An intelligent carbon emission analysis method and system based on a knowledge graph

Abstract

The present invention relates to the technical field of intelligent carbon emission analysis, and specifically discloses an intelligent carbon emission analysis method and system based on a knowledge graph, which specifically includes the following steps: Step 1: Construct a knowledge graph about carbon emissions; Step 2: Analyze the constructed knowledge graph to determine whether there are any anomalies and generate anomaly signals; Step 3: Based on the anomaly signals, obtain the group of entity 2 corresponding to the unreasonable entity 1 in the knowledge graph, and add new triples to improve the knowledge graph. The present invention first obtains data to establish a knowledge graph, analyzes the triples in the knowledge graph, obtains the number of unreasonable entity 1, calculates the proportion of unreasonable entity 1, compares the proportion of unreasonable entity 1 with a threshold value to determine whether there are any anomalies in the constructed knowledge graph, so as to timely discover the abnormal situation in the knowledge graph and provide strong and accurate quantitative support for subsequent carbon emission analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent carbon emission analysis, and specifically relates to an intelligent carbon emission analysis method and system based on a knowledge graph. Background Art

[0002] With the increasingly severe global climate change, the monitoring and management of carbon emissions have become the focus of widespread concern in the international community. Traditional carbon emission analysis methods mainly rely on a single data source, with low ability to integrate and analyze data from different sources. At the same time, the existing carbon emission analysis methods are often not deep enough in the time dimension analysis, thus affecting the formulation of emission reduction strategies.

[0003] In view of this, we propose an intelligent carbon emission analysis method and system based on a knowledge graph. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent carbon emission analysis method and system based on a knowledge graph to solve the technical problems in the above background.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] In the first aspect, the present invention provides an intelligent carbon emission analysis method based on a knowledge graph, specifically including the following steps:

[0007] Step 1: Construct a knowledge graph about carbon emissions;

[0008] Step 2: Analyze the constructed knowledge graph to determine whether there are anomalies and generate anomaly signals;

[0009] Step 3: Based on the anomaly signals, obtain the group of entity 2 corresponding to the unreasonable entity 1 in the knowledge graph, and add new triples to improve the knowledge graph;

[0010] Step 4: Analyze the changing trend of carbon emissions based on all triples;

[0011] Among them, the changing trend includes a stable trend and an unstable trend;

[0012] Step 5: Further analyze the carbon emissions based on the marked unstable trend.

[0013] As a further solution of the present invention: the process of generating the anomaly signal:

[0014] Obtain the proportion of the unreasonable entity 1, and compare the proportion of the unreasonable entity 1 with the proportion threshold of the unreasonable entity 1;

[0015] If the proportion of the unreasonable entity 1 is greater than the proportion threshold of the unreasonable entity 1, an anomaly signal is generated.

[0016] As a further solution of the present invention: the process of obtaining the ratio of the unreasonable entity 1 is as follows:

[0017] Count the number of entity 2 in each triple;

[0018] Compare the number of entity 2 in the triple with the expected number of entity 2 respectively;

[0019] If the number of entity 2 is greater than the expected number of entity 2, mark it as the unreasonable entity 1;

[0020] Count the number of unreasonable entity 1, and process the ratio of the number of unreasonable entity 1 to the total number of entity 1 in the knowledge graph to obtain the ratio of the unreasonable entity 1.

[0021] As a further solution of the present invention: the process of step three is as follows:

[0022] Based on the generated abnormal signal, obtain the unreasonable entity 1 and the corresponding group of entity 2 in the knowledge graph;

[0023] For each entity 1, obtain the time period corresponding to the value of entity 2 in the group of entity 2 and record the time stamp;

[0024] Sort the time periods in ascending order and number them, calculate the difference between adjacent time periods to obtain the time period difference;

[0025] Compare the time period difference with the time period difference threshold. If the time period difference is greater than the time period difference threshold, generate a large deviation signal;

[0026] Obtain the number of the time period corresponding to the generated large deviation signal, and classify the time period group;

[0027] Sum and average the time periods in each group to obtain the time period average value, and add new triples according to the time period average value and the recorded time stamp;

[0028] Basic triple: enterprise name - carbon emission - carbon emission value;

[0029] Time period triple: enterprise name - recorded time stamp - recorded time value;

[0030] Classification triple: enterprise name - time period average value - including recorded time value.

