Signal processing method and apparatus

Abstract

The signal processing method and device provided by the embodiments of the present application relate to the technical field of electric power and can effectively improve the accuracy of household transformer relationship identification. The method comprises the following steps: obtaining first data and a plurality of second data from a node. Then, the node determines a plurality of first similarities according to the first data and the plurality of second data. Next, the node determines a target master node according to the plurality of first similarities. The first data is power frequency cycle characteristic data collected by the node in a target period, the power frequency cycle characteristic data is used to indicate the cycle characteristics of the working frequency of the power grid, the plurality of second data is power frequency cycle characteristic data collected by a plurality of master nodes in the target period respectively, the plurality of second data corresponds to the plurality of master nodes one by one, the plurality of first similarities correspond to the plurality of master nodes one by one, and the target master node is one of the plurality of master nodes.

Description

Technical Field

[0001] This application relates to the field of power technology, and in particular to signal processing methods and apparatus. Background Technology

[0002] Within a power supply area, electrical energy is supplied to users through various stages of the power network, including transmission, transformation, and distribution. During the transmission and distribution of electrical energy, each component of the power network incurs a certain amount of active power loss and energy loss. Line loss is usually calculated by subtracting the total "power supply" and total "power sales" measured by the electricity meter. In low-voltage power supply, the "power supply" within a distribution area can be directly obtained from the power company's metering equipment. However, the "power sales" need to be calculated based on the distribution area identification results. In other words, the accuracy of distribution area identification directly determines the accuracy of the "power sales" statistics. In a power system, a distribution area can refer to the power supply range or region of a single transformer. Distribution area identification, also known as user-transformer relationship identification, refers to identifying which distribution area a power grid device (such as a smart switch, meter, or data collector) belongs to within the power network.

[0003] However, existing methods for identifying the relationship between households and transformers have low accuracy and are prone to misidentifying the relationship, thus failing to provide a reliable basis for calculating line loss. Summary of the Invention

[0004] This application provides a signal processing method and apparatus that can effectively improve the accuracy of household-transformer relationship identification.

[0005] In a first aspect, this application provides a signal processing method, which includes: acquiring first data and multiple second data from a slave node; then, the slave node determines multiple first similarities based on the first data and the multiple second data; and then, the slave node determines a target master node based on the multiple first similarities. Wherein, the first data is power frequency cycle feature data collected by the slave node within a target time period, the power frequency cycle feature data being used to indicate the periodic characteristics of the power grid operating frequency; the multiple second data are power frequency cycle feature data collected by multiple master nodes within the target time period, each of the multiple second data corresponding one-to-one with the multiple master nodes; the multiple first similarities also corresponding one-to-one with the multiple master nodes; and the target master node is one of the multiple master nodes.

[0006] Existing technologies determine the main node (i.e., the target main node) of a transformer substation by collecting power frequency cycle characteristic data from multiple main nodes at different time periods. The signal processing method provided in this application, however, can acquire power frequency cycle characteristic data collected by both the slave node and multiple main nodes within the same time period (i.e., the target time period), and then determine the target main node based on this data. Since power frequency cycle characteristic data collected by multiple main nodes within the same time period has higher discriminative power than power frequency cycle characteristic data collected by multiple main nodes at different time periods, the signal processing method provided in this application has higher accuracy than existing methods for identifying transformer substation relationships. This effectively improves the accuracy of transformer substation relationship identification and provides a reliable basis for calculating line loss.

[0007] Optionally, the aforementioned power frequency cycle characteristic data includes N power frequency zero-crossing times, where N is a positive integer. For example, the aforementioned power frequency cycle characteristic data may include 1000 power frequency zero-crossing times.

[0008] Optionally, the aforementioned zero-crossing time of the power frequency may include at least one of the following: the zero-crossing time of the rising edge of the power frequency voltage, the zero-crossing time of the falling edge of the power frequency voltage, the zero-crossing time of the rising edge of the power frequency current, or the zero-crossing time of the falling edge of the power frequency current. For example, the aforementioned zero-crossing time of the power frequency may only include the zero-crossing time of the falling edge of the power frequency voltage.

[0009] In one possible implementation, before acquiring the plurality of second data, the method further includes: the slave node transmitting second indication information to the plurality of master nodes, the second indication information being used to instruct each of the plurality of master nodes to collect the power frequency cycle characteristic data within the target time period.

[0010] In this way, by transmitting the second indication information to multiple master nodes, these multiple master nodes can collect power frequency cycle characteristic data in the same time period.

[0011] In one possible implementation, the slave node determines the target master node based on the plurality of first similarities, including: the slave node determining whether the first master node meets the target conditions, wherein the first master node is the master node with the highest first similarity among the plurality of master nodes; if the first master node meets the target conditions, the slave node determines the first master node as the target master node; if the first master node does not meet the target conditions, the slave node obtains first information of a plurality of transformer substations and determines the target master node based on the first information of the plurality of transformer substations, wherein the plurality of transformer substations includes the transformer substations where the plurality of master nodes are located.

[0012] By determining whether the first master node meets the target conditions, it can be concluded whether the power frequency cycle characteristic data of the multiple master nodes has high distinguishability. When the power frequency cycle characteristic data of the multiple master nodes does not have high distinguishability, the target master node is determined by the first information of the multiple transformer areas corresponding to the multiple master nodes, so as to ensure the accuracy of household-transformer relationship identification.

[0013] Optionally, the first information may include at least one of the following: the lowest effective level, the signal-to-noise ratio (SNR) value, or the attenuation value. For example, the first information may include the lowest effective level, the SNR, and the attenuation value.

[0014] In one possible implementation, the slave node determines whether the first master node meets the target condition by: the slave node determining a first difference based on the plurality of first similarities, wherein the first difference is the difference between the highest first similarity among the plurality of first similarities and the second highest first similarity among the plurality of first similarities; if the first difference is greater than a first threshold, the slave node determines that the first master node meets the target condition; if the first difference is less than or equal to the first threshold, the slave node determines that the first master node does not meet the target condition.

[0015] In this way, the magnitude of the first difference can be used to determine whether the power frequency cycle characteristic data of the multiple master nodes have high distinguishability.

[0016] When the first difference is greater than the first threshold, it means that the power frequency cycle characteristic data of the other master nodes, except for the first master node, are not similar to the power frequency cycle characteristic data of the slave nodes. At this time, the power frequency cycle characteristic data of each master node has a high degree of differentiation. The relationship between households and transformers can be accurately identified through the power frequency cycle characteristic data of multiple master nodes. Therefore, the first master node can be determined as the target master node.

[0017] Conversely, when the first difference is less than or equal to the first threshold, it indicates that among the multiple master nodes, there is a master node with a first similarity close to the first master node. In this case, the power frequency cycle feature data of the master node is also extremely similar to the power frequency cycle feature data of the slave node. The master node may also be the target master node. At this time, the power frequency cycle feature data does not have high distinguishability, and the relationship between households and transformers may not be accurately identified through the power frequency cycle feature data of multiple master nodes.

[0018] In another possible implementation, the slave node determines whether the first master node meets the target condition by: the slave node determining a plurality of second similarities based on the plurality of second data, wherein the plurality of second similarities are respectively used to indicate the similarity between the second data corresponding to each master node other than the first master node and the second data corresponding to the first master node; if all of the plurality of second similarities are less than a second threshold, the slave node determines that the first master node meets the target condition; if the plurality of second similarities are not all less than the second threshold, the slave node determines that the first master node does not meet the target condition.

[0019] In this way, the degree of the second similarity can be used to determine whether the power frequency cycle feature data of the multiple master nodes have a high degree of distinguishability.

[0020] The fact that the second similarity scores are all less than the second threshold indicates that the similarity between the power frequency cycle feature data of the multiple master nodes other than the first master node and the power frequency cycle feature data of the first master node is not very high. At this time, the power frequency cycle feature data has a high degree of distinguishability. The relationship between households and transformers can be accurately identified through the power frequency cycle feature data of multiple master nodes. Therefore, the first master node can be identified as the target master node.

[0021] Conversely, if multiple second similarities are greater than or equal to the second threshold, it indicates that among the multiple master nodes other than the first master node, there exists a master node whose power frequency cycle characteristic data is similar to that of the first master node. In this case, the transformer area of ​​that master node may also be the target master node. However, the power frequency cycle characteristic data of each master node does not have high distinguishability, and the relationship between households and transformers may not be accurately identified through the power frequency cycle characteristic data of multiple master nodes.

[0022] Optionally, the second information includes the transformer area identifier, level, SNR value, and attenuation value.

[0023] In one possible implementation, the step of obtaining first information about multiple distribution areas from a slave node includes: the slave node obtaining multiple target messages, wherein the target messages are messages sent by multiple nodes of the multiple distribution areas within a preset time; the slave node determining second information about multiple first nodes of the multiple distribution areas based on the multiple target messages; and the slave node determining first information about the multiple distribution areas based on the second information of the multiple first nodes, wherein the first node is a node among the multiple nodes that satisfies a first condition.

[0024] Optionally, the first condition includes at least one of the following: the number of messages sent to the slave node within the preset time is greater than a sixth threshold, the SNR value is greater than a seventh threshold, or the attenuation value is less than an eighth threshold.

[0025] In one possible implementation, the slave node determines the first information of the plurality of transformer substations based on the second information of the plurality of first nodes, including: the slave node determining the lowest effective level of the first transformer substation based on the levels of the plurality of first nodes of the first transformer substation, wherein the first transformer substation is any one of the plurality of transformer substations; the slave node determining the SNR value of the first transformer substation based on the SNR values ​​of the plurality of first nodes of the first transformer substation, wherein the second node is the node with the lowest level among the plurality of first nodes of the first transformer substation; and the slave node determining the attenuation value of the first transformer substation based on the attenuation values ​​of the plurality of first nodes of the first transformer substation.

[0026] In another possible implementation, the slave node determines the first information of the plurality of transformer substations based on the second information of the plurality of first nodes, including: the slave node determines the lowest effective level of the first transformer substation based on the levels of the plurality of first nodes of the first transformer substation, wherein the first transformer substation is any one of the plurality of transformer substations; the slave node determines the SNR value of the first transformer substation based on the SNR value of the second node, wherein the second node is the node with the lowest level among the plurality of first nodes of the first transformer substation; and the slave node determines the attenuation value of the first transformer substation based on the attenuation value of the second node.

[0027] In one possible implementation, determining the target master node based on the first information of the plurality of transformer substations includes: if there is only one second transformer substation among the plurality of transformer substations, the slave node determines the master node of the second transformer substation as the target master node, wherein the second transformer substation is the transformer substation with the smallest lowest effective level among the plurality of transformer substations; if there are multiple second transformer substations among the plurality of transformer substations, the slave node determines the target master node based on the SNR value and the attenuation value of the multiple second transformer substations.

[0028] This approach allows for direct identification of the target master node when multiple master node levels are highly distinguishable. Conversely, when multiple master node levels are not highly distinguishable, the target master node is identified using other information. This ensures the accuracy of household change relationship identification.

[0029] In one possible implementation, determining the target master node from the plurality of second transformer substations based on their SNR values ​​and attenuation values ​​includes: the slave node determining a second difference based on the SNR values ​​of the plurality of second transformer substations, wherein the second difference is the difference between the SNR values ​​of a third transformer substation and a fourth transformer substation, wherein the third transformer substation is the second transformer substation with the largest SNR value among the plurality of second transformer substations, and the fourth transformer substation is the second transformer substation with the second largest SNR value among the plurality of second transformer substations; if the second difference is greater than or equal to a third threshold, the slave node determines the target master node based on the SNR values ​​of the plurality of second transformer substations; if the second difference is less than the third threshold, the slave node determines the target master node based on the attenuation values ​​of the plurality of second transformer substations.

[0030] For example, if the second difference is greater than or equal to the third threshold, the slave node determines that the target master node is the master node of the third station area; if the second difference is less than the third threshold, the slave node determines that the target master node is the master node of the station area with the smaller attenuation value among the third station area and the fourth station area.

[0031] This allows us to determine whether the SNR data has high discriminative power using the second difference. If the SNR data has high discriminative power, the target master node can be directly identified using the SNR data. Conversely, if the SNR data does not have high discriminative power, the target master node is identified using the attenuation value data, ensuring the accuracy of household-transformer relationship identification.

