A Multi-Loop Energy Metering Method Based on Dynamic Resource Allocation of Semaphores

Abstract

This invention provides a multi-loop energy metering method based on dynamic resource allocation using semaphores. The steps include: collecting multi-loop energy signal data, calculating dynamic priorities, determining system status, and outputting a multi-channel dynamically bound metering resource scheduling strategy. This invention, through differentiated adaptation between multi-resource reuse metering strategies and single-resource independent metering strategies, avoids idle sampling channels and computing units during idle periods, effectively reducing hardware configuration costs and adapting to the resource elasticity requirements of multi-loop scenarios. Furthermore, synchronous sampling across multiple channels during idle periods improves the reliability of metering data, while independent resource allocation during busy periods avoids data queuing and loss, ensuring metering accuracy and response speed under dynamically fluctuating load scenarios. Moreover, through dynamic priority and system status determination, it can flexibly adapt to various scenarios and meet collaborative scheduling requirements.

Description

Technical Field

[0001] This invention belongs to the field of electrical variable measurement technology, specifically relating to a multi-loop energy meter metering method based on dynamic resource allocation of signal quantities. Background Technology

[0002] In the field of power load metering, scenarios such as charging piles, industrial power distribution, and commercial building power distribution increasingly demand centralized, high-precision, and high-reliability power metering. Taking charging piles as an example, with the surge in the number of new energy vehicles, the number of charging piles at a single station often reaches 10-20, requiring simultaneous support for multiple types of power demand, including emergency charging, regular fast charging, and slow charging. Metering data is directly linked to user billing and operator revenue. In industrial power distribution scenarios, the load differences of circuits such as main motors, auxiliary water pumps, and air conditioners on production lines are significant. The metering accuracy requirements for voltage and current fluctuations of some key equipment must meet specified accuracy standards and withstand strong electromagnetic interference, high temperatures, and dust pollution. Commercial buildings involve multi-tenant individual metering, requiring support for time-of-use billing and load peak-valley adjustment, while also requiring metering equipment to have low power consumption and remote operation and maintenance capabilities. The common requirements of these scenarios place higher demands on the resource utilization efficiency, redundancy and fault tolerance, and dynamic adaptability of power metering systems. The current mainstream "one circuit, one meter" traditional metering model is no longer suitable for the digital upgrade needs of these scenarios.

[0003] The core flaw of the traditional "one circuit, one meter" model lies in hardware configuration and operation and maintenance management. From the perspective of hardware cost and installation, different circuits have different rated currents (e.g., the emergency gun circuit current of the charging pile can reach 150A, while the slow charging circuit is only 30A) and voltage levels, requiring the purchase of different models of single-phase or three-phase energy meters. This not only increases the complexity of procurement and inventory management costs, but may also lead to excessive metering errors due to model mismatch. In the installation process, each meter needs to be independently connected to a current transformer (CT) and a voltage transformer (PT), with a single circuit wiring length of 5-10 meters. The total wiring cost for 20 circuits is higher than that of a centralized solution. At the same time, in order to avoid electromagnetic interference and heat dissipation issues between meters, a 5-10cm spacing needs to be reserved between meters, resulting in a 30%-50% increase in the size of the distribution box. Especially in scenarios such as outdoor cabinets of charging piles and confined spaces in industrial workshops, the installation adaptability is significantly insufficient.

[0004] In terms of data reliability, traditional models lack redundancy design and struggle to cope with failure risks under complex operating conditions. In charging pile environments, frequent start-stop cycles during vehicle charging generate instantaneous inrush currents, and in industrial settings, devices such as frequency converters and rectifiers introduce strong electromagnetic interference. These factors can easily lead to sampling channel failures or metering chip malfunctions in independent meters. Because each meter operates independently without backup data links, once a meter in a loop fails (such as a damaged sampling chip or communication module failure), the energy data for that loop will be completely lost and cannot be recovered or traced through other meters. For example, a failure of a meter in a critical equipment loop on an industrial production line may result in missing energy consumption data for that period, affecting production energy consumption analysis and cost accounting. In commercial buildings, meter failures by tenants may lead to billing disputes, requiring maintenance personnel to replace meters on-site and manually re-enter data, further increasing management costs.

