Power grid power consumption information collection and power management system and control method

Abstract

The application belongs to the technical field of power system automation and energy management, and particularly discloses a power grid electricity information collection and power management system and a control method. The system comprises a main power supply monitoring module, an energy storage management module, a communication environment sensing module, an energy efficiency strategy decision core and a task priority scheduling module. By capturing the state of the power grid, the battery and the channel in real time, the communication step and the transmission power are dynamically adjusted, and the data scheduling is performed according to the priority. By adopting the above technical scheme, the application realizes fine distribution of electric energy, reduces invalid standby power consumption, prolongs the survival period of the collection terminal in the power-off state, ensures the reliable reporting of key information in extreme working conditions, and provides data support for power grid fault research and judgment.

Description

Technical Field

[0001] This invention belongs to the field of power system automation and energy management technology, specifically relating to a power grid electricity consumption information collection and backup power management system and control method. Background Technology

[0002] With the deep integration of smart grids and the energy internet, electricity consumption information collection systems, as the core infrastructure for end-point sensing and data interaction in the power grid, play a crucial role in achieving real-time electricity consumption monitoring, accurate electricity metering, and distribution network fault analysis. Collection terminals are widely deployed in various complex electricity consumption sites. By constructing a data collection network with broad coverage and high timeliness, they support the digital transformation and intelligent upgrading of power grid operations. The stability and reliability of their operation directly affect the continuity of power grid dispatching decisions and marketing operations.

[0003] The backup power management system is a core component of the power information acquisition terminal that maintains basic operation under abnormal main power supply conditions or extreme operating conditions. Its basic principle is to utilize backup energy storage units to ensure that the terminal can still perform necessary fault alarm and status data transmission tasks during power outages. To achieve remote interaction, these terminals typically integrate a data transmission module based on wireless communication technology and are equipped with corresponding energy management control logic, aiming to ensure real-time communication while rationally allocating and efficiently scheduling limited backup power resources.

[0004] In existing technologies, in data acquisition scenarios using wireless communication, the communication module, due to its high-frequency interaction characteristics, becomes the main power source for the power consumption acquisition terminal and also the main power load of the backup power system. Traditional periodic wake-up communication mechanisms lack sensitivity to actual communication needs and environmental changes, resulting in low wake-up efficiency and a significant amount of valuable backup power being wasted on ineffective wireless link listening and idle handshake processes.

[0005] In extreme situations such as abnormal master station commands or large-scale power grid outages, this extensive energy dispatch strategy is prone to depleting backup power before effective data transmission is completed, leading to complete terminal disconnection or loss of critical alarm information. This highlights a key technical challenge that urgently needs to be addressed in the field of backup power management. Summary of the Invention

[0006] The purpose of this invention is to provide a power grid electricity consumption information collection and backup power management system and control method to solve the problems of high communication energy consumption, lack of environmental awareness in wake-up mechanism, and loss of key data caused by uneven energy distribution under extreme conditions in the existing technology of electricity consumption information collection terminal in backup power working mode.

[0007] This invention provides a power grid electricity consumption information collection and backup power management system, comprising:

[0008] The main power supply monitoring module is used to capture the voltage waveform characteristics, frequency offset and phase vector data of the external AC power supply bus in real time. It converts the physical voltage signal into digital power parameters through the built-in high-speed voltage sampling circuit at a sampling frequency of no less than 4,000 times per second, and calculates the instantaneous slope of voltage drop to determine the power supply stability level of the main power grid.

[0009] The energy storage management module is electrically connected to the backup energy storage battery pack. It is used to collect the terminal voltage, charging and discharging current, cell surface temperature and ohmic internal resistance of the backup energy storage battery pack in real time using the high-frequency pulse current excitation method. It also uses a composite algorithm that combines charge integration and open-circuit voltage correction to calculate the state of charge and health data of the backup energy storage battery pack.

[0010] The communication environment perception module is used to drive the wireless communication module to perform a full-spectrum scan of the energy density of the current wireless channel, obtain the received signal strength indication, signal-to-noise ratio, channel occupancy rate, and physical layer load status of the current serving base station, and establish a real-time evaluation matrix that reflects the quality of the wireless transmission link.

[0011] The energy efficiency strategy decision core interacts bidirectionally with the main power supply monitoring module, the energy storage management module, and the communication environment perception module. Based on the acquired main power supply status, remaining power, and channel quality, the energy efficiency strategy decision core dynamically calculates the optimal communication interval step size and transmission power level.

[0012] The task priority scheduling module is used to mark the electricity information data packets to be sent, and divide them into four priority levels according to the data attributes: emergency alarm, normal load, historical curve, and system maintenance. According to the output instructions of the energy efficiency strategy decision core, the module sequentially executes the encapsulation and queuing of data frames.

[0013] Preferably, the main power supply monitoring module further includes a zero-crossing detection circuit;

[0014] The zero-crossing detection circuit is used to determine the phase start point of the voltage waveform;

[0015] When the main power supply monitoring module detects that the effective voltage value is lower than 70% of the rated voltage for three consecutive cycles, it sends a main power abnormality interruption signal to the energy efficiency strategy decision core to trigger the switch to backup power operation mode.

[0016] Preferably, the energy storage management module has a built-in temperature compensation circuit and a battery balancing circuit;

[0017] The temperature compensation circuit relies on a preset temperature power consumption mapping table to achieve its function. This mapping table is compiled using measured data of the backup energy storage battery pack in the typical operating temperature range of -10℃ to 60℃, and combined with the environmental temperature distribution characteristics of the power grid power consumption information collection scenario, to match the actual power consumption parameters of the backup energy storage battery pack for different temperature ranges.

[0018] The temperature compensation circuit is used to correct the state of charge in real time based on a preset temperature power consumption mapping table.

[0019] The battery balancing circuit is used to perform millivolt-level voltage difference compensation on the voltage of each series-connected cell in the backup energy storage battery pack during the charging process.

