Underground space low-carbon operation management method based on environmental condition dynamic adjustment

Abstract

The invention provides an underground space low-carbon operation management method based on environmental condition dynamic adjustment, and the method comprises the following steps: 1, training an energy consumption prediction model based on a historical data set; wherein the historical data set comprises input data and output data; the input data are environmental conditions of the underground space; the output data is the daily operation energy consumption of each system of the underground space; step 2, acquiring N groups of operation parameters; step 3, optimizing the N groups of operating parameters based on a particle swarm algorithm to obtain a group of optimal operating parameters; and step 4, transmitting the optimal operation parameters to an underground space operation management system. According to the invention, regulation and control of operation parameters such as ventilation, illumination, drainage and the like are realized, and equipment energy consumption and carbon emission level are reduced.

Description

Technical Field

[0001] The present invention belongs to the field of infrastructure carbon emissions, and particularly relates to a low-carbon operation management method for underground spaces dynamically adjusted based on environmental conditions. Background Art

[0002] Underground space projects, such as tunnels, underground complexes, underground parking lots, etc., have significant characteristics in terms of energy consumption of ventilation, lighting, drainage, and temperature control systems due to their special environmental conditions and operation requirements. The operation of these systems directly affects the energy consumption level and carbon emissions of underground spaces, becoming a key problem in low-carbon operation management. How to reduce equipment energy consumption and carbon emission levels while ensuring the environmental comfort and operation safety of underground spaces is an important technical challenge currently faced.

[0003] Existing underground space operation management methods mostly rely on fixed parameters or preset operation modes, lacking the ability to respond in real time to the dynamic changes in environmental conditions. Ventilation, lighting, and drainage equipment usually operates continuously at high loads, resulting in energy waste and high carbon emissions. In addition, these methods fail to effectively combine the special environmental characteristics of underground spaces (such as high humidity, poor air circulation, and small temperature fluctuations), and cannot achieve refined regulation and energy consumption optimization. Even if some systems introduce energy-saving management technologies, they fail to quantitatively analyze the dynamic relationship between environmental conditions and equipment energy consumption, resulting in limited emission reduction effects of optimization measures.

[0004] Therefore, there is an urgent need for a low-carbon operation management method that can combine the dynamic changes in underground space environmental conditions. Based on real-time data collection and dynamic adjustment models, it optimizes the operation parameters of ventilation, lighting, and drainage equipment to reduce energy consumption and carbon emission levels, while improving the operation efficiency and environmental adaptability of underground spaces. Summary of the Invention

[0005] The purpose of the present invention is to provide a low-carbon operation management method for underground spaces dynamically adjusted based on environmental conditions. By real-time monitoring of underground space environmental conditions (such as temperature, humidity, air quality, etc.), combined with dynamic adjustment models and optimization algorithms, it realizes precise regulation of the operation parameters of ventilation, lighting, drainage, and other equipment, significantly reduces equipment energy consumption and carbon emission levels, and at the same time meets the requirements of environmental comfort and operation safety of underground spaces. The technical solutions adopted are as follows:

[0006] A low-carbon operation management method for underground spaces dynamically adjusted based on environmental conditions, comprising the following steps:

[0007] Step 1: Based on the historical data set, train an energy consumption prediction model;

[0008] Among them, the historical data set includes input data and output data; the input data is the environmental conditions of the underground space; the output data is the daily operating energy consumption of each system in the underground space.

[0009] Step 2: Obtain N groups of operating parameters, which specifically include the following steps:

[0010] Step 2A: Collect the environmental conditions of the underground space, and then input them into the trained energy consumption prediction model to obtain the daily operating energy consumption of each system; among them, a group of energy consumption includes the daily operating energy consumption of all systems.

[0011] Step 2B: Collect a group of operating parameters corresponding to a group of energy consumption under the environmental conditions of Step 2A; a group of operating parameters includes the operating parameters of all systems.

[0012] Step 2C: Repeat Steps 2A to 2B until N groups of operating parameters corresponding to N groups of energy consumption are finally obtained.

[0013] Step 3: Optimize the N groups of operating parameters based on the particle swarm algorithm to obtain a group of optimal operating parameters.

[0014] Among them, the fitness function is the sum of the carbon emissions of all systems in a group of energy consumption.

[0015] Carbon emissions = energy consumption * carbon emissions generated per unit of energy consumption.

[0016] Step 4: Transmit the optimal operating parameters to the underground space operation and management system.

