A smart energy management system based on a data visualization platform

Abstract

The present application relates to the technical field of intelligent energy management system, and more particularly to an intelligent energy management system based on a data visualization platform, which generates data packets by collecting data through a data collection module; determines a benchmark data value based on the data packets and compares the data value to determine whether to perform energy efficiency processing; generates a spatial aggregation dataset by spatial aggregation through a data hub module; determines an energy efficiency processing space based on the dataset and generates a corresponding signal through a spatial management module; performs corresponding operations based on the signal through a primary regulation module; and determines secondary regulation based on a lag index, adjusts an energy saving intensity coefficient based on a fluctuation coefficient, and corrects in combination with personnel off-site parameters through a secondary regulation module. The present application uniformly associates equipment, data and control strategies based on spatial identification, effectively avoids equipment wear and tear and user comfort decline caused by power oscillation and excessive energy saving, thereby improving energy utilization efficiency and system stability.

Description

Technical Field

[0001] This invention relates to the field of smart energy management system technology, and in particular to a smart energy management system based on a data visualization platform. Background Technology

[0002] Currently, smart meters, environmental sensors, and IoT gateways are widely used in various buildings and industrial parks. Energy management systems built upon these systems can perform basic equipment status monitoring, energy consumption data statistics, and simple alarm prompts, providing some data support for energy conservation management. However, existing systems generally suffer from the following shortcomings: a lack of a unified data storage and spatial master data management system, making it difficult to effectively link spatial-dimensional energy consumption analysis and automated control; and visualizations are mostly static reports, failing to form a cross-module integrated real-time visual monitoring interface.

[0003] Chinese Patent Application Publication No. CN115775077A discloses a smart energy management platform with energy-saving and emission-reduction data visualization function. The related technical solution includes an energy optimization system for using optimization algorithms to reduce equipment operating energy consumption, and also for using redundant control algorithms and multi-target tracking algorithms to ensure system operation; a cloud server for controlling the energy optimization system and receiving its data; a cooling optimization control system for reducing the overall energy consumption of the air conditioning system; a heating optimization control system for controlling indoor temperature; and a visualization data output system for outputting data to a display device.

[0004] However, the various systems in the technical solution are relatively independent, failing to achieve integrated analysis of the systems; the energy consumption data of the equipment lacks a clear correlation mapping mechanism with the corresponding spatial location, making it impossible to perform energy consumption analysis and automated control based on the spatial dimension.

[0005] Therefore, there is an urgent need for a smart energy management system that can take space as the core dimension to achieve unified association and closed-loop collaboration of equipment, data and control strategies in the spatial dimension. Summary of the Invention

[0006] To address this, the present invention provides a smart energy management system based on a data visualization platform, which overcomes the shortcomings of existing technologies where the lack of spatial dimension prevents data from being linked to devices, easily leads to misjudgments and omissions of abnormal spaces, thereby affecting the degree of regulation and causing power oscillations, ultimately resulting in low energy efficiency and unstable operation of the system in actual operation.

[0007] To achieve the above objectives, the present invention provides a smart energy management system based on a data visualization platform, comprising: The data acquisition module is used to preprocess the raw data collected within the energy management space to generate data packets; An energy efficiency pre-judgment module is used to determine a baseline data value based on the data packet, and to determine whether energy efficiency processing is required based on the comparison result between the data value in the data packet and the baseline data value. The data platform module is used to perform spatial aggregation based on the spatial identifier in the data packet to generate several spatial aggregation datasets, wherein the spatial aggregation datasets include the personnel activity index and the deviation of electricity intensity in the corresponding space. The space management module is used to determine the corresponding space as an energy efficiency processing space and generate a first energy efficiency trigger signal based on the personnel activity index and the electricity intensity deviation, and to determine the corresponding space as a non-energy efficiency processing space and generate a second energy efficiency trigger signal. A primary control module is used to receive the first energy efficiency trigger signal and the second energy efficiency trigger signal to perform primary control, wherein the first energy efficiency trigger signal is to reduce the operating power of the terminal device controlled by the energy efficiency processing space, and the second energy efficiency trigger signal is to maintain the operating power of several terminal devices in the energy efficiency processing space. The secondary control module is used to obtain the total power time series of the corresponding space after the first control under the preset control period to obtain the power response lag index and the power fluctuation coefficient. Based on the power response lag index and the preset power response lag index, it determines whether to perform secondary control. If it is determined to perform secondary control, it determines the energy-saving intensity coefficient of the terminal equipment in the energy efficiency treatment space based on the power fluctuation coefficient and the preset power fluctuation coefficient.

[0008] Furthermore, the energy efficiency pre-judgment module is also used to determine the corresponding historical data value based on the spatial identifier, timestamp, and data type in the data packet, and sort the historical data values ​​to determine the median as the benchmark data value.

[0009] Furthermore, the energy efficiency pre-judgment module is also used to determine whether the current space needs to undergo energy efficiency processing based on the comparison result that the data value is greater than the benchmark data value.

[0010] Furthermore, the data platform module is also used to determine the corresponding historical power value based on the spatial identifier, the timestamp, and the data type in the data packet, sort the historical power values ​​to determine the median as the reference power value, and calculate the power intensity deviation based on the current power value and the reference power value. The data platform module is also used to determine the carbon dioxide change rate based on the timestamp and the data value in the data packet, and to perform a weighted calculation based on the carbon dioxide change rate and the infrared movement frequency in the data packet to obtain the personnel activity index.

[0011] Furthermore, the space management module is also used to determine the corresponding space as the energy efficiency processing space based on the comparison results of the personnel activity index being less than or equal to the preset personnel activity index and the comparison results of the electricity intensity deviation being greater than the preset electricity intensity deviation. The space management module is also used to determine the corresponding space as the non-energy-efficiency processing space based on the comparison result that the personnel activity index is greater than the preset personnel activity index, or based on the comparison result that the personnel activity index is less than or equal to the preset personnel activity index and the power consumption deviation is less than or equal to the preset power consumption deviation.

[0012] Furthermore, the secondary control module is also used to determine the execution of the secondary control based on the comparison result of the power response lag being greater than the preset power response lag index.

[0013] Furthermore, the secondary control module is also used to determine the energy-saving intensity coefficient of the terminal equipment in the energy efficiency processing space based on the comparison result that the power fluctuation coefficient is less than or equal to the preset power fluctuation coefficient; The secondary control module is also used to determine the energy-saving intensity coefficient of the terminal equipment in the energy efficiency treatment space based on the comparison result that the power fluctuation coefficient is greater than the preset power fluctuation coefficient.

[0014] Furthermore, the secondary control module is also used to determine the improvement of the energy-saving intensity coefficient based on the comparison result between the fluctuation coefficient difference and the preset fluctuation coefficient difference, and the improvement magnitude is positively correlated with the fluctuation coefficient difference; The secondary control module is also used to determine the reduction of the energy-saving intensity coefficient based on the comparison result between the fluctuation coefficient offset value and the preset fluctuation coefficient offset value, and the reduction magnitude is positively correlated with the fluctuation coefficient offset value.

