Environment-friendly low-carbon monitoring system for energy-saving transformation of old community

Abstract

The invention discloses a green low-carbon monitoring system for energy-saving transformation of an old community, and relates to the field of data management monitoring, and the system comprises a central management and control module which is used for deploying a management and control intranet, and carrying out the instruction editing and submitting of all functional modules and units; the data acquisition module is used for acquiring actual energy consumption coefficients and operation setting attributes of power supply, gas supply and water supply equipment in a to-be-transformed area in a plurality of preset periods; the model construction module is used for constructing and training an energy consumption prediction model, and outputting predicted energy consumption coefficients of the devices in a plurality of periods in the future based on the current energy consumption coefficients and the current operation setting attributes of the devices as input; historical data, real-time data and external environment variables are analyzed, a prediction result and an adjustment strategy are dynamically updated, the energy consumption trend of a future period is accurately predicted, a transformation strategy is adjusted in time, potential energy-saving opportunities are mined, carbon emission loss of future transformation is analyzed, and managers are helped to identify potential energy-saving potential and emission reduction paths.

Description

Technical Field

[0001] The present invention relates to the technical field of data management and monitoring, and in particular to a green and low-carbon monitoring system for energy-saving transformation of old residential areas. Background Art

[0002] Old residential communities usually have problems such as outdated facilities, which leads to serious energy waste. Energy-saving renovation and carbon emission monitoring of old residential communities are one of the important measures to achieve sustainable development. They can optimize energy utilization efficiency and reduce unnecessary resource consumption, which can not only reduce the economic burden of residents, but also improve the resource utilization efficiency of the entire region. With the development of intelligent technology, carbon emission monitoring of old residential communities can be combined with technologies such as the Internet of Things and big data analysis to form an intelligent monitoring and management system. Through real-time data monitoring and analysis, community managers can allocate resources more scientifically, optimize management strategies, and achieve improved management efficiency and economic benefits.

[0003] Existing technologies often monitor carbon emissions separately for projects such as power supply, water supply, and gas supply. They lack the ability to conduct multi-dimensional analysis and cross-system integration, making it difficult to comprehensively analyze the use of various energy sources, reducing the potential for energy saving. Most of them only analyze the anomalies in existing data, lack analysis of abnormal carbon emissions caused by future cycles and energy-saving transformation, making the data limited at present, resulting in the final transformation strategy being affected by "pseudo-normal" equipment. Some equipment in normal operation may cover up potential energy-saving opportunities, making it difficult to effectively predict and intervene in the energy consumption trends of several energy-consuming equipment in the future;

[0004] Moreover, it lacks adaptive capabilities and is not easy to dynamically adjust and optimize according to actual operating data, resulting in poor results in subsequent energy-saving renovation measures. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the above-mentioned shortcomings of the prior art, the present invention provides a green and low-carbon monitoring system for energy-saving transformation of old residential areas, which can effectively solve the problems of the prior art.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] The present invention discloses a green and low-carbon monitoring system for energy-saving transformation of old residential areas, comprising:

[0010] The central control module is used to deploy the control intranet and edit and submit instructions for each functional module and unit;

[0011] A data collection module is used to collect the actual energy consumption coefficient and operation setting attributes of the power supply, gas supply and water supply equipment in the area to be transformed within a preset period;

[0012] A threshold setting module, used to define a standard energy consumption coefficient threshold based on the initial operation settings of the power supply, gas supply, and water supply equipment to be transformed;

[0013] A model building module is used to build and train an energy consumption prediction model, which outputs the predicted energy consumption coefficients of several devices in several future cycles based on the current energy consumption coefficients and current operation setting properties of several devices as inputs;

[0014] A judgment and marking module is used to compare the actual energy consumption coefficient and the predicted energy consumption coefficient of several cycles collected by the data collection module with the standard energy consumption coefficient threshold value respectively;

[0015] A loss calculation module is used to mark the energy consumption coefficient and its associated equipment that violate the standard threshold based on the comparison result under the standard energy consumption coefficient threshold of the judgment marking module, and output the actual carbon emission loss coefficient and the predicted carbon emission loss coefficient of the marked equipment respectively based on the marked energy consumption coefficient;

[0016] A transformation analysis unit, used to extract the marked equipment associated with the actual carbon emission loss coefficient and the predicted carbon emission loss coefficient, calculate the future transformation carbon emission, and obtain the transformation carbon emission loss coefficient;

[0017] The weight setting module is used to preset the weights of actual, predicted and modified carbon emission loss coefficients, and perform weighted calculations on the current actual, predicted and modified carbon emission loss coefficients to obtain the total carbon emission loss of several devices;

[0018] The summary module is used to summarize the total carbon emission loss data of all equipment, generate a visual report and submit it to the central control module.

