Energy scheduling method and device and medium

Abstract

The invention relates to an energy scheduling method and device and a medium. Comprising the following steps: acquiring historical original flow data and channel state information of an underwater node; generating a traffic distribution matrix according to the historical original traffic data and the channel state information; obtaining a multi-dimensional optimization model; inputting the flow distribution matrix into a multi-dimensional optimization model to obtain an energy scheduling signaling; and the underwater node receives the energy scheduling signaling and executes energy scheduling operation. According to the technical scheme, a base station obtains historical original flow data and channel state information of an underwater node; and then, the base station generates a flow distribution matrix according to the historical original flow data and the channel state information, and inputs the flow distribution matrix into the multi-dimensional optimization model to generate energy scheduling signaling. Therefore, the base station predicts the flow of the underwater node according to the historical original data and the channel state information of the underwater node, and then dynamically generates the energy scheduling signaling according to the prediction result of the flow, thereby effectively reducing the energy loss of the underwater node.

Description

Technical Field

[0001] This application relates to the field of submarine communications, and more particularly to an energy dispatching method, apparatus and medium. Background Technology

[0002] In recent years, the country has successively established seabed observation networks in regions such as the East China Sea and the South China Sea. The seabed observation network has deployed multiple seabed observation nodes, which transmit the collected data to shore-based platforms or surface buoys to achieve long-term, continuous and real-time observation of key sea areas.

[0003] Currently, seabed observation nodes transmit data to shore-based platforms or surface buoys using a fixed energy scheduling method. Specifically, seabed observation nodes transmit data to shore-based platforms or surface buoys within fixed communication time slots, using fixed transmission power and modulation methods.

[0004] However, the seabed environment is complex. The amount of data collected by seabed observation nodes is not constant. When the amount of data collected by a seabed observation node is large, the node may not be able to complete the data transmission within a fixed communication time slot. This forces the node to switch its operating state multiple times to complete the data transmission, thereby increasing the energy consumption of the seabed observation node. Summary of the Invention

[0005] This application provides an energy scheduling method, apparatus, and medium, aiming to solve the technical problem of high energy loss at seabed observation nodes.

[0006] In a first aspect, embodiments of this application provide an energy scheduling method, which is applied to an energy scheduling system, the energy scheduling system including a base station and multiple underwater nodes, comprising:

[0007] The base station acquires historical raw traffic data of each of the plurality of underwater nodes and channel state information of each of the plurality of underwater nodes;

[0008] The base station generates a traffic distribution matrix based on the historical raw traffic data of each of the plurality of underwater nodes and the channel state information of each of the plurality of underwater nodes. The traffic distribution matrix includes the traffic prediction values ​​of the plurality of underwater nodes.

[0009] The base station acquires a multi-dimensional optimization model, which is used to generate energy scheduling signaling for each of the plurality of underwater nodes.

[0010] The base station inputs the traffic distribution matrix into the multidimensional optimization model to obtain the energy scheduling signaling for each of the multiple underwater nodes.

[0011] The base station sends energy scheduling signaling for each of the plurality of underwater nodes to the underwater node;

[0012] Each of the plurality of underwater nodes receives energy scheduling signaling from the underwater node of the base station;

[0013] Each of the plurality of underwater nodes performs an energy scheduling operation according to the energy scheduling signaling of the underwater node, the energy scheduling operation being used to adjust the power consumption of the underwater node.

[0014] Optionally, the base station generates a traffic distribution matrix based on the historical raw traffic data of each of the plurality of underwater nodes and the channel state information of each of the plurality of underwater nodes, including:

[0015] The base station preprocesses the historical raw flow data of each of the plurality of underwater nodes to obtain the preprocessed flow data of each of the plurality of underwater nodes.

[0016] The base station inputs the preprocessed flow data of each of the plurality of underwater nodes into the autoregressive integral moving average model to obtain the baseline predicted flow value of each of the plurality of underwater nodes. The autoregressive integral moving average model is used to predict the flow of each of the plurality of underwater nodes.

