A green and low-carbon energy service management system

Abstract

The present invention discloses a green and low-carbon energy service management system, which belongs to the field of energy management technology, and includes a platform end and a user end; the platform end is used and managed by the platform party, and includes a monitoring and analysis module; the monitoring and analysis module is used to perform monitoring and reserve analysis, set up several energy estimation models, and configure corresponding energy estimation models for the user end; the user end includes a monitoring module, a display module and an energy-saving analysis module; the monitoring module is used to monitor the energy consumption data of various energy-consuming equipment in the enterprise in real time, merge and classify the energy-consuming equipment, and obtain several representative classifications and independent equipment; the monitoring equipment is arranged according to the representative classifications and independent equipment; the representative classifications and independent equipment are monitored for energy consumption in real time according to the energy estimation model, and the energy consumption monitoring data of the energy-consuming equipment are obtained; the display module is used to perform monitoring and display; the energy-saving analysis module is used to perform real-time energy-saving analysis according to the energy consumption simulation model, and obtain corresponding emission reduction plans.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy management, and in particular is a green and low-carbon energy service management system. Background Art

[0002] As energy demand continues to increase, developing green, low-carbon energy, improving energy efficiency, and reducing carbon emissions have become the goals and aspirations of current social development. Traditional energy management systems often suffer from high energy consumption, large carbon emissions, and insufficient intelligence, failing to meet society's urgent needs for energy conservation, emission reduction, and sustainable development. In recent years, with the rapid development of information technology and the increasing level of intelligence, green, low-carbon energy service management systems have become increasingly feasible. By integrating advanced sensor technology, the Internet of Things, and big data analytics, these systems enable real-time monitoring, intelligent analysis, and efficient management of energy consumption. Furthermore, the continuous innovation and application of green, low-carbon technologies, such as the utilization of clean energy and the promotion of energy-saving and efficiency-enhancing technologies, have provided strong technical support for the development of green, low-carbon energy service management systems.

[0003] However, despite the huge potential and market prospects of green and low-carbon energy service management systems, some challenges and problems still exist in the market. For example, there may be compatibility issues between devices of different brands and models, especially when enterprises subsequently add new equipment.

[0004] Based on this, in order to solve the green and low-carbon energy service management needs of enterprises, the present invention provides a green and low-carbon energy service management system. Summary of the Invention

[0005] In order to solve the problems existing in the above solutions, the present invention provides a green and low-carbon energy service management system.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A green and low-carbon energy service management system, including a platform end and a user end;

[0008] The platform end is used and managed by the platform party, including the monitoring and analysis module;

[0009] The monitoring and analysis module is used to perform monitoring and reserve analysis, identify the platform's monitoring scope, and generate and update the reserve equipment list in real time based on the monitoring scope; set up several energy consumption estimation models based on the reserve equipment list, and update the energy consumption estimation model based on the update of the reserve equipment list; and configure the corresponding energy consumption estimation model for the user end.

[0010] The user terminal includes a monitoring module, a display module and an energy-saving analysis module;

[0011] The monitoring module is used to monitor the energy consumption data of each energy-consuming device in the enterprise in real time, obtain the energy-consuming device list of the enterprise, merge and classify the energy-consuming devices in the energy-consuming device list to obtain a number of representative classifications and independent devices; and deploy monitoring devices according to the representative classifications and independent devices;

[0012] Real-time energy consumption monitoring is performed on representative classifications and independent devices according to the energy consumption estimation model to obtain energy consumption monitoring data of the energy-consuming devices.

[0013] Furthermore, the method for setting the energy-consuming equipment list includes:

[0014] Acquire enterprise information, and generate a potential device list based on the enterprise information, wherein the potential device list is used to collect statistics on corresponding potential device information;

[0015] A device calibration model is established, and the expression of the device calibration model is:

[0016]

[0017] Where: (si, FLi) is the input data, si represents the potential device information of the corresponding potential device in the potential device list, i represents the potential device, i = 1, 2, ..., n, and n is the number of corresponding potential devices in the potential device list; FLi represents the verification material of the corresponding potential device; si → FLi indicates that the corresponding potential device information and verification material have been verified; the output data is the device calibration value SP(si, FLi), which is 1 or 0;

[0018] Collect calibration material data according to the potential device list; analyze the potential device list and calibration material data through the device calibration model to obtain the device calibration value of the corresponding potential device information;

[0019] The potential equipment information with the equipment calibration value of 1 is integrated to establish a list of energy-consuming equipment.

