Intelligent energy management and control method based on RFID technology

Abstract

The invention discloses an intelligent energy management and control method based on the RFID technology, and the method comprises the steps: fixedly installing passive RFID tags on a plurality of pieces of energy consumption equipment, and enabling each passive RFID tag to be physically integrated with a current sensor and a voltage sensor; periodically reading an identification code, a real-time current value and a real-time voltage value of each passive RFID tag through a plurality of fixed RFID reader-writers deployed in a management and control area; the data processing unit receives the identification codes, the real-time current values and the real-time voltage values uploaded by all the RFID reader-writers and generates real-time power data of all the energy consumption devices; predicting a regional total load change trend in a future time period by adopting a time sequence analysis method; generating a scheduling instruction according to a preset equipment priority strategy; and the scheduling instruction is reversely written into the storage area of the corresponding passive RFID tag through the RFID reader-writer. The overall management and control efficiency of the system can be effectively improved, and the human intervention cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management and control. More specifically, the present invention relates to an intelligent energy management and control method based on RFID technology. Background Art

[0002] In the field of intelligent management and control of energy-consuming devices, realizing real-time monitoring of device energy consumption, load prediction, and dynamic scheduling is a key requirement for ensuring the stable operation of the power grid and improving energy utilization efficiency. There are several problems to be solved in the existing technologies in aspects such as data collection, load prediction, scheduling control, and device communication. These problems make it difficult for the management and control system to meet the actual requirements in terms of reliability and effectiveness.

[0003] In terms of data collection and device integration, the energy consumption monitoring of traditional energy-consuming devices usually relies on independent sensor modules and wired communication methods. However, independent sensor modules require additional power supply lines and communication interfaces, increasing the hardware cost and installation complexity. In terms of load prediction and scheduling strategies, existing energy management and control systems mostly perform load prediction based on fixed thresholds or simple historical data statistics, without fully considering the dynamic correlation between the real-time operating state of devices and historical energy consumption patterns. For example, the total regional load is predicted only by simply accumulating real-time power data, ignoring the differences in energy consumption characteristics among different device types, resulting in a deviation between the prediction result and the actual load change trend. When the predicted total load approaches the power grid supply threshold, traditional methods lack refined scheduling strategies based on device priorities, and may perform a one-size-fits-all power reduction operation, affecting the normal operation of key devices; or due to the single triggering condition of scheduling instructions, they cannot dynamically adjust the management and control intensity according to the load growth rate, increasing the risk of power grid load imbalance.

[0004] Therefore, it is necessary to design a technical solution that can overcome the above defects to a certain extent. Summary of the Invention

[0005] An object of the present invention is to provide an intelligent energy management and control method based on RFID technology, which can effectively improve the overall management and control efficiency of the system and reduce the cost of human intervention.

[0006] To achieve these objects and other advantages of the present invention, according to one aspect of the present invention, there is provided an energy intelligent control method based on RFID technology, including: S1: Passive RFID tags are fixedly installed on multiple energy-consuming devices, and each passive RFID tag is physically integrated with a current sensor and a voltage sensor. The current sensor is used to collect the real-time current value of the energy-consuming device, and the voltage sensor is used to collect the real-time voltage value of the energy-consuming device; S2: Through multiple fixed RFID readers deployed in the control area, the identification code, real-time current value, and real-time voltage value of each passive RFID tag are periodically read; S3: The data processing unit receives the identification code, real-time current value, and real-time voltage value uploaded by all RFID readers, performs a product operation on the real-time current value and real-time voltage value associated with the same identification code, and generates the real-time power data of each energy-consuming device; S4: The load prediction unit, based on the real-time power data and in combination with the device operation mode records in the historical energy consumption database, uses the time series analysis method to predict the change trend of the total regional load in the future time period; S5: The control unit compares the change trend of the total regional load with the power grid supply threshold in real time. When the predicted total load exceeds a preset ratio of the power grid supply threshold, a scheduling instruction is generated according to the preset device priority strategy; S6: The scheduling instruction is reversely written into the storage area of the corresponding passive RFID tag through the RFID reader, and the relay switch built into the target energy-consuming device is triggered to perform a power reduction operation or a delayed start operation.

[0007] Further, S3 includes: S3.1: Perform a first-level filtering on the received real-time current value. When the current value exceeds the range of 80% to 120% of the rated current value for 3 consecutive sampling periods, activate the current sensor diagnostic flag; S3.2: Perform a second-level filtering on the received real-time voltage value. When the voltage value continuously deviates from the nominal voltage value by ±10% for more than 5 sampling periods, activate the voltage sensor diagnostic flag; S3.3: When neither the current sensor diagnostic flag nor the voltage sensor diagnostic flag is activated, perform the product operation of the real-time current value and the real-time voltage value to generate effective real-time power data; S3.4: When any diagnostic flag is activated, perform an alternative calculation: If the current sensor diagnostic flag is activated, use the average historical current value in the last 5 minutes to replace the real-time current value; If the voltage sensor diagnostic flag is activated, use the power grid nominal voltage value to replace the real-time voltage value; Perform a product operation based on the replacement value to generate compensated real-time power data; S3.5: Store the effective real-time power data or the compensated real-time power data associated with the corresponding identification code into the real-time power database.

[0008] Further, if the real-time current values in three consecutive sampling periods exceed the range of 80% to 120% of the rated current value of the device, and the standard deviation of the current change rate is greater than 200% of the standard deviation of the rated current change rate within the three consecutive sampling periods, the current sensor diagnostic flag is activated; if the real-time voltage value deviates from the nominal grid voltage value by ±10% for five consecutive sampling periods, and the absolute value of the difference between adjacent sampling points of the real-time voltage value is less than 0.5% of the nominal voltage value during the deviation period, the voltage sensor diagnostic flag is activated.

[0009] Further, S4 includes: S4.1: Based on the real-time power data and combined with the device operation mode records in the historical energy consumption database, identify the current operation status flags of each energy-consuming device. The operation status flags include the operation status, standby status, and shutdown status; extract the device historical power sequences that match the current operation status flags from the historical energy consumption database, group them by device type. The device types include continuous production type, intermittent operation type, and auxiliary equipment, and use the time series analysis method to generate a single-device load prediction curve; S4.2: Real-time collect the multi-dimensional feature vectors of the device through the dynamic device energy efficiency portrait engine. The feature vectors include: the device inherent attribute vector, which includes the device type code, rated power, and historical average energy efficiency ratio; the real-time status vector, which includes the current operation status flag, status duration, and power change rate. The power change rate is calculated by dividing the absolute value of the difference between the current power and the power in the previous sampling period by the sampling interval time; the energy efficiency health vector, which includes the deviation degree from the energy efficiency baseline model and the sensor diagnostic flag. The sensor diagnostic flag is the abnormal activation status of the current or voltage sensor; S4.3: Input the multi-dimensional feature vectors into the portrait scoring matrix and output the dynamic weight coefficients according to the following rules: when the power change rate in the real-time status vector exceeds 15% of the rated power change rate, increase the weight coefficient by 0.1 to 0.3; when the real-time power in the standby status in the real-time status vector continues to be greater than 5% of the rated power for three consecutive sampling periods, decrease the weight coefficient by 0.1 to 0.2; when the deviation degree in the energy efficiency health vector exceeds the preset threshold, freeze the weight coefficient and generate a device-level energy efficiency alarm; S4.4: Perform a weighted operation on the dynamic weight coefficients and the single-device load prediction curve in the time dimension to generate a weighted prediction curve; superimpose the weighted prediction curves of all devices in the time dimension to generate the regional total load change trend.