[0005] Insufficient operational efficiency and functional scalability also restrict the application of traditional models. During operation and maintenance, staff must check the wiring of each meter individually, diagnose hardware status, and calibrate metering errors. A single inspection of 20 circuits typically takes 2-3 hours, which is more than 60% less efficient than a centralized solution. If a fault is encountered, power must be cut off and tested one by one, potentially causing production line shutdowns in industrial settings and requiring partial suspension of charging services for charging piles, resulting in a decline in user experience and operational losses. Regarding functional scalability, traditional meters have fixed metering functions. Adding features such as harmonic metering, demand analysis, and remote cost control requires replacing the entire meter, which cannot be achieved through software upgrades. For example, if commercial buildings later need to introduce peak-valley electricity pricing, traditional meters, which do not support remote configuration of time-of-use parameters, must be replaced in batches with smart meters. This not only requires power outages during the upgrade process, affecting merchants' normal electricity use, but also increases costs.

[0006] In recent years, some improved solutions have attempted to achieve multi-loop metering by integrating multiple metering units, but in essence, they are still integrated designs of "one metering unit per loop." While such solutions reduce installation space, they do not reduce hardware costs, and many metering units remain idle during off-peak hours, resulting in insufficient resource utilization. During peak hours, due to a lack of reasonable scheduling, critical loops share resources with ordinary loops, leading to extended sampling intervals and metering errors exceeding standard requirements. Furthermore, existing solutions cannot identify sampling channel faults or grid interference, and still suffer from low data reliability, making it difficult to meet the metering reliability and accuracy requirements of industrial and charging pile scenarios.

[0007] In summary, the traditional "one circuit, one meter" model and existing improvement solutions have significant shortcomings in terms of resource utilization efficiency, metering real-time performance and accuracy, redundancy and fault tolerance, operation and maintenance costs, and functional scalability. They cannot meet the dynamic metering, precise control, and collaborative scheduling needs of charging piles, industrial power distribution, commercial buildings, and other fields. Summary of the Invention

[0008] This invention addresses the problems existing in the prior art by providing a method for scheduling metering resources by collecting multi-loop signal data, calculating dynamic priorities, determining system status, and outputting multi-channel dynamic binding.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0010] This invention provides a multi-loop energy metering method based on dynamic resource allocation of semaphores, comprising the following steps:

[0011] Collect signal data from each circuit of a multi-circuit energy meter;

[0012] The dynamic priority of each loop is calculated based on the signal data;

[0013] The system state is calculated based on the dynamic priority and activity level of each loop.

[0014] Based on the system status and mapping relationship, output a metering resource scheduling strategy;

[0015] The metering resource scheduling strategy is a multi-channel dynamic binding loop metering, and at least a multi-resource reuse metering strategy and a single-resource independent metering strategy are provided.

[0016] In the multi-resource multiplexing metering strategy, at least some circuits in the multi-circuit energy meter correspond to multiple sampling channels; in the single-resource independent metering strategy, each circuit corresponds to one sampling channel.

[0017] Multi-channel dynamic binding loop metering is performed according to the metering resource scheduling strategy.

[0018] Furthermore, the signal data includes historical electrical data and loop tags;

[0019] Calculating the dynamic priority of each loop involves the following steps:

[0020] Based on the loop labels and preset information, a priority base value is assigned to each loop;

[0021] The historical electrical data is used to generate predicted electrical data for each circuit in the next time period;

[0022] Priority bonuses are calculated using the predicted electrical data;

[0023] The basic priority value and the additional priority value of each loop are summed to obtain the dynamic priority of each loop.

[0024] Furthermore, the signal data also includes real-time electrical data;

[0025] The method for calculating the system state includes the following steps:

[0026] The number of active circuits is counted using the real-time electrical data, and the activity level of the circuits is calculated using a ratio method.

