Electric energy meter data acquisition method and system based on channel state dynamic scheduling

Abstract

The invention relates to the technical field of electric energy meter monitoring, in particular to an electric energy meter data acquisition method and system based on channel state dynamic scheduling. Comprising the steps of setting multiple interaction points according to distribution parameters of an electric energy meter; constructing a data interaction network according to all the interaction points, wherein the data interaction network comprises an uplink transmission network and a plurality of downlink transmission networks; and acquiring channel monitoring data of each interaction point, and setting a data acquisition strategy of each interaction point according to all the channel monitoring data and a preset acquisition control model. By additionally arranging an acquisition control model, rapid adaptation can be performed according to real-time channel states of interaction points, so that data acquisition parameters of each electric energy meter are adjusted, the channel utilization rate and the success rate of acquisition tasks are improved, a plurality of interaction points are set according to distribution parameters of the electric energy meters, and a standby uplink channel is established, so that the data acquisition efficiency is improved. The risk of regional data interruption caused by operation fault of a single interaction point or channel state fluctuation is avoided, and the data acquisition efficiency of the electric energy meter is improved.

Description

Technical Field

[0001] This application relates to the field of electricity meter monitoring technology, and in particular to an electricity meter data acquisition method and system based on dynamic channel state scheduling. Background Technology

[0002] In the construction of smart grids, electricity information collection systems bear the critical task of automatically and reliably aggregating massive amounts of electricity meter data. With the widespread adoption of smart meters and the rapid development of new electricity services (such as high-frequency data acquisition and real-time load monitoring), the number of electricity meters requiring access to the collection terminals has surged, and the data volume is growing exponentially, posing a severe challenge to the carrying capacity and reliability of communication networks. Traditional data acquisition schemes typically employ fixed-period polling or static scheduling strategies based on simple priority ranking. These methods fail to fully consider the fundamental impact of the time-varying characteristics of communication channels on data transmission performance.

[0003] In real-world operating environments, especially in scenarios relying on public wireless networks or complex private networks for remote communication, channel conditions are significantly affected by various factors such as environmental interference, network congestion, weather changes, and building obstructions, exhibiting marked randomness and dynamism. Existing static scheduling methods, even when channel quality deteriorates, continue to initiate communication in a predetermined order or period, which can easily lead to packet loss, excessive transmission delays, and increased communication failure rates, ultimately causing data integrity issues and data acquisition task timeouts. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for acquiring electricity meter data based on dynamic channel state scheduling in order to solve the above-mentioned technical problems, thereby improving the data acquisition efficiency and quality of electricity meters.

[0005] In some embodiments of this application, by adding a data acquisition control model, it is possible to quickly adapt to the real-time channel status of the interaction point, thereby adjusting the data acquisition parameters of each electricity meter to improve channel utilization and the success rate of acquisition tasks, and optimize network resource utilization efficiency.

[0006] In some embodiments of this application, multiple interaction points are set according to the distribution parameters of the electricity meter, and auxiliary interaction points are selected for each interaction point to establish a backup uplink channel, thereby improving the transmission quality of electricity meter data, avoiding the risk of regional data interruption due to the operation failure of a single interaction point or fluctuations in channel status, and improving the data acquisition efficiency of the electricity meter.

[0007] In some embodiments of this application, a method for acquiring electricity meter data based on dynamic channel state scheduling is provided, including:

[0008] Multiple interaction points are set according to the distributed parameters of the electricity meter; A data interaction network is constructed based on all interaction points, and the data interaction network includes an uplink transmission network and multiple downlink transmission networks; Acquire channel monitoring data from each interaction point, and set the data acquisition strategy for each interaction point based on all channel monitoring data and the preset acquisition control model.

[0009] In some embodiments of this application, the construction of the data interaction network includes: Establish a sequence of interaction points A, A=(a1,a2…ai…an), where ai is the i-th interaction point; n is the number of interaction points; Based on the sequence of interaction points A, ai is sequentially set as the target interaction point; A mapping device area for the target interaction point is generated based on the distribution parameters of the electricity meters, and the mapping device area includes multiple electricity meter nodes; Establish a downlink transmission network for the target interaction point based on the mapping device area; Select auxiliary interaction points for the target interaction point; Establish an uplink subnetwork for the target interaction point based on the auxiliary interaction points; Establish the downlink transmission network and uplink subnetwork for each interaction point in sequence; Establish an uplink transmission network based on all uplink subnetworks; A data interaction network is constructed based on the uplink transmission network and all downlink transmission networks.

