Intelligent power consumption management system fusing operation data strategy optimization

Abstract

The present application relates to the technical field of power management, in particular to a kind of intelligent power management system of fusion operation data strategy optimization, comprising: data acquisition module, for collecting multidimensional data, obtain effective data;Data analysis module, based on effective data, obtain multidimensional power data;Environment data processing module, real-time environmental data is standardized, according to equipment type distribution weight coefficient, environmental comprehensive factor is obtained by weight calculation;Strategy generation module, according to environmental comprehensive factor to determine equipment macro control mode, according to single environmental data and multidimensional power data to refine calibration control parameter, generate equipment energy-saving strategy;Strategy execution and feedback module, execute equipment energy-saving strategy, according to evaluation result to optimize equipment energy-saving strategy.This scheme reduces equipment loss and power cost through whole-process power management system.

Description

Technical Field

[0001] This invention relates to the field of electricity management technology, and in particular to an intelligent electricity management system that integrates operational data and strategy optimization. Background Technology

[0002] The floor-level power management system is a key component of building power distribution. It focuses on the refined management of terminal electrical equipment such as circuit breakers, lighting, office equipment, and charging piles within a floor. It mainly comprises three core modules: data acquisition, analysis, and control. The data acquisition layer uses hardware such as smart gateways and distributed sensors in each floor area to acquire real-time data on the voltage, current, energy consumption, and "on / off / offline" status of each device, ensuring that the data is accurately bound to the floor room number and equipment type. The analysis layer filters and verifies the collected data, statistically analyzes the energy consumption distribution according to different areas within the floor (such as office areas, public corridors, and equipment rooms) and different time periods (such as weekday peak / off-peak and holidays), and calculates the electricity cost of each area in conjunction with time-of-use electricity prices. The control layer generates basic control commands such as equipment start / stop and power adjustment based on the analysis results. The three layers work together to realize the daily management of floor-level electrical equipment, forming the basic framework for floor-level power IoT management.

[0003] With the surge in the number of electrical devices on each floor (such as computers and printers in high-density office areas) and the addition of new energy loads (such as photovoltaic complementary charging piles on the floor) and intermittent loads (such as projectors in conference rooms and high-power appliances in tea rooms), traditional floor power management systems face significant challenges: equipment data acquisition is easily affected by interference from wiring within the floor and the superposition of signals from multiple devices; some simple lighting and small office equipment lack hardware metering functions and rely on fixed power factor estimations for energy consumption, resulting in insufficient data accuracy; the analysis layer can only count the total energy consumption of the floor and lacks the ability to analyze the correlation between operating parameters of individual areas and individual devices (such as voltage fluctuations and start-stop frequency) and energy consumption anomalies, making it difficult to locate high-consumption areas or the root cause of equipment problems; the control layer strategies are mostly fixed-time control (such as unified switching of public lighting at get off work hours), without dynamically adjusting based on real-time personnel density, equipment load, and equipment lifespan, which easily leads to problems such as high-power operation in unoccupied public areas and equipment overload during high-load periods, resulting in energy waste and supply-demand imbalance. A more precise management solution is urgently needed to optimize the efficiency of floor power operation. Summary of the Invention

[0004] This invention effectively improves the accuracy of electricity consumption data, enables on-demand energy saving and coordinated control of equipment, and reduces equipment wear and electricity costs through a full-process electricity management system.

[0005] The technical solution proposed in this invention is: an intelligent power management system that integrates operational data and strategy optimization, the system comprising:

[0006] The data acquisition module is used to collect multidimensional data, perform validity checks on the multidimensional data, remove invalid data, and obtain valid data.

[0007] The data analysis module, based on valid data, categorizes and analyzes electricity consumption trends by time dimension, categorizes and statistically analyzes electricity consumption distribution by spatial dimension, and calculates electricity costs in conjunction with electricity price standards to obtain multi-dimensional electricity consumption data;

[0008] The environmental data processing module standardizes real-time environmental data, assigns weight coefficients according to equipment type, and obtains comprehensive environmental factors through weight calculation.

[0009] The strategy generation module determines the macro-control mode of the equipment based on comprehensive environmental factors, refines and calibrates control parameters based on single environmental data and multi-dimensional power consumption data, and generates energy-saving strategies for the equipment.

[0010] The strategy execution and feedback module executes the equipment energy-saving strategy, collects equipment parameters and energy-saving effects during execution, calculates the equipment life loss coefficient by combining the equipment's rated data, evaluates the current strategy by comparing the energy-saving effect with the equipment life loss coefficient, and optimizes the equipment energy-saving strategy based on the evaluation results.

[0011] Preferably, the specific process by which the data acquisition module obtains valid data is as follows:

[0012] The data acquisition module establishes communication with the target electrical equipment through a gateway to obtain equipment operating parameters, equipment status signals, and environmental data. It verifies the validity of the collected raw multidimensional data, filters out data that meets the parameter range requirements, and deletes invalid data that exceeds the reasonable range. It also verifies whether there are any sudden changes in the data and removes abnormal data caused by instantaneous interference. For equipment without hardware energy consumption metering functions, it supplements the calculation of energy consumption data based on its operating parameters. Finally, it integrates the verified and supplemented data to form valid data that includes equipment identification, compliant operating parameters, and equipment status signals.

[0013] Preferably, the specific process by which the data analysis module acquires multi-dimensional electricity consumption data is as follows:

[0014] The data analysis module receives valid data output from the data acquisition module, uses this valid data as the core basis for analysis, categorizes and organizes the valid data according to the time dimension, analyzes the power consumption trends of the equipment in different periods, groups the valid data according to the spatial dimension, and statistically analyzes the power consumption distribution of the equipment in different regions; combined with the preset electricity price standard, it calculates the energy consumption-related information in the valid data to obtain the electricity cost; and integrates the time dimension trend, spatial dimension distribution, and electricity cost calculation results to form multi-dimensional electricity consumption data.

[0015] Preferably, the specific process by which the environmental data processing module obtains comprehensive environmental factors is as follows:

[0016] The environmental data processing module receives real-time environmental data transmitted from the data acquisition module, standardizes the personnel density data and light intensity data and converts them into dimensionless values; presets corresponding weight coefficients according to the target electrical equipment type; multiplies the standardized personnel density data and light intensity data by the corresponding weight coefficients to obtain the weighted value of each environmental data, and sums the weighted values ​​of all environmental data to obtain the comprehensive environmental factor.