[0027] Sum the dynamic priorities of each loop to obtain the total system priority;

[0028] The total system priority and the loop activity are normalized to obtain system state variables;

[0029] The system state is obtained using a threshold method based on the system state variables.

[0030] Furthermore, the method for calculating the system state also includes the following steps:

[0031] A threshold is set for the total system priority to trigger a priority strategy. When the total system priority exceeds or falls below the preset priority threshold, the system state calculation process is skipped, and a preset system state is output.

[0032] Furthermore, the system state includes at least an idle state and a busy state;

[0033] The mapping relationship is as follows: the idle state corresponds to the multi-resource reuse metering strategy; the busy state corresponds to the single-resource independent metering strategy.

[0034] Furthermore, the multi-resource reuse metering strategy includes the following steps:

[0035] The idle sampling channels in the multi-circuit energy meter are counted and allocated and bound according to the dynamic priority of each circuit;

[0036] In the same loop where multiple sampling channels are bound together, the error rate of multiple channels is calculated using the redundancy check method;

[0037] When the error rate is less than or equal to the error rate threshold, the electrical energy value of the corresponding circuit is calculated using the mean value method.

[0038] Furthermore, the multi-resource reuse metering strategy also includes the following steps:

[0039] When the error rate exceeds the error rate threshold, the multi-loop energy meter triggers an early warning and selects adjacent historical data based on the sliding window method to correct the energy value.

[0040] Furthermore, in the multi-resource reuse metering strategy, when the multi-loop energy meter triggers an early warning, the multi-channel sampling data of the early warning loop is uploaded to the server and stored.

[0041] Furthermore, the single-resource independent metering strategy includes the following steps:

[0042] Based on the continuity condition of the sampling channel, a sampling channel is bound to each circuit in the multi-circuit energy meter.

[0043] Furthermore, in the single-resource independent metering strategy, the computing resources of the multi-loop energy meter are reallocated, including the following steps:

[0044] The idle computing resources of the multi-circuit energy meter are statistically analyzed.

[0045] The idle computing resources are allocated based on the dynamic priority of each loop, and the reallocation of computing resources for each loop is dynamically adjusted according to the response delay of each loop during the allocation.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] This invention, through differentiated adaptation of multi-resource reuse metering strategies and single-resource independent metering strategies, avoids idle sampling channels and computing units during idle periods, effectively reducing hardware configuration costs and adapting to the resource elasticity requirements of multi-loop scenarios. Furthermore, multi-channel synchronous sampling during idle periods improves the reliability of metering data, while independent resource allocation during busy periods avoids data queuing and loss, ensuring metering accuracy and response speed under dynamic load fluctuation scenarios. Moreover, through dynamic priority and system status determination, it can flexibly adapt to various scenarios and meet the requirements of collaborative scheduling. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart of a method in a specific embodiment of the present invention;

[0050] Figure 2 This is a schematic diagram of the configuration of a multi-circuit energy meter in a specific embodiment of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of 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, 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.

[0052] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0053] It should be noted that, unless otherwise specified, the methods used in this invention are conventional methods; and the raw materials and apparatus used are, unless otherwise specified, conventional commercially available products.

[0054] It should also be noted that, for ease of understanding, the method steps in the specific embodiments of the present invention are described in a certain order, but those skilled in the art can change the order of the steps according to actual needs, so this should not be used as a limiting condition.

[0055] like Figure 1 As shown, this embodiment proposes a multi-loop energy metering method based on dynamic resource allocation of semaphores. For ease of description, the application scenario is a charging station, and the hardware configuration is as shown in the attached diagram. Figure 2 Taking the structure shown as an example, a multi-circuit energy meter specifically includes the following modules:

[0056] Power supply section: It adopts a switching power supply design, which is divided into 3 independent power supply branches to provide stable voltage for the metering module, control unit and communication module respectively, ensuring the continuous operation of the entire metering system and providing power support for subsequent multi-channel dynamic binding loop metering work.