[0020] The calculation logic of the state of charge is as follows: the state of charge at the current moment is obtained by subtracting the amount consumed during the discharge process from the state of charge at the initial moment; the amount consumed is obtained by integrating the outflowing current during the discharge time and dividing it by the rated capacity of the battery; during the integration process, it is corrected by the dynamic coulombic efficiency factor which is affected by the cell temperature in real time.

[0021] Preferably, the communication environment perception module executes multi-threshold decision logic;

[0022] When the received signal strength is lower than the first preset threshold and the signal-to-noise ratio is lower than the second preset threshold, the communication environment perception module determines that the current environment is a weak coverage environment.

[0023] The first preset threshold is determined by combining the minimum receiving sensitivity standard of the power grid communication system with the minimum signal strength requirement for data transmission in the backup power system. It is achieved through actual measurements of signal strength and transmission success rate at multiple test points in a typical power grid deployment environment, with the minimum signal strength corresponding to "data transmission success rate ≥ 99%". The second preset threshold is determined by verifying the signal-to-noise ratio and data error rate through laboratory simulation and on-site measurement based on the anti-interference parameters of the communication module and the actual electromagnetic noise level of the power grid. It is achieved with the minimum signal-to-noise ratio corresponding to "bit error rate ≤ 0.1%".

[0024] When the channel occupancy rate exceeds 80%, the communication environment perception module determines that the current environment is congested.

[0025] The communication environment perception module also encapsulates environmental parameters into an environmental state vector and transmits it to the energy efficiency strategy decision core as an input variable for adjusting the wake-up cycle of the wireless communication module.

[0026] Preferably, the energy efficiency strategy decision core operates a dynamic trade-off algorithm;

[0027] The dynamic trade-off algorithm determines the adjustment strategy for the wake-up frequency by calculating the energy efficiency weight factor.

[0028] The calculation logic of the energy efficiency weighting factor is as follows: the weighted sum of the remaining power contribution value and the communication link quality contribution value is subtracted from the weighted value of the load pressure term of the task to be sent.

[0029] The remaining power contribution value is determined based on the ratio of the current remaining capacity to the full capacity;

[0030] The communication link quality contribution value is determined based on the ratio of the weighted value of the real-time sensed signal-to-noise ratio and signal strength to the ideal state reference value;

[0031] The pending task load pressure term is determined based on the ratio of the number of data bytes backed up in the current sending queue to the maximum buffer capacity.

[0032] Preferably, the system further includes an intelligent fault isolation subunit;

[0033] The intelligent fault isolation subunit is connected in series between the backup energy storage battery pack and the main circuit, and it has a built-in current ripple analysis algorithm.

[0034] When a short circuit or abnormal current change is detected in the back-end load, the intelligent fault isolation subunit uses a microsecond-level electronic switch to shut off the energy output.

[0035] This invention also provides a control method for a power grid electricity consumption information collection and backup power management system, comprising the following steps:

[0036] Step 1: System startup initialization. The main power supply monitoring module, energy storage management module, and communication environment sensing module enter real-time inspection state and establish the initial electrical energy parameter benchmark.

[0037] Step 2: The main power supply monitoring module continuously compares the input voltage with the preset safety threshold. The safety threshold is based on the rated operating voltage range of the power grid electricity consumption information acquisition terminal, combined with the voltage fluctuation tolerance standard of the main power supply, and is determined by measuring the voltage critical value when the main power supply is interrupted. When an abnormal input voltage is detected and it is determined that the main power supply is interrupted, the main power supply monitoring module sends a high-priority interrupt to the system kernel, forcing the system to switch from main power supply to backup energy storage battery pack power supply.

[0038] Step 3: The system enters the backup power mode. The energy storage management module starts high-frequency coulomb counting to lock the initial state of charge of the backup energy storage battery pack. At the same time, the communication environment perception module conducts a comprehensive mapping of the surrounding base station signals to determine the optimal combination of communication physical parameters.

[0039] Step 4: The energy efficiency strategy decision core calculates the current energy efficiency weight factor based on the acquired state of charge, channel environment parameters, and the current backlog of tasks to be sent. It then compares the energy efficiency weight factor with the preset execution threshold. The execution threshold is determined through simulation testing of the energy efficiency strategy, based on the remaining power threshold of the backup energy storage battery pack, the priority weight of the tasks to be sent, and the power consumption classification standard of the wireless communication module. This generates a wake-up scheduling table for the wireless communication module.

[0040] Step 5: Activate the wireless communication module at the wake-up time. The task priority scheduling module extracts data packets from the data buffer and sends them in descending order. During the sending process, the transmission power is monitored in real time. If no acknowledgment is received within 3 attempts, the energy efficiency strategy decision core will recalculate the next wake-up time.

[0041] Step 6: Monitor the battery terminal voltage in real time during the discharge process. When the battery terminal voltage drops to 0.2 volts above the cutoff voltage protection line, the system triggers the final words operation, transfers all unsent key historical data to the low-power storage area, and cuts off the power supply to the peripheral devices to enter the standby state.

[0042] Step 7: The main power supply monitoring module continuously detects the AC bus status. When it detects that the main power supply has returned to normal and the duration exceeds 60 seconds, the system performs a switchback operation.

[0043] Preferably, in step 4, a traffic prediction mechanism is also introduced:

[0044] The energy efficiency strategy decision-making core is based on the communication traffic distribution over the past 24 cycles, and uses a weighted moving average algorithm to predict the probability of potential instruction issuance in the next hour.

[0045] When a need for issuing inspection commands is predicted, the energy efficiency strategy decision core increases the wake-up frequency.

[0046] Preferably, in step 5, an adaptive power ramp strategy is adopted for the transmit power control of the wireless communication module:

[0047] The initial transmission attempts to use the minimum effective power. If no acknowledgment is received, the transmission power is increased by 3 dB on the next retransmission until the maximum allowable transmission power is reached.

