Smart park carbon emission management system and method

Abstract

The invention discloses a smart park carbon emission management system and method. The system comprises a building carbon portrait module, a data monitoring module, a park carbon map module and a carbon emission analysis management module. The building carbon portrait module presents the hidden carbon emission level of the single building in a visual form; the data monitoring module monitors and records data such as energy consumption, temperature and humidity in the park in real time, and monitors and manages Internet of Things equipment; the park carbon map module presents the park operation carbon emission level in real time in a thermodynamic diagram form; and the carbon emission analysis management module analyzes the carbon emission of the park and gives out early warning according to a set carbon emission threshold. According to the method, rapid calculation analysis, visual presentation and early warning management of park carbon emission can be realized, specific suggestions and measures can be provided for park carbon reduction, and the method serves construction and management of low-carbon parks.

Description

Technical Field

[0001] The present invention belongs to the field of carbon emission evaluation and management, and specifically relates to a smart park carbon emission management system and method. Background Art

[0002] As the world pays more and more attention to the issue of climate change, countries have adopted carbon emission reduction policies. Industrial parks are the basic units of cities. At present, there are more than 200 national economic and technological development zones in my country, and there are many provincial or municipal industrial parks in various places, such as entrepreneurial parks, science and technology parks, industrial parks, etc. The industrial park economy accounts for nearly a quarter of the national GDP. The development of industrial parks has also brought about greater energy consumption and carbon emissions. Taking industrial parks as an example, their carbon dioxide emissions account for about 31% of the total national carbon dioxide emissions. It is of great significance to manage and reduce the carbon emissions of industrial parks. However, the current carbon emission management of industrial parks still lacks a unified management platform and systematic solutions. This leads to untimely monitoring of carbon emissions and inability to effectively respond to high emissions.

[0003] In order to comprehensively and accurately monitor the carbon emissions of the park, accurately assess the carbon emission level of the park, and formulate emission reduction strategies in a timely manner, it is particularly important to build a comprehensive monitoring and centralized carbon emission management system; the rapid development of information technology can also provide strong support for this management system. Summary of the invention

[0004] To solve the above problems, the present invention discloses a smart park carbon emission management system and method, which aims to integrate multiple technical means through digital empowerment to carry out refined management of the park's carbon emissions, thereby achieving the goal of promoting the low-carbon development of industrial parks.

[0005] To achieve the above object, the technical solution of the present invention is as follows:

[0006] A smart park carbon emission management system, including a building carbon portrait module, a data monitoring module, a park carbon map module, a carbon emission analysis and management module, and a carbon reduction strategy module;

[0007] The building carbon portrait module collects carbon emission data of individual buildings from material production to construction, calculates the implicit carbon emissions of the building using the carbon emission factor method, and displays the building carbon emission level in a visual form;

[0008] The data monitoring module uses IoT sensors to monitor the energy consumption data and temperature and humidity data of the park in real time, records the energy consumption of buildings, and supports inquiries by region and time period;

[0009] The park carbon map module displays the overall carbon emission level of the park through data collection, and displays the carbon emission result values ​​by region and time period in the form of a visual heat map;

[0010] The carbon emission analysis and management module sets a threshold for carbon emissions and issues early warnings for buildings or areas with excessive carbon emissions.

[0011] The carbon reduction strategy module outputs specific emission reduction strategy recommendations based on the early warning information of the park's carbon emissions and the park's emission reduction case database through a case reasoning model.

[0012] Furthermore, the data monitoring module includes a park operation data monitoring submodule and an equipment management submodule.

[0013] The park operation data monitoring submodule directly monitors and obtains data such as electricity consumption, water consumption, gas consumption, photovoltaic equipment power generation, park vehicle flow, park passenger flow, temperature, humidity, etc. within the park through the deployment of sensors, and supports data query by region and time period.

[0014] The device management submodule supports querying the operating status of sensor devices and displays detailed information about the number, name, location, online status, and installation time of the monitoring equipment. Users can adjust, add, and delete sensor device information. This submodule will also report sensors in abnormal status to the IoT device in a timely manner.