[0031] As a further solution of the present invention: the process of step four is as follows:

[0032] For the same enterprise name, extract the record timestamps included in the mean value of the same time period, and obtain the carbon emissions corresponding to these record timestamps;

[0033] Sum up all the carbon emissions and take the mean value to obtain the mean carbon emissions. Calculate the difference between all the carbon emissions and the mean carbon emissions, take the absolute value of the difference to obtain the carbon emission deviation value, sum up all the carbon emission deviation values and take the mean value to obtain the mean carbon emission deviation;

[0034] Compare the carbon emission deviation value with the mean carbon emission deviation;

[0035] If the carbon emission deviation value is less than or equal to the mean carbon emission deviation, generate a stable value;

[0036] If the carbon emission deviation value is greater than the mean carbon emission deviation, generate an unstable value;

[0037] Count the number of stable values and the number of unstable values, and sum them up to obtain the total number. Calculate the ratio of the number of stable values to the total number to obtain the stable value ratio;

[0038] Compare the stable value ratio with the stable value ratio threshold;

[0039] If the stable value ratio is greater than the stable value ratio threshold, mark it as a stable trend;

[0040] If the stable value ratio is less than or equal to the stable value ratio threshold, mark it as a non - stable trend.

[0041] As a further solution of the present invention: The process of step five is as follows:

[0042] Obtain the growth sub - curve segment ratio and the mean value of the growth slope deviation ratio;

[0043] Substitute into the formula , and calculate to obtain the analysis and judgment value FX, where ZB represents the growth sub - curve segment ratio, PC represents the mean value of the growth slope deviation ratio, and a1, a2 are preset proportional coefficients.

[0044] As a further solution of the present invention: The process of obtaining the growth sub - curve segment ratio is as follows:

[0045] Taking time as the abscissa and carbon emissions as the ordinate, plot the carbon emission change curve in the plane coordinate system;

[0046] Mark the curve between adjacent coordinate points in the carbon emission change curve as a sub - curve segment;

[0047] Calculate the slope of each sub - curve segment, count the number of sub - curve segments with a positive slope, and mark them as growth sub - curve segments;

[0048] It should be noted that the slope of the sub-curve segment is calculated from two adjacent coordinate points;

[0049] The ratio of the number of growing sub-curve segments to the total number of sub-curve segments is processed to obtain the proportion of growing sub-curve segments.

[0050] As a further solution of the present invention: the process of obtaining the average value of the growth slope deviation ratio is as follows:

[0051] Extract the slope of the growing sub-curve segment, calculate the difference between the slope of the growing sub-curve segment and the slope limit value, take the absolute value of the difference and process it with the slope limit value to obtain the growth slope deviation ratio, sum up all the growth slope deviation ratios and take the average value to obtain the average value of the growth slope deviation ratio.

[0052] As a further solution of the present invention: the process of step five further includes:

[0053] Obtain the analysis and judgment value, and compare the analysis and judgment value with the analysis and judgment threshold.

[0054] If the analysis and judgment value is less than or equal to the analysis and judgment threshold, generate a non-analysis signal;

[0055] If the analysis and judgment value is greater than the analysis and judgment threshold, generate an analysis signal.

[0056] In a second aspect, the present invention provides an intelligent carbon emission analysis system based on a knowledge graph, and the system includes:

[0057] Knowledge graph construction module: construct a knowledge graph about carbon emissions;

[0058] Knowledge graph anomaly judgment module: analyze the constructed knowledge graph, judge whether there is an anomaly, and generate an anomaly signal;

[0059] Knowledge graph optimization module: based on the anomaly signal, obtain the group of entity 2 corresponding to the unreasonable entity 1 in the knowledge graph, and add new triples to improve the knowledge graph;

[0060] Carbon emission trend judgment module: analyze the change trend of carbon emissions based on all triples;

[0061] Among them, the change trend includes a stable trend and an unstable trend;

[0062] Carbon emission analysis module: further analyze the carbon emissions based on the marked unstable trend.