[0032] In another possible implementation, determining the target master node from the plurality of second stations based on the SNR values ​​and attenuation values ​​of the plurality of second stations includes: if the attenuation value of the fifth station is less than a fourth threshold, the slave node determines the target master node based on the attenuation values ​​of the plurality of second stations, wherein the fifth station is the station with the smallest attenuation value among the plurality of second stations; if the attenuation value of the fifth station is greater than or equal to the fourth threshold, the slave node determines the target master node based on the SNR values ​​of the plurality of second stations.

[0033] For example, if the attenuation value of the fifth station area is less than the fourth threshold, the slave node determines the target master node as the master node of the fifth station area; if the attenuation value of the fifth station area is greater than or equal to the fourth threshold, the slave node determines the target master node as the master node of the sixth station area, which is the station area with the largest SNR value among the plurality of second station areas.

[0034] This allows us to determine whether the attenuation value data has high discriminative power. If the attenuation value data has high discriminative power, the target master node can be directly determined based on the attenuation value. Conversely, if the attenuation value data does not have high discriminative power, the target master node is determined based on the SNR data to ensure the accuracy of household-transformer relationship identification.

[0035] In another possible implementation, determining the target master node from the plurality of second stations based on the SNR values ​​and attenuation values ​​of the plurality of second stations includes: if the SNR value of the sixth station is greater than a fifth threshold, the slave node determines the target master node based on the SNR values ​​of the plurality of second stations, wherein the sixth station is the station with the largest SNR value among the plurality of second stations; if the SNR value of the sixth station is less than or equal to the fifth threshold, the slave node determines the target master node based on the attenuation values ​​of the plurality of second stations.

[0036] For example, if the SNR value of the sixth station area is greater than the fifth threshold, the slave node determines the target master node as the master node of the sixth station area; if the SNR value of the sixth station area is less than or equal to the fifth threshold, the slave node determines the target master node as the master node of the fifth station area.

[0037] This allows us to determine whether the SNR data has high discriminative power. If the SNR data has high discriminative power, the target master node can be directly determined using the SNR data. Conversely, if the SNR data does not have high discriminative power, the target master node is determined using the attenuation value data, ensuring the accuracy of household-transformer relationship identification.

[0038] Secondly, this application provides another signal processing method, which includes: a second master node acquiring first data, the first data being power frequency cycle feature data collected by slave nodes within a target time period, the power frequency cycle feature data being used to indicate the cycle characteristics of the power grid operating frequency; the second master node acquiring multiple sets of second data, the multiple sets of second data including the power frequency cycle feature data collected by multiple master nodes within the target time period, the multiple master nodes including the second master node, the multiple sets of second data corresponding one-to-one with the multiple master nodes; the second master node determining multiple sets of first similarity based on the first data and the multiple sets of second data, the multiple sets of first similarity corresponding one-to-one with the multiple master nodes; and the second master node determining a target master node based on the multiple sets of first similarity, the target master node being one of the multiple master nodes.

[0039] Optionally, the aforementioned power frequency cycle characteristic data includes N power frequency zero-crossing times, where N is a positive integer.

[0040] Optionally, the aforementioned zero-crossing time of the power frequency may include at least one of the following: the zero-crossing time of the rising edge of the power frequency voltage, the zero-crossing time of the falling edge of the power frequency voltage, the zero-crossing time of the rising edge of the power frequency current, or the zero-crossing time of the falling edge of the power frequency current.

[0041] In one possible implementation, before the second master node acquires the plurality of second data, the method further includes: the second master node sending second instruction information to the third master node, the second instruction information being used to instruct each of the third master nodes to collect the power frequency cycle characteristic data within the target time period, the third master node including each of the plurality of master nodes other than the second master node.

[0042] In one possible implementation, the second master node determines the target master node based on the plurality of first similarities, including: the second master node determining whether the first master node meets the target conditions, wherein the first master node is the master node with the highest first similarity among the plurality of master nodes; if the first master node meets the target conditions, the second master node determines the first master node as the target master node; if the first master node does not meet the target conditions, the second master node obtains the first information of the plurality of transformer substations and determines the target master node based on the first information of the plurality of transformer substations.

[0043] Optionally, the first information includes at least one of the following: the lowest effective level, the signal-to-noise ratio (SNR) value, or the attenuation value.

[0044] In another possible implementation, the second master node determines the target master node based on the plurality of first similarities, including: the second master node determining whether the first master node meets the target conditions, wherein the first master node is the master node with the highest first similarity among the plurality of master nodes; if the first master node meets the target conditions, the second master node determines the first master node as the target master node; if the first master node does not meet the target conditions, the second master node sends a first request to the slave node and receives a first result sent by the slave node. The first request is used to request a target result, and the target result is used to indicate the target master node.

[0045] In one possible implementation, the second master node determines whether the first master node meets the target condition by: the second master node determining a first difference based on the plurality of first similarities, wherein the first difference is the difference between the highest first similarity among the plurality of first similarities and the second highest first similarity among the plurality of first similarities; if the first difference is greater than a first threshold, the second master node determines that the first master node meets the target condition; if the first difference is less than or equal to the first threshold, the second master node determines that the first master node does not meet the target condition.

[0046] In another possible implementation, the second master node determines whether the first master node meets the target condition by: the second master node determining a plurality of second similarities based on the plurality of second data, wherein the plurality of second similarities are respectively used to indicate the similarity between the second data corresponding to each master node other than the first master node and the second data corresponding to the first master node; if all of the plurality of second similarities are less than a second threshold, the second master node determines that the first master node meets the target condition; if the plurality of second similarities are not all less than the second threshold, the second master node determines that the first master node does not meet the target condition.

[0047] In one possible implementation, the second master node acquires first information about multiple transformer substations by: the second master node sending a second request to the slave node, the second request being used to request the first information about the multiple transformer substations; and the second master node receiving the first information about the multiple transformer substations sent by the slave node.

[0048] In one possible implementation, determining the target master node based on the first information of the plurality of transformer substations includes: if there is only one second transformer substation among the plurality of transformer substations, the second master node determines the master node of the second transformer substation as the target master node, wherein the second transformer substation is the transformer substation with the smallest lowest effective level among the plurality of transformer substations; if there are multiple second transformer substations among the plurality of transformer substations, the second master node determines the target master node based on the SNR value and the attenuation value of the multiple second transformer substations.

[0049] In one possible implementation, the second master node determines the target master node based on the SNR values ​​and attenuation values ​​of the plurality of second transformer areas, including: the second master node determines a second difference based on the SNR values ​​of the plurality of second transformer areas, the second difference being the difference between the SNR value of a third transformer area and the SNR value of a fourth transformer area, wherein the third transformer area is the second transformer area with the largest SNR value among the plurality of second transformer areas, and the fourth transformer area is the transformer area with the second largest SNR value among the plurality of second transformer areas; if the second difference is greater than or equal to a third threshold, the slave node determines the target master node based on the SNR values ​​of the plurality of second transformer areas; if the second difference is less than the third threshold, the slave node determines the target master node based on the attenuation values ​​of the plurality of second transformer areas.

[0050] In another possible implementation, the second master node determines the target master node from the plurality of second substations based on the SNR values ​​and attenuation values ​​of the plurality of second substations, including: if the attenuation value of the fifth substation is less than a fourth threshold, the slave node determines the target master node based on the attenuation values ​​of the plurality of second substations, wherein the fifth substation is the substation with the smallest attenuation value among the plurality of second substations; if the attenuation value of the fifth substation is greater than or equal to the fourth threshold, the slave node determines the target master node based on the SNR values ​​of the plurality of second substations.

[0051] In another possible implementation, the second master node determines the target master node from the plurality of second substations based on the SNR values ​​and attenuation values ​​of the plurality of second substations, including: if the SNR value of the sixth substation is greater than a fifth threshold, the slave node determines the target master node based on the SNR values ​​of the plurality of second substations, wherein the sixth substation is the substation with the largest SNR value among the plurality of second substations; if the SNR value of the sixth substation is less than or equal to the fifth threshold, the slave node determines the target master node based on the attenuation values ​​of the plurality of second substations.

[0052] Thirdly, this application provides another signal processing method, which includes: a slave node first receiving a first request sent by a second master node for requesting a target result; then the slave node acquiring first information of multiple distribution areas; subsequently, the slave node determining the target master node based on the first information of the multiple distribution areas; and then the slave node sending the target result to the second master node to indicate the target master node. The first information includes at least one of the following: a minimum effective level, an SNR value, or an attenuation value; the multiple distribution areas include distribution areas of multiple master nodes; the multiple master nodes correspond one-to-one with the multiple distribution areas; the target master node is one of the multiple master nodes; and the multiple master nodes include the second master node.

[0053] Existing technologies determine the master node (i.e., the target master node) of a transformer substation based on its SNR data. However, in some scenarios (such as extremely high or low ambient noise), the SNR data of a transformer substation has low discriminative power, making it impossible to accurately determine the target master node solely based on SNR data. This application, in determining the target master node, considers not only SNR data but also the lowest effective level and attenuation value. Therefore, the signal processing method provided in this application has higher accuracy than existing methods for identifying transformer-household relationships, effectively improving the accuracy of such identification and providing a reliable basis for calculating line loss.

[0054] In one possible implementation, the step of obtaining first information about multiple distribution areas from a slave node includes: the slave node obtaining multiple target packets, wherein the target packets are packets sent by multiple nodes of the multiple distribution areas within a preset time; the slave node determining second information about multiple first nodes of the multiple distribution areas based on the multiple target packets, wherein the second information includes a distribution area identifier, level, SNR value, and attenuation value, and the first node is a node among the multiple nodes that meets a first condition; and the slave node determining the first information about the multiple distribution areas based on the second information of the multiple first nodes.

[0055] Optionally, the first condition includes at least one of the following: the number of messages sent to the slave node within the preset time is greater than a sixth threshold, the SNR value is greater than a seventh threshold, or the attenuation value is less than an eighth threshold.

[0056] In one possible implementation, the slave node determines the first information of the plurality of transformer substations based on the second information of the plurality of first nodes, including: the slave node determining the lowest effective level of the first transformer substation based on the levels of the plurality of first nodes of the first transformer substation, wherein the first transformer substation is any one of the plurality of transformer substations; the slave node determining the SNR value of the first transformer substation based on the SNR value of the plurality of first nodes of the first transformer substation or the SNR value of a second node, wherein the second node is the node with the lowest level among the plurality of first nodes of the first transformer substation; and the slave node determining the attenuation value of the first transformer substation based on the attenuation value of the plurality of first nodes of the first transformer substation or the attenuation value of the second node.

[0057] In one possible implementation, the slave node determines the target master node based on the first information of the plurality of transformer substations, including: if there is only one second transformer substation among the plurality of transformer substations, the slave node determines the master node of the second transformer substation as the target master node, wherein the second transformer substation is the transformer substation with the smallest lowest effective level among the plurality of transformer substations; if there are multiple second transformer substations among the plurality of transformer substations, the slave node determines the target master node based on the SNR value and the attenuation value of the multiple second transformer substations.

[0058] In one possible implementation, the slave node determines the target master node based on the SNR values ​​and attenuation values ​​of the plurality of second transformer areas, including: the slave node determines a second difference based on the SNR values ​​of the plurality of second transformer areas, the second difference being the difference between the SNR values ​​of a third transformer area and a fourth transformer area, wherein the third transformer area is the transformer area with the largest SNR value among the plurality of second transformer areas, and the fourth transformer area is the transformer area with the second largest SNR value among the plurality of second transformer areas; if the second difference is greater than or equal to a third threshold, the slave node determines the target master node based on the SNR values ​​of the plurality of second transformer areas; if the second difference is less than the third threshold, the slave node determines the target master node based on the attenuation values ​​of the plurality of second transformer areas.

[0059] In another possible implementation, the slave node determines the target master node based on the SNR values ​​and attenuation values ​​of the plurality of second stations, including: if the attenuation value of the fifth station is less than a fourth threshold, the slave node determines the target master node based on the attenuation values ​​of the plurality of second stations, wherein the fifth station is the station with the smallest attenuation value among the plurality of second stations; if the attenuation value of the fifth station is greater than or equal to the fourth threshold, the slave node determines the target master node based on the SNR values ​​of the plurality of second stations.

