A multi - spatio - temporal scale energy visualization analysis method

Abstract

The present invention discloses a multi - spatio - temporal scale energy visualization analysis method, which relates to the technical field of energy analysis. This multi - spatio - temporal scale energy visualization analysis method divides the currently to - be - analyzed area into multiple sub - areas to be analyzed, determines the area types of each sub - area to be analyzed, and uploads the area types of the sub - areas to be analyzed to the visualization platform for display; determines the time scales of each sub - area to be analyzed, and uploads the comprehensive state indicators of each sub - area to be analyzed to the visualization platform for display; uploads the energy data sets corresponding to each sub - area to be analyzed to the visualization platform for display; determines the evaluation results of each sub - area to be analyzed, and uploads the evaluation results of each sub - area to be analyzed to the visualization platform for display, solving the problem in the prior art that energy visualization analysis has a vague determination of time scales in area division and a single analysis method, resulting in poor visualization effects.

Description

Technical Field

[0001] The present invention relates to the technical field of energy analysis, and particularly to a multi - spatio - temporal scale energy visualization analysis method. Background Art

[0002] With the accelerated promotion of the planning and construction of the new power system, new energy sources such as distributed photovoltaics are massively connected to the distribution network, and the operation mechanism of the power system and the power supply - demand balance mode are undergoing profound changes. The large - scale connection of distributed photovoltaics increases the volatility of the power grid, which will increase the load pressure of the distribution network, resulting in problems such as voltage fluctuations, frequency fluctuations, overload of distribution network equipment, and increased line losses. At the same time, the large - scale access of thermal storage electric heating equipment to the distribution network leads to a sharp increase in the power grid load, and phenomena such as "overload, heavy load, and three - phase imbalance" occur in the distribution transformer, affecting the stable operation of the power grid.

[0003] The Chinese invention patent with the publication number CN112256789B discloses a data intelligent visualization analysis method and device. A chart knowledge base is constructed, and the chart knowledge base contains knowledge structure information of multiple chart types; a data set to be analyzed is obtained, and the field information of samples in the data set and the relationship information between fields are extracted; according to the field information of samples in the data set and the relationship information between fields, the matching quality score between the data set to be analyzed and any chart type in the chart knowledge base is calculated; based on the matching quality score, the chart configuration of the data set to be analyzed is output. By constructing a chart knowledge base and calculating the matching quality score between the data set to be analyzed and any chart type in the chart knowledge base, the chart configuration of the data set to be analyzed is output, enabling data developers to quickly complete the data visualization analysis of the data set to be analyzed, reducing the development cost of data visualization, and improving the development efficiency of data visualization.

[0004] However, the existing technology has problems such as fuzzy determination of the time scale in regional division and single analysis method in energy visualization analysis, resulting in poor visualization effects. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the present invention provides a multi - spatio - temporal scale energy visualization analysis method, which solves the problems of fuzzy determination of the time scale in regional division and single analysis method in energy visualization analysis in the existing technology, resulting in poor visualization effects.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A multi-temporal and spatial scale energy visualization analysis method, comprising the following steps: obtaining a dataset of regional characteristic information of the currently to-be-analyzed area, dividing the currently to-be-analyzed area into multiple sub-areas to be analyzed, and determining the regional types of each sub-area to be analyzed, and uploading the regional types of the sub-areas to be analyzed to a visualization platform for display; obtaining energy datasets of each sub-area to be analyzed, based on the energy datasets, obtaining comprehensive status indicators of each sub-area to be analyzed, and determining the time scales of each sub-area to be analyzed, and uploading the comprehensive status indicators of each sub-area to be analyzed to the visualization platform for display, wherein the energy datasets include power generation data, distributed photovoltaic data, energy consumption data, and carbon emission data; based on the time scales of each sub-area to be analyzed, uploading the corresponding energy datasets of each sub-area to be analyzed to the visualization platform for display; after determining the time scales of each sub-area to be analyzed, comprehensively analyzing the energy datasets of each sub-area to be analyzed to determine the evaluation results of each sub-area to be analyzed; according to the evaluation results, uploading the evaluation results of each sub-area to be analyzed to the visualization platform for display.

