Efficient data interaction method for intelligent power distribution cabinet

Abstract

The invention relates to the technical field of power distribution cabinet data interaction, in particular to an efficient data interaction method for an intelligent power distribution cabinet. The method comprises the following steps that S1, an intelligent gateway gathers multiple paths of electrical parameters in the power distribution cabinet, and standardized electrical data is generated after edge side processing; and S2, identifying an operation cooperation relationship between the power distribution cabinets, and performing dynamic networking for the corresponding intelligent gateways according to the operation cooperation relationship to form an edge cluster capable of cooperative calculation. When the intelligent gateway detects a local anomaly, the abnormal data packet is forwarded in the edge cluster instead of depending on a possibly unstable uplink of the intelligent gateway, and other intelligent gateways with healthy uplinks in the edge cluster cooperate with each other, so that on one hand, a plurality of optional routing paths are constructed, the transmission reliability is greatly improved, and on the other hand, the transmission efficiency is improved; on the other hand, redundant operation is provided for subsequent control, and the fault influence range is limited within the minimum range.

Description

Technical Field

[0001] This invention relates to the field of data interaction technology for power distribution cabinets, and more specifically, to an efficient data interaction method for intelligent power distribution cabinets. Background Technology

[0002] Against the backdrop of the accelerated integration of the Industrial Internet and smart grids, power distribution systems are rapidly developing towards digitalization and intelligence. As the core node of the power distribution network, the operating status of intelligent distribution cabinets directly affects the safety, stability, and efficiency of the entire power supply system. Real-time and reliable acquisition and interaction of massive electrical parameters (such as voltage, current, power, temperature, harmonics, etc.) within intelligent distribution cabinets is the cornerstone of building intelligent power distribution systems.

[0003] Traditional smart distribution cabinet data interaction methods typically employ a centralized architecture. The basic model involves smart sensors and power measurement devices within each distribution cabinet collecting raw data to a local smart gateway via a fieldbus (such as RS-485). Subsequently, each smart gateway, acting as an independent data silo, periodically or unconditionally uploads the data stream directly to a remote cloud platform or monitoring center via uplinks such as 4G / 5G or Ethernet.

[0004] To reduce the load on communication links, current smart gateways generally adopt a periodic reporting mechanism, which involves packaging the collected data and sending it to the cloud platform after a preset time window has elapsed. While this mechanism effectively reduces network bandwidth usage, it introduces a significant problem: when the system encounters abnormal data that needs to be reported immediately, the upload request becomes highly random.

[0005] This makes the upload timing of abnormal data unpredictable, and it is very easy for it to overlap with the periodic reporting tasks of other gateways, thus competing for limited uplink channel resources. As a result, uplink channel congestion is often caused, leading to transmission delays of the abnormal data itself, and consequently delays in fault warning and control. Summary of the Invention

[0006] The purpose of this invention is to provide an efficient data interaction method for intelligent power distribution cabinets. This method solves the problem mentioned in the background art by using a cloud-edge collaborative redundancy protection design, namely, the problem that the upload request has strong randomness when abnormal data that needs to be reported immediately occurs.

[0007] To achieve the above objectives, the efficient data interaction method for intelligent power distribution cabinets includes the following steps: S1. The intelligent gateway aggregates multiple electrical parameters from the power distribution cabinet, processes them at the edge, and generates standardized electrical data. S2. Identify the operational coordination relationship between power distribution cabinets, and dynamically network the corresponding smart gateways accordingly to form an edge cluster capable of collaborative computing. S3. Plan the uploading strategy for the smart gateways in the edge cluster to the cloud platform. The uploading strategy adopts the uploading time polling mechanism and configures the time offset for the data uploading cycle of each smart gateway. S4. When the smart gateway detects a local anomaly, if it is not in the reporting cycle, it will synchronize the abnormal data to the smart gateway in the edge cluster that meets the reporting cycle. S5. When the intelligent gateway receiving abnormal data reports the abnormal data to the cloud platform, it triggers the local rule engine to make a judgment: if the preset security conditions are met, it sends control instructions to the relevant power distribution cabinet.