[0010] In some embodiments of this application, establishing the uplink subnetwork of the target interaction point includes: Establish the first communication link between the target interaction point and the data platform; Generate the communication dependency values ​​between the target interaction point and each interaction point; Set the interaction point corresponding to the maximum value among all communication dependency values ​​as the auxiliary interaction point of the target interaction point; Establish a second communication link between the target interaction point and the auxiliary interaction point; An uplink subnetwork of the target interaction point is established based on the first and second communication links.

[0011] In some embodiments of this application, the preset acquisition and control model includes: Obtain historical monitoring data from electricity meters; Multiple data collection sub-tasks are set based on historical monitoring data; Establish a sequence H of subtasks for data collection, H=(h1,h2…hi…hm), where hi is the i-th subtask for data collection; m is the number of subtasks for data collection. Generate various channel characteristic indicators based on historical monitoring data; Establish a channel state sequence B, B=(b1, b2…bi…br) based on all channel characteristic indicators, where bi is the i-th channel state and r is the number of channel states; A downlink acquisition model is established based on the acquisition sub-task sequence H and the channel state sequence B; Construct an uplink transmission model based on the uplink transmission network; A data acquisition and control model is established, which includes a downlink data acquisition model and an uplink transmission model.

[0012] In some embodiments of this application, establishing the downlink acquisition model includes: Based on the sequence of subtasks H, hi is sequentially set as the target subtask; An evaluation sub-model for the target sub-tasks was established based on historical monitoring data; Based on the channel state sequence B, bi is sequentially set as the target state; Based on the evaluation sub-model, set the collection sub-strategy for the target sub-task in the target state; Sequentially set the acquisition sub-strategies for the target sub-tasks in each channel state; Set the downlink acquisition strategy for the target sub-task based on all acquisition sub-strategies; Configure the downlink acquisition strategy for each acquisition subtask in sequence; A downlink acquisition model is constructed based on all downlink acquisition strategies.

[0013] In some embodiments of this application, the setting of data acquisition strategies for each interaction point includes: Multiple preset acquisition cycles; Based on the interaction point sequence A, ai is sequentially set as the control point; Based on the data acquisition sub-task sequence H, set multiple data acquisition time nodes for the control points in the current data acquisition cycle; Generate a set of associated tasks for the control point at the current acquisition time node, wherein the set of associated tasks includes multiple associated sub-tasks; The downlink status of the control point at the current acquisition time node is set based on the channel monitoring data of the control point; Generate similarity values ​​between the downlink state and each channel state; The channel state corresponding to the maximum value among similar values ​​is set as the anchoring state; Generate a primary acquisition strategy for the control point based on the acquisition sub-strategies of each associated subtask in the anchored state. Set the primary transmission strategy for the control point based on the channel monitoring data of the control point; The data acquisition strategy for the control points is generated based on the primary acquisition strategy and the primary transmission strategy.

[0014] In some embodiments of the present application, generating an associated task set of the to-be-controlled point at the current acquisition time node includes: Sequentially setting hi as the to-be-evaluated subtask according to the acquisition subtask sequence H; Generating an execution association value c of the to-be-evaluated subtask at the to-be-controlled point at the current acquisition time node; Presetting an execution association value threshold C1; If c > C1, setting the to-be-evaluated subtask as the associated subtask of the to-be-controlled point at the current acquisition time node; Sequentially determining whether each acquisition subtask is an associated subtask of the to-be-controlled point at the current acquisition time node; Generating an associated task set according to the judgment result.

[0015] In some embodiments of the present application, setting the first-level transmission strategy of the to-be-controlled point includes: Obtaining a plurality of electric meter data packets according to the first-level acquisition strategy; Generating a first uplink value f1 according to the channel monitoring data of the to-be-controlled point; Presetting an uplink value threshold F1; If f1 > F1, generating a first-level transmission instruction; If f1 < F1, generating a second uplink value f2 of the to-be-controlled point. When f2 > F1, generating a second-level transmission instruction; when f2 < F1, generating a third-level transmission instruction.

[0016] In some embodiments of the present application, there is provided an electric meter data acquisition system based on dynamic channel state scheduling, including: An acquisition unit, including a plurality of acquisition sub-modules, where the acquisition unit is used to acquire the operation data of each electric meter; A central control unit, used to set a plurality of interaction points according to the distribution parameters of the electric meters; The central control unit includes a plurality of control sub-modules, and the control sub-modules are arranged at each interaction point; A monitoring unit, including a plurality of monitoring sub-modules, where the monitoring unit is used to acquire the channel monitoring data of each interaction point; The central control unit further includes: A first processing module, used to construct a data interaction network according to all interaction points, and the data interaction network includes an uplink transmission network and a plurality of downlink transmission networks; A second processing module, used to set the data acquisition strategy of each interaction point according to all channel monitoring data and a preset acquisition control model.

