A park intelligent carbon reduction assessment optimization method and system

Abstract

The present invention provides a method and system for evaluating and optimizing intelligent carbon reduction in a park, wherein the method includes: evaluating the carbon reduction potential information of the park; formulating a carbon reduction optimization plan based on the carbon reduction potential information; executing the carbon reduction optimization plan in the park; tracking the execution performance information of the carbon reduction optimization plan; and visually outputting the execution performance information. The method and system for evaluating and optimizing intelligent carbon reduction in a park of the present invention automatically formulates a carbon reduction optimization plan based on the evaluated carbon reduction potential information, and executes it in the park. During execution, the execution performance information of the carbon reduction optimization plan is tracked. There is no need to arrange special technical personnel to study and formulate carbon reduction optimization plans, which reduces labor costs and improves the efficiency of formulating carbon reduction optimization plans. There is no need for technical personnel to track the execution performance of carbon reduction optimization plans, which further reduces labor costs.

Description

Technical Field

[0001] The present invention relates to the field of computer processing technology, and in particular to a park intelligent carbon reduction assessment optimization method and system. Background Art

[0002] The trend of global warming is becoming increasingly obvious, and it is necessary to reduce the emission of greenhouse gases such as carbon dioxide, that is, to implement carbon emission reduction. As the main source of carbon emissions, the implementation of carbon emission reduction in enterprise parks is of great significance. Usually, the park will formulate carbon emission reduction optimization plans for each enterprise in the park, that is, to guide enterprises on how to implement carbon emission reduction. However, carbon emission reduction optimization plans are all studied and formulated by specialized technicians, with high labor costs and low efficiency. In addition, when enterprises implement carbon emission reduction optimization plans, technicians also need to track the implementation results of carbon emission reduction optimization plans, which further increases labor costs.

[0003] In view of this, a solution is urgently needed. Summary of the invention

[0004] The present invention provides an intelligent carbon reduction assessment and optimization method and system for a park, which automatically formulates a carbon emission reduction optimization plan based on the assessed carbon emission reduction potential information, and executes it in the park. During execution, the implementation performance information of the carbon emission reduction optimization plan is tracked. There is no need to arrange special technical personnel to study and formulate the carbon emission reduction optimization plan, which reduces labor costs and improves the efficiency of formulating the carbon emission reduction optimization plan. There is no need for technical personnel to track the implementation performance of the carbon emission reduction optimization plan, which further reduces labor costs.

[0005] The present invention provides a park intelligent carbon reduction assessment and optimization method, comprising:

[0006] Evaluate the carbon reduction potential of the park;

[0007] Develop carbon reduction optimization plans based on carbon reduction potential information;

[0008] Implement carbon reduction optimization plans within the park;

[0009] Track the implementation performance of carbon reduction optimization plans;

[0010] Visually output execution performance information.

[0011] Preferably, the carbon reduction potential of the park is assessed, including:

[0012] Construct a carbon reduction potential assessment model;

[0013] Obtain carbon emission data of enterprises in the park;

[0014] Input the enterprise carbon emission data into the carbon emission reduction potential assessment model to obtain carbon emission reduction potential information;

[0015] Among them, the construction of carbon emission reduction potential assessment model includes:

[0016] Update the preset carbon emission reduction potential information assessment database; the carbon emission reduction potential information assessment database includes: multiple sets of one-to-one corresponding potential standards and potential items;

[0017] Based on the preset model architecture template and the carbon emission reduction potential information assessment library, a carbon emission reduction potential assessment model is constructed.

[0018] Preferably, based on the carbon reduction potential information, a carbon reduction optimization plan is formulated, including:

[0019] Construct a model for formulating carbon emission reduction optimization plans;

[0020] Inputting the carbon emission reduction potential information into the carbon emission reduction optimization plan formulation model to obtain the carbon emission reduction optimization plan;

[0021] Among them, the construction of a carbon emission reduction optimization plan formulation model includes:

[0022] Search for training sample extraction sources;

[0023] Analyze and determine whether the training sample extraction source meets the training sample extraction conditions;

[0024] When the conditions are met, extracting training samples from a training sample extraction source;

[0025] Based on the training samples, the preset neural network model is trained;

[0026] The neural network model trained to convergence is used as a model for formulating carbon emission reduction optimization plans;

[0027] Among them, the training sample extraction conditions include:

[0028] The source type of the training sample extraction source is an active source and the first association weight sum of the source party of the training sample extraction source is greater than or equal to a preset first weight sum threshold; the first association weight sum includes: the sum of the first association weights of at least one first association record corresponding to the source party in the preset source party association record library corresponding to the park;

[0029] or,

[0030] The source type is a passive source and the sum of the second association weights of the park generated by the training sample extraction source is greater than or equal to the preset second weight and threshold; the second association weight includes: in the preset park association record library, the sum of the second association weights of at least one corresponding second association record between the parks.