[0060] In another possible implementation, the slave node determines the target master node based on the SNR values ​​and attenuation values ​​of the plurality of second stations, including: if the SNR value of the sixth station is greater than a fifth threshold, the slave node determines the target master node based on the SNR values ​​of the plurality of second stations, wherein the sixth station is the station with the largest SNR value among the plurality of second stations; if the SNR value of the sixth station is less than or equal to the fifth threshold, the slave node determines the target master node based on the attenuation values ​​of the plurality of second stations.

[0061] Fourthly, embodiments of this application also provide a signal processing apparatus for performing the methods described in the foregoing aspects or any possible implementations thereof. Specifically, the signal processing apparatus may include units for performing the methods described in the foregoing aspects or any possible implementations thereof.

[0062] Optionally, the signal processing device can be either the aforementioned slave node or the aforementioned second master node.

[0063] Fifthly, embodiments of this application also provide a signal processing apparatus, comprising: a memory, at least one processor, a transceiver, and instructions stored in the memory and executable on the processor. Further, the memory, the processor, and the communication interface communicate with each other via an internal connection path. The at least one processor executes the instructions to cause the signal processing apparatus to implement the methods described in the foregoing aspects or any possible implementation thereof.

[0064] Optionally, the signal processing device can be either the aforementioned slave node or the aforementioned second master node.

[0065] Sixthly, this application also provides a computer-readable storage medium for storing a computer program that includes methods for implementing the foregoing aspects or any possible implementation thereof.

[0066] In a seventh aspect, embodiments of this application also provide a computer program product containing instructions that, when run on a computer, cause the computer to implement the methods described in the above aspects or any possible implementation thereof.

[0067] The signal processing method, signal processing device, computer storage medium, and computer program product provided in this embodiment are all used to execute the signal processing method provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the signal processing method provided above, and will not be repeated here. Attached Figure Description

[0068] Figure 1 This is a diagram of the power frequency waveform of an electric power line.

[0069] Figure 2 This is a hierarchical diagram of the distribution area;

[0070] Figure 3 A schematic diagram of a power network structure provided in an embodiment of this application;

[0071] Figure 4 This is a schematic diagram of the structure of a signal processing device provided in an embodiment of this application;

[0072] Figure 5 A schematic flowchart of a signal processing method provided in an embodiment of this application;

[0073] Figure 6 A schematic flowchart illustrating another signal processing method provided in an embodiment of this application;

[0074] Figure 7 A schematic flowchart illustrating another signal processing method provided in an embodiment of this application;

[0075] Figure 8 A schematic flowchart illustrating another signal processing method provided in an embodiment of this application;

[0076] Figure 9 This is a schematic diagram of the structure of a chip provided in an embodiment of this application. Detailed Implementation

[0077] 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, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0078] In this application, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0079] The terms "first" and "second," etc., used in the specification and drawings of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0080] Furthermore, the terms “comprising” and “having”, and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0081] It should be noted that in the description of the embodiments of this application, the words "exemplarily" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplarily" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0082] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0083] First, some of the terms used in this application will be explained to facilitate understanding by those skilled in the art.

[0084] Power frequency:

[0085] Power frequency can characterize the rated frequency used by power generation, transmission, transformation and distribution equipment, as well as industrial and civil electrical equipment in a power system. The unit is Hertz (HZ). At the standard power frequency of 50Hz, each power frequency cycle is 20 milliseconds (ms).

[0086] Zero crossing of power frequency:

[0087] The point in a power frequency cycle where the voltage or current is zero can be called a power frequency zero-crossing point. The power frequency waveform of a power line is as follows: Figure 1 As shown in the figure, this diagram illustrates the waveform of voltage change on a power line over time. It can be seen that there are two points where the voltage is zero within one power frequency cycle. Based on the different voltage trends at these zero-crossing points, they can be further divided into zero-crossing points at the rising edge and zero-crossing points at the falling edge. It can be understood that in the waveform of current change over time, the power frequency zero-crossing point is the point where the current is zero.

[0088] Power frequency cycle characteristics:

[0089] Generally, a zero-crossing point (such as the zero-crossing point of a voltage drop edge) is used as the starting point of each power frequency cycle. Power frequency cycle characteristics can be used to characterize changes in the power frequency zero-crossing point, also known as power frequency zero-crossing point time characteristics. Power frequency cycle characteristic data for a certain time period includes N consecutive power frequency zero-crossing point times collected within that time period, where N is a positive integer (e.g., 1000). Power frequency cycle characteristic data can be represented by a sequence of zero-crossing point times. For example, the power frequency cycle characteristic data for time 1 includes N consecutive power frequency zero-crossing point times collected starting from time 1, where these N power frequency zero-crossing point times are T1 to TN. Therefore, the power frequency cycle characteristic data for time 1 can be represented as T1, T2, ..., TN. Power frequency cycle characteristic data can also be represented by a power frequency cycle sequence. For example, the power frequency cycle characteristic data at time 1 includes N consecutive power frequency zero-crossing times collected starting from time 1. These N power frequency zero-crossing times are T1 to TN. Taking T1 as the network time base (NTB), the power frequency cycle characteristic data at time 1 can be represented as T2-T1, T3-T2, ..., TN-TN-1.

[0090] Level:

[0091] A hierarchy represents the communication distance between nodes within a transformer area and the area's master node. A transformer area can include multiple hierarchies. For example... Figure 2As shown, the master node of each distribution area is located at level 0, and the slave nodes directly connected to the master node in each distribution area are located at level 1. The slave nodes in each distribution area connected to the master node via one slave node are located at level 2. The level can also be determined based on other data, such as the signal strength between the slave and master nodes.

[0092] On actual power lines, due to various factors (wiring patterns, load variations, etc.), the position of the zero-crossing point of a node fluctuates within a very small range (typically on the order of microseconds). The patterns of these zero-crossing points vary between different transformer substations due to differences in wiring, load, and other environmental factors, but tend to be consistent within the same substation. Therefore, the substation to which a node belongs can be determined by the periodic characteristics of its power frequency.

[0093] In existing technology, master nodes collect their own power frequency cycle characteristic data and broadcast it to the power network. Slave nodes receive the power frequency cycle characteristic data broadcast by each master node and compare this data with their own power frequency cycle characteristic data collected at the same time to calculate the power frequency cycle characteristic similarity between themselves and each master node. Then, they determine the transformer area where the master node with the highest power frequency cycle characteristic similarity to themselves is located as their own transformer area.

[0094] However, the power frequency cycle characteristics of nodes change frequently, and the instantaneous similarity of power frequency cycle characteristics between slave nodes and master nodes of various transformer areas also fluctuates constantly. Current technical solutions do not synchronize the acquisition time of power frequency cycle characteristics of adjacent master nodes, resulting in low distinguishability of power frequency cycle characteristics at different times, leading to identification errors in some scenarios.

[0095] The following example illustrates the situation with multiple master nodes, including master node 1 and master node 2, where the slave nodes should actually belong to master node 1. When the power frequency cycle feature acquisition times of the two master nodes are not aligned (i.e., the acquisition times of the power frequency cycle features of master node 1 and master node 2 are not exactly the same), the similarity data of the power frequency cycle features calculated for the slave nodes is shown in Table 1.

[0096] Table 1

[0097] Similarity with master node 1 -- 80 30 -- -- 20 -- 30 Similarity with master node 2 85 -- -- 35 55 -- 85 --

[0098] As shown in Table 1, whether comparing adjacent data or calculating the average similarity of power frequency cycle features, the overall power frequency cycle feature similarity of master node 2 is higher. However, referring to the data in Table 2 after aligning the power frequency cycle feature acquisition times between master nodes, it can be seen that the power frequency cycle feature similarity between slave node and master node 1 is actually higher.

[0099] Table 2

[0100] Similarity with master node 1 90 80 30 40 60 20 90 30 Similarity with master node 2 85 75 25 35 55 15 85 25

[0101] In summary, the existing methods for identifying the relationship between households and transformers have low accuracy and are prone to misidentifying the relationship, which cannot provide a reliable basis for calculating line loss and significantly impacts the user experience.

[0102] Therefore, embodiments of this application provide a signal processing method and apparatus that can effectively improve the accuracy of household change relationship identification.

[0103] The signal processing method provided in this application is applicable to power grids. Figure 3 A schematic block diagram of the power network is shown, which includes two distribution areas, namely distribution area 1 and distribution area 2. Each distribution area includes one concentrator and N user equipments. Each concentrator is configured with one master node, and each user equipment is configured with one slave node, where N is a positive integer.

[0104] The aforementioned user equipment may be a smart meter, a data collector, or other devices; this application embodiment does not limit this.

[0105] The master node and slave node can communicate with each other. For example, the slave node can send power frequency cycle characteristic data to the master node via a power line carrier (PLC).

[0106] Master nodes in different transformer areas can communicate with each other in various ways. For example, master nodes in different transformer areas can communicate directly. Alternatively, master nodes in different transformer areas can communicate through slave nodes. This application does not limit this approach.

[0107] Optionally, the aforementioned master node can be integrated into the concentrator as a functional module or chip device. The master node can also exist as a standalone entity, i.e., it can be a separate device or apparatus. For example, the master node can be connected to the concentrator via a line (such as a data cable), or it can be connected to the concentrator via other means, such as a wireless network. This application does not impose any limitations on these embodiments.

[0108] Optionally, the aforementioned slave node can also be integrated as a functional module or chip device into other user equipment. The aforementioned slave node can also exist independently, that is, it can be a separate device or apparatus, etc. For example, the slave node can be connected to the user equipment via a line, or it can be connected to the user equipment via other means such as a wireless network, etc. This application embodiment does not impose any limitations.

[0109] In this embodiment of the application, the aforementioned wireless network may be Ethernet, radio access network (RAN), wireless local area network (WLAN), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared (IR), etc., and this embodiment of the application does not limit it.

[0110] It should be noted that the embodiments of this application are only illustrated by taking a power network including two transformer substations as an example. The number of transformer substations in the embodiments of this application can also be other values ​​(such as 10, 100, 1000, etc.), and the embodiments of this application do not limit this.

[0111] The signal processing method provided in this application embodiment can be executed by a signal processing device. This signal processing device can be... Figure 3 The main node of the station area or the device located in the main node; the signal processing device can also be... Figure 3 The slave nodes in the middle platform area are either the slave nodes or the devices set in the slave nodes. Figure 4 One hardware structure of the signal processing device is shown. For example... Figure 4 As shown, the signal processing device 400 may include a processor 401, a communication line 402, a memory 403, and a transmission interface 404.

[0112] The illustrated structure of this embodiment does not constitute a limitation on the signal processing device 400. It may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of both.

[0113] Processor 401 may include one or more processing units, such as an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). These different processing units may be independent devices or integrated into one or more processors.

[0114] The controller acts as the decision-maker, directing the various components of the signal processing device 400 to coordinate their operations according to instructions. It serves as the nerve center and command center of the signal processing device 400. Based on the instruction opcode and timing signals, the controller generates operation control signals to control the fetching and execution of instructions.

[0115] The processor 401 may also include a memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory, which can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from the memory. This avoids repeated accesses, reduces the processor's waiting time, and thus improves system efficiency.

[0116] In some embodiments, the processor 401 may include interfaces. Interfaces may include inter-integrated circuit (I2C) interfaces, inter-integrated circuit sound (I2S) interfaces, pulse code modulation (PCM) interfaces, universal asynchronous receiver / transmitter (UART) interfaces, mobile industry processor interfaces (MIPI), general-purpose input / output (GPIO) interfaces, subscriber identity module (SIM) interfaces, and / or universal serial bus (USB) interfaces, etc.

[0117] Communication line 402 is used to transmit information between the processor 401 and the memory 403.

[0118] The memory 403 is used to store instructions for execution by the computer and is controlled by the processor 401 for execution.

[0119] The memory 403 may exist independently and be connected to the processor via communication line 402. The memory 403 may be volatile memory or non-volatile memory, or may include both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), and enhanced synchronous dynamic random access memory (ESDRAM). It should be noted that the memory of the systems and devices described herein is intended to include, but is not limited to, these and any other memory suitable for the business type.

[0120] The transmission interface 404 is used for communication with other devices or communication networks. These communication networks can be Ethernet, radio access network (RAN), wireless local area network (WLAN), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared (IR), etc.