[0048] Preferably, in step 6, the dying words operation further includes:

[0049] The system generates a self-diagnostic report that includes the battery status at the last moment, channel quality statistics, and fault feature vectors.

[0050] The fault feature vector records the original sampled waveform features within the last 100 milliseconds before the main power supply interruption;

[0051] The self-diagnostic report is stored in a specific sector of non-volatile memory and is reported preferentially after the main power is restored.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] 1. This invention, by introducing a three-in-one sensing framework integrating main power supply monitoring, energy reserve management, and communication environment perception, completely changes the blind and mechanical wake-up mechanism in traditional backup power management. The system can accurately capture subtle changes in the power grid status and the dynamic characteristics of the wireless channel, thereby reducing the ineffective standby power consumption of the backup power system and extending the effective lifespan of the acquisition terminal in the event of a power outage, while ensuring that critical power consumption information is not lost. 2. This invention utilizes an adaptive scheduling logic implemented by an energy efficiency strategy decision core to establish a multi-dimensional energy allocation system. This energy allocation system can dynamically adjust the wake-up step size and transmission power based on real-time battery state of charge and channel quality, enabling the limited backup power to be optimally allocated according to the urgency of the service. Especially in extreme conditions such as poor channel environment or critical power shortages, the system can prioritize the successful reporting of emergency alarms through a degraded operation strategy, improving the timeliness of power grid fault assessment. 3. This invention achieves refined data management through a task priority scheduling module and priority management mechanism. By dividing data into four levels and employing techniques such as fragmented transmission and incremental compression, the problem of low data reporting success rates caused by communication network congestion during large-scale power outages is effectively solved. Simultaneously, the system's built-in "last words" operation and self-diagnostic reporting mechanism provide detailed data support for fault analysis and system self-healing after grid restoration. 4. This invention also features in-depth optimization in hardware protection and battery health management. The introduction of intelligent fault isolation subunits and cycle life degradation assessment logic not only improves the system's operational safety in complex environments but also realizes a transformation from simple energy allocation to full lifecycle asset management, providing solid physical and logical guarantees for the long-term stable operation of the smart grid electricity information collection network. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention;

[0055] Figure 2 This is a logical flowchart of the control method for the power grid electricity consumption information collection and backup power management system proposed in this invention;

[0056] Figure 3 This is a logical interaction diagram of the three-in-one framework of main power supply monitoring, energy storage management and communication environment perception in this invention;

[0057] Figure 4 This is a logical framework diagram of data hierarchical processing and cache management in the task priority scheduling module of this invention. Detailed Implementation

[0058] Example 1: Please refer to the appendix Figure 1 The power grid electricity consumption information collection and backup power management system disclosed in this embodiment has its overall architecture deeply integrated into the hardware layer of the electricity consumption information collection terminal, and is highly coupled with the terminal's main control core at both the logical and physical levels. The core design of this system lies in building an intelligent backup power guarantee system with environmental perception capabilities and adaptive energy allocation capabilities, thereby solving the problem of how the collection terminal can achieve an optimal balance between communication performance and lifespan under limited backup energy constraints when a large-scale power outage or power supply anomaly occurs in the main power grid.

[0059] Combined with appendix Figure 1 With appendix Figure 4 This system consists of multiple complementary hardware modules and software algorithm kernels that coordinate data processing. First, the system includes a main power supply monitoring module. This module serves as the primary entry point for the system to perceive the external power grid status, responsible for capturing various key power parameters of the external AC power supply bus in real time and at high frequency. In its specific circuit implementation, the main power supply monitoring module integrates a high-speed voltage sampling circuit and a zero-crossing detection circuit.

[0060] The high-speed voltage sampling circuit employs a 16-bit resolution analog-to-digital converter chip to discretize the AC voltage at an ultra-high sampling frequency of no less than 4000 times per second. This means that for a 50Hz power frequency AC current, each cycle is subdivided into 80 sampling points, thus accurately outlining subtle distortions in the voltage waveform. By processing these discrete sampling points using an RMS algorithm, the system can calculate the current voltage amplitude in real time.

[0061] Meanwhile, the zero-crossing detection circuit in the main power supply monitoring module utilizes a highly sensitive voltage comparator to accurately determine the moment each voltage waveform crosses zero potential, thereby locking the phase start point of the voltage. Through frequency analysis of multiple consecutive cycles, the main power supply monitoring module can also identify frequency deviations in the main power grid. The main power supply monitoring module not only focuses on the absolute value of the voltage but also incorporates logic for calculating the instantaneous slope of voltage drops. When an external power grid experiences an instantaneous voltage drop due to a fault, the main power supply monitoring module obtains the rate of voltage decrease by calculating the first derivative of the sampling sequence.

[0062] If the effective voltage value is detected to be below 70% of the rated voltage for three consecutive cycles, and the instantaneous slope exceeds the preset safety threshold, the main power supply monitoring module will immediately send a high-priority interrupt signal to the system kernel through the high-speed optocoupler isolation interface. This signal will serve as the sole trigger source for switching to the backup power operation mode, ensuring that the system completes state preservation and energy switching preparation within microseconds before the main power completely disappears.

[0063] Closely integrated with the main power supply monitoring module is the energy storage management module. This module is physically electrically connected to the backup battery pack and bears the crucial responsibility of monitoring and managing the electrochemical energy state. At the hardware level, the energy storage management module includes a high-precision shunt current detection circuit, a multi-point distributed temperature sensor array, and a voltage monitoring circuit. This module not only acquires the terminal voltage of the backup battery pack in real time but also measures the ohmic internal resistance of the battery cells using a high-frequency pulse current excitation method to assess changes in the battery's internal physical characteristics.