[0031] Preferably, the implementation performance information of the carbon emission reduction optimization plan is tracked, including:

[0032] Obtain the basis for evaluating the effectiveness of the carbon emission reduction optimization plan;

[0033] Based on the preset effectiveness evaluation template and according to the effectiveness evaluation basis, the execution effectiveness information is evaluated.

[0034] Preferably, the visual output of execution performance information includes:

[0035] Retrieve the preset map model corresponding to the park;

[0036] Analyze the execution performance information and obtain multiple sets of one-to-one corresponding carbon emission reduction enterprise and local performance information;

[0037] Go through the carbon emission reduction enterprises one by one;

[0038] During each traversal, the local area model corresponding to the traversed carbon emission reduction enterprise is determined from the map model, and the local performance information corresponding to the traversed carbon emission reduction enterprise is mapped into the local area model;

[0039] After traversing the carbon emission reduction enterprises, the map model is adjusted based on the map model adjustment requirements to obtain the adjusted model;

[0040] Output the adjusted model;

[0041] Among them, map model adjustment requirements include:

[0042] The shortest distance between at least N local area models and the model boundary of the adjusted model falls within a preset distance interval;

[0043] The two parts of the results information do not block each other;

[0044] The target vector angle of local performance information of the same performance type in each local area model in the target local area model set is the same; the carbon emission reduction enterprises corresponding to each local area model in the target local area model set have corresponding association relationships in a preset enterprise association library; the target vector angle is the vector angle between a first direction vector constructed based on the model position of the local performance information and the text direction of the local performance information, and a second direction vector constructed based on the model position and the straight line direction from the model position to the model center of the local area model where the local performance information is located.

[0045] Preferably, the park intelligent carbon reduction assessment and optimization method also includes:

[0046] Based on the preset feature description template, the carbon emission reduction potential information, carbon emission reduction optimization plan and implementation effect information are characterized to obtain the feature description results;

[0047] Based on the feature description results, a search for a carbon emission reduction optimization relay solution is triggered;

[0048] Determine the type of carbon reduction object from the carbon reduction optimization relay plan;

[0049] Carbon emission reduction enterprises that are carbon emission reduction object types shall be regarded as target carbon emission reduction enterprises;

[0050] The carbon emission reduction optimization relay plan is mapped into the target semicircle; the center of the target semicircle is the midpoint of the model center line between the local area models corresponding to the target carbon emission reduction enterprises in the adjusted model, the radius of the target semicircle is a preset length value, and the target semicircle is located on the side of the model center line close to the model center of the adjusted model;

[0051] Display the model center line;

[0052] Among them, based on the feature description results, the search for carbon emission reduction optimization relay scheme is triggered, including:

[0053] Obtaining preset multiple sets of one-to-one corresponding standard feature description results and trigger search rules;

[0054] When the standard feature description results are completely present in the feature description results, the search for the carbon emission reduction optimization relay scheme is triggered based on the corresponding trigger search rules.

[0055] The present invention provides a park intelligent carbon reduction assessment and optimization system, comprising:

[0056] Carbon emission reduction potential information assessment module, used to assess the carbon emission reduction potential information of the park;

[0057] The carbon emission reduction optimization plan formulation module is used to formulate a carbon emission reduction optimization plan based on carbon emission reduction potential information;

[0058] Carbon emission reduction optimization plan execution module, used to implement the carbon emission reduction optimization plan in the park;

[0059] Implementation performance information tracking module, used to track the implementation performance information of the carbon emission reduction optimization plan;

[0060] The execution performance information output module is used to visually output the execution performance information.

[0061] Preferably, the carbon emission reduction potential information evaluation module evaluates the carbon emission reduction potential information of the park, including:

[0062] Construct a carbon reduction potential assessment model;

[0063] Obtain carbon emission data of enterprises in the park;

[0064] Input the enterprise carbon emission data into the carbon emission reduction potential assessment model to obtain carbon emission reduction potential information;

[0065] Among them, the construction of carbon emission reduction potential assessment model includes:

[0066] Update the preset carbon emission reduction potential information assessment database; the carbon emission reduction potential information assessment database includes: multiple sets of one-to-one corresponding potential standards and potential items;

[0067] Based on the preset model architecture template and the carbon emission reduction potential information assessment library, a carbon emission reduction potential assessment model is constructed.