[0010] Further, the S4.4 includes: S4.4.1: Establish an equipment association rule library to store the mapping relationship between the preset equipment state combinations and the load compensation coefficients, where the equipment state combinations are composed of the operating state marks of at least two energy-consuming devices; S4.4.2: Real-time detect the operating state marks of all current energy-consuming devices. When there is a state combination that matches the equipment association rule library, extract the corresponding load compensation coefficient; S4.4.3: Multiply the load compensation coefficient by the single-device load prediction curve of the energy-consuming device with the largest rated power in the state combination to generate a compensated prediction curve; S4.4.4: Superimpose the compensated prediction curve and the single-device load prediction curves of other energy-consuming devices that have not triggered compensation in the time dimension to generate the regional total load change trend.

[0011] Further, the S5 includes: S5.1: Calculate the instantaneous slope of the regional total load change trend, where the instantaneous slope is obtained by dividing the difference between the predicted load values at two consecutive future time points by the time interval; S5.2: When the predicted total load exceeds a preset percentage of the grid supply threshold and the absolute value of the instantaneous slope is less than 0.05 kW / s, generate a first-level scheduling instruction, and the first-level scheduling instruction only triggers the energy-consuming device with the lowest priority in the equipment priority strategy to perform a power reduction operation; S5.3: When the predicted total load exceeds a preset percentage of the grid supply threshold and the absolute value of the instantaneous slope is greater than or equal to 0.05 kW / s and less than 0.1 kW / s, generate a second-level scheduling instruction, and the second-level scheduling instruction triggers the energy-consuming device with the lowest priority and the energy-consuming device with the second-lowest priority in the equipment priority strategy to perform a power reduction operation; S5.4: When the predicted total load exceeds a preset percentage of the grid supply threshold and the absolute value of the instantaneous slope is greater than or equal to 0.1 kW / s, generate a third-level scheduling instruction, and the third-level scheduling instruction triggers all target energy-consuming devices in the equipment priority strategy to perform a power reduction operation or a delayed start operation according to the equipment priority strategy.

[0012] Further, it also includes: Real-time calculate the current grid load margin, where the grid load margin is equal to the grid supply threshold minus the real-time total load value; When the grid load margin continuously exceeds 10% of the grid supply threshold and the duration exceeds the set value of the equipment recovery delay counter, perform the following recovery operations: For the energy-consuming devices triggered by the first-level scheduling instruction, immediately cancel the power reduction operation when the grid load margin is greater than 15%; For the energy-consuming devices triggered by the second-level scheduling instruction, cancel the power reduction operation when the grid load margin is greater than 12%; For the energy-consuming devices triggered by the third-level scheduling instruction, cancel the power reduction operation or allow the start operation when the grid load margin is greater than 10%; After each execution of the recovery operation, reset the equipment recovery delay counter to the initial value.

[0013] Further, the S6 includes: S6.1: Split the scheduling instruction into an instruction header and a data body. The instruction header contains the identification code of the target passive RFID tag and the operation type flag, and the data body contains the power reduction parameter or the delay start parameter; S6.2: Send the instruction header to the target RFID reader through the first wireless communication channel, and at the same time send the data body to the same target RFID reader through the second wireless communication channel. The first wireless communication channel uses the UHF band, and the second wireless communication channel uses the HF band; S6.3: After the target RFID reader receives the complete instruction header and data body, perform dual-channel data verification: When the CRC check code in the instruction header matches the CRC check code in the data body, write the recombined complete instruction in reverse to the corresponding passive RFID tag storage area; When the CRC check codes do not match or data is lost in a single channel, request the data processing unit to retransmit the instruction; S6.4: After the passive RFID tag receives the complete instruction, output a pulse signal to the control end of the relay switch through the built-in I / O interface to trigger the execution of the power reduction operation or the delay start operation.

[0014] Further, the S6.3 also includes: S6.3.1: When the target RFID reader receives multiple instruction headers at the same time, sort them according to the reception timestamp and extract the first complete instruction header, and store the remaining instruction headers in the conflict buffer queue; S6.3.2: Allocate a dynamic frequency point for the currently processed instruction header. The dynamic frequency point is selected from a preset ultra-high frequency hopping sequence, and the frequency range of the ultra-high frequency hopping sequence is from 902 MHz to 928 MHz, and send a frequency point synchronization instruction to the corresponding passive RFID tag through the high-frequency channel; S6.3.3: Receive the data body at the dynamic frequency point. If the reception fails, switch to the next frequency point in the ultra-high frequency hopping sequence to resend the frequency point synchronization instruction and receive the data body. The maximum number of retries is 3 times; S6.3.4: Perform two-level verification on the successfully received instruction header and data body: The first-level verification: Compare the cyclic redundancy check codes of the instruction header and the data body; The second-level verification: Verify the logical consistency between the operation type flag in the instruction header and the power reduction parameter or the delay start parameter in the data body; S6.3.5: Only when both levels of verification pass, write the recombined instruction into the passive RFID tag storage area; If any level of verification fails, trigger the processing flow of the next instruction header in the conflict buffer queue.

[0015] The present invention has at least the following beneficial effects: The present invention physically integrates a passive RFID tag with current and voltage sensors. While maintaining the maintenance-free characteristics of the device, it significantly simplifies the hardware deployment process, reduces the initial installation cost of the system and the long-term operation and maintenance difficulty, and is particularly suitable for the rapid networking and real-time monitoring of a large number of energy-consuming devices in scenarios such as industrial plants and commercial buildings. Through the correlation analysis of the real-time power calculation model and the device operation status, combined with the device operation modes in the historical energy consumption database, it can accurately identify the electricity consumption characteristics of different types of devices, generate a more accurate prediction of the regional total load change trend that fits the actual operation situation, and provide a more reliable basis for scheduling decisions. Compared with traditional methods, this prediction mechanism fully considers the dynamic coupling of the real-time state of the device and the historical operation law, effectively improving the fit between the load prediction and the actual electricity consumption situation. A hierarchical strategy based on device priority is introduced, and the control intensity is dynamically adjusted according to the comparison result of the load change trend and the power grid supply threshold, combined with the load growth rate.