[0008] As a further improvement to this technical solution, the method for generating standardized electrical data in S1 is as follows: S1.1 Establish communication with all different types of devices and sensors in the power distribution cabinet through the protocol interface of the smart gateway, and read the raw data; S1.2 Clean and calculate the raw data to obtain electrical data.

[0009] As a further improvement to this technical solution, the electrical data includes current, voltage, active power, power factor, electrical energy, and harmonic distortion rate.

[0010] As a further improvement to this technical solution, the method for forming an edge cluster in S2 is as follows: S2.1, Keep the clocks of all smart gateways synchronized; S2.2 The smart gateway continuously calculates the effective voltage value from electrical data and sets a dynamic threshold. When the voltage value exceeds the dynamic threshold and continues to exceed the preset time, it is determined as a voltage event. S2.3 Record the key features of the voltage event, including the start timestamp, voltage drop / rise depth, event duration, and waveform snapshot; S2.4 When multiple gateways detect voltage events, determine whether the voltage events originate from the same source; S2.5. Smart gateways corresponding to voltage events originating from the same source are assigned to the same edge cluster, and smart gateways within the same edge cluster are interconnected. S2.6. Compare the event start timestamps reported by the smart gateway, sort them in order, and obtain the electrical connection sequence of the power distribution cabinet.

[0011] As a further improvement to this technical solution, the judgment condition for determining whether voltage events originate from the same source in S2.4 is as follows: The start time of voltage events detected by different smart gateways conforms to the preset time window; The waveform distortion features in the waveform snapshots recorded by different smart gateways meet the preset similarity. The amplitude changes of voltage events detected by different smart gateways conform to the preset attenuation conditions; When all the above conditions are met, it indicates that the voltage events originate from the same source; otherwise, they do not originate from the same source.

[0012] As a further improvement to this technical solution, the method for planning the reporting strategy in S3 is as follows: S3.1 Obtain the reporting cycle of non-abnormal data from the smart gateway. ; S3.2 Obtain the number of smart gateways in the same edge cluster ; S3.3 Calculation time interval = / ; S3.4 Assign an index number to each smart gateway according to the electrical connection sequence. ; S3.5, Configure reporting cycle offset = .

[0013] As a further improvement to this technical solution, the method for detecting local anomalies in S4 is as follows: S4.1 Set corresponding abnormal thresholds for different electrical parameters in the electrical data; S4.2 Real-time monitoring of electrical parameters. When an electrical parameter exceeds the corresponding abnormal threshold, it is marked as abnormal data; otherwise, no action is taken.

[0014] As a further improvement to this technical solution, step S4 also includes the following method: S4.3 Mark the smart gateway that detects abnormal data as an abnormal gateway; S4.4 Obtain the time when abnormal data occurs The time point at which the abnormal gateway reports an error. If the time point for abnormal gateway reporting is not reached, it means that the smart gateway is not in the reporting cycle; S4.5, Obtain the time corresponding to the reporting time node within the edge cluster. If there is no corresponding smart gateway, the abnormal gateway will directly upload the data to the cloud platform. If there is a corresponding smart gateway, the abnormal gateway will transmit the abnormal data to the corresponding smart gateway. S4.6 Mark the smart gateway that receives abnormal data as a collaborative gateway. The collaborative gateway uploads its normal electrical data and abnormal data to the cloud platform according to its own reporting time node.

[0015] As a further improvement to this technical solution, the method for triggering the local rule engine judgment in S5 is as follows: S5.1 The abnormal gateway uploads the control commands it receives in real time within the edge cluster, and the collaborative gateway subscribes to the control commands uploaded by the abnormal gateway within the edge cluster. S5.2 The collaborative gateway analyzes the abnormal information of the abnormal data and obtains the corresponding control instructions based on the abnormal information; S5.3 Establish a control command delay sending procedure. If the collaborative gateway receives the subscribed control command within the delay period, the collaborative gateway cancels the issuance of the control command; otherwise, the collaborative gateway issues the control command to the corresponding distribution cabinet after the delay.