[0063] The beneficial effects of the present invention:

[0064] (1) First, the present invention obtains data, constructs a knowledge graph, analyzes the triples in the knowledge graph, obtains the number of unreasonable entities 1, calculates the proportion of unreasonable entities 1, compares the proportion of unreasonable entities 1 with a threshold value, and determines whether the constructed knowledge graph is abnormal, so as to timely discover abnormal situations in the knowledge graph and provide strong and accurate quantitative support for subsequent carbon emission analysis;

[0065] (2) The present invention obtains the group of entities 2 corresponding to the unreasonable entity 1, obtains the time period corresponding to the entity 2 value and the recording timestamp, analyzes the time period corresponding to the entity 2 value, divides the time periods that are close into one group, sums and averages the time periods of the same group to obtain the average time period, and adds new triples according to the average time period and the recording timestamp, so as to more accurately reflect the carbon emission situation of the enterprise in different time periods, help the enterprise more accurately analyze the carbon emission level, formulate more effective emission reduction strategies, and monitor the emission reduction effect;

[0066] (3) The present invention obtains the recording timestamps included in the same average time period, obtains the carbon emissions corresponding to these recording timestamps, and conducts a trend analysis on these carbon emissions. Thus, it can integrate carbon emission-related data using the knowledge graph, accurately judge the change trend of carbon emissions through average calculation and deviation analysis of carbon emissions, and provide data support for the enterprise to formulate an emission reduction plan;

[0067] (4) The present invention statistically analyzes the sub-curve segments with an increasing trend in the carbon emission change curve, analyzes the sub-curve segments with an increasing trend in carbon emissions, calculates the proportion of the increasing sub-curve segments and the average value of the growth slope deviation ratio, makes an analysis and judgment based on the proportion of the increasing sub-curve segments and the average value of the growth slope deviation ratio, and compares it with a threshold value to generate an analysis signal or a non-analysis signal. Based on the non-analysis signal, continuously monitor the carbon emissions without immediately taking in-depth analysis or intervention measures. Based on the analysis signal, generate a detailed carbon emission analysis report for the corresponding enterprise, propose targeted emission reduction suggestions or measures, help the enterprise optimize the carbon emission management strategy, thereby providing a quantitative analysis of the carbon emission growth trend, enabling the enterprise to more accurately grasp its own carbon emission situation and take corresponding management measures in a timely manner. At the same time, by providing specific emission reduction suggestions for the enterprise, it can also help the enterprise achieve more environmentally friendly and sustainable development, not only improving the accuracy and efficiency of carbon emission analysis, but also providing the enterprise with scientific and feasible emission reduction strategies. Brief Description of the Drawings

[0068] The present invention will be further described below with reference to the accompanying drawings.

[0069] Figure 1 is a flowchart of a carbon emission intelligent analysis method based on a knowledge graph according to the present invention;

[0070] Figure 2 It is a block diagram of the process for obtaining the analysis and judgment value in an intelligent carbon emission analysis method based on a knowledge graph according to the present invention;

[0071] Figure 3 It is a block diagram of an intelligent carbon emission analysis system based on a knowledge graph according to the present invention. Specific embodiments

[0072] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0073] Embodiment 1:

[0074] Please refer to Figure 1 As shown, an intelligent carbon emission analysis method based on a knowledge graph according to an embodiment of the present invention specifically includes the following steps:

[0075] Step 1: Construct a knowledge graph about carbon emissions;

[0076] In some embodiments, carbon emission data is obtained, where the carbon emission data includes but is not limited to: carbon emission amount, carbon emission intensity, carbon emission trend;

[0077] It should be noted that the sources for obtaining carbon emission data include but are not limited to: government reports, international agency data, professional research, enterprise reports;

[0078] Preprocess the obtained carbon emission data, where the preprocessing process includes but is not limited to: cleaning, removing duplicate, incorrect or invalid data;

[0079] According to the characteristics of the carbon emission field, define the entity types in the graph, and the defined entity types include but are not limited to: enterprises, regions, industries, energy types, emission sources;

[0080] Define the carbon emission association relationships between entities, and the association relationships include but are not limited to: emission amount, emission trend, emission intensity, carbon footprint, energy utilization efficiency;

[0081] Extract the knowledge related to carbon emissions from the original data, where the extraction methods include but are not limited to: text parsing, natural language processing;

[0082] Store the extracted knowledge in a graph database, construct a data layer in the graph database, and store the extracted knowledge in the form of triples (entity 1 - relationship - entity 2) or (entity - attribute - value).