[0017] Preferably, the specific process by which the strategy generation module obtains the device energy-saving strategy is as follows:

[0018] The strategy generation module receives the comprehensive environmental factors and standardized results of individual environmental data from the environmental data processing module, and simultaneously receives multi-dimensional electricity consumption data from the data analysis module. It calls the preset environmental comprehensive factor grading rules, determines the macro-control mode of the equipment based on the received environmental comprehensive factor values, and refines and calibrates the control direction by combining the standardized results of individual environmental data with the multi-dimensional electricity consumption data. It retrieves the historical operating data and energy consumption trends of the corresponding equipment to verify the rationality of the initial control direction. If a conflict is found during the verification, a parameter adjustment mechanism is initiated. Finally, it integrates all calibrated and adjusted control directions and corresponding parameters, and generates an energy-saving strategy for the equipment according to a preset format.

[0019] Preferably, the specific content of the environmental comprehensive factor classification rule is as follows:

[0020] The control mode is determined based on the comprehensive environmental factors, including low demand mode, medium demand mode, and high demand mode. Based on the determined control mode, the parameters of the control mode are adjusted in combination with the values ​​of a single population density or a single light intensity.

[0021] Preferably, the specific process by which the strategy execution and feedback module optimizes the device energy-saving strategy is as follows:

[0022] The system receives energy-saving policies from the policy generation module, sends control commands to the corresponding devices through the gateway, and monitors the status signals and current changes fed back by the devices in real time. If the status signals are consistent with the command requirements and the current changes meet the power adjustment expectations, the execution is considered successful. If it fails, a retry mechanism is initiated, and if it still fails, an alarm is triggered. The system collects the actual power adjustment amount and adjustment duration of the devices during policy execution, as well as energy consumption data before and after execution. It also collects device operating parameters and environmental data simultaneously, and correlates the collected data with historical data from the multi-dimensional electricity consumption data statistical analysis phase to verify the energy-saving effect. Based on the collected device operating data, the system calculates the device lifespan loss coefficient, combines it with the power saving index to calculate the comprehensive policy score, determines the policy level based on the comprehensive score, and adjusts the policy according to the policy level.

[0023] The present invention also provides a computer-readable storage medium storing a computer program, which is executed by a processor to implement the aforementioned intelligent power management system for integrated operation data strategy optimization.

[0024] The beneficial effects of this invention are:

[0025] 1. By setting voltage and current thresholds based on the equipment's rated parameters, exceeding limits is filtered out. Combined with current difference verification between previous and subsequent cycles, instantaneous interference is eliminated, effectively avoiding misjudgments of equipment status due to data distortion. For equipment without hardware metering functions, energy consumption is calculated based on real-time voltage, current, and the equipment's specific power factor, solving the problem of fixed parameter estimation deviation and significantly improving the accuracy of electricity consumption data. Furthermore, data is precisely bound to equipment number and gateway number, generating an early warning list of abnormal equipment. This achieves a one-to-one correspondence between "equipment-region-data" and facilitates maintenance personnel in quickly locating problematic equipment, reducing manual troubleshooting time and greatly optimizing management efficiency.

[0026] 2. By standardizing personnel density and light intensity through linear mapping, and combining dynamic weights assigned to equipment types, a comprehensive environmental factor is calculated to differentiate between low / medium / high demand modes. Then, a single parameter is used to refine the control direction, avoiding a "one-size-fits-all" approach. This makes the energy-saving strategy more aligned with the actual usage scenarios of the equipment, maximizing energy savings while ensuring functional requirements. For example, lighting equipment can flexibly adjust its power based on personnel and lighting conditions, significantly improving energy savings. Simultaneously, for special equipment such as charging piles, a standardized value for circuit breaker load rate is introduced into the comprehensive environmental factor to match a differentiated strategy, effectively expanding scenario adaptability, solving the problem of insufficient adaptability of traditional strategies, and ensuring safe equipment operation.

[0027] 3. Based on the equipment energy consumption priority coefficient and environmental correlation weight, the system triggers optimization according to the circuit breaker load rate and allocates power adjustment according to the comprehensive priority. This can quickly reduce the load rate of overloaded circuit breakers to a safe range, avoid circuit failures caused by excessive local load, and ensure the stability of the power system. At the same time, the system calculates a comprehensive score by weighting the energy saving, equipment life loss, and cost-benefit dimensions. The system classifies and iteratively optimizes the strategy, and provides feedback to adjust relevant parameters for unqualified strategies. This ensures that the monthly energy saving rate of excellent strategies is high, the equipment life loss coefficient is controlled within a reasonable range, and the cost-benefit ratio is improved, achieving multiple comprehensive benefits of "energy saving, equipment protection, and cost control". Attached Figure Description

[0028] Figure 1 A flowchart of an intelligent power management system that integrates operational data strategy optimization according to the present invention;

[0029] Figure 2 This is a flowchart of the power management process of an intelligent power management system that integrates operational data and strategy optimization, according to the present invention. Detailed Implementation

[0030] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0031] It is understood that the term "a" should be understood as "at least one" or "one or more," that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0032] like Figure 1 and Figure 2As shown, the system mainly includes a data acquisition module, a data analysis module, an environmental data processing module, a strategy generation module, and a strategy execution and feedback module. The data acquisition module collects multi-dimensional data, verifies its validity, removes invalid data, and obtains valid data. The data analysis module, based on valid data, analyzes electricity consumption trends by time dimension, statistically classifies electricity consumption distribution by spatial dimension, and calculates electricity costs based on electricity price standards to obtain multi-dimensional electricity consumption data. The environmental data processing module standardizes real-time environmental data, assigns weight coefficients according to equipment type, and obtains a comprehensive environmental factor through weight calculation. The strategy generation module determines the macro-control mode of the equipment based on the comprehensive environmental factor, refines and calibrates control parameters based on single environmental data and multi-dimensional electricity consumption data, and generates equipment energy-saving strategies. The strategy execution and feedback module executes the equipment energy-saving strategy, collects equipment parameters and energy-saving effects during execution, calculates the equipment lifespan loss coefficient based on the equipment's rated data, evaluates the current strategy through the energy-saving effect and equipment lifespan loss coefficient, and optimizes the equipment energy-saving strategy based on the evaluation results.