[0015] Furthermore, the secondary control module is also used to acquire personnel departure parameters under the pre-observation window, and to determine whether to correct the energy-saving intensity coefficient after secondary control based on the comparison result between the personnel departure parameters and the preset personnel departure parameters. The energy-saving intensity coefficient after the secondary regulation is improved is determined based on the comparison result that the personnel departure parameter is greater than the preset personnel departure parameter; The energy-saving intensity coefficient after reducing the secondary regulation is determined based on the comparison result that the personnel departure parameter is less than or equal to the preset personnel departure parameter.

[0016] Furthermore, the secondary control module is also used to determine the improvement range of the energy-saving intensity coefficient after the secondary control based on the comparison result between the difference of the off-field parameters and the preset difference of the off-field parameters, and the improvement range is positively correlated with the difference of the off-field parameters; The secondary control module is also used to determine the reduction range of the energy-saving intensity coefficient after the secondary control based on the comparison result between the off-field parameter offset value and the preset off-field parameter offset value, and the reduction range is positively correlated with the off-field parameter offset value.

[0017] Compared with existing technologies, the advantages of this invention are as follows: By using an energy efficiency pre-judgment module to compare data values ​​with baseline data values ​​in data packets, it determines whether energy efficiency processing is necessary. This avoids the shortcomings of fixed thresholds that cannot adapt to load differences in different spaces and time periods, and pre-screens abnormal spatial states, reducing the processing burden of the module and thus lowering false alarm and false negative rates. Furthermore, by using a data platform module to perform spatial aggregation based on spatial identifiers in each data packet, it aligns and merges data from different devices and data types within the same space in both time and space dimensions, forming a unified dataset based on space units. This overcomes the data silos and misaligned decision-making granularity problems caused by traditional device-centric systems, allowing subsequent energy efficiency judgment and control to be based on complete and synchronized spatial state information, thereby improving the accuracy of anomaly detection and the targeting of control strategies. Finally, by using the personnel activity index and electricity intensity deviation generated by the data platform module, it can comprehensively analyze the degree of energy consumption deviation and the presence of personnel. This invention comprehensively characterizes the energy efficiency of a space to avoid the limitations of a single indicator. The space management module determines the energy efficiency processing space based on personnel activity index and electricity intensity deviation, generating corresponding trigger signals. This prevents erroneous power reduction from affecting personnel comfort or equipment wastage due to neglecting unoccupied areas. It also provides clear action instructions to the primary control management module, improving inter-module response speed. The primary control module receives trigger signals and executes power reduction or maintenance operations, decoupling from the space management module, facilitating system expansion, and improving response speed while avoiding delays caused by layered judgments. The secondary control module extracts the power response lag index and power fluctuation coefficient based on the total power time series after primary control, determining whether to execute secondary control and adjusting the energy-saving intensity coefficient. This avoids frequent equipment start-ups and shutdowns, load cycle oscillations, and energy waste caused by power overshoot or under-control due to over-adjustment, ensuring that energy-saving efforts match the actual system stability, thereby improving energy utilization and operational stability. With this setup, the invention significantly improves energy utilization efficiency and long-term operational stability while ensuring stable system operation and personnel comfort.

[0018] Furthermore, the present invention also sorts historical data values ​​based on the energy efficiency pre-judgment module and takes the median as the benchmark data value, which can effectively eliminate the contamination of the benchmark value by occasional abnormal data (such as equipment startup peaks and instantaneous sensor interference), so that the benchmark data value truly reflects the normal energy consumption level under typical operating conditions. By comparing the data value with the benchmark data value in a one-way manner, unnecessary energy efficiency processing is avoided due to normal low fluctuations (such as equipment standby and unattended periods), and the abnormal energy consumption space can be accurately identified, thereby improving the accuracy and pertinence of energy efficiency pre-judgment.

[0019] Furthermore, this invention also determines historical power values ​​based on spatial identifiers, timestamps, and data types, and takes the median as the benchmark power value, thereby calculating the power intensity deviation, so that the deviation reflects the degree of fluctuation of the current power relative to the historical normal of the corresponding space; at the same time, it calculates the personnel activity index based on the carbon dioxide change rate and infrared movement frequency weighted, which solves the problems of missed detection and false detection caused by a single infrared sensor when personnel are stationary or asleep, as well as the response lag of a single CO2 sensor, thereby improving the accuracy and robustness of detecting personnel on site.

[0020] Furthermore, this invention uses a personnel activity index less than or equal to a preset personnel activity index and a power consumption deviation greater than a preset power consumption deviation as the criteria for determining an energy efficiency processing space. This avoids both the unwarranted power reduction affecting comfort caused by relying solely on power consumption intensity and the omission of energy-saving situations when no one is present, based solely on personnel activity. Conversely, when the personnel activity index is greater than the preset personnel activity index, or when there are few people but the power consumption does not deviate, it is determined to be a non-energy efficiency processing space. This allows the system to accurately determine whether a space is an energy efficiency processing space based on the actual usage status.

[0021] Furthermore, this invention also filters out invalid interventions during the natural stabilization process by comparing the power response lag index with a preset power response lag index. Intervention is only performed when the power response lag is greater than the preset power response lag index. This not only filters out invalid interventions during the natural stabilization process, thereby reducing the computational load and bus communication pressure on the secondary control module, but also prevents adjustment oscillations caused by premature intervention, making the control process converge smoothly, improving the operating efficiency of each module and the feasibility of large-scale deployment.

[0022] Furthermore, this invention dynamically adjusts the energy-saving level based on the comparison between the power fluctuation coefficient and the preset power fluctuation coefficient. This avoids damage to terminal equipment due to over-adjustment and energy waste due to under-adjustment or failure to adjust in a timely manner. At the same time, based on the fluctuation difference and fluctuation offset value, the increase and decrease ranges are determined respectively, so that the adjustment intensity matches the actual fluctuation level. This avoids over-adjustment due to too small deviation or under-adjustment due to too large deviation, thereby achieving a correspondence between the energy-saving level adjustment step size and the power oscillation intensity, ensuring that the control process is smooth and quickly stabilizes.

[0023] Furthermore, this invention also corrects the energy-saving intensity coefficient after secondary regulation by introducing personnel departure parameters, avoiding the problem of personnel comfort being reduced due to the power fluctuation coefficient adjusting the terminal equipment and thus ignoring the personnel inside the corresponding space; at the same time, the front observation window effectively prevents false triggering caused by personnel leaving briefly or sensor obstruction, improving the reliability of personnel departure parameter settings.

[0024] Furthermore, the present invention also prevents overcorrection or undercorrection caused by excessively small or large deviations by using the difference and offset values ​​of the departure parameters, and ensures that the degree of correction is proportional to the actual degree of unmanned operation. This avoids energy waste caused by excessive power backoff due to short-term personnel presence, and also avoids continuous discomfort caused by insufficient power backoff due to long-term personnel presence, thereby improving the accuracy of correction actions and the stability of equipment operation. Attached Figure Description

[0025] Figure 1 This is a block diagram of a smart energy management system based on a data visualization platform, as described in an embodiment of the present invention. Figure 2 This is a flowchart illustrating the usage method of the smart energy management system based on a data visualization platform in an embodiment of the present invention. Figure 3 This is a flowchart illustrating the logic of determining secondary control based on the comparison between the power response hysteresis index and the preset power response hysteresis index in an embodiment of the present invention. Figure 4 This is a flowchart illustrating the logic of determining the energy-saving intensity coefficient after secondary regulation based on the comparison between personnel departure parameters and preset personnel departure parameters in an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0027] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0028] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0029] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0030] Please see Figure 1 As shown, it is a block diagram of a smart energy management system based on a data visualization platform in an embodiment of the present invention.