[0017] The base station acquires the location information of each of the plurality of underwater nodes;

[0018] The base station acquires the event trigger signal of each of the plurality of underwater nodes, and the event trigger signal of the underwater node is the event trigger signal received by the underwater node;

[0019] The base station generates a traffic correction value for each of the multiple underwater nodes based on the traffic reference prediction value of each underwater node, the location information of each underwater node, the event triggering signal of each underwater node, and the channel state information of each underwater node.

[0020] The base station determines the predicted flow value of each underwater node based on the baseline predicted flow value of each underwater node and the corrected flow value of each underwater node.

[0021] The base station generates the flow distribution matrix based on the flow prediction value of each of the plurality of underwater nodes.

[0022] Optionally, the base station preprocesses the historical raw flow data of each of the plurality of underwater nodes to obtain preprocessed flow data of each of the plurality of underwater nodes, including:

[0023] The base station performs differential stabilization on the preprocessed flow data of each of the plurality of underwater nodes to obtain differential preprocessed data of each of the plurality of underwater nodes.

[0024] The base station performs a normalization operation on the differential preprocessed data of each of the plurality of underwater nodes to obtain the preprocessed flow data of each of the plurality of underwater nodes.

[0025] Optionally, the base station determines the predicted flow value for each underwater node based on the baseline predicted flow value for each underwater node and the corrected flow value for each underwater node, including:

[0026] The base station performs a weighted operation on the baseline predicted flow value and the corrected flow value of each of the plurality of underwater nodes to obtain the predicted flow value of each of the plurality of underwater nodes.

[0027] Optionally, the base station acquires a multi-dimensional optimization model, including:

[0028] The base station constructs a three-dimensional optimization model that includes underwater node sleep strategy, underwater node power control and underwater node modulation method. The three-dimensional optimization model aims to minimize the underwater node transmission energy consumption, underwater node listening energy consumption and underwater node state switching energy consumption.

[0029] Optionally, the energy scheduling signaling includes: a sleep period and a wake-up interval. Each of the plurality of underwater nodes performs an energy scheduling operation according to the energy scheduling signaling of the underwater node, including:

[0030] Each of the plurality of underwater nodes performs a hibernation operation according to its hibernation cycle;

[0031] Each of the plurality of underwater nodes performs a wake-up operation according to the wake-up interval of the underwater node.

[0032] Optionally, the energy scheduling signaling further includes: a working state type, wherein each of the plurality of underwater nodes performs an energy scheduling operation according to the energy scheduling signaling of the underwater node, including:

[0033] Each of the plurality of underwater nodes adjusts its transmission power according to its operating state type.

[0034] Optionally, the energy scheduling signaling further includes a modulation scheme, wherein each of the plurality of underwater nodes performs an energy scheduling operation according to the energy scheduling signaling of the underwater node, including:

[0035] Each of the plurality of underwater nodes adjusts its modulation method according to its own modulation method.

[0036] Secondly, embodiments of this application also provide an energy dispatching device, which includes a unit for performing the above-described method.

[0037] Thirdly, embodiments of this application also provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0038] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the above-described method.

[0039] This application provides an energy scheduling method, apparatus, and medium. The method is applied to an energy scheduling system, which includes a base station and multiple underwater nodes. The method includes: the base station acquiring historical raw traffic data and channel state information for each of the multiple underwater nodes; the base station generating a traffic distribution matrix based on the historical raw traffic data and channel state information of each of the multiple underwater nodes, the traffic distribution matrix including predicted traffic values ​​for the multiple underwater nodes; and the base station acquiring a multidimensional optimization model, the multidimensional optimization model being used to generate... The base station generates energy scheduling signaling for each of the plurality of underwater nodes; the base station inputs the traffic distribution matrix into the multidimensional optimization model to obtain energy scheduling signaling for each of the plurality of underwater nodes; the base station sends the energy scheduling signaling for each of the plurality of underwater nodes to each of the plurality of underwater nodes; each of the plurality of underwater nodes receives the energy scheduling signaling for its underwater node from the base station; each of the plurality of underwater nodes performs an energy scheduling operation according to the energy scheduling signaling for its underwater node, the energy scheduling operation being used to adjust the power consumption of the underwater node. Therefore, in the technical solution of this application, the base station obtains historical raw traffic data and channel state information for each of the plurality of underwater nodes. Then, the base station generates a traffic distribution matrix based on the historical raw traffic data and channel state information for each of the plurality of underwater nodes, and inputs the traffic distribution matrix into the multidimensional optimization model to generate energy scheduling signaling for each of the plurality of underwater nodes. Therefore, it can be seen that the base station predicts the traffic of the underwater node based on the historical raw data and channel state information of the underwater node, and then dynamically generates energy scheduling signaling based on the traffic prediction results, so that the underwater node can complete the data transmission without switching the working state multiple times, effectively reducing the energy consumption of the underwater node. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0043] Figure 1 A flowchart illustrating an energy scheduling method provided in an embodiment of this application;