[0016] Other advantages, objectives, and features of the present invention will be partially reflected by the following description, and partially will also be understood by those skilled in the art through the research and practice of the present invention. Brief Description of the Drawings

[0017] Figure 1 It is a flowchart of an embodiment of the present application. Detailed Embodiment

[0018] The following further elaborates on the present invention in detail so that those skilled in the art can implement it with reference to the description in the specification.

[0019] It should be understood that terms such as "having", "including", and "comprising" used in the embodiments of the present application do not exclude the existence or addition of one or more other elements or their combinations. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present application are only used to explain the relative position relationship and movement conditions between components in a specific posture. If the specific posture changes, the directional indication will also change accordingly. When an element is referred to as being "fixed to" or "disposed on" another element, it can be directly on the other element or there may be an intermediate element at the same time. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or it can also be indirectly connected to the other element through an intermediate element. The descriptions involving "first", "second", etc. in the embodiments of the present application are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one of such features.

[0020] It should be noted that the technical solutions between the various embodiments of the present application can be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present application.

[0021] As Figure 1 shown, the embodiments of the present application provide an energy intelligent control method based on RFID technology, including: S1: Passive RFID tags are fixedly installed on multiple energy-consuming devices, and each passive RFID tag is physically integrated with a current sensor and a voltage sensor. The current sensor is used to collect the real-time current value of the energy-consuming device, and the voltage sensor is used to collect the real-time voltage value of the energy-consuming device; S2: Through multiple fixed RFID readers deployed in the control area, the identification code, real-time current value, and real-time voltage value of each passive RFID tag are periodically read; S3: The data processing unit receives the identification code, real-time current value, and real-time voltage value uploaded by all RFID readers, performs a product operation on the real-time current value and real-time voltage value associated with the same identification code, and generates the real-time power data of each energy-consuming device; S4: The load prediction unit predicts the change trend of the total regional load in the future time period by using the time series analysis method based on the real-time power data and combining the device operation mode records in the historical energy consumption database; S5: The control unit compares the change trend of the total regional load with the grid supply threshold in real time. When the predicted total load exceeds a preset ratio of the grid supply threshold, a scheduling instruction is generated according to the preset device priority strategy; S6: The scheduling instruction is reversely written into the storage area of the corresponding passive RFID tag through the RFID reader, and the relay switch built into the target energy-consuming device is triggered to perform a power reduction operation or a delayed start operation.

[0022] Exemplarily, the passive RFID tag in S1 can select products from NXP, which support high-frequency or ultra-high-frequency communication and can obtain energy from the RFID reader through electromagnetic induction. The current sensor can select the Hall effect sensor from LEM or the linear current sensor from Allegro, which is used to collect real-time current in series in the phase wire loop of the energy-consuming device. The voltage sensor can adopt the voltage sensor module from TE or the differential voltage sensor from Intersil, which is connected in parallel at the device power input terminal to obtain the real-time voltage value. The tag and the sensor are fixed near the power connection box on the device shell with epoxy resin glue, and the signal wire of the sensor is connected to the integrated interface of the tag through a shielded wire. The fixed RFID reader in S2 can select products from ThingMagic or Zebra, which are deployed on the workshop ceiling or the device bracket and transmit radio frequency signals through the antenna to activate the tag and read data at a cycle of 5 seconds, 10 seconds or 15 seconds. The data processing unit in S3 can adopt an embedded industrial computer from Advantech or Dell, group the received current and voltage data according to the tag identification code, and obtain the real-time power through multiplication operation. For example, when the real-time current of a certain device is 5A and the voltage is 220V, the real-time power is 5×220 = 1100W. The load forecasting unit in S4 uses the statsmodels library in Python to implement the ARIMA model or the forecast package in R language to implement the exponential smoothing method. First, it conducts a stationarity test on the real-time power data (such as the ADF test). If the data is not stationary, it performs differencing processing, and then combines the operation mode records such as the device start-stop time and load cycle in the historical database to determine the model parameters (such as p, d, q values) through the AIC criterion, and forecasts the total regional load in the next 1 hour, 2 hours or 4 hours. The power grid supply threshold in S5 is provided by the power company or set according to 40%-62.5% of the transformer rated capacity, and the preset ratio can be 10%, 15% or 20%. The device priority strategy is configured in advance through the management software. For example, the main motor of the production line is set to high priority, and the lighting equipment is set to low priority. The relay switch in S6 can select products from Omron or Schneider. After the reader writes the scheduling instruction into the tag storage area, the tag outputs a pulse signal of 5V, 12V or 24V through the I / O interface to trigger the relay action, realizing device power adjustment or start-up delay. In this embodiment, first, passive RFID tags integrated with current sensors and voltage sensors are installed on each energy-consuming device to ensure that the sensors are correctly connected to the device power circuit. The reader collects tag data at a set period and uploads it to the data processing unit. After calculation, the real-time power is generated and transmitted to the load prediction unit to predict the load trend in combination with historical data. The control unit compares the prediction result with the grid threshold. When the proportion exceeds the preset ratio, scheduling instructions are generated according to the priority, and the relay action is triggered by writing to the tag through the reader. This method realizes the real-time monitoring of equipment energy consumption and the dynamic regulation of the grid load. Without changing the original circuit structure of the equipment, through standardized hardware integration and algorithm processing, it improves the compatibility and reliability of the energy management and control system, providing a feasible technical solution for the stable operation of the grid and equipment energy conservation.

[0023] In another embodiment, in the energy intelligent management and control method based on RFID technology, S3 includes: S3.1: Perform first-level filtering on the received real-time current value. When the current value exceeds the range of 80% to 120% of the rated current value for 3 consecutive sampling periods, activate the current sensor diagnostic flag; S3.2: Perform second-level filtering on the received real-time voltage value. When the voltage value continuously deviates from the nominal voltage value by ±10% for more than 5 sampling periods, activate the voltage sensor diagnostic flag; S3.3: When neither the current sensor diagnostic flag nor the voltage sensor diagnostic flag is activated, perform the multiplication operation of the real-time current value and the real-time voltage value to generate effective real-time power data; S3.4: When any diagnostic flag is activated, perform alternative calculations: if the current sensor diagnostic flag is activated, use the average historical current value in the last 5 minutes to replace the real-time current value; if the voltage sensor diagnostic flag is activated, use the grid nominal voltage value to replace the real-time voltage value; perform the multiplication operation based on the replacement value to generate compensated real-time power data; S3.5: Store the effective real-time power data or the compensated real-time power data associated with the corresponding identification code in the real-time power database.