[0020] Furthermore, the method for merging and classifying the energy-consuming equipment in the energy-consuming equipment list includes:

[0021] Define independent devices, where independent devices refer to energy-consuming devices that are independently monitored for energy consumption; identify the energy-consuming device list according to the independent device definition, obtain corresponding independent device information, and mark the non-independent device information in the energy-consuming device list as candidate information;

[0022] An energy connection mode corresponding to the information to be selected is obtained, and the information to be selected is merged according to the energy connection mode to obtain a plurality of unit classifications; and a representative classification is determined according to the unit classifications.

[0023] Furthermore, the method for merging the selected information according to the connection mode includes:

[0024] Obtain unit classification standards; establish a unit judgment model, the expression of the unit judgment model is:

[0025]

[0026] Where: (LK, DB) is the input data, LK is the energy connection mode of the two candidate information for the corresponding merge analysis, and DB is the unit classification standard; LK→DB means that LK meets the unit classification standard; the output data is the unit judgment value DP(LK, DB), which is 1 or 0;

[0027] By analyzing the unit judgment model, the unit judgment values ​​between the corresponding candidate information are obtained, and the corresponding candidate information with a unit judgment value of 1 is assigned to the same unit category; and so on, until all the candidate information is assigned.

[0028] Furthermore, the method of determining the representative classification according to the unit classification includes:

[0029] Setting a merger upper limit; performing a merger simulation on the unit classification based on the merger upper limit to obtain several candidate merger schemes;

[0030] Setting an energy consumption simulation model according to the unit classification; obtaining the enterprise's historical energy consumption data, and setting a verification data set based on the historical energy consumption data and the energy consumption simulation model, wherein the verification data set includes simulated usage and energy consumption of the corresponding energy-consuming equipment;

[0031] Performing energy consumption monitoring simulation on the candidate merging scheme according to the verification data set and the energy consumption simulation model to obtain a scheme simulation result of the candidate merging scheme, wherein the scheme simulation result is composed of energy consumption simulation errors of corresponding energy-consuming equipment;

[0032] The candidate merging schemes are screened according to the simulation results of the schemes to determine a target merging scheme, and the unit classifications are merged according to the target merging scheme to obtain several representative classifications.

[0033] Furthermore, the method for screening the merger solutions to be selected based on the solution simulation results includes:

[0034] The energy-consuming devices in the energy-consuming device list are marked as j, where j = 1, 2, ..., m, and m is the number of energy-consuming devices in the energy-consuming device list;

[0035] Identify the energy consumption simulation error of the corresponding energy-consuming equipment according to the simulation results of the scheme, marked as WCj; identify the energy consumption of the energy-consuming equipment, marked as MHj;

[0036] The screening value of the corresponding candidate merger solution is calculated according to the screening formula. The screening formula is:

[0037]

[0038] Where: SA is the screening value; βj represents the weight coefficient of the corresponding energy-consuming equipment, which is set by the platform according to user needs;

[0039] The candidate merging solution with the smallest screening value is selected as the target merging solution.

[0040] Furthermore, the method for monitoring energy consumption of representative categories based on the energy consumption estimation model includes:

[0041] Acquire representative energy consumption data corresponding to a representative classification in real time; identify energy-consuming devices corresponding to the representative classification and acquire the operating status of the energy-consuming devices in real time; integrate the operating status and energy-consuming device information of each energy-consuming device corresponding to the representative classification into representative analysis data;

[0042] The representative analysis data and the representative energy consumption data are analyzed by the energy consumption estimation model to obtain energy consumption monitoring data of the corresponding energy-using equipment in the representative classification.