[0121] Figure 5This illustration shows a schematic flowchart of a signal processing method provided in an embodiment of this application. This method can be applied to, for example... Figure 3 The power network shown is composed of Figure 4 The signal processing device shown is executed at this time. Figure 4 The signal processing device shown is Figure 3 The transformer area shown is either a slave node or configured within a slave node. For example... Figure 5 As shown, the signal processing method includes:

[0122] S501, Obtain the first data from the node.

[0123] The first data mentioned above is the power frequency cycle characteristic data collected by the slave node within the target time period. The power frequency cycle characteristic data is used to indicate the cycle characteristics of the power grid operating frequency.

[0124] In this embodiment, the aforementioned power frequency cycle characteristic data includes N power frequency zero-crossing points, where N is a positive integer. The aforementioned target time period can be any time period.

[0125] Optionally, the aforementioned zero-crossing point of the power frequency may include at least one of the following: zero-crossing point of the rising edge of the power frequency voltage, zero-crossing point of the falling edge of the power frequency voltage, zero-crossing point of the rising edge of the power frequency current, or zero-crossing point of the falling edge of the power frequency current. For example, the aforementioned zero-crossing point of the power frequency may only include the zero-crossing point of the falling edge of the power frequency voltage.

[0126] For example, Table 3 shows the first data obtained by the slave node. Referring to Table 3, it can be seen that the first data includes the power frequency zero-crossing times (i.e., T11, T12, T13, T14 and T15) of five consecutive zero-crossing points (i.e., zero-crossing point 1, zero-crossing point 2, zero-crossing point 3, zero-crossing point 4 and zero-crossing point 5) collected by the slave node within the target time period.

[0127] Table 3

[0128] From the zero-crossing moment of the power frequency of the node T11 T12 T13 T14 T15

[0129] S502, Obtain multiple second data from the node.

[0130] The aforementioned secondary data includes the power frequency cycle characteristic data collected by each of the aforementioned master nodes within the aforementioned target time period. Each of the aforementioned secondary data corresponds one-to-one with each of the aforementioned master nodes. For example, if the master nodes include master node 1 and master node 2, and the secondary data includes second data 1 and second data 2, then master node 1 corresponds to second data 1, and master node 2 corresponds to second data 2.

[0131] For example, taking multiple master nodes including master node 1 and master node 2 as an example, Table 4 shows the second data 1 collected by master node 1 during the target time period and the second data 2 collected by master node 2 during the target time period. Referring to Table 4, it can be seen that the second data 1 includes the power frequency zero-crossing times of the five consecutive zero-crossing points collected by master node 1 during the target time period (i.e., T21, T22, T23, T24 and T25), and the second data 2 includes the power frequency zero-crossing times of the five consecutive zero-crossing points collected by master node 2 at the beginning of the target time period (i.e., T31, T32, T33, T34 and T35).

[0132] Table 4

[0133] Zero-crossing time of master node 1 T21 T22 T23 T24 T25 Zero-crossing time of master node 2 T31 T32 T33 T34 T35

[0134] In one possible implementation, the slave node can receive second data sent by multiple master nodes respectively. For example, the slave node can first receive second data 1 sent by master node 1, and then receive second data 2 sent by master node 2.

[0135] In one possible implementation, before acquiring the second data from the slave node, the slave node may also transmit first indication information to the multiple master nodes. This first indication information instructs the multiple master nodes to collect power frequency cycle characteristic data within the target time period.

[0136] In this embodiment of the application, there may be asynchronous master nodes among the multiple master nodes, that is, master nodes that cannot collect power frequency cycle characteristic data within the target time period. Therefore, the above-mentioned multiple second data may include second data collected outside the target time period.

[0137] S503, The slave node determines multiple first similarities based on the aforementioned first data and the aforementioned multiple second data.

[0138] Each of the aforementioned first similarities indicates the similarity between each of the aforementioned second data and the aforementioned first data. Each of the aforementioned first similarities corresponds one-to-one with the aforementioned master nodes.

[0139] For example, consider multiple master nodes, including master node 1 and master node 2. A slave node can determine a first similarity 1, representing the degree of similarity between the second data of master node 1 and the first data of the slave node, based on the first data collected by the slave node within the target time period in Table 5 and the second data 1 collected by master node 1 within the target time period in Table 5. Then, the slave node can determine a first similarity 2, representing the degree of similarity between the second data of master node 2 and the first data of the slave node, based on the first data collected by the slave node within the target time period in Table 5 and the second data 2 collected by master node 2 within the target time period in Table 5.

[0140] Table 5

[0141] The zero-crossing point of the power frequency at the node (first data) T11 T12 T13 T14 T15 Zero-crossing time of power frequency of master node 1 (second data 1) T21 T22 T23 T24 T25 Zero-crossing time of power frequency of master node 2 (second data 2) T31 T32 T33 T34 T35

[0142] In this embodiment, the slave node can determine the multiple similarities in various ways, and this embodiment does not limit the methods used. For example, the slave node can calculate the variance between each of the multiple second data and the first data, and then use the variance between each second data and the first data as the similarity corresponding to each master node. Alternatively, the slave node can calculate the similarity coefficient between each of the multiple second data and the first data, and then use the similarity coefficient between each second data and the first data as the similarity corresponding to each master node.

[0143] Optionally, S501 to S503 can be repeated multiple times from the node to determine multiple sets of first similarity.

[0144] In this embodiment of the application, when the slave node contains second data collected during a non-target time period among the above-mentioned multiple second data, the determination of the first similarity corresponding to the asynchronous master node needs to be based on the power frequency cycle feature data collected by the slave node and the asynchronous master node in the same time period.

[0145] S504. The slave node determines the target master node based on the above multiple first similarities.

[0146] In one possible implementation, the slave node can first determine whether the first master node meets the target conditions. Then, if the first master node meets the target conditions, the slave node determines the first master node as the target master node. Conversely, if the first master node does not meet the target conditions, the slave node obtains first information from multiple transformer substations and determines the target master node based on this information. The first master node can be the master node with the highest similarity among the multiple master nodes. The multiple transformer substations include the substations where the multiple master nodes are located.

[0147] Optionally, the first information may include at least one of the following: the lowest effective level, the signal-to-noise ratio (SNR) value, or the attenuation value.

[0148] It should be noted that a higher first similarity score for a master node indicates a higher similarity between the power frequency cycle characteristics of that master node and the slave node. In other words, a higher first similarity score for a master node indicates a higher probability that the master node and the slave node belong to the same distribution area. By determining whether the master node with the highest first similarity score among multiple master nodes (i.e., the aforementioned first master node) satisfies the above first condition, it can be determined that the power frequency cycle characteristic data of the aforementioned multiple master nodes have high discriminative power.

[0149] The fact that the first master node satisfies the first condition indicates that the power frequency cycle characteristic data of the multiple master nodes have high distinguishability. Therefore, when the first master node satisfies the first condition, it can be identified as the target master node.

[0150] Conversely, if the first master node does not meet the first condition, it indicates that the power frequency cycle characteristic data of the multiple master nodes does not have high distinguishability. Therefore, when the first master node does not meet the first condition, it may not be possible to accurately determine the target master node solely based on the power frequency cycle characteristic data of the multiple master nodes. It is necessary to determine the target node by using the first information of the transformer substations of the multiple master nodes to ensure accurate identification of the target master node.

[0151] In one possible implementation, the slave node determining whether the first master node meets the target condition may include: the slave node first determines a first difference based on the plurality of first similarities; when the first difference is greater than a first threshold, the slave node determines that the first master node meets the target condition; conversely, when the first difference is less than or equal to the first threshold, the slave node determines that the first master node does not meet the target condition. The first difference is the difference between the highest first similarity among the plurality of first similarities and the second highest first similarity among the plurality of first similarities.

[0152] It should be noted that when the first difference is greater than the first threshold, it means that the power frequency cycle characteristics of the other master nodes, except for the first master node, are not similar to those of the slave nodes. In this case, the power frequency cycle characteristics of each master node have a high degree of distinguishability. The relationship between households and transformers can be accurately identified through the power frequency cycle characteristics of multiple master nodes. Therefore, the first master node can be identified as the target master node.

[0153] Conversely, when the first difference is less than or equal to the first threshold, it indicates that among the multiple master nodes, there is a master node with a first similarity close to the first master node. In this case, the power frequency cycle characteristics of the master node are also very similar to those of the slave nodes. The master node may also be the target master node. At this time, the power frequency cycle characteristics do not have high distinguishability, and the relationship between households and transformers may not be accurately identified through the power frequency cycle characteristics of multiple master nodes.

[0154] For example, consider multiple master nodes including master node 1, master node 2, and master node 3, with the first threshold set to 5. The first similarity score (1) of master node 1 is 89, the first similarity score (2) of master node 2 is 87, and the first similarity score (3) of master node 3 is 80. It can be seen that among the three first similarities, first similarity score 1 is the highest, and first similarity score 2 is the second highest. Therefore, master node 1 can be determined as the first master node, and the first difference is 89-87=2. It can be seen that the first difference (2) is less than the first threshold (5). Therefore, this slave node can determine that master node 1 does not meet the above target condition.

[0155] In another possible implementation, the slave node determining whether the first master node meets the target condition may include: the slave node first determining multiple second similarities based on the multiple second data. Then, when all of the multiple second similarities are less than a second threshold, the slave node determines that the first master node meets the target condition; conversely, when none of the multiple second similarities are less than the second threshold, the slave node determines that the first master node does not meet the target condition. Here, the multiple second similarities are used to indicate the similarity between the second data of each master node (excluding the first master node) and the second data of the first master node.

[0156] It should be noted that if the above-mentioned second similarity is less than the above-mentioned second threshold, it means that the similarity between the power frequency cycle characteristics of the multiple master nodes other than the first master node and the power frequency cycle characteristics of the first master node is not very high. At this time, the power frequency cycle characteristics have high distinguishability. The relationship between households and transformers can be accurately identified through the power frequency cycle characteristics of multiple master nodes. Therefore, the first master node can be identified as the target master node.

[0157] Conversely, if the aforementioned second similarity values ​​are all greater than or equal to the aforementioned second threshold, it indicates that among the multiple master nodes other than the first master node, there is a master node whose power frequency cycle characteristics are similar to those of the first master node. In this case, the power frequency cycle characteristics of each master node do not have high distinguishability, and the relationship between households and transformers may not be accurately identified through the power frequency cycle characteristics of multiple master nodes.

[0158] For example, taking multiple master nodes including master node 1, master node 2, and master node 3, with master node 1 being the first master node and the aforementioned second threshold being 80, the slave node can determine, based on the aforementioned multiple second data, that the second similarity 1 (89) used to characterize the similarity between the power frequency cycle characteristics of master node 2 and the power frequency cycle characteristics of the first master node, and the second similarity 2 (75) used to characterize the similarity between the power frequency cycle characteristics of master node 3 and the power frequency cycle characteristics of the first master node. It can be seen that the second similarity 1 (89) is greater than the aforementioned second threshold (80). Therefore, the slave node can determine that the aforementioned master node 1 does not meet the aforementioned target condition.

[0159] In another possible implementation, the slave node can determine the first master node based on the above multiple sets of first similarity, and then when the first master node satisfies the above target conditions, the slave node determines the first master node as the target master node.

[0160] In this embodiment of the application, the slave node can determine the probability relationship between any two master nodes that are the first master node by the first similarity between any two master nodes in multiple sets of first similarity. When the probability of a certain master node being the first master node is higher than the probability of all master nodes other than that master node being the first master node, the master node is determined to be the first master node.

[0161] For example, taking multiple master nodes including master node 1 and master node 2, with first similarity scores of first similarity 1 and first similarity 2 respectively, or multiple sets of first similarity scores including three sets, the magnitudes of first similarity 1 and second similarity 2 in each of these three sets of first similarity scores can be compared. If first similarity 1 is greater than second similarity 2, the score of master node 1 is increased by 1 point; if first similarity 1 is less than second similarity 2, the score of master node 2 is increased by 1 point. After comparing the magnitudes of first similarity 1 and first similarity 2 in these three sets of first similarity scores, the relationship between the scores of master node 1 and master node 2 is determined. If the score of master node 1 is greater than the score of master node 2, the probability that master node 1 is the first master node is greater than the probability that master node 2 is the first master node; conversely, if the score of master node 2 is greater than the score of master node 1, the probability that master node 2 is the first master node is greater than the probability that master node 1 is the first master node. The initial values ​​of the scores of master node 1 and master node 2 are the same.