[0019] Furthermore, the central control module is interactively connected to a feedback module via a wireless network. The feedback module is used to receive the actual energy consumption coefficient collected by the data collection module in the next cycle or the energy consumption coefficient written manually, and submit it to the threshold setting module for judgment. When it is judged that the threshold of the threshold setting module is violated, it jumps to the loss calculation module to calculate the actual carbon emission loss coefficient, compares the current actual carbon emission loss coefficient with the predicted carbon emission coefficient of the previous cycle, and calculates the difference as the adjustment reference coefficient of the energy consumption prediction model of the model construction module.

[0020] Furthermore, the energy consumption prediction model in the model building module is constructed by a regression analysis algorithm, and the expression is:

[0021]

[0022] In the formula, Ei,t+k represents the predicted energy consumption coefficient of device i in the next k cycles, P o represents the intercept term of the model, m represents the number of independent variables used for prediction, and P j Represents the value corresponding to each independent variable X i,j,t The regression coefficient, X i,j,t represents the value of the jth independent variable of device i at time t, n represents the number of other external factors affecting energy consumption, and Y represents the value corresponding to each external factor Z i,l,t The regression coefficient, Z i,l,t represents the value of the lth external factor of device i at time t, e i,j,t represents the error term.

[0023] Furthermore, the energy consumption prediction model training process in the model building module is:

[0024] Step 1: Use the data acquisition module to obtain the actual energy consumption coefficient of each device in different time periods, record the type, operation mode and setting parameters of the equipment, collect climate conditions, time factors and socio-economic factors, and convert them into a model-readable format to form a training set;

[0025] Step 2: Select the characteristic independent variables that are closely related to the energy consumption of the equipment;

[0026] Step 3: Use the training set to determine the corresponding regression coefficients by the least squares method, and continue to optimize the parameters;

[0027] Step 4: Put the trained energy consumption prediction model into real-time prediction calculation, receive evaluation feedback of the prediction results and add them to the training set.

[0028] Furthermore, during the comparison operation stage, the judgment and marking module triggers a jump based on the comparison result. When it is judged that the comparison result does not violate the standard threshold state, it jumps to the threshold setting module and the model building module to run. When it is judged that the comparison result violates the standard threshold state, it jumps to the loss calculation module to run. During the stage of feedback of the comparison result to the loss calculation module, the judgment and marking module simultaneously feedbacks to the data source threshold setting module and the model building module.

[0029] Furthermore, the central control module is interactively connected to a storage module via a wireless network, and the storage module is used to receive the collected data and output data of each functional module, perform classified storage, and support cloud backup and reading and writing of external storage media.

[0030] Furthermore, the transformation analysis unit is provided with submodules at the lower level, and the submodules include an extraction module, an index module and a definition module, the extraction module and the index module are interactively connected via a wireless network, and the index module and the definition module are interactively connected via a wireless network, wherein:

[0031] An extraction module, used for extracting a list of devices marked as violating a threshold by the judgment and marking module, and creating a label for each device;

[0032] An indexing module is used to use the tag generated by the extraction module as a keyword to query and obtain the historical carbon emission loss data of the tag-associated device in the internal network, and to query the network carbon emission loss data of the tag-associated device in the Internet;

[0033] A definition module is used to obtain the historical and network carbon emission loss data obtained by the index module, integrate the carbon emission loss data of the devices that violate the threshold based on the device type and running time, and output the integrated associated carbon emission loss data.

[0034] Furthermore, the marking content of the extraction module includes: the current status of the device, operating properties, marking reason and marking time. After the marking content is generated, it is synchronously jumped to the central management and control module to request storage permission.

[0035] Furthermore, the weight setting module retains historical preset data during the actual, predicted and modified carbon emission loss coefficient weight setting process. When the energy consumption coefficient error is within the allowable error range for weighted calculation of the same type of equipment, it directly jumps to the historical preset data for application.

