node.

[0218] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0219] Constructing an initial energy-saving power supply evaluation model, the initial energy-saving power supply evaluation model includes:

[0220]

[0221] y i =F(U i )=1 / [1+exp(-U i )]

[0222] Among them, ω ij represents the weight between power supply node i and power consumption node j, k represents the number of nodes, U i Represents the model input value, y i Indicates the energy-saving power supply evaluation value, F(U i ) indicates about U i The evaluation function, θ j represents the amplitude, O j represents frequency, f j Indicates the fundamental frequency, h j Indicates phase;

[0223] The initial energy-saving power supply evaluation model is trained and tested using a training set and a test set generated by historical parameter information;

[0224] In response to detecting that the test result meets the preset standard, the target energy-saving power supply evaluation model is output to complete the construction of the energy-saving power supply evaluation model.

[0225] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0226] Obtain energy-saving power supply evaluation results of multiple target nodes within a target range;

[0227] The energy-saving power supply evaluation results of the multiple target nodes are fitted using a fitting function to generate a target fitting result, wherein the fitting function includes:

[0228]

[0229] Among them, X k represents the stable value in the node sequence, K t Represents the periodic correction factor, E t Represents the linear value, y i =y 1 ,y 2 ,...,y k represents the energy-saving power supply evaluation result of the node, R s represents the fitting value, and t represents the time period;

[0230] The fitting value is defined as the target fitting result.

[0231] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0232] In response to detecting that the target fitting result is greater than a preset threshold, selecting a preset number of nodes within the target range and sending a message notification;

[0233] In response to not detecting the response information of the message notification within the preset time period, the power supply to the electric device corresponding to the selected node is stopped.

[0234] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods 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 memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0235] 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 specification.

[0236] The above-mentioned 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 invention patent. 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 patent of the present application shall be subject to the attached claims.

Claims

1. An energy-saving power supply intelligent control method based on a sine wave tracking mechanism, characterized in that: The method comprises: Based on the structural data of the load-side electrical equipment, construct an electrical equipment graph structure, and obtain a first sinusoidal wave signal corresponding to the target node according to the electrical equipment graph structure; Based on the sine wave tracking mechanism, filtering the first sine wave signal to obtain a second sine wave signal; Inputting relevant parameters of the second sinusoidal wave signal into a pre-built energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of the target node; Fitting the energy-saving power supply evaluation results of multiple target nodes to generate target fitting results; Based on the target fitting result, the power consumption data within the target range composed of multiple target nodes is predicted to determine an energy-saving power supply adjustment plan.

2. The energy-saving power supply intelligent control method based on the sine wave tracking mechanism according to claim 1 is characterized in that: Based on the structural data of the load-side electrical equipment, the electrical equipment diagram structure is constructed including: Acquire structural data of the load-end electrical equipment, wherein the structural data of the load-end electrical equipment at least includes location distribution of the electrical equipment in the target area, electrical equipment identification, connection relationship between the electrical equipment and the power supply node, and / or connection relationship between each electrical equipment; Determine the nodes and edges of the electric equipment graph structure based on the location distribution and various connection relationships of the electric equipment in the target area; The electric device graph structure is constructed according to the nodes and edges, and the electric device identifier is mapped to a corresponding node of the electric device graph structure.

3. The energy-saving power supply intelligent control method based on the sine wave tracking mechanism according to claim 2 is characterized in that: Based on the sine wave tracking mechanism, filtering the first sine wave signal to obtain a second sine wave signal includes: Based on the first sinusoidal wave signal, a system state equation is constructed, including: x(k)=A*x(k-1)+w(k-1) And the observation equations, including: z(k)=H*x(k)+v(k) Among them, x(k) represents the system state value, A represents the state transfer matrix, w(k-1) represents the noise, z(k) represents the observation value, H represents the observation matrix, and v(k) represents the observation noise; The first sinusoidal wave signal is estimated and corrected based on the system state equation, observation equation and Kalman filter algorithm to generate a second sinusoidal wave signal.

4. The energy-saving power supply intelligent control method based on the sine wave tracking mechanism according to claim 3 is characterized in that: Inputting the relevant parameters of the second sinusoidal wave signal into the pre-built energy-saving power supply evaluation model to obtain the energy-saving power supply evaluation result of the target node includes: Acquire a second sinusoidal wave signal to obtain a sinusoidal wave signal graph within a target time period; Based on the sine wave signal graph, obtaining relevant parameters of the second sine wave signal, the relevant parameters at least including: amplitude, frequency and phase; Normalizing the relevant parameters to obtain a target data set; The target data set is input into a pre-built energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of the target node.

5. The energy-saving power supply intelligent control method based on the sine wave tracking mechanism according to claim 4 is characterized in that: The method for constructing the energy-saving power supply evaluation model includes: Constructing an initial energy-saving power supply evaluation model, the initial energy-saving power supply evaluation model includes: y i =F(U i )=1 / [1+exp(-U i )] Among them, ω ij represents the weight between power supply node i and power consumption node j, k represents the number of nodes, U i Represents the model input value, y i Indicates the energy-saving power supply evaluation value, F(U i ) indicates about U i The evaluation function, θ j represents the amplitude, O j represents frequency, f j Indicates the fundamental frequency, h j Indicates phase; The initial energy-saving power supply evaluation model is trained and tested using a training set and a test set generated by historical parameter information; In response to detecting that the test result meets the preset standard, the target energy-saving power supply evaluation model is output to complete the construction of the energy-saving power supply evaluation model.

6. The energy-saving power supply intelligent control method based on the sine wave tracking mechanism according to claim 5 is characterized in that: Fit the energy-saving power supply evaluation results of multiple target nodes to generate target fitting results including: Obtain energy-saving power supply evaluation results of multiple target nodes within a target range; The energy-saving power supply evaluation results of the multiple target nodes are fitted using a fitting function to generate a target fitting result, wherein the fitting function includes: Among them, X k represents the stable value in the node sequence, K t Represents the periodic correction factor, E t Represents the linear value, y i =y1,y2,...,y k represents the energy-saving power supply evaluation result of the node, R s represents the fitting value, and t represents the time period; The fitting value is defined as the target fitting result.

7. The energy-saving power supply intelligent control method based on the sine wave tracking mechanism according to claim 6 is characterized in that: Based on the target fitting result, the power consumption data within the target range composed of multiple target nodes is predicted to determine the energy-saving power supply adjustment plan including: In response to detecting that the target fitting result is greater than a preset threshold, selecting a preset number of nodes within the target range and sending a message notification; In response to not detecting the response information of the message notification within the preset time period, the power supply to the electric device corresponding to the selected node is stopped.

8. An energy-saving power supply intelligent control device based on a sine wave tracking mechanism, characterized in that: The device comprises: A first sinusoidal wave signal acquisition module, configured to construct an electric device graph structure based on the structural data of the load-side electric device, and acquire a first sinusoidal wave signal corresponding to a target node according to the electric device graph structure; A second sinusoidal wave signal acquisition module, configured to filter the first sinusoidal wave signal based on a sinusoidal wave tracking mechanism to obtain a second sinusoidal wave signal; An energy-saving power supply evaluation result acquisition module, used for inputting relevant parameters of the second sinusoidal wave signal into a pre-built energy-saving power supply evaluation model to obtain an energy-saving power supply evaluation result of a target node; A fitting module, used for fitting the energy-saving power supply evaluation results of multiple target nodes to generate target fitting results; The adjustment module is used to predict the power consumption data within the target range composed of multiple target nodes based on the target fitting result to determine an energy-saving power supply adjustment plan.

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