[0043] The display module is used to perform monitoring and display, obtain an energy consumption simulation model, and dynamically display the energy consumption monitoring data of the energy-consuming equipment according to the energy consumption simulation model;

[0044] The energy-saving analysis module is used to perform real-time energy-saving analysis based on the energy consumption simulation model, obtain corresponding emission reduction plans, and display the emission reduction plans to users.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] By monitoring energy consumption in real time, the system can promptly identify energy waste and excessive carbon emissions, enabling the implementation of appropriate energy-saving measures and emission reduction strategies. This will help reduce a company's energy consumption and carbon emissions, achieving energy conservation and emission reduction goals. Furthermore, to adapt to changes in a company's energy-consuming equipment and technological developments, intelligent monitoring of energy-consuming equipment can be implemented, addressing the inflexibility and inconvenience of existing energy consumption monitoring systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] 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 description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0049] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] like Figure 1 As shown, a green and low-carbon energy service management system includes a platform end and a user end;

[0051] The platform end is used and managed by the platform party, including the monitoring and analysis module;

[0052] The monitoring and analysis module is used to perform monitoring and reserve analysis, identify the platform's monitoring scope, which is the range of various enterprises that the platform needs to serve, and generate and update the reserve equipment list in real time based on the monitoring scope. The reserve equipment list includes information on various energy-consuming equipment that can perform energy consumption monitoring;

[0053] The platform sets up several energy consumption estimation models based on the reserve equipment list, and updates the corresponding energy consumption estimation models according to the update of the reserve equipment list, so that the corresponding energy consumption estimation models can meet the monitoring needs of the corresponding updated energy-consuming equipment; the energy consumption estimation model is used to analyze the operating status, energy-consuming equipment information and overall energy consumption monitoring data of several energy-consuming equipment to determine the energy consumption monitoring data of each energy-consuming equipment; and mark the corresponding equipment application scope for the energy consumption estimation model; configure the corresponding energy consumption estimation model for the corresponding user terminal, and when the user terminal updates a new energy-consuming equipment, analyze whether it is within the equipment application scope. When it is not within the equipment application scope, the platform will update the model.

[0054] The user terminal is used by users and includes a monitoring module, a display module and an energy-saving analysis module;

[0055] The monitoring module is used to monitor the energy consumption data of various energy-consuming equipment in the enterprise in real time and obtain the energy-consuming equipment list of the enterprise. The energy-consuming equipment list is used to collect information about various energy-consuming equipment in the enterprise, such as equipment type, power, production period and other related information;

[0056] The energy-consuming equipment in the energy-consuming equipment list is combined and classified to obtain several representative classifications and independent equipment; monitoring equipment is deployed according to the representative classifications and independent equipment, which is equivalent to setting one monitoring equipment for each independent equipment and one monitoring equipment for each representative classification;

[0057] According to the energy consumption estimation model, real-time energy consumption monitoring is performed on representative classifications and independent equipment to obtain energy consumption monitoring data of corresponding energy-consuming equipment.

[0058] In one embodiment, the energy-consuming device list can be set by the user according to actual conditions.

[0059] In one embodiment, a method for setting the energy-consuming device list includes:

[0060] Obtain enterprise information, including enterprise type, business scope, location, and other related information; search in real time based on enterprise information for various energy-consuming equipment that the enterprise may apply, marking them as potential equipment. Specifically, various energy-consuming equipment that may be applied by similar enterprises can be summarized, such as by searching and matching based on big data analysis technology to form a potential equipment list;

[0061] Establish an equipment calibration model. The equipment calibration model is used to analyze the potential equipment information in the potential equipment list to determine whether it is the enterprise's energy-consuming equipment. The expression of the equipment calibration model is:

[0062]

[0063] Where: (si, FLi) is the input data, si represents the potential device information of the corresponding potential device in the potential device list, i represents the potential device, i = 1, 2, ..., n, and n is the number of corresponding potential devices in the potential device list; FLi represents the verification material of the corresponding potential device, which can be the enterprise's confirmation record of the corresponding potential device or the monitoring data within the enterprise. The monitoring data is then used to identify whether such equipment exists, and then determine the detailed equipment information, thereby reducing the enterprise's confirmation workload; si→FLi indicates that the corresponding potential device information and verification material have been verified successfully; the output data is the device calibration value SP(si, FLi), which is 1 or 0;

[0064] Obtain corresponding calibration material data according to the potential device list. The calibration material data is the integrated data of multiple calibration materials. Analyze the potential device list and calibration material data using the device calibration model to obtain the device calibration value of the corresponding potential device information.

[0065] The potential device information with a device calibration value of 1 is integrated to establish a list of energy-consuming devices; and the list of energy-consuming devices can be updated subsequently according to the above method.