[0083] It should be noted that the data layer should contain data of all entities and relationships.

[0084] Construct a schema layer on top of the data layer. The schema layer should contain refined knowledge ontologies and schema information to standardize and manage entities, relationships, and the types and attributes of entities, etc.

[0085] Step 2: Analyze the constructed knowledge graph to determine if there are any anomalies.

[0086] In some embodiments, based on the constructed knowledge graph, traverse the triples in the knowledge graph.

[0087] In one possible embodiment, taking the entity 1 - relationship - entity 2 triple as an example, count the number of entity 2 in each triple.

[0088] Among them, the entity 1 - relationship - entity 2 triple can be set as: enterprise name - carbon emissions - carbon emission value.

[0089] Exemplarily, if entity 1 is enterprise A and the relationship is carbon emissions, and if the corresponding entity 2 is 100 tons, 300 tons, 500 tons, 1000 tons, then the number of entity 2 in the counted triple is 4. If the corresponding entity 2 is 100 tons, then the number of entity 2 in the counted triple is 1.

[0090] Compare the number of entity 2 in the triple with the expected number of entity 2 respectively.

[0091] If the number of entity 2 is less than or equal to the expected number of entity 2, mark it as reasonable entity 1. If the number of entity 2 is greater than the expected number of entity 2, mark it as unreasonable entity 1.

[0092] It should be explained that unreasonable entity 1 means that when the corresponding entity 2 exceeds the expected number, new triple knowledge extraction and construction need to be carried out for entity 2.

[0093] Count the number of unreasonable entity 1, and perform a ratio process on the number of unreasonable entity 1 and the total number of entity 1 in the knowledge graph to obtain the unreasonable entity 1 occupancy ratio.

[0094] Compare the unreasonable entity 1 occupancy ratio with the unreasonable entity 1 occupancy ratio threshold. Among them, the unreasonable entity 1 occupancy ratio threshold is set by those skilled in the art according to experience and multiple historical experiments.

[0095] If the proportion of unreasonable entity 1 is less than or equal to the unreasonable entity 1 proportion threshold, there are fewer unreasonable entities 1 in the knowledge graph, and a normal signal is generated;

[0096] If the proportion of unreasonable entity 1 is greater than the unreasonable entity 1 proportion threshold, there are more unreasonable entities 1 in the knowledge graph, and an abnormal signal is generated;

[0097] The technical solution of the embodiment of the present invention is mainly as follows: First, data is obtained to establish a knowledge graph, and then the triples in the knowledge graph are analyzed to obtain the number of unreasonable entities 1, calculate the proportion of unreasonable entities 1, compare the proportion of unreasonable entities 1 with the threshold, and determine whether there are abnormalities in the constructed knowledge graph, so as to timely discover abnormal situations in the knowledge graph and provide strong and accurate quantitative support for subsequent carbon emission analysis.

[0098] Embodiment 2:

[0099] On the basis of Embodiment 1, please refer to Figure 1 As shown, a carbon emission intelligent analysis method based on a knowledge graph according to an embodiment of the present invention specifically further includes the following steps:

[0100] Step 3: Based on the abnormal signal, obtain the entity 2 group corresponding to the unreasonable entity 1 in the knowledge graph, and add new triples to improve the knowledge graph;

[0101] In some embodiments, based on the generated abnormal signal, obtain the unreasonable entity 1 and the corresponding entity 2 group in the knowledge graph;

[0102] In some possible embodiments, for each entity 1, obtain the time period corresponding to the entity 2 value in the entity 2 group and the recording timestamp;

[0103] It should be noted that the time period is in days. Exemplarily, the values in the entity 2 group may include 100 tons, 300 tons, 500 tons, 1000 tons. Then the time period corresponding to 100 tons may be 30 days, the time period corresponding to 300 tons may also be 30 days, the time period corresponding to 500 tons may be 60 days, and the time period corresponding to 1000 tons may be 90 days;