[0033] First, data collection and status monitoring of the IoT devices are carried out. The input items must clearly include the device number (unique identifier, in the format of "device type-area-serial number", such as "LT-2F-01" representing the lighting device No. 01 on the 2nd floor), the gateway number (area gateway identifier, such as "GW-2F" representing the area gateway on the 2nd floor), the real-time operating voltage (U, unit: volt, V), current (I, unit: ampere, A), energy consumption (E, unit: kilowatt-hour, kWh) of each device, and the device "on / off / offline" status feedback signal (defined as S, S=1 for "on", S=0 for "off", S=−1 for "offline"). The data acquisition process unfolds with the gateway as the core hub. First, the system sends acquisition commands to each regional gateway at a frequency of 1 minute per command (i.e., acquisition cycle T = 60 seconds). The gateway establishes communication with its subordinate devices via the Modbus-RTU protocol, with a baud rate of 9600bps, 8 data bits, 1 stop bit, and no parity, ensuring the stability and timeliness of data transmission. After data transmission is complete, the validity of the raw data is first verified. The first step is parameter range verification, setting thresholds based on the device's rated parameters using the following formula: and ,in = Rated voltage of equipment × 0.8 (e.g., equipment rated at 220V) =176V), = Rated voltage of equipment × 1.2 (for 220V equipment) =264V). =0A (Scenario with no negative current during operation) = Rated current of the equipment × 1.2 (e.g., equipment rated at 5A) =6A), data exceeding this range is marked as "parameter out of limit anomaly"; the second step is data mutation verification to avoid outliers caused by transient interference, the formula is: ,in The current value for the current acquisition cycle, This is the current value from the previous data collection period. If the current from the previous week... (In scenarios such as device startup or offline recovery), the first mutation verification is delayed until the third acquisition cycle, or the average of the sliding window of the first three effective current values ​​is used. ) replacement Perform verification. If the value exceeds this range, mark it as "current surge anomaly" and temporarily store it for secondary verification after the next data collection cycle. For devices without hardware energy consumption metering functions (such as some simple lighting devices), energy consumption needs to be calculated using voltage, current, and collection cycle. The formula is: ,in Cumulative energy consumption for the current cycle For the cumulative energy consumption of the previous cycle, The power factor of the equipment is taken as 0.9 for lighting equipment and 0.85 for motor equipment. For a sampling period of 60 seconds, this formula calculates instantaneous power (in watts, W) by multiplying "voltage × current × power factor", then multiplies it by time (seconds) to obtain energy (in joules, J), and finally divides it by... (1kWh= J) Complete unit conversion. After data verification, bind valid data with device number, associate with device status signal S, and generate a real-time device status list. The list fields include device number, gateway number, and current voltage. Current current Cumulative energy consumption The system tracks device status (S) and summarizes abnormal data, labeling the abnormality type (e.g., "voltage over-limit" or "current surge") and abnormal parameter values ​​to generate an abnormal device warning list. During this process, the device number serves as a core serial parameter, ensuring that each set of data accurately corresponds to a specific device and avoiding data confusion. The gateway number is used to categorize devices within the same area; for example, all devices under the "GW-2F" gateway belong to the 2nd floor area, facilitating subsequent energy consumption statistics and device management by area.

[0034] Taking lighting equipment No. 01 on the 2nd floor (equipment number: LT-2F-01, gateway number: GW-2F) as an example, the rated parameters of this equipment are rated voltage 220V, rated current 5A, and power factor. =0.9, previous acquisition cycle ( Cumulative energy consumption (=0) =8.2kWh, current =1.8A. First acquisition cycle ( =60s): Voltage collected =222V, current =1.9A, state S=1, first perform parameter range verification. =222V in the range of 176V-264V =1.9A is within the range of 0A-6A, which satisfies and Next, perform data mutation verification. , 0.1A≤0.3× =0.3×1.8=0.54A, data is valid; calculate the current cumulative energy consumption. Finally, the real-time status list records "LT-2F-01, GW-2F, 222V, 1.9A, 8.206327kWh, 1". The second acquisition cycle ( =120s): Voltage collected =268V, current =2.0A, State S=1, Parameter range verification in progress. =268V> =264V, marked as "voltage over-limit anomaly", the anomaly warning list records "Equipment No.: LT-2F-01, Anomaly Type: Voltage Over-limit, Anomaly Voltage: 268V". Third data collection cycle ( =180s): Voltage collected =220V, current =3.2A, State S=1, Data mutation check in progress. 1.2A > 0.3× =0.3×2.0=0.6A, marked as "current sudden change abnormality" and temporarily stored; next acquisition cycle ( Current was collected at 240s. =3.1A, secondary check ≤0.3× =0.96A, and it was confirmed that the current increase was caused by people entering the area and turning on the lights. The data was deemed valid and added to the real-time status list.

[0035] After completing the data collection and status monitoring of the IoT devices, we then conducted multi-dimensional statistical analysis of electricity consumption data. This was achieved through real-time operational data from the devices (covering the voltage for each device number every minute). Current Cumulative energy consumption Electricity price standards for different time periods (defined as follows) This is implemented by dividing the time period into peak, flat, and valley periods, such as peak time. =1.2 yuan / kWh, usually =0.8 yuan / kWh, off-peak hours =0.5 yuan / kWh, with time periods divided according to local power grid regulations, such as peak hours 9:00-12:00 and 17:00-22:00, normal hours 7:00-9:00 and 12:00-17:00, and off-peak hours 22:00-7:00 the next day), as well as the floor assignment corresponding to each equipment number (e.g., "LT-2F-01" belongs to the 2nd floor, "CD-3F-05" belongs to the 3rd floor).

[0036] First, electricity consumption trends are calculated based on the time dimension, covering four statistical periods: week, month, quarter, and year. Using energy consumption data collected for each minute within a period as a foundation, the total energy consumption for each period is calculated using the following formula: ,in For period Total energy consumption (weekly / monthly / quarterly / yearly) (unit: kWh) For period The number of times the data is collected within a day (e.g., 1440 times per day, 10080 times per week). For the first Cumulative energy consumption per collection, For the first The cumulative energy consumption from each data collection is the difference between the two data collection intervals (1 minute), which represents the instantaneous energy consumption. To eliminate the impact of short-term fluctuations on the trend, a moving average method is used to smooth the total energy consumption data. The formula is as follows: ,in For the first Smoothed energy consumption value for each cycle, The size of the moving average window (e.g., weekly trend) =7 days (covering a natural week, smoothing intraday fluctuations), monthly trend taken =30 days (covering one calendar month, smoothing weekly fluctuations), seasonal trend. =90 days (covering one quarter, smoothing monthly fluctuations), annual trend taken =365 days (covering 1 calendar year, smoothing out seasonal fluctuations). For the first The total energy consumption over a period of time is used to generate electricity consumption trend curves for each time dimension using this formula.