[0007] Further, dividing the currently to-be-analyzed area into multiple sub-areas to be analyzed and determining the regional types of each sub-area to be analyzed specifically include the following steps: comparing the dataset of regional characteristic information with the reference regional characteristic information datasets stored in the database to obtain respective matching similarities, wherein the dataset of regional characteristic information includes regional geographical information characteristic data, regional energy characteristic data, and regional socio-economic characteristic data, and the reference regional characteristic information datasets include reference regional geographical information characteristic data, reference regional energy characteristic data, and reference regional socio-economic characteristic data; arranging the respective matching similarities in ascending order to determine the maximum matching similarity; based on the maximum matching similarity, determining the reference regional characteristic information dataset of the corresponding reference area, and obtaining the sub-area division criteria of the reference area and the regional types corresponding to each sub-area from the database; based on the determined sub-area division criteria and the regional types corresponding to each sub-area, dividing the currently to-be-analyzed area to obtain multiple sub-areas to be analyzed, and determining the regional types of each sub-area to be analyzed.

[0008] Further, obtaining the respective matching similarities specifically includes the following steps: comparing the regional geographical information characteristic data with the reference regional geographical information characteristic data of each reference area to obtain respective geographical matching similarities; comparing the regional energy characteristic data with the reference regional energy characteristic data of each reference area to obtain respective energy matching similarities; comparing the regional socio-economic characteristic data with the reference regional socio-economic characteristic data of each reference area to obtain respective socio-economic matching similarities; comprehensively analyzing the geographical matching similarities, energy matching similarities, and socio-economic matching similarities to obtain the respective matching similarities.

[0009] Furthermore, the comprehensive status indicators of each sub-region to be analyzed are obtained, which specifically include the following steps: Obtain the power generation data of each sub-region to be analyzed to obtain the power generation characteristic indicators of each sub-region to be analyzed. The power generation data includes the power generation volatility factor, the power generation power stability factor, and the unit operation status stability factor; Obtain the distributed photovoltaic data of each sub-region to be analyzed to obtain the photovoltaic distribution characteristic indicators of each sub-region to be analyzed. The distributed photovoltaic data includes the distribution quantity, the installed capacity density, and the installed capacity; Obtain the energy consumption data of each sub-region to be analyzed to obtain the energy consumption characteristic indicators of each sub-region to be analyzed. The energy consumption data includes the electricity consumption, the consumption power factor, and the average electricity consumption during peak hours; Obtain the carbon emission data of each sub-region to be analyzed to obtain the carbon emission characteristic indicators of each sub-region to be analyzed. The carbon emission data includes the total carbon emission, the carbon emission intensity, and the carbon emission peak; Perform a comprehensive analysis on the power generation characteristic indicators, photovoltaic distribution characteristic indicators, energy consumption characteristic indicators, and carbon emission characteristic indicators corresponding to each sub-region to be analyzed to obtain the comprehensive status indicators of each sub-region to be analyzed; Based on the comprehensive status indicators of each sub-region to be analyzed, determine the time scale of each sub-region to be analyzed.

[0010] Furthermore, to determine the time scale of each sub-region to be analyzed, the specific analysis steps are as follows: Obtain the minimum threshold and maximum threshold of the comprehensive status indicators corresponding to each region type from the database based on the region type of each sub-region to be analyzed; Compare the comprehensive status indicator of the sub-region to be analyzed with the minimum threshold of the comprehensive status indicator: If the comprehensive status indicator is less than the minimum threshold of the comprehensive status indicator, the time scale of the sub-region to be analyzed is the short term; If the comprehensive status indicator is not less than the minimum threshold of the comprehensive status indicator, then compare the comprehensive status indicator with the maximum threshold of the comprehensive status indicator: If the comprehensive status indicator is not greater than the maximum threshold of the comprehensive status indicator, the time scale of the sub-region to be analyzed is the medium term; If the comprehensive status indicator is greater than the maximum threshold of the comprehensive status indicator, the time scale of the sub-region to be analyzed is the long term.