[0104] Sort the time periods in ascending order and number them, calculate the difference between adjacent time periods to obtain the time period difference;

[0105] Compare the time period difference with the time period difference threshold, and the time period difference threshold is set by those skilled in the art according to experience;

[0106] If the time period difference is less than or equal to the time period difference threshold, a small deviation signal is generated. If the time period difference is greater than the time period difference threshold, a large deviation signal is generated;

[0107] Obtain the numbers of the time periods corresponding to the generated large deviation signal, and classify the time period groups;

[0108] Exemplarily, if the numbers of the time periods corresponding to the generated large deviation signal are 10, 20, and 30 respectively, the time periods are divided into four groups, namely numbers 1 - 9, numbers 10 - 19, numbers 20 - 29, and the remaining numbers;

[0109] Sum and average the time periods in each group to obtain the time period average value, and add new triples according to the time period average value and the recorded timestamp;

[0110] Basic triple: enterprise name - carbon emission - carbon emission value;

[0111] Time period triple: enterprise name - recorded timestamp - recorded time value;

[0112] Classification triple: enterprise name - time period average value - including recorded time value;

[0113] Exemplarily, assume that the total carbon emission recorded by enterprise A on April 1, 2023 is 100 tons, and the time period average value is 30 days. Then, we can construct the following triples:

[0114] Basic triple: enterprise A - carbon emission - 100 tons;

[0115] Time period triple: enterprise A - carbon emission recording time value - April 1, 2023;

[0116] Classification triple: enterprise A - 30 - day carbon emission - including April 1, 2023;

[0117] The technical solution of the embodiment of the present invention is mainly: by obtaining the group of entity 2 corresponding to the unreasonable entity 1, obtaining the time period corresponding to the entity 2 value and the recorded timestamp, analyzing the time period corresponding to the entity 2 value, dividing the time periods with close proximity into one group, summing and averaging the time periods in the same group to obtain the time period average value, and adding new triples according to the time period average value and the recorded timestamp, so as to more accurately reflect the carbon emission situation of the enterprise in different time periods, help the enterprise more accurately analyze the carbon emission level, formulate more effective emission reduction strategies, and monitor the emission reduction effect.

[0118] Embodiment 3:

[0119] Based on Embodiment 1 and Embodiment 2, please refer toFigure 1 As shown in Figure 1 , a carbon emission intelligent analysis method based on a knowledge graph according to an embodiment of the present invention further specifically includes the following steps:

[0120] Step Four: Analyze the change trend of carbon emissions based on all triples;

[0121] Among them, the change trend includes a stable trend and an unstable trend;

[0122] In some embodiments, for the same enterprise name, extract the recorded timestamps included in the mean value of the same time period, and obtain the carbon emissions corresponding to these recorded timestamps;

[0123] Sum up all the carbon emissions and take the mean value to obtain the mean value of carbon emissions. Calculate the difference between all the carbon emissions and the mean value of carbon emissions, take the absolute value of the difference to obtain the carbon emission deviation value, sum up all the carbon emission deviation values and take the mean value to obtain the mean value of carbon emission deviation;

[0124] Compare the carbon emission deviation value with the mean value of carbon emission deviation;

[0125] If the carbon emission deviation value is less than or equal to the mean value of carbon emission deviation, it indicates that the deviation between the carbon emissions and the mean value of carbon emissions is small, and a stable value is generated;

[0126] If the carbon emission deviation value is greater than the mean value of carbon emission deviation, it indicates that the deviation between the carbon emissions and the mean value of carbon emissions is large, and an unstable value is generated;

[0127] Count the number of stable values and the number of unstable values, and sum them up to obtain the total number. Calculate the ratio of the number of stable values to the total number to obtain the proportion of stable values;

[0128] Compare the proportion of stable values with the threshold of the proportion of stable values. Among them, the threshold of the proportion of stable values is a critical value used to judge whether the change trend of carbon emissions is stable, and is set by those skilled in the art based on historical experimental data from multiple experiments;

[0129] If the proportion of stable values is greater than the threshold of the proportion of stable values, it indicates that the carbon emissions corresponding to the mean value of the same time period are relatively stable, and it is marked as a stable trend;