[0024] Exemplarily, in S3.1, the first-stage filtering adopts a 3-point moving average filter to perform arithmetic averaging on the real-time current values for three consecutive cycles in each sampling period to smooth short-term fluctuations. The rated current value is obtained from the nameplate parameters of the energy-consuming device or the initial configuration data. For example, the rated current of a certain motor is 30A. When the current values collected in three consecutive cycles are 25A, 28A, and 35A respectively (exceeding the range of 80% i.e., 24A to 120% i.e., 36A), and the original current value in each cycle triggers the threshold judgment, the current sensor diagnostic flag is activated. In S3.2, the second-stage filtering adopts a 5-point median filter to take the median value of the real-time voltage value for five consecutive cycles as the filtering result. The nominal voltage value is the rated operating voltage of the device (such as 220V for single-phase devices and 380V for three-phase devices). If the filtered voltage values are 245V, 243V, 240V, 238V, and 235V for five consecutive cycles (continuously higher than +10% of 220V i.e., 242V), the voltage sensor diagnostic flag is activated. In S3.4 during the alternative calculation, if the current diagnostic flag is activated, the data processing unit retrieves all the current sampling values of the device in the past 5 minutes from the historical database (sampling once per second, a total of 300 data points), calculates the arithmetic average as the alternative current; if the voltage diagnostic flag is activated, directly use the grid nominal voltage value (such as 220V) to replace the real-time voltage, and then calculate the compensation power through the power formula (Power = Current × Voltage). In S3.5, the real-time power database adopts a relational database structure, with the device identification code as the primary key, indexed by the timestamp. Each record contains the identification code, sampling time, power value (valid or compensation flag), and the sensor status flag, facilitating subsequent query and analysis. In this embodiment, the data processing unit first performs hierarchical filtering on the current and voltage data, identifies abnormalities through threshold judgment in consecutive cycles, directly calculates the power when normal, and compensates based on historical data or nominal values when abnormal, ensuring the continuity of power data. This processing method effectively distinguishes sensor failures from normal device fluctuations, avoids calculation errors caused by abnormal data in individual cycles, provides a stable data source for load forecasting, and improves the robustness of the data processing link. In another embodiment, in the energy intelligent management and control method based on RFID technology, if the real-time current value in three consecutive sampling periods exceeds the range of 80% to 120% of the rated current value of the device, and within three consecutive sampling periods, the standard deviation of the current change rate is greater than 200% of the rated current change rate standard deviation, the current sensor diagnostic flag is activated; if the real-time voltage value deviates from the ±10% of the grid nominal voltage value for five consecutive sampling periods, and during the deviation period, the absolute value of the difference between adjacent sampling points of the real-time voltage value is less than 0.5% of the nominal voltage value, the voltage sensor diagnostic flag is activated.

[0025] Exemplarily, the rate of change of current is calculated by dividing the difference in current between adjacent sampling periods by the sampling interval time. For example, if the sampling interval is 2 seconds and the current values for three consecutive periods are 18A, 22A, and 20A, the rates of change are (22 - 18) / 2 = 2A / s and (20 - 22) / 2 = -1A / s respectively. The standard deviation of the rated current change rate is obtained through statistics of historical data during stable operation of the device (such as the standard deviation of the rate of change during stable operation is 0.8A / s). The standard deviation of the rate of change for the current three periods is 1.5A / s, which is greater than 200% of the rated value (1.6A / s. Here, it is assumed that 200% of the rated value of 0.8A / s is 1.6A / s, and the actual calculation needs to be strictly in accordance with the formula), then the current diagnosis flag is activated. In terms of voltage, the nominal voltage is 220V, and the absolute value of the difference between adjacent sampling points needs to be less than 0.5%, that is, 1.1V. If the voltage values for five consecutive periods are 195V, 194.5V, 194V, 193.5V, and 193V (the difference between each adjacent value is 0.5V, all less than 1.1V, and continuously lower than -10%, that is, 198V), then the voltage diagnosis flag is activated. This dual-condition judgment avoids misjudgment caused by current surges (large rate of change but small standard deviation) or transient voltage fluctuations (large difference but short duration) during normal start-up and shutdown of the device.

[0026] In this embodiment, current diagnosis combines a threshold range with the standard deviation of the rate of change, and voltage diagnosis combines the deviation amplitude with the adjacent difference to more accurately identify sensor abnormalities. For example, when the current surges rapidly during device startup but the standard deviation of the rate of change is large, it belongs to normal fluctuations and does not trigger diagnosis; while continuous small deviations caused by sensor drift will be effectively captured. This mechanism reduces false triggering of data compensation, ensures the accuracy of real-time power calculation, and provides more reliable input data for subsequent load prediction models.

[0027] In another embodiment, in the energy intelligent control method based on RFID technology, S4 includes: S4.1: Based on the real-time power data and combined with the device operation mode records in the historical energy consumption database, identify the current operation status markers of each energy-consuming device. The operation status markers include the operation status, standby status, and shutdown status; extract the device historical power sequences that match the current operation status markers from the historical energy consumption database, group them by device type, and the device types include continuous production type, intermittent operation type, and auxiliary equipment. Use the time series analysis method to generate the single-device load prediction curve; S4.2: Real-time collect the multi-dimensional feature vectors of the device through the dynamic device energy efficiency portrait engine. The feature vectors include: the device inherent attribute vector, which includes the device type code, rated power, and historical average energy efficiency ratio; the real-time status vector, which includes the current operation status marker, status duration, and power change rate. The power change rate is calculated by dividing the absolute value of the difference between the current power and the power in the previous sampling period by the sampling interval time; the energy efficiency health vector, which includes the deviation degree from the energy efficiency baseline model of the current power and the sensor diagnosis marker. The sensor diagnosis marker is the abnormal activation status of the current or voltage sensor; S4.3: Input the multi-dimensional feature vectors into the portrait scoring matrix and output the dynamic weight coefficients according to the following rules: When the power change rate in the real-time status vector exceeds 15% of the rated power change rate, increase the weight coefficient by 0.1 to 0.3; when the real-time power in the standby status in the real-time status vector continues to be greater than 5% of the rated power for 3 sampling periods, decrease the weight coefficient by 0.1 to 0.2; when the deviation degree in the energy efficiency health vector exceeds the preset threshold, freeze the weight coefficient and generate a device-level energy efficiency alarm; S4.4: Perform a weighted operation on the dynamic weight coefficients and the single-device load prediction curve in the time dimension to generate a weighted prediction curve; superimpose the weighted prediction curves of all devices in the time dimension to generate the regional total load change trend.