[0066] In one embodiment, a method for merging and classifying corresponding energy-consuming devices in a list of energy-consuming devices includes:

[0067] Define independent devices. Independent devices refer to energy-consuming devices that are independently monitored for energy consumption. The specific number is determined based on user needs. Based on the definition of independent devices, the energy-consuming device list is identified to obtain the corresponding independent device information. The non-independent device information in the energy-consuming device list is marked as candidate information.

[0068] Obtain the energy connection mode corresponding to the selected information, such as if computer A, computer B, and computer C are all connected through socket A; merge the selected information according to the obtained energy connection mode to obtain several unit classifications, that is, determine the unit classification according to the lowest monitoring node, and generally merge according to the energy connection mode corresponding to a single line. For example, if computer A, computer B, and computer C are all connected through socket A, then computer A, computer B, and computer C are merged into one unit classification;

[0069] Determine the representative classification based on the unit classification; each unit classification can correspond to one representative classification, or multiple unit classifications that can be merged can be merged into one representative classification based on user needs, and the merging standard is that energy consumption can be monitored uniformly; the specific determination is based on user needs.

[0070] In one embodiment, the method for merging the selected information according to the connection mode includes:

[0071] Obtain unit classification standards. Unit classification standards are set by the platform based on user needs, such as defining unit classification standards based on circuits, sockets, etc.

[0072] A unit judgment model is established. The unit judgment model is used to judge whether the energy connection mode of the corresponding candidate information meets the unit classification standard. The expression of the unit judgment model is:

[0073]

[0074] Where: (LK, DB) is the input data, LK is the energy connection mode of the two candidate information for the corresponding merge analysis, and DB is the unit classification standard; LK→DB means that LK meets the unit classification standard; the output data is the unit judgment value DP(LK, DB), which is 1 or 0;

[0075] The unit judgment model analyzes the corresponding candidate information and obtains the unit judgment value. The candidate information with a unit judgment value of 1 is assigned to the same unit category. This process is repeated until all candidate information is assigned. Subsequent additions of new energy-consuming equipment can also be intelligently assigned to the corresponding unit category.

[0076] In one embodiment, a method for determining a representative classification based on a unit classification includes:

[0077] Set a merging upper limit. The merging upper limit is set by the platform to limit the number of energy-consuming devices corresponding to the representative category and the upper limit of the overall energy consumption. This is because when there are too many energy-consuming devices or the overall energy consumption is too high, the accuracy of the energy consumption of each energy-consuming device in the representative category will be reduced. The upper limit is mainly set based on the estimated accuracy of the historical merging records. It is generally set manually by the platform staff, and can also be set in combination with existing intelligent analysis technology;

[0078] Based on the merging upper limit, each unit classification is merged and simulated to determine various merging schemes. That is, each representative classification corresponding to each merging scheme meets the merging upper limit requirement. For example, each unit classification is randomly combined and then selected based on the merging upper limit to determine various merging schemes and mark them as candidate merging schemes.

[0079] Energy consumption simulation models are set up based on the classification of each unit. The energy consumption simulation model is used to display the connection relationship and energy consumption relationship of each energy-consuming device. It can be established based on existing modeling technology to form a visual data model, such as using lines to represent circuits and using simple blocks to represent corresponding energy-consuming devices and monitoring equipment. The platform will set up this model based on the actual situation of the user.

[0080] Obtain the company's historical energy consumption data, which is the historical usage of each energy-consuming equipment in the company; set up a verification data set based on the historical energy consumption data and the energy consumption simulation model. The verification data set includes the simulated usage mode and energy consumption of each energy-consuming equipment, that is, use the historical energy consumption data and the energy consumption simulation model to set the simulated usage mode of each energy-consuming equipment. Generally, the self-simulated usage mode is set according to the situation with the highest usage probability, such as computer A and computer B are used from 9 am to 12 pm, and computer A is used from 12 pm to 1:30 pm and computer B is not used; the energy consumption is simulated according to the simulated usage mode, and the corresponding energy consumption is detected. It can also be counted based on historical data with clear energy consumption; the platform can simulate the usage and energy consumption of various equipment on the spot to form a material library, which is then matched according to the energy-consuming equipment and the simulated usage mode, and integrated into a verification data set; the most accurate method is to conduct simulated monitoring in the enterprise, use the usage mode as the simulated usage mode, and record the corresponding energy consumption; there are many ways to set up the verification data set;

[0081] Perform energy consumption monitoring simulation on the selected merged scheme based on the verification data set and energy consumption simulation model to obtain the energy consumption simulation error of each energy-consuming device. That is, use the equipment according to the simulated usage method in the verification data and monitor it according to the subsequent energy consumption monitoring method to obtain the monitored energy consumption of the corresponding energy-consuming equipment. Compare it with the energy consumption in the verification data set to determine the energy consumption simulation error; and integrate it into the scheme simulation result.