[0017] In some embodiments of the present application, the first processing module is further used for: Establish a sequence of interaction points A, A=(a1,a2…ai…an), where ai is the i-th interaction point; n is the number of interaction points; Based on the sequence of interaction points A, ai is sequentially set as the target interaction point; A mapping device area for the target interaction point is generated based on the distribution parameters of the electricity meters, and the mapping device area includes multiple electricity meter nodes; Establish a downlink transmission network for the target interaction point based on the mapping device area; Select auxiliary interaction points for the target interaction point; Establish an uplink subnetwork for the target interaction point based on the auxiliary interaction points; Establish the downlink transmission network and uplink subnetwork for each interaction point in sequence; Establish an uplink transmission network based on all uplink subnetworks; A data interaction network is constructed based on the uplink transmission network and all downlink transmission networks.

[0018] Compared with existing technologies, the beneficial effects of the energy meter data acquisition method and system based on channel state dynamic scheduling proposed in this application are as follows: By adding a data acquisition and control model, it is possible to quickly adapt to the real-time channel status of the interaction points, thereby adjusting the data acquisition parameters of each electricity meter to improve channel utilization and the success rate of acquisition tasks, and optimize network resource utilization efficiency.

[0019] Multiple interaction points are set according to the distributed parameters of the electricity meter, and auxiliary interaction points are selected for each interaction point to establish a backup uplink channel. This improves the transmission quality of electricity meter data, avoids the risk of regional data interruption due to the operation failure of a single interaction point or fluctuations in channel status, and improves the data acquisition efficiency of the electricity meter. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a preferred embodiment of a method for collecting electricity meter data based on dynamic channel state scheduling. Detailed Implementation

[0021] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0022] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0023] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0024] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0025] like Figure 1 As shown in the preferred embodiment of this application, a method for collecting electricity meter data based on dynamic channel state scheduling includes: S101: Set multiple interaction points according to the distributed parameters of the electricity meter; S102: Construct a data interaction network based on all interaction points. The data interaction network includes an uplink transmission network and multiple downlink transmission networks. S103: Acquire channel monitoring data from each interaction point, and set the data acquisition strategy for each interaction point based on all channel monitoring data and the preset acquisition control model.

[0026] Specifically, the distributed parameters refer to the location data of all electricity meters that need to be collected. Multiple interaction points are set according to the distributed parameters, and each interaction point is equipped with a concentrator. Each interaction point is used to periodically read the operating data (such as daily frozen electricity, real-time load, and event logs) of each electricity meter within the corresponding mapped device area.

[0027] Specifically, building a data interaction network includes: Establish a sequence of interaction points A, A=(a1,a2…ai…an), where ai is the i-th interaction point; n is the number of interaction points; Based on the sequence of interaction points A, ai is sequentially set as the target interaction point; The mapping device area of ​​the target interaction point is generated based on the distribution parameters of the electricity meter. The mapping device area includes multiple electricity meter nodes. Establish a downlink transmission network for the target interaction point based on the mapping device area; Select auxiliary interaction points for the target interaction point; Establish an uplink subnetwork for the target interaction point based on the auxiliary interaction points; Establish the downlink transmission network and uplink subnetwork for each interaction point in sequence; Establish an uplink transmission network based on all uplink subnetworks; A data interaction network is constructed based on the uplink transmission network and all downlink transmission networks.

[0028] Specifically, based on the distribution parameters of the electricity meter, each transformer in the required monitoring area is set as an interaction point, and the mapping device area of ​​each interaction point is the power supply range corresponding to that transformer.

[0029] Specifically, multiple energy meter nodes are set according to the energy meter parameters in the mapping equipment area, where a single energy meter node represents an energy meter in the mapping equipment area.

[0030] Specifically, a communication channel is established between the concentrator of the target interaction point and the collectors corresponding to each electricity meter node within the corresponding mapping device area, thereby building a downlink transmission network for the target interaction point. Through the downlink transmission network, the target interaction point can periodically query each electricity meter within the mapping device area and obtain the operating data of the electricity meters collected by the collectors of each electricity meter node.

[0031] Specifically, establishing the uplink subnetwork of the target interaction point includes: Establish the first communication link between the target interaction point and the data platform; Generate the communication dependency values ​​between the target interaction point and each interaction point; Set the interaction point corresponding to the maximum value among all communication dependency values ​​as the auxiliary interaction point of the target interaction point; Establish a second communication link between the target interaction point and the auxiliary interaction point; An uplink subnetwork of the target interaction point is established based on the first and second communication links.

[0032] Specifically, the data platform is the data center of the main station system, and the first communication link refers to the communication channel between the concentrator of the target interaction point and the data platform. Through the first communication link, the operating data of the electricity meter in the concentrator of the target interaction point can be transmitted to the data platform, that is, the data center of the main station system.