[0031] This embodiment includes a data acquisition module, an energy efficiency prediction module, a data platform module, a space management module, a primary control module, a secondary control module, and a visualization platform.

[0032] The data acquisition module includes a sensor interface unit, a data cleaning unit, a spatial identification mapping unit, and a data packet encapsulation unit. The sensor interface unit receives raw data from the space, such as electricity, water, gas, heat, temperature and humidity, carbon dioxide concentration, and personnel movement frequency, which are collected by corresponding smart meters, water meters, gas meters, heat meters, temperature and humidity sensors, carbon dioxide sensors, and infrared sensors, respectively. The data cleaning unit removes outliers that exceed reasonable ranges and marks missing data points. The spatial identification mapping unit queries a pre-stored device-space mapping table based on the device identifier and adds a corresponding spatial identifier to each data entry. The data packet encapsulation unit assembles the cleaned and labeled data into a unified format data packet, with each data packet including a spatial identifier, timestamp, data type, and data value.

[0033] The energy efficiency pre-judgment module is connected to the data acquisition module and includes a historical data storage unit, a time period classification unit, a median calculation unit, and a threshold comparison unit. The historical data storage unit maintains a historical data queue for each space, each data type, and each time period category. The time period classification unit categorizes the current data packet into a preset time period based on its timestamp. The median calculation unit extracts the median from the historical data of the same time period category as a benchmark data value. The threshold comparison unit determines whether energy efficiency processing is required based on the comparison result between the current data value and the benchmark data value.

[0034] The data platform module is connected to the energy efficiency prediction module, and includes a spatial aggregation unit, an electricity intensity deviation calculation unit, and a personnel activity index calculation unit. The spatial aggregation unit classifies and aligns all data packets in the same space based on spatial identifiers. The electricity intensity deviation calculation unit obtains the current total power of the corresponding space and the median power of the same historical time period to calculate the electricity intensity deviation. The personnel activity index calculation unit counts the carbon dioxide concentration slope and infrared movement frequency within a preset time window to obtain the personnel activity index.

[0035] The space management module is connected to the data platform module and includes a priority determination unit and a signal generation unit. The priority determination unit determines whether the corresponding space is an energy efficiency processing space based on the comparison results of the personnel activity index and the preset personnel threshold, as well as the comparison results of the electricity intensity deviation and the preset deviation threshold. The signal generation unit generates the corresponding first or second energy efficiency trigger signal based on the determination results.

[0036] The primary control module is connected to the space management module and includes an energy efficiency triggering unit and a power regulation unit. The energy efficiency triggering unit receives a first energy efficiency triggering signal or a second energy efficiency triggering signal to perform corresponding primary control. The power regulation unit sends a power reduction command or a power maintenance command to the corresponding device.

[0037] The secondary control module is connected to the primary control module and includes a power sampling unit, a hysteresis index calculation unit, a fluctuation coefficient calculation unit, and an energy-saving level decision unit. After the primary control is completed, the power sampling unit continuously collects the total power of the corresponding space at a fixed sampling interval to form a time series. The hysteresis index calculation unit analyzes the response time and rebound of the power from the start of its decline to entering the stable range, and calculates the power response hysteresis index. The fluctuation coefficient calculation unit calculates the ratio of the standard deviation to the mean of the power series after stabilization to obtain the power fluctuation coefficient. The energy-saving level decision unit triggers secondary control based on the comparison result of the power response hysteresis index and the corresponding preset threshold, and further adjusts the energy-saving intensity coefficient after secondary control based on the comparison result of the power fluctuation coefficient and the corresponding preset threshold.

[0038] The data visualization platform connects to the data middleware module, space management module, primary control module, and secondary control module. It includes a space energy consumption dashboard, a personnel activity monitoring panel, a control status window, and a parameter configuration interface. The space energy consumption dashboard displays the deviation of electricity consumption intensity and the current total power of each space in real time, in the form of a heat map or dashboard. The personnel activity monitoring panel dynamically displays the personnel activity index and its historical change curve for each space. The control status window records the trigger time, control object, and changes in energy-saving intensity coefficient for primary and secondary control, presented in a timeline format. The parameter configuration interface allows users to preset key parameters such as electricity consumption deviation threshold, personnel activity index threshold, and preset adjustment cycle, and supports manual intervention (such as temporarily shutting down automatic control, manually restoring equipment power, or adjusting the energy-saving intensity coefficient). The visualization platform communicates with each module in real time via web sockets or Hypertext Transfer Protocol interfaces, ensuring a data refresh delay of less than 1 second.

[0039] In this embodiment, the energy management space can be exemplarily set as a university campus, and the terminal equipment includes lighting fixtures, air conditioning terminals, fresh air units, water dispensers, water heaters, vending machines, ordinary fans, and humidifiers, etc.

[0040] Please see Figure 2 The diagram shown is a flowchart illustrating the usage method of a smart energy management system based on a data visualization platform according to an embodiment of the present invention. The process includes at least the following steps: S1: Preprocess the raw data collected within the energy management space to generate data packets; Specifically, the data acquisition module polls or actively receives raw data uploaded by each terminal device based on a preset sampling period. Each piece of raw data includes a device identifier, acquisition time, data type, and data value. In this embodiment, the preset sampling period can be set to 30 seconds.

[0041] After receiving the raw data, preprocessing is performed to remove obviously erroneous data, such as negative power values ​​or temperatures exceeding the -40℃ to 80℃ range, and missing data points are marked as invalid. Based on the device-space mapping relationship and device identifier in the cloud configuration table, the spatial identifier corresponding to each data entry is determined. If it cannot be determined, the corresponding data is stored in a pending processing queue, and the system administrator periodically checks and manually binds the spatial identifier. Timestamps reported by different devices are uniformly converted to the system standard time and formatted as ISO 8601 standard strings. The data after the above operations is assembled into a unified format data packet, including spatial identifier, timestamp, data type, and data value, using JavaScript object representation, with the unified format: {"Spatial Identifier": "Building A, 3rd Floor", "Timestamp": "2025-06-15T14:30:05.123Z", "Data Type": "CO2", "Data Value": "423.5"}. In this embodiment, the standard string can be set to 2025-06-15T14:30:05.123Z.

[0042] Data packets are sent to the energy efficiency prediction module and the data platform module via message queues or a real-time stream processing engine. If the network experiences a momentary interruption, failed data packets are temporarily stored in a circular buffer and retransmitted once the network is restored. If the circular buffer reaches its storage limit, the earliest timestamp data packet is discarded first.

[0043] The data packet also performs calculations based on the original data, and saves the calculation results and the original data together after establishing a mapping relationship through the same spatial identifier and timestamp, so as to generate a corresponding trend curve for retrospective analysis and early warning.

[0044] S2: Determine the baseline data value based on the data packet, and determine whether energy efficiency treatment is required based on the comparison result between the data value in the data packet and the baseline data value; Specifically, the spatial identifier, timestamp, data type, and data value in the data packet are extracted through the energy efficiency pre-judgment module.

[0045] In this embodiment, the data type is exemplarily selected as the original current waveform. Based on the acquired original current waveform, the effective value of the current I is calculated using the root mean square (RMS) method, and the effective value of the fundamental current I1 and the effective values ​​of each harmonic current Ih are extracted using the fast Fourier transform, where h = 2, 3, ..., n.