[0017] Preferably, in Step 1, the underground space includes a ventilation system, a lighting system, and a drainage system.

[0018] Preferably, the operating parameter of the ventilation system is the ventilation volume; the operating parameter of the lighting system is the regional brightness condition; the operating parameter of the drainage system is the operating duration.

[0019] Preferably, in Step 1, the environmental conditions include: temperature, humidity, and carbon dioxide concentration.

[0020] 1. Air quality requirements

[0021] Goal: Ensure that the air quality in the underground space meets safety, health, and comfort standards.

[0022] Carbon dioxide concentration control:

[0023] Air quality requirements: Control the carbon dioxide concentration and the concentrations of other air pollutants (such as PM2.5 or harmful gases) within the specified healthy range.

[0024] Environmental comfort requirements: Include appropriate temperature and humidity, proper brightness, and noise control, etc.

[0025] Set a reasonable range (such as 400 ppm to 800 ppm). When the concentration exceeds the standard (such as > 1000 ppm), automatically increase the ventilation volume; when the concentration decreases, reduce the ventilation frequency.

[0026] Related technology: The concentration is monitored in real time through a carbon dioxide sensor and is linked with the ventilation system for regulation.

[0027] Control of other pollutants:

[0028] Monitor the concentrations of particulate matter (PM2.5) and harmful gases (such as carbon monoxide and formaldehyde) to ensure they are below the national or local standard limits.

[0029] Related technology: Install an air quality sensor and dynamically adjust it in combination with intelligent ventilation and filtration devices.

[0030] 2. Environmental comfort requirements

[0031] Goal: Provide a temperature, humidity, light intensity, and noise environment that meets human comfort, and ensure the low-carbon operation of the underground space.

[0032] Temperature and humidity control:

[0033] Temperature: Maintain it between 20°C and 26°C according to the season and usage requirements.

[0034] Humidity: Control it in the range of 40% to 70% to avoid mold growth caused by high humidity or discomfort caused by low humidity.

[0035] Related technology: The temperature and humidity are monitored in real time by sensors and are linked with the drainage system and ventilation system for regulation (such as increasing the drainage frequency when the humidity is high).

[0036] Light intensity control:

[0037] Brightness range: The lighting should meet the functional requirements. For example, the brightness in the pedestrian area should be ≥ 300 lux, and in the vehicle passage area should be ≥ 150 lux. At the same time, the brightness in the vacant area can be reduced to 20% - 30%.

[0038] Related technology: LED lighting and zoning dynamic control technology (adjusted in real time based on the flow of people and vehicles).

[0039] Noise control:

[0040] Standard: The noise generated by equipment operation should be lower than the national standard (generally ≤ 55 dB).

[0041] Related technology: Through shock absorption devices and low-noise

[0042] 3. Comprehensive optimization and evaluation

[0043] Adaptive adjustment: By dynamically adjusting the model, it takes into account air quality, environmental comfort, and energy consumption optimization. For example, when the temperature rises, the lighting power is reduced; when the humidity is too high, the drainage system is preferentially operated.

[0044] Multi-objective trade-off: Based on optimization algorithms (such as particle swarm optimization), it comprehensively weighs air quality, comfort, and energy consumption to ensure that the system operates at the multi-objective equilibrium point.

[0045] Compared with the prior art, the advantages of the present invention are as follows:

[0046] 1) Real-time dynamic response

[0047] By collecting temperature, humidity, air quality, and equipment operation status data in real time, the present invention can immediately respond to the dynamic changes in environmental conditions, optimize equipment operation parameters, and reduce energy waste.

[0048] 2) Precise energy consumption optimization

[0049] Using the dynamic adjustment model to quantify the relationship between environmental conditions and equipment energy consumption, combined with optimization algorithms to generate low-carbon operation strategies, realizing precise control of ventilation volume, lighting brightness, and drainage scheduling, and reducing energy consumption while meeting environmental requirements.

[0050] 3) Global low-carbon goal

[0051] Starting from the overall perspective of the underground space, the present invention optimizes the coordinated operation of multiple systems (such as ventilation, lighting, and drainage), avoids resource waste caused by the separate operation of each device, and realizes a systematic reduction in global carbon emissions.

[0052] 4) Strong environmental adaptability

[0053] Combined with the special environmental requirements of the underground space, the present invention ensures that it can still efficiently manage equipment operation under complex conditions such as high humidity and poor air circulation, improving the adaptability and stability of low-carbon operation.