[0033] Specifically, the first dependency value between the target interaction point and the current interaction point is set based on the communication volatility between the target interaction point and the current interaction point (i.e., the volatility of the communication link between the target interaction point and the current interaction point). The higher the communication volatility (i.e., the more unstable the communication between the two), the smaller the corresponding first dependency value. The mapping relationship between the two can be set according to historical parameters.

[0034] Specifically, a second dependency value is generated between the target interaction point and the current interaction point based on the average data collection volume within the mapped device area corresponding to the current interaction point (i.e., the average amount of data required to collect at a single collection time node) and the number of times the current interaction point is selected (i.e., the number of interaction points preceding the target interaction point that use the current interaction point as an auxiliary interaction point). The larger the average data collection volume and the more times it is selected (i.e., the greater its own data transmission pressure), the smaller the corresponding second dependency value. The mapping relationship between the two can be set according to historical parameters, and the value ranges corresponding to the first dependency value and the second dependency value are the same.

[0035] Specifically, communication dependency values ​​for the target interaction point and the current interaction point are generated based on the weighted result of the first dependency value and the second dependency value. The weight coefficients of the first dependency value and the second dependency value can be set according to historical parameters, and the sum of the weight coefficients of the two is 1.

[0036] Specifically, the larger the communication dependency value, the more stable the communication between the current interaction point and the target interaction point, and the higher the feasibility of the current interaction point as a backup communication channel for the target interaction point.

[0037] Specifically, the second communication link refers to the communication channel between the concentrator at the target interaction point and the concentrator at the auxiliary interaction point. Through the second communication link, the energy meter operation data in the concentrator at the target interaction point can be transmitted to the auxiliary interaction point, and then transmitted to the data platform through the auxiliary interaction point.

[0038] It is understood that in the above embodiments, multiple interaction points are set according to the distribution parameters of the electricity meter, and auxiliary interaction points are selected for each interaction point to establish a backup uplink channel, thereby improving the transmission quality of electricity meter data, avoiding the risk of regional data interruption due to the operation failure of a single interaction point or fluctuations in channel status, and improving the data acquisition efficiency of the electricity meter.

[0039] In a preferred embodiment of this application, the preset acquisition and control model includes: Obtain historical monitoring data from electricity meters; Multiple data collection sub-tasks are set based on historical monitoring data; Establish a sequence H of subtasks for data collection, H=(h1,h2…hi…hm), where hi is the i-th subtask for data collection; m is the number of subtasks for data collection. Generate various channel characteristic indicators based on historical monitoring data; Establish a channel state sequence B, B=(b1, b2…bi…br) based on all channel characteristic indicators, where bi is the i-th channel state and r is the number of channel states; A downlink acquisition model is established based on the acquisition sub-task sequence H and the channel state sequence B; Construct an uplink transmission model based on the uplink transmission network; Establish an acquisition and control model, which includes a downlink acquisition model and an uplink transmission model.

[0040] Specifically, historical monitoring data includes operational data collected from various electricity meters, channel status data during transmission, and transmission-related data (success rate, delay rate, packet loss rate, etc.) under different channel conditions.

[0041] Specifically, by analyzing the operational data collected by the electricity meter, various data collection sub-tasks are generated, where each sub-task represents a category of operational data.

[0042] Specifically, the operational data includes, but is not limited to: basic metering data (cumulative power, real-time voltage, real-time current, etc.), event log data (opening event, voltage loss / phase loss / current loss event, voltage over-limit / current over-limit, time synchronization event, etc.), status data (power, voltage, current, etc. parameters frozen and stored at fixed time intervals), and quality data (voltage / current harmonics, voltage deviation, frequency deviation, voltage imbalance, etc.).

[0043] Specifically, channel characteristic indicators include, but are not limited to, multiple parameters reflecting the channel state such as received signal strength, signal-to-noise ratio, bit error rate, communication success rate, and round-trip delay. By quantifying each channel characteristic indicator, the reference values ​​of each channel characteristic indicator are made to be within the same range. Multiple value intervals for each channel characteristic indicator are set sequentially, and multiple channel states are established based on random combinations of all value intervals. Among these, the value intervals of each channel characteristic indicator corresponding to any two channel states are not exactly the same.

[0044] Specifically, the uplink transmission model is used to determine whether the data at each interaction point is transmitted to the data platform via the first communication link or via the second communication link.