[0015] Furthermore, the carbon emission analysis and management module uses the carbon emission factor method to calculate the carbon emissions during construction and operation respectively;

[0016] The total amount of embodied carbon in a single building during the construction process is calculated using the following formula:

[0017] E=E1+E2+E3+E4

[0018] Where E is the total carbon emissions of the single building, E1 is the carbon emissions in the material production stage, E2 is the carbon emissions in the off-site processing stage, E3 is the carbon emissions in the material transportation stage, and E4 is the carbon emissions in the construction stage.

[0019] The carbon emissions during the material production phase are calculated using the following formula:

[0020]

[0021] Where m is the material type; Q m is the amount of material m used; f m is the carbon emission factor of material m;

[0022] The carbon emissions from the off-site processing of the materials are calculated using the following formula:

[0023]

[0024] Where h is the type of off-site processing equipment; r is the fuel type; Q his the total use time of the mechanical equipment h; is the consumption of fuel r by mechanical equipment h; f r is the carbon emission factor of the rth fuel;

[0025] The carbon emissions during the material transportation phase are calculated using the following formula:

[0026]

[0027] Where t is the vehicle type, Q t The distance travelled by vehicle t, is the consumption of the rth type of fuel per unit driving distance of vehicle t;

[0028] The carbon emissions during the construction phase are calculated using the following formula:

[0029]

[0030] Where s is the type of construction equipment, Q s is the usage time of the construction machine s, The consumption of the rth type of fuel per unit usage time of construction machine s;

[0031] The carbon emissions during the operation of the park are calculated using the following formula:

[0032] C(t)=C1(t)+C2(t)+C3(t)+C4(t)-C5(t)

[0033] Where C(t) is the total carbon emissions from park operations within time t, C1(t) is the carbon emissions caused by building operations within time t (tCO2), C2(t) is the carbon emissions caused by vehicles running in the park within time t (tCO2), C3(t) is the carbon emissions caused by lighting in the open space of the park within time t (tCO2), C4(t) is the carbon emissions generated by greening pruning and road maintenance (tCO2), and C5(t) is the carbon fixation amount of the park green space carbon sink system within time t (tCO2).

[0034]

[0035] Where i is the terminal energy type consumed by the building, including electricity, gas, oil, municipal heat, water, etc.; E i (t) is the energy consumption of the i-th type of building at time t, f i is the carbon emission factor value of the i-th type of energy.

[0036]

[0037] Where D k (t) is the actual distance traveled by the vehicle in the park within time t, f kCarbon emission factor value per kilometer traveled by the kth type of vehicle (tCO2 / km).

[0038]

[0039] Where E m (t) is the power consumed by the mth type of street lamp within time t, f l is the carbon emission factor value of electricity (tCO2 / kw·h).

[0040]

[0041] Where C i (t) is the amount of money spent on the i-th type of maintenance activity within time t (ten thousand yuan), is the carbon emission factor value of the i-th type of maintenance activity (tCO2 / 10,000 yuan).

[0042]

[0043] Where A j is the green area of ​​plant type j (㎡), LA j is the leaf area index of plant type j, C j (t) is the carbon dioxide sequestration per unit leaf area of ​​type j plant in month t (tCO2 / ㎡), and d(t) is the number of days in month t.

[0044] Furthermore, the carbon emission analysis management module includes an equipment information management submodule and a carbon emission threshold warning submodule.

[0045] The device information management submodule supports users to delete sensor device information and query whether the device is online; this module displays the number, name, ID, monitoring location, online status, installation time and device details of the monitoring device, and reports on devices in abnormal status; it supports users to query various types of monitoring data, and supports annual, monthly, weekly and real-time data queries for a single monitoring device.

[0046] The carbon emission threshold warning submodule is used to count the carbon emission peak of the natural day, and use 20% exceeding the average value as the carbon emission warning threshold, and compare the carbon accounting data with the carbon emission average of the region; if the carbon emissions of a building exceed the regional average, the building will be marked as a building to be optimized.

[0047] Furthermore, the carbon reduction strategy module includes a park emission reduction case library submodule and a carbon reduction strategy output submodule;

[0048] The park emission reduction case library submodule stores park cases that are of reference significance for the formulation of park energy conservation and emission reduction strategies. Each case contains the basic parameter information of the park, carbon emission alarm type, energy conservation and emission reduction measures, and the emission reduction effect after the implementation of the measures.