[0028] Exemplarily, when identifying the operating state in S4.1, the operating state refers to the state where the device is currently consuming power and is in a normal working state. The standby state refers to the state where the device is powered on but not operating at full load and has a low power consumption. The shutdown state refers to the state where the device is powered off and has no power consumption. The historical energy consumption database can store the power data of the device for the past 1 month, 3 months, or 6 months. Among the device types, the continuous production type can be the main motor of a factory production line, the intermittent operation type can be a welding machine, and the auxiliary device can be a lighting system. The time series analysis method can adopt the aforementioned exponential smoothing method. In S4.2, the dynamic device energy efficiency portrait engine can adopt an industrial Internet of Things gateway. The device type code in the device inherent attribute vector is represented by 01 for the continuous production type, 02 for the intermittent operation type, and 03 for the auxiliary device. The rated power is obtained from the device nameplate. For example, the rated power of the main motor is 200 kW. The historical average energy efficiency ratio is the ratio of active power to input apparent power, and the average value for the past week is taken. The sampling interval time in the real-time state vector is set to 1 minute, 2 minutes, or 5 minutes. The power change rate is calculated as follows: if the current power is 150 kW, the previous cycle is 140 kW, and the interval is 1 minute, then the change rate is (150 - 140) / 1 = 10 kW / min. The energy efficiency baseline model is established through the historical power data when the device is operating normally. For example, the normal operating power baseline of the main motor is 180 ± 10 kW. The deviation is calculated as |current power - baseline power| / baseline power × 100%. When the deviation exceeds 15%, 20%, or 25%, an exception is triggered. In S4.3, the portrait scoring matrix can adopt a preset weighted table. The rated power change rate is obtained through the statistics of the historical data when the device is operating stably. For example, the rated change rate of a certain device is 5 kW / min. When the real-time power change rate exceeds 5 × 15% = 0.75 kW / min, the weight is increased. If the current power change rate of a continuous production type device is 1.5 kW / min, exceeding 15% (0.75 kW / min) of the rated change rate, the weight coefficient can be increased by 0.2 according to the rule, so that the weight changes from the initial 1.0 to 1.2. If an auxiliary device is in the standby state and the real-time power is continuously 15 kW for 3 sampling periods (each period is 5 minutes), and its rated power is 200 kW, 15 kW exceeds 5% (10 kW) of the rated power, then the weight coefficient is decreased by 0.1 to 0.9. In S4.4, the weighted operation is to multiply the predicted power at each time point by the dynamic weight coefficient. For example, at t = 30 minutes, the predicted power of a single main motor device is 100 kW, and the weight coefficient is 1.2. After weighting, it is 120 kW. At the same time period, the predicted power of an intermittent operation welding machine is 80 kW, and the weight coefficient is 1.0. After weighting, it is 80 kW. The predicted power of a lighting device is 20 kW, and the weight coefficient is 0.9. After weighting, it is 18 kW. Add the weighted power values of the three at t = 30 minutes, 120 kW + 80 kW + 18 kW = 218 kW, that is, the predicted value of the regional total load at this time point is 218 kW.

[0029] In this embodiment, first, through operation status recognition and device type grouping, combined with time series analysis, a single-device prediction curve is generated. Then, a multi-dimensional feature vector is collected using a dynamic device energy efficiency portrait engine, and the dynamic weight coefficient is calculated through a scoring matrix. After weighting the prediction curve and superimposing it, the regional total load trend is obtained. This method fully considers the real-time operation status, energy efficiency health status, and historical patterns of the devices. Compared with the traditional prediction method that simply accumulates based on historical data, it can more accurately capture the dynamic changes of the device load. The adjustment of the weight coefficient reflects the additional impact of the current operation mode of the device on the load, making the prediction result more in line with the actual operation situation, avoiding prediction deviations caused by device state fluctuations, providing a more reliable basis for subsequent load scheduling, and effectively improving the response accuracy and adaptability of the energy management and control system to load changes.

[0030] In another embodiment, in the energy intelligent management and control method based on RFID technology, S4.4 includes: S4.4.1: Establish a device association rule library to store the mapping relationship between preset device state combinations and load compensation coefficients, where the device state combination is composed of the operation status marks of at least two energy-consuming devices; S4.4.2: Real-time detect the operation status marks of all current energy-consuming devices. When there is a state combination that matches the device association rule library, extract the corresponding load compensation coefficient; S4.4.3: Multiply the load compensation coefficient by the single-device load prediction curve of the energy-consuming device with the largest rated power in this state combination to generate a compensated prediction curve; S4.4.4: Superimpose the compensated prediction curve and the single-device load prediction curves of other energy-consuming devices that have not triggered compensation in the time dimension to generate the regional total load change trend.

[0031] Exemplarily, the device association rule library in S4.4.1 is established through historical data statistics. For example, when collecting the load data when the combination of "air compressor (running) + chiller (running)" appears, it is found that the actual load is 10% higher than the sum of individual predictions, then a compensation coefficient of 1.1 is set; the combination of "three electric welders (running) + lighting (fully on)" often causes a 20% sudden increase in load, and a compensation coefficient of 1.2 is set. The rule library adopts a key-value pair structure, where the key is the device status combination (such as "device A: running, device B: running"), and the value is the compensation coefficient (1.05 - 1.3). In S4.4.2, the real-time detection module traverses all device statuses every 5 seconds. When a combination that meets the rule library (such as at least two devices are in the running state at the same time and there is a collaborative load effect in history) is found, compensation is triggered. In S4.4.3, for the combination of "air compressor + chiller", if the rated power of the air compressor is 500 kW (greater than the 300 kW of the chiller), the power value at each time point of its prediction curve is multiplied by the compensation coefficient of 1.1 to generate a compensated curve. In S4.4.4, when superimposing, first generate a compensation curve for the device that triggers compensation, and then add it point by point to the original prediction curves of other devices. For example, the original prediction of the main device is 100 kW, and after compensation it is 110 kW, and the prediction of another device is 50 kW, and the total load is 160 kW.

[0032] In this embodiment, by mining historical data to establish the mapping between device status combinations and compensation coefficients, the load sudden increase effect during the collaborative operation of multiple devices can be captured. For example, when the air compressor and the chiller are running simultaneously, the additional energy consumption of their cooling systems is often ignored by traditional predictions. After being corrected by the compensation coefficient, the prediction curve is closer to the actual load. This mechanism avoids the underestimation of prediction values caused by the neglect of device correlation, providing a more reliable basis for the scheduling system to respond to load peaks in advance.