[0045] Specifically, establishing a downlink acquisition model includes: Based on the sequence of subtasks H, hi is sequentially set as the target subtask; An evaluation sub-model for the target sub-tasks was established based on historical monitoring data; Based on the channel state sequence B, bi is sequentially set as the target state; Based on the evaluation sub-model, set the collection sub-strategy for the target sub-task in the target state; Sequentially set the acquisition sub-strategies for the target sub-tasks in each channel state; Set the downlink acquisition strategy for the target sub-task based on all acquisition sub-strategies; Configure the downlink acquisition strategy for each acquisition subtask in sequence; A downlink acquisition model is constructed based on all downlink acquisition strategies.

[0046] Specifically, multiple energy meter evaluation indicators are set based on historical monitoring data. These indicators include, but are not limited to, energy meter failure frequency, cumulative running time, fluctuation of basic data (i.e., the fluctuation of each basic data point within a unit of time), and task execution interval (i.e., the time interval between the current energy meter's last execution of the current data acquisition sub-task and the current time point). By quantifying each energy meter evaluation indicator, the reference values ​​of each indicator are kept within the same range.

[0047] Specifically, an evaluation sub-model is constructed based on the data types to be collected for the target sub-task. The evaluation sub-model sets the weight coefficients for each energy meter evaluation index, and the weight coefficients for each energy meter evaluation index are set according to their correlation with the target sub-task. The greater the correlation, the greater the corresponding weight coefficient (for example, if the target sub-task is to collect event record data, then the weight coefficients for energy meter evaluation indices that map energy meter faults, such as fault frequency and cumulative running time, are greater).

[0048] Specifically, by establishing an evaluation sub-model, the collected evaluation values ​​of each electricity meter (i.e., the weighted result of the real-time reference values ​​of each electricity meter's evaluation indicators) are generated when the target sub-task is executed. The larger the collected evaluation value, the higher the collection priority of the electricity meter.

[0049] Specifically, by analyzing historical monitoring data related to the target state, the percentage of electricity meters that need to be delayed in collection under the target state is generated. Based on the set percentage of electricity meters that need to be delayed in collection and the evaluation sub-model, the collection sub-strategy of the target sub-task under the target state is generated.

[0050] It is understood that, in the above embodiments, by adding a data acquisition and control model, it is possible to quickly adapt to the real-time channel status of the interaction points, thereby adjusting the data acquisition parameters of each electricity meter to improve channel utilization and the success rate of acquisition tasks, and optimizing network resource utilization efficiency.

[0051] In a preferred embodiment of this application, a data acquisition strategy is set for each interaction point, including: Multiple preset acquisition cycles; Based on the interaction point sequence A, ai is sequentially set as the control point; Based on the data acquisition sub-task sequence H, set multiple data acquisition time nodes for the control points in the current data acquisition cycle; Generate a set of associated tasks for the control point at the current data collection time node. The set of associated tasks includes multiple associated subtasks. The downlink status of the control point at the current acquisition time node is set based on the channel monitoring data of the control point; Generate similarity values ​​between the downlink state and each channel state; The channel state corresponding to the maximum value among similar values ​​is set as the anchoring state; Generate a primary acquisition strategy for the control point based on the acquisition sub-strategies of each associated subtask in the anchored state. Set the primary transmission strategy for the control point based on the channel monitoring data of the control point; The data acquisition strategy for the control points is generated based on the primary acquisition strategy and the primary transmission strategy.

[0052] Specifically, the duration of the data collection period can be set according to historical parameters, and in this application, it is preferably 1 day.

[0053] Specifically, the channel monitoring data includes downlink network monitoring data of each interaction point (i.e., real-time reference values ​​of each channel characteristic indicator in the downlink channel) and overall uplink network monitoring data (i.e., reference values ​​of each channel characteristic indicator corresponding to the first and second communication links of each interaction point).

[0054] Specifically, multiple data collection time nodes are set based on the historical monitoring data of the points to be controlled. The time interval between adjacent data collection time nodes can be set according to the number of electricity meter nodes corresponding to the points to be controlled. The more electricity meter nodes there are, the shorter the corresponding time interval will be. The mapping relationship between the two can be set according to historical parameters.

[0055] Specifically, a similarity value is set based on the difference between the real-time reference value of each channel characteristic indicator in the downlink state and the reference value of each channel characteristic indicator in the current channel state. The smaller the difference, the larger the corresponding similarity value. The mapping relationship between the two can be set according to historical parameters.

[0056] Specifically, a higher similarity value indicates a greater degree of matching between the downlink state of the control point and the current channel state. This also means higher execution efficiency of the various acquisition sub-strategies corresponding to the current channel state at the control point.