[0046] The current harmonic distortion rate T is calculated based on the effective value of the fundamental current I1 and the effective values ​​of each harmonic current Ih. The calculation process is T=(√(ΣIh²)) / I1×100%. In this embodiment, the current harmonic distortion rate T is selected as the parameter for determining the energy efficiency treatment space because T can be used to characterize the degree of power quality degradation and reflect the additional energy loss caused by harmonics, comprehensively reflecting the operating status of various nonlinear loads in the space.

[0047] Based on each spatial identifier, all historical current harmonic distortion rates for the corresponding space within the past 7 days that belong to the same time period category as the current timestamp are collected. If the number of selected historical current harmonic distortion rates reaches a preset number, the historical current harmonic distortion rates are sorted in ascending order, and the median is taken as the benchmark current harmonic distortion rate T0. If the number is insufficient, no judgment is made temporarily to wait for data accumulation. During the accumulation period of historical current harmonic distortion rates, the corresponding space is not compared with T0 until the data volume reaches the preset number. In this embodiment, the preset number can be exemplarily 20, and the time period category is divided by hour.

[0048] In other embodiments, industrial or building energy consumption data often contain occasional spikes (such as equipment startup moments, communication errors, and momentary sensor malfunctions). The mean is susceptible to outliers; a single outlier can cause a significant shift in the baseline value, resulting in distorted subsequent deviation calculations. The median, on the other hand, depends only on the middle position after sorting and is unaffected by extreme values ​​at both ends, accurately reflecting the typical level of the data. Therefore, the median is chosen as the baseline value.

[0049] In this embodiment, the reference current harmonic distortion rate T0 is obtained from the data platform module by the energy efficiency pre-judgment module. These samples are historical current harmonic distortion rates that have been screened and confirmed to have no significant energy efficiency anomalies (i.e., electricity intensity deviation E is less than or equal to the preset electricity intensity deviation E0 and personnel activity index Q is less than or equal to the preset personnel activity index Q0). When the number of valid samples in the same space and time period reaches a preset number (e.g., 20), the samples are sorted in ascending order, and the median is taken as T0. For example, T0 = 2.5%. The comparison process between the current harmonic distortion rate T and the reference current harmonic distortion rate T0 is as follows: If T is greater than T0, it indicates that the power quality in the current space has deteriorated significantly (such as increased nonlinear load, inverter failure, and equipment aging), resulting in increased harmonic losses and decreased equipment efficiency, which can easily affect the normal operation of other equipment on the same circuit. Therefore, the energy efficiency pre-judgment module determines that the current space needs to undergo energy efficiency treatment.

[0050] If T is less than or equal to T0, it indicates that the current power quality of the space is at or better than the normal level of the same period in history, the additional harmonic loss is within the normal range, and the equipment efficiency is not significantly affected. Therefore, the energy efficiency pre-judgment module determines that the current space does not need to undergo energy efficiency treatment.

[0051] In this embodiment, the data type can also be, for example, power factor. The energy efficiency pre-judgment module extracts data values ​​of type power factor from the data packet, denoted as the current power factor PF. Based on each spatial identifier, all historical power factor data values ​​for the corresponding space within the past 7 days that belong to the same time period category as the current timestamp are collected. If the number of selected power factor data values ​​reaches a preset number (e.g., 20), the power factor data values ​​are sorted in ascending order, and the median is taken as the baseline power factor PF0; if the number is insufficient, no judgment is made until data accumulation. The power factor deviation D is calculated as D = PF0 - PF.

[0052] The energy efficiency pre-judgment module pre-sets a power factor deviation threshold D0, and determines whether energy efficiency treatment is required based on the comparison between the power factor deviation D and the power factor deviation threshold D0.

[0053] In this embodiment, the power factor deviation threshold D0 is dynamically established based on historical power factor deviation samples under normal operating conditions stored in the data platform module. Specifically, the energy efficiency pre-judgment module extracts power factor deviation samples D from the data platform within time periods that have been screened and confirmed to have no significant energy efficiency anomalies, and stores them, along with spatial identifiers and time period categories, in the historical sample library of the data platform. When the number of valid samples within the same space and time period category reaches a preset number (e.g., 20), the maximum value of the samples is taken as D0. For example, D0 = 0.1 can be set. The comparison process between the power factor deviation D and the power factor deviation threshold D0 is as follows: If D is greater than D0, it indicates that the power factor of the current space is significantly lower than the normal level of the same period in history. At this time, the reactive current increases, the line loss increases significantly, and the utilization rate of transformers and power distribution equipment decreases. Long-term operation at a low power factor will also accelerate the aging of equipment insulation. Therefore, the energy efficiency pre-judgment module determines that energy efficiency treatment is required.

[0054] If D is less than or equal to D0, it indicates that the power factor of the current space is at or better than the normal level of the same period in history, the reactive power loss has not significantly exceeded the economic operating range, the power distribution system is operating efficiently, and no additional intervention is required. Therefore, the energy efficiency pre-judgment module determines that no energy efficiency processing is required.

[0055] S3: Perform spatial aggregation based on the spatial identifier in the data packet to generate several spatial aggregation datasets, wherein the spatial aggregation datasets include the personnel activity index and the deviation of electricity intensity in the corresponding space; Specifically, the data platform module categorizes all data packets belonging to the same space based on the spatial identifier in the data packets. For each space, the data packets are sorted and aligned according to the timestamp to form a data stream of the corresponding space on a continuous time axis.

[0056] Within a preset time window Δt, the data platform module counts the total number of data packets uploaded by all infrared pyroelectric sensors within the corresponding space whose data type is infrared trigger and whose data value is 1, in order to obtain the infrared movement frequency P. In this embodiment, the preset time window Δt can be set to 5 minutes for example.

[0057] Within the same preset time window Δt, the initial carbon dioxide concentration Cs and the final carbon dioxide concentration Ce in the corresponding space are obtained by the data platform module based on the timestamp and data value in the data packet. The total carbon dioxide change rate ΔC is calculated as ΔC = (Ce - Cs) / Δt, and the minimum value of ΔC is 0, which is used to characterize the concentration increase caused by human respiration.

[0058] The environmental impact in the carbon dioxide change rate ΔC is eliminated by using the infrared movement frequency P. If P is equal to 0 and the duration is greater than or equal to Δt, then ΔC in the corresponding time period is recorded as the carbon dioxide change rate ΔC1 during the unoccupied period, and ΔC1 is cached for correction in subsequent occupied periods. If P is greater than 0, then the most recently cached ΔC1 is taken (ΔC1 is 0 if there is no cache), and the carbon dioxide change rate ΔCp related to personnel is calculated as ΔCp = ΔC - ΔC1, where ΔCp is used to characterize the impact of ventilation and outdoor atmospheric background on CO2 concentration.

[0059] The carbon dioxide change rate ΔCp and the infrared movement frequency P are normalized to obtain the normalized carbon dioxide change rate ΔCp1 and the normalized infrared movement frequency P1, respectively. A weighted average is then used to calculate the human activity index Q, which is calculated as Q = w1 × P1 + w2 × ΔCp1. Here, w1 and w2 are the weighting coefficients of the normalized infrared movement frequency P1 and the normalized carbon dioxide change rate ΔCp1, respectively, and w1 + w2 = 1. The normalization method can be either Min-Max normalization or Z-Score normalization.