[0057] Signal acquisition module: Configured with N sets of current transformers (CTs), N sets of voltage transformers (PTs), and a multi-channel AD sampling chip. In this embodiment, N is 10, and the number of sampling channels is greater than 10. The current and voltage transformers are used to acquire the analog current and voltage signals of each circuit. The AD sampling chip converts the analog signals into digital quantities, realizing the hardware function of real-time acquisition of the electrical energy signal to be measured, providing raw data for subsequent signal data processing. The voltage transformers, current transformers, and corresponding sampling channels form voltage and current sampling circuits, such as voltage and current sampling circuit 1 to voltage and current sampling circuit N.

[0058] Control Unit: A high-performance microcontroller unit (MCU) is selected to undertake the core tasks of signal data processing, dynamic priority calculation, system status determination, and metering resource scheduling. The MCU can read the signal identifiers (such as loop IDs) and load fluctuation values ​​of each loop in real time to meet control requirements.

[0059] Metering Module: Includes an energy calculation unit and a redundancy verification module. The energy calculation unit consists of one core calculation unit and two backup calculation units, used to calculate the active power, reactive power, and cumulative energy value of each circuit; the redundancy verification module is used to compare the consistency of multiple sets of sampled data.

[0060] Communication module: Composed of an RS485 module and a 4G communication module. The RS485 module supports local parameter reading, allowing maintenance personnel to read metering data through local devices; the 4G communication module supports remote monitoring of meter operating status and can also achieve remote upgrades and fault data reporting through the 4G antenna interface.

[0061] Storage module: Used to store historical electrical data of each circuit (such as current and voltage change records of the past 24 hours), real-time metering data and fault warning logs, providing data storage support for subsequent generation of predictive information based on historical data and tracing abnormal data.

[0062] Furthermore, the multi-circuit energy meter is equipped with a human-machine interaction module, which integrates display, button, infrared communication, power outage wake-up and other functional modules.

[0063] Based on the above-mentioned multi-circuit energy meter, its metering method includes the following steps:

[0064] 1. Signal monitoring;

[0065] Data acquisition of multi-circuit power signals: The control unit synchronously acquires three types of signal data from 10 charging pile circuits at a frequency of 100ms / time via the signal acquisition module, specifically including:

[0066] Circuit tag information: Functional tags are pre-configured for 10 circuits, including 2 emergency charging gun circuits, 5 ordinary fast charging pile circuits, and 3 slow charging pile circuits. The tag information is used to distinguish the basic importance of the circuits and provide a basis for subsequent calculations.

[0067] Historical electrical data: The storage module reads historical data of each circuit over the past 24 hours, including time series data such as average current, average voltage, and power fluctuation amplitude every 5 minutes. This data is used to analyze the load change patterns of the circuit.

[0068] Real-time electrical data: Instantaneous current and voltage of each circuit are collected in real time through current transformers and voltage transformers, and the on / off status of the circuit is monitored synchronously (current greater than 0.5A is judged as active), load fluctuation intensity (current change exceeding 5A within 1 minute is marked as high fluctuation) and signal priority (emergency charging gun is marked as high priority).

[0069] 2. System status determination;

[0070] Calculating dynamic priority and system status: Based on the acquired signal data, the control unit performs dynamic priority calculation and system status determination, including the following steps:

[0071] Dynamic priority calculation: First, the basic priority value of each circuit is determined based on the circuit label information. One preset information is that the basic priority value for emergency charging guns is 8-10 points, for ordinary fast charging piles it is 5-7 points, and for slow charging piles it is 2-4 points. The principle of setting the basic priority value is to obtain the initial assignment based on expert evaluation, and then dynamically adjust it to the optimal score after feedback optimization based on actual operation. The goal of feedback optimization is to improve the accuracy of electricity metering. It is understandable that emergency charging guns are used under urgent conditions, so they are assigned the highest value, while fast charging has a larger demand than slow charging, so its score is higher than that of slow charging. Predictive electrical data for each circuit in the next time period (such as predicting the current fluctuation trend in the next 10 minutes) is generated by using historical electrical data. The prediction method can adopt time-series prediction. Since the collected data has time-series characteristics, a pre-trained prediction model can be deployed in the electricity meter after being lightweighted, so that rapid prediction can be achieved. Priority bonuses can be calculated based on predicted electrical data. For example, if the predicted current shows high fluctuations, the bonus is 3 points; if it shows a high-priority signal, the bonus is 5 points; and if it shows a stable load signal, the bonus is -1 point. Finally, the base priority value and the bonus are summed to obtain the dynamic priority of each circuit; each circuit receives a dynamic priority value. Furthermore, the method for obtaining the priority bonus can refer to the base priority value. An initial value is assigned based on expert evaluation, and then dynamically adjusted to the optimal score after feedback optimization based on actual operating conditions. The goal of feedback optimization is to improve the accuracy of electricity metering.

[0072] System status determination: First, the number of active circuits is counted. Circuits with a current greater than 0.5A in the real-time electrical data are identified as active circuits. The formula for calculating circuit activity is: number of active circuits / total number of circuits. Then, the dynamic priority values ​​of all circuits are summed to obtain the total system priority. The total system priority and circuit activity are normalized, and then summed to obtain the system status variables. Furthermore, in the data normalization process, this embodiment specifically uses the Min-Max normalization method to map the total system priority (assuming a value range of 0-100) and circuit activity (value range of 0-100%) to the 0-1 interval respectively. The normalized formula for the total system priority is: Normalized Total System Priority = (Actual Total System Priority - Minimum Possible Value) / (Maximum Possible Value - Minimum Possible Value); Assuming the total system priority is calculated to be 61, then the normalized total system priority = (61-0) / (100-0) = 0.61; Three loops are in an active state, i.e., the normalized loop activity = 3 / 10 = 0.3. A weighted summation method is used for calculation, i.e.: System state variable = 0.6 × Normalized Total System Priority + 0.4 × Normalized Loop Activity = 0.6 × 0.61 + 0.4 × 0.3 = 0.486. The weights 0.6 and 0.4 are used to adjust the calculation results, and are set based on the degree of influence of priority on resource scheduling in the charging pile scenario. They can be adjusted according to the actual scenario and operating conditions.

[0073] Based on system state variables, the system state is obtained using a threshold method. First, the system state is divided into idle and busy states according to its operating status. It can be understood that this could be further divided into multiple levels depending on the scale of the charging station; however, this embodiment uses a two-level state structure for ease of understanding. A preset system state variable threshold of 0.5 is designed, with a system state variable ≤ 0.5 indicating an idle state and a system state variable > 0.5 indicating a busy state. Therefore, in the aforementioned example, the system state variable is 0.486, which is less than the system state variable threshold of 0.5, and thus can be determined as an idle state.

[0074] Furthermore, to meet the needs of special power consumption scenarios, a threshold-triggered priority strategy can be set for the total system priority. That is, when the total system priority exceeds or falls below a preset priority threshold, the above system state calculation process is skipped, and the preset system state is directly output. Specifically, in this embodiment, if there is at least one high-priority signal (such as the emergency charging gun starting), regardless of the number of active circuits, it is directly determined to be in a busy state.

[0075] 3. Resource allocation and data processing;

[0076] Output metering resource scheduling strategy: Based on the determined system state and the mapping relationship, output the corresponding metering resource scheduling strategy:

[0077] In this embodiment, the mapping relationship is as follows: the idle state corresponds to the multi-resource reuse metering strategy; the busy state corresponds to the single-resource independent metering strategy.

[0078] The multi-channel dynamic binding loop metering operation is as follows:

[0079] A. If the system is in an idle state, output a multi-resource reuse metering strategy. Under this strategy, due to low metering pressure, there is redundancy in resources such as sampling channels of the metering module, so metering accuracy can be improved through multi-channel reuse.