[0011] Furthermore, the calculation formula for the power generation characteristic indicator is:

[0012]

[0013] In the formula, is the power generation characteristic indicator of the j-th sub-region to be analyzed, Bd j is the power generation volatility factor of the j-th sub-region to be analyzed, Gl j is the power generation power stability factor of the j-th sub-region to be analyzed, Yx j is the unit operation status stability factor of the j-th sub-region to be analyzed.

[0014] Furthermore, the calculation formula for the photovoltaic distribution characteristic index is as follows:

[0015]

[0016] In the formula, is the photovoltaic distribution characteristic index of the j-th sub-region to be analyzed, Fb j is the distribution quantity of the j-th sub-region to be analyzed, Zj j is the installed capacity density of the j-th sub-region to be analyzed, Rl j is the installed capacity of the j-th sub-region to be analyzed.

[0017] Furthermore, the calculation formula for the energy consumption characteristic index is as follows:

[0018]

[0019] In the formula, is the energy consumption characteristic index of the j-th sub-region to be analyzed, Yd j is the electricity consumption of the j-th sub-region to be analyzed, Xh j is the consumption power factor of the j-th sub-region to be analyzed, Pj j is the average consumption electricity during the peak period of the j-th sub-region to be analyzed.

[0020] Furthermore, the calculation formula for the carbon emission characteristic index is as follows:

[0021]

[0022] In the formula, is the carbon emission characteristic index of the j-th sub-region to be analyzed, Tp j is the total carbon emission of the j-th sub-region to be analyzed, Qd j is the carbon emission intensity of the j-th sub-region to be analyzed, Fz j is the carbon emission peak of the j-th sub-region to be analyzed.

[0023] Further, determine the evaluation results of each sub-region to be analyzed, specifically including the following steps: Obtain the characteristic index data of each sub-region to be analyzed, where the characteristic index data includes power generation characteristic indexes, photovoltaic distribution characteristic indexes, energy consumption characteristic indexes, and carbon emission characteristic indexes; Based on the reference region corresponding to the maximum matching similarity, obtain the reference characteristic index data of each sub-region in the database, where the reference characteristic index data includes reference power generation characteristic indexes, reference photovoltaic distribution characteristic indexes, reference energy consumption characteristic indexes, and reference carbon emission characteristic indexes; Process the characteristic index data and the reference characteristic index data to obtain the comprehensive evaluation value of each sub-region to be analyzed; Compare the comprehensive evaluation value with the corresponding comprehensive evaluation threshold of the reference sub-region to be analyzed stored in the database: If the comprehensive evaluation value is less than the corresponding comprehensive evaluation threshold of the reference sub-region to be analyzed, then mark the sub-region to be analyzed corresponding to this comprehensive evaluation value as an area to be improved, and upload the result to the visualization platform for display; If the comprehensive evaluation value is not less than the corresponding comprehensive evaluation threshold of the reference sub-region to be analyzed, then mark the sub-region to be analyzed corresponding to this comprehensive evaluation value as an advanced area, and upload the result to the visualization platform for display.

[0024] The present invention has the following beneficial effects:

[0025] This multi-temporal and multi-spatial scale energy visualization analysis method can achieve multi-dimensional comprehensive analysis, integrate multiple factors such as geography, energy, and social economy and data of various energy types in the consideration of regional division and energy data sets, avoid one-sidedness, provide rich and accurate basis for decision-making, enable the data to be visually displayed according to the corresponding time scale and spatial scale as required, has intuitiveness, is convenient for understanding and comparison, facilitates timely optimization management, ensures the safe and stable operation of the power grid, can also accurately locate the areas that need to be improved, promote the efficient and green development of energy, and effectively improve the quality and effectiveness of energy analysis, management, and optimization work.