[0049] The carbon reduction strategy submodule uses case-based reasoning algorithms to calculate the similarity between the current park and the case parks in the case library, accurately retrieves the emission reduction cases that are closest to the carbon emission alarm type of the current park, reuses the cases, and conducts detailed analysis and adjustments to past plans based on the actual situation of the evaluated park to generate corresponding carbon reduction strategies. After effective emission reduction, the park case is added to the case library.

[0050] The case-based reasoning (CBR) model is used to calculate the similarity between the current park and the case park in the case library. The Sklearn library in Python is used for implementation. The core statement is the Consline Similarity module. The specific formula is as follows:

[0051]

[0052] Among them, C1 is the vector of the current park case, C2 is the vector of the case park in the case library, and C 1k and C 2k is the component of two vectors, k ≥ 1, g k is the Gini coefficient weight of the kth attribute. The cosine similarity between cases ranges from [0,1]. The larger the calculated value of the cosine similarity, the higher the similarity between the case and the current park. Cases with a cosine similarity greater than 0.7 are selected for reuse. The CBR model will learn the above cases, and adjust and modify the solutions of similar cases based on the actual data of the current case, and propose a carbon reduction strategy for the current park.

[0053] The CBR model uses a framework representation method to structure the case as C=<Z,A,B>, where Z is the basic information subframe of the park, including six basic attributes: park type, park area, location (latitude and longitude), building area, green area, and year of construction, expressed as Z=<Z1,Z2,Z3,Z4,Z5,Z6>. A is the carbon emission quantum framework during the construction of the park, including four stages: material production, off-site processing, material transportation, and construction site, expressed as A=<A1,A2,A3,A4>. B is the carbon emission quantum framework during the operation of the park, including five dimensions: building operation, vehicle operation, outdoor lighting, green space carbon fixation, and park maintenance, expressed as B=<B1,B2,B3,B4,B5>

[0054] The structured representation of the case described quantifies the characteristic attributes of the park type as shown in Table 1, and other attributes can be numerically represented as characteristic attributes.

[0055] Table 1: Quantitative table of park type characteristic attributes

[0056]

[0057] After all park attributes are quantified, weight calculation will be performed. The characteristic attribute Gini coefficient is calculated using the following formula, assuming G k is the Gini coefficient value of the kth indicator, n is the total number of indicator data, a ik is the kth characteristic attribute value of the i-th item, then the Gini coefficient value of the kth characteristic attribute is calculated using the following formula:

[0058] Secondly, the Gini coefficient values ​​of each indicator are normalized to obtain the Gini coefficient weight g of the kth indicator k , the calculation formula is as follows:

[0059]

[0060] Where g k is the Gini coefficient weight of the kth attribute; m is the number of indicators.

[0061] The carbon emission management method using the above-mentioned smart park carbon emission management system includes the following steps:

[0062] Step 1: Based on the data of building material production, transportation, off-site processing and construction stages, the carbon emission factor method is used to calculate the embodied carbon emissions of the park, and the embodied carbon emission level of a single building is visualized;

[0063] Step 2: Use the Internet of Things and sensor devices to monitor and record the energy consumption data and temperature and humidity data in the park in real time, monitor the operating status of the Internet of Things devices, and transmit the data back to the system;

[0064] Step 3: Based on the data obtained in step 2, the carbon emission factor method is used to calculate the park's operational carbon emissions, and the park's operational carbon emission level is displayed in the form of a heat map, which supports the display of the park's operational carbon emission intensity by region and time period;

[0065] Step 4: Analyze the results of steps 1 and 3, identify hot spots, issue early warnings for high-carbon emission areas, develop corresponding improvement plans, and provide energy-saving and emission reduction strategy recommendations.

[0066] The beneficial effects of the present invention are:

[0067] The present invention can realize rapid calculation and analysis, visual presentation and early warning management of the park's carbon emissions, carry out refined management of the park's carbon emissions, and effectively improve the efficiency of the park's carbon emissions management. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a system structure diagram of the present invention.

[0069] Figure 2 It is a diagram of the implementation steps of the present invention.