[0054] 5) Quantifiable optimization effect

[0055] Through the carbon emission monitoring and evaluation module, record the energy consumption and carbon emission data before and after the implementation of the optimization strategy, intuitively display the emission reduction effect of the optimization measures, and provide a scientific basis for subsequent operation optimization.

[0056] 6) Wide applicability

[0057] The present invention is applicable to the low-carbon operation management of various underground projects such as tunnels, underground complexes, and parking lots, can significantly improve the green level of the underground space, and provide technical support for achieving the carbon neutrality goal. Description of the drawings

[0058] Figure 1Flowchart of the low-carbon operation management method for underground space dynamically adjusted based on environmental conditions.

[0059] Figure 2 Diagram of the systems involved in the underground space and corresponding parameters.

[0060] Figure 3 Diagram of the optimization process of the particle swarm algorithm.

[0061] Figure 4 Diagram of the 24-hour system energy consumption comparison and carbon emissions.

[0062] Figure 5 Schematic diagram of the influence of different factors on energy consumption. Detailed implementation method

[0063] The following will describe in more detail the low-carbon operation management method for underground space dynamically adjusted based on environmental conditions in conjunction with the schematic diagrams, which show the preferred embodiments of the present invention. It should be understood that those skilled in the art can modify the present invention described herein while still achieving the beneficial effects of the present invention. Therefore, the following description should be understood as broad guidance for those skilled in the art and not as a limitation to the present invention.

[0064] By combining the dynamic changes in environmental conditions with the operation requirements of the underground space, the present invention reduces the energy consumption and carbon emission levels of the ventilation, lighting, and drainage systems, improves the operation efficiency and environmental adaptability of the underground space, and is applicable to the low-carbon operation management of various underground projects such as tunnels, underground complexes, and underground parking lots.

[0065] Such as Figures 1 to 5 , the following embodiments take an underground parking lot as the research object to specifically illustrate how the present invention realizes the low-carbon operation management of dynamically adjusting the ventilation, lighting, and drainage systems.

[0066] Such as Figures 1 to 5 , a low-carbon operation management method for underground space dynamically adjusted based on environmental conditions, includes the following steps:

[0067] Step 1: Train an energy consumption prediction model based on the historical data set;

[0068] Among them, the historical data set includes input data and output data.

[0069] The input data is the environmental conditions of the underground space; the environmental conditions include: temperature, humidity, and carbon dioxide concentration.

[0070] The output data is the daily operation energy consumption of each system in the underground space; the underground space includes a ventilation system, a lighting system, and a drainage system.

[0071] Among them, the "energy consumption of the system" is the energy consumption of the equipment.

[0072] Step 2: Obtain N sets of operating parameters.

[0073] Step 2A: Collect the environmental conditions of the underground space in real time and then input them into the trained energy consumption prediction model to obtain the daily operating energy consumption of each system.

[0074] Among them, one set of energy consumption includes the daily operating energy consumption of all systems.

[0075] (1) Collect the temperature through a temperature sensor.

[0076] Specifically, use a high-precision digital temperature sensor (such as DS18B20 or DHT22), which supports wide-temperature range measurement (-40°C to +125°C) and has an accuracy within ±0.5°C.

[0077] According to the zoning characteristics of the underground space, arrange temperature sensors in each main functional area (such as the parking area and the passage area). It is recommended to arrange one sensor every 500 - 1000 square meters.

[0078] Data collection and transmission:

[0079] Real-time monitoring: The sensor measures the temperature once every 1 minute through a timing trigger mechanism.

[0080] Data transmission: The data is sent to the central control system through the RS485 interface or a wireless transmission method (such as LoRa).

[0081] Data processing and application:

[0082] When the temperature exceeds the set range (such as above 26°C), the ventilation or cooling system is automatically triggered to operate; when it is below the lower limit (such as below 20°C), the ventilation volume is reduced to save energy.

[0083] (2) Collect the humidity through a humidity sensor.

[0084] Use an integrated temperature and humidity sensor (such as SHT31 or DHT22), with a humidity measurement range of 0% - 100% and an accuracy of ±2%RH.

[0085] In areas that may be affected by water vapor (such as the drainage pump room or the underground pipe gallery), appropriately increase the sensor layout density to ensure comprehensive data.