[0064] To accurately monitor remaining battery power, the energy storage management module employs a composite calculation logic that deeply integrates charge integration and open-circuit voltage correction algorithms. When the battery is discharging, the system accumulates the outflowing charge using an ampere-hour meter method; while when the battery is idle or just starting up, the calculation results are statically corrected using the mapping curve between open-circuit voltage and state of charge.

[0065] In this embodiment, the energy storage management module calculates the state of charge according to the following algorithm principle. The initial state of charge is set as... In the time interval The discharge current inside is The rated capacity of the battery is Furthermore, considering the coulomb efficiency factor during the discharge process... The formula for calculating the state of charge at the current moment is as follows:

[0066] ;

[0067] This represents the percentage of battery state of charge calculated at the current moment, with a value ranging from 0 to 100. The integral term represents the total charge consumed from the initial moment to the current moment. Coulomb efficiency factor. It is a dynamic variable, and its value is affected by the magnitude of the discharge current and the real-time temperature of the battery cell. The energy storage management module extracts the corresponding correction coefficient from the preset temperature power consumption mapping table through the built-in temperature compensation circuit.

[0068] Because the electrochemical activity of the backup energy storage battery pack decreases and its discharge efficiency declines at low temperatures, the system automatically lowers the assessed value of the available capacity. Furthermore, the energy storage management module includes a battery balancing circuit. This circuit uses active or passive balancing technology to precisely compensate for voltage deviations between series-connected cells at the millivolt level during the charging phase. By monitoring the terminal voltage of each cell in real time, when the voltage difference exceeds 30 millivolts, the balancing circuit activates a shunt resistor or charge transfer pump to ensure that the overall battery pack's capacity utilization rate remains consistently above 95%.

[0069] This system also includes a communication environment sensing module, which empowers the data acquisition terminal with a deep understanding of the radio electromagnetic environment. This module does not simply maintain the communication link; instead, it actively drives the wireless communication module to perform a full-spectrum scan of the energy density of the current operating frequency band and its adjacent bands. This module can acquire real-time received signal strength indication, signal-to-noise ratio, and the physical layer load status of the current serving base station. More importantly, it can calculate channel occupancy, i.e., the proportion of time the channel is busy within a specific time window.

[0070] The communication environment perception module establishes a multi-dimensional real-time evaluation matrix to quantify the quality of the wireless transmission link. When the wireless communication module is activated, the perception module first detects the noise floor of the current environment. If the received signal strength is below a first preset threshold and the signal-to-noise ratio (SNR) is below a second preset threshold, the system automatically determines that it is in a weak coverage environment. This means that data transmission requires higher transmit power or more retransmissions. The first preset threshold is determined by combining the minimum receiving sensitivity standard of the power grid communication system with the minimum signal strength requirement for backup power system data transmission. It is determined through actual measurements of signal strength and transmission success rate at multiple test points in a typical power grid deployment environment, with a minimum signal strength corresponding to a "data transmission success rate ≥ 99%". The second preset threshold is determined based on the communication module's anti-interference parameters and the actual electromagnetic noise level of the power grid. It is verified through laboratory simulation and on-site measurements of SNR and data error rate, with a minimum SNR corresponding to a "bit error rate ≤ 0.1%". If the channel occupancy rate exceeds 80%, it is determined to be a congested environment. These perceived environmental parameters are encapsulated into an environmental state vector, serving as a key input variable for the core of energy efficiency strategy decision-making.

[0071] In addition, the communication environment perception module also has an interference avoidance function. When it detects co-channel interference or severe electromagnetic noise at the currently used frequency, it will coordinate with the main control program to initiate a frequency hopping request and find the channel with the least interference among multiple preset alternative frequency points to re-establish the connection, thereby avoiding ineffective energy consumption caused by harsh environment.

[0072] The energy efficiency strategy decision-making core is the intelligent hub of the entire system, enabling bidirectional data interaction with the aforementioned main power supply monitoring module, energy storage management module, and communication environment sensing module. Its core function is to run a dynamic trade-off algorithm, aiming to establish an optimization decision-making model with multiple objectives, including maximizing battery life and data transmission success rate. Based on the acquired battery state of charge, channel environment state vector, and current task urgency, this energy efficiency strategy decision-making core dynamically calculates the optimal communication interval step size and transmit power level.

[0073] When generating the wake-up scheduling table, the core of the energy efficiency strategy decision-making process calculates an energy efficiency weighting factor. The calculation logic of the energy efficiency weighting factor is as follows:

[0074] ;

[0075] This represents the energy efficiency weighting factor, which is the basis for the system to adjust the wake-up frequency. The first term on the right side of the equation represents the contribution value of the remaining power. This represents the current remaining capacity. To full capacity, The first term represents the power weighting coefficient. The second term represents the contribution value to the communication link quality. This is a weighted value of the real-time perceived signal-to-noise ratio and signal strength. This is the baseline value under ideal link conditions. This represents the link weight coefficient. The third term represents the load factor of the pending task. This represents the number of data bytes currently backed up in the send queue. This is the system's maximum cache capacity. This represents the load adjustment coefficient. The core of the energy efficiency strategy decision-making process solves the function in real time, when... When the data falls below a specific execution threshold, the system will automatically lengthen the wake-up cycle or reduce the frequency of reporting unnecessary data.

[0076] Specifically, in the initial stage of mains power loss, if the state of charge reported by the energy storage management module is above 60%, the energy efficiency strategy decision core instructs the wireless communication module to maintain a regular wake-up monitoring every 15 minutes to ensure real-time delivery of main station commands. However, as energy is consumed, when the state of charge drops to the moderate range of 30% to 60%, the decision core will incorporate data from the communication environment perception module. If the link quality is good at this time, the system considers the energy consumption of a single transmission to be low and will appropriately extend the wake-up interval to 30 minutes; but if the perceived link quality is poor and the signal-to-noise ratio is extremely low, in order to avoid the severe power loss caused by repeated retransmissions under poor channel conditions, the decision core will decisively extend the wake-up interval to 60 minutes. When the state of charge further drops below 30%, the system is forced into a deep breathing mode, at which point the wake-up interval is set to no less than 120 minutes, retaining only the most basic survival status reporting.