[0016] As a further improvement to this technical solution, the delay time in S5.3 is the time consumed from the start of abnormal data transmission to the receipt of the subscribed control command by the cooperative gateway when the network is not congested.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This efficient data interaction method for intelligent power distribution cabinets employs a redundant transmission mechanism involving collaboration among multiple intelligent gateways. When an intelligent gateway detects a local anomaly, it does not rely solely on its own potentially unstable uplink. Instead, it forwards the abnormal data packet within the edge cluster, where other intelligent gateways with healthy uplinks collaborate. This approach constructs multiple optional routing paths, significantly improving transmission reliability. Furthermore, it provides redundant operations for subsequent control, minimizing the impact of the fault.

[0018] 2. In this efficient data interaction method for intelligent power distribution cabinets, the cloud acts as the first line of defense, performing global optimization and strategic control; the collaborative gateway acts as the second line of defense, quickly taking over when the cloud fails. This redundant architecture ensures that critical protection functions remain available even if any single link fails, elevating system reliability from reliance on a single node to high availability at the architecture level. Attached Figure Description

[0019] Figure 1 This is a flowchart of the overall method of the present invention. Detailed Implementation

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] To address the issue of highly random upload requests when abnormal data requiring immediate reporting occurs, this invention provides an efficient data interaction method for intelligent power distribution cabinets. For example... Figure 1 As shown, it includes the following steps: S1. The intelligent gateway aggregates multiple electrical parameters from the power distribution cabinet, processes them at the edge, and generates standardized electrical data. S2. Identify the operational coordination relationship between power distribution cabinets, and dynamically network the corresponding smart gateways accordingly to form an edge cluster capable of collaborative computing. S3. Plan the uploading strategy for the smart gateways in the edge cluster to the cloud platform. The uploading strategy adopts the uploading time polling mechanism and configures the time offset for the data uploading cycle of each smart gateway. S4. When the smart gateway detects a local anomaly, if it is not in the reporting cycle, it will synchronize the abnormal data to the smart gateway in the edge cluster that meets the reporting cycle. S5. When the intelligent gateway receiving abnormal data reports the abnormal data to the cloud platform, it triggers the local rule engine to make a judgment: if the preset security conditions are met, it sends control instructions to the relevant power distribution cabinet.

[0022] To achieve efficient data interaction, this solution employs a redundant transmission mechanism involving multiple intelligent gateways. When an intelligent gateway detects a local anomaly, it does not rely solely on its own potentially unstable uplink. Instead, it forwards the abnormal data packets within the edge cluster, where other intelligent gateways with healthy uplinks collaborate. This approach not only constructs multiple optional routing paths, significantly improving transmission reliability, but also provides redundant operations for subsequent control, minimizing the impact of the fault. The following sections will elaborate on key capabilities such as electrical data acquisition, dynamic networking, reporting strategies, abnormal data collaboration, and redundant operations.

[0023] First, the smart gateway establishes communication with all different types of devices and sensors in the distribution cabinet through various protocol interfaces and reads raw data. Protocol interfaces include: RS-485 serial port (connecting to smart circuit breakers and power meters), digital input (DI) channel (receiving switching signals, such as circuit breaker opening and closing status), and analog input (AI) channel (receiving temperature sensor signals). Then, the raw data is cleaned and calculated to obtain electrical data. Cleaning methods include: checking if the data is within a reasonable range (e.g., voltage values ​​between 0-500V); values ​​outside this range are marked as invalid; and checking if the difference between two adjacent data points exceeds a reasonable rate of change (e.g., current changes exceeding 100A per second); values ​​exceeding this rate of change are marked as invalid. Electrical data includes current, voltage, active power, power factor, energy, and harmonic distortion rate.