[0070] Figure 3 It is a structural framework diagram of the present invention. DETAILED DESCRIPTION

[0071] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0072] As shown in the figure, a smart park carbon emission management system described in the present invention includes a building carbon portrait module, a data monitoring module, a park carbon map module, a carbon emission analysis and management module, and a carbon reduction strategy module;

[0073] The building carbon portrait module collects carbon emission data of individual buildings from material production to construction, calculates the implicit carbon emissions of the building using the carbon emission factor method, and displays the building carbon emission level in a visual form;

[0074] The data monitoring module uses IoT sensors to monitor the energy consumption data and temperature and humidity data of the park in real time, records the energy consumption of buildings, and supports inquiries by region and time period;

[0075] The park carbon map module displays the overall carbon emission level of the park through data collection, and displays the carbon emission results of different areas by day, month, quarter and other time periods;

[0076] The carbon emission analysis and management module sets a threshold for carbon emissions and issues early warnings for buildings or areas with excessive carbon emissions.

[0077] The carbon reduction strategy module uses the park’s emission reduction case database to perform case reasoning based on the park’s carbon emission warning information and outputs specific emission reduction strategy recommendations.

[0078] Furthermore, the data monitoring module includes a park operation data monitoring submodule and an equipment management submodule.

[0079] The park operation data monitoring submodule directly monitors and obtains data on electricity consumption, water consumption, gas consumption, photovoltaic equipment power generation, park vehicle flow, park passenger flow, temperature, and humidity in the park through the deployment of sensors, and supports data query by region and time period;

[0080] The device management submodule supports querying the operating status of sensor devices and displays detailed information about the number, name, location, online status, and installation time of the monitoring equipment. Users can adjust, add, and delete sensor device information. This submodule will also report sensors in abnormal status to the IoT device in a timely manner.

[0081] Furthermore, the carbon emission analysis management module uses the carbon emission factor method to calculate the carbon emissions during the construction and operation process. The construction process mainly considers the carbon emissions from material production, off-site processing, construction and transportation; the operation process considers the carbon emissions caused by energy consumption and the carbon sequestration capacity of the park's carbon sink.

[0082] The total amount of embodied carbon in a single building during the construction process is calculated using the following formula:

[0083] E=E1+E2+E3+E4

[0084] Where E is the total carbon emissions of the single building, E1 is the carbon emissions in the material production stage, E2 is the carbon emissions in the off-site processing stage, E3 is the carbon emissions in the material transportation stage, and E4 is the carbon emissions in the construction stage.

[0085] The carbon emissions during the material production phase are calculated using the following formula:

[0086]

[0087] Where m is the material type; Q m is the amount of material m used; f m is the carbon emission factor of material m;

[0088] The carbon emissions from the off-site processing of the materials are calculated using the following formula:

[0089]

[0090] Where h is the type of off-site processing equipment; r is the fuel type; Q h is the total use time of the mechanical equipment h; is the consumption of fuel r by mechanical equipment h; f r is the carbon emission factor of the rth fuel;

[0091] The carbon emissions during the material transportation phase are calculated using the following formula:

[0092]

[0093] Where t is the vehicle type, Q t The distance travelled by vehicle t, is the consumption of the rth type of fuel per unit driving distance of vehicle t;

[0094] The carbon emissions during the construction phase are calculated using the following formula:

[0095]

[0096] Where s is the type of construction equipment, Q s is the usage time of the construction machine s, The consumption of the rth type of fuel per unit usage time of construction machine s;

[0097] The carbon emissions during the operation of the park are calculated using the following formula:

[0098] C(t)=C1(t)+C2(t)+C3(t)+C4(t)-C5(t)

[0099] Where C(t) is the total carbon emissions from park operations within time t, C 1(t) is the carbon emissions caused by building operation within time t (tCO2), C2(t) is the carbon emissions caused by vehicle operation in the park within time t (tCO2), C3(t) is the carbon emissions caused by open space lighting in the park within time t (tCO2), C4(t) is the carbon emissions generated by greening pruning and road maintenance (tCO2), and C5(t) is the carbon fixation amount of the park green space carbon sink system at time t (tCO2).

[0100]

[0101] Where i is the terminal energy type consumed by the building, including electricity, gas, oil, municipal heat, and water; E i (t) is the energy consumption of the i-th type of building at time t, f i is the carbon emission factor value of the i-th type of energy;

[0102]

[0103] Where D k (t) is the actual distance traveled by the vehicle in the park within time t, f k Carbon emission factor value per kilometer traveled by the kth type of vehicle (tCO2 / km).