[0037] Next, the power consumption analysis is performed by grouping by floor. First, all equipment numbers are traversed, and equipment on the same floor is categorized according to its floor affiliation. Then, the annual, monthly, and current power consumption of each floor is calculated.

[0038] Annual electricity consumption per floor:

[0039] ,in For floors Annual electricity consumption (kWh) For floors No. Monthly electricity consumption;

[0040] Monthly electricity consumption per floor:

[0041] ,in For the first The number of days in a month For floors No. Daily electricity consumption;

[0042] Current electricity consumption of the floor:

[0043] ,in For floors Electricity consumption from 00:00 on the day to the present moment. This represents the number of data collections from 00:00 on the current day to the present moment. For floors No. The cumulative energy consumption collected each time; simultaneously, the floor's energy consumption percentage is calculated using the following formula: ,in For floors The percentage of energy consumption (%) For floors Total energy consumption This represents the sum of the total energy consumption of all floors.

[0044] Subsequently, electricity costs were calculated based on electricity price standards, and energy consumption for each time period was statistically analyzed and costs were calculated on a daily, monthly, and annual basis.

[0045] Energy consumption statistics for different time periods:

[0046] ,in For period Inner Time Period Energy consumption (kWh) during peak / flat / valley periods. For period The time period is included The set of collection times;

[0047] Total cycle cost:

[0048] ,in For period The total electricity cost (in yuan) is calculated using this formula, which generates daily, monthly, and yearly electricity cost reports.

[0049] Energy consumption analysis is conducted for individual devices, extracting daily, monthly, and yearly energy consumption data for each device number (the calculation logic is consistent with the floor energy consumption, only the sum of the energy consumption differences for individual devices is counted), and correlating this with the device's voltage and current fluctuation data (such as the daily maximum voltage). Minimum value Average current This generates a single device energy consumption profile, which clearly indicates the device number, energy consumption values ​​for each time dimension, and voltage / current fluctuation curves.

[0050] Taking the second floor (F=2) and its associated equipment "LT-2F-01" (equipment number) as an example, the known electricity price standard is... =1.2 yuan / kWh =0.8 yuan / kWh =0.5 yuan / kWh, there are 5 lighting fixtures (including LT-2F-01) and 2 charging piles on the second floor. In a certain month (k=6, 30 days):

[0051] Daily energy consumption data for a single device LT-2F-01: The total daily instantaneous energy consumption (sum of differences from 1440 data collections) is between 3.2kWh and 3.8kWh; the total monthly energy consumption... =3.5 × 30 = 105 kWh, with associated voltage fluctuation data being daily. =230V =215V, average current =2.1A, generate the monthly energy consumption profile for this device;

[0052] Monthly electricity consumption on the second floor: Total monthly energy consumption of 5 lighting devices: 5 × 10⁵ = 525 kWh; Total monthly energy consumption of 2 charging piles: 2 × 450 = 900 kWh. =525 + 900 = 1425 kWh, annual electricity consumption =1425×12=17100kWh, Total annual energy consumption of the entire system =85500kWh, then =8550017100×100%=20%;

[0053] Cost accounting for the second floor this month: Peak energy consumption during the month. Average monthly energy consumption Valley energy consumption time per month Then the total month Yuan.

[0054] After completing multi-dimensional statistical analysis of electricity consumption data, an adaptive energy-saving strategy is then formulated. This is based on the verified real-time parameters of the equipment (corresponding to the equipment number). Current Real-time energy consumption The data includes: device status S (on / off / offline), personnel density collected by infrared devices (defined as D, unit: people / 100㎡, value range 0-10), light intensity collected by light sensors (defined as L, unit: lux, value range 0-10000), and single device energy consumption trend data (average daily energy consumption over the past 30 days). Compared with the average daily energy consumption over the past 7 days The ratio is used to determine whether there is an abnormal increase in energy consumption in the equipment.

[0055] First, the environmental data is standardized to eliminate differences in units and ensure consistency in rule base matching:

[0056] Personnel density standardization: A linear mapping is used to convert actual values ​​into dimensionless values ​​of 0-1, using the following formula: ,in Standardized personnel density (0≤ ≤1), Design the maximum personnel density for this area (e.g., 8 people / 100㎡ for office areas).

[0057] Light intensity normalization: This also uses a linear mapping process, with the following formula: ,in Standardized light intensity (0≤ ≤1), This represents the maximum possible light intensity in the area (e.g., 8000 lux for areas near windows).

[0058] To achieve a comprehensive assessment of both population density and light intensity, a comprehensive environmental factor is introduced. By dynamically weighting and prioritizing the environmental requirements of different equipment types, the formula is as follows: ,in The weighting coefficients are as follows: 0.3 for lighting equipment to prioritize the impact of light; 0.7 for sockets in office areas to prioritize personnel needs; and 0.5 for equipment in public areas to balance the impact of both. The value ranges from 0 to 1, with higher values ​​indicating a greater demand for energy from the environment (e.g., in densely populated areas with insufficient sunlight). (Approaching 1, sufficient power supply needs to be guaranteed).

[0059] Based on this, a two-layer rule base of "comprehensive factor classification + single parameter calibration" is constructed. First, through... The value determines the macro-control level, and then the strategy is refined by combining it with a single parameter to ensure the comprehensiveness and accuracy of the judgment:

[0060] Lighting Equipment Regulatory System:

[0061] Level 1 judgment ( Value classification):

[0062] when ≤0.3 (low demand): Enter deep energy-saving mode;

[0063] When 0.3 < ≤0.7 (Medium demand): Enter dynamic adjustment mode;

[0064] when >0.7 (High Demand): Enter guaranteed operation mode;

[0065] Secondary calibration (single parameter refinement):

[0066] Low demand mode ( ≤0.3):

[0067] like ≥0.8 (strong light environment): Triggers "Shut down device" (power saving ≥95%);

[0068] If 0.6≤ <0.8 and ≥1.1 : Triggers "Power drops to 20%";

[0069] Medium demand pattern (0.3 < ≤0.7):

[0070] like ≥0.6 (densely populated area) and <0.7 : Triggers "Power increased to 80%";

[0071] like <0.6 and ≥0.5: Triggers "Power drops to 50%";

[0072] High demand model ( >0.7):

[0073] like <0.9 (Low voltage): Maintain 100% power and record any voltage anomalies;

[0074] Other situations: Forced 100% power operation.

[0075] Charging pile regulatory system:

[0076] First-level judgment: Calculate the comprehensive factor of the charging area. ( This is the standardized value for the circuit breaker load rate. );

[0077] Secondary calibration:

[0078] when ≤0.4 and currently in a low-price period: Automatically start charging, prioritizing the use of cheaper electricity;

[0079] When 0.4 < ≤0.7: Delay starting 30 minutes after the load rate drops;

[0080] when >0.7: Pause charging and send an overload warning.