[0026] Of course, it is not necessary for any product implementing the present invention to simultaneously achieve all the above-mentioned advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flowchart of a multi-temporal and multi-spatial scale energy visualization analysis method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] 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. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0029] Please refer to Figure 1 , the embodiments of the present invention provide a technical solution: a multi - spatio - temporal scale energy visualization analysis method, including the following steps:

[0030] Obtain the regional characteristic information data set of the current area to be analyzed, divide the current area to be analyzed into multiple sub - areas to be analyzed, determine the regional types of each sub - area to be analyzed, and upload the regional types of the sub - areas to be analyzed to the visualization platform for display.

[0031] Compare the regional characteristic information data set with the reference regional characteristic information data sets of each reference area stored in the database to obtain each matching similarity. The regional characteristic information data set includes regional geographical information characteristic data, regional energy characteristic data, and regional social - economic characteristic data. The reference regional characteristic information data set includes reference regional geographical information characteristic data, reference regional energy characteristic data, and reference regional social - economic characteristic data.

[0032] Arrange each matching similarity in ascending order, and determine the maximum matching similarity: Based on the maximum matching similarity, determine the reference regional characteristic information data set of the corresponding reference area, and obtain the sub - area division criteria of the reference area and the regional types corresponding to each sub - area from the database; Based on the determined sub - area division criteria and the regional types corresponding to each sub - area, divide the current area to be analyzed to obtain multiple sub - areas to be analyzed, and determine the regional types of each sub - area to be analyzed.

[0033] Compare the regional geographical information characteristic data with the reference regional geographical information characteristic data of each reference area to obtain each geographical matching similarity; Compare the regional energy characteristic data with the reference regional energy characteristic data of each reference area to obtain each energy matching similarity; Compare the regional social - economic characteristic data with the reference regional social - economic characteristic data of each reference area to obtain each social - economic matching similarity; Comprehensively analyze each geographical matching similarity, energy matching similarity, and social - economic matching similarity to obtain each matching similarity.

[0034] The calculation formula for the matching similarity is:

[0035]

[0036] In the formula, is the matching similarity corresponding to the current area to be analyzed and the i - th reference area, is the geographical matching similarity corresponding to the current area to be analyzed and the i - th reference area, is the energy matching similarity corresponding to the current area to be analyzed and the i - th reference area, $SIM_{SE}$ is the social and economic matching similarity corresponding to the current area to be analyzed and the $i$-th reference area, $\alpha_1$ is the weight factor of stored in the database, and $\alpha_2$ is the weight factor of stored in the database, and $\alpha_3$ is the weight factor of stored in the database.

[0037] The geographical matching similarity, energy matching similarity, and social and economic matching similarity are all obtained by the cosine similarity calculation method. For example, the calculation formula for the geographical matching similarity is:

[0038]

[0039] In the formula, is the representation of the regional geographical information feature vector of the current area to be analyzed, which is used to represent the regional geographical information feature data, is the representation of the reference regional geographical information feature vector of the $i$-th reference area stored in the database, which is used to represent the reference regional geographical information feature data of the reference area, is 's modulus, is 's modulus.

[0040] Collect the regional feature information dataset, covering geographical, energy, social and economic aspects of information, understand the actual situation of the area to be analyzed, and provide basic data support for subsequent analysis and regional division. Divide the area into multiple sub-regions and determine their types. Among them, the regional types include section areas, administrative areas, substation areas, etc., which can realize the targeted analysis and management of different spatial regions, make the analysis granularity finer, and avoid the inaccuracy caused by general analysis with the whole area as the object. Upload the regional type to the visualization platform for display, which can enable users (such as energy managers, decision-makers, researchers, etc.) to intuitively see the classification of different spatial regions, quickly master the overall layout of the area to be analyzed and the general characteristics of the area, and facilitate macro-control and subsequent specific data viewing and comparison.