[0077] The task priority scheduling module is responsible for the fine-grained management of the electricity consumption information data packets to be sent. This module does not simply process data in chronological order, but rather divides all tasks into four strict priority levels based on data attributes. The first priority is for emergency alarm data, covering power outage timestamps, outage location information, and severe equipment fault codes. The second priority is for routine load data, including real-time operating parameters such as voltage, current, and power factor. The third priority is for historical curve data, such as daily and monthly frozen energy readings. The fourth priority is for system maintenance data, including log information and configuration parameter update requests.

[0078] During standby mode operation, the task priority scheduling module implements a strict cache management strategy. For emergency alarm data with the highest priority, the system has the highest transmission weight and can immediately trigger the random access procedure of the wireless communication module for reporting, regardless of the current wake-up cycle limit. This design ensures that in the event of a large-scale power grid failure, emergency repair teams can obtain accurate outage range data as soon as possible.

[0079] For data with priorities of second, third, and fourth, the task priority scheduling module will perform fragmented transmission and incremental compression. For large historical data packets, the task priority scheduling module will divide them into multiple smaller data fragments. Within the limited time window of each wake-up, the number of data fragments to be sent is determined based on the remaining energy prediction value given by the energy efficiency strategy decision core.

[0080] If the power is extremely low, the system only sends the critical incremental part of each data packet, or only sends the data checksum, to ensure that even in extreme cases, the master station can know the existence status of the data, thereby avoiding energy waste caused by the failure to send large data packets.

[0081] In addition, this system includes an intelligent fault isolation subunit. This subunit is physically connected in series between the backup energy storage battery pack and the main circuit power supply path. It not only functions as an electronic fuse but also integrates a current ripple analysis algorithm. When the subunit detects a momentary short-circuit fault or abnormal current surge in the downstream load circuit, it rapidly shuts off the energy output path using a microsecond-level power semiconductor electronic switch. This mechanism effectively prevents overheating, fire, and other safety accidents caused by short-term high-current discharge of the backup energy storage battery pack when the acquisition terminal suffers from strong external electromagnetic interference or internal hardware damage leading to a short circuit, thus protecting the physical safety of the entire backup power system.

[0082] The energy storage management module further integrates cycle life degradation assessment logic. This logic establishes a battery life degradation model and continuously records the number of full charge-discharge cycles, average depth of discharge, and peak temperature fluctuations experienced by the battery throughout its entire lifespan. Using this accumulated data, the system estimates the percentage reduction in the effective usable capacity of the backup energy storage battery pack—i.e., the health status data—using empirical formulas.

[0083] When the assessed effective capacity is less than 80% of the factory rated capacity, the system automatically generates a battery replacement recommendation signal. This battery replacement recommendation signal will be displayed as a visual alarm on the local screen of the acquisition terminal, and simultaneously encapsulated in a system maintenance data packet, which will be reported to the remote power marketing management system via a wireless channel, realizing the transformation from traditional passive fault replacement to proactive preventive maintenance.

[0084] Example 2: This example details a control method based on the aforementioned power grid electricity consumption information collection and backup power management system. The method divides the system's operational logic into seven closely linked, closed-loop control steps, ensuring the robust operation of the data acquisition terminal under various complex operating conditions.

[0085] Step 1 is the system startup and initialization phase. After the acquisition terminal is powered on or reset, the main control program immediately activates the main power supply monitoring module, energy storage management module, and communication environment sensing module. Each module enters real-time inspection mode, confirming the linearity of the sensors and the zero-point drift of the sampling circuit through internal self-test programs. The system establishes initial electrical energy parameter benchmarks, including recording the current stable median voltage of the main power grid, the initial open-circuit voltage of the backup energy storage battery pack, and the static signal strength benchmarks of surrounding base stations.

[0086] Step 2 is the real-time power supply status comparison stage. The main power supply monitoring module continuously compares the real-time input voltage sample value with a preset safety threshold at the hardware level every 250 microseconds. The system maintains a sliding window to store the voltage waveform data of the most recent 10 cycles. When the amplitude of the input voltage drops by more than 30%, or the frequency deviation exceeds ±0.5 Hz and the duration exceeds the set threshold, the logic determines that the main power supply is interrupted. At this time, the main power supply monitoring module issues a non-maskable high-priority interrupt to the system kernel, forcing the hardware-level power management unit to smoothly switch from main power supply to backup energy storage battery pack power supply. The voltage fluctuation during the switching process is controlled within 5%, ensuring that the main control chip does not restart.

[0087] Step 3 is the backup power operating environment locking phase. After the system officially enters backup power mode, the energy storage management module immediately initiates a high-frequency coulomb counter statistical program. By integrating the voltage drop across the shunt, it locks the initial state of charge of the backup energy storage battery pack at the moment of power failure. Simultaneously, the communication environment sensing module is activated and enters deep mapping mode to scan the signals of all accessible base stations in the vicinity. This step not only acquires signal strength but also determines the optimal combination of communication physical layer parameters under the current environment through tentative handshake interactions, including selecting a suitable modulation and coding scheme, spreading factor, and initial transmit power. These parameters constitute a baseline configuration set to guide subsequent communication activities.

[0088] Step 4 is the energy efficiency strategy decision generation stage. This is the intelligent core of the entire control method. The energy efficiency strategy decision core summarizes the acquired battery state of charge percentage, channel quality assessment matrix, and the backlog of tasks to be sent in the current task priority scheduling module.