[0086] Data collection and transmission:

[0087] The sensor collects the relative humidity of the air in real time and generates a measurement data point every 1 - 5 minutes.

[0088] The data is transmitted to the control center through the Modbus protocol or wireless technology.

[0089] Data processing and application:

[0090] When the humidity exceeds 75%, increase the operating frequency of the drainage pump; when the humidity is below 40%, reduce ventilation to retain air humidity.

[0091] (3) Specific implementation of carbon dioxide concentration acquisition

[0092] Equipment selection and installation:

[0093] Use a non-dispersive infrared (NDIR) CO2 sensor (such as MH-Z19C air S8), which supports measurements in the range of 0 ppm to 5000 ppm with an accuracy of ±50 ppm.

[0094] Layout principle:

[0095] High traffic areas: Focus on layout at parking areas and entrances / exits, with at least one installed per 500 square meters.

[0096] Low traffic areas: For auxiliary monitoring, the layout density can be reduced.

[0097] Data acquisition and transmission:

[0098] Acquisition method: Conduct data sampling once every 1 minute. The ADC built into the sensor converts the measurement results into digital signals.

[0099] Transmission method: Data is connected to the central data processing module through the UART interface or transmitted using a wireless module (such as Wi-Fi, LoRa).

[0100] Data processing and application:

[0101] When the concentration exceeds the standard (such as exceeding 800 ppm): Automatically increase the air volume of the ventilation system to give priority to ensuring air quality.

[0102] When the concentration returns to the normal level (such as 400 - 600 ppm): Adjust the ventilation system to operate in a low-power mode to save energy.

[0103] Operation data of the underground space:

[0104] Ventilation system: The total power of the fans is 50 kW, and the current operating state is continuous operation;

[0105] Lighting system: Uses LED lights, divided into 6 zones, with a power of 5 kW for each zone and a total power of 30 kW;

[0106] Drainage system: The power of the drainage pump is 10 kW.

[0107] Among them, the rule between environmental conditions and operating parameters is:

[0108] When the temperature rises, the energy consumption of the ventilation system increases linearly;

[0109] When the humidity increases, the operating frequency of the drainage system needs to be dynamically adjusted;

[0110] When the air quality deteriorates (such as the carbon dioxide concentration exceeds 1000 ppm), the ventilation demand increases significantly.

[0111] Step 2B: Collect a set of operating parameters corresponding to a set of energy consumption under the environmental conditions of Step 2A; a set of operating parameters includes the operating parameters of all systems.

[0112] The operating parameter of the ventilation system is the ventilation volume;

[0113] The operating parameter of the lighting system is the regional brightness condition; the regional brightness condition includes the brightness of different regions.

[0114] The operating parameter of the drainage system is the operating duration.

[0115] (1) Collect the ventilation volume

[0116] 1.1 Use a digital air volume sensor (such as a Pitot tube sensor or an anemometer) combined with the duct area to measure the real-time air volume of the ventilation system.

[0117] The sensor is installed near the outlet of the fan or other key positions in the ventilation duct to ensure the accuracy of the measurement data.

[0118] 1.2 Data collection and processing:

[0119] Real-time collection: The air volume sensor reads the wind speed (unit: m / s) every 5 seconds and calculates the air volume (unit: m 2 ) in combination with the cross-sectional area of the duct (unit: m 3 / s).

[0120] Data transmission: The sensor sends the measured data to the central control system through the Modbus or RS485 interface.

[0121] Data processing:

[0122] Combined with the operating mode of the ventilation system, determine whether the current air volume reaches the set value.

[0123] When the data is abnormal (such as a sudden decrease or excessive increase in the air volume), trigger an alarm to indicate equipment failure.

[0124] 1.3 Application scenarios:

[0125] When the air volume is low (such as below 30%), the system automatically increases the fan power;

[0126] When the air volume is high (such as exceeding 70%), the system reduces the fan power to save energy.

[0127] (2) Collect the regional brightness

[0128] 2.1 Equipment Selection and Installation:

[0129] Use a light intensity sensor (such as BH1750 or TSL2561), with the measurement unit being lux, suitable for area brightness monitoring.

[0130] The sensor is installed at key points in the area, such as: the main vehicle passage area (ensuring the driver's line of sight); the pedestrian passage (ensuring pedestrian safety); the vacant area (monitoring ineffective brightness energy consumption).

[0131] 2.2 Data Collection and Processing:

[0132] Collection Interval: Collect the light intensity every 1 minute, with the unit being lux.