[0162] For example, taking multiple master nodes including master node 1 and master node 2, with first similarity scores of first similarity 1 and first similarity 2 respectively, and multiple sets of first similarity scores including three sets of first similarity scores as an example, the ratio of first similarity 1 to first similarity 2 in each of these three sets of first similarity scores is determined to obtain three ratios, and then the average of these three ratios is determined. If the average value is greater than 1, then the probability of master node 1 being the first master node is greater than the probability of master node 2 being the first master node; conversely, if the average value is less than 1, then the probability of master node 2 being the first master node is greater than the probability of master node 1 being the first master node.

[0163] In this embodiment of the application, for scenarios where there are asynchronous master nodes among multiple master nodes, when determining the probability relationship between any two master nodes as the first master node based on the first similarity values ​​corresponding to any two master nodes in multiple sets of first similarity values, it is necessary to first determine whether the second data of these two master nodes in the multiple second data corresponding to each set of first similarity values ​​are synchronous data. Specifically, if the interval between the start times of the collection periods of these two master nodes is less than a preset threshold, then the second data of these two master nodes are determined to be synchronous data. If the interval between the start times of the collection periods of these two master nodes is greater than a preset threshold, then the second data of these two master nodes are determined to be asynchronous data.

[0164] For example, consider multiple master nodes, including master node 1 and master node 2, with first similarity scores of first similarity 1 and first similarity 2 respectively. Alternatively, consider three sets of first similarity scores. The magnitudes of first similarity 1 and second similarity 2 within each of these three sets of first similarity scores can be compared.

[0165] If the first similarity score 1 is greater than the second similarity score 2, and the second data of the main node 1 and main node 2 corresponding to the first similarity score in this group are synchronized data, then the score of main node 1 is increased by 10 points; if the first similarity score 1 is less than the second similarity score 2, and the second data of the main node 1 and main node 2 corresponding to the first similarity score in this group are synchronized data, then the score of main node 2 is increased by 10 points; if the first similarity score 1 is greater than the second similarity score 2, and the second data of the main node 1 and main node 2 corresponding to the first similarity score in this group are asynchronous data, then the score of main node 1 is increased by 1 point .... If the second data for master node 1 and master node 2 corresponding to a similarity score are asynchronous, then the score of master node 1 is increased by 1 point. After comparing the scores of first similarity 1 and first similarity 2 in these three sets, the relationship between the scores of master node 1 and master node 2 is determined. If the score of master node 1 is greater than the score of master node 2, then the probability that master node 1 is the first master node is greater than the probability that master node 2 is the first master node; conversely, if the score of master node 2 is greater than the score of master node 1, then the probability that master node 2 is the first master node is greater than the probability that master node 1 is the first master node. The initial values ​​of the scores of master node 1 and master node 2 are the same.

[0166] If the second data of any one of the multiple master nodes is asynchronous with the second data of the first master node, the second similarity between the master node and the first master node can be determined as either the difference between the first similarity between the master node and the first master node.

[0167] In one possible implementation, the slave node obtains the first information of multiple transformer substations, including: the slave node first obtains multiple target packets, then the slave node determines the second information of multiple first nodes based on the multiple target packets, and finally the slave node determines the first information of the multiple transformer substations based on the second information of the multiple first nodes.

[0168] The target message refers to a message sent by multiple nodes in the aforementioned multiple transformer areas within a preset time period. The first node is the node among the multiple nodes that meets the first condition. The second information includes the transformer area identifier, level, SNR value, and attenuation value.

[0169] Optionally, the first condition mentioned above includes at least one of the following: the number of corresponding target packets is greater than the sixth threshold, the SNR value is greater than the seventh threshold, or the attenuation value is less than the eighth threshold.

[0170] In this embodiment of the application, the slave node can obtain a set of target data based on each acquired target message. As shown in Table 6, the target data may include a node identifier, a substation identifier, a level, an SNR value, and an attenuation value. The node identifier indicates the node that sent the target message, the substation identifier indicates the substation of the node that sent the target message, the level indicates the level of the node that sent the target message within the substation, the SNR value characterizes the SNR value of the target message during transmission, and the attenuation value characterizes the attenuation value of the target message during transmission.

[0171] Table 6

[0172] Node identifier Station signage hierarchy SNR value Attenuation value

[0173] In one possible implementation, when the slave node receives a target message, it will calculate the signal strength and noise intensity of the target message at the time of receipt, and determine the signal-to-noise ratio of the target message as the ratio of the signal strength to the noise intensity.

[0174] In one possible implementation, when the slave node receives the target message, it will count the signal strength of the target message when it is received. Since the signal strength of the message when it is transmitted is fixed and known, the slave node can determine the attenuation value of the message during transmission based on the signal strength of the message when it is transmitted and received.

[0175] Optionally, the slave node can determine the second information of the node based on the target messages sent by each node. For example, if node 1 sends multiple target messages, the slave node can determine the SNR value of node 1 based on the multiple SNR values ​​corresponding to the multiple messages, and determine the attenuation value of node 1 based on the multiple attenuation values ​​corresponding to the multiple messages. The slave node can determine the SNR value of the node by statistically averaging the multiple SNR values ​​(or attenuation values) corresponding to the multiple messages, statistically averaging over a recent period, statistically averaging over multiple time periods, or obtaining the real-time value through alpha filtering.

[0176] In one possible implementation, the slave node determines the first information of the plurality of transformer substations based on the second information of the plurality of first nodes, including: the slave node determining the lowest effective level of the first transformer substation based on the hierarchy of the plurality of first nodes in the first transformer substation; the slave node determining the SNR value of the first transformer substation based on the SNR values ​​of the plurality of first nodes in the first transformer substation; and the slave node determining the attenuation value of the first transformer substation based on the attenuation values ​​of the plurality of first nodes in the first transformer substation. Wherein, the first transformer substation is any one of the plurality of transformer substations. The lowest effective level of the first transformer substation is the hierarchy of the second node in the first transformer substation, and the second node in the first transformer substation is the node with the lowest hierarchy among the plurality of first nodes in the first transformer substation.

[0177] For example, consider a transformer substation 1 comprising 80 first nodes of level 1 and 160 first nodes of level 2. It can be seen that the lowest effective level of substation 1 is 1. The slave node determines the SNR value of level 1 of substation 1 based on the weighted average of the SNR values ​​of the 50% (40) of the first nodes with the highest SNR values ​​among the 80 level 1 first nodes. The slave node determines the SNR value of level 2 of substation 1 based on the weighted average of the SNR values ​​of the 50% (80) of the first nodes with the highest SNR values ​​among the 160 level 2 first nodes. Then, the SNR value of substation 1 is determined based on the weighted average of the SNR values ​​of level 1 and level 2 of substation 1. Finally, the slave node determines the attenuation value of level 1 of substation 1 based on the weighted average of the attenuation values ​​of the 50% (40) of the first nodes with the highest attenuation values ​​among the 80 level 1 first nodes. The attenuation value of transformer area 1 at level 2 is determined by the weighted average of the attenuation values ​​of the 50% (i.e., 80) of the first-level nodes with the largest attenuation values ​​among the 160 first-level nodes of level 2. Then, the attenuation value of transformer area 1 is determined by the weighted average of the attenuation values ​​of transformer area 1 at level 1 and transformer area 1 at level 2.

[0178] In another possible implementation, the slave node determines the first information of the plurality of transformer substations based on the second information of the plurality of first nodes, including: the slave node determines the lowest effective level of the first transformer substation based on the hierarchy of the plurality of first nodes of the first transformer substation; the slave node determines the SNR value of the first transformer substation based on the SNR value of the second node of the first transformer substation; and the slave node determines the attenuation value of the first transformer substation based on the attenuation value of the second node of the first transformer substation.

[0179] For example, consider a transformer substation 1 comprising 80 first nodes at level 1 and 160 first nodes at level 2. It can be seen that the lowest effective level of substation 1 is 1, and the first nodes at level 1 in substation 1 are the second nodes of substation 1. The slave node determines the SNR value of substation 1 based on the weighted average of the SNR values ​​of these 80 first nodes at level 1. The slave node determines the attenuation value of substation 1 based on the weighted average of the attenuation values ​​of these 80 first nodes at level 1.

[0180] In one possible implementation, determining the target master node based on the first information of the plurality of transformer substations includes: when only one second transformer substation exists among the plurality of substations, the slave node determines the master node of the second transformer substation as the target master node; conversely, when multiple second transformer substations exist among the plurality of substations, the slave node determines the target master node based on the SNR values ​​and attenuation values ​​of the multiple second transformer substations. Wherein, the second transformer substation is the substation with the smallest lowest effective level among the plurality of substations.

[0181] It should be noted that the lowest effective level of a transformer area can be used to characterize the communication distance between the transformer area and the slave node. The lower the lowest effective level of a transformer area, the closer the communication distance between the transformer area and the slave node. In other words, among multiple transformer areas, the transformer area with the lowest lowest effective level is the one with the closest communication distance to the slave node. When there is only one second transformer area among the above multiple transformer areas, it means that there is only one transformer area with the closest communication distance to the slave node. In this case, the lowest effective level of the transformer area has high distinguishability, so the master node of the second transformer area can be identified as the target master node.

[0182] However, when there are multiple second substations among the aforementioned multiple substations, it means that there are multiple substations with the closest communication distance to the slave node. In this case, the lowest effective level of the substation does not have high distinguishability, and the target master node may not be accurately determined based solely on the lowest effective level of the substation. In this situation, the slave node can determine the target master node based on the SNR value and attenuation value of the aforementioned multiple second substations.

[0183] For example, taking the aforementioned multiple transformer substations including substation 1, substation 2, and substation 3, where the lowest effective level of substation 1 is 1, the lowest effective level of substation 2 is 2, and the lowest effective level of substation 3 is 2, it can be seen that substation 1 has the lowest effective level among the aforementioned multiple transformer substations. Since the second transformer substation only includes substation 1, the slave node determines the master node of substation 1 as the target master node.

[0184] For example, consider the aforementioned multiple transformer substations including substation 1, substation 2, and substation 3, where the lowest effective level of substation 1 is 1, the lowest effective level of substation 2 is 1, and the lowest effective level of substation 3 is 2. It can be seen that substation 1 and substation 2 have the lowest effective levels among the multiple substations. Since the second substation includes multiple (2) substations, the slave node determines the target master node based on the SNR values ​​of these multiple substations (i.e., substation 1 and substation 2) and the attenuation values ​​of these multiple second substations.

[0185] In one possible implementation, the slave node can determine the target master node based on the SNR values ​​and attenuation values ​​of the multiple second transformer areas. This can include: the slave node determining a second difference based on the SNR values ​​of the multiple second transformer areas. If the second difference is greater than or equal to a third threshold, the slave node determines the target master node based on the SNR values ​​of the multiple second transformer areas. Conversely, if the second difference is less than the third threshold, the slave node determines the target master node based on the attenuation values ​​of the multiple second transformer areas. Here, the second difference is the difference between the SNR values ​​of the third transformer area and the fourth transformer area, where the third transformer area is the second transformer area with the largest SNR value, and the fourth transformer area is the transformer area with the second largest SNR value.

[0186] For example, when the second difference is greater than or equal to the third threshold, the slave node determines the target master node as the master node of the third transformer area; conversely, when the second difference is less than the third threshold, the slave node determines the target master node as the master node of the transformer area with the smaller attenuation value among the third transformer area and the fourth transformer area.

[0187] It should be noted that a higher SNR value for a transformer area indicates a stronger correlation between the transformer area and the slave node. When the second difference is greater than or equal to the third threshold, it means that there is a significant difference in SNR between the transformer area with the highest SNR value (i.e., the third transformer area) and the transformer area with the second highest SNR value (i.e., the fourth transformer area). In this case, the SNR values ​​of the multiple transformer areas have high distinguishability, so the target master node can be determined based on the SNR values ​​of the multiple transformer areas.