[0060] Among them, since infrared movement frequency reflects short-term human activity (such as walking, entering and exiting), it has a weak ability to detect stationary human beings; while carbon dioxide change rate can detect the respiratory metabolism of stationary human beings, and has a higher coverage and stability in determining the presence status of human beings in space. Therefore, w1 is set to 0.3 and w2 is set to 0.7 for example.

[0061] Furthermore, the data platform module collects all historical active power data values ​​for each spatial identifier within the past 7 days that belong to the same time period category as the current timestamp. If the number of selected active power data values ​​reaches a preset quantity, the active power data values ​​are sorted in ascending order, and the median is taken as the baseline power value P0; if the quantity is insufficient, no determination is made temporarily to wait for data accumulation. The data values ​​of data packets with active power data uploaded by all terminal devices within the same spatial area are accumulated in real time to obtain the current power value P. In this embodiment, the preset quantity can be set to 20, and the time period categories are divided by hour.

[0062] The data platform module calculates the power intensity deviation E based on the current power value P and the reference power value P0. The calculation process is E=(P—P0) / P0. When the system is in operation at night and all terminal devices are turned off, there is still leakage current to ground of the power distribution line (including insulation resistance and distributed capacitance effect) and no-load loss of various switching power supplies. The active power data value is always greater than 0, so the reference power value P0 is always greater than 0. During the accumulation of historical active power data values, the power intensity deviation E is not calculated for the corresponding space. The calculation is performed after the data volume reaches the preset amount.

[0063] S4: Determine the corresponding space as an energy efficiency processing space based on the personnel activity index and the deviation of electricity intensity and generate a first energy efficiency trigger signal; determine the corresponding space as a non-energy efficiency processing space and generate a second energy efficiency trigger signal. Specifically, the space management module determines the corresponding space as an energy efficiency processing space and generates a first energy efficiency trigger signal based on the comparison results of the personnel activity index Q with the preset personnel activity index Q0 and the electricity intensity deviation E with the preset electricity intensity deviation E0. It also determines the corresponding space as a non-energy efficiency processing space and generates a second energy efficiency trigger signal.

[0064] In this embodiment, the preset personnel activity index Q0 is dynamically established based on the personnel activity index samples of historical unoccupied periods stored in the data platform module. Specifically, the space management module extracts unoccupied periods with infrared movement frequency P=0 and duration ≥Δt from the front observation window, and stores the personnel activity index Q value provided by the data platform module within the corresponding period, along with the space identifier and period category, in the historical sample library of the data platform. When the number of valid samples in the same space and the same period category reaches a preset number (e.g., 20), the maximum value of the sample set is taken as Q0, which can be exemplarily set as Q0=0.15.

[0065] The preset electricity consumption deviation E0 is dynamically established based on historical electricity consumption deviation samples under normal operating conditions stored in the data platform module. Specifically, the energy efficiency pre-judgment module extracts electricity consumption deviation E samples from the data platform within time periods that have been screened and confirmed to have no significant energy efficiency anomalies (i.e., current harmonic distortion rate T≤T0 and personnel activity index Q≤Q0), and stores these samples, along with their spatial identifiers and time period categories, in the historical sample database of the data platform. When the number of valid samples within the same spatial and time period category reaches a preset number (e.g., 20), the mean μE and standard deviation σE of the samples are calculated, and E0 is set to μE + k × σE, where k is a configurable coefficient (in this embodiment, k=2 is exemplarily used), and E0 can be set to 0.2.

[0066] The comparison process based on the personnel activity index Q and the preset personnel activity index Q0, and the electricity intensity deviation E and the preset electricity intensity deviation E0 is as follows: If Q is less than or equal to Q0 and E is greater than E0, it indicates that there is no one in the space or that people are stationary for a long time (e.g., at night, during lunch break, or during unattended periods). At the same time, the deviation of the power consumption is higher than the threshold, which means that the energy consumption is abnormally high when there is no demand for personnel activity (e.g., equipment running idle, lights left on, air conditioning over-cooling). In this case, energy efficiency treatment will not affect the comfort of personnel and can eliminate energy waste. Therefore, the space management module determines the corresponding space as an energy efficiency treatment space.

[0067] If Q is less than or equal to Q0 and E is less than or equal to E0, it indicates that there is no one in the space or the personnel are stationary, and the power consumption is at or above the historical normal level (e.g., the equipment is turned off or in low-power standby). At this time, there is no additional energy-saving space. If forced regulation is applied, it will increase the equipment start-up and shutdown losses. Therefore, the space management module determines the corresponding space as a non-energy-efficiency processing space.

[0068] If Q is greater than Q0 and E is greater than E0, it indicates that there are people in the space and they are active. Although the power consumption deviation is high, since people have basic needs for thermal environment, illuminance and air quality, priority should be given to ensuring the comfort and work efficiency of the people. It is not advisable to implement energy efficiency control that reduces comfort (such as reducing air conditioning power or turning off lighting). Therefore, the space management module determines the corresponding space as a non-energy efficiency treatment space.

[0069] If Q is greater than Q0 and E is less than or equal to E0, it indicates that there are people in the space and the power consumption is normal. The system has neither an urgent need for energy saving nor a need for regulation. It can maintain the current operating state. Therefore, the space management module determines the corresponding space as a non-energy-efficiency processing space.

[0070] S5: Receive the first energy efficiency trigger signal and the second energy efficiency trigger signal to perform a regulation, wherein the first energy efficiency trigger signal is to reduce the operating power of the terminal equipment in the energy efficiency processing space, and the second energy efficiency trigger signal is to maintain the operating power of several terminal equipment in the non-energy efficiency processing space. Specifically, when the primary control module receives an energy efficiency trigger signal, it analyzes the signal type: if it is the first energy efficiency trigger signal, it indicates that the corresponding space is an energy efficiency processing space, that is, the primary control module reduces the operating power of the terminal device that needs to be controlled; if it is the second energy efficiency trigger signal, it indicates that the corresponding space is a non-energy efficiency processing space, that is, the primary control module maintains the operating power of several terminal devices.

[0071] Furthermore, when the primary control module receives the first energy efficiency trigger signal, it reads the duration from the first receipt of the first energy efficiency trigger signal in the corresponding space to the current time, and records it as the cumulative duration DF. Based on the cumulative duration DF and the spatial weight factor γ, the spatial equivalent duration QF is determined. The calculation process is QF=DF×γ. In this embodiment, the spatial weight factor γ of the corresponding space is preset by the space management module. For example, the corresponding space can be set as an unmanned dormitory, and γ is set to 1.2 for example.

[0072] A preset spatial equivalent duration QF0 is pre-set by the control module, and the difference between the spatial equivalent duration QF and the preset spatial equivalent duration QF0 is calculated to obtain the duration difference ΔQF, thereby determining the required reduction in the operating power of the terminal device. In this embodiment, the terminal device can be exemplarily selected as a lighting fixture.