[0044] Figure 2 This application provides an overall architecture diagram of an energy dispatching system.

[0045] Figure 3 A schematic block diagram of an energy dispatching device provided in an embodiment of this application;

[0046] Figure 4 A computer device provided in an embodiment of this application. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0048] The following disclosure provides numerous different embodiments or examples for implementing various structures of this application. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of this application. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0049] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0050] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0051] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0052] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrases "if determined" or "if [described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [described condition or event] is detected," or "in response to detection of [described condition or event]."

[0053] To address the technical problem of high energy loss in existing seabed observation nodes, this application provides a device for transmitting energy scheduling signaling, which can reduce the energy loss of seabed observation nodes.

[0054] Figure 1 This is a flowchart illustrating an energy scheduling method provided in an embodiment of this application. In one embodiment, the method is applied to an energy scheduling system, which includes a base station and multiple underwater nodes. The method includes:

[0055] S1. The base station acquires the historical raw traffic data of each underwater node and the channel status information of each underwater node among multiple underwater nodes.

[0056] Please see Figure 2 , Figure 2 This application provides an overall architecture diagram of an energy dispatching system. The historical raw flow data of the underwater nodes represents communication data between the underwater nodes and other nodes or base stations over a specific historical period. The status information of the underwater nodes includes, but is not limited to, water layer information, seawater temperature, and seawater salinity.

[0057] S2. The base station generates a traffic distribution matrix based on the historical raw traffic data of each underwater node and the channel state information of each underwater node.

[0058] The flow distribution matrix includes predicted flow values ​​for multiple underwater nodes. It should be noted that the generation of the flow distribution matrix will be described in detail in later embodiments of this application. Therefore, it will not be repeated here.

[0059] S3, Base station acquires multi-dimensional optimization model.

[0060] The multidimensional optimization model is used to generate energy scheduling signaling for each of the multiple underwater nodes. The energy scheduling command includes a sleep period, a wake-up interval, an operating state type, and a modulation scheme. The sleep period instructs the underwater node to enter a sleep state. The wake-up interval instructs the underwater node to perform a wake-up operation. The operating state type includes full-power operating state and half-power operating state. The modulation scheme includes high-order modulation schemes and low-order modulation schemes. It should be noted that this application will describe in detail how to obtain the multidimensional optimization model in later embodiments. This will not be repeated here.

[0061] S4. The base station inputs the traffic distribution matrix into the multidimensional optimization model to obtain the energy scheduling signaling for each underwater node among multiple underwater nodes.

[0062] The flow distribution matrix includes the flow forecast for each of the multiple underwater nodes. A multidimensional optimization model generates energy scheduling signaling for each underwater node based on its flow forecast.

[0063] S5. The base station sends energy scheduling signaling for each of the multiple underwater nodes.

[0064] The base station can send energy scheduling signaling to multiple underwater nodes via underwater acoustic communication. The energy scheduling signaling for each underwater node can be the same or different. Specifically, the energy scheduling signaling for each underwater node is determined by S4.

[0065] S6. Each of the multiple underwater nodes receives energy scheduling signaling from the underwater node of the base station.

[0066] Among them, multiple underwater nodes can receive energy scheduling signaling from underwater nodes of the base station through underwater acoustic communication.

[0067] S7. Each of the multiple underwater nodes performs energy scheduling operations according to the energy scheduling signaling of the underwater node.

[0068] Upon receiving energy scheduling signaling from the base station, the underwater node performs energy scheduling operations according to the signaling. These energy scheduling operations are used to adjust the power consumption of the underwater node. It should be noted that this application will describe in detail how the underwater node performs energy scheduling based on the energy scheduling information in later embodiments. This will not be repeated here.