[0036] Furthermore, the central control module is interactively connected to the data acquisition module, the threshold setting module and the model building module through a wireless network, the judgment marking module is interactively connected to the threshold setting module, the model building module and the loss calculation module through a wireless network, the loss calculation module is interactively connected to the transformation analysis unit through a wireless network, and the weight setting module is interactively connected to the loss calculation module, the transformation analysis unit and the summary module through a wireless network.

[0037] (III) Beneficial effects

[0038] Compared with the known prior art, the technical solution provided by the present invention has the following beneficial effects:

[0039] 1. By building an energy consumption prediction model, analyzing historical data, real-time data and external environmental variables, generating in-depth insight reports, dynamically updating prediction results and adjustment strategies to adapt to the ever-changing operating environment, accurately predicting energy consumption trends in future cycles, and adjusting transformation strategies in a timely manner to reduce the impact of "pseudo-normal" equipment, the multi-dimensional analysis mechanism can not only identify data anomalies, but also explore potential energy-saving opportunities and improve energy-saving efficiency.

[0040] 2. Through real-time data collection and analysis, a judgment and marking mechanism is established. When the monitoring indicators exceed the set range, the fault can be quickly identified and early warning can be given to avoid waste of resources and potential losses. By summarizing and analyzing various data, regular reports and monitoring charts are generated, and the data is fed back into the transformation strategy to achieve a data-driven decision-making process. Through the feedback control mechanism, the system monitors various indicators and adjusts parameters in real time during operation to ensure the best energy-saving effect.

[0041] 3. By analyzing the carbon emission losses of future renovations, managers can identify potential energy-saving potential and emission reduction paths, and provide feasibility analysis for renovation plans. By quantitatively analyzing the carbon emission losses, communities can clarify the economic benefits when implementing energy-saving renovations, thereby allocating funds and resources wisely. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 It is a schematic diagram of the framework of the present invention;

[0044] Figure 2 It is a flowchart of the energy consumption prediction model training process in the present invention.

[0045] The numbers in the figure represent: 1. Central control module; 2. Data collection module; 3. Threshold setting module; 4. Model building module; 5. Judgment marking module; 6. Loss calculation module;

[0046] 7. Transformation and analysis unit; the transformation and analysis unit includes: 71. extraction module; 72. index module; 73. definition module;

[0047] 8. Weight setting module; 9. Summarization module; 10. Feedback module; 11. Storage module. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0049] The present invention will be further described below in conjunction with the embodiments.

[0050] Example 1

[0051] The green low-carbon monitoring system for energy-saving transformation of old residential areas in this embodiment is as follows: Figure 1 As shown, including:

[0052] Central control module 1 is used to deploy the control intranet and edit and submit instructions for each functional module and unit;

[0053] Data collection module 2, used to collect the actual energy consumption coefficient and operation setting attributes of the power supply, gas supply and water supply equipment in the area to be transformed within a preset period, and the operation setting attributes include: equipment type, operation mode and setting parameters;

[0054] The threshold setting module 3 is used to define the standard energy consumption coefficient threshold based on the initial operation settings of the power supply, gas supply and water supply equipment to be transformed; based on the initial operation settings, a reasonable standard energy consumption coefficient threshold is defined to provide an effective comparison baseline for the system, ensure the scientificity and accuracy of long-term monitoring, and reduce the waste of resources;

[0055] Model building module 4 is used to build and train the energy consumption prediction model. Based on the current energy consumption coefficients of several devices and the current operation setting attributes as input, the predicted energy consumption coefficients of several devices in the future several cycles are output, which provides a forward-looking analysis for the system, so that managers can take energy-saving measures in advance and optimize the operation of the equipment;

[0056] The judging and marking module 5 is used to compare the actual energy consumption coefficient and the predicted energy consumption coefficient of several cycles collected by the data acquisition module 2 with the standard energy consumption coefficient threshold value respectively; the judging and marking module 5 triggers a jump based on the comparison result in the comparison operation stage, and when it is judged that the comparison result does not violate the standard threshold state, it jumps to the threshold setting module 3 and the model building module 4 to run, and when it is judged that the comparison result violates the standard threshold state, it jumps to the loss calculation module 6 to run, and the judging and marking module 5 feeds back the comparison result to the loss calculation module 6, and simultaneously feeds back the data source threshold setting module 3 and the model building module 4; the actual and predicted energy consumption coefficients are automatically compared, and the state judgment is performed, which improves the automation degree of the system. When a violation of the standard threshold is found, the next loss calculation process can be effectively triggered, thereby improving the response speed;