[0024] After obtaining the electrical data, a smart gateway can be used to upload it to the cloud platform. Due to the special nature of abnormal data, the smart gateway will upload abnormal data in real time. To avoid the occurrence of such random events, the next step is to identify the operational coordination relationships between distribution cabinets and establish a collaborative computing edge cluster.

[0025] Regarding the identification of the operational coordination relationship of the distribution cabinet, this solution adopts voltage identification. First, a Grandmaster Clock is deployed in the network, and all smart gateways act as PTP slave clocks, continuously calibrating their local clocks by exchanging timestamp messages to maintain synchronization at the microsecond or even nanosecond level. Then, the smart gateways continuously calculate the effective voltage (RMS) from electrical data and set dynamic thresholds (e.g., ±10% of the nominal voltage). When the voltage value exceeds the threshold and persists for a certain period (pre-set, e.g., two cycles), it is determined to be a voltage event. Key characteristics of the voltage event are recorded, including the start timestamp, voltage dip / rise depth, event duration, and waveform snapshot. When multiple gateways detect a voltage event, it is determined whether the voltage event originates from the same source, with the following specific criteria: The voltage events detected by different smart gateways start at a preset time window (e.g., 1-2 milliseconds). Because the disturbance propagates very quickly, a large time difference may indicate that they are not the same event. The waveform distortion features (such as drop shape and oscillation frequency) in the waveform snapshots recorded by different smart gateways meet the preset similarity. The amplitude changes of voltage events detected by different smart gateways conform to the preset attenuation conditions (the farther away from the event source, the smaller the amplitude change is usually).

[0026] When all the above conditions are met, it indicates that the voltage events originate from the same source; otherwise, they do not originate from the same source.

[0027] Then, smart gateways originating from the same source are grouped into the same edge cluster, and smart gateways within the same edge cluster are interconnected. Next, the start timestamps of the events reported by the smart gateways are compared and sorted to obtain the electrical connection sequence of the distribution cabinet.

[0028] For example: Assume there are three distribution cabinets, corresponding to gateways A, B, and C. Assume gateway B first detects an 8% voltage sag at time T0, gateway A detects a -2% voltage sag at T0+100μs, and gateway C detects a -7% voltage sag at T0+150μs. At this point, it can be concluded that gateways A, B, and C are in the same edge cluster.

[0029] Furthermore, gateway A also sensed the disturbance, but slightly later and with a smaller amplitude. This indicates that gateway A is upstream of gateway B. Gateway C sensed the disturbance later than B but earlier than A, and its amplitude was similar to that of B. This indicates that gateway C is downstream of gateway B. Therefore, the electrical connection sequence is A→B→C.

[0030] After obtaining the edge cluster data, the next step is to plan the reporting strategy. First, obtain the reporting cycle for non-abnormal data from the smart gateway. Number of smart gateways in the same edge cluster Then calculate the time interval. Time interval = / Then, an index number is assigned to each smart gateway according to the electrical connection sequence. Configure the reporting cycle offset, reporting cycle offset = .

[0031] For example, reporting cycle For 3 seconds, the number of smart gateways There are 3, and the time interval is 3. = / =3 / 3=1 second, the index numbers of the three smart gateways The numbers are 0, 1, and 2 respectively. At this time, smart gateway 0 reports at 0 seconds, 3 seconds, 6 seconds…; smart gateway 1 reports at 1 second, 4 seconds, 7 seconds…; and smart gateway 2 reports at 2 seconds, 5 seconds, 8 seconds… The electrical connection sequence for smart gateways 0, 1, and 2 is A→B→C.

[0032] Next, a real-time threshold rule is used to determine local anomalies. First, corresponding anomaly thresholds are set for different electrical parameters in the electrical data. Then, the electrical parameters are monitored in real time. When an electrical parameter exceeds the corresponding anomaly threshold, it is marked as abnormal data; otherwise, no action is taken.