[0057] Specifically, the associated sub-task refers to the data collection sub-task that needs to be executed at the current data collection time node. Based on the evaluation sub-model corresponding to the current associated sub-task, the data collection evaluation values ​​of each electricity meter node corresponding to the control point are generated. Then, based on the data collection sub-strategy corresponding to the current associated sub-task in the anchored state, the proportion of electricity meters requiring delayed data collection is obtained. Finally, based on the data collection evaluation values ​​of each electricity meter node, specific electricity meter nodes requiring delayed data collection are selected (i.e., the electricity meter nodes with smaller data collection evaluation values; the number selected is the product of the proportion of electricity meters requiring delayed data collection in the data collection sub-strategy and the total number of electricity meter nodes corresponding to the control point). Thus, the electricity meter nodes corresponding to the current associated sub-task that need to be executed are selected, and this process is repeated for each associated sub-task, generating the corresponding primary data collection strategy.

[0058] Specifically, the set of associated tasks for the control point at the current data collection time node is generated, including: Based on the sequence of subtasks H collected, hi is sequentially set as the subtask to be evaluated; Generate the execution correlation value c of the subtask to be evaluated at the control point at the current data acquisition time node; Preset execution association value threshold C1; If c>C1, the subtask to be evaluated is set as the associated subtask of the control point at the current acquisition time node; Determine in turn whether each data collection subtask is a related subtask of the control point at the current data collection time node; Generate a set of related tasks based on the judgment results.

[0059] Specifically, a first correlation value is set based on the ratio between the time interval between the last execution time node and the current execution time node of the subtask to be evaluated and the expected execution interval (i.e., the time interval between two execution time nodes set according to the regular collection frequency of the subtask to be evaluated). The larger the ratio, the larger the corresponding first correlation value. The mapping relationship between the two can be set according to historical parameters.

[0060] Specifically, a second correlation value is set based on the feedback data (data anomalies, number of uncollected energy meters) of the subtask to be evaluated during its last execution. The more data anomalies and the larger the number of uncollected energy meters, the larger the corresponding second correlation value. The mapping relationship between the two can be set according to historical parameters, and the first and second correlation values ​​have the same range. The execution correlation value of the subtask to be evaluated is generated based on the weighted result of the first and second correlation values. The weight coefficients of the first and second correlation values ​​can be set according to historical parameters, and the sum of the weight coefficients of the two is 1.

[0061] Specifically, the execution association value threshold can be set according to historical parameters. If the execution association of the to-be-evaluated subtask is greater than the preset execution association value threshold, the to-be-evaluated subtask needs to be executed at the current acquisition time node.

[0062] Specifically, the execution association value threshold can be set according to historical parameters.

[0063] Specifically, the first-level transmission strategy for the to-be-controlled point is set, including: Obtain multiple electric meter data packets according to the first-level acquisition strategy; Generate the first uplink value f1 according to the channel monitoring data of the to-be-controlled point; Preset the uplink value threshold F1; If f1 > F1, generate a first-level transmission instruction; If f1 < F1, generate the second uplink value f2 of the to-be-controlled point. When f2 > F1, generate a second-level transmission instruction; when f2 < F1, generate a third-level transmission instruction.

[0064] Specifically, the first uplink value is generated according to the real-time reference values of each channel characteristic index corresponding to the first communication link of the to-be-controlled point in the channel monitoring data. The larger the first uplink value, the higher the data transmission efficiency of the first communication link of the to-be-controlled point.

[0065] Specifically, the uplink value threshold can be set according to historical parameters. When the first uplink value is lower than the preset uplink value threshold, it indicates that there are communication fluctuations in the first communication link of the to-be-controlled point, and there are transmission risks (packet loss, delay, etc.). It is necessary to timely judge the state of the second communication link and generate the second uplink value of the second communication link. Similarly, if the second uplink value is also less than the preset uplink value threshold, it indicates that there are also transmission risks in the second communication link.

[0066] Specifically, the first-level transmission instruction means that the to-be-controlled point directly transmits the collected electric energy meter data to the data platform through the first communication link. The second-level transmission instruction means that the collected electric energy meter data is transmitted to the data platform through the second communication link, and the third transmission instruction means to perform transmission.

[0067] Based on another preferred embodiment of the electric energy meter data acquisition method based on channel state dynamic scheduling in any one of the above preferred embodiments, in this preferred embodiment, an electric energy meter data acquisition system based on channel state dynamic scheduling is provided, including: An acquisition unit, including multiple acquisition sub-modules. The acquisition unit is used to acquire the operation data of each electric energy meter; A central control unit, used to set multiple interaction points according to the distribution parameters of the electric energy meters; The central control unit includes multiple control sub-modules, and the control sub-modules are set at each interaction point; The monitoring unit includes multiple monitoring sub-modules. The monitoring unit is used to collect channel monitoring data from each interaction point. The central control unit also includes: The first processing module is used to construct a data interaction network based on all interaction points. The data interaction network includes an uplink transmission network and multiple downlink transmission networks. The second processing module is used to set the data acquisition strategy for each interaction point based on all channel monitoring data and the preset acquisition control model.