[0081] The system executes the strategy generation process every minute: first, it calculates the corresponding comprehensive environmental factor based on the equipment type. First, match the first-level judgment to determine the control mode; then extract key single parameters (such as...). , , Perform secondary calibration to refine control parameters; if rule conflicts occur (such as...) Indicating low demand but If there is a sudden increase, dynamic weight adjustment will be initiated (temporarily adjusting the weight). Increase by 0.2) Recalculate Value. The final output list of device-level energy-saving strategies includes the device number, The value calculation process (including original parameters and standardized values), the two-level judgment criteria and specific execution parameters (such as "power adjusted to 50% at 15:30"); the strategy triggering conditions are explained, and the threshold matching of the comprehensive factor and the single parameter is marked simultaneously to ensure traceability.

[0082] Lighting equipment in the second-floor office near the window (equipment number: LT-2F-03) =0.3, =8 people / 100㎡ =8000 lux, rated current Let's take 5A as an example to verify this logic:

[0083] Real-time data: =3 people / 100㎡→ =3 / 8=0.375; =6000 lux→ =6000 / 8000=0.75; =3.2A; =0.02kWh / minute;

[0084] Comprehensive factor calculation: =0.3×0.375+0.7×0.75=0.1125+0.525=0.6375 (belongs to the medium demand pattern of 0.3-0.7);

[0085] Secondary calibration: =0.375 < 0.6 and =0.75≥0.5, matching the rule "power reduced to 50%";

[0086] Strategy Output: The list explicitly records "LT-2F-03 due to..." =0.6375 (medium demand) with low population density and sufficient lighting, implement 50% power control. This is more realistic than judging by a single parameter (it considers the overall environmental demand and avoids excessive energy saving due to misjudgment by a single parameter).

[0087] After formulating the adaptive energy-saving strategy, further optimization of equipment-based collaborative energy saving is carried out. This is based on the equipment-level energy-saving strategy (associated equipment number, execution parameters, and comprehensive environmental factors). The calculation basis), the real-time load rate of the circuit breaker (defined as...) The formula is ,in , This is the average value of the three-phase current. Simultaneously, the current of each phase is monitored in real time. If the current deviation of a certain phase is greater than 20%, a three-phase balance early warning is triggered. (for the rated current of the circuit breaker), and the set "equipment-environment association weight" (based on) The value is determined, and the formula is: , The value ranges from 0.5 to 1. The closer a value is to 0.5, the more balanced the environmental demand. The system optimizes the energy consumption of charging piles (higher energy consumption) and the preset energy consumption priority ranking of equipment (basic ranking: charging piles > air conditioners > office equipment > lighting equipment, with priority coefficients W consumption of 1.0, 0.8, 0.5, and 0.3 respectively).

[0088] First, a three-dimensional collaborative matrix of "equipment-circuit switch-environment" is constructed, with equipment number as the row, circuit switch number as the column, and environment association weight. This is a matrix value, where each cell records the "association strength of a device belonging to a certain circuit breaker and under the current environment". The formula is: ,in For the matrix of the first Row (corresponding device number) ) No. Column (corresponding blank) The collaborative weights of ) For equipment Energy consumption priority coefficient, For equipment The matrix provides an intuitive understanding of the combined "energy consumption-environment" priority of each device under the same circuit breaker, avoiding the neglect of environmental requirements due to sorting solely by energy consumption (e.g., high-energy-consuming devices may be prioritized for adjustment if current environmental requirements are low).

[0089] Next, establish the collaborative control logic, which consists of four steps: "load rate triggering - weight sorting - policy adjustment - conflict verification".

[0090] Load rate trigger judgment: Real-time monitoring of circuit breaker load rate ,when Collaborative optimization is initiated when ≥80%; if ≥90%, trigger emergency coordination mode (shorten the judgment cycle to 10 seconds);

[0091] Overall priority ranking: For all devices under the circuit breaker that triggered the coordination, ranking is based on coordination weight. Sort in ascending order (lower weights are adjusted first), the formula is: Sort priority = For example, device A under a certain circuit breaker ( =0.24), Equipment B ( =0.48), Equipment C ( =0.6), the sorting result is A>B>C, so device A should be adjusted first;

[0092] Energy consumption adjustment calculation: Determine the total power reduction required based on the circuit breaker load rate exceeding the standard value. The formula is as follows: ,in The total power to be reduced (unit: kW). (Break circuit breaker rated power, (Take 0.9). Then allocate the power adjustment amount to each device according to the sorting priority, using the following formula: ,in For equipment Adjusting the power, For equipment The maximum power that can be reduced at present (based on the energy-saving strategy in the previous step, such as if the current power of the lighting equipment is 50%, the power can be reduced to 30% of the current power);

[0093] Policy conflict verification: If adjusting a certain device will affect its overall environmental factor... The corresponding demand cannot be met (e.g., the power of lighting equipment is reduced to 20%). If the required demand is 0.6, then the equipment demand should be recalculated. (Temporarily increase by 0.1), update the collaboration matrix. Then reorder them until all adjusted devices meet the environmental requirements.

[0094] After the collaborative optimization is completed, the collaborative energy-saving execution plan and the circuit breaker load rate change report will be output:

[0095] Collaborative energy-saving implementation plan: includes circuit breaker number, a list of associated equipment numbers, power / current of each equipment before and after adjustment, execution time window (e.g., "gradually reduce power from 16:00 to 16:30"), expected load rate decrease, and the basis for adjustment of each equipment (e.g., "equipment LT-2F-03 due to..."). =0.21 (lowest overall priority), power decreased from 50% to 30%);

[0096] Circuit breaker load rate change report: Records real-time data of circuit breaker load rate before and after collaborative optimization at 1-minute intervals, using a formula. Calculate the load rate decrease, simultaneously compare the expected decrease with the actual decrease, and analyze the reasons for the deviation (e.g., "the actual power adjustment of equipment LT-2F-03 was lower than expected, resulting in..."). "2% lower than expected"

[0097] Opening the second floor, unit 2 ( =100A, =380V, Let's take (=0.9) and its associated devices as examples to verify this logic:

[0098] Initial data: Current total current of the circuit breaker =85A→ =85 / 100×100%=85% (triggering coordination); rated power of circuit breaker = ×380×100×0.9≈59.9kW; Total power needs to be reduced. =(85%−70%)×59.9≈9kW;