[0130] If the proportion of stable values is less than or equal to the threshold of the proportion of stable values, it indicates that the carbon emissions corresponding to the mean value of the same time period are unstable, and it is marked as an unstable trend;

[0131] The technical solution of the embodiment of the present invention is mainly as follows: by obtaining the recorded timestamps included in the average value of the same time period, obtaining the carbon emissions corresponding to these recorded timestamps, and performing trend analysis on these carbon emissions, it is possible to integrate carbon emission-related data using a knowledge graph. By calculating the average value and deviation analysis of the carbon emissions, the change trend of the carbon emissions can be accurately judged, providing support for enterprises to formulate emission reduction plans.

[0132] Embodiment 4:

[0133] Based on Embodiment 1, Embodiment 2, and Embodiment 3, please refer to Figure 1 and Figure 2 As shown, a carbon emission intelligent analysis method based on a knowledge graph according to an embodiment of the present invention further specifically includes the following steps:

[0134] Step Five: Based on the marked unstable trend, further analyze the carbon emissions;

[0135] In some embodiments, with time as the abscissa and carbon emissions as the ordinate, a carbon emission change curve is plotted in a plane coordinate system;

[0136] The curve between adjacent coordinate points in the carbon emission change curve is marked as a sub-curve segment;

[0137] Calculate the slope of each sub-curve segment, count the number of sub-curve segments with a positive slope, and mark it as the growing sub-curve segment;

[0138] It should be noted that the slope of the sub-curve segment is calculated from two adjacent coordinate points;

[0139] Perform a ratio process on the number of growing sub-curve segments and the total number of sub-curve segments to obtain the proportion of growing sub-curve segments;

[0140] Extract the slope of the growing sub-curve segment, calculate the difference between the slope of the growing sub-curve segment and the slope limit value, take the absolute value of the difference and perform a ratio process with the slope limit value to obtain the growth slope deviation ratio. Sum and average all the growth slope deviation ratios to obtain the average value of the growth slope deviation ratio;

[0141] Among them, the slope limit value is set by those skilled in the art based on historical experimental data from multiple experiments;

[0142] Substitute into the formula , and calculate the analysis and judgment value FX. Among them, ZB represents the proportion of growing sub-curve segments, PC represents the average value of the growth slope deviation ratio, a1 and a2 are preset proportionality coefficients. Among them, the value of a1 is 2.52, and the value of a2 is 1.48;

[0143] It should be noted that the meaning reflected by the analysis and judgment value is as follows: the analysis and judgment value is calculated from the proportion of the growth sub-curve segments and the average value of the growth slope deviation ratio. Among them, the proportion of the growth sub-curve segments represents the proportion of the number of growth sub-curve segments in the carbon emission change curve. The larger the proportion of the growth sub-curve segments, the more growth sub-curve segments there are, and the more the carbon emissions show an increasing trend. The larger the average value of the growth slope deviation ratio, the more sub-curve segments with a larger slope in the growth sub-curve segments, and the faster the growth rate of the carbon emissions;

[0144] Compare the analysis and judgment value with the analysis and judgment threshold, where the analysis and judgment threshold is set by those skilled in the art based on historical experimental data from multiple experiments;

[0145] If the analysis and judgment value is less than or equal to the analysis and judgment threshold, it indicates that there are fewer growth sub-curve segments in the carbon emission change curve and fewer sub-curve segments with a large slope in the growth sub-curve segments, and a non-analysis signal is generated;

[0146] Based on the non-analysis signal, continuously monitor the carbon emissions, but do not immediately take in-depth analysis or intervention measures;

[0147] If the analysis and judgment value is greater than the analysis and judgment threshold, it indicates that there are more growth sub-curve segments in the carbon emission change curve and more sub-curve segments with a large slope in the growth sub-curve segments, and an analysis signal is generated;

[0148] Based on the analysis signal, generate a detailed carbon emission analysis report for the corresponding enterprise, put forward targeted emission reduction suggestions or measures, and help the enterprise optimize the carbon emission management strategy;

[0149] It should be noted that there are more sub-curve segments with a growth trend and a larger slope, which means that the carbon emissions are continuously growing rapidly, so as to provide scientific carbon emission management suggestions for the enterprise;