[0068] Preferably, the carbon emission reduction optimization program formulation module formulates a carbon emission reduction optimization program based on the carbon emission reduction potential information, including:

[0069] Construct a model for formulating carbon emission reduction optimization plans;

[0070] Inputting the carbon emission reduction potential information into the carbon emission reduction optimization plan formulation model to obtain the carbon emission reduction optimization plan;

[0071] Among them, the construction of a carbon emission reduction optimization plan formulation model includes:

[0072] Search for training sample extraction sources;

[0073] Analyze and determine whether the training sample extraction source meets the training sample extraction conditions;

[0074] When the conditions are met, extracting training samples from a training sample extraction source;

[0075] Based on the training samples, the preset neural network model is trained;

[0076] The neural network model trained to convergence is used as a model for formulating carbon emission reduction optimization plans;

[0077] Among them, the training sample extraction conditions include:

[0078] The source type of the training sample extraction source is an active source and the first association weight sum of the source party of the training sample extraction source is greater than or equal to a preset first weight sum threshold; the first association weight sum includes: the sum of the first association weights of at least one first association record corresponding to the source party in the preset source party association record library corresponding to the park;

[0079] or,

[0080] The source type is a passive source and the sum of the second association weights of the park generated by the training sample extraction source is greater than or equal to the preset second weight and threshold; the second association weight includes: in the preset park association record library, the sum of the second association weights of at least one corresponding second association record between the parks.

[0081] Preferably, the execution performance information tracking module tracks the execution performance information of the carbon emission reduction optimization plan, including:

[0082] Obtain the basis for evaluating the effectiveness of the carbon emission reduction optimization plan;

[0083] Based on the preset effectiveness evaluation template and according to the effectiveness evaluation basis, the execution effectiveness information is evaluated.

[0084] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0085] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0087] Figure 1 This is a flow chart of a method for optimizing the evaluation of intelligent carbon reduction in a park according to an embodiment of the present invention;

[0088] Figure 2 It is a schematic diagram of an intelligent carbon reduction assessment and optimization method for a park in an embodiment of the present invention. DETAILED DESCRIPTION

[0089] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0090] The present invention provides a park intelligent carbon reduction assessment and optimization method, such as Figure 1 As shown, including:

[0091] Step S1: Evaluate the carbon emission reduction potential information of the park;

[0092] Step S2: Formulate a carbon emission reduction optimization plan based on the carbon emission reduction potential information;

[0093] Step S3: Implementing the carbon emission reduction optimization plan in the park;

[0094] Step S4: Tracking the implementation performance information of the carbon emission reduction optimization plan;

[0095] Step S5: Visually output execution performance information.

[0096] Among them, the carbon emission reduction potential information includes: the potential means for enterprises in the park to implement carbon emission reduction, such as: replacing coal with electricity, replacing oil with electricity, etc.; the carbon emission reduction optimization plan includes: the plan for enterprises in the park to implement carbon emission reduction; when implementing the carbon emission reduction optimization plan, each enterprise in the park shall implement it accordingly; the implementation results information includes: the results of carbon emission reduction implemented by enterprises in the park based on the carbon emission reduction optimization plan, such as: changes in carbon emission reduction amount, changes in carbon emission reduction range, etc.; the implementation results information is output visually for users to view.

[0097] This application automatically formulates a carbon emission reduction optimization plan based on the assessed carbon emission reduction potential information, and implements it within the park. During implementation, it tracks the implementation performance information of the carbon emission reduction optimization plan. There is no need to arrange special technical personnel to research and formulate carbon emission reduction optimization plans, which reduces labor costs and improves the efficiency of formulating carbon emission reduction optimization plans. There is also no need for technical personnel to track the implementation results of the carbon emission reduction optimization plan, further reducing labor costs.