[0188] Conversely, when the second difference is less than the third threshold, it indicates that the SNR values ​​between the third and fourth substations are relatively similar, meaning the SNR values ​​of the multiple second substations do not have high distinguishability. The smaller the attenuation value of a substation, the smaller the attenuation between the substation and the slave node. Therefore, when the SNR values ​​of the multiple second substations do not have high distinguishability, the slave node can determine the target master node based on the attenuation values ​​of the multiple second substations.

[0189] In another possible implementation, the slave node can determine the target master node based on the SNR values ​​and attenuation values ​​of the multiple second transformer areas. This can include: if the attenuation value of the fifth transformer area is less than a fourth threshold, the slave node determines the target master node based on the attenuation values ​​of the multiple second transformer areas. Conversely, if the attenuation value of the fifth transformer area is greater than or equal to the fourth threshold, the slave node determines the target master node based on the SNR values ​​of the multiple second transformer areas. The fifth transformer area is the transformer area with the smallest attenuation value among the multiple second transformer areas.

[0190] Optionally, the fourth threshold can be 50 or other values, and this application embodiment does not limit this.

[0191] For example, when the attenuation value of the fifth distribution zone is less than the fourth threshold, the slave node determines the target master node as the master node of the fifth distribution zone; conversely, when the attenuation value of the fifth distribution zone is greater than or equal to the fourth threshold, the slave node determines the target master node as the master node of the sixth distribution zone. The sixth distribution zone is the distribution zone with the highest SNR value among the second distribution zones.

[0192] It should be noted that if the attenuation value of the fifth distribution area is less than the fourth threshold, it means that there are distribution areas among the multiple second distribution areas whose attenuation value is less than the fourth threshold. Since the attenuation value is less than the fourth threshold, it means that the attenuation value has a good correlation with the channel, that is, the attenuation value has a high degree of distinguishability. Therefore, the target master node can be determined based on the attenuation values ​​of the multiple second distribution areas.

[0193] Conversely, if the attenuation value of the fifth distribution area is greater than or equal to the fourth threshold, it indicates that there are no distribution areas among the multiple second distribution areas with an attenuation value less than the fourth threshold. In this case, the attenuation value does not have high distinguishability, and the target master node cannot be accurately determined based on the attenuation value. Therefore, the target master node can be determined based on the SNR values ​​of the multiple second distribution areas.

[0194] In another possible implementation, the slave node can determine the target master node from the plurality of second substations based on the SNR values ​​and attenuation values ​​of the plurality of second substations. This can include: if the SNR value of the sixth substation is greater than a fifth threshold, the slave node determines the target master node based on the SNR values ​​of the plurality of second substations; conversely, if the SNR value of the sixth substation is less than or equal to the fifth threshold, the slave node determines the target master node based on the attenuation values ​​of the plurality of second substations.

[0195] Optionally, the fifth threshold mentioned above can be 15 or other values, and this application embodiment does not limit this.

[0196] For example, when the SNR value of the sixth transformer area is greater than the fifth threshold, the slave node determines the target master node as the master node of the sixth transformer area; conversely, when the SNR value of the sixth transformer area is less than or equal to the fifth threshold, the slave node determines the target master node as the master node of the fifth transformer area.

[0197] It should be noted that if the SNR value of the sixth substation is greater than the fifth threshold, it means that there is a substation among the multiple second substations with an SNR value greater than the fifth threshold. An SNR value greater than the fifth threshold indicates that the SNR value has a good correlation with the channel. In other words, the SNR values ​​of the multiple second substations have high distinguishability. Therefore, the target master node can be determined based on the SNR values ​​of the multiple second substations.

[0198] Conversely, if the SNR value of the sixth distribution area is less than or equal to the fifth threshold, it indicates that there is no distribution area among the multiple second distribution areas with an SNR value greater than the fifth threshold. In this case, the SNR values ​​of the multiple second distribution areas do not have high distinguishability, and the target master node cannot be accurately determined based on the SNR value. Therefore, the target master node is determined based on the attenuation values ​​of the multiple second distribution areas.

[0199] As can be seen from S501 to S504, the signal processing method provided in this application embodiment can acquire power frequency cycle feature data collected by slave nodes and multiple master nodes within the same time period (i.e., the target time period), and then determine the target master node based on the power frequency cycle feature data collected by slave nodes and multiple master nodes within the same time period. Since the power frequency cycle feature data collected by multiple master nodes within the same time period has higher distinguishability than the power frequency cycle feature data collected by multiple master nodes within different time periods, the signal processing method provided in this application has higher accuracy than existing household transformer relationship identification methods, effectively improving the accuracy of household transformer relationship identification and providing a reliable basis for line loss calculation.

[0200] Figure 6This illustration shows a schematic flowchart of a signal processing method provided in an embodiment of this application. This method can be applied to, for example... Figure 3 The power network shown below. (The following is in conjunction with...) Figure 6 The communication interaction between the slave node and the master node in the signal processing method provided in this application embodiment is described in detail. The communication interaction between the slave node and the master node specifically includes the following steps:

[0201] S601, The first instruction information is transmitted from the slave node to multiple master nodes.

[0202] Specifically, the first indication information is used to instruct each of the plurality of master nodes to collect power frequency cycle characteristic data within the target time period. The aforementioned power frequency cycle characteristic data is used to indicate the cycle characteristics of the power grid's operating frequency.

[0203] For example, multiple master nodes include Figure 6 Taking master node 1 and master node 2 as an example, master node 1 sends the aforementioned first instruction information to the slave node, and the slave node receives the aforementioned first instruction information and forwards it to master node 2.

[0204] Optionally, the source sender of the first indication information can be the fourth master node among the aforementioned master nodes. That is, the first indication information is sent from the fourth master node to the slave node, and then forwarded by the slave node to the master nodes other than the fourth master node among the multiple master nodes. The fourth master node can be the master node with the largest medium access control (MAC) address among the aforementioned master nodes.

[0205] In one possible implementation, multiple master nodes can send each other a first message carrying their own collection time period and target information, and the multiple master nodes will update their own collection time period based on the first message received.

[0206] Optionally, the target information may include the MAC address and synchronization information sequence number pre-stored by each master node.

[0207] In one possible implementation, if the MAC address in the first message received by any of the multiple master nodes is greater than the MAC address pre-stored by that master node, the master node updates its own collection period to the collection period in the first message and updates the pre-stored MAC address to the MAC address in the first message and the synchronization information sequence number stored in the first message.

[0208] In another possible implementation, if the MAC address in the first message received by any of the aforementioned master nodes is equal to the MAC address pre-stored by the master node and the synchronization information sequence number in the first message is greater than the synchronization information sequence number stored by the master node, then the master node updates its own collection period to the collection period in the first message and updates the stored synchronization information sequence number to the synchronization information sequence number in the first message.

[0209] In another possible implementation, if the MAC address in the first message received by any of the aforementioned master nodes is equal to the MAC address pre-stored by that master node, and the synchronization information sequence number in the first message is less than or equal to the synchronization information sequence number stored by that master node, then that master node does not update its own collection time period and pre-stored MAC address.

[0210] In another possible implementation, if the MAC address in the first message received by any of the aforementioned master nodes is less than the MAC address pre-stored by that master node, that master node will not update its own collection time period and pre-stored MAC address.

[0211] It's worth noting that if the MAC address in the first message received by the master node is equal to the master node's own MAC address, it means that other master nodes are updating their own collection periods using the master node's collection period. Each time the master node sends the first message to other master nodes, it increments the synchronization information sequence number in the first message, allowing other master nodes to distinguish between new and old messages based on the sequence number.

[0212] It is understandable that by sending each other a first message carrying its own collection period and target information, multiple master nodes can synchronize their collection periods, thereby enabling them to collect data representing their own power frequency cycle characteristics within the same time period.

[0213] S602. The slave node and the aforementioned multiple master nodes collect their respective power frequency cycle characteristic data during the target time period.

[0214] S603, The first data is obtained from the node.

[0215] The first data mentioned above refers to the power frequency cycle characteristic data collected by the slave node during the target time period.

[0216] S604, The second data sent by multiple master nodes to the slave node.

[0217] Accordingly, the slave node receives multiple sets of second data.

[0218] The aforementioned secondary data includes the power frequency cycle characteristic data collected by each of the aforementioned master nodes within the aforementioned target time period. Each of the aforementioned secondary data corresponds one-to-one with each of the aforementioned master nodes.

[0219] S605, The slave node determines multiple first similarities based on the aforementioned first data and the aforementioned multiple second data.

[0220] Among them, the aforementioned multiple first similarities are used to indicate the similarity between each of the aforementioned multiple second data and the aforementioned first data, and the aforementioned multiple first similarities correspond one-to-one with the aforementioned multiple master nodes;

[0221] S606. The slave node determines the target master node based on the above multiple first similarities.

[0222] The specific implementation of S606 can be referred to the description of S504 above, and will not be repeated here.

[0223] Figure 7 This illustration shows a schematic flowchart of a signal processing method provided in an embodiment of this application. This method can be applied to, for example... Figure 3 The power network shown is composed of Figure 4 The signal processing device shown is executed at this time. Figure 7 The signal processing device shown is Figure 3 The shown area's main node is configured within the main node. For example... Figure 7 The signal processing method shown includes:

[0224] S701, the second master node obtains the first data.

[0225] The first data mentioned above is the power frequency cycle characteristic data collected by the slave node within the target time period. The power frequency cycle characteristic data is used to indicate the cycle characteristics of the power grid operating frequency.

[0226] S702, the second master node acquires multiple second data.

[0227] The aforementioned multiple second data include the power frequency cycle characteristic data collected by each of the multiple master nodes within the aforementioned target time period, the aforementioned multiple master nodes include the second master node, and the aforementioned multiple second data and the aforementioned multiple master nodes correspond one-to-one.

[0228] Optionally, before acquiring multiple sets of second data, the second master node may also send a second instruction message to the third master node. This second instruction message instructs the third master node to collect power frequency cycle characteristic data within the target time period. The third master node includes each of the multiple master nodes other than the second master node.

[0229] S703, the second master node determines multiple first similarities based on the aforementioned first data and the aforementioned multiple second data.

[0230] Each of the aforementioned first similarities is used to indicate the similarity between each of the aforementioned second data and the aforementioned first data, and the aforementioned first similarities correspond one-to-one with the aforementioned master nodes.

[0231] S704. The second master node determines the target master node based on the above multiple first similarities.

[0232] In one possible implementation, the second master node can first determine whether the first master node meets the target conditions. Then, if the first master node meets the target conditions, the second master node determines the first master node as the target master node. Conversely, if the first master node does not meet the target conditions, the second master node obtains first information about multiple transformer substations and determines the target master node based on the first information of the multiple transformer substations. The first master node is the master node with the highest first similarity among the multiple master nodes. The multiple transformer substations include the substations of each of the multiple master nodes.

[0233] Optionally, the first information may include at least one of the following: the lowest effective level, the SNR value, or the attenuation value.

[0234] In another possible implementation, the second master node can first determine whether the first master node meets the target conditions. Then, when the first master node meets the target conditions, the second master node determines the first master node as the target master node. Conversely, when the first master node does not meet the target conditions, it sends a first request to the slave node and receives a first result from the slave node. The first request is used to request the target result, and the target result is used to instruct the target master node.

[0235] In this embodiment, after receiving the first request from the second master node, the slave node first obtains first information about multiple transformer substations, then determines the target master node based on the first information of the multiple transformer substations, and then sends the target result to the second master node. The specific implementation method of the slave node determining the target master node based on the first information of the multiple transformer substations can be referred to the description in S504 above, and will not be repeated here.

[0236] In one possible implementation, the second master node determining whether the first master node meets the target condition may include: the second master node first determines a first difference based on the plurality of first similarities; when the first difference is greater than a first threshold, the second master node determines that the first master node meets the target condition; conversely, when the first difference is less than or equal to the first threshold, the second master node determines that the first master node does not meet the target condition. The first difference is the difference between the highest first similarity among the plurality of first similarities and the second highest first similarity among the plurality of first similarities.

[0237] In another possible implementation, the second master node determining whether the first master node meets the target condition may include: the second master node first determining multiple second similarities based on the multiple second data. Then, when all of the multiple second similarities are less than a second threshold, the second master node determines that the first master node meets the target condition; conversely, when none of the multiple second similarities are less than the second threshold, the second master node determines that the first master node does not meet the target condition. The multiple second similarities are used to indicate the similarity between the second data of each master node (excluding the first master node) and the second data of the first master node.