[0104]

[0105] Where E m (t ) is the power consumed by the mth type of street lamp in time t, f l is the carbon emission factor value of electricity (tCO2 / kw·h).

[0106]

[0107] Where Ci (t) is the amount of money spent on the i-th type of maintenance activity within time t (ten thousand yuan), is the carbon emission factor value of the i-th type of maintenance activity (tCO2 / 10,000 yuan);

[0108]

[0109] Where A j is the green area of ​​plant type j (㎡), LA j is the leaf area index of plant type j, C j (t) is the carbon dioxide sequestration per unit leaf area of ​​type j plant in month t (tCO2 / ㎡), and d(t) is the number of days in month t.

[0110] Furthermore, the building carbon portrait module analyzes and processes the collected and calculated carbon emission data of the park, converts the data into visual charts, indicators and reports, and displays the carbon emission characteristics and conditions of the park. At the same time, it provides detailed information on each building, including building area, height, design life, structure type, green area, single carbon emission, carbon emission index, and whether there is any over-standard situation. Clicking on a single building can obtain the carbon emission data of the area.

[0111] Furthermore, the park carbon map module is used to display the park's operating carbon emissions and determine whether they exceed the standard. This module includes building operation carbon emissions, park vehicle driving carbon emissions, open space lighting carbon emissions in the park, and carbon absorption by green plants.

[0112] Further, the carbon emission analysis management module includes an equipment information management submodule and a carbon emission threshold warning submodule;

[0113] The device information management submodule supports users to delete sensor device information and query whether the device is online. This module displays the number, name, ID, monitoring location, online status, installation time and device details of the monitoring device, and reports devices in abnormal status. It supports users to query various monitoring data and supports annual, monthly, weekly and real-time data query for a single monitoring device.

[0114] The carbon emission threshold warning submodule is used to count the carbon emission peak of the natural day, and uses 20% exceeding the average value as the carbon emission warning threshold, and compares the carbon accounting data with the carbon emission average of the region; if the carbon emissions of a building exceed the regional average, the building will be marked as a building to be optimized.

[0115] Furthermore, the carbon reduction strategy module includes a park emission reduction case library submodule and a carbon reduction strategy output submodule;

[0116] The park emission reduction case library submodule stores park cases that are of reference significance for the formulation of park energy conservation and emission reduction strategies. Each case contains the basic parameter information of the park, carbon emission alarm type, energy conservation and emission reduction measures, and the emission reduction effect after the implementation of the measures.

[0117] The carbon reduction strategy submodule uses case-based reasoning algorithms to calculate the similarity between the current park and the case parks in the case library, accurately retrieves the emission reduction cases that are closest to the carbon emission alarm type of the current park, reuses the cases, and conducts detailed analysis and adjustments to past plans based on the actual situation of the evaluated park to generate corresponding carbon reduction strategies. After effective emission reduction, the park case is added to the case library.

[0118] The CBR model is used to calculate the similarity between the current park case and the case in the case library. The Sklearn library in Python is used for implementation. The core statement is the Consline Similarity module. The specific formula is as follows:

[0119]

[0120] Among them, C1 is the vector of the current park case, C2 is the vector of the case in the case library, and C 1k and C 2k is the component of two vectors, k ≥ 1, g k is the Gini coefficient weight of the kth attribute. The cosine similarity between cases ranges from [0,1]. The larger the calculated value of the cosine similarity, the higher the similarity between the case and the current park. Cases with a cosine similarity greater than 0.7 are selected for reuse. The CBR model will learn the above case parks, and adjust and modify the solutions of similar cases based on the actual data of the evaluated parks, and propose carbon reduction strategies for the current parks.

[0121] The CBR model uses a framework representation method to structure the case as C=<Z,A,B>, where Z is the basic information subframe of the park, including six basic attributes: park type, park area, location (latitude and longitude), building area, green area, and year of construction, expressed as Z=<Z1,Z2,Z3,Z4,Z5,Z6>. A is the carbon emission quantum framework during the construction of the park, including four stages: material production, off-site processing, material transportation, and construction site, expressed as A=<A1,A2,A3,A4>. B is the carbon emission quantum framework during the operation of the park, including five dimensions: building operation, vehicle operation, outdoor lighting, green space carbon fixation, and park maintenance, expressed as B=<B1,B2,B3,B4,B5>

[0122] The structured representation of the case described quantifies the characteristic attributes of the park type as shown in Table 1, and other attributes can be numerically represented as characteristic attributes.