[0057] The loss calculation module 6 is used to mark the energy consumption coefficient and its associated equipment that violate the standard threshold based on the comparison result under the standard energy consumption coefficient threshold of the judgment marking module 5, and output the actual carbon emission loss coefficient and the predicted carbon emission loss coefficient of the marked equipment based on the marked energy consumption coefficient; by marking all illegal energy consumption and its associated equipment, and calculating their actual and predicted carbon emission loss coefficients, specific data support is provided for subsequent transformation, thereby achieving effective tracking and analysis and improving the operability of data;

[0058] The transformation analysis unit 7 is used to extract the marked equipment associated with the actual carbon emission loss coefficient and the predicted carbon emission loss coefficient, calculate the future transformation carbon emission, obtain the transformation carbon emission loss coefficient, and extract data from the actual and predicted losses to calculate the carbon emission loss coefficient of the future transformation, thereby providing a quantitative basis for transformation investment decisions and ensuring the economy and environmental protection of the transformation plan;

[0059] The weight setting module 8 is used to preset the weights of the actual, predicted and transformed carbon emission loss coefficients, and perform weighted calculations on the current actual, predicted and transformed carbon emission loss coefficients to obtain the total carbon emission loss of several devices; the weight setting module 8 retains the historical preset data in the process of presetting the weights of the actual, predicted and transformed carbon emission loss coefficients. When the weighted calculation is performed on the same type of equipment whose energy consumption coefficient error meets the allowable error range, it directly jumps to the historical preset data for application; effective weighted calculations are performed through the preset weights to achieve a comprehensive assessment of the carbon emission losses of different equipment, and the historical data retention mechanism allows for rapid response within the error range, reduces repeated calculations, and improves efficiency;

[0060] The summary module 9 is used to summarize the total carbon emission loss data of all equipment, generate a visual report and submit it to the central control module 1, which helps the management to quickly evaluate the current carbon emission reduction effect;

[0061] The central control module 1 is interactively connected to the feedback module 10 through a wireless network. The feedback module 10 is used to receive the actual energy consumption coefficient collected by the data collection module 2 for the next cycle or the energy consumption coefficient written manually, and submit it to the threshold setting module 3 for judgment. When it is judged that the threshold of the threshold setting module 3 is violated, it jumps to the loss calculation module 6 to calculate the actual carbon emission loss coefficient, compares the current actual carbon emission loss coefficient with the predicted carbon emission coefficient of the previous cycle, and calculates the difference as the adjustment reference coefficient of the energy consumption prediction model of the model construction module 4; it realizes instant feedback and processing of data, improves system quality, and enhances interactivity with users;

[0062] The central control module 1 is interactively connected to the storage module 11 via a wireless network. The storage module 11 is used to receive the collected data and output data of each functional module, classify and store them, support cloud backup and reading and writing of external storage media, strengthen the management and protection of historical data, and ensure the security and traceability of data.

[0063] As an implementation method in this embodiment, Figure 1 As shown, the central control module 1 is interactively connected with the data acquisition module 2, the threshold setting module 3 and the model building module 4 through a wireless network, the judgment marking module 5 is interactively connected with the threshold setting module 3, the model building module 4 and the loss calculation module 6 through a wireless network, the loss calculation module 6 is interactively connected with the transformation analysis unit 7 through a wireless network, and the weight setting module 8 is interactively connected with the loss calculation module 6, the transformation analysis unit 7 and the summary module 9 through a wireless network.

[0064] Compared with existing technologies, it has significant advantages in accurate data collection, efficient carbon emission monitoring, prediction capabilities and decision support. Its high degree of automation and modular design not only improves the efficiency of energy management, but also provides managers with real-time and accurate decision-making basis, promotes the green and low-carbon transformation of old communities, and is conducive to improving resource utilization efficiency, reducing operating costs, complying with the concept of sustainable development, and providing strong technical support for future urban management.