[0089] In this step, the decision-making core introduces a business traffic prediction mechanism. It analyzes historical traffic distribution over the past 24 scheduling cycles and uses a weighted moving average algorithm to predict the potential probability of the main station issuing control commands or inspection requests within the next hour. If a large-scale business interaction demand is predicted in the near future, the decision-making core will appropriately reduce the wake-up interval, prioritizing timely command response even when battery power is at a moderate level. By calculating energy efficiency weighting factors and matching them with a preset execution threshold table, the system ultimately generates a wake-up scheduling table for the wireless communication module.

[0090] Step 5 is the task scheduling and power adaptation phase. Whenever the wake-up scheduler reaches its predetermined trigger time, the wireless communication module is activated from deep sleep and enters the working state. The task priority scheduling module retrieves data packets to be sent from the non-volatile memory buffer according to their priority from high to low. During this process, the system adopts an adaptive power ramp-up strategy. The initial transmission attempt uses the minimum effective transmit power determined by the communication environment awareness module in step 3.

[0091] If no acknowledgment is received from the master station within the preset waiting time after transmission, the system determines that the current link has experienced momentary fading or interference. Subsequently, the transmission power is increased by 3 dB during the next retransmission. This process is repeated a maximum of 3 times. If it still fails, the current link is determined to be completely faulty. At this point, the task priority scheduling module immediately suspends the transmission process of all non-urgent tasks, saves the current transmission pointer position, and the decision core recalculates the next wake-up time, thus avoiding the depletion of limited power through blind retransmissions.

[0092] Step 6 is the ultra-low power protection and final warning stage. During the later stages of backup power operation, the energy storage management module continuously and closely monitors the battery terminal voltage. When the terminal voltage drops to a critical threshold of 0.2 volts above the cutoff voltage protection line, the system determines that the energy is about to be depleted.

[0093] At this point, the system triggers a final command. The task priority scheduling module quickly generates a self-diagnostic report, which includes the battery voltage, remaining power, channel quality statistics, and operational trajectory since the fault occurred. Using its remaining energy, the system attempts to transfer this self-diagnostic report and all unreported critical historical data to a low-power ferroelectric memory region. Subsequently, the system executes a shutdown sequence, cutting off power to all peripherals except the real-time clock circuit and wake-up interrupt circuit, entering an extremely low-power standby state with a current consumption of only microamps. This state will persist until mains power is restored.

[0094] Step 7 is the smooth power restoration and switchback phase. The main power supply monitoring module maintains minimal detection activity while the system is in standby or backup mode. When it detects that the AC bus voltage has recovered to more than 90% of its rated value and this stable state lasts for more than 60 seconds, the system performs a switchback operation. The power management unit reconnects the load to the main power supply, and the energy storage management module immediately initiates a high-current charging mode to recharge the battery pack. Simultaneously, the task priority scheduling module clears all cached pending data, the wireless communication module resumes full-function operation, and the system re-enters full-load operation.

[0095] The power grid power consumption information acquisition and backup power management system and its control method disclosed in this invention significantly improve the survivability of the acquisition terminal under complex power grid conditions through the deep integration of hardware sensing and software intelligent decision-making. The synergy of main power supply monitoring, energy management and environmental perception enables the system to adopt the most reasonable energy allocation scheme according to the dynamic changes of external conditions, ensuring the continuity of power data acquisition while achieving long-term protection of equipment hardware.

[0096] Example 3: Based on Examples 1 and 2, this example further refines the system's operating strategy under extreme electromagnetic interference environments and at the end of the battery's life cycle, to demonstrate the technical depth of the invention in dealing with special operating conditions.

[0097] To address common transient overvoltages or strong electromagnetic pulse interference in power grids, this embodiment incorporates a digital filtering preprocessing step in the main power supply monitoring module. Before the sampling sequence enters the logical decision-making process, the system performs algorithmic processing based on a combination of median and mean filtering to eliminate isolated noise points in the sampled signal. This ensures that the system will not make erroneous switching actions due to instantaneous waveform distortion caused by lightning strikes or the switching of large switching equipment. Simultaneously, the voltage drop instantaneous slope calculation of the main power supply monitoring module serves not only as a switching basis but also as an assessment of the type of power grid fault. For example, when an extremely high negative slope is detected accompanied by a sudden frequency change, the system predicts a severe permanent power outage and, immediately upon switching to backup power mode, instructs the task priority scheduling module to prepare for the transmission of an emergency alarm packet, eliminating subsequent decision-making waiting time.

[0098] In the energy storage management module, this embodiment emphasizes the importance of dynamic monitoring of the battery's ohmic internal resistance. The system monitors the battery's voltage drop response in real time by discharging a small current pulse every 10 minutes, thereby calculating the real-time dynamic internal resistance. This parameter, together with the state of charge, determines the battery's instantaneous high-current output capability at the current moment. When the task priority scheduling module is preparing to initiate wireless communication, the decision core will first query this output capability.

[0099] If the system detects that the battery's internal resistance is too high to support the peak current demand of the wireless communication module at maximum power transmission, it will automatically force a reduction in transmission power or adopt a shorter data packet transmission strategy to prevent the battery voltage drop caused by a sudden surge in current from triggering low-voltage shutdown protection. This precise control based on physical characteristics greatly extends the actual usable time of older batteries or batteries operating in low-temperature environments.

[0100] Regarding the communication environment perception module, this embodiment introduces base station load adaptive compensation logic. During large-scale regional power outages, surrounding wireless communication base stations often experience severe channel congestion due to a large number of terminals simultaneously attempting to report power loss information. The perception module of this system can quantitatively assess the current network congestion level by analyzing the paging message density sent by the base station and the collision rate during random access.

[0101] When the congestion level exceeds 90%, the task priority scheduling module will proactively execute a backoff algorithm. Instead of forcibly sending data according to a fixed wake-up schedule, the system adjusts the transmission time based on a backoff factor generated by random numbers, thus physically avoiding mass channel collisions. This strategy not only improves the data reporting success rate of the local terminal but also alleviates the instantaneous pressure on the entire power grid communication network.