[0238] In one possible implementation, the second master node obtains first information about multiple transformer substations by: sending a second request to the slave node, and then receiving the first information about the multiple transformer substations sent by the slave node. The second request is used to request the first information about the multiple transformer substations.

[0239] In this embodiment of the application, after receiving the second request sent by the second master node, the slave node will send the first information of the plurality of transformer substations to the second master node.

[0240] In one possible implementation, determining the target master node based on the first information of the plurality of distribution areas includes: when only one second distribution area exists among the plurality of distribution areas, the second master node determines the master node of the second distribution area as the target master node; conversely, when multiple second distribution areas exist among the plurality of distribution areas, the second master node determines the target master node based on the SNR values ​​and attenuation values ​​of the multiple second distribution areas. Wherein, the second distribution area is the distribution area with the smallest lowest effective level among the plurality of distribution areas.

[0241] In one possible implementation, the second master node can determine the target master node based on the SNR values ​​and attenuation values ​​of the multiple second transformer areas. This can include: the second master node determining a second difference based on the SNR values ​​of the multiple second transformer areas. If the second difference is greater than or equal to a third threshold, the second master node determines the target master node based on the SNR values ​​of the multiple second transformer areas; conversely, if the second difference is less than the third threshold, the second master node determines the target master node based on the attenuation values ​​of the multiple second transformer areas. Here, the second difference is the difference between the SNR value of the third transformer area and the SNR value of the fourth transformer area, where the third transformer area is the one with the largest SNR value among the multiple second transformer areas, and the fourth transformer area is the one with the second largest SNR value among the multiple second transformer areas.

[0242] In another possible implementation, the second master node can determine the target master node from the plurality of second substations based on the SNR values ​​and attenuation values ​​of the plurality of second substations. This can include: if the attenuation value of the fifth substation is less than a fourth threshold, the second master node determines the target master node based on the attenuation values ​​of the plurality of second substations. Conversely, if the attenuation value of the fifth substation is greater than or equal to the fourth threshold, the second master node determines the target master node based on the SNR values ​​of the plurality of second substations. The fifth substation is the substation with the smallest attenuation value among the plurality of second substations.

[0243] In another possible implementation, the second master node can determine the target master node from the plurality of second transformer substations based on the SNR values ​​and attenuation values ​​of the plurality of second transformer substations. This can include: if the SNR value of the sixth transformer substation is greater than a fifth threshold, the second master node determines the target master node based on the SNR values ​​of the plurality of second transformer substations; conversely, if the SNR value of the sixth transformer substation is less than or equal to the fifth threshold, the second master node determines the target master node based on the attenuation values ​​of the plurality of second transformer substations. Wherein, the sixth transformer substation is the substation with the highest SNR value among the aforementioned second transformer substations.

[0244] Figure 8 This illustration shows a schematic flowchart of a signal processing method provided in an embodiment of this application. This method can be applied to, for example... Figure 3 The power network shown below. (The following is in conjunction with...) Figure 8 The communication interaction between the slave node and the master node in the signal processing method provided in this application embodiment is described in detail. The communication interaction between the slave node and the master node specifically includes the following steps:

[0245] S801, the second master node sends the second instruction information to the third master node.

[0246] The second instruction information is used to instruct each of the third master nodes to collect power frequency cycle characteristic data within the target time period. The third master node includes each of the multiple master nodes except for the second master node.

[0247] For example, the second master node is Figure 8 The primary node 1 shown, the third primary node includes Figure 8 As shown in the diagram, Master Node 1 sends a second instruction message to Master Node 2.

[0248] S802. The aforementioned master nodes and slave nodes collect their respective power frequency cycle characteristic data during the target time period.

[0249] Among them, the aforementioned multiple master nodes include the second master node.

[0250] S803: The slave node sends the first data to the second master node.

[0251] Accordingly, the second master node receives the first data.

[0252] The first data mentioned above is the power frequency cycle characteristic data collected from the node during the target time period.

[0253] S804, The second data sent by the third master node to the second master node.

[0254] Accordingly, the second master node receives the second data sent by the aforementioned third master node.

[0255] For example, the second master node is Figure 8 The primary node 1 shown, the third primary node includes Figure 8 As shown in the diagram, Master Node 2 sends the second data to Master Node 1.

[0256] Optionally, the third master node can send the second data directly to the second master node, or the third master node can send the second data to the second master node through the slave node. That is, the third master node first sends the second data to the slave node, and then the slave node forwards it to the second master node.

[0257] S805, the second master node determines multiple first similarities based on the aforementioned first data and multiple second data.

[0258] The aforementioned plurality of second data includes the power frequency cycle characteristic data collected by each of the plurality of master nodes within the aforementioned target time period. Each of the plurality of second data corresponds one-to-one with each of the plurality of master nodes, which includes the second master node and the third master node. The plurality of first similarities are used to indicate the similarity between each of the plurality of second data and the aforementioned first data, and each of the plurality of first similarities corresponds one-to-one with the plurality of master nodes.

[0259] S806, The second master node determines the target master node based on the above multiple first similarities.

[0260] The specific implementation of S806 can be referred to the description of S704 above, and will not be repeated here.

[0261] This application also provides a computer storage medium storing computer instructions. When the computer instructions are executed on a signal processing device, the signal processing device performs the aforementioned related method steps to implement the signal processing method described above.

[0262] This application also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned related steps to implement the signal processing method described above.

[0263] This application also provides a signal processing apparatus, which may specifically be a chip, integrated circuit, component, or module. Specifically, the apparatus may include a connected processor and a memory for storing instructions, or the apparatus may include at least one processor for fetching instructions from external memory. When the apparatus is running, the processor can execute the instructions to cause the chip to perform the signal processing methods described in the above method embodiments.

[0264] Please see Figure 9 , Figure 9 This is a schematic diagram of another signal processing device provided in an embodiment of this application. The signal processing device can be the aforementioned slave node or the aforementioned second master node. The signal processing device 900 includes: at least one CPU, memory (the type of memory may include, for example, SRAM and ROM), a microcontroller unit (MCU), a WLAN subsystem, a bus, a transmission interface, etc. Although... Figure 9 As not shown, the signal processing device 900 may also include an application processor (AP), an NPU or other dedicated processors, as well as other subsystems such as a power management subsystem, a clock management subsystem, and a power consumption management subsystem.

[0265] The various parts of the signal processing device 900 are coupled together by connectors. For example, the connectors include various interfaces, transmission lines or buses, etc. These interfaces are usually electrical communication interfaces, but may also be mechanical interfaces or other forms of interfaces. This embodiment does not limit them.

[0266] Optionally, the CPU can be a single-core or multi-core processor; alternatively, the CPU can be a processor group consisting of multiple processors, which are coupled to each other through one or more buses. In one optional case, the CPU implements any of the signal processing methods described in the above method embodiments by calling program instructions stored in the aforementioned on-chip memory or off-chip memory. In one optional case, the CPU and MCU jointly implement any of the signal processing methods described in the above method embodiments; for example, the CPU completes some steps of the signal processing method, while the MCU completes other steps. In one optional case, the AP or other dedicated processor implements any of the signal processing methods described in the above method embodiments by calling program instructions stored in the aforementioned on-chip memory or off-chip memory.

[0267] This transmission interface can serve as the interface for the processor chip to receive and send data. It typically includes multiple interfaces; optionally, it may include an Inter-Integrated Circuit (I2C) interface, a Serial Peripheral Interface (SPI), a Universal Asynchronous Receiver-Transmitter (UART) interface, or a General-Purpose Input / Output (GPIO) interface. It should be understood that these interfaces can achieve different functions by multiplexing the same physical interface.

[0268] In an alternative embodiment, the transmission interface may also include High Definition Multimedia Interface (HDMI), V-By-One interface, Embedded Display Port (eDP), Mobile Industry Processor Interface (MIPI), or Display Port (DP), etc.

[0269] In one alternative scenario, the aforementioned components are integrated onto the same chip; in another alternative scenario, the memory may be a separate chip.

[0270] A WLAN subsystem may include, for example, radio frequency circuitry and a baseband.

[0271] The chip involved in this application embodiment is a system manufactured on the same semiconductor substrate using integrated circuit technology, also called a semiconductor chip. It can be a collection of integrated circuits formed on a substrate (usually a semiconductor material such as silicon) using integrated circuit technology, and its outer layer is typically encapsulated by semiconductor packaging materials. The aforementioned integrated circuits can include various functional devices, each including logic gates, metal-oxide-semiconductor (MOS) transistors, bipolar transistors, or diodes, and may also include other components such as capacitors, resistors, or inductors. Each functional device can operate independently or under the action of necessary driving software, and can realize various functions such as communication, computation, or storage.

[0272] In this embodiment, the signal processing device, computer storage medium, and computer program product are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0273] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0274] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. 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 implementation should not be considered beyond the scope of this application.

[0275] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0276] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed between the units may be through some interfaces; the indirect coupling or communication connection between the apparatuses or units may be electrical, mechanical, or other forms.

[0277] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0278] In addition, 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.

[0279] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion 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 instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0280] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included 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. A signal processing method, characterized in that, include: The first data is obtained from the node, which is the power frequency cycle characteristic data collected by the slave node within the target time period. The power frequency cycle characteristic data is used to indicate the cycle characteristics of the power grid operating frequency. The slave node acquires multiple second data, which includes the power frequency cycle characteristic data collected by each of the multiple master nodes within the target time period, and the multiple second data corresponds one-to-one with the multiple master nodes; The slave node determines multiple first similarities based on the first data and the multiple second data, and the multiple first similarities correspond one-to-one with the multiple master nodes; The slave node determines the target master node based on the plurality of first similarities, and the target master node is one of the plurality of master nodes; Wherein, the slave node determines the target master node based on the plurality of first similarities, including: The slave node determines whether the first master node meets the target condition. The first master node is the master node with the highest similarity among the plurality of master nodes. The target condition is used to indicate that the power frequency cycle feature data of the plurality of master nodes have high distinguishability. If the first master node satisfies the target condition, the slave node determines the first master node as the target master node; If the first master node does not meet the target condition, the slave node obtains the first information of multiple distribution areas and determines the target master node based on the first information of the multiple distribution areas. The multiple distribution areas include the distribution areas where the multiple master nodes are located. The first information includes the lowest effective level. The lower the lowest effective level, the closer the communication distance between the distribution area and the slave node.

2. The method according to claim 1, characterized in that, The power frequency cycle characteristic data includes N power frequency zero-crossing times, where N is a positive integer. The power frequency zero-crossing times include at least one of the following: the zero-crossing time of the rising edge of the power frequency voltage, the zero-crossing time of the falling edge of the power frequency voltage, the zero-crossing time of the rising edge of the power frequency current, or the zero-crossing time of the falling edge of the power frequency current.

3. The method according to claim 1, characterized in that, Before acquiring the plurality of second data, the method further includes: The slave node transmits first indication information to the plurality of master nodes, the first indication information being used to instruct each of the plurality of master nodes to collect the power frequency cycle characteristic data within the target time period.

4. The method according to claim 1, characterized in that, The process of determining whether the first master node meets the target conditions by the slave node includes: The slave node determines a first difference based on the plurality of first similarities, wherein the first difference is the difference between the highest first similarity among the plurality of first similarities and the second highest first similarity among the plurality of first similarities; If the first difference is greater than the first threshold, the slave node determines that the first master node satisfies the target condition; or, The slave node determines multiple second similarities based on the multiple second data, and the multiple second similarities are respectively used to indicate the similarity between the second data corresponding to each master node (excluding the first master node) and the second data corresponding to the first master node; If all of the multiple second similarities are less than the second threshold, the slave node determines that the first master node satisfies the target condition.

5. The method according to claim 4, characterized in that, The acquisition of first information for multiple transformer substations includes: The node acquires multiple target messages, which are messages sent by multiple nodes of the multiple transformer areas within a preset time period; The slave node determines the second information of multiple first nodes of the multiple distribution areas based on the multiple target messages. The second information includes distribution area identifier, level, SNR value and attenuation value. The first node is the node among the multiple nodes that meets the first condition. The slave node determines the first information of the plurality of transformer substations based on the second information of the plurality of first nodes.