[0098] In one embodiment, the carbon emission reduction potential information of the park is evaluated, including:

[0099] Construct a carbon reduction potential assessment model;

[0100] Obtain carbon emission data of enterprises in the park;

[0101] Input the enterprise carbon emission data into the carbon emission reduction potential assessment model to obtain carbon emission reduction potential information;

[0102] Among them, the construction of carbon emission reduction potential assessment model includes:

[0103] Update the preset carbon emission reduction potential information assessment database; the carbon emission reduction potential information assessment database includes: multiple sets of one-to-one corresponding potential standards and potential items;

[0104] Based on the preset model architecture template and the carbon emission reduction potential information assessment library, a carbon emission reduction potential assessment model is constructed.

[0105] Among them, enterprise carbon emission data include: carbon emissions generated by various carbon emission sources within the enterprise (such as oil combustion, coal stone combustion, etc.); when the enterprise carbon emission data meets the potential standard, the corresponding potential item is output as carbon emission reduction potential information, for example: the potential standard is that the carbon emissions generated by the enterprise's oil combustion decreases month by month, which means that the enterprise can continue to reduce oil combustion, and the potential item is replacing oil with electricity; the model architecture template is a tree query structure, and its tree nodes are various potential standards, leaf node potential items, and the oil carbon emission potential assessment model is queried one by one to determine whether the enterprise carbon emission data meets the potential standards on the tree nodes. When it meets the requirements, the potential items on the corresponding leaf nodes are output as carbon emission reduction potential information.

[0106] In one embodiment, based on the carbon emission reduction potential information, a carbon emission reduction optimization plan is formulated, including:

[0107] Construct a model for formulating carbon emission reduction optimization plans;

[0108] Inputting the carbon emission reduction potential information into the carbon emission reduction optimization plan formulation model to obtain the carbon emission reduction optimization plan;

[0109] Among them, the construction of a carbon emission reduction optimization plan formulation model includes:

[0110] Search for training sample extraction sources;

[0111] Analyze and determine whether the training sample extraction source meets the training sample extraction conditions;

[0112] When the conditions are met, extracting training samples from a training sample extraction source;

[0113] Based on the training samples, the preset neural network model is trained;

[0114] The neural network model trained to convergence is used as a model for formulating carbon emission reduction optimization plans;

[0115] Among them, the training sample extraction conditions include:

[0116] The source type of the training sample extraction source is an active source and the first association weight sum of the source party of the training sample extraction source is greater than or equal to a preset first weight sum threshold; the first association weight sum includes: the sum of the first association weights of at least one first association record corresponding to the source party in the preset source party association record library corresponding to the park;

[0117] or,

[0118] The source type is a passive source and the sum of the second association weights of the park generated by the training sample extraction source is greater than or equal to the preset second weight and threshold; the second association weight includes: in the preset park association record library, the sum of the second association weights of at least one corresponding second association record between the parks.

[0119] Among them, there are two types of source types for training sample extraction sources: one is active source, such as: carbon emission reduction optimization plans corresponding to carbon emission reduction potential information of different enterprises actively provided by carbon emission reduction experts and technicians; the other is passive source, such as: real cases of carbon emission reduction in history; training samples can be extracted from the two types of training sample extraction sources, the active source directly extracts the carbon emission reduction optimization plans corresponding to the carbon emission reduction potential information of different enterprises as training samples, and the passive source extracts the carbon emission reduction potential information of enterprises in real cases and the corresponding carbon emission reduction optimization plan training samples; after the neural network model is trained based on the training samples, when the neural network model is trained to convergence, the neural network model can automatically formulate a carbon emission reduction optimization plan based on the input carbon emission reduction potential information. The carbon emission reduction optimization plan formulation model is used to determine the carbon emission reduction optimization plan, which improves the accuracy and efficiency of determining the carbon emission reduction optimization plan.

[0120] The first weight and threshold value can be, for example, 80; the source party includes: carbon emission reduction experts, technicians, etc.; the source party related records include: historical corporate carbon emission data that have browsed the park, visited the park, worked for the park's internal employees, and formulated carbon emission reduction optimization plans for enterprises in the park, etc.; the first association weight represents the degree to which the source party's association records can reflect the reliability of the source party, which can be preset in advance by technicians according to actual needs; the second weight and threshold value can be, for example, 100; the generating park includes: other parks that have produced real cases of carbon emission reduction in history; the second association records include: the same type of enterprises in the park, and the same carbon emission reduction optimization plans have been implemented by enterprises in the park in history, etc.; the second association weight represents the degree to which the training sample extraction source represented by the second association record is reliable, which can be preset in advance by technicians according to actual needs; the training sample extraction condition is introduced, and when the training sample extraction source meets the training sample extraction condition, the training sample is extracted from it, which improves the quality of the training sample, thereby improving the construction quality of the carbon emission reduction optimization plan formulation model and the working ability for formulating carbon emission reduction optimization plans.