[0150] The technical solution of the embodiment of the present invention is mainly as follows: by statistically analyzing the sub-curve segments with a growth trend in the carbon emission change curve, calculating the proportion of the growth sub-curve segments and the average value of the growth slope deviation ratio, making an analysis and judgment based on the proportion of the growth sub-curve segments and the average value of the growth slope deviation ratio, comparing with a threshold value to generate an analysis signal or a non-analysis signal. Based on the non-analysis signal, continuously monitor the carbon emissions without immediately taking in-depth analysis or intervention measures. Based on the analysis signal, generate a detailed carbon emission analysis report for the corresponding enterprise, put forward targeted emission reduction suggestions or measures to help the enterprise optimize the carbon emission management strategy, thereby providing a quantitative analysis of the carbon emission growth trend, enabling the enterprise to more accurately grasp its own carbon emission situation and take corresponding management measures in a timely manner. At the same time, by providing specific emission reduction suggestions for the enterprise, it can also help the enterprise achieve more environmentally friendly and sustainable development, not only improving the accuracy and efficiency of carbon emission analysis, but also providing a scientific and feasible emission reduction strategy for the enterprise.

[0151] Embodiment 5:

[0152] Based on Embodiment 1, Embodiment 2 and Embodiment 3, please refer to Figure 3 As shown, an intelligent carbon emission analysis system based on a knowledge graph according to an embodiment of the present invention includes:

[0153] Knowledge graph construction module: construct a knowledge graph about carbon emissions;

[0154] Knowledge graph anomaly judgment module: analyze the constructed knowledge graph to judge whether there are anomalies;

[0155] Knowledge graph optimization module: based on the anomaly signal, obtain the group of entity 2 corresponding to the unreasonable entity 1 in the knowledge graph, and add new triples to improve the knowledge graph;

[0156] Carbon emission trend judgment module: analyze the carbon emission change trend based on all triples;

[0157] Carbon emission analysis module: further analyze the carbon emissions based on the marked unstable trend.

[0158] The above has described an embodiment of the present invention in detail, but the described content is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the present invention application shall still fall within the scope covered by the patent of the present invention.

Claims

1. A carbon emission intelligent analysis method based on knowledge graph, characterized in that: The specific steps include: Step 1: Build a knowledge graph about carbon emissions; Step 2: Analyze the constructed knowledge graph to determine whether there are any anomalies and generate anomaly signals; Step 3: Based on the abnormal signal, obtain the entity group 2 corresponding to the unreasonable entity 1 in the knowledge graph, and add new triples to improve the knowledge graph; Step 4: Analyze the trend of carbon emissions based on all triples; Among them, the changing trend includes stable trend and unstable trend; For the same enterprise name, extract the record timestamps contained in the mean of the same time period and obtain the carbon emissions corresponding to these record timestamps; All carbon emissions are summed up and averaged to obtain the mean carbon emissions, all carbon emissions are calculated with the mean carbon emissions, the difference is taken as the absolute value of the difference to obtain the carbon emissions deviation value, all carbon emissions deviation values ​​are summed up and averaged to obtain the carbon emissions deviation mean; Compare the carbon emission deviation value with the carbon emission deviation mean; If the carbon emission deviation value is less than or equal to the carbon emission deviation mean value, a stable value is generated; If the carbon emission deviation value is greater than the carbon emission deviation mean, an unstable value is generated; Count the number of stable values ​​and the number of unstable values, and sum them up to get the total number. Ratio the number of stable values ​​to the total number to get the proportion of stable values. comparing the stable value ratio with a stable value ratio threshold; If the stable value ratio is greater than the stable value ratio threshold, it is marked as a stable trend; If the stable value ratio is less than or equal to the stable value ratio threshold, it is marked as an unstable trend; Step 5: Further analysis of carbon emissions based on the marked unstable trend.

2. According to claim 1, a carbon emission intelligent analysis method based on knowledge graph is characterized in that: The process of generating abnormal signals: Obtaining an unreasonable entity 1 ratio value, and comparing the unreasonable entity 1 ratio value with an unreasonable entity 1 ratio threshold; If the unreasonable entity 1 ratio value is greater than the unreasonable entity 1 ratio threshold, an abnormal signal is generated.