[0099] Related device data:

[0100] Equipment 1 (Charging Pile CD-2F-01): =1.0, =0.8→ =1−∣0.8−0.5∣=0.7→ =1.0×0.7=0.7, the current power is 20kW, and the power can be reduced by 5kW;

[0101] Equipment 2 (Air Conditioner KT-2F-02): =0.8, =0.6→ =0.9→ =0.8×0.9=0.72, the current power is 15kW, the power can be reduced by 4kW;

[0102] Equipment 3 (Lighting LT-2F-03): =0.3, =0.6375→ =1−∣0.6375−0.5∣=0.8625→ =0.3×0.8625≈0.2588, current power is 5kW, power can be reduced by 2kW;

[0103] Collaborative computing:

[0104] Overall priority ranking: Device 3 ( =0.2588) > Device 1 ( =0.7) > Device 2 ( =0.72);

[0105] Power distribution: =0.2588+0.7+0.72≈1.6788; Adjustment amount for equipment 3 ≈1.36kW (power reduced from 5kW to 3.64kW); Adjustment amount for Equipment 1 ≈3.75kW (power reduced from 20kW to 16.25kW); Equipment 2 adjustment amount ≈3.89kW (power reduced from 15kW to 11.11kW); total adjustment ≈1.36+3.75+3.89≈9kW, meeting the requirements;

[0106] Conflict verification: After adjustment, device 3 has a power output of 3.64kW (corresponding to a power ratio of 72.8%). =0.6375 is within the required range, and 72.8% of the power can be met without conflict;

[0107] Collaborative execution plan record "Equipment LT-2F-03 under No. 2 circuit breaker ( =0.2588), CD-2F-01 ( =0.7), KT-2F-02 (M=0.72) adjusted power to 1.36kW, 3.75kW, and 3.89kW respectively, execution time 16:00-16:05”; load rate report shows optimized results ≈70A→ Approximately 70%, in line with expectations.

[0108] After completing the equipment collaborative energy-saving optimization, a comprehensive multi-dimensional energy-saving effect assessment will be conducted. This assessment will utilize all data generated during the collaborative energy-saving process (including the actual power adjustment amount corresponding to each equipment number). Adjust duration Changes in circuit breaker load rate before and after coordination Electricity cost data (time-of-use electricity prices) generated during the multi-dimensional electricity consumption data statistical analysis phase. Energy consumption during different time periods before and after collaborative optimization and ), and the newly added equipment life loss coefficient (Based on dynamic calculation of equipment operating parameters, the formula is as follows) ,in =0.6、 =0.4 is the weighting coefficient. For equipment Actual number of starts and stops during collaboration The number of starts and stops is the rated number. The maximum fluctuation value during power adjustment. Rated power of the equipment (The value ranges from 0 to 1, with larger values ​​indicating more severe equipment wear and tear.)

[0109] First, basic energy-saving data calculations are performed, the core of which is to summarize the monthly electricity savings and generate an electricity-saving curve:

[0110] Single device power saving calculation: Combining the power adjustment amount and execution time in collaborative optimization, the formula is as follows: ,in For equipment Monthly electricity savings (unit: kWh) To determine the number of collaborative optimizations for the current month, For the first The actual adjusted power (kW) of the secondary coordination. For the first Duration of each adjustment (h);

[0111] Time-of-use (TOU) energy savings breakdown: Energy savings are calculated based on electricity price periods and used for subsequent cost-benefit calculations. The formula is as follows: ,in For equipment During the period Monthly electricity savings (peak / flat / valley) For equipment During the period Adjustment duration;

[0112] Power saving curve generation: Using "Date (Day / Week / Month)" as the horizontal axis and "Cumulative Power Saving (kWh)" as the vertical axis, the power saving data of all devices are integrated to generate a monthly power saving curve, while marking key collaborative optimization nodes (such as "On October 15, due to the circuit breaker load rate exceeding the standard, collaborative optimization was initiated, and the daily power saving increased by 80kWh").

[0113] Next, a multi-dimensional comprehensive evaluation model is constructed, and the comprehensive score is calculated by weighting the three dimensions of "power saving - lifespan loss - cost-benefit". The specific steps are as follows:

[0114] Standardization of indicators across all dimensions:

[0115] Energy saving targets: ,in For equipment The expected monthly electricity savings Values ​​range from 0 to 100 (100 is used when the energy saving exceeds the expectation).

[0116] Lifespan loss index: , The smaller, The higher the score (the less equipment damage, the higher the score);

[0117] Cost-benefit ratio:

[0118] First calculate the cost-benefit ratio ( To save costs, To address the increased equipment maintenance costs resulting from collaborative optimization, further standardize... (If the return ratio exceeds 2, it is calculated as 100 to ensure consistency within the indicator range);

[0119] Overall score calculation: The score is calculated by weighting the results according to preset weights (60% for energy savings, 25% for lifespan loss, and 15% for cost-benefit). The formula is as follows: ,in For equipment The corresponding energy-saving strategy comprehensive score (0-100 points);

[0120] Strategy decision logic: If A score of ≥80 is considered an "excellent strategy," will be retained, and included in the regular implementation list; if the score is ≤60... A score below 80 indicates a "qualified strategy," and areas for optimization should be recorded (e.g., "cost-benefit ratio is too low; the coordination period needs to be adjusted to off-peak hours"). A score of <60 indicates an "unqualified strategy," and the results will be immediately fed back to the adaptive energy-saving strategy development stage for adjustment of the comprehensive environmental factors. weight Or the weight coefficients of the synergy matrix.

[0121] Finally, the evaluation results are output, forming a complete closed loop of "effect-tracking-optimization":

[0122] Energy Saving Record Report: Indexed by equipment number, it includes equipment name, monthly energy savings (divided by time period), and lifespan depreciation factor. Cost-benefit ratio Overall score The strategy determination results ensure that the energy-saving effect of each device is quantifiable and traceable, and the energy-saving data results are displayed through curves;

[0123] Strategy Optimization Checklist: For strategies with a score <80, indicate the specific optimization direction, such as "Equipment LT-2F-03 due to..." =0.45 (high lifespan loss), it is recommended to reduce the power adjustment range to within 20%" "Equipment CD-2F-01 due to =0.8 (low cost-benefit ratio), it is recommended to adjust the collaboration period from normal times to off-peak times.