[0069] This application provides an energy scheduling method. The method is applied to an energy scheduling system, which includes a base station and multiple underwater nodes. The method includes: the base station acquiring historical raw traffic data and channel state information for each of the multiple underwater nodes; the base station generating a traffic distribution matrix based on the historical raw traffic data and channel state information of each of the multiple underwater nodes, the traffic distribution matrix including predicted traffic values ​​for the multiple underwater nodes; and the base station acquiring a multidimensional optimization model, the multidimensional optimization model being used to generate... The base station generates energy scheduling signaling for each of the plurality of underwater nodes; the base station inputs the traffic distribution matrix into the multidimensional optimization model to obtain energy scheduling signaling for each of the plurality of underwater nodes; the base station sends the energy scheduling signaling for each of the plurality of underwater nodes to each of the plurality of underwater nodes; each of the plurality of underwater nodes receives the energy scheduling signaling for its underwater node from the base station; each of the plurality of underwater nodes performs an energy scheduling operation according to the energy scheduling signaling for its underwater node, the energy scheduling operation being used to adjust the power consumption of the underwater node. Therefore, in the technical solution of this application, the base station obtains historical raw traffic data and channel state information for each of the plurality of underwater nodes. Then, the base station generates a traffic distribution matrix based on the historical raw traffic data and channel state information for each of the plurality of underwater nodes, and inputs the traffic distribution matrix into the multidimensional optimization model to generate energy scheduling signaling for each of the plurality of underwater nodes. Therefore, it can be seen that the base station predicts the traffic of the underwater node based on the historical raw data and channel state information of the underwater node, and then dynamically generates energy scheduling signaling based on the traffic prediction results, so that the underwater node can complete the data transmission without switching the working state multiple times, effectively reducing the energy consumption of the underwater node.

[0070] In one embodiment, the base station generates a traffic distribution matrix based on the historical raw traffic data of each of the plurality of underwater nodes and the channel state information of each of the plurality of underwater nodes, including:

[0071] S21. The base station preprocesses the historical raw traffic data of each of the multiple underwater nodes to obtain the preprocessed traffic data of each of the multiple underwater nodes.

[0072] In one embodiment, the base station preprocesses the historical raw traffic data of each of the plurality of underwater nodes to obtain preprocessed traffic data for each of the plurality of underwater nodes, including:

[0073] S211. The base station performs differential stabilization on the preprocessed traffic data of each of the multiple underwater nodes to obtain differential preprocessed data of each of the multiple underwater nodes.

[0074] It should be noted that in this embodiment of the application, the base station performs differential stabilization operation to eliminate the impact of periodic fluctuations in the underwater environment, thereby improving the stability of differential preprocessed data.

[0075] S212. The base station performs a normalization operation on the differential preprocessed data of each of the multiple underwater nodes to obtain the preprocessed traffic data of each of the multiple underwater nodes.

[0076] It should be noted that in this embodiment, the base station performs a normalization operation to make the features fall within a similar range, which is beneficial for subsequent data analysis.

[0077] S22. The base station inputs the preprocessed flow data of each of the multiple underwater nodes into the autoregressive integral moving average model to obtain the baseline predicted flow value of each of the multiple underwater nodes.

[0078] Among them, the autoregressive integral moving average model is used to predict the flow rate of each underwater node among multiple underwater nodes.

[0079] S23. The base station obtains the location information of each underwater node among multiple underwater nodes.

[0080] Each of the multiple underwater nodes is equipped with a position sensor to acquire its location information. This location information includes the underwater node's longitude, latitude, and depth.

[0081] S24. The base station acquires the event trigger signal of each of the multiple underwater nodes.

[0082] The event trigger signal of the underwater node is the event trigger signal received by the underwater node. For example, the event trigger signal can be the signal triggered by a tsunami event.

[0083] S25. The base station generates a traffic correction value for each underwater node based on the traffic baseline prediction value of each underwater node, the location information of each underwater node, the event triggering signal of each underwater node, and the channel state information of each underwater node.