[0121] In one embodiment, tracking the execution performance information of the carbon emission reduction optimization plan includes:

[0122] Obtain the basis for evaluating the effectiveness of the carbon emission reduction optimization plan;

[0123] Based on the preset effectiveness evaluation template and according to the effectiveness evaluation basis, the execution effectiveness information is evaluated.

[0124] Among them, the basis for effectiveness evaluation includes: the carbon emissions of enterprises in the park before and after the implementation of the carbon emission reduction optimization plan; the effectiveness evaluation template includes: calculating the difference in carbon emissions before and after the implementation of the carbon emission reduction optimization plan by enterprises in the park, determining the reduction in carbon emissions based on the positive or negative value and the size of the difference, etc., thereby determining the implementation effectiveness information.

[0125] In one embodiment, the visual output of the execution performance information includes:

[0126] Retrieve the preset map model corresponding to the park;

[0127] Analyze the execution performance information and obtain multiple sets of one-to-one corresponding carbon emission reduction enterprise and local performance information;

[0128] Go through the carbon emission reduction enterprises one by one;

[0129] During each traversal, the local area model corresponding to the traversed carbon emission reduction enterprise is determined from the map model, and the local performance information corresponding to the traversed carbon emission reduction enterprise is mapped into the local area model;

[0130] After traversing the carbon emission reduction enterprises, the map model is adjusted based on the map model adjustment requirements to obtain the adjusted model;

[0131] Output the adjusted model;

[0132] Among them, the map model is a 3D map model of the park; the execution performance information includes different carbon emission reduction enterprises and local performance information, and the local performance information is the carbon emission reduction performance achieved by the carbon emission reduction enterprises; the local area model is a partial 3D map model of the carbon emission reduction enterprises in the map model; by mapping the local performance information to the local area model, users can intuitively see the carbon emission reduction performance of each enterprise;

[0133] Among them, map model adjustment requirements include:

[0134] The shortest distance between at least N local area models and the model boundary of the adjusted model falls within a preset distance interval; N is a positive integer and can be preset in advance by technical personnel according to actual needs; the distance interval can be, for example, 20 meters to 50 meters; the unit of the shortest distance is the map distance unit on the map model; setting this map model adjustment requirement can eliminate areas on the map model that are not related to the local area model as much as possible, making the adjusted model more streamlined, reducing resource usage, and making it easier for users to intuitively view the carbon emission reduction results of each enterprise;

[0135] The local results information should not block each other. Setting this map model adjustment requirement can make the local results information staggered to avoid clutter and affect user viewing.

[0136] The target vector angle of local performance information of the same performance type in each local area model in the target local area model set is the same; the carbon emission reduction enterprises corresponding to each local area model in the target local area model set have corresponding association relationships in a preset enterprise association library; the target vector angle is the vector angle between a first direction vector constructed based on the model position of the local performance information and the text direction of the local performance information, and a second direction vector constructed based on the model position and the straight line direction from the model position to the model center of the local area model where the local performance information is located. The types of results include: carbon emission reduction range, carbon emission reduction value, etc.; the enterprise association database stores the associations between different enterprises in the park, such as: belonging to the same enterprise type, belonging to the same parent company, and the same carbon emission source type between enterprises; the model location is the location of the local performance information in the map model; generally, when users view, they will have this requirement: compare the local performance information of the same performance type of enterprises with associations. Traditionally, when solving this user demand, the data is usually analyzed and summarized into a table. However, when comparing, users need to combine the company's own situation (for example: company type, company carbon emission source, company If the user looks at the table alone, the user needs to find the remaining required data by himself, which is very cumbersome. When solving this user demand, the embodiment of the present invention ensures that the target vector angles of the local performance information of the same performance type in each local area model in the target local area model set are the same. In this way, the user can visually see and compare this type of information more clearly and intuitively, and can also directly view from the map model: enterprise type, enterprise carbon emission source, enterprise location in the park, etc., to make a comprehensive decision on the next carbon emission reduction optimization plan, which is more humane and improves the user's work efficiency.