3. According to claim 1, a carbon emission intelligent analysis method based on knowledge graph is characterized in that: The process of obtaining the proportion of unreasonable entity 1 is as follows: Count the number of entity 2s in each triple; Compare the number of entity 2 in the triples with the expected number of entity 2 respectively; If the number of entity 2 is greater than the expected number of entity 2, it is marked as unreasonable entity 1; Count the number of unreasonable entities 1, and ratio the number of unreasonable entities 1 to the total number of entities 1 in the knowledge graph to obtain the proportion of unreasonable entities 1.

4. According to claim 1, a carbon emission intelligent analysis method based on knowledge graph is characterized in that: The process of step three is: Based on the generated abnormal signal, the unreasonable entity 1 and the corresponding entity 2 group in the knowledge graph are obtained; For each entity 1, obtain the time period and record timestamp corresponding to the entity 2 value in the entity 2 group; Sort the time periods in ascending order and number them, calculate the difference between adjacent time periods, and obtain the time period difference; The time period difference is compared with the time period difference threshold, and if the time period difference is greater than the time period difference threshold, a large deviation signal is generated; Get the number of the time period corresponding to the generation of the large deviation signal and classify the time period groups; Sum and average the time periods in each group to get the time period mean, and add a new triplet based on the time period mean and the record timestamp; Basic triplet: company name - carbon emissions - carbon emissions value; Time period triplet: enterprise name - record timestamp - record time value; Classification triplet: company name - time period average - including recording time value.

5. According to claim 1, a carbon emission intelligent analysis method based on knowledge graph is characterized in that: The process of step five is: Obtain the proportion of growth sub-curve segments and the mean value of growth slope deviation ratio; Substitute into the formula , the analysis and judgment value FX is calculated, where ZB represents the proportion of the growth sub-curve segment, PC represents the mean value of the growth slope deviation ratio, and a1 and a2 are preset proportional coefficients.

6. According to claim 5, a carbon emission intelligent analysis method based on knowledge graph is characterized in that: The process of obtaining the proportion of the growth sub-curve segment is as follows: With time as the horizontal axis and carbon emissions as the vertical axis, a carbon emissions variation curve is drawn in a plane coordinate system; Mark the curve between adjacent coordinate points in the carbon emission change curve as a sub-curve segment; Calculate the slope of each sub-curve segment, count the number of sub-curve segments with positive slopes, and mark them as growing sub-curve segments; The ratio of the number of growing sub-curve segments to the total number of sub-curve segments is processed to obtain the proportion of growing sub-curve segments.

7. According to claim 6, a carbon emission intelligent analysis method based on knowledge graph is characterized in that: The process of obtaining the mean value of the growth slope deviation ratio is as follows: Extract the slope of the growth sub-curve segment, calculate the difference between the slope of the growth sub-curve segment and the slope limit, take the absolute value of the difference and ratio it with the slope limit to obtain the growth slope deviation ratio, sum and average all the growth slope deviation ratios to obtain the mean of the growth slope deviation ratios.

8. According to claim 5, a carbon emission intelligent analysis method based on knowledge graph is characterized in that: The process of step five also includes: Obtaining an analysis judgment value, comparing the analysis judgment value with an analysis judgment threshold, If the analysis judgment value is less than or equal to the analysis judgment threshold, a no-analysis signal is generated; If the analysis judgment value is greater than the analysis judgment threshold, an analysis signal is generated.

9. A carbon emission intelligent analysis system based on knowledge graph, characterized in that: The system is used to execute the method described in any one of claims 1 to 8, and the system comprises: Knowledge graph construction module: construct a knowledge graph about carbon emissions; Knowledge graph anomaly judgment module: analyzes the constructed knowledge graph, determines whether there are anomalies, and generates anomaly signals; Knowledge graph optimization module: Based on abnormal signals, obtain entity 2 groups corresponding to unreasonable entity 1 in the knowledge graph, and add new triples to improve the knowledge graph; Carbon emission trend judgment module: analyzes the trend of carbon emission changes based on all triples; Among them, the changing trend includes stable trend and unstable trend; Carbon emissions analysis module: Further analysis of carbon emissions based on marked unstable trends.

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