[0084] It should be noted that the underwater nodes are affected by ocean waves, causing their positions to shift. The location of these underwater nodes can affect communication between them or between them and the base station. Event triggering signals and channel state information of the underwater nodes also affect communication between them or between them and the base station. In this embodiment, the base station generates a traffic correction value for each underwater node to eliminate the effects of changes in underwater node location, channel state, and the underwater environment.

[0085] S26. The base station determines the predicted flow value of each underwater node among multiple underwater nodes based on the baseline predicted flow value of each underwater node among multiple underwater nodes and the corrected flow value of each underwater node among multiple underwater nodes.

[0086] In one embodiment, the base station determines the predicted flow value of each underwater node based on the baseline predicted flow value of each underwater node and the corrected flow value of each underwater node, including:

[0087] S261. The base station performs a weighted operation on the traffic baseline prediction value and the traffic correction value of each underwater node among multiple underwater nodes to obtain the traffic prediction value of each underwater node among multiple underwater nodes.

[0088] The weighting coefficient between the baseline predicted flow rate and the corrected flow rate at the underwater node is set by the applicant based on experience. Specifically, a weighting ratio of 7:3 can be used. Of course, other weighting ratios can also be used. This application does not impose any restrictions on this.

[0089] S27. The base station generates a traffic distribution matrix based on the traffic prediction value of each underwater node among multiple underwater nodes.

[0090] The base station combines the predicted traffic values ​​of each underwater node from multiple underwater nodes into a multi-dimensional traffic distribution matrix. Each element of the traffic distribution matrix corresponds to a predicted traffic value for one underwater node. It should be noted that the dimension of the traffic distribution matrix is ​​related to the number of underwater nodes. This application does not impose any limitations on this.

[0091] In one embodiment, the base station acquires a multidimensional optimization model, including:

[0092] S31. The base station constructs a three-dimensional optimization model that includes underwater node sleep strategy, underwater node power control, and underwater node modulation method.

[0093] The three-dimensional optimization model aims to minimize the energy consumption of underwater node transmission, underwater node listening, and underwater node state switching. Specifically, the base station first fixes the transmission power and modulation scheme of the underwater node and solves for the optimal sleep period that satisfies the maximum latency constraint of the underwater node. Next, while fixing the optimal sleep period, the transmission power of the underwater node is dynamically modulated to ensure that the signal-to-noise ratio (SNR) received by the underwater node meets the requirements. The lowest transmission power that meets the SNR requirement is then selected. Finally, while fixing the optimal sleep period and the lowest transmission power, the optimal modulation scheme is selected to ensure that the bit error rate (BER) of the underwater node meets the requirements.

[0094] In one embodiment, the energy scheduling signaling includes: a sleep period and a wake-up interval. Each of the plurality of underwater nodes performs an energy scheduling operation according to the energy scheduling signaling of the underwater node, including:

[0095] S71. Each of the multiple underwater nodes performs a hibernation operation according to the hibernation cycle of the underwater node.

[0096] For example, the hibernation cycle can be 12 hours. The underwater node enters hibernation every 12 hours.

[0097] S72. Each of the multiple underwater nodes performs a wake-up operation according to the wake-up interval of the underwater node.

[0098] For example, the wake-up interval could be 5 hours. After the underwater node has been sleeping for 5 hours, the wake-up action can be triggered.

[0099] It should be noted that the sleep cycle and wake-up interval will affect the overall power consumption of the underwater node. The shorter the sleep cycle and the shorter the wake-up interval, the greater the power consumption of the underwater node, and vice versa.

[0100] In one embodiment, the energy scheduling signaling further includes: a working state type, wherein each of the plurality of underwater nodes performs an energy scheduling operation according to the energy scheduling signaling of the underwater node, including:

[0101] S73. Each of the multiple underwater nodes adjusts its transmission power according to its operating status.

[0102] The operating state types include full-power operating state and half-power operating state. When the underwater node is in full-power operating state, its transmission power is relatively high. For example, the transmission power of the underwater node is 20W. When the underwater node is in half-power operating state, its transmission power is relatively low. For example, the transmission power of the underwater node is 2W.

[0103] It should be noted that the greater the transmission power of the underwater node, the greater its power consumption, and vice versa.