[0137] In one embodiment, the park intelligent carbon reduction assessment and optimization method further includes:

[0138] Based on the preset feature description template, the carbon emission reduction potential information, carbon emission reduction optimization plan and implementation effect information are characterized to obtain the feature description results;

[0139] Based on the feature description results, a search for a carbon emission reduction optimization relay solution is triggered;

[0140] Determine the type of carbon reduction object from the carbon reduction optimization relay plan;

[0141] Carbon emission reduction enterprises that are carbon emission reduction object types shall be regarded as target carbon emission reduction enterprises;

[0142] The carbon emission reduction optimization relay plan is mapped into the target semicircle; the center of the target semicircle is the midpoint of the model center line between the local area models corresponding to the target carbon emission reduction enterprises in the adjusted model, the radius of the target semicircle is a preset length value, and the target semicircle is located on the side of the model center line close to the model center of the adjusted model;

[0143] Display the model center line;

[0144] Among them, based on the feature description results, the search for carbon emission reduction optimization relay scheme is triggered, including:

[0145] Obtaining preset multiple sets of one-to-one corresponding standard feature description results and trigger search rules;

[0146] When the standard feature description results are completely present in the feature description results, the search for the carbon emission reduction optimization relay scheme is triggered based on the corresponding trigger search rules.

[0147] Among them, the feature description results include: what type of enterprise's what carbon emission reduction optimization plan has achieved results, what type of enterprise's what carbon emission reduction optimization plan has not achieved results, etc.; the carbon emission reduction optimization relay plan is a plan for relaying carbon emission reduction for park enterprises; the carbon emission reduction object type is the type of enterprise to which the carbon emission reduction optimization relay plan is applicable, such as: chemical plant, etc.; set the target semicircle, and map the carbon emission reduction optimization relay plan into the target semicircle, so that the user can intuitively view the carbon emission reduction optimization relay plan that is commonly applicable to the target carbon emission reduction enterprises for reference, and the displayed model center line allows the user to see the commonly applicable target carbon emission reduction enterprises at a glance. In addition, when there are many and scattered target semicircles, control the target semicircle to be located on the side of the model center line close to the model center of the adjusted model, so that the user can visually see that the carbon emission reduction optimization relay plan is within a polygon, avoiding conflicts with the carbon emission reduction optimization relay plans determined by target carbon emission reduction enterprises of other carbon emission reduction object types, improving rationality, and indirectly improving user work efficiency.

[0148] The present invention provides a park intelligent carbon reduction assessment and optimization system, such as Figure 2 As shown, including:

[0149] Carbon emission reduction potential information assessment module 1, used to assess the carbon emission reduction potential information of the park;

[0150] Carbon emission reduction optimization plan formulation module 2, used to formulate a carbon emission reduction optimization plan based on carbon emission reduction potential information;

[0151] The carbon emission reduction optimization plan execution module 3 is used to execute the carbon emission reduction optimization plan in the park;

[0152] The execution performance information tracking module 4 is used to track the execution performance information of the carbon emission reduction optimization plan;

[0153] The execution performance information output module 5 is used to visually output the execution performance information.

[0154] Carbon emission reduction potential information assessment module 1 assesses the carbon emission reduction potential information of the park, including:

[0155] Construct a carbon reduction potential assessment model;

[0156] Obtain carbon emission data of enterprises in the park;

[0157] Input the enterprise carbon emission data into the carbon emission reduction potential assessment model to obtain carbon emission reduction potential information;

[0158] Among them, the construction of carbon emission reduction potential assessment model includes:

[0159] Update the preset carbon emission reduction potential information assessment database; the carbon emission reduction potential information assessment database includes: multiple sets of one-to-one corresponding potential standards and potential items;

[0160] Based on the preset model architecture template and the carbon emission reduction potential information assessment library, a carbon emission reduction potential assessment model is constructed.

[0161] Carbon emission reduction optimization plan formulation module 2 formulates a carbon emission reduction optimization plan based on carbon emission reduction potential information, including:

[0162] Construct a model for formulating carbon emission reduction optimization plans;

[0163] Inputting the carbon emission reduction potential information into the carbon emission reduction optimization plan formulation model to obtain the carbon emission reduction optimization plan;

[0164] Among them, the construction of a carbon emission reduction optimization plan formulation model includes:

[0165] Search for training sample extraction sources;

[0166] Analyze and determine whether the training sample extraction source meets the training sample extraction conditions;

[0167] When the conditions are met, extracting training samples from a training sample extraction source;

[0168] Based on the training samples, the preset neural network model is trained;

[0169] The neural network model trained to convergence is used as a model for formulating carbon emission reduction optimization plans;

[0170] Among them, the training sample extraction conditions include:

[0171] The source type of the training sample extraction source is an active source and the first association weight sum of the source party of the training sample extraction source is greater than or equal to a preset first weight sum threshold; the first association weight sum includes: the sum of the first association weights of at least one first association record corresponding to the source party in the preset source party association record library corresponding to the park;

[0172] or,

[0173] The source type is a passive source and the sum of the second association weights of the park generated by the training sample extraction source is greater than or equal to the preset second weight and threshold; the second association weight includes: in the preset park association record library, the sum of the second association weights of at least one corresponding second association record between the parks.