[0080] Multi-resource reuse metering strategy: Idle sampling channels in the statistical metering module are allocated according to the dynamic priority of each loop and temporarily bound to the loop. For example, loops with high priority are allocated 2-3 sampling channels, and they are allocated in order of priority (sorted by numerical value, with larger ones first) until there are no idle sampling channels.

[0081] Furthermore, since multiple sampling channels are bound to the same circuit, and the sampling results are not consistent, a redundancy check method is used to calculate the error rate of multiple channels. Specifically, the redundancy check module compares the error rates of multiple sets of sampling data, that is, taking the smallest sampling data as the benchmark, subtracting the benchmark from the sampling data of other channels, and then dividing by the benchmark to obtain the error rate. If the error rate of any channel in the circuit is less than or equal to the error rate threshold (e.g., 0.2%), the mean method is used to solve the problem: the average value of the sampling data of multiple sampling channels in the circuit is taken as the final metering value; if the error rate exceeds the error rate threshold, the multi-circuit energy meter triggers a fault warning, and the energy value is corrected by selecting adjacent historical data based on the sliding window method. The correction method involves selecting historical energy data from the storage module within the same window period (with similar or identical start and end times) or from a complete window (5 minutes) preceding the occurrence of the anomaly. The average energy change rate within this window (e.g., energy increment per minute) is calculated, and combined with previously collected valid data (e.g., data from the minute before the fault), a corrected energy value for the fault period is generated. Simultaneously, the anomaly data from the warning loop is uploaded to the server and stored via a 4G communication module. During data output, multi-channel raw data is included for the backend system to trace and verify.

[0082] B. If the system is in a busy state, output a single-resource independent metering strategy. Under this strategy,

[0083] Single-resource independent metering strategy execution: Each active circuit is bound to an independent sampling channel based on the principle of sampling channel continuity; that is, the sampling channel is preferentially bound to the circuit bound in the most recent single-resource independent metering strategy. Due to the high demand for computing resources during busy periods, the computing resources of multi-circuit energy meters are reallocated. Specifically, the computing unit allocates computing power according to the dynamic priority of each circuit, and each circuit independently completes the energy calculation. Data is stored and categorized by circuit ID during output, directly outputting independent metering values. If a sampling channel fails, a fault response is triggered within 1 second, switching to a backup channel. The data from the failed channel is marked with a "correction flag" to alert the backend for manual review.

[0084] Specifically, computing power is reflected in the sampling rate. To ensure real-time metering, one signal is bound to one independent sampling channel to avoid data queuing and loss. The sampling frequency is dynamically adjusted according to load characteristics. Loops with high dynamic priority use a sampling rate of 12.8kHz, while loops with low dynamic priority use a sampling rate of 6.4kHz. The computing units are allocated computing power according to dynamic priority, and the reallocation of computing resources for each loop is dynamically adjusted based on the response delay of each loop. For example, high-priority signals occupy core computing units first, ensuring that their response delay is ≤10ms (if this is not met, the computing resources allocated to that loop are further increased). Low-priority signals use backup computing units, with a response delay of ≤50ms, to meet the requirement of prioritizing the real-time metering of critical loops under busy conditions.

[0085] This embodiment achieves dynamic metering of 10-circuit charging piles through the above process, and is also suitable for scenarios with large dynamic load fluctuations, multi-path parallel metering, and high operation and maintenance efficiency requirements, such as industrial power distribution and commercial buildings. By adopting a hierarchical priority calculation method, the result of a single calculation can be applied to the entire process, including system status judgment and resource allocation, thereby reducing the overall computational load. Furthermore, the priority values, through data combination, quantify different influencing factors and incorporate them into status judgment or resource allocation, making them more suitable for the scenario requirements. Therefore, after actual engineering implementation, it can be found that the metering accuracy error is reduced, with a significant improvement in accuracy compared to the traditional "one circuit, one meter" mode; the reuse rate of resources such as sampling channels and computing units is improved, effectively reducing hardware costs; and the fault response speed is also reduced accordingly. Through redundancy verification and backup channel switching, the data loss problem of the traditional mode is solved, achieving the technical goals of dynamic metering, precise control, and collaborative scheduling.