[0065] Example 2

[0066] In other aspects, this embodiment also provides a process for constructing and training an energy consumption prediction model, such as Figure 2 As shown in the figure, the energy consumption prediction model is constructed through regression analysis algorithm, and the expression is:

[0067]

[0068] In the formula, E i,t+k represents the predicted energy consumption coefficient of device i in the next k cycles, P o represents the intercept term of the model, represents the predicted energy consumption coefficient when all independent variables are zero, m represents the number of independent variables used for prediction, and each independent variable has a certain influence weight on energy consumption, P j Represents the value corresponding to each independent variable X i,j,t The regression coefficient of X represents the influence of independent variable j on the energy consumption coefficient of equipment i. i,j,t represents the value of the jth independent variable of device i at time t, including: device type, current energy consumption coefficient, set parameters and operation mode, n represents the number of other external factors affecting energy consumption, which are actively added manually, and Y represents the value corresponding to each external factor Z i,l,tThe regression coefficient of , which indicates the influence of external factor l on the energy consumption coefficient of equipment i, Z i,l,t represents the value of the lth external factor of device i at time t, including: time factor, environmental factor and socio-economic factor, e i,j,t represents the error term, which contains random fluctuations or noise;

[0069] like Figure 2 As shown in the figure, the energy consumption prediction model training process is:

[0070] Step 1: Obtain the actual energy consumption coefficient of each device in different time periods through the data acquisition module 2, record the type, operation mode and setting parameters of the equipment, collect climate conditions, time factors and socio-economic factors, and convert them into a model-readable format to form a training set;

[0071] Step 2: Select the characteristic independent variables that are closely related to the energy consumption of the equipment;

[0072] Step 3: Use the training set to determine the corresponding regression coefficients by the least squares method, and continue to optimize the parameters;

[0073] Step 4: Put the trained energy consumption prediction model into real-time prediction calculation, receive evaluation feedback of the prediction results and add them to the training set.

[0074] Through the above formulas and steps, the energy consumption prediction model can provide quantitative data support for the energy-saving transformation of old communities, and help promote the implementation and optimization of energy-saving measures.

[0075] Example 3

[0076] In this embodiment, Figure 1 As shown, the transformation analysis unit 7 is provided with submodules at the lower level, and the submodules include an extraction module 71, an index module 72 and a definition module 73. The extraction module 71 is interactively connected with the index module 72 through a wireless network, and the index module 72 is interactively connected with the definition module 73 through a wireless network, wherein:

[0077] The extraction module 71 is used to extract the list of devices marked as violating the threshold by the judgment and marking module 5, create a label for each device, and customize the device label so that the management personnel can directly understand the actual situation of the device, which is helpful to quickly take targeted measures. The label content includes: the current status of the device, operating properties, labeling reasons and labeling time. After the label content is generated, it will be synchronously jumped to the central control module 1 to request storage permission, ensuring the timely storage and backup of the label content in the internal network, improving the security and reliability of the data, and effectively preventing data loss or leakage;

[0078] The indexing module 72 is used to use the tag generated by the extraction module 71 as a keyword to query and obtain the historical carbon emission loss data of the tag-associated device in the internal network, and query the network carbon emission loss data of the tag-associated device in the Internet; perform a two-way data query to obtain the historical carbon emission loss data of the tag-associated device. The multi-channel data acquisition method improves the integrity and accuracy of the data and ensures the diversity of decision-making basis;

[0079] Definition module 73 is used to obtain the historical and network carbon emission loss data obtained by index module 72, integrate the carbon emission loss data of the equipment that violates the threshold based on the equipment type and operation time, and output the integrated associated carbon emission loss data, so that data analysis can generate more representative and accurate carbon emission loss data. Compared with the isolated analysis of traditional methods, this integration can reveal deeper rules.

[0080] Compared with existing technologies, by associating the historical carbon emission data of the equipment with its current status, it can help managers evaluate the effectiveness of the current renovation on the one hand, and provide data support for future renovation measures on the other hand, making decisions more scientific and accurate. Through multi-dimensional data collection, dynamic feedback mechanism and customized equipment labeling, the ability of the energy-saving renovation system for old communities to monitor and analyze carbon emission losses has been improved.

[0081] In summary, the present invention overcomes the shortcomings of the prior art through innovations in unified platform, multi-dimensional analysis, dynamic prediction, adaptive management, visual reporting and real-time monitoring, improves the efficiency and accuracy of energy management, helps to effectively reduce energy consumption and carbon emissions, and can also provide a more scientific basis for decision-makers, promoting green and low-carbon sustainable development.