[0102] Furthermore, the energy efficiency strategy decision-making core in this embodiment integrates a multi-dimensional health model. This multi-dimensional health model not only counts the number of charge-discharge cycles but also introduces a weighting factor for the contribution of discharge current multiples to lifespan. The system records the average discharge current during each backup power operation. If the battery frequently experiences high-current discharge, its health degradation rate is calculated using weighted averages. This refined health assessment data can provide accurate replacement cycle predictions for the power grid asset management system.

[0103] When the health status index approaches the 80% critical point, the energy efficiency strategy decision core will automatically tighten the energy allocation threshold and increase the wake-up cycle in backup power mode by 20% globally, thereby extending the battery's service life through this degraded operation.

[0104] Regarding the "last words" operation described in Example 2, this example standardizes and expands the fields of the self-diagnostic report. The generated report not only includes regular operating parameters but also a data segment called the "fault feature vector." This data segment records the original sampled waveform characteristics within the last 100 milliseconds before the main power supply interruption. Using this data, maintenance personnel can reconstruct the actual physical scenario of the power grid at the time of the fault, determining whether it was due to an outage in the upstream power grid, a short circuit in the local line, or an abnormal voltage surge. This design concept, which transforms the backup power management system into a power black box, provides an irreplaceable data source for fault tracing in smart grids.

[0105] Finally, this system adds an incremental update verification mechanism to the task priority scheduling module. In standby mode, if the system successfully sends a fragmented data frame, the master station will send back an acknowledgment frame containing the offset of the received bytes. The task priority scheduling module will update its sending pointer accordingly. Even if the system shuts down due to power depletion during transmission, it can accurately resume transmission from the last interrupted position after the main power is restored and restarted, instead of retransmitting the entire data packet. This breakpoint resumption mechanism maximizes the conservation of precious power and channel resources in extremely fragile communication environments.

[0106] In summary, this invention, through in-depth exploration of hardware module functions and meticulous optimization of software control strategies, constructs a comprehensive and multi-dimensional power grid consumption information collection and backup power management solution. Its three-in-one sensing framework, energy efficiency-weighted adaptive decision-making logic, and refined task scheduling mechanism jointly ensure the efficient survival of the data collection terminal in the event of power failure and the highly reliable transmission of critical data, providing solid technical support for the stable operation and fault self-healing of modern smart grids. All technical details disclosed in this embodiment, while adhering to system physical constraints, achieve a leapfrog improvement over existing backup power management technologies.

[0107] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0108] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A power grid electricity information collection and power management system, characterized in that, include: The main power supply monitoring module is used to capture the voltage waveform characteristics, frequency offset and phase vector data of the external AC power supply bus in real time. It converts the physical voltage signal into digital power parameters through the built-in high-speed voltage sampling circuit at a sampling frequency of no less than 4,000 times per second, and calculates the instantaneous slope of the voltage drop to determine the power supply stability level of the main power grid. The energy storage management module is electrically connected to the backup energy storage battery pack. It is used to collect the terminal voltage, charging and discharging current, cell surface temperature and ohmic internal resistance of the backup energy storage battery pack in real time using the high-frequency pulse current excitation method. It also uses a composite algorithm that combines charge integration and open-circuit voltage correction to calculate the state of charge and health data of the backup energy storage battery pack. The communication environment perception module is used to drive the wireless communication module to perform a full-spectrum scan of the energy density of the current wireless channel, obtain the received signal strength indication, signal-to-noise ratio, channel occupancy rate, and physical layer load status of the current serving base station, and establish a real-time evaluation matrix that reflects the quality of the wireless transmission link. The energy efficiency strategy decision core interacts bidirectionally with the main power supply monitoring module, the energy storage management module, and the communication environment perception module. Based on the acquired main power supply status, remaining power, and channel quality, the energy efficiency strategy decision core dynamically calculates the optimal communication interval step size and transmission power level. The task priority scheduling module is used to mark the electricity information data packets to be sent, and divide them into four priority levels according to the data attributes: emergency alarm, normal load, historical curve, and system maintenance. According to the output instructions of the energy efficiency strategy decision core, the module sequentially executes the encapsulation and queuing of data frames.

2. The power grid electricity consumption information collection and backup power management system according to claim 1, characterized in that, The main power supply monitoring module also includes a zero-crossing detection circuit; The zero-crossing detection circuit is used to determine the phase start point of the voltage waveform; When the main power supply monitoring module detects that the effective voltage value is lower than 70% of the rated voltage for three consecutive cycles, it sends a main power abnormality interruption signal to the energy efficiency strategy decision core to trigger the switch to backup power operation mode. The energy storage management module has a built-in temperature compensation circuit and a battery balancing circuit. The temperature compensation circuit relies on a preset temperature power consumption mapping table to achieve its function. This mapping table is compiled using measured data of the backup energy storage battery pack in the typical operating temperature range of -10℃ to 60℃, and combined with the environmental temperature distribution characteristics of the power grid power consumption information collection scenario, to match the actual power consumption parameters of the backup energy storage battery pack for different temperature ranges.

3. The power grid electricity consumption information collection and backup power management system according to claim 2, characterized in that, The temperature compensation circuit is used to correct the state of charge in real time based on a preset temperature power consumption mapping table. The battery balancing circuit is used to perform millivolt-level voltage difference compensation on the voltage of each series-connected cell in the backup energy storage battery pack during the charging process. The calculation logic of the state of charge is as follows: the state of charge at the current moment is obtained by subtracting the amount consumed during the discharge process from the state of charge at the initial moment; the amount consumed is obtained by integrating the outflowing current during the discharge time and dividing it by the rated capacity of the battery; during the integration process, it is corrected by the dynamic coulombic efficiency factor which is affected by the cell temperature in real time.