[0086] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions made by those skilled in the art to the technical solution of the present invention do not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A multi-loop energy meter metering method based on dynamic resource allocation of semaphores, characterized in that, Includes the following steps: Collect signal data from each circuit of a multi-circuit energy meter; The dynamic priority of each loop is calculated based on the signal data; The system state is calculated based on the dynamic priority and activity level of each loop. Based on the system status and mapping relationship, output a metering resource scheduling strategy; The metering resource scheduling strategy is a multi-channel dynamic binding loop metering, and at least a multi-resource reuse metering strategy and a single-resource independent metering strategy are provided. In the multi-resource multiplexing metering strategy, at least some circuits in the multi-circuit energy meter correspond to multiple sampling channels; in the single-resource independent metering strategy, each circuit corresponds to one sampling channel. Perform multi-channel dynamic binding loop metering work according to the metering resource scheduling strategy; The signal data includes historical electrical data and loop tags; Calculating the dynamic priority of each loop involves the following steps: Based on the loop labels and preset information, a priority base value is assigned to each loop; The historical electrical data is used to generate predicted electrical data for each circuit in the next time period; Priority bonuses are calculated using the predicted electrical data; Sum the base priority value and the additional priority value of each loop to obtain the dynamic priority of each loop; The signal data also includes real-time electrical data; The method for calculating the system state includes the following steps: The number of active circuits is counted using the real-time electrical data, and the activity level of the circuits is calculated using a ratio method. Sum the dynamic priorities of each loop to obtain the total system priority; The total system priority and the loop activity are normalized to obtain system state variables; Based on the system state variables, the system state is obtained using a threshold method. The multi-resource reuse metering strategy includes the following steps: The idle sampling channels in the multi-circuit energy meter are counted and allocated and bound according to the dynamic priority of each circuit; In the same loop where multiple sampling channels are bound together, the error rate of multiple channels is calculated using the redundancy check method; When the error rate is less than or equal to the error rate threshold, the electrical energy value of the corresponding circuit is calculated by the mean value method; The single-resource independent metering strategy includes the following steps: Based on the continuity condition of the sampling channel, a sampling channel is bound to each circuit of the multi-circuit energy meter; In the single-resource independent metering strategy, the computing resources of the multi-loop energy meter are reallocated, including the following steps: The idle computing resources of the multi-circuit energy meter are statistically analyzed. The idle computing resources are allocated based on the dynamic priority of each loop, and the reallocation of computing resources for each loop is dynamically adjusted according to the response delay of each loop during the allocation.

2. The multi-loop energy meter metering method based on dynamic resource allocation of semaphores according to claim 1, characterized in that, The method for calculating the system state also includes the following steps: A threshold is set for the total system priority to trigger a priority strategy. When the total system priority exceeds or falls below the preset priority threshold, the system state calculation process is skipped, and a preset system state is output.

3. The multi-loop energy meter metering method based on dynamic resource allocation of semaphores according to claim 1, characterized in that, The system state includes at least an idle state and a busy state; The mapping relationship is as follows: the idle state corresponds to the multi-resource reuse metering strategy; the busy state corresponds to the single-resource independent metering strategy.

4. The multi-loop energy meter metering method based on dynamic resource allocation of semaphores according to claim 1, characterized in that, The multi-resource reuse metering strategy also includes the following steps: When the error rate exceeds the error rate threshold, the multi-loop energy meter triggers an early warning and selects adjacent historical data based on the sliding window method to correct the energy value.

5. The multi-loop energy meter metering method based on dynamic resource allocation of semaphores according to claim 4, characterized in that, In the multi-resource reuse metering strategy, when the multi-loop energy meter triggers an early warning, the multi-channel sampling data of the early warning loop is uploaded to the server and stored.

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