[0041] Find the reference area that is most similar to the current area to be analyzed in terms of multiple features through data comparison. Using this as a bridge, utilize the existing relevant standards, division situations, etc. of the reference area to assist in determining key elements such as the sub-region division and regional type of the current area, and reasonably and efficiently determine the sub-region division standard and regional type of the current area to be analyzed. Ensure that the division result can not only fit the actual characteristics of the current area but also be consistent with past analysis experience and standards, build a standardized and accurate spatial region framework for subsequent energy visualization analysis, and improve the analysis efficiency and accuracy. It provides an important intermediate transition basis for further refining and accurately obtaining relevant information of each sub-region of the current area and carrying out energy visualization analysis that conforms to the actual situation.

[0042] The above-set α1, α2, and α3 are obtained from the database and calculated historically based on historical data. And Establish A mapping set of it and its corresponding weight factors to obtain the current α1, α2, and α3. β1, β2, β3, β4, γ1, γ2, γ3, and γ4 in the following text are also obtained through the mapping set of historical data and weight factors established in the database, that is, the corresponding weight factors are obtained according to the current data.

[0043] Obtain the energy data sets of each sub-region to be analyzed. Based on the energy data sets, obtain the comprehensive state indicators of each sub-region to be analyzed, and determine the time scales of each sub-region to be analyzed. Upload the comprehensive state indicators of each sub-region to be analyzed to the visualization platform for display. The energy data sets include power generation data, distributed photovoltaic data, energy consumption data, and carbon emission data; based on the time scales of each sub-region to be analyzed, upload the corresponding energy data sets of each sub-region to the visualization platform for display.

[0044] The time scales are mainly divided into three scales: short-term, medium-term, and long-term. The short-term can be monitored in seconds, minutes, and hours, the medium-term can be monitored in days, months, and seasons, and the long-term is monitored in years.

[0045] Obtain the power generation data of each sub-region to be analyzed to obtain the power generation characteristic indicators of each sub-region to be analyzed. The power generation data includes the power generation volatility factor, the power generation power stability factor, and the unit operation state stability factor.

[0046] The calculation formula for the power generation characteristic indicators is:

[0047]

[0048] In the formula, is the power generation characteristic indicator of the jth sub-region to be analyzed, Bd j is the power generation volatility factor of the jth sub-region to be analyzed, Gl j is the power generation power stability factor of the jth sub-region to be analyzed, Yx j is the unit operation state stability factor of the jth sub-region to be analyzed.

[0049] The power generation volatility factor reflects the degree of fluctuation of power generation within a certain time period. It can be obtained by calculating the ratio of the standard deviation to the average value of power generation. The greater the volatility, the more unstable the power generation supply, and the more necessary it is to monitor in a short period of time. The power generation power stability factor is used to evaluate the stability of power generation power over a period of time and is measured by the standard deviation of power output. High stability means that there will be no large fluctuations in power during the power supply process, and it can be monitored over a long period of time. The unit operation state stability factor is used to measure the stability of the generator set during operation and can be represented by recording the failure rate of the unit. The higher the stability, the lower the failure rate of the unit, and it can be monitored over a long period of time.

[0050] Obtain the distributed photovoltaic data of each sub-region to be analyzed, and obtain the photovoltaic distribution characteristic indexes of each sub-region to be analyzed. The distributed photovoltaic data includes the distribution quantity, the installed capacity density, and the installed capacity.

[0051] The calculation formula for the photovoltaic distribution characteristic index is:

[0052]

[0053] In the formula, is the photovoltaic distribution characteristic index of the jth sub-region to be analyzed, Fb j is the distribution quantity of the jth sub-region to be analyzed, Zj j is the installed capacity density of the jth sub-region to be analyzed, Rl j is the installed capacity of the jth sub-region to be analyzed.