[0123] Table 1: Quantitative table of park type characteristic attributes

[0124]

[0125] After all park attributes are quantified, weight calculation will be performed. The characteristic attribute Gini coefficient is calculated using the following formula, assuming G k is the Gini coefficient value of the kth indicator, n is the total number of indicator data, a ik is the kth characteristic attribute value of the i-th item, then the Gini coefficient value of the kth characteristic attribute is calculated using the following formula:

[0126] Secondly, the Gini coefficient values ​​of each indicator are normalized to obtain the Gini coefficient weight g of the kth indicator k , the calculation formula is as follows:

[0127]

[0128] Where g k is the Gini coefficient weight of the kth attribute; m is the number of indicators.

[0129] A smart park carbon emission management method comprises the following steps:

[0130] Step 1: Based on the data of building material production, transportation, off-site processing and construction stages, the carbon emission factor method is used to calculate the embodied carbon emissions of the park, and the embodied carbon emission level of a single building is visualized;

[0131] Step 2: Use the Internet of Things and sensor devices to monitor and record the energy consumption data and temperature and humidity data in the park in real time, monitor the operating status of the Internet of Things devices, and transmit the data back to the system;

[0132] Step 3: Based on the data obtained in step 2, the carbon emission factor method is used to calculate the park's operational carbon emissions, and the park's operational carbon emission level is displayed in the form of a heat map, which supports the display of the park's operational carbon emission intensity by region and time period;

[0133] Step 4: Analyze the results of steps 1 and 3, identify hot spots, issue early warnings for high-carbon emission areas, develop corresponding improvement plans, and provide energy-saving and emission reduction strategy recommendations.

[0134] It should be noted that the above content only illustrates the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. For ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications all fall within the protection scope of the claims of the present invention.

Claims

1. A smart park carbon emission management system, characterized by: It includes building carbon profiling module, data monitoring module, park carbon map module, carbon emission analysis and management module and carbon reduction strategy module; The building carbon portrait module collects carbon emission data of individual buildings from material production to construction, calculates the implicit carbon emissions of the building using the carbon emission factor method, and displays the building carbon emission level in a visual form; The data monitoring module uses IoT sensors to monitor the energy consumption data and temperature and humidity data of the park in real time, records the energy consumption of buildings, and supports inquiries by region and time period; The park carbon map module displays the overall carbon emission level of the park through data collection, and displays the carbon emission result values ​​by region and time period in the form of a visual heat map; The carbon emission analysis and management module sets a threshold for carbon emissions and issues early warnings for buildings or areas with excessive carbon emissions. The carbon reduction strategy module uses the park’s emission reduction case database to perform case reasoning based on the park’s carbon emission warning information and outputs specific emission reduction strategy recommendations.

2. The smart park carbon emission management system according to claim 1 is characterized by: The data monitoring module includes a park operation data monitoring submodule and an equipment management submodule; The park operation data monitoring submodule directly monitors and obtains data on electricity consumption, water consumption, gas consumption, photovoltaic equipment power generation, park vehicle flow, park passenger flow, temperature, and humidity in the park through the deployment of sensors, and supports data query by region and time period; The device management submodule supports querying the operating status of sensor devices, displaying the number, name, location, online status, and installation time of the monitoring equipment. Users can adjust, add, and delete sensor equipment information. This submodule will also report abnormal sensors to IoT devices in a timely manner.