[0104] In one embodiment, the energy scheduling signaling further includes a modulation scheme, and each of the plurality of underwater nodes performs an energy scheduling operation according to the energy scheduling signaling of the underwater node, including:

[0105] S74. Each of the multiple underwater nodes adjusts its modulation method according to its own modulation method.

[0106] Modulation methods include high-order modulation methods and low-order modulation methods. Low-order modulation methods include, but are not limited to, amplitude shift keying (APS) and quadrature phase shift keying (QPS). High-order modulation methods include, but are not limited to, 256QAM and 64QAM.

[0107] See Figure 3 , Figure 3 This is a schematic block diagram of an energy scheduling device provided in an embodiment of this application. Corresponding to the above energy scheduling method, this application also provides an energy scheduling device. The energy scheduling device includes a unit for executing the above energy scheduling method, and the energy scheduling device can be configured in a terminal such as a desktop computer, tablet computer, or laptop computer. Specifically, the energy scheduling device includes:

[0108] The first acquisition unit 301 is used to acquire the historical raw traffic data of each of the multiple underwater nodes and the channel state information of each of the multiple underwater nodes.

[0109] The generation unit 302 is used to generate a traffic distribution matrix based on the historical raw traffic data of each of the plurality of underwater nodes and the channel state information of each of the plurality of underwater nodes. The traffic distribution matrix includes the traffic prediction values ​​of the plurality of underwater nodes.

[0110] The second acquisition unit 303 acquires a multi-dimensional optimization model, which is used to generate energy scheduling signaling for each of the plurality of underwater nodes.

[0111] Input unit 304 is used to input the flow distribution matrix into the multidimensional optimization model to obtain the energy scheduling signaling of each underwater node among the multiple underwater nodes;

[0112] The sending unit 305 is used to send the energy scheduling signaling of the underwater node to each of the plurality of underwater nodes, wherein the energy scheduling signaling of the underwater node is used to instruct the underwater node to perform energy scheduling.

[0113] The receiving unit 306 is used for each of the plurality of underwater nodes to receive energy scheduling signaling from the base station.

[0114] The execution unit 307 is used for each of the plurality of underwater nodes to perform an energy scheduling operation according to the energy scheduling signaling of the underwater node, the energy scheduling operation being used to adjust the power consumption of the underwater node.

[0115] In one embodiment, the generation unit 302 is specifically used by the base station to preprocess the historical raw flow data of each of the plurality of underwater nodes to obtain the preprocessed flow data of each of the plurality of underwater nodes;

[0116] The base station inputs the preprocessed flow data of each of the multiple underwater nodes into the autoregressive integral moving average model to obtain the baseline predicted flow value of each of the multiple underwater nodes. The autoregressive integral moving average model is used to predict the flow of each of the multiple underwater nodes.

[0117] The base station acquires the location information of each of the plurality of underwater nodes;

[0118] The base station acquires the event trigger signal of each of the plurality of underwater nodes, wherein the event trigger signal of the underwater node is the event trigger signal received by the underwater node;

[0119] The base station generates a traffic correction value for each of the multiple underwater nodes based on the traffic reference prediction value of each underwater node, the location information of each underwater node, the event triggering signal of each underwater node, and the channel state information of each underwater node.

[0120] The base station determines the predicted flow value of each underwater node based on the baseline predicted flow value of each underwater node and the corrected flow value of each underwater node.

[0121] The base station generates the flow distribution matrix based on the flow prediction value of each of the plurality of underwater nodes.

[0122] In one embodiment, the generation unit 302 is further specifically used by the base station to perform differential stabilization operation on the preprocessed flow data of each of the plurality of underwater nodes to obtain differential preprocessed data of each of the plurality of underwater nodes;

[0123] The base station performs a normalization operation on the differential preprocessed data of each of the plurality of underwater nodes to obtain the preprocessed flow data of each of the plurality of underwater nodes.

[0124] In one embodiment, the generation unit 302 is further configured to perform a weighted operation on the traffic baseline prediction value of each of the plurality of underwater nodes and the traffic correction value of each of the plurality of underwater nodes to obtain the traffic prediction value of each of the plurality of underwater nodes.