[0033] The smart gateway that detects abnormal data is marked as an abnormal gateway, and the time when the abnormal data occurs is obtained. The time point at which the abnormal gateway reports an error. If the time point for reporting an anomaly is not within the reporting cycle, it indicates that the smart gateway is not in its reporting period. Next, obtain the corresponding time point for the reporting within the edge cluster. If there is no corresponding smart gateway, the abnormal gateway will directly upload the data to the cloud platform. If there is a corresponding smart gateway, the abnormal gateway will transmit the abnormal data to the corresponding smart gateway.

[0034] Subsequently, the smart gateway receiving the abnormal data is marked as a collaborative gateway. The collaborative gateway uploads its own normal electrical data and abnormal data to the cloud platform according to its own reporting time points. In this solution, the collaborative gateway also has the ability to control relevant distribution cabinets based on abnormal data. Specifically: First, the abnormal gateway uploads the control commands it receives in real time within the edge cluster, while the collaborating gateway subscribes to the control commands uploaded by the abnormal gateway within the edge cluster. Next, the collaborating gateway analyzes the anomaly information in the abnormal data, obtains the corresponding control policy based on the anomaly information, and then sends the control commands to the corresponding distribution cabinets with a delay according to the control policy. The delay time is the time consumed from the start of abnormal data transmission to the collaborating gateway receiving the subscribed control commands when the network is not congested. During the delay time, if the collaborating gateway receives the subscribed control commands, it cancels the sending of the control commands; otherwise, the collaborating gateway sends the control commands to the corresponding distribution cabinets after the delay.

[0035] For example, if the abnormal data is overvoltage, the collaborative gateway will report the overvoltage event to the cloud platform. If the cloud platform does not issue a tripping command to the abnormal gateway within a specified time (e.g., 1 second), the collaborative gateway will directly send a tripping command to the corresponding distribution cabinet. Since the distribution cabinets in the edge cluster are in an upstream and downstream relationship, this design allows for the protection of other distribution cabinets in the edge cluster even if the cloud platform cannot control them in a timely manner, thus preventing damage to downstream distribution cabinets.

[0036] In summary, this design constructs a dual protection barrier of "cloud-based main control + edge backup." The cloud serves as the first line of defense, performing global optimization and strategic control; the collaborative gateway acts as the second line of defense, quickly taking over in the event of cloud failure. This redundant architecture ensures that critical protection functions remain available even if any single component fails, elevating system reliability from reliance on a single node to architecture-level high availability.

[0037] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A highly efficient data interaction method for intelligent power distribution cabinets, characterized in that: Includes the following steps: S1. The intelligent gateway aggregates multiple electrical parameters from the power distribution cabinet, processes them at the edge, and generates standardized electrical data. S2. Identify the operational coordination relationship between power distribution cabinets, and dynamically network the corresponding smart gateways accordingly to form an edge cluster capable of collaborative computing. S3. Plan the uploading strategy for the smart gateways in the edge cluster to the cloud platform. The uploading strategy adopts the uploading time polling mechanism and configures the time offset for the data uploading cycle of each smart gateway. S4. When the smart gateway detects a local anomaly, if it is not in the reporting cycle, it will synchronize the abnormal data to the smart gateway in the edge cluster that meets the reporting cycle. S5. When the intelligent gateway receiving abnormal data reports the abnormal data to the cloud platform, it triggers the local rule engine to make a judgment: if the preset security conditions are met, it sends control instructions to the relevant power distribution cabinet.

2. The efficient data interaction method for intelligent power distribution cabinets according to claim 1, characterized in that: The method for generating standardized electrical data in S1 is as follows: S1.1 Establish communication with all different types of devices and sensors in the power distribution cabinet through the protocol interface of the smart gateway, and read the raw data; S1.2 Clean and calculate the raw data to obtain electrical data.

3. The efficient data interaction method for intelligent power distribution cabinets according to claim 2, characterized in that: The electrical data includes current, voltage, active power, power factor, electrical energy, and harmonic distortion rate.