4. The power grid electricity consumption information collection and backup power management system according to claim 3, characterized in that, The communication environment perception module executes multi-threshold decision logic; When the received signal strength is lower than the first preset threshold and the signal-to-noise ratio is lower than the second preset threshold, the communication environment perception module determines that the current environment is a weak coverage environment. The first preset threshold is determined by combining the minimum receiving sensitivity standard of the power grid communication system with the minimum signal strength requirement for data transmission in the backup power system. It is achieved through actual measurements of signal strength and transmission success rate at multiple test points in a typical power grid deployment environment, with the minimum signal strength corresponding to "data transmission success rate ≥ 99%". The second preset threshold is determined by verifying the signal-to-noise ratio and data error rate through laboratory simulation and on-site measurement based on the anti-interference parameters of the communication module and the actual electromagnetic noise level of the power grid. It is achieved with the minimum signal-to-noise ratio corresponding to "bit error rate ≤ 0.1%". When the channel occupancy rate exceeds 80%, the communication environment perception module determines that the current environment is congested. The communication environment perception module also encapsulates environmental parameters into an environmental state vector and transmits it to the energy efficiency strategy decision core as an input variable for adjusting the wake-up cycle of the wireless communication module.

5. The power grid electricity consumption information collection and backup power management system according to claim 4, characterized in that, The core of the energy efficiency strategy decision-making process operates a dynamic trade-off algorithm. The dynamic trade-off algorithm determines the adjustment strategy for the wake-up frequency by calculating the energy efficiency weight factor. The calculation logic of the energy efficiency weighting factor is as follows: the weighted sum of the remaining power contribution value and the communication link quality contribution value is subtracted from the weighted value of the load pressure term of the task to be sent. The remaining power contribution value is determined based on the ratio of the current remaining capacity to the full capacity; The communication link quality contribution value is determined based on the ratio of the weighted value of the real-time sensed signal-to-noise ratio and signal strength to the ideal state reference value; The pending task load pressure term is determined based on the ratio of the number of data bytes backed up in the current sending queue to the maximum buffer capacity.

6. The power grid electricity consumption information collection and backup power management system according to claim 1, characterized in that, The system also includes an intelligent fault isolation subunit; The intelligent fault isolation subunit is connected in series between the backup energy storage battery pack and the main circuit, and it has a built-in current ripple analysis algorithm. When a short circuit or abnormal current change is detected in the back-end load, the intelligent fault isolation subunit uses a microsecond-level electronic switch to shut off the energy output.

7. A control method for a power grid electricity consumption information collection and backup power management system, characterized in that, Includes the following steps: Step 1: System startup initialization. The main power supply monitoring module, energy storage management module, and communication environment sensing module enter real-time inspection state and establish the initial electrical energy parameter benchmark. Step 2: The main power supply monitoring module continuously compares the input voltage with the preset safety threshold. The safety threshold is based on the rated operating voltage range of the power grid electricity consumption information acquisition terminal, combined with the voltage fluctuation tolerance standard of the main power supply, and is determined by measuring the voltage critical value when the main power supply is interrupted. When an abnormal input voltage is detected and it is determined that the main power supply is interrupted, the main power supply monitoring module sends a high-priority interrupt to the system kernel, forcing the system to switch from main power supply to backup energy storage battery pack power supply. Step 3: The system enters the backup power mode. The energy storage management module starts high-frequency coulomb counting to lock the initial state of charge of the backup energy storage battery pack. At the same time, the communication environment perception module conducts a comprehensive mapping of the surrounding base station signals to determine the optimal combination of communication physical parameters. Step 4: The energy efficiency strategy decision core calculates the current energy efficiency weight factor based on the acquired state of charge, channel environment parameters, and the current backlog of tasks to be sent. It then compares the energy efficiency weight factor with the preset execution threshold. The execution threshold is determined through simulation testing of the energy efficiency strategy, based on the remaining power threshold of the backup energy storage battery pack, the priority weight of the tasks to be sent, and the power consumption classification standard of the wireless communication module. This generates a wake-up scheduling table for the wireless communication module. Step 5: Activate the wireless communication module at the wake-up time. The task priority scheduling module extracts data packets from the data buffer and sends them in descending order. During the sending process, the transmission power is monitored in real time. If no acknowledgment is received within 3 attempts, the energy efficiency strategy decision core will recalculate the next wake-up time. Step 6: Monitor the battery terminal voltage in real time during the discharge process. When the battery terminal voltage drops to 0.2 volts above the cutoff voltage protection line, the system triggers the final words operation, transfers all unsent key historical data to the low-power storage area, and cuts off the power supply to the peripheral devices to enter the standby state. Step 7: The main power supply monitoring module continuously detects the AC bus status. When it detects that the main power supply has returned to normal and the duration exceeds 60 seconds, the system performs a switchback operation.

8. The control method for the power grid electricity consumption information collection and backup power management system according to claim 7, characterized in that, In step 4, a traffic prediction mechanism is also introduced: The energy efficiency strategy decision-making core is based on the communication traffic distribution over the past 24 cycles, and uses a weighted moving average algorithm to predict the probability of potential instruction issuance in the next hour. When a need for issuing inspection commands is predicted, the energy efficiency strategy decision core increases the wake-up frequency.

9. The control method for the power grid electricity consumption information collection and backup power management system according to claim 8, characterized in that, In step 5, an adaptive power ramp strategy is adopted for the transmit power control of the wireless communication module: The initial transmission attempts to use the minimum effective power. If no acknowledgment is received, the transmission power is increased by 3 dB on the next retransmission until the maximum allowable transmission power is reached.

10. The control method for the power grid electricity consumption information collection and backup power management system according to claim 9, characterized in that, In step 6, the process of delivering a dying message also includes: The system generates a self-diagnostic report that includes the battery status at the last moment, channel quality statistics, and fault feature vectors. The fault feature vector records the original sampled waveform features within the last 100 milliseconds before the main power supply interruption; The self-diagnostic report is stored in a specific sector of non-volatile memory and is reported preferentially after the main power is restored.

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