[0125] In one embodiment, the second acquisition unit 303 is specifically used for the base station to construct a three-dimensional optimization model including underwater node sleep strategy, underwater node power regulation and underwater node modulation mode. The three-dimensional optimization model takes the weighted minimization of underwater node transmission energy consumption, underwater node listening energy consumption and underwater node state switching energy consumption as the optimization objective.

[0126] In one embodiment, the energy scheduling signaling includes a sleep cycle and a wake-up interval, and the execution unit 307 is specifically used for each of the plurality of underwater nodes to perform a sleep operation according to the sleep cycle of the underwater node;

[0127] Each of the plurality of underwater nodes performs a wake-up operation according to the wake-up interval of the underwater node.

[0128] In one embodiment, the energy scheduling signaling further includes: a working state type, and the execution unit 307 is specifically used to adjust the transmission power of each of the plurality of underwater nodes according to the working state type of the underwater node.

[0129] In one embodiment, the energy scheduling signaling further includes a modulation scheme, and the execution unit 307 is specifically used to adjust the modulation scheme of each of the plurality of underwater nodes according to the modulation scheme of the underwater node.

[0130] like Figure 4 As shown, this application provides a computer device including a processor 41, a communication interface 42, a memory 43, and a communication bus 44. The processor 41, the communication interface 42, and the memory 43 communicate with each other through the communication bus 44. The memory 43 is used to store computer programs.

[0131] In one embodiment of this application, when the processor 41 executes the program stored in the memory 43, it implements the energy scheduling control method provided in any of the foregoing method embodiments.

[0132] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program signaling related hardware. The computer program may be stored in a storage medium, which is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0133] Therefore, embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the energy scheduling method provided in any of the foregoing method embodiments.

[0134] The storage medium is a physical, non-transitory storage medium, such as a USB flash drive, external hard drive, read-only memory (ROM), magnetic disk, or optical disk, or any other physical storage medium capable of storing program code. The computer-readable storage medium can be non-volatile or volatile.

[0135] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0136] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0137] The steps in the methods of this application embodiment can be adjusted, merged, or deleted according to actual needs. The units in the apparatus of this application embodiment can be merged, divided, or deleted according to actual needs. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0138] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of this application, 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. This computer software product is stored in a storage medium and includes several signaling messages to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0139] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0140] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Since these modifications and variations fall within the scope of the claims and their equivalents, this application also intends to include these modifications and variations.

[0141] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An energy scheduling method, characterized in that, The method is applied to an energy dispatching system, which includes a base station and multiple underwater nodes, and the method includes: The base station acquires historical raw traffic data of each of the plurality of underwater nodes and channel state information of each of the plurality of underwater nodes; The base station generates a traffic distribution matrix based on the historical raw traffic data of each of the plurality of underwater nodes and the channel state information of each of the plurality of underwater nodes. The traffic distribution matrix includes the traffic prediction values ​​of the plurality of underwater nodes. The base station acquires a multi-dimensional optimization model, which is used to generate energy scheduling signaling for each of the plurality of underwater nodes. The base station inputs the traffic distribution matrix into the multidimensional optimization model to obtain the energy scheduling signaling for each of the multiple underwater nodes. The base station sends energy scheduling signaling for each of the plurality of underwater nodes to the underwater node; Each of the plurality of underwater nodes receives energy scheduling signaling from the underwater node of the base station; Each of the plurality of underwater nodes performs an energy scheduling operation according to the energy scheduling signaling of the underwater node, the energy scheduling operation being used to adjust the power consumption of the underwater node.

2. The method according to claim 1, characterized in that, The base station generates a traffic distribution matrix based on the historical raw traffic data of each of the plurality of underwater nodes and the channel state information of each of the plurality of underwater nodes, including: The base station preprocesses the historical raw flow data of each of the plurality of underwater nodes to obtain the preprocessed flow data of each of the plurality of underwater nodes. The base station inputs the preprocessed flow data of each of the plurality of underwater nodes into the autoregressive integral moving average model to obtain the baseline predicted flow value of each of the plurality of underwater nodes. The autoregressive integral moving average model is used to predict the flow of each of the plurality of underwater nodes. The base station acquires the location information of each of the plurality of underwater nodes; The base station acquires the event trigger signal of each of the plurality of underwater nodes, and the event trigger signal of the underwater node is the event trigger signal received by the underwater node; The base station generates a traffic correction value for each of the multiple underwater nodes based on the traffic reference prediction value of each underwater node, the location information of each underwater node, the event triggering signal of each underwater node, and the channel state information of each underwater node. The base station determines the predicted flow value of each underwater node based on the baseline predicted flow value of each underwater node and the corrected flow value of each underwater node. The base station generates the flow distribution matrix based on the flow prediction value of each of the plurality of underwater nodes.