4. The efficient data interaction method for intelligent power distribution cabinets according to claim 1, characterized in that: The method for forming edge clusters in S2 is as follows: S2.1, Keep the clocks of all smart gateways synchronized; S2.2 The smart gateway continuously calculates the effective voltage value from electrical data and sets a dynamic threshold. When the voltage value exceeds the dynamic threshold and continues to exceed the preset time, it is determined as a voltage event. S2.3 Record the key features of the voltage event, including the start timestamp, voltage drop / rise depth, event duration, and waveform snapshot; S2.4 When multiple gateways detect voltage events, determine whether the voltage events originate from the same source; S2.

5. Smart gateways corresponding to voltage events originating from the same source are assigned to the same edge cluster, and smart gateways within the same edge cluster are interconnected. S2.

6. Compare the event start timestamps reported by the smart gateway, sort them in order, and obtain the electrical connection sequence of the power distribution cabinet.

5. The efficient data interaction method for intelligent power distribution cabinets according to claim 4, characterized in that: The judgment conditions for determining whether voltage events originate from the same source in S2.4 are as follows: The start time of voltage events detected by different smart gateways conforms to the preset time window; The waveform distortion features in the waveform snapshots recorded by different smart gateways meet the preset similarity. The amplitude changes of voltage events detected by different smart gateways conform to the preset attenuation conditions; When all the above conditions are met, it indicates that the voltage events originate from the same source; otherwise, they do not originate from the same source.

6. The efficient data interaction method for intelligent power distribution cabinets according to claim 1, characterized in that: The method for planning and reporting strategies in S3 is as follows: S3.1 Obtain the reporting cycle of non-abnormal data from the smart gateway. ; S3.2 Obtain the number of smart gateways in the same edge cluster ; S3.3 Calculation time interval = / ; S3.4 Assign an index number to each smart gateway according to the electrical connection sequence. ; S3.5, Configure reporting cycle offset = .

7. The efficient data interaction method for intelligent power distribution cabinets according to claim 1, characterized in that: The method for detecting local anomalies in S4 is as follows: S4.1 Set corresponding abnormal thresholds for different electrical parameters in the electrical data; S4.2 Real-time monitoring of electrical parameters. When an electrical parameter exceeds the corresponding abnormal threshold, it is marked as abnormal data; otherwise, no action is taken.

8. The efficient data interaction method for intelligent power distribution cabinets according to claim 7, characterized in that: S4 also includes the following method: S4.3 Mark the smart gateway that detects abnormal data as an abnormal gateway; S4.4 Obtain the time when abnormal data occurs The time point at which the abnormal gateway reports an error. If the time point for abnormal gateway reporting is not reached, it means that the smart gateway is not in the reporting cycle; S4.5, Obtain the time corresponding to the reporting time node within the edge cluster. If there is no corresponding smart gateway, the abnormal gateway will directly upload the data to the cloud platform. If there is a corresponding smart gateway, the abnormal gateway will transmit the abnormal data to the corresponding smart gateway. S4.6 Mark the smart gateway that receives abnormal data as a collaborative gateway. The collaborative gateway uploads its normal electrical data and abnormal data to the cloud platform according to its own reporting time node.

9. The efficient data interaction method for intelligent power distribution cabinets according to claim 8, characterized in that: The method for triggering the local rule engine judgment in S5 is as follows: S5.1 The abnormal gateway uploads the control commands it receives in real time within the edge cluster, and the collaborative gateway subscribes to the control commands uploaded by the abnormal gateway within the edge cluster. S5.2 The collaborative gateway analyzes the abnormal information of the abnormal data and obtains the corresponding control instructions based on the abnormal information; S5.3 Establish a control command delay sending procedure. If the collaborative gateway receives the subscribed control command within the delay period, the collaborative gateway cancels the issuance of the control command; otherwise, the collaborative gateway issues the control command to the corresponding distribution cabinet after the delay.

10. The efficient data interaction method for intelligent power distribution cabinets according to claim 9, characterized in that: In S5.3, the delay time is the time consumed from the start of abnormal data transmission to the time the cooperative gateway receives the subscribed control command when the network is not congested.

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