[0082] The merging schemes to be selected are screened according to the scheme simulation results to determine the target merging scheme, such as presetting the weight coefficients of each energy-consuming equipment, and then screening according to the scheme simulation results. Other methods can also be used to screen according to the scheme simulation results.

[0083] Merge the unit classifications according to the target merging scheme to form representative classifications.

[0084] In one embodiment, a method for real-time energy consumption monitoring of representative categories based on an energy consumption estimation model includes:

[0085] Real-time acquisition of energy consumption monitoring data corresponding to the representative classification, marked as representative energy consumption data, that is, the total energy consumption monitoring data of each energy-consuming device corresponding to the representative classification;

[0086] Identify the energy-consuming equipment corresponding to the representative classification and obtain the operating status of the energy-consuming equipment in real time, such as whether it is running and the gear it is in during operation. The operating status is collected based on the actual energy-consuming equipment. This can be obtained through video surveillance, installation of corresponding sensors, etc., or input by enterprise staff. For some smart devices, the corresponding operating status can be directly accessed; the operating status of the energy-consuming equipment corresponding to the representative classification and the energy-consuming equipment information are integrated into representative analysis data;

[0087] The representative analysis data and representative energy consumption data are analyzed through the energy consumption estimation model to determine the energy consumption monitoring data of each energy-consuming equipment in the representative classification; that is, the energy consumption proportion of each energy-consuming equipment is estimated using the operating status and energy-consuming equipment information, and then the representative energy consumption data is divided according to the energy consumption proportion to form the energy consumption monitoring data of the corresponding energy-consuming equipment.

[0088] The display module is used to monitor and display, obtain an energy consumption simulation model, and dynamically display the energy consumption monitoring data of the corresponding energy-consuming equipment according to the energy consumption simulation model; for example, display its working status according to the energy consumption monitoring data and display the corresponding energy consumption data.

[0089] The energy-saving analysis module is used to perform real-time energy-saving analysis based on the energy consumption simulation model, obtain corresponding emission reduction plans, and display the emission reduction plans to users.

[0090] In one embodiment, after the actual energy consumption data is determined according to the energy consumption simulation model, it can be analyzed according to the existing energy conservation and emission reduction analysis method to set an emission reduction plan.

[0091] For example, deep learning algorithms such as neural networks and convolutional neural networks can be used to build intelligent models, and intelligent analysis can be performed through successfully trained intelligent models to generate emission reduction plans.

[0092] The above formulas are all calculated by removing dimensions and taking their numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technicians in this field according to actual conditions or obtained by simulating a large amount of data.

[0093] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A green and low-carbon energy service management system, characterized in that: Including platform side and user side; The platform end is used and managed by the platform party, including the monitoring and analysis module; The monitoring and analysis module is used to perform monitoring and reserve analysis, identify the platform's monitoring scope, and generate and update the reserve equipment list in real time based on the monitoring scope; set up several energy consumption estimation models based on the reserve equipment list, and update the energy consumption estimation models based on the update of the reserve equipment list; and configure the corresponding energy consumption estimation model for the user terminal; The user terminal includes a monitoring module, a display module and an energy-saving analysis module; The monitoring module is used to monitor the energy consumption data of each energy-consuming device in the enterprise in real time, obtain the energy-consuming device list of the enterprise, merge and classify the energy-consuming devices in the energy-consuming device list to obtain a number of representative classifications and independent devices; and deploy monitoring devices according to the representative classifications and independent devices; Performing real-time energy consumption monitoring on representative classifications and independent devices according to the energy consumption estimation model to obtain energy consumption monitoring data of the energy-consuming devices; The display module is used to perform monitoring and display, obtain an energy consumption simulation model, and dynamically display the energy consumption monitoring data of the energy-consuming equipment according to the energy consumption simulation model; The energy-saving analysis module is used to perform real-time energy-saving analysis based on the energy consumption simulation model, obtain corresponding emission reduction plans, and present the emission reduction plans to users; Methods for merging and classifying energy-consuming equipment in the energy-consuming equipment catalog include: Define independent devices, where independent devices refer to energy-consuming devices that are independently monitored for energy consumption; identify the energy-consuming device list according to the independent device definition, obtain corresponding independent device information, and mark the non-independent device information in the energy-consuming device list as candidate information; Obtaining energy connection methods corresponding to the selected information, merging the selected information according to the energy connection methods to obtain a plurality of unit classifications; and determining a representative classification based on the unit classifications; Methods for determining representative classifications based on unit classifications include: Setting a merger upper limit; performing a merger simulation on the unit classification based on the merger upper limit to obtain several candidate merger schemes; Setting an energy consumption simulation model according to the unit classification; obtaining the enterprise's historical energy consumption data, and setting a verification data set based on the historical energy consumption data and the energy consumption simulation model, wherein the verification data set includes simulated usage and energy consumption of the corresponding energy-consuming equipment; Performing energy consumption monitoring simulation on the candidate merging scheme according to the verification data set and the energy consumption simulation model to obtain a scheme simulation result of the candidate merging scheme, wherein the scheme simulation result is composed of energy consumption simulation errors of corresponding energy-consuming equipment; The candidate merging schemes are screened according to the simulation results of the schemes to determine a target merging scheme, and the unit classifications are merged according to the target merging scheme to obtain several representative classifications.