[0068] In a preferred embodiment of this application, the first processing module is further configured to: Establish a sequence of interaction points A, A=(a1,a2…ai…an), where ai is the i-th interaction point; n is the number of interaction points; Based on the sequence of interaction points A, ai is sequentially set as the target interaction point; The mapping device area of ​​the target interaction point is generated based on the distribution parameters of the electricity meter. The mapping device area includes multiple electricity meter nodes. Establish a downlink transmission network for the target interaction point based on the mapping device area; Select auxiliary interaction points for the target interaction point; Establish an uplink subnetwork for the target interaction point based on the auxiliary interaction points; Establish the downlink transmission network and uplink subnetwork for each interaction point in sequence; Establish an uplink transmission network based on all uplink subnetworks; A data interaction network is constructed based on the uplink transmission network and all downlink transmission networks.

[0069] According to the first concept of this application, by adding a data acquisition and control model, it is possible to quickly adapt to the real-time channel status of the interaction point, thereby adjusting the data acquisition parameters of each electricity meter to improve channel utilization and the success rate of acquisition tasks, and optimizing network resource utilization efficiency.

[0070] According to the second concept of this application, multiple interaction points are set according to the distributed parameters of the electricity meter, and auxiliary interaction points are selected for each interaction point to establish a backup uplink channel, thereby improving the transmission quality of electricity meter data, avoiding the risk of regional data interruption caused by the operation failure of a single interaction point or channel status fluctuation, and improving the data acquisition efficiency of the electricity meter.

[0071] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.

Claims

1. A method for acquiring electricity meter data based on dynamic scheduling of channel state, characterized in that, include: Multiple interaction points are set according to the distributed parameters of the electricity meter; A data interaction network is constructed based on all interaction points, and the data interaction network includes an uplink transmission network and multiple downlink transmission networks; Acquire channel monitoring data from each interaction point, and set the data acquisition strategy for each interaction point based on all channel monitoring data and the preset acquisition control model.

2. The method for acquiring electricity meter data based on dynamic scheduling of channel state as described in claim 1, characterized in that, The construction of the data interaction network includes: Establish a sequence of interaction points A, A=(a1,a2…ai…an), where ai is the i-th interaction point; n is the number of interaction points; Based on the sequence of interaction points A, ai is sequentially set as the target interaction point; A mapping device area for the target interaction point is generated based on the distribution parameters of the electricity meters, and the mapping device area includes multiple electricity meter nodes; Establish a downlink transmission network for the target interaction point based on the mapping device area; Select auxiliary interaction points for the target interaction point; Establish an uplink subnetwork for the target interaction point based on the auxiliary interaction points; Establish the downlink transmission network and uplink subnetwork for each interaction point in sequence; Establish an uplink transmission network based on all uplink subnetworks; A data interaction network is constructed based on the uplink transmission network and all downlink transmission networks.

3. The energy meter data acquisition method based on channel state dynamic scheduling as described in claim 2, characterized in that, The uplink subnetwork for establishing the target interaction point includes: Establish the first communication link between the target interaction point and the data platform; Generate the communication dependency values ​​between the target interaction point and each interaction point; Set the interaction point corresponding to the maximum value among all communication dependency values ​​as the auxiliary interaction point of the target interaction point; Establish a second communication link between the target interaction point and the auxiliary interaction point; An uplink subnetwork of the target interaction point is established based on the first and second communication links.

4. The energy meter data acquisition method based on channel state dynamic scheduling as described in claim 3, characterized in that, The preset acquisition and control model includes: Obtain historical monitoring data from electricity meters; Multiple data collection sub-tasks are set based on historical monitoring data; Establish a sequence H of subtasks for data collection, H=(h1,h2…hi…hm), where hi is the i-th subtask for data collection; m is the number of subtasks for data collection. Generate various channel characteristic indicators based on historical monitoring data; Establish a channel state sequence B, B=(b1, b2…bi…br) based on all channel characteristic indicators, where bi is the i-th channel state and r is the number of channel states; A downlink acquisition model is established based on the acquisition sub-task sequence H and the channel state sequence B; Construct an uplink transmission model based on the uplink transmission network; A data acquisition and control model is established, which includes a downlink data acquisition model and an uplink transmission model.