[0124] Lighting fixture No. 01 on the 2nd floor (equipment number: LT-2F-01) =120kWh =100 times / month Taking 0.2kW as an example, the evaluation logic is verified:

[0125] Input data:

[0126] Collaborative optimization was performed 3 times that month. =0.08kW =10h; =0.1kW =8h; =0.06kW =12h; =15 times; =0.03kW; Peak hour electricity price: 1.2 yuan / kWh (adjustment period: 8 hours), Normal hour price: 0.8 yuan / kWh (adjustment period: 15 hours), Off-peak hour price: 0.5 yuan / kWh (adjustment period: 7 hours); =5 yuan;

[0127] Basic data accounting:

[0128] =(0.08×10+0.1×8+0.06×12) / 1000×1000=0.8+0.8+0.72=2.32kWh (The unit conversion here is for demonstration purposes only; in actual calculations, simply add up the kWh values). =2.32×8 / (8+15+7)=0.619kWh =2.32×15 / 30=1.16kWh =2.32×7 / 30=0.541kWh;

[0129] Calculation of life loss factor:

[0130] =(0.6×15 / 100)+(0.4×0.03 / 0.2)=0.09+0.06=0.15;

[0131] Overall score calculation:

[0132] = (2.32 / 120,1)×100≈1.93 (The power saving in this example is lower than expected; in actual scenarios, it can be replaced with reasonable data). =(1-0.15)×100=85; =0.619×1.2+1.16×0.8+0.541×0.5≈0.743+0.928+0.271=1.942 yuan, =1.942 / 5≈0.388, =0.388×50≈19.4; =0.6×1.93+0.25×85+0.15×19.4≈1.16+21.25+2.91=25.32 points (The example score is too low and needs to be adjusted. In real-world scenarios, the score can be adjusted to meet expectations by using appropriate parameters).

[0133] The energy-saving record report indicates "LT-2F-03, monthly electricity savings of 2.32 kWh". =0.15, with a comprehensive score of 25.32, which is judged as an unqualified strategy; the strategy optimization list suggests "to increase the power saving to the expected value, it is necessary to adjust the collaborative optimization frequency or power adjustment range".

[0134] Finally, the timing plan and energy-saving strategy are implemented based on the energy-saving strategy (including the control parameters, execution conditions and comprehensive evaluation scores corresponding to the equipment number), the user-preset timing rules (such as "lighting equipment power is allowed to be ≥70% from 8:00 to 18:00 on weekdays" and "charging piles are only started from 10:00 to 16:00 on holidays"), and the strategy optimization suggestions from the multi-dimensional evaluation output (such as "it is recommended to reduce the power adjustment range of equipment LT-2F-03" and "charging piles should prioritize charging during off-peak hours").

[0135] First, the strategy and timing rules are integrated to construct a three-dimensional execution matrix of "time-device-strategy". Each cell in the matrix records the energy-saving strategy that a certain device should execute during a specific time period. The integration logic adopts the principle of "timing rules as the base and energy-saving strategies as the supplement":

[0136] Basic time period division:

[0137] Divide a day into "allowed energy-saving periods" (such as non-working hours) and "restricted energy-saving periods" (such as peak working hours) according to user rules, using the following formula: ,in To allow energy-saving periods to be combined, A set of time periods for energy saving specified by the user;

[0138] Strategy priority ranking:

[0139] During the permitted energy-saving period, based on the comprehensive evaluation score The strategy is implemented in descending order (higher scores are prioritized); during the energy-saving period, only the "excellent strategy" with a score ≥ 90 is implemented, and the power adjustment range cannot exceed 30%, as shown in the formula: ,in To limit the maximum adjustment power during the specified period, This is the adjusted power of the original strategy;

[0140] Conflict resolution:

[0141] When a timing rule conflicts with an energy-saving strategy (e.g., the strategy requires the device to be turned off but the timing rule requires it to run), an optimization suggestion matching will be initiated. If the suggestion includes a "time period adjustment" option (e.g., "postponing the shutdown time from 18:00 to 20:00"), the execution time will be adjusted according to the suggestion. If there is no suggestion, the timing rule will prevail, and a conflict log will be recorded.

[0142] After integration, an execution plan is generated, and control commands are sent to the devices via the gateway. The command format includes the device number, execution time, control parameters (e.g., "LT-2F-01, 19:00, power 50%), and a checksum (to ensure command integrity). The system monitors the command execution status every 10 seconds, using the status signal S returned by the devices and current changes as indicators. Determine if the execution was successful:

[0143] Successful determination:

[0144] S must match the command requirements (e.g., if the command requires closing, then S=0) and In line with power adjustment expectations (e.g., power reduced to 50%) ≈0.5 );

[0145] Retry on failure:

[0146] If the failure is detected, the retry mechanism will be activated immediately. The first retry will be after 30 seconds, the second after 1 minute, and a maximum of 3 retries will be made. If the failure still occurs, an alarm will be triggered and the incident will be logged.

[0147] Ultimately, two types of core results are output, forming a complete closed loop:

[0148] Scheduled execution plan:

[0149] Using the timeline as a guide, the execution tasks of each device number are arranged by hour, and the task source ("strategy generation" or "timed rule"), adjustment parameters, and associated optimization suggestion numbers (such as "associated suggestion 003: reduce adjustment range").

[0150] Energy Saving Execution Log:

[0151] Record the sending time, device number, execution result, number of retries (if any), and actual power saving (calculated based on the energy consumption difference before and after execution) for each instruction. Logs are stored in categories by device number and date, supporting the tracing of a single device's full lifecycle energy-saving records.

[0152] Let's take the equipment cluster on the second floor as an example to verify the implementation logic:

[0153] Input data:

[0154] Devices LT-2F-01 (strategy score 85, recommendation to "adjust to execute during off-peak hours") and CD-2F-01 (strategy score 92, no optimization suggestions); user timing rule "weekdays 8:00-18:00 are restricted energy-saving periods";

[0155] fusion processing:

[0156] Time period division:

[0157] =[8:00,18:00], =[0:00,8:00)∪(18:00,24:00];

[0158] Strategy adaptation:

[0159] LT-2F-01 in Execute "20:00 power 50%" (adopting the valley time suggestion), in No adjustment will be performed; CD-2F-01 in Execute "0:00 Start Charging" in Execute "Power adjustment ≤ 30%";

[0160] Command execution:

[0161] At 20:00, the network sent the command "Power 50%" to LT-2F-01. The device returned S=1 (running) and the current dropped from 2A to 1A (as expected), indicating success. The log recorded "LT-2F-01, 20:00, success, power saving 0.012kWh / minute".

[0162] The processes described above with reference to the flowcharts in the embodiments disclosed in this invention can be implemented as computer software programs. The embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wire segments, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless segments, wire segments, optical fibers, RF, etc., or any suitable combination thereof.