[0082] In the actual implementation process, after the central control module 1 performs management identity authentication, the present invention issues instructions, and the data acquisition module 2 collects the energy consumption parameters of the power supply, gas supply, and water supply equipment to be transformed in the detected area within a preset number of cycles, obtains the operation setting attributes of the power supply, gas supply, and water supply equipment to be transformed, and calculates the standard energy consumption parameter threshold of the operation setting of the power supply, gas supply, and water supply equipment to be transformed through the threshold setting module 3;

[0083] The energy consumption parameters collected for several cycles are compared with the standard energy consumption parameter threshold by the judging and marking module 5, and the energy consumption parameters and their associated devices that violate the standard threshold are marked. Based on the marked energy consumption coefficient, the current carbon emission loss coefficient of the marked device is calculated by the loss calculation module 6;

[0084] The model building module 4 builds a prediction model, based on the current energy consumption parameters of several devices and the current operation setting attributes as input, outputs the predicted energy consumption coefficients of several devices in several future cycles, compares the predicted energy consumption parameters with the standard energy consumption parameter threshold, marks the energy consumption parameters and associated devices that violate the standard threshold, and calculates the predicted carbon emission loss coefficients of the marked devices through the loss calculation module 6 based on the marked energy consumption coefficients;

[0085] The extraction module 71 extracts all the marked devices associated with the carbon emission loss coefficient, obtains the transformation data of the associated devices available in the history and the Internet through the index module 72, and the definition module 73 combines and calculates the future transformation carbon emissions to obtain the transformation carbon emission loss coefficient;

[0086] The weight setting module 8 presets the weights of the current, predicted and transformed carbon emission loss coefficients, and assigns corresponding weights to the current, predicted and transformed carbon emission loss coefficients. The summary module 9 summarizes the total carbon emission loss of several devices and submits it to the management end. The feedback module 10 defines the actual data of the future cycle for feedback, and the storage module 11 records all collected and analyzed data.

[0087] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. The green and low-carbon monitoring system for energy-saving transformation of old residential areas is characterized by: include: Central control module (1), used to deploy the control intranet, edit and submit instructions for each functional module and unit; A data collection module (2) is used to collect actual energy consumption coefficients and operation setting attributes of power supply, gas supply and water supply equipment in the area to be transformed within a preset period; A threshold setting module (3) is used to define a standard energy consumption coefficient threshold based on the initial operation settings of the power supply, gas supply and water supply equipment to be transformed; A model building module (4) is used to build and train an energy consumption prediction model, based on the current energy consumption coefficients of a number of devices and current operation setting properties as input, and output the predicted energy consumption coefficients of a number of devices in a number of future cycles; A judgment marking module (5) is used to compare the actual energy consumption coefficient and the predicted energy consumption coefficient of several cycles collected by the data collection module (2) with the standard energy consumption coefficient threshold value respectively; A loss calculation module (6) is used to mark the energy consumption coefficient and its associated equipment that violate the standard threshold value based on the comparison result under the standard energy consumption coefficient threshold value of the judgment marking module (5), and output the actual carbon emission loss coefficient and the predicted carbon emission loss coefficient of the marked equipment respectively based on the marked energy consumption coefficient; A transformation analysis unit (7) is used to extract the marked equipment associated with the actual carbon emission loss coefficient and the predicted carbon emission loss coefficient, calculate the future transformation carbon emission, and obtain the transformation carbon emission loss coefficient; A weight setting module (8) is used to preset the weights of actual, predicted and modified carbon emission loss coefficients, and to perform weighted calculations on the current actual, predicted and modified carbon emission loss coefficients to obtain the total carbon emission loss of a number of devices; The summary module (9) is used to summarize the total carbon emission loss data of all equipment, generate a visual report and submit it to the central control module (1).

2. The green and low-carbon monitoring system for energy-saving transformation of old residential areas according to claim 1 is characterized in that: The central control module (1) is interactively connected to a feedback module (10) via a wireless network. The feedback module (10) is used to receive the actual energy consumption coefficient collected by the data collection module (2) for the next cycle or the energy consumption coefficient written manually, and submit it to the threshold setting module (3) for judgment. When it is judged that the threshold of the threshold setting module (3) is violated, it jumps to the loss calculation module (6) to calculate the actual carbon emission loss coefficient, compares the current actual carbon emission loss coefficient with the predicted carbon emission coefficient of the previous cycle, and calculates the difference as the adjustment reference coefficient of the energy consumption prediction model of the model construction module (4).