5. The energy meter data acquisition method based on channel state dynamic scheduling as described in claim 4, characterized in that, The establishment of the downlink acquisition model includes: Based on the sequence of subtasks H, hi is sequentially set as the target subtask; An evaluation sub-model for the target sub-tasks was established based on historical monitoring data; Based on the channel state sequence B, bi is sequentially set as the target state; Based on the evaluation sub-model, set the collection sub-strategy for the target sub-task in the target state; Sequentially set the acquisition sub-strategies for the target sub-tasks in each channel state; Set the downlink acquisition strategy for the target sub-task based on all acquisition sub-strategies; Configure the downlink acquisition strategy for each acquisition subtask in sequence; A downlink acquisition model is constructed based on all downlink acquisition strategies.

6. The energy meter data acquisition method based on channel state dynamic scheduling as described in claim 5, characterized in that, The data acquisition strategy for each interaction point is defined as follows: Multiple preset acquisition cycles; Based on the interaction point sequence A, ai is sequentially set as the control point; Set multiple acquisition time nodes of the to-be-controlled point in the current acquisition period according to the acquisition sub-task sequence H; Generate an associated task set of the to-be-controlled point at the current acquisition time node, where the associated task set includes multiple associated sub-tasks; Set the downlink state of the to-be-controlled point at the current acquisition time node according to the channel monitoring data of the to-be-controlled point; Generate similarity values between the downlink state and each channel state; Set the channel state corresponding to the maximum value in the similarity values as the anchored state; Generate the first-level acquisition strategy of the to-be-controlled point according to the acquisition sub-strategies of each associated sub-task in the anchored state; Set the first-level transmission strategy of the to-be-controlled point according to the channel monitoring data of the to-be-controlled point; Generate the data acquisition strategy of the to-be-controlled point according to the first-level acquisition strategy and the first-level transmission strategy.

7. The energy meter data acquisition method based on channel state dynamic scheduling as described in claim 6, characterized in that, The generation of the associated task set of the to-be-controlled point at the current acquisition time node includes: Set hi as the to-be-evaluated sub-task in sequence according to the acquisition sub-task sequence H; Generate the execution association value c of the to-be-evaluated sub-task at the current acquisition time node at the to-be-controlled point; Preset the execution association value threshold C1; If c > C1, set the to-be-evaluated sub-task as the associated sub-task of the to-be-controlled point at the current acquisition time node; Judge whether each acquisition sub-task is the associated sub-task of the to-be-controlled point at the current acquisition time node in sequence; Generate the associated task set according to the judgment result.

8. The energy meter data acquisition method based on channel state dynamic scheduling as described in claim 6, characterized in that, The setting of the first-level transmission strategy of the to-be-controlled point includes: Obtain multiple electricity meter data packets according to the first-level acquisition strategy; Generate the first uplink value f1 according to the channel monitoring data of the to-be-controlled point; Preset the uplink value threshold F1; If f1 > F1, generate a first-level transmission instruction; If f1 < F1, generate the second uplink value f2 of the to-be-controlled point. When f2 > F1, generate a second-level transmission instruction; when f2 < F1, generate a third-level transmission instruction.

9. A data acquisition system for electricity meters based on dynamic channel state scheduling, employing the data acquisition method for electricity meters based on dynamic channel state scheduling as described in any one of claims 1-8, characterized in that, Includes: An acquisition unit, including multiple acquisition sub-modules, where the acquisition unit is used to acquire the operation data of each electric energy meter; A central control unit, used to set multiple interaction points according to the distribution parameters of the electric energy meters; The central control unit includes multiple control sub-modules, and the control sub-modules are arranged at each interaction point; A monitoring unit, including multiple monitoring sub-modules, where the monitoring unit is used to acquire the channel monitoring data of each interaction point; The central control unit further includes: A first processing module, used to construct a data interaction network according to all interaction points, and the data interaction network includes an uplink transmission network and multiple downlink transmission networks; A second processing module, used to set the data acquisition strategy of each interaction point according to all channel monitoring data and a preset acquisition control model.

10. The energy meter data acquisition system based on channel state dynamic scheduling as described in claim 9, characterized in that, The first processing module is further used for: Establish an interaction point sequence A, A = (a1, a2…ai…an), where ai is the i-th interaction point; n is the number of interaction points; Set ai as the target interaction point in sequence according to the interaction point sequence A; Generate the mapped device area of the target interaction point according to the distribution parameters of the electric energy meters, and multiple electric energy meter nodes are included in the mapped device area; Establish the downlink transmission network of the target interaction point according to the mapped device area; Select the auxiliary interaction point of the target interaction point; Establish the uplink sub-network of the target interaction point according to the auxiliary interaction point; Establish the downlink transmission network and the uplink sub-network of each interaction point in sequence; Establish an uplink transmission network based on all uplink subnetworks; A data interaction network is constructed based on the uplink transmission network and all downlink transmission networks.

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