[0033] In another embodiment, in the energy intelligent control method based on RFID technology, S5 includes: S5.1: Calculate the instantaneous slope of the total load change trend in the area. The instantaneous slope is obtained by dividing the difference between the predicted load values at two consecutive future time points by the time interval; S5.2: When the predicted total load exceeds a preset ratio of the grid supply threshold and the absolute value of the instantaneous slope is less than 0.05 kW / s, generate a first-level scheduling instruction, and the first-level scheduling instruction only triggers the energy-consuming device with the lowest priority in the device priority policy to perform a power reduction operation; S5.3: When the predicted total load exceeds a preset ratio of the grid supply threshold and the absolute value of the instantaneous slope is greater than or equal to 0.05 kW / s and less than 0.1 kW / s, generate a second-level scheduling instruction, and the second-level scheduling instruction triggers the energy-consuming device with the lowest priority and the energy-consuming device with the second lowest priority in the device priority policy to perform a power reduction operation; S5.4: When the predicted total load exceeds a preset ratio of the grid supply threshold and the absolute value of the instantaneous slope is greater than or equal to 0.1 kW / s, generate a third-level scheduling instruction, and the third-level scheduling instruction triggers all target energy-consuming devices in the device priority policy to perform a power reduction operation or a delayed start operation according to the device priority policy. Exemplarily, in S5.1, the two consecutive future time points are the t-th minute and the (t + 1)-th minute of the predicted curve, the time interval is 60 seconds, and the instantaneous slope = (load(t + 1) - load(t)) / 60. The grid supply threshold is set to 1000 kW according to the rated capacity of the transformer, and the preset ratio is 15%, that is, 150 kW. The comparison is started when the predicted total load reaches 1150 kW. In S5.2, if the instantaneous slope is 0.03 kW / s (slow increase), only the lighting equipment with the lowest priority is scheduled, and the power reduction amplitude is 20% of the rated power (for example, if the lighting rated power is 50 kW, it is reduced to 40 kW). In S5.3, the slope is 0.08 kW / s (medium-speed growth), and both the lighting (lowest priority) and the auxiliary fan (second lowest priority) are scheduled, and the power reduction amplitude is 15% for both. In S5.4, the slope is 0.15 kW / s (rapid increase), and all non-critical devices are triggered in the order of priority. The power of the running machine tool is reduced (the feed speed is reduced, and the power is reduced from 200 kW to 160 kW), and the air compressor to be started is delayed (delayed by 15 minutes). In this embodiment, the generation of the scheduling instruction combines the load exceeding the threshold ratio and the growth rate. The slow increase scenario only affects non-critical devices, the scheduling range is expanded during medium-speed growth, and comprehensive regulation is performed during rapid increase. This hierarchical strategy avoids the interference of traditional "one-size-fits-all" scheduling to production. For example, only the lighting system is adjusted during slow load increase, without affecting the main equipment of the production line; during rapid increase, the load growth rate is quickly reduced to ensure the stability of the power grid. At the same time, triggering according to priority ensures the continuity of the operation of key devices and improves the practicality of the scheduling strategy. In another embodiment, the energy intelligent management and control method based on RFID technology also includes: real-time calculation of the current grid load margin, the grid load margin is equal to the grid supply threshold minus the real-time total load value; when the grid load margin is continuously greater than 10% of the grid supply threshold and the duration exceeds the set value of the equipment recovery delay counter, perform the following recovery operations: for energy-consuming equipment triggered by the first-level scheduling instruction, immediately release the power reduction operation when the grid load margin is greater than 15%; for energy-consuming equipment triggered by the second-level scheduling instruction, release the power reduction operation when the grid load margin is greater than 12%; for energy-consuming equipment triggered by the third-level scheduling instruction, release the power reduction operation or allow the startup operation when the grid load margin is greater than 10%; after each recovery operation is performed, reset the equipment recovery delay counter to the initial value. For example, when the grid supply threshold is 1200kW and the real-time total load is 1000kW, the load margin is 200kW (16.7%). If the duration reaches the initial value of the equipment recovery delay counter of 15 minutes (which can be adjusted according to the equipment type, such as long delay for motor equipment and short delay for lighting equipment), the power reduction of the lighting equipment scheduled at the first level will be lifted and normal power supply will be restored. When the load further drops to 950kW (margin 250kW, 20.8%), the recovery threshold of 12% (144kW) of the second-level equipment (auxiliary fan) is met, and it lasts for 10 minutes, its power reduction will be lifted. When the third-level equipment (such as non-emergency production lines) has a margin of 120kW (10%) and lasts for 20 minutes, the equipment in standby is allowed to start. The recovery delay counter prevents frequent start and stop of equipment when the load fluctuates, and resets to the initial value after each recovery. For example, after the first level is released, the counter is reset to 15 minutes to avoid a small increase in load after 10 minutes triggering scheduling again. In this embodiment, the calculation of load margin and differentiated recovery threshold combined with delay counter realizes the orderly release of scheduling instructions. The third-level equipment with high priority scheduling can be restored when the margin is low to ensure production efficiency; the first-level equipment with low priority requires a higher margin to avoid secondary overload caused by the grid being restored as soon as it meets the standard. This mechanism strikes a balance between grid stability and equipment operating efficiency, reduces the number of equipment starts and stops, and extends the service life of the equipment. In another embodiment, in the energy intelligent control method based on RFID technology, S6 includes: S6.1: Split the scheduling instruction into an instruction header and a data body. The instruction header contains the identification code of the target passive RFID tag and an operation type flag, and the data body contains a power reduction parameter or a delay start parameter; S6.2: Send the instruction header to the target RFID reader through a first wireless communication channel, and at the same time send the data body to the same target RFID reader through a second wireless communication channel, where the first wireless communication channel uses the UHF band and the second wireless communication channel uses the HF band; S6.3: After the target RFID reader receives the complete instruction header and data body, perform dual-channel data verification: When the CRC check code in the instruction header matches the CRC check code in the data body, write the recombined complete instruction reversely into the corresponding passive RFID tag storage area; When the CRC check codes do not match or data is lost in a single channel, request the data processing unit to retransmit the instruction; S6.4: After receiving the complete instruction, the passive RFID tag outputs a pulse signal to the control terminal of the relay switch through the built-in I / O interface, triggering the execution of a power reduction operation or a delay start operation. Exemplarily, the format of the instruction header in S6.1 is: 8-byte tag ID + 2-byte operation type (0x01 for power reduction, 0x02 for delay start) + 2-byte CRC check code, and the format of the data body is: 4-byte parameter (power reduction percentage or delay time, in seconds) + 2-byte CRC check code. In S6.2, the UHF band (902 - 928 MHz) is used to transmit the instruction header, taking advantage of its long-distance characteristic to ensure tag identification; the HF band (13.56 MHz) is used to transmit the data body, taking advantage of its high stability to reduce parameter loss. In S6.3, after the reader receives the instruction header and data body, it first calculates the CRC check codes separately. After comparison and consistency, it recombines the instruction (instruction header + data body) and writes it into a specific storage area of the tag (such as user storage area address 0x0010 - 0x0020) through backscatter modulation. If data is lost in any channel or the verification fails, the reader sends a retransmission request to the data processing unit, attaching the lost instruction header ID and timestamp. In S6.4, the tag I / O interface outputs a pulse signal (such as 5V, 12V, or 24V) that matches the rated voltage of the relay coil, with a pulse width of 200 ms, triggering the relay contact to switch, realizing equipment power adjustment (such as adjusting the output frequency of the frequency converter) or start delay (such as disconnecting the contactor coil power supply and closing after a preset time delay). In this embodiment, dual-channel transmission and CRC check ensure the integrity of the instructions. The UHF band quickly locates the target tag, and the HF band reliably transmits parameters, avoiding the signal attenuation problem of a single channel in a complex environment. The check and retransmission mechanism of the reader effectively solves the data error caused by electromagnetic interference in the industrial field, ensures the reliability of the control instructions from generation to execution, enables the device to accurately respond to the scheduling instructions, and realizes the stability of closed-loop control. In another embodiment, in the energy intelligent management and control method based on RFID technology, S6.3 further includes: S6.3.1: When the target RFID reader receives multiple instruction headers simultaneously, sort them according to the reception timestamp and extract the first complete instruction header, and store the remaining instruction headers in the conflict buffer queue; S6.3.2: Allocate a dynamic frequency point for the currently processed instruction header. The dynamic frequency point is selected from a preset ultra-high frequency hopping sequence, and the frequency range of the ultra-high frequency hopping sequence is 902 MHz to 928 MHz. Send a frequency point synchronization instruction to the corresponding passive RFID tag through the high-frequency channel; S6.3.3: Receive the data body at the dynamic frequency point. If the reception fails, switch to the next frequency point in the ultra-high frequency hopping sequence to resend the frequency point synchronization instruction and receive the data body. The maximum number of retries is 3 times; S6.3.4: Perform a two-level check on the successfully received instruction header and data body: The first-level check: Compare the cyclic redundancy check codes of the instruction header and the data body; The second-level check: Verify the logical consistency between the operation type flag in the instruction header and the power-down parameter or delay start parameter in the data body; S6.3.5: Only when both levels of checks pass, write the reorganized instruction into the storage area of the passive RFID tag; if any level of check fails, trigger the processing flow of the next instruction header in the conflict buffer queue. Exemplarily, in S6.3.1, the reader memory sets a conflict buffer queue with a capacity of 50 to store the unprocessed instruction headers according to the FIFO principle, and the timestamp is accurate to the millisecond level to ensure processing in the reception order. In S6.3.2, the ultra-high frequency hopping sequence contains 10 frequency points (such as 905 MHz, 910 MHz, 915 MHz, 920 MHz, etc.). Each time an instruction header is processed, a random unused frequency point is selected (to avoid co-frequency interference), and a frequency point synchronization instruction (including the frequency point number and working duration) is sent to the tag through the HF channel. After receiving it, the tag adjusts the internal tuning circuit to the specified frequency point. In S6.3.3, the determination of data body reception failure is: the signal strength is lower than -85 dBm or the CRC check is incorrect. Each time it fails, switch to the next frequency point, and retry at most 3 times. If it still fails, record the error log and skip the current instruction. In S6.3.4, the logical consistency check includes: when the operation type is power-down, the data body parameter needs to be within the range of 10% - 50%; when it is delay start, the parameter needs to be greater than 0 seconds and less than 3600 seconds, otherwise it is determined as a logical error (such as parameter 0 or negative number). In this embodiment, the conflict buffer queue handles the problem of multi-instruction concurrency, dynamic frequency hopping reduces frequency band interference, and double-layer verification ensures the correctness of instructions. For example, when multiple devices need to adjust their power simultaneously, the reader processes them in the order of reception to avoid instruction chaos; when data reception fails due to frequency point interference, it automatically switches to another frequency point and retries, improving the communication success rate in complex environments. This mechanism effectively solves the instruction conflicts and transmission errors in high-density device scenarios, ensures the orderly execution of control instructions, and improves the anti-interference ability and control precision of the system.