[0073] In this embodiment, the preset spatial equivalent duration QF0 is dynamically established based on historical successful control data stored in the primary control module (i.e., after a control operation, the power intensity deviation E decreases to less than or equal to E0 within a subsequent preset time window without any complaints about user comfort or equipment malfunctions). Specifically, the primary control module extracts QF samples from its internal cache or associated historical data storage area, confirming the effectiveness of the control within a selected time period, and stores these samples, along with the spatial identifier and time period category, in the primary control module's historical sample library. When the number of valid samples within the same space and time period category reaches a preset number (e.g., 20), the maximum value of the samples is taken as QF0. For example, QF0 can be set to 5 minutes.

[0074] The preset time difference ΔQF0 includes a first preset time difference ΔQF1 and a second preset time difference ΔQF2, and exemplarily set ΔQF1 = 1 minute and ΔQF2 = 2 minutes. The reduction in power consumption corresponding to the terminal device is determined based on the comparison between the time difference ΔQF and ΔQF1 and ΔQF2. As the time difference ΔQF increases, it indicates that the corresponding space has been in a state requiring energy-saving control for a long time. If a small power reduction is still used, continuous energy waste will accumulate. Therefore, the reduction in power consumption corresponding to the terminal device increases with the increase of the time difference ΔQF. If ΔQF is less than or equal to ΔQF1, the operating power of the terminal device is reduced to 0.8 times the current power based on the generated first operating power reduction adjustment command. For example, if the current operating power of the terminal device is set to 100W, the reduced operating power will be 80W. If ΔQF is greater than ΔQF1 and less than or equal to ΔQF2, the operating power of the terminal device is reduced to 0.75 times the current power based on the generated second operating power reduction adjustment command. If ΔQF is greater than ΔQF2, the operating power of the terminal device is reduced to 0.7 times the current power based on the generated third operating power reduction adjustment command. It should be noted that none of these power reductions will have a negative impact on the terminal device.

[0075] S6: Obtain the total power time series of the corresponding space after one regulation under the preset regulation period to obtain the power response lag index and power fluctuation coefficient. Based on the power response lag index and the preset power response lag index, determine whether to perform secondary regulation. If secondary regulation is performed, determine the energy-saving intensity coefficient of the terminal equipment in the energy efficiency treatment space based on the power fluctuation coefficient and the preset power fluctuation coefficient.

[0076] Specifically, after the preset adjustment period Ts ends, the secondary control module obtains the total power time series V(t) after the first control, starting from the start time t0 of the first control, with a sampling interval of δ. In this embodiment, Ts can be set to 15 minutes and δ to 1 minute.

[0077] The power change rate ΔV(t) is obtained by first-order difference of the total power time series V(t) after one regulation, calculated as ΔV(t) = [V(t+δ) - V(t)] / δ. By setting a power response threshold ε, starting from time t0, the system sequentially judges along the positive time axis to find the first time when ΔV(t) is less than or equal to ε, denoted as the power response stabilization time t1. This indicates that the power change rate ΔV has first decayed to within the preset threshold ε, meaning that the transient power fluctuations after one regulation have essentially subsided, and the system has entered a quasi-steady-state phase. The power response hysteresis index H is calculated as H = (t1 - t0) / Ts, where if t1 is not found within Ts, then H = 1. For example, ε is set to 0.5 kW / min.

[0078] A stable window of [t0+Ts, t0+Ts+L] is selected. The power subsequence after the first regulation within the window is extracted through the secondary regulation module, and its mean μ1 and standard deviation σ1 are calculated. The calculation process of the power fluctuation coefficient F is F=σ1 / μ1, where L is the time length of the stable window, and L can be set to 60 minutes for example.

[0079] A preset power response hysteresis index H0 is pre-set through the secondary control module, and the decision on whether to perform secondary control is based on the comparison result with the power response hysteresis index H.

[0080] In this embodiment, the preset power response lag index H0 is dynamically established based on historical successful control samples stored in the secondary control module. Specifically, the secondary control module stores the calculated H value after each control operation, along with the spatial identifier, time period category, and effective control marker (i.e., normal power response and improved energy efficiency after one control operation), in its internal cache or associated historical data storage area. When the number of effective samples in the same spatial and time period category reaches a preset number (e.g., 20), the samples are sorted in ascending order, and the 90th percentile is taken as H0. For example, H0 = 0.5 can be set.

[0081] The comparison process between the power response hysteresis index H and the preset power response hysteresis index H0 is as follows: If H is less than or equal to H0, it indicates that after the first control command is issued, the actual power begins to respond within an acceptable delay time, and the power change rate ΔC(t) reaches or exceeds the threshold ε. That is, the control command is executed in time, the energy efficiency treatment has taken effect, and no additional intervention is required. Therefore, the secondary control module determines not to execute secondary control.

[0082] If H is greater than H0, it indicates that after the first control command is issued, the actual power response is slow and the expected power decrease does not occur for most of the preset adjustment period Ts (or even there is no response at all). That is, the first energy efficiency treatment failed to effectively reduce the power intensity and the control needs to be triggered again to eliminate the energy efficiency abnormality. Therefore, the secondary control module determines to perform secondary control.

[0083] The secondary control module determines the energy-saving intensity coefficient of the terminal equipment in the energy efficiency treatment space by pre-setting a preset power fluctuation coefficient F0 and comparing it with the power fluctuation coefficient F.

[0084] Please see Figure 3 As shown, it is a flowchart of the logic for determining the secondary control based on the comparison result between the power response hysteresis index and the preset power response hysteresis index in an embodiment of the present invention.

[0085] In this embodiment, the preset power fluctuation coefficient F0 is dynamically established based on the power fluctuation coefficient F samples from historical successful controls stored in the secondary control module. Specifically, the secondary control module stores the F value calculated after each control operation, along with the spatial identifier, time period category, and effective control marker (i.e., H≤H0), in its internal cache or associated historical data storage area. When the number of effective samples in the same space and time period category reaches a preset number (e.g., 20), the mean μF and standard deviation σF of the samples are calculated, and F0 is set to μF + 2σF. For example, F0 = 0.2 can be set.

[0086] The comparison process between the power fluctuation coefficient F and the preset power fluctuation coefficient F0 is as follows: If F is less than or equal to F0, it indicates that the power fluctuation within the stable window after the first regulation is completed is small, the regulation action does not cause obvious power oscillation or large jump, and the system operates smoothly. Therefore, the secondary regulation module determines the energy-saving intensity coefficient of the terminal equipment in the energy efficiency treatment space to further reduce the operating power of the terminal equipment to save energy.

[0087] The difference between the preset power fluctuation coefficient F0 and the power fluctuation coefficient F is calculated and denoted as the fluctuation coefficient difference ΔFX. The preset fluctuation coefficient difference ΔFX0 includes a first preset fluctuation coefficient difference ΔFX1 and a second preset fluctuation coefficient difference ΔFX2. For example, ΔFX1 = 0.03 and ΔFX2 = 0.05 are set. Based on the comparison between the fluctuation coefficient difference ΔFX and ΔFX1 and ΔFX2, the increase in the energy-saving intensity coefficient is determined. As the fluctuation coefficient difference ΔFX increases, it indicates that the power fluctuation after one adjustment is smaller and the system operation is more stable, and the space for further increasing energy saving is larger. Therefore, the increase in the energy-saving intensity coefficient increases with the increase in the fluctuation coefficient difference ΔFX. If ΔFX is less than or equal to ΔFX1, the energy-saving intensity coefficient is increased to 1.1 times its initial value based on the generated first energy-saving intensity coefficient increase adjustment command. If the initial energy-saving intensity coefficient is set to 1, the increased energy-saving intensity coefficient is 1.1. If ΔFX is greater than ΔFX1 and less than or equal to ΔFX2, the energy-saving intensity coefficient is increased to 1.2 times its initial value based on the generated second energy-saving intensity coefficient increase adjustment command. If ΔFX is greater than ΔFX2, the energy-saving intensity coefficient is increased to 1.3 times its initial value based on the generated third energy-saving intensity coefficient increase adjustment command.