[0133] Data Transmission: Use a wireless protocol (such as ZigBee or LoRa) to send the data to the lighting control module.

[0134] Data Processing:

[0135] Compare the collected data with the set values (such as 300 lux in the pedestrian area, 150 lux in the vehicle area, 20 - 30 lux in the vacant area), and automatically adjust the lighting power.

[0136] If the brightness of a certain area is persistently low, it indicates that the sensor or lighting equipment may be abnormal.

[0137] 2.3 Application Scenarios:

[0138] During peak hours, the brightness of the vehicle area is increased to 100%;

[0139] At night, the brightness of the vacant area is reduced to 20% to achieve energy conservation.

[0140] (3) Collection of Operating Duration

[0141] 3.1 Equipment Selection and Installation:

[0142] Use a time recorder or the built-in timing function of the PLC module to record the cumulative operating time of equipment such as the ventilation system, lighting system, and drainage system.

[0143] Configure an operating duration recording module for each key piece of equipment (such as fans, lamps, drainage pumps).

[0144] 3.2 Data Collection and Processing:

[0145] Operating Time Monitoring:

[0146] When the system starts, record the current timestamp as the starting point; when the system stops, record the stop timestamp. The difference between the two is the single operating duration.

[0147] Cumulative time statistics:

[0148] Summarize the total running time of the equipment by day, week, and month for analyzing the equipment workload and optimizing the maintenance plan.

[0149] Data transmission: The time recorder transmits data to the central database via the CAN bus or Wi-Fi.

[0150] 3.3 Application scenarios:

[0151] If the drainage pump runs for more than the set duration (e.g., 6 hours) per day, it indicates that there may be an abnormality in the drainage system (such as a blocked pipeline).

[0152] Statistical running time is used to predict the equipment life and prompt preventive maintenance or replacement plans.

[0153] For example: When the temperature is 22°C, the humidity is 65%, and the carbon dioxide concentration is 400 ppm, the energy consumption of the ventilation system, lighting system, and drainage system are Q1, Q2, and Q3 respectively.

[0154] At this time, the ventilation volume is f1, the regional brightness condition is z1, and the running time is s1

[0155] Q1, Q2, and Q3 form a set of energy consumption.

[0156] f1, z1, and s1 form a set of operating parameters.

[0157] Step 2C: Repeat steps 2A - 2B to finally obtain N sets of operating parameters corresponding to N sets of energy consumption.

[0158] Set the environmental conditions in the underground space to be constant within a day, so collect the environmental conditions within N days.

[0159] Step 3: Optimize the N sets of operating parameters based on the particle swarm algorithm.

[0160] Among them, the fitness function is the sum of the carbon emissions of all systems in a set of energy consumption;

[0161] Carbon emissions = energy consumption * carbon emissions generated per unit of energy consumption.

[0162] When the sum of the 3 daily running energy consumptions is the smallest, the corresponding set of energy consumption is the optimal energy consumption, and the corresponding set of operating parameters is the optimal operating parameters.

[0163] 1.1 Optimal operating parameters of the system air volume

[0164] Goal: Dynamically adjust the air volume of the ventilation system to reduce energy consumption while ensuring air quality.

[0165] Optimal air volume range: Set the ventilation air volume based on the carbon dioxide concentration.

[0166] When the CO2 concentration is between 400 ppm and 600 ppm, the air volume is set to 30% of the total system air volume;

[0167] When the CO2 concentration rises above 800 ppm, gradually increase the air volume to 70%;

[0168] When the concentration continuously remains above 1000 ppm, start the forced ventilation mode (air volume 100%).

[0169] Technical implementation: Real-time collect the current air volume through a wind speed sensor, and dynamically adjust the fan power according to the air volume control strategy optimized by the particle swarm algorithm.

[0170] 1.2 Optimal distribution of regional brightness

[0171] Goal: On the basis of ensuring functional requirements, dynamically adjust the lighting brightness according to the partition function.

[0172] Partition brightness configuration:

[0173] Driving area: Keep the brightness ≥ 150 lux, and increase it to 200 lux during peak hours (such as daytime on weekdays);

[0174] Pedestrian area: Keep the brightness ≥ 300 lux;

[0175] Vacant area: When under low load, reduce the brightness to 20% - 30% (such as below 50 lux).