[0034] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and embodiments. It can be fully applied to various fields suitable for the present invention. For those familiar with the field, additional modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and the embodiments shown and described herein.

Claims

1. An energy intelligent control method based on RFID technology, characterized in that, Including: S1: Passive RFID tags are fixedly installed on multiple energy-consuming devices. Each passive RFID tag is physically integrated with a current sensor and a voltage sensor. The current sensor is used to collect the real-time current value of the energy-consuming device, and the voltage sensor is used to collect the real-time voltage value of the energy-consuming device; S2: Multiple fixed RFID readers deployed in the control area periodically read the identification code, real-time current value, and real-time voltage value of each passive RFID tag; S3: The data processing unit receives the identification code, real-time current value, and real-time voltage value uploaded by all RFID readers, performs a multiplication operation on the real-time current value and real-time voltage value associated with the same identification code, and generates the real-time power data of each energy-consuming device; S4: The load forecasting unit, based on the real-time power data and combined with the device operation mode records in the historical energy consumption database, uses the time series analysis method to predict the regional total load change trend in the future time period; S5: The control unit compares the regional total load change trend with the grid supply threshold in real time. When the predicted total load exceeds a preset proportion of the grid supply threshold, a scheduling instruction is generated according to the preset device priority strategy; S6: The scheduling instruction is reversely written into the storage area of the corresponding passive RFID tag through the RFID reader, triggering the relay switch built into the target energy-consuming device to perform a power reduction operation or a delayed start operation.

2. The energy intelligent control method based on RFID technology according to claim 1, wherein, The S3 includes: S3.1: Perform a first-level filtering on the received real-time current value. When the current value exceeds the range of 80% to 120% of the rated current value for 3 consecutive sampling periods, activate the current sensor diagnostic flag; S3.2: Perform a second-level filtering on the received real-time voltage value. When the voltage value continuously deviates from the nominal voltage value by ±10% for more than 5 sampling periods, activate the voltage sensor diagnostic flag; S3.3: When neither the current sensor diagnostic flag nor the voltage sensor diagnostic flag is activated, perform a multiplication operation on the real-time current value and the real-time voltage value to generate effective real-time power data; S3.4: When any diagnostic flag is activated, perform an alternative calculation: if the current sensor diagnostic flag is activated, use the average historical current value in the last 5 minutes to replace the real-time current value; if the voltage sensor diagnostic flag is activated, use the grid nominal voltage value to replace the real-time voltage value; perform a multiplication operation based on the replacement value to generate compensated real-time power data; S3.5: Store the effective real-time power data or the compensated real-time power data associated with the corresponding identification code in the real-time power database.

3. The energy intelligent control method based on RFID technology according to claim 2, characterized in that, If the real-time current value in 3 consecutive sampling periods exceeds the range of 80% to 120% of the device rated current value, and within the 3 consecutive sampling periods, the standard deviation of the current change rate is greater than 200% of the rated current change rate standard deviation, then activate the current sensor diagnostic flag; If the real-time voltage value continuously deviates from the grid nominal voltage value by ±10% for 5 sampling periods, and during the deviation period, the absolute value of the difference between adjacent sampling points of the real-time voltage value is less than 0.5% of the nominal voltage value, then activate the voltage sensor diagnostic flag.