[0088] If F is greater than F0, it indicates that the power fluctuation is large within the stable window after the first regulation is completed. The regulation action may cause unstable phenomena such as frequent start-stop of equipment, periodic load oscillation or power overshoot. The current energy-saving intensity coefficient has exceeded the tolerance range of the load characteristics of this space. If the intensity is maintained or increased, it may lead to increased equipment loss, decreased comfort or deterioration of power quality. Therefore, the secondary regulation module determines to reduce the energy-saving intensity coefficient of the terminal equipment in the energy efficiency treatment space to avoid negative impacts caused by excessive regulation.

[0089] The difference between the power fluctuation coefficient F and the preset power fluctuation coefficient F0 is calculated and recorded as the fluctuation coefficient offset value ΔFY. The preset fluctuation coefficient difference ΔFY0 includes a first preset fluctuation coefficient offset value ΔFY1 and a second preset fluctuation coefficient offset value ΔFY2, and ΔFY1=0.03 and ΔFY2=0.06 are set for example. Based on the comparison between the fluctuation coefficient offset value ΔFY and ΔFY1 and ΔFY2, the reduction magnitude corresponding to the energy-saving intensity coefficient is determined. As the fluctuation coefficient offset value ΔFY increases, it indicates that the power fluctuation after one adjustment is more severe, the system operation is more unstable, and the current energy-saving intensity coefficient has exceeded the tolerance range of the spatial load characteristics. A greater degree of adjustment of the energy-saving intensity is needed to restore system stability. Therefore, the reduction magnitude corresponding to the energy-saving intensity coefficient increases with the increase of the fluctuation coefficient offset value ΔFY. If ΔFY is less than or equal to ΔFY1, the energy-saving intensity coefficient will be reduced to 0.95 times its initial value based on the generated first energy-saving intensity coefficient reduction adjustment command. If the initial energy-saving intensity coefficient is set to 1, the reduced energy-saving intensity coefficient will be 0.95. If ΔFY is greater than ΔFY1 and less than or equal to ΔFY2, the energy-saving intensity coefficient will be reduced to 0.85 times its initial value based on the generated second energy-saving intensity coefficient reduction adjustment command. If ΔFY is greater than ΔFY2, the energy-saving intensity coefficient will be reduced to 0.75 times its initial value based on the generated third energy-saving intensity coefficient reduction adjustment command. It should be noted that neither the increase nor decrease in the energy-saving intensity coefficient will have a negative impact on the terminal device.

[0090] Furthermore, the secondary control module obtains the longest continuous duration within the pre-observation window U where P equals 0 and ΔCp is less than or equal to 0, denoted as the unattended duration J (minutes). Based on the pre-observation window U and the unattended duration J, the personnel departure parameter λ is obtained, calculated as λ = J / U. λ is used to characterize the proportion of time in the corresponding space where there is no personnel (no movement and no respiration). In this embodiment, the pre-observation window U can be exemplarily set to [tp, tp + 10 min], where tp is the end time of the secondary control.

[0091] Please see Figure 4 As shown, it is a flowchart of the logic of determining the energy-saving intensity coefficient after correction and secondary regulation based on the comparison result of personnel departure parameters and preset personnel departure parameters in an embodiment of the present invention.

[0092] The secondary control module determines whether to correct the energy-saving intensity coefficient after secondary control by pre-setting a preset personnel departure parameter λ0 and comparing the result with the personnel departure parameter λ.

[0093] In this embodiment, the preset personnel departure parameter λ0 is dynamically established based on the personnel departure parameter λ samples stored in the historical successful departure judgments stored in the secondary control module. Specifically, the secondary control module stores the λ value calculated in the pre-observation window U after each secondary control, along with the spatial identifier, time period category, and departure validity marker (i.e., no rapid return after actual departure and improved energy efficiency), in the module's internal cache or associated historical data storage area. When the number of valid samples in the same space and time period category reaches a preset number (e.g., 20), the mean μλ and standard deviation σλ of the samples are calculated, and λ0 is set to max(0, μλ - σλ), which can be exemplarily set to λ0 = 0.8.

[0094] The comparison process between the personnel departure parameter λ and the preset personnel departure parameter λ0 is as follows: If λ is greater than λ0, it indicates that the people in the space have truly left, and further increasing energy saving will not affect the comfort of the people. Therefore, the secondary control module determines to correct and improve the energy-saving intensity coefficient after secondary control.

[0095] The difference between the personnel departure parameter λ and the preset personnel departure parameter λ0 is calculated and recorded as the departure parameter difference ΔλY. The preset departure parameter difference ΔλY0 includes a first preset departure parameter difference ΔλY1 and a second preset departure parameter difference ΔλY2. For example, ΔλY1 = 0.1 and ΔλY2 = 0.2 are set. Based on the comparison between the departure parameter difference ΔλY and ΔλY1 and ΔλY2, the increase in the energy-saving intensity coefficient after secondary control is determined. As the departure parameter difference ΔλY increases, it indicates a higher proportion of unattended time, and a greater increase in the energy-saving intensity coefficient after secondary control. Therefore, the increase in the energy-saving intensity coefficient after secondary control increases with the increase in the departure parameter difference ΔλY. If ΔλY is less than or equal to ΔλY1, the energy-saving intensity coefficient is increased to 1.2 times the current level based on the generated first energy-saving intensity coefficient increase correction instruction. If the energy-saving intensity coefficient after the second adjustment is set to 0.95, the adjusted energy-saving intensity coefficient is 1.14. If ΔλY is greater than ΔλY1 and less than or equal to ΔλY2, the energy-saving intensity coefficient is increased to 1.25 times the current level based on the generated second energy-saving intensity coefficient increase correction instruction. If ΔλY is greater than ΔλY2, the energy-saving intensity coefficient is increased to 1.3 times the current level based on the generated third energy-saving intensity coefficient increase correction instruction.

[0096] If λ is less than or equal to λ0, it indicates that there are still people in the space (who may be stationary or have left briefly). In order to avoid excessive energy saving affecting the comfort of people, the energy saving intensity is further reduced. Therefore, the secondary control module determines to correct and reduce the energy saving intensity coefficient after secondary control.