[0174] The execution performance information tracking module 4 tracks the execution performance information of the carbon emission reduction optimization plan, including:

[0175] Obtain the basis for evaluating the effectiveness of the carbon emission reduction optimization plan;

[0176] Based on the preset effectiveness evaluation template and according to the effectiveness evaluation basis, the execution effectiveness information is evaluated.

[0177] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A park intelligent carbon reduction assessment and optimization method, characterized in that: include: Evaluate the carbon reduction potential of the park; Formulate a carbon emission reduction optimization plan based on the carbon emission reduction potential information; Implementing the carbon emission reduction optimization plan in the park; Tracking the implementation performance information of the carbon emission reduction optimization plan; Visually outputting the execution performance information; The carbon reduction potential information of the evaluation park includes: Construct a carbon reduction potential assessment model; Obtain carbon emission data of enterprises in the park; Inputting the enterprise carbon emission data into the carbon emission reduction potential assessment model to obtain the carbon emission reduction potential information; The step of constructing a carbon emission reduction potential assessment model includes: Updating a preset carbon emission reduction potential information evaluation library; the carbon emission reduction potential information evaluation library includes: multiple groups of one-to-one corresponding potential standards and potential items; Based on a preset model architecture template and according to the carbon emission reduction potential information assessment library, construct the carbon emission reduction potential assessment model; The step of formulating a carbon emission reduction optimization plan based on the carbon emission reduction potential information includes: Construct a model for formulating carbon emission reduction optimization plans; Inputting the carbon emission reduction potential information into the carbon emission reduction optimization plan formulation model to obtain the carbon emission reduction optimization plan; The construction of a carbon emission reduction optimization program formulation model includes: Search for training sample extraction sources; Analyze and determine whether the training sample extraction source meets the training sample extraction conditions; When the conditions are met, extracting training samples from the training sample extraction source; Based on the training samples, training a preset neural network model; The neural network model trained to convergence is used as the carbon emission reduction optimization plan formulation model; The training sample extraction conditions include: The source type of the training sample extraction source is an active source and the first association weight sum of the source party of the training sample extraction source is greater than or equal to a preset first weight sum threshold; the first association weight sum includes: the sum of the first association weights of at least one first association record corresponding to the source party in the preset source party association record library corresponding to the park; or, The source type is a passive source and the second association weight sum of the generating park of the training sample extraction source is greater than or equal to a preset second weight sum threshold; the second association weight sum includes: the sum of the second association weights of at least one second association record corresponding to the generating park and the park in a preset park association record library.

2. The method for optimizing the intelligent carbon reduction assessment in a park according to claim 1, characterized in that: The tracking of the execution performance information of the carbon emission reduction optimization plan includes: Obtaining the basis for evaluating the effectiveness of the carbon emission reduction optimization plan; Based on a preset effectiveness evaluation template and according to the effectiveness evaluation basis, the execution effectiveness information is evaluated.

3. The method for optimizing the intelligent carbon reduction assessment in a park according to claim 1, characterized in that: The visual output of the execution performance information includes: Retrieving a preset map model corresponding to the park; Analyze the execution performance information to obtain multiple groups of one-to-one corresponding carbon emission reduction enterprise and local performance information; Traversing the carbon emission reduction enterprises in sequence; During each traversal, a local area model corresponding to the traversed carbon emission reduction enterprise is determined from the map model, and the local performance information corresponding to the traversed carbon emission reduction enterprise is mapped into the local area model; After traversing the carbon emission reduction enterprises, adjusting the map model based on the map model adjustment requirements to obtain an adjusted model; outputting the adjusted model; The map model adjustment requirements include: The shortest distance between at least N of the local area models and the model boundary of the adjusted model falls within a preset distance interval; The local performance information described in pairs does not block each other; The target vector angles of the local performance information of the same performance type in each of the local area models in the target local area model set are the same; the carbon emission reduction enterprises corresponding to each of the local area models in the target local area model set have corresponding association relationships in a preset enterprise association library; the target vector angle is a vector angle between a first direction vector constructed based on the model position of the local performance information and the text direction of the local performance information, and a second direction vector constructed based on the model position and a straight line direction from the model position to the model center of the local area model where the local performance information is located.