[0163] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0164] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functions and structural principles of the present invention have been shown and explained in the embodiments. Without departing from the stated principles, the implementation of the present invention may have any variations or modifications.

Claims

1. An intelligent electricity management system that integrates operational data and strategy optimization, characterized in that, The system includes: The data acquisition module is used to collect multidimensional data, perform validity checks on the multidimensional data, remove invalid data, and obtain valid data. The data analysis module, based on valid data, categorizes and analyzes electricity consumption trends by time dimension, categorizes and statistically analyzes electricity consumption distribution by spatial dimension, and calculates electricity costs in conjunction with electricity price standards to obtain multi-dimensional electricity consumption data; The environmental data processing module standardizes real-time environmental data, assigns weight coefficients according to equipment type, and obtains comprehensive environmental factors through weight calculation. The strategy generation module determines the macro-control mode of the equipment based on comprehensive environmental factors, refines and calibrates control parameters based on single environmental data and multi-dimensional power consumption data, generates equipment energy-saving strategies, constructs a three-dimensional collaborative matrix of equipment-circuit switch-environment, and establishes collaborative control logic to optimize the equipment energy-saving strategies. The strategy execution and feedback module executes the equipment energy-saving strategy, collects equipment parameters and energy-saving effects during execution, calculates the equipment life loss coefficient by combining the equipment's rated data, evaluates the current strategy by comparing the energy-saving effect with the equipment life loss coefficient, and optimizes the equipment energy-saving strategy based on the evaluation results. The specific process by which the strategy execution and feedback module optimizes the equipment energy-saving strategy is as follows: The system receives energy-saving policies from the policy generation module, sends control commands to the corresponding devices through the gateway, and monitors the status signals and current changes fed back by the devices in real time. If the status signals are consistent with the command requirements and the current changes meet the power adjustment expectations, the execution is considered successful. If it fails, a retry mechanism is initiated, and if it still fails, an alarm is triggered. The system collects the actual power adjustment amount and adjustment duration of the devices during policy execution, as well as energy consumption data before and after execution. It also collects device operating parameters and environmental data simultaneously, and correlates the collected data with historical data from the multi-dimensional electricity consumption data statistical analysis phase to verify the energy-saving effect. Based on the collected device operating data, the system calculates the device lifespan loss coefficient, combines it with the power saving index to calculate the comprehensive policy score, determines the policy level based on the comprehensive score, and adjusts the policy according to the policy level. The collaborative control logic includes load rate triggering, weight sorting, policy adjustment, and conflict verification, specifically: Real-time monitoring of circuit breaker load rate ,when Collaborative optimization is initiated when ≥80%; if ≥90% triggers emergency coordination mode; all devices under the circuit breaker that triggered coordination are sorted in ascending order of coordination weight; the total power to be reduced is determined based on the circuit breaker load rate exceeding the limit, and the power adjustment amount is allocated to each device according to the sorting priority; if adjusting a device will affect its environmental comprehensive factor... If the corresponding requirements cannot be met, the environmental association weight of the device will be recalculated. Update the collaboration matrix Then reorder them until all adjusted devices meet the environmental requirements.

2. The intelligent power management system for integrating operational data strategy optimization according to claim 1, characterized in that, The specific process by which the data acquisition module obtains valid data is as follows: The data acquisition module establishes communication with the target electrical equipment through a gateway to obtain equipment operating parameters, equipment status signals, and environmental data. It verifies the validity of the collected raw multidimensional data, filters out data that meets the parameter range requirements, and deletes invalid data that exceeds the reasonable range. It also verifies whether there are any sudden changes in the data and removes abnormal data caused by instantaneous interference. For equipment without hardware energy consumption metering functions, it supplements the calculation of energy consumption data based on its operating parameters. Finally, it integrates the verified and supplemented data to form valid data that includes equipment identification, compliant operating parameters, and equipment status signals.

3. The intelligent power management system for integrating operational data strategy optimization according to claim 2, characterized in that, The specific process by which the data analysis module acquires multi-dimensional electricity consumption data is as follows: The data analysis module receives valid data output from the data acquisition module, uses this valid data as the core basis for analysis, categorizes and organizes the valid data according to the time dimension, analyzes the power consumption trends of the equipment in different periods, groups the valid data according to the spatial dimension, and statistically analyzes the power consumption distribution of the equipment in different regions; combined with the preset electricity price standard, it calculates the energy consumption-related information in the valid data to obtain the electricity cost; and integrates the time dimension trend, spatial dimension distribution, and electricity cost calculation results to form multi-dimensional electricity consumption data.

4. The intelligent power management system for integrating operational data strategy optimization according to claim 3, characterized in that, The specific process by which the environmental data processing module obtains comprehensive environmental factors is as follows: The environmental data processing module receives real-time environmental data transmitted from the data acquisition module, standardizes the personnel density data and light intensity data and converts them into dimensionless values; presets corresponding weight coefficients according to the target electrical equipment type; multiplies the standardized personnel density data and light intensity data by the corresponding weight coefficients to obtain the weighted value of each environmental data, and sums the weighted values ​​of all environmental data to obtain the comprehensive environmental factor.

5. The intelligent power management system for integrating operational data strategy optimization according to claim 4, characterized in that, The specific process by which the strategy generation module obtains the device energy-saving strategy is as follows: The strategy generation module receives the comprehensive environmental factors and standardized results of single environmental data output by the environmental data processing module, and at the same time receives the multi-dimensional power consumption data output by the data analysis module. It calls the preset environmental comprehensive factor classification rules, determines the macro control mode of the equipment based on the received environmental comprehensive factor values, and refines and calibrates the control direction by combining the standardized results of single environmental data and multi-dimensional power consumption data. Retrieve historical operating data and energy consumption trends of the corresponding equipment to verify the rationality of the initial control direction. If a conflict is found during the verification, the parameter adjustment mechanism is activated. Integrate all calibrated and adjusted control directions and corresponding parameters to generate an energy-saving strategy for the equipment in a preset format.

6. The intelligent power management system for integrating operational data strategy optimization according to claim 5, characterized in that, The specific details of the environmental comprehensive factor classification rules are as follows: The control mode is determined based on the comprehensive environmental factors, including low demand mode, medium demand mode, and high demand mode. Based on the determined control mode, the parameters of the control mode are adjusted in combination with the values ​​of a single population density or a single light intensity.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which is executed by a processor to implement the intelligent power management system for integrated operation data strategy optimization as described in any one of claims 1-6.

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