3. The smart park carbon emission management system according to claim 1 is characterized by: The carbon emission analysis management module adopts the carbon emission factor method to calculate the carbon emissions during construction and operation respectively; The total amount of embodied carbon in a single building during the construction process is calculated using the following formula: E=E1+E2+E3+E4 Where E is the total carbon emissions of the single building, E1 is the carbon emissions in the material production stage, E2 is the carbon emissions in the off-site processing stage, E3 is the carbon emissions in the material transportation stage, and E4 is the carbon emissions in the construction stage; The carbon emissions during the material production phase are calculated using the following formula: Where m is the material type; Q m is the amount of material m used; f m is the carbon emission factor of material m; The carbon emissions from the off-site processing of the materials are calculated using the following formula: Where h is the type of off-site processing equipment; r is the fuel type; Q h is the total use time of the mechanical equipment h; is the consumption of fuel r by mechanical equipment h; f r is the carbon emission factor of the rth fuel; The carbon emissions during the material transportation phase are calculated using the following formula: Where t is the vehicle type, Q t The distance travelled by vehicle t, is the consumption of the rth type of fuel per unit driving distance of vehicle t; The carbon emissions during the construction phase are calculated using the following formula: Where s is the type of construction equipment, Q s is the usage time of the construction machine s, The consumption of the rth type of fuel per unit usage time of construction machine s; The carbon emissions during the operation of the park are calculated using the following formula: C(t)=C1(t)+C2(t)+C3(t)+C4(t)-C5(t) Where C(t) is the total carbon emission of the park operation within time t, C1(t) is the carbon emission caused by building operation within time t (tCO2), C2(t) is the carbon emission caused by vehicles running in the park within time t (tCO2), C3(t) is the carbon emission caused by lighting in the open space of the park within time t (tCO2), C4(t) is the carbon emission generated by greening pruning and road maintenance (tCO2), and C5(t) is the carbon fixation amount of the park green space carbon sink system within time t (tCO2); Where i is the terminal energy type consumed by the building, including electricity, gas, oil, municipal heat, and water; E i (t) is the energy consumption of the i-th type of building at time t, f i is the carbon emission factor value of the i-th type of energy; Where D k (t) is the actual distance traveled by the vehicle in the park within time t, f k Carbon emission factor value per kilometer traveled by the k-th type of vehicle (tCO2 / km); Where E m (t) is the power consumed by the mth type of street lamp within time t, f l is the carbon emission factor value of electricity (tCO2 / kw·h); Where C i (t) is the amount of money spent on the i-th type of maintenance activity within time t (ten thousand yuan), is the carbon emission factor value of the i-th type of maintenance activity (tCO2 / 10,000 yuan); Where A j is the green area of ​​plant type j (㎡), LA j is the leaf area index of plant type j, C j (t) is the carbon dioxide sequestration per unit leaf area of ​​type j plant in month t (tCO2 / ㎡), and d(t) is the number of days in month t.

4. The smart park carbon emission management system according to claim 1 is characterized by: The building carbon portrait module analyzes and processes the collected and calculated carbon emission data of the park, converts the data into visual charts, indicators and reports to display the carbon emission characteristics and conditions of the park; at the same time, it provides detailed information on each building, including building area, height, design life, structure type, green area, single carbon emissions, carbon emission indicators and whether there is any excessive situation. Click on a single building to obtain the carbon emission data of the area.

5. The smart park carbon emission management system according to claim 1 is characterized by: The park carbon map module is used to display the carbon emissions of project operations and to determine whether they exceed the standard.

6. The smart park carbon emission management system according to claim 1 is characterized by: The carbon emission analysis and management module includes an equipment information management submodule and a carbon emission threshold warning submodule; The device information management submodule supports users to delete sensor device information and query whether the device is online; This module displays the number, name, ID, monitoring location, online status, installation time and device details of the monitoring device, and reports the devices in abnormal status; Support users to query various monitoring data, and support annual, monthly, weekly and real-time data query of a single monitoring device; The carbon emission threshold warning submodule is used to count the carbon emission peak value of natural days, taking 20% ​​exceeding the average value as the carbon emission warning threshold, and comparing the carbon accounting data with the average carbon emission value of the region; If the carbon emissions of a building exceed the regional average, the building will be marked as a building to be optimized.

7. The smart park carbon emission management system according to claim 1 is characterized by: The carbon reduction strategy module includes a park emission reduction case library submodule and a carbon reduction strategy output submodule; The park emission reduction case library submodule stores park cases that are of reference significance for the formulation of park energy conservation and emission reduction strategies. Each case contains the basic parameter information of the park, carbon emission alarm type, energy conservation and emission reduction measures, and emission reduction effects after the implementation of the measures; The carbon reduction strategy submodule uses a case-based reasoning algorithm to calculate the similarity between the current park and the case parks in the case library, accurately retrieves the emission reduction cases that are closest to the carbon emission alarm type of the current park, reuses the cases, and conducts detailed analysis and adjustments to past plans based on the actual situation of the evaluated park to generate corresponding carbon reduction strategies; After effective emission reduction, the park case will be added to the case library.