[0176] Technical implementation: Adopt partition intelligent lighting technology, combine with a light sensor to real-time monitor the ambient brightness, and dynamically adjust the power of the LED lights.

[0177] 1.3 Optimal operation duration of the drainage system

[0178] Goal: Optimize the operation frequency and duration of the drainage pump according to the humidity conditions to reduce ineffective operation.

[0179] Operation duration configuration:

[0180] When the humidity is below 75%, it runs 2 times a day, each time for 1.5 hours;

[0181] When the humidity exceeds 80%, the daily operation increases to 3 times, each time for 2 hours.

[0182] Technical implementation: The humidity sensor real-time monitors the air humidity, and dynamically adjusts the start and stop time of the drainage pump in combination with the historical data model and algorithm optimization results.

[0183] 1.4 Ambient conditions corresponding to the optimal operation parameters

[0184] Objective: Ensure that the optimal parameters are adapted to the typical range of underground space environmental changes.

[0185] Environmental condition configuration:

[0186] Temperature: 22°C to 28°C;

[0187] Humidity: 65% to 85%;

[0188] CO2 concentration: 400 ppm to 1000 ppm;

[0189] Illumination intensity: Optimized according to the functional partition.

[0190] Dynamic adjustment mechanism: When the environmental conditions exceed the above range, trigger a warning and further optimize the operating parameters to cope with emergencies.

[0191] 3.1 Ventilation system optimization:

[0192] When the CO2 concentration is 400 ppm - 600 ppm, the ventilation system air volume is set to 30% operation; when the CO2 concentration exceeds 800 ppm, it is gradually increased to 70% operation.

[0193] 3.2 Lighting system optimization:

[0194] Zonal dynamic regulation, adjust the brightness of different areas according to the traffic flow and the density of people. The lighting power of the vacant area is reduced to 20%, and the busy area operates at 100% power;

[0195] 3.3 Drainage system optimization:

[0196] When the humidity is below 75%, the drainage pump operates 2 times a day, each time for 1.5 hours; when the humidity exceeds 80%, the drainage pump is increased to 3 times a day, each time for 2 hours;

[0197] Step 4: Transmit a set of optimal operating parameters to the underground space operation management system.

[0198] 4.1 Ventilation system: The fan air volume is adjusted according to the optimization strategy, and the CO2 concentration is maintained within the range of 400 ppm - 800 ppm, and the system energy consumption is reduced by 15%;

[0199] 4.2 Lighting system: The brightness of the busy partition is maintained at 100%, the brightness of the vacant partition is reduced to 20%, and the overall lighting energy consumption is reduced by 20%;

[0200] 4.3 Drainage system: When the humidity conditions change, automatically adjust the start-stop frequency and duration of the drainage pump, and the drainage system energy consumption is reduced by 10%.

[0201] Step Five: Carbon emission monitoring and assessment

[0202] 5.1 Carbon emission calculation before optimization:

[0203] The daily operating energy consumption of the ventilation system is 50 kW × 24 h = 1200 kWh;

[0204] The daily operating energy consumption of the lighting system is 30 kW × 12 h = 360 kWh;

[0205] The daily operating energy consumption of the drainage system is 10 kW × 2 h × 3 times = 60 kWh;

[0206] The total energy consumption is 1620 kWh, and the daily carbon emission is 1620 kWh × 0.6 kgCO2 / kWh = 972 kgCO2.

[0207] 5.2 Carbon emission calculation after optimization:

[0208] The energy consumption of the ventilation system is reduced by 15%, and the daily energy consumption is 1020 kWh;

[0209] The energy consumption of the lighting system is reduced by 20%, and the daily energy consumption is 288 kWh;

[0210] The energy consumption of the drainage system is reduced by 10%, and the daily energy consumption is 54 kWh;

[0211] The total energy consumption is 1362 kWh, and the daily carbon emission is 1362 kWh × 0.6 kgCO2 / kWh = 817.2 kgCO2;

[0212] 1.1 Energy consumption calculation of the ventilation system before optimization:

[0213] The daily operating energy consumption before optimization is 1200 kWh (i.e., the total energy consumption before optimization).

[0214] Assuming that the energy consumption ratio of the ventilation system is the main source, the energy consumption needs to be reduced by adjusting the ventilation volume after optimization.

[0215] 1.2 Optimization calculation of the ventilation system energy consumption:

[0216] Energy consumption formula of the ventilation system:

[0217] E 通风 = P × t × f where:

[0218] P: Fan power (kW);

[0219] t: Daily operating time (h);

[0220] f: Ventilation volume adjustment coefficient (optimized ratio).