4. The energy intelligent control method based on RFID technology according to claim 1, wherein The S4 includes: S4.1: Based on the real-time power data and combined with the device operation mode records in the historical energy consumption database, identify the current operation status markers of each energy-consuming device. The operation status markers include the operation status, standby status, and shutdown status. Extract the device historical power sequences that match the current operation status markers from the historical energy consumption database, group them by device type. The device types include continuous production type, intermittent operation type, and auxiliary equipment. Use time series analysis method to generate the single-device load prediction curve. S4.2: Real-time collect the multi-dimensional feature vectors of the device through the dynamic device energy efficiency portrait engine. The feature vectors include: device inherent attribute vector, which contains device type code, rated power, and historical average energy efficiency ratio; real-time status vector, which contains the current operation status marker, status duration, and power change rate. The power change rate is calculated by dividing the absolute value of the difference between the current power and the power in the previous sampling period by the sampling interval time; energy efficiency health vector, which contains the deviation degree from the energy efficiency baseline model and the sensor diagnosis marker. The sensor diagnosis marker is the abnormal activation status of the current or voltage sensor. S4.3: Input the multi-dimensional feature vectors into the portrait scoring matrix and output the dynamic weight coefficients according to the following rules: When the power change rate in the real-time status vector exceeds 15% of the rated power change rate, increase the weight coefficient by 0.1 to 0.3; When the real-time power in the standby status in the real-time status vector continues to be greater than 5% of the rated power for 3 sampling periods, decrease the weight coefficient by 0.1 to 0.2; When the deviation degree in the energy efficiency health vector exceeds the preset threshold, freeze the weight coefficient and generate a device-level energy efficiency warning. S4.4: Perform a weighted operation on the dynamic weight coefficients and the single-device load prediction curve in the time dimension to generate a weighted prediction curve; Superimpose the weighted prediction curves of all devices in the time dimension to generate the regional total load change trend.

5. The energy intelligent control method based on RFID technology according to claim 4, characterized in that The S4.4 includes: S4.4.1: Establish a device association rule library to store the mapping relationship between the preset device status combinations and the load compensation coefficients, where the device status combinations are composed of the operation status markers of at least two energy-consuming devices. S4.4.2: Real-time detect the operation status markers of all current energy-consuming devices. When there is a status combination that matches the device association rule library, extract the corresponding load compensation coefficient. S4.4.3: Multiply the load compensation coefficient by the single-device load prediction curve of the energy-consuming device with the largest rated power in this status combination to generate a compensated prediction curve. S4.4.4: Superimpose the compensated prediction curve and the single-device load prediction curves of other energy-consuming devices that have not triggered compensation in the time dimension to generate the regional total load change trend.

6. The energy intelligent control method based on RFID technology according to claim 1, characterized in that, The S5 includes: S5.1: Calculate the instantaneous slope of the regional total load change trend. The instantaneous slope is obtained by dividing the difference between the predicted load values at two consecutive future time points by the time interval. S5.2: When the predicted total load exceeds a preset proportion of the grid supply threshold and the absolute value of the instantaneous slope is less than 0.05 kW / s, a first-level dispatch instruction is generated, and the first-level dispatch instruction only triggers the energy consumption device with the lowest priority in the device priority strategy to perform a power reduction operation; S5.3: when the predicted total load exceeds a preset proportion of the grid supply threshold and the absolute value of the instantaneous slope is greater than or equal to 0.05 kW / s and less than 0.1 kW / s, a second-level dispatch instruction is generated, and the second-level dispatch instruction triggers the energy consuming device with the lowest priority and the energy consuming device with the second lowest priority in the device priority strategy to perform a power reduction operation; S5.4: When the predicted total load exceeds the preset proportion of the grid supply threshold and the absolute value of the instantaneous slope is greater than or equal to 0.1kW / s, a third-level scheduling instruction is generated. The third-level scheduling instruction triggers all target energy-consuming devices in the device priority strategy to perform power reduction operations or delayed start operations based on the device priority strategy.

7. The energy intelligent control method based on RFID technology according to claim 6, characterized in that, Also includes: Calculate the current grid load margin in real time, where the grid load margin is equal to the grid supply threshold minus the real-time total load value; When the grid load margin is continuously greater than 10% of the grid supply threshold and the duration exceeds the setting value of the device recovery delay counter, the following recovery operations are performed: For energy-consuming equipment triggered by the first-level dispatching instruction, the power reduction operation will be immediately lifted when the grid load margin is greater than 15%; For energy-consuming equipment triggered by the second-level dispatching instruction, the power reduction operation will be lifted when the grid load margin is greater than 12%; For energy-consuming equipment triggered by the third-level dispatching instruction, the power reduction operation is lifted or the startup operation is allowed when the grid load margin is greater than 10%; After each restore operation, reset the device restore delay counter to the initial value.

8. The energy intelligent control method based on RFID technology according to claim 1, characterized in that, The S6 includes: S6.1: Split the scheduling instruction into an instruction header and a data body, wherein the instruction header includes an identification code of a target passive RFID tag and an operation type tag, and the data body includes a power reduction parameter or a delayed start parameter; S6.2: sending the instruction header to the target RFID reader / writer through a first wireless communication channel, and sending the data body to the same target RFID reader / writer through a second wireless communication channel, wherein the first wireless communication channel uses a UHF frequency band and the second wireless communication channel uses a HF frequency band; S6.3: After receiving the complete instruction header and data body, the target RFID reader performs a dual-channel data check: when the CRC check code in the instruction header matches the CRC check code in the data body, the reorganized complete instruction is reversely written into the corresponding passive RFID tag storage area; when the CRC check code does not match or the single channel data is lost, the data processing unit is requested to retransmit the instruction; S6.4: After receiving the complete command, the passive RFID tag outputs a pulse signal to the control end of the relay switch through the built-in I / O interface, triggering the execution of a power reduction operation or a delayed start operation.

9. The energy intelligent control method based on RFID technology according to claim 8, characterized in that Said S6.3 also includes: S6.3.1: When the target RFID reader receives multiple instruction headers simultaneously, sort them by the reception timestamp and extract the first complete instruction header, storing the remaining instruction headers in the conflict buffer queue; S6.3.2: Allocate a dynamic frequency point for the currently processed instruction header. The dynamic frequency point is selected from a preset ultra-high frequency hopping sequence with a frequency range of 902 MHz to 928 MHz, and send a frequency point synchronization instruction to the corresponding passive RFID tag through the high-frequency channel; S6.3.3: Receive the data body at the dynamic frequency point. If the reception fails, switch to the next frequency point in the ultra-high frequency hopping sequence to resend the frequency point synchronization instruction and receive the data body, with a maximum retry count of 3 times; S6.3.4: Perform a two-level verification on the successfully received instruction header and data body: First-level verification: Compare the cyclic redundancy check codes of the instruction header and the data body; Second-level verification: Verify the logical consistency between the operation type marker in the instruction header and the power-down parameter or delay start parameter in the data body; S6.3.5: Only when both levels of verification pass, write the reorganized instruction to the storage area of the passive RFID tag; if any level of verification fails, trigger the processing flow of the next instruction header in the conflict buffer queue.

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