[0097] The difference between the preset personnel departure parameter λ0 and the preset personnel departure parameter λ is calculated and recorded as the departure parameter offset value ΔλX. The preset departure parameter offset value ΔλX0 includes a first preset departure parameter offset value ΔλX1 and a second preset departure parameter offset value ΔλX2, and ΔλX1=0.25 and ΔλX2=0.35 are set for example. Based on the comparison between the departure parameter offset value ΔλX and ΔλX1 and ΔλX2, the reduction magnitude of the energy-saving intensity coefficient after secondary adjustment is determined. As the departure parameter offset value ΔλX increases, it indicates that the longer the personnel are present, the greater the potential impact of the current energy-saving intensity coefficient on personnel comfort, requiring a greater reduction in the energy-saving intensity coefficient after secondary adjustment. Therefore, the reduction magnitude of the energy-saving intensity coefficient after secondary adjustment increases with the increase of the departure parameter offset value ΔλX. If ΔλX is less than or equal to ΔλX1, the energy-saving intensity coefficient is reduced to 0.9 times the current level based on the generated first energy-saving intensity coefficient reduction correction instruction. Wherein, if the energy-saving intensity coefficient after the second adjustment is set to 0.95, the corrected energy-saving intensity coefficient is 0.855. If ΔλX is greater than ΔλX1 and less than or equal to ΔλX2, the energy-saving intensity coefficient is reduced to 0.8 times the current level based on the generated second energy-saving intensity coefficient reduction correction instruction. If ΔλX is greater than ΔλX2, the energy-saving intensity coefficient is reduced to 0.7 times the current level based on the generated third energy-saving intensity coefficient reduction correction instruction.

[0098] It should be noted that the safe range of the energy-saving intensity coefficient for primary regulation, secondary regulation, and correction regulation is [0.8, 1.8]. If the energy-saving intensity coefficient after each adjustment is less than 0.8, the energy-saving intensity coefficient is set to 0.8; if it is greater than 1.8, the energy-saving intensity coefficient is set to 1.8. The increase or decrease of the energy-saving intensity coefficient after secondary regulation and correction regulation will not have a negative impact on the terminal equipment.

[0099] All technologies not mentioned in the above embodiments are existing technologies. It is understood that no specific limitation is made to any preset parameter or critical parameter in the embodiments of the present invention, and the above values ​​are not limited thereto. Those skilled in the art can adjust the preset parameters or critical parameters accordingly based on actual needs, analysis of historical data, or equipment usage.

[0100] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A smart energy management system based on a data visualization platform, characterized in that, include: The data acquisition module is used to preprocess the raw data collected within the energy management space to generate data packets; An energy efficiency pre-judgment module is used to determine a baseline data value based on the data packet, and to determine whether energy efficiency processing is required based on the comparison result between the data value in the data packet and the baseline data value. The data platform module is used to perform spatial aggregation based on the spatial identifier in the data packet to generate several spatial aggregation datasets, wherein the spatial aggregation datasets include the personnel activity index and the deviation of electricity intensity in the corresponding space. The space management module is used to determine the corresponding space as an energy efficiency processing space and generate a first energy efficiency trigger signal based on the personnel activity index and the electricity intensity deviation, and to determine the corresponding space as a non-energy efficiency processing space and generate a second energy efficiency trigger signal. A primary control module is used to receive the first energy efficiency trigger signal and the second energy efficiency trigger signal to perform primary control, wherein the first energy efficiency trigger signal is to reduce the operating power of the terminal equipment in the energy efficiency processing space, and the second energy efficiency trigger signal is to maintain the operating power of several terminal equipment in the non-energy efficiency processing space. The secondary control module is used to obtain the total power time series of the corresponding space after the first control under the preset control period to obtain the power response lag index and the power fluctuation coefficient. Based on the power response lag index and the preset power response lag index, it determines whether to perform secondary control. If it is determined to perform secondary control, it determines the energy-saving intensity coefficient of the terminal equipment in the energy efficiency treatment space based on the power fluctuation coefficient and the preset power fluctuation coefficient.

2. The smart energy management system based on a data visualization platform according to claim 1, characterized in that, The energy efficiency pre-judgment module is also used to determine the corresponding historical data value based on the spatial identifier, timestamp and data type in the data packet, and sort the historical data value to determine the median as the benchmark data value.

3. The smart energy management system based on a data visualization platform according to claim 2, characterized in that, The energy efficiency pre-judgment module is also used to determine whether the current space needs energy efficiency processing based on the comparison result that the data value is greater than the benchmark data value.

4. The smart energy management system based on a data visualization platform according to claim 2, characterized in that, The data platform module is also used to determine the corresponding historical power value based on the spatial identifier, the timestamp, and the data type in the data packet, sort the historical power values ​​to determine the median as the reference power value, and calculate the power intensity deviation based on the current power value and the reference power value. The data platform module is also used to determine the carbon dioxide change rate based on the timestamp and the data value in the data packet, and to perform a weighted calculation based on the carbon dioxide change rate and the infrared movement frequency in the data packet to obtain the personnel activity index.

5. The smart energy management system based on a data visualization platform according to claim 4, characterized in that, The space management module is also used to determine the corresponding space as the energy efficiency processing space based on the comparison results of the personnel activity index being less than or equal to the preset personnel activity index and the comparison results of the electricity intensity deviation being greater than the preset electricity intensity deviation. The space management module is also used to determine the corresponding space as the non-energy-efficiency processing space based on the comparison result that the personnel activity index is greater than the preset personnel activity index, or based on the comparison result that the personnel activity index is less than or equal to the preset personnel activity index and the power consumption deviation is less than or equal to the preset power consumption deviation.

6. The smart energy management system based on a data visualization platform according to claim 1, characterized in that, The secondary control module is also used to determine the execution of the secondary control based on the comparison result that the power response hysteresis index is greater than the preset power response hysteresis index.

7. The smart energy management system based on a data visualization platform according to claim 6, characterized in that, The secondary control module is also used to determine the energy-saving intensity coefficient of the terminal equipment in the energy efficiency treatment space based on the comparison result that the power fluctuation coefficient is less than or equal to the preset power fluctuation coefficient. The secondary control module is also used to determine the energy-saving intensity coefficient of the terminal equipment in the energy efficiency treatment space based on the comparison result that the power fluctuation coefficient is greater than the preset power fluctuation coefficient.

8. The smart energy management system based on a data visualization platform according to claim 7, characterized in that, The secondary control module is also used to determine the increase of the energy-saving intensity coefficient based on the comparison result between the fluctuation coefficient difference and the preset fluctuation coefficient difference, and the increase is positively correlated with the fluctuation coefficient difference. The secondary control module is also used to determine the reduction of the energy-saving intensity coefficient based on the comparison result between the fluctuation coefficient offset value and the preset fluctuation coefficient offset value, and the reduction magnitude is positively correlated with the fluctuation coefficient offset value.

9. The smart energy management system based on a data visualization platform according to claim 8, characterized in that, The secondary control module is also used to acquire personnel departure parameters under the pre-observation window, and to determine whether to correct the energy-saving intensity coefficient after secondary control based on the comparison result between the personnel departure parameters and the preset personnel departure parameters. The energy-saving intensity coefficient after the secondary regulation is improved is determined based on the comparison result that the personnel departure parameter is greater than the preset personnel departure parameter; The energy-saving intensity coefficient after reducing the secondary regulation is determined based on the comparison result that the personnel departure parameter is less than or equal to the preset personnel departure parameter.

10. The smart energy management system based on a data visualization platform according to claim 9, characterized in that, The secondary control module is also used to determine the energy-saving intensity coefficient after the secondary control is corrected and improved based on the comparison result between the difference of the off-field parameters and the preset difference of the off-field parameters, and the magnitude of the correction and improvement is positively correlated with the difference of the off-field parameters. The secondary control module is also used to determine the energy-saving intensity coefficient after the secondary control is corrected and reduced based on the comparison result between the off-field parameter offset value and the preset off-field parameter offset value, and the correction reduction magnitude is positively correlated with the off-field parameter offset value.

Images