8. The carbon emission management method according to claim 1 is characterized by: The following steps are involved: Step 1: Based on the data of building material production, transportation, off-site processing and construction stages, the carbon emission factor method is used to calculate the embodied carbon emissions of the park, and the embodied carbon emission level of a single building is visualized; Step 2: Use the Internet of Things and sensor devices to monitor and record the energy consumption data and temperature and humidity data in the park in real time, monitor the operating status of the Internet of Things devices, and transmit the data back to the system; Step 3: Based on the data obtained in step 2, the carbon emission factor method is used to calculate the park's operational carbon emissions, and the park's operational carbon emission level is displayed in the form of a heat map, which supports the display of the park's operational carbon emission intensity by region and time period; Step 4: Analyze the results of steps 1 and 3, identify hot spots, issue early warnings for high-carbon emission areas, develop corresponding improvement plans, and provide energy-saving and emission reduction strategy recommendations.

9. The carbon emission management method according to claim 8, characterized in that: The fourth step is to output the park emission reduction strategy based on the CBR model, which specifically includes the following four key steps: Step 1: Build a park emission reduction case library to store cases that are of reference significance for the formulation of park energy conservation and emission reduction strategies. Each case contains basic parameter information of the park, carbon emission alarm type, implemented energy conservation and emission reduction measures, and actual emission reduction effects; Step 2: Use cosine similarity to calculate the similarity between the current park and the case parks in the case library, and accurately retrieve the case that is closest to the carbon emission alarm type of the current park; Step 3: Case reuse, extract the energy-saving and emission-reduction technical solutions from the most similar cases, and use them as preliminary solutions to solve the current energy-saving and emission-reduction problems in the park; combine the actual situation of the park, and conduct detailed analysis and adjustments on the preliminary solutions to ensure that the proposed strategic recommendations are feasible and effective; Step 4: According to the current carbon emission alarm problem of the park, modify or adjust the existing strategy plan, and feedback the emission reduction strategy plan to the user; Step 5: Evaluate the current strategic plan. When the energy conservation and emission reduction issues are effectively resolved, the current park case will be added to the park emission reduction case library.

10. The carbon emission management method according to claim 9, characterized in that: The cosine similarity in step 2 is used to calculate the similarity between the current park and the case park in the case library. It is implemented using the Sklearn library in Python. The core statement is the Consline Similarity module. The specific formula is as follows: Among them, C1 is the vector of the current park case, C2 is the vector of the case park in the case library, and C 1k and C 2k is the component of two vectors, k ≥ 1, g k is the Gini coefficient weight of the kth attribute. The cosine similarity between cases ranges from [0,1]. The larger the calculated value of the cosine similarity, the higher the similarity between the case and the current park. Cases with a cosine similarity greater than 0.7 are selected for reuse. The CBR model will learn the above cases and adjust and modify the solutions to similar cases based on the actual situation of the park, and propose a carbon reduction strategy for the current park. The CBR model uses a framework representation method to structurally represent the case as C=<Z,A,B>, where Z is the basic information subframe of the park, including six basic attributes: park type, park area, location, building area, green area, and construction year, expressed as Z=<Z1,Z2,Z3,Z4,Z5,Z6>; A is the carbon emission subframe during the construction of the park, including four stages: material production, off-site processing, material transportation, and construction site, expressed as A=<A1,A2,A3,A4>; B is the carbon emission subframe during the operation of the park, including five dimensions: building operation, vehicle operation, outdoor lighting, green carbon sequestration, and park maintenance, expressed as B=<B1,B2,B3,B4,B5>; After all park attributes are quantified, weight calculation will be performed. First, the Gini coefficient of the characteristic attribute should be calculated. Let G k is the Gini coefficient value of the kth indicator, n is the total number of indicator data, a ik is the kth characteristic attribute value of the i-th item, then the Gini coefficient value of the kth characteristic attribute is calculated using the following formula: Secondly, the Gini coefficient values ​​of each indicator are normalized to obtain the Gini coefficient weight g of the kth indicator k , the calculation formula is as follows: Where g k is the Gini coefficient weight of the kth attribute; m is the number of indicators.

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