3. The green and low-carbon monitoring system for energy-saving transformation of old residential areas according to claim 1 is characterized in that: The energy consumption prediction model in the model construction module (4) is constructed by a regression analysis algorithm, and the expression is: In the formula, E i,t+k represents the predicted energy consumption coefficient of device i in the next k cycles, P o represents the intercept term of the model, m represents the number of independent variables used for prediction, and P j Represents the value corresponding to each independent variable X i,j,t The regression coefficient, X i,j,t represents the value of the jth independent variable of device i at time t, n represents the number of other external factors affecting energy consumption, and Y represents the value corresponding to each external factor Z i,l,t The regression coefficient, Z i,l,t represents the value of the lth external factor of device i at time t, e i,j,t represents the error term.

4. The green and low-carbon monitoring system for energy-saving transformation of old residential areas according to claim 1 is characterized in that: The energy consumption prediction model training process in the model building module (4) is as follows: Step 1: Obtain the actual energy consumption coefficient of each device in different time periods, record the type, operation mode and setting parameters of the equipment, collect climate conditions, time factors and socio-economic factors, and convert them into a model-readable format to form a training set; Step 2: Select the characteristic independent variables that are closely related to the energy consumption of the equipment; Step 3: Use the training set to determine the corresponding regression coefficients by the least squares method, and continue to optimize the parameters; Step 4: Put the trained energy consumption prediction model into real-time prediction calculation, receive evaluation feedback of the prediction results and add them to the training set.

5. The green and low-carbon monitoring system for energy-saving transformation of old residential areas according to claim 1 is characterized in that: The judgment marking module (5) triggers a jump based on the comparison result during the comparison operation phase. When it is judged that the comparison result does not violate the standard threshold state, it jumps to the threshold setting module (3) and the model building module (4) for operation. When it is judged that the comparison result violates the standard threshold state, it jumps to the loss calculation module (6) for operation. The judgment marking module (5) feeds back the comparison result to the loss calculation module (6) and simultaneously feeds back the comparison result to the data source threshold setting module (3) and the model building module (4).

6. The green and low-carbon monitoring system for energy-saving transformation of old residential areas according to claim 1 is characterized in that: The central control module (1) is interactively connected to a storage module (11) via a wireless network. The storage module (11) is used to receive the collected data and output data of each functional module, perform classified storage, and support cloud backup and reading and writing of external storage media.

7. The green and low-carbon monitoring system for energy-saving transformation of old residential areas according to claim 1 is characterized in that: The transformation analysis unit (7) is provided with a submodule at a lower level, and the submodule includes an extraction module (71), an index module (72) and a definition module (73). The extraction module (71) and the index module (72) are interactively connected via a wireless network, and the index module (72) and the definition module (73) are interactively connected via a wireless network, wherein: An extraction module (71) is used to extract a list of devices marked as violating a threshold by the determination and marking module (5) and create a label for each device; An indexing module (72) is used to use the tag generated by the extraction module (71) as a keyword to query and obtain historical carbon emission loss data of devices associated with the tag in the internal network, and to query network carbon emission loss data of devices associated with the tag in the Internet; A definition module (73) is used to obtain the historical and network carbon emission loss data obtained by the index module (72), integrate the carbon emission loss data of the devices that violate the threshold based on the device type and the running time, and output the integrated associated carbon emission loss data.

8. The green and low-carbon monitoring system for energy-saving transformation of old residential areas according to claim 7 is characterized in that: The marking content of the extraction module (71) includes: the current state of the device, operating attributes, marking reason and marking time. After the marking content is generated, it is synchronously jumped to the central control module (1) to request storage permission.

9. The green and low-carbon monitoring system for energy-saving transformation of old residential areas according to claim 1 is characterized in that: The weight setting module (8) retains historical preset data during the process of presetting the actual, predicted and modified carbon emission loss coefficient weights, and when weighted calculation is performed for the same type of equipment whose energy consumption coefficient error is within the allowable error range, it directly jumps to the historical preset data for application.

10. The green and low-carbon monitoring system for energy-saving transformation of old residential areas according to claim 1 is characterized in that: The central control module (1) is interactively connected with the data acquisition module (2), the threshold setting module (3) and the model building module (4) via a wireless network; the judgment mark module (5) is interactively connected with the threshold setting module (3), the model building module (4) and the loss calculation module (6) via a wireless network; the loss calculation module (6) is interactively connected with the transformation analysis unit (7) via a wireless network; and the weight setting module (8) is interactively connected with the loss calculation module (6), the transformation analysis unit (7) and the summary module (9) via a wireless network.

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