3. The method according to claim 2, characterized in that, The base station preprocesses the historical raw flow data of each of the plurality of underwater nodes to obtain preprocessed flow data for each of the plurality of underwater nodes, including: The base station performs differential stabilization on the preprocessed flow data of each of the plurality of underwater nodes to obtain differential preprocessed data of each of the plurality of underwater nodes. The base station performs a normalization operation on the differential preprocessed data of each of the plurality of underwater nodes to obtain the preprocessed flow data of each of the plurality of underwater nodes.

4. The method according to claim 2, characterized in that, The base station determines the predicted flow value for each of the plurality of underwater nodes based on the baseline predicted flow value for each underwater node and the corrected flow value for each underwater node, including: The base station performs a weighted operation on the baseline predicted flow value and the corrected flow value of each of the plurality of underwater nodes to obtain the predicted flow value of each of the plurality of underwater nodes.

5. The method according to any one of claims 1 to 4, characterized in that, The base station acquires a multi-dimensional optimization model, including: The base station constructs a three-dimensional optimization model that includes underwater node sleep strategy, underwater node power control and underwater node modulation method. The three-dimensional optimization model aims to minimize the underwater node transmission energy consumption, underwater node listening energy consumption and underwater node state switching energy consumption.

6. The method according to any one of claims 1 to 4, characterized in that, The energy scheduling signaling includes a sleep cycle and a wake-up interval. Each of the plurality of underwater nodes performs an energy scheduling operation according to the energy scheduling signaling of the underwater node, including: Each of the plurality of underwater nodes performs a hibernation operation according to its hibernation cycle; Each of the plurality of underwater nodes performs a wake-up operation according to the wake-up interval of the underwater node.

7. The method according to claim 6, characterized in that, The energy scheduling signaling further includes: a working state type, wherein each of the plurality of underwater nodes performs energy scheduling operations according to the energy scheduling signaling of the underwater node, including: Each of the plurality of underwater nodes adjusts its transmission power according to its operating state type.

8. The method according to claim 7, characterized in that, The energy scheduling signaling also includes a modulation scheme. Each of the plurality of underwater nodes performs an energy scheduling operation according to the energy scheduling signaling of the underwater node, including: Each of the plurality of underwater nodes adjusts its modulation method according to its own modulation method.

9. An energy dispatching device, characterized in that, include: The first acquisition unit is used by the base station to acquire historical raw traffic data of each of the plurality of underwater nodes and channel state information of each of the plurality of underwater nodes; The generation unit is used by the base station to generate a traffic distribution matrix based on the historical raw traffic data of each of the plurality of underwater nodes and the channel state information of each of the plurality of underwater nodes, wherein the traffic distribution matrix includes the traffic prediction values ​​of the plurality of nodes; The second acquisition unit is used for the base station to acquire a multi-dimensional optimization model, and the multi-dimensional optimization model is used to generate energy scheduling signaling for each of the plurality of underwater nodes. The input unit is used by the base station to input the traffic distribution matrix into the multidimensional optimization model to obtain the energy scheduling signaling of each of the multiple underwater nodes. The transmitting unit is used by the base station to send energy scheduling signaling of the underwater node to each of the plurality of underwater nodes, wherein the energy scheduling signaling is used to instruct the underwater node to perform energy scheduling. A receiving unit is configured to allow each of the plurality of underwater nodes to receive energy scheduling signaling from the base station. An execution unit is configured to perform an energy scheduling operation on each of the plurality of underwater nodes according to the energy scheduling signaling of the underwater node, the energy scheduling operation being used to adjust the power consumption of the underwater node.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, can implement the method as described in any one of claims 1 to 8.

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