2. A green and low-carbon energy service management system according to claim 1, characterized in that: Methods for setting the energy-consuming equipment list include: Acquire enterprise information, and generate a potential device list based on the enterprise information, wherein the potential device list is used to collect statistics on corresponding potential device information; A device calibration model is established, and the expression of the device calibration model is: ； Where: (si, FLi) is the input data, si represents the potential device information of the corresponding potential device in the potential device list, i represents the potential device, i=1, 2, ..., n, and n is the number of corresponding potential devices in the potential device list; FLi represents the verification material of the corresponding potential device; si→FLi indicates that the corresponding potential device information and verification material have been verified; the output data is the device calibration value SP(si, FLi), which is 1 or 0; Collect calibration material data according to the potential device list; analyze the potential device list and calibration material data through the device calibration model to obtain the device calibration value of the corresponding potential device information; The potential equipment information with the equipment calibration value of 1 is integrated to establish a list of energy-consuming equipment.

3. A green and low-carbon energy service management system according to claim 1, characterized in that: Methods for merging selected information according to the connection mode include: Obtain unit classification standards; establish a unit judgment model, the expression of the unit judgment model is: ； Where: (LK, DB) is the input data, LK is the energy connection mode of the two candidate information for the corresponding merge analysis, DB is the unit classification standard; LK→DB means that LK meets the unit classification standard; the output data is the unit judgment value DP(LK, DB), which is 1 or 0; By analyzing the unit judgment model, the unit judgment values ​​between the corresponding candidate information are obtained, and the corresponding candidate information with a unit judgment value of 1 is assigned to the same unit category; and so on, until all the candidate information is assigned.

4. A green and low-carbon energy service management system according to claim 1, characterized in that: Methods for screening merger plans based on simulation results include: The energy-consuming devices in the energy-consuming device list are marked as j, where j = 1, 2, ..., m, and m is the number of energy-consuming devices in the energy-consuming device list; Identify the energy consumption simulation error of the corresponding energy-consuming equipment according to the simulation results of the scheme, marked as WCj; identify the energy consumption of the energy-consuming equipment, marked as MHj; The screening value of the corresponding candidate merger solution is calculated according to the screening formula. The screening formula is: ； Where: SA is the screening value; βj represents the weight coefficient of the corresponding energy-consuming equipment; The candidate merging solution with the smallest screening value is selected as the target merging solution.

5. A green and low-carbon energy service management system according to claim 1, characterized in that: Methods for monitoring energy consumption of representative categories based on energy consumption estimation models include: Acquire representative energy consumption data corresponding to a representative classification in real time; identify energy-consuming devices corresponding to the representative classification and acquire the operating status of the energy-consuming devices in real time; integrate the operating status and energy-consuming device information of each energy-consuming device corresponding to the representative classification into representative analysis data; The representative analysis data and the representative energy consumption data are analyzed by the energy consumption estimation model to obtain energy consumption monitoring data of the corresponding energy-using equipment in the representative classification.

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