[0221] After optimization, the ventilation volume is reduced from full load (100%) to optimized load (such as 70% operation), and the energy consumption decreases accordingly.

[0222] Assume that after optimization, the ventilation volume decreases by 30%, and the corresponding energy consumption decreases by 15%. We get:

[0223] E 通风优化后 = E 通风优化前 ×(1 - 15%) = 1200×0.85 = 1020 kWh

[0224] 2.1 Energy consumption optimization of lighting system:

[0225] Optimize the lighting system through zonal control:

[0226] Energy consumption before optimization:

[0227] Before the lighting system is optimized, the daily operating power is 30 kW, and assume the operating duration is 12 hours.

[0228] E 照明优化前 = 30 kW×12 h = 360 kWh

[0229] Energy consumption after optimization:

[0230] Optimization measures (such as dynamically adjusting the brightness of the vacant area to 20%) reduce the energy consumption by 20%.

[0231] E 照明优化后 = E 照明优化前 ×(1 - 20%) = 360×0.8 = 288 kWh

[0232] 2.2 Energy consumption optimization of drainage system:

[0233] Optimize the drainage system by dynamically adjusting the operating duration:

[0234] Energy consumption before optimization:

[0235] The power of the drainage pump is 10 kW, it operates 3 times a day, each time for 2 hours, and the total operating duration is 6 hours.

[0236] E 排水优化前 = 10 kW×6 h = 60 kWh

[0237] Energy consumption after optimization:

[0238] Adjust the drainage frequency and duration under different humidity conditions (such as reducing the operating times or duration when the humidity is low), reducing the energy consumption by 10%.

[0239] E 排水优化后 = E 排水优化前 ×(1 - 10%) = 60×0.9 = 54 kWh

[0240] 5.3 Evaluation of emission reduction effect:

[0241] The daily carbon emission reduction is 972 kgCO2 - 817.2 kgCO2 = 154.8 kgCO2, and the emission reduction rate is 15.9%.

[0242] The above are only the preferred embodiments of the present invention and do not impose any limitation on the present invention. Any person skilled in the art within the technical field, without departing from the technical solution of the present invention, makes any form of equivalent substitution or modification and other changes to the technical solution and technical content disclosed by the present invention, which are all within the content of the technical solution of the present invention and still fall within the protection scope of the present invention.

Claims

1. A low-carbon operation management method for underground space dynamically adjusted based on environmental conditions, characterized in that It includes the following steps: Step 1: Train an energy consumption prediction model based on a historical dataset; Among them, the historical dataset includes input data and output data; the input data is the environmental conditions of the underground space; the output data is the daily operating energy consumption of each system in the underground space; Step 2: Obtain N groups of operating parameters, which specifically include the following steps: Step 2A: Collect the environmental conditions of the underground space, and then input them into the trained energy consumption prediction model to obtain the daily operating energy consumption of each system; among them, a set of energy consumption includes the daily operating energy consumption of all systems; Step 2B: Collect a set of operating parameters corresponding to a set of energy consumption under the environmental conditions of Step 2A; a set of operating parameters includes the operating parameters of all systems; Step 2C: Repeat Steps 2A to 2B to finally obtain N groups of operating parameters corresponding to N groups of energy consumption; Step 3: Optimize the N groups of operating parameters based on the particle swarm algorithm to obtain a set of optimal operating parameters; Among them, the fitness function is the sum of the carbon emissions of all systems in a set of energy consumption; Carbon emissions = energy consumption * carbon emissions generated per unit of energy consumption; Step 4: Transmit the optimal operating parameters to the underground space operation and management system.

2. The low-carbon operation management method for underground space dynamically adjusted based on environmental conditions according to claim 1, wherein In Step 1, the underground space includes a ventilation system, a lighting system, and a drainage system.

3. The low-carbon operation management method for underground space dynamically adjusted based on environmental conditions according to claim 2, characterized in that, The operating parameter of the ventilation system is the ventilation volume; the operating parameter of the lighting system is the regional brightness condition; the operating parameter of the drainage system is the operating duration.

4. The low-carbon operation management method for underground space dynamically adjusted based on environmental conditions according to claim 1, characterized in that, In Step 1, the environmental conditions include: temperature, humidity, and carbon dioxide concentration.

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