4. The method for optimizing the intelligent carbon reduction assessment in a park as claimed in claim 3, characterized in that: Also includes: Based on a preset feature description template, feature description is performed on the carbon emission reduction potential information, the carbon emission reduction optimization plan and the implementation performance information to obtain a feature description result; Based on the characteristic description results, a search for a carbon emission reduction optimization relay solution is triggered; Determining the type of carbon emission reduction object from the carbon emission reduction optimization relay plan; Taking the carbon emission reduction enterprise of the carbon emission reduction object type as the target carbon emission reduction enterprise; Mapping the carbon emission reduction optimization relay plan into the target semicircle; the center of the target semicircle is the midpoint of the model center line between the two local area models corresponding to the target carbon emission reduction enterprises in the adjusted model, the radius of the target semicircle is a preset length value, and the target semicircle is located on one side of the model center line close to the model center of the adjusted model; Display the center line of the model; The triggering of searching for a carbon emission reduction optimization relay scheme based on the feature description result includes: Obtaining preset multiple sets of one-to-one corresponding standard feature description results and trigger search rules; When the standard feature description result completely exists in the feature description result, based on the corresponding trigger search rule, the carbon emission reduction optimization relay scheme is triggered to search.

5. A park intelligent carbon reduction assessment and optimization system, characterized in that: include: Carbon emission reduction potential information assessment module, used to assess the carbon emission reduction potential information of the park; A carbon emission reduction optimization plan formulation module, used to formulate a carbon emission reduction optimization plan based on the carbon emission reduction potential information; A carbon emission reduction optimization program execution module, used to execute the carbon emission reduction optimization program in the park; An execution performance information tracking module, used to track the execution performance information of the carbon emission reduction optimization plan; An execution performance information output module, used for visually outputting the execution performance information; The carbon emission reduction potential information evaluation module evaluates the carbon emission reduction potential information of the park, including: Construct a carbon reduction potential assessment model; Obtain carbon emission data of enterprises in the park; Inputting the enterprise carbon emission data into the carbon emission reduction potential assessment model to obtain the carbon emission reduction potential information; The step of constructing a carbon emission reduction potential assessment model includes: Updating a preset carbon emission reduction potential information evaluation library; the carbon emission reduction potential information evaluation library includes: multiple groups of one-to-one corresponding potential standards and potential items; Based on a preset model architecture template and according to the carbon emission reduction potential information assessment library, construct the carbon emission reduction potential assessment model; The carbon emission reduction optimization plan formulation module formulates a carbon emission reduction optimization plan based on the carbon emission reduction potential information, including: Construct a model for formulating carbon emission reduction optimization plans; Inputting the carbon emission reduction potential information into the carbon emission reduction optimization plan formulation model to obtain the carbon emission reduction optimization plan; The construction of a carbon emission reduction optimization program formulation model includes: Search for training sample extraction sources; Analyze and determine whether the training sample extraction source meets the training sample extraction conditions; When the conditions are met, extracting training samples from the training sample extraction source; Based on the training samples, training a preset neural network model; The neural network model trained to convergence is used as the carbon emission reduction optimization plan formulation model; The training sample extraction conditions include: The source type of the training sample extraction source is an active source and the first association weight sum of the source party of the training sample extraction source is greater than or equal to a preset first weight sum threshold; the first association weight sum includes: the sum of the first association weights of at least one first association record corresponding to the source party in the preset source party association record library corresponding to the park; or, The source type is a passive source and the second association weight sum of the generating park of the training sample extraction source is greater than or equal to a preset second weight sum threshold; the second association weight sum includes: the sum of the second association weights of at least one second association record corresponding to the generating park and the park in a preset park association record library.

6. The intelligent carbon reduction evaluation and optimization system for a park as claimed in claim 5, characterized in that: The execution performance information tracking module tracks the execution performance information of the carbon emission reduction optimization plan, including: Obtaining the basis for evaluating the effectiveness of the carbon emission reduction optimization plan; Based on a preset effectiveness evaluation template and according to the effectiveness evaluation basis, the execution effectiveness information is evaluated.

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