6. The method according to claim 5, characterized in that, The first condition includes at least one of the following: The number of messages sent to the slave node within the preset time period is greater than the sixth threshold, the SNR value is greater than the seventh threshold, or the attenuation value is less than the eighth threshold.

7. The method according to claim 5, characterized in that, The slave node determines the first information of the plurality of transformer substations based on the second information of the plurality of first nodes, including: The slave node determines the lowest effective level of the first transformer area based on the levels of the multiple first nodes in the first transformer area, where the first transformer area is any one of the multiple transformer areas. The slave node determines the SNR value of the first transformer area based on the SNR values ​​of the multiple first nodes or the SNR value of the second node in the first transformer area, wherein the second node is the lowest level node among the multiple first nodes in the first transformer area. The slave node determines the attenuation value of the first transformer area based on the attenuation values ​​of multiple first nodes or the attenuation value of the second node in the first transformer area.

8. The method according to any one of claims 1 to 7, characterized in that, The step of determining the target master node based on the first information of the plurality of transformer substations includes: If there is only one second transformer area among the plurality of transformer areas, the slave node determines the master node of the second transformer area as the target master node, and the second transformer area is the transformer area with the smallest lowest effective level among the plurality of transformer areas; If there are multiple second stations among the multiple stations, the slave node determines the target master node based on the SNR value and attenuation value of the multiple second stations.

9. The method according to claim 8, characterized in that, The step of determining the target master node based on the SNR values ​​and attenuation values ​​of the plurality of second transformer areas includes: The slave node determines a second difference based on the SNR values ​​of the plurality of second transformer areas. The second difference is the difference between the SNR value of the third transformer area and the SNR value of the fourth transformer area. The third transformer area is the transformer area with the largest SNR value among the plurality of second transformer areas, and the fourth transformer area is the transformer area with the second largest SNR value among the plurality of second transformer areas. If the second difference is greater than or equal to the third threshold, the slave node determines the target master node based on the SNR values ​​of the plurality of second stations; If the second difference is less than the third threshold, the slave node determines the target master node based on the attenuation values ​​of the plurality of second stations. or, If the attenuation value of the fifth station area is less than the fourth threshold, the slave node determines the target master node based on the attenuation values ​​of the plurality of second station areas, wherein the fifth station area is the station area with the smallest attenuation value among the plurality of second station areas; If the attenuation value of the fifth station area is greater than or equal to the fourth threshold, the slave node determines the target master node based on the SNR values ​​of the plurality of second stations. or, If the SNR value of the sixth station area is greater than the fifth threshold, the slave node determination is based on the SNR values ​​of the plurality of second stations to determine the target master node, wherein the sixth station area is the station area with the largest SNR value among the plurality of second stations. If the SNR value of the sixth station area is less than or equal to the fifth threshold, the slave node determines the target master node based on the attenuation values ​​of the plurality of second stations.

10. A signal processing method, characterized in that, include: The second master node acquires the first data, which is the power frequency cycle characteristic data collected by the slave node within the target time period. The power frequency cycle characteristic data is used to indicate the cycle characteristics of the power grid operating frequency. The second master node acquires multiple second data, which includes the power frequency cycle characteristic data collected by each of the multiple master nodes within the target time period. The multiple master nodes include the second master node, and the multiple second data corresponds one-to-one with the multiple master nodes. The second master node determines multiple first similarities based on the first data and the multiple second data, and the multiple first similarities correspond one-to-one with the multiple master nodes; The second master node determines the target master node based on the plurality of first similarities, wherein the target master node is one of the plurality of master nodes; Wherein, the second master node determines the target master node based on the plurality of first similarities, including: The second master node determines whether the first master node meets the target conditions, and the first master node is the master node with the highest similarity among the plurality of master nodes; If the first master node meets the target condition, the second master node determines the first master node as the target master node; If the first master node does not meet the target condition, the second master node obtains the first information of multiple distribution areas and determines the target master node based on the first information of the multiple distribution areas. Alternatively, the second master node sends a first request to the slave node and receives a first result sent by the slave node. The multiple distribution areas include the distribution areas where the multiple master nodes are located. The first information includes the lowest valid level. The first request is used to request the target result. The target result is used to indicate the target master node. The lower the lowest valid level, the closer the communication distance between the distribution area and the slave node.

11. The method according to claim 10, characterized in that, The power frequency cycle characteristic data includes N power frequency zero-crossing times, where N is a positive integer. The zero-crossing times include at least one of the following: the zero-crossing time of the rising edge of the power frequency voltage, the zero-crossing time of the falling edge of the power frequency voltage, the zero-crossing time of the rising edge of the power frequency current, or the zero-crossing time of the falling edge of the power frequency current.

12. The method according to claim 10, characterized in that, Before the second master node acquires the plurality of second data, the method further includes: The second master node sends a second instruction message to the third master node. The second instruction message is used to instruct each master node in the third master node to collect the power frequency cycle characteristic data during the target time period. The third master node includes each master node other than the second master node among the plurality of master nodes.

13. The method according to claim 10, characterized in that, The second master node determines whether the first master node meets the target conditions, including: The second master node determines a first difference based on the plurality of first similarities, wherein the first difference is the difference between the highest first similarity among the plurality of first similarities and the second highest first similarity among the plurality of first similarities; If the first difference is greater than the first threshold, the second master node determines that the first master node satisfies the target condition; or, The second master node determines multiple second similarities based on the multiple second data, and the multiple second similarities are respectively used to indicate the similarity between the second data corresponding to each master node other than the first master node and the second data corresponding to the first master node; If all of the second similarities are less than the second threshold, the second master node determines that the first master node satisfies the target condition.

14. The method according to claim 13, characterized in that, The acquisition of first information for multiple transformer substations includes: The second master node sends a second request to the slave node, the second request being used to request the first information of the plurality of transformer areas; The second master node receives the first information of the multiple transformer areas sent by the slave node.

15. The method according to any one of claims 10 to 14, characterized in that, The step of determining the target master node based on the first information of the plurality of transformer substations includes: If there is only one second transformer area among the plurality of transformer areas, the second master node determines the master node of the second transformer area as the target master node, and the second transformer area is the transformer area with the smallest lowest effective level among the plurality of transformer areas; If there are multiple second stations among the multiple stations, the second master node determines the target master node based on the SNR value and attenuation value of the multiple second stations.

16. The method according to claim 15, characterized in that, The step of determining the target master node based on the SNR values ​​and attenuation values ​​of the plurality of second transformer areas includes: The second master node determines a second difference based on the SNR values ​​of the plurality of second stations. The second difference is the difference between the SNR value of the third station and the SNR value of the fourth station. The third station is the second station with the largest SNR value among the plurality of second stations, and the fourth station is the station with the second largest SNR value among the plurality of second stations. If the second difference is greater than or equal to the third threshold, the slave node determines the target master node based on the SNR values ​​of the plurality of second stations; If the second difference is less than the third threshold, the slave node determines the target master node based on the attenuation values ​​of the plurality of second stations. or, If the attenuation value of the fifth station area is less than the fourth threshold, the slave node determines the target master node based on the attenuation values ​​of the plurality of second station areas, wherein the fifth station area is the station area with the smallest attenuation value among the plurality of second station areas; If the attenuation value of the fifth station area is greater than or equal to the fourth threshold, the slave node determines the target master node based on the SNR values ​​of the plurality of second stations. or, If the SNR value of the sixth station area is greater than the fifth threshold, the slave node determination is based on the SNR values ​​of the plurality of second stations to determine the target master node, wherein the sixth station area is the station area with the largest SNR value among the plurality of second stations. If the SNR value of the sixth station area is less than or equal to the fifth threshold, the slave node determines the target master node based on the attenuation values ​​of the plurality of second stations.

17. A signal processing method, characterized in that, include: The slave node receives a first request sent by the second master node, the first request being used to request a target result, the target result being used to instruct the target master node; The slave node obtains first information about multiple distribution areas, which includes distribution areas where multiple master nodes are located. Each master node corresponds to one of the multiple distribution areas. The first information includes the lowest effective level. The lower the lowest effective level, the closer the communication distance between the distribution area and the slave node. The slave node determines the target master node based on the first information of the plurality of transformer areas, and the target master node is one of the plurality of master nodes; The slave node sends the target result to the second master node, and the plurality of master nodes includes the second master node.

18. The method according to claim 17, characterized in that, The process of obtaining the first information of multiple transformer substations from the node includes: The node acquires multiple target messages, which are messages sent by multiple nodes of the multiple transformer areas within a preset time period; The slave node determines the second information of multiple first nodes of the multiple distribution areas based on the multiple target messages. The second information includes distribution area identifier, level, SNR value and attenuation value. The first node is the node among the multiple nodes that meets the first condition. The slave node determines the first information of the plurality of transformer substations based on the second information of the plurality of first nodes.

19. The method according to claim 18, characterized in that, The first condition includes at least one of the following: The number of messages sent to the slave node within the preset time period is greater than the sixth threshold, the SNR value is greater than the seventh threshold, or the attenuation value is less than the eighth threshold.

20. The method according to claim 18, characterized in that, The slave node determines the first information of the plurality of transformer substations based on the second information of the plurality of first nodes, including: The slave node determines the lowest effective level of the first transformer area based on the levels of the multiple first nodes in the first transformer area, where the first transformer area is any one of the multiple transformer areas. The slave node determines the SNR value of the first transformer area based on the SNR values ​​of the multiple first nodes or the SNR value of the second node in the first transformer area, wherein the second node is the lowest level node among the multiple first nodes in the first transformer area. The slave node determines the attenuation value of the first transformer area based on the attenuation values ​​of multiple first nodes or the attenuation value of the second node in the first transformer area.

21. The method according to any one of claims 17 to 20, characterized in that, The slave node determines the target master node based on the first information of the plurality of transformer areas, including: If there is only one second transformer area among the plurality of transformer areas, the slave node determines the master node of the second transformer area as the target master node, and the second transformer area is the transformer area with the smallest lowest effective level among the plurality of transformer areas; If there are multiple second stations among the multiple stations, the slave node determines the target master node based on the SNR value and attenuation value of the multiple second stations.

22. The method according to claim 21, characterized in that, The slave node determines the target master node based on the SNR values ​​and attenuation values ​​of the plurality of second stations, including: The slave node determines a second difference based on the SNR values ​​of the plurality of second transformer areas. The second difference is the difference between the SNR value of the third transformer area and the SNR value of the fourth transformer area. The third transformer area is the transformer area with the largest SNR value among the plurality of second transformer areas, and the fourth transformer area is the transformer area with the second largest SNR value among the plurality of second transformer areas. If the second difference is greater than or equal to the third threshold, the slave node determines the target master node based on the SNR values ​​of the plurality of second stations; If the second difference is less than the third threshold, the slave node determines the target master node based on the attenuation values ​​of the plurality of second stations. or, If the attenuation value of the fifth station area is less than the fourth threshold, the slave node determines the target master node based on the attenuation values ​​of the plurality of second station areas, wherein the fifth station area is the station area with the smallest attenuation value among the plurality of second station areas; If the attenuation value of the fifth station area is greater than or equal to the fourth threshold, the slave node determines the target master node based on the SNR values ​​of the plurality of second stations. or, If the SNR value of the sixth station area is greater than the fifth threshold, the slave node determination is based on the SNR values ​​of the plurality of second stations to determine the target master node, wherein the sixth station area is the station area with the largest SNR value among the plurality of second stations. If the SNR value of the sixth station area is less than or equal to the fifth threshold, the slave node determines the target master node based on the attenuation values ​​of the plurality of second stations.

23. A signal processing apparatus, comprising at least one processor and an interface circuit, wherein the at least one processor and the interface circuit are coupled, characterized in that, The at least one processor executes a program or instructions stored in a memory to cause the signal processing apparatus to implement the method of any one of claims 1 to 9, any one of claims 10 to 16, or any one of claims 17 to 22.

24. A computer-readable storage medium for storing a computer program, characterized in that, The computer program includes instructions for implementing the method of any one of claims 1 to 9, any one of claims 10 to 16, or any one of claims 17 to 22.

25. A computer program product, the computer program product comprising instructions, characterized in that, When the instructions are executed on a computer or processor, the computer or processor performs the method of any one of claims 1 to 9, 10 to 16, or any one of claims 17 to 22.

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