[0054] The distribution quantity is the number of photovoltaic power generation devices (such as solar panels) installed in the jth sub-region to be analyzed, which reflects the degree of development of photovoltaic resources in this region. The installed capacity density represents the number of photovoltaic devices per unit area in the jth sub-region to be analyzed. The installed capacity refers to the total installed capacity of all photovoltaic devices in the jth sub-region to be analyzed, usually calculated in kilowatts (kW) or megawatts (MW).

[0055] Integrate the distribution quantity, the installed capacity density, and the installed capacity to evaluate the overall layout of distributed photovoltaics in each sub-region to be analyzed, including the scale size, the distribution density, etc., so as to reflect the development potential and utilization efficiency of solar energy resources in the region. The larger the photovoltaic distribution characteristic index, the smaller the photovoltaic distribution scale, and the long-term monitoring method can be adopted. On the contrary, the corresponding monitoring time interval is smaller.

[0056] Obtain the energy consumption data of each sub-region to be analyzed, and obtain the energy consumption characteristic indexes of each sub-region to be analyzed. The energy consumption data includes the electricity consumption, the consumption power factor, and the average electricity consumption during peak hours.

[0057] The calculation formula for the energy consumption characteristic index is:

[0058]

[0059] Wherein, is the energy consumption characteristic index of the j-th sub-region to be analyzed, Yd j is the electricity consumption of the j-th sub-region to be analyzed, Xh j is the consumption power factor of the j-th sub-region to be analyzed, pj j is the average power consumption during the peak period of the j-th sub-region to be analyzed.

[0060] Electricity consumption is usually expressed in kilowatt-hours (kWh) and reflects the energy demand of the region. The consumption power factor is used to measure the efficiency of electricity use and is calculated as the ratio of active power to apparent power, with an ideal value of 1. A low power factor (e.g., close to 0) means inefficient use of electrical energy and may indicate problems such as equipment aging or poor loads. The average power consumption during the peak period is used to measure the average power consumption during the peak electricity consumption period and usually measures the maximum load situation within a specific time period.

[0061] The smaller the energy consumption characteristic index, the more energy is consumed, and monitoring needs to be carried out on a shorter time scale to ensure the stability of the system and to detect any problems in a timely manner. The larger the energy consumption characteristic index, the longer the time scale for monitoring can be.

[0062] Obtain the carbon emission data of each sub-region to be analyzed to obtain the carbon emission characteristic index of each sub-region to be analyzed. The carbon emission data includes the total carbon emissions, carbon emission intensity, and carbon emission peak.

[0063] The calculation formula for the carbon emission characteristic index is:

[0064]

[0065] Wherein, is the carbon emission characteristic index of the j-th sub-region to be analyzed, Tp j is the total carbon emissions of the j-th sub-region to be analyzed, Qd j is the carbon emission intensity of the j-th sub-region to be analyzed, Fz j is the carbon emission peak of the j-th sub-region to be analyzed.

[0066] The total carbon emissions represent the total amount of carbon dioxide emissions within a specific time, usually measured in tons, and this index reflects the degree of impact of the region on the environment. The carbon emission intensity refers to the amount of carbon emissions generated per unit of energy consumption or electricity generation. The carbon emission peak represents the highest carbon emissions within a specific time period and is often used to evaluate the environmental impact during a certain period.

[0067] The smaller the carbon emission characteristic index, the greater the challenges faced by the region in carbon emission management and environmental impact, and the less environmentally friendly it is. Monitoring needs to be carried out at shorter time intervals to ensure the effectiveness of emission reduction measures, evaluate the adaptability of policies, analyze changes in economic activities, and meet the needs of climate change. The larger the carbon emission characteristic index, the longer time scale can be monitored.

[0068] Comprehensively analyze the power generation characteristic index, photovoltaic distribution characteristic index, energy consumption characteristic index, and carbon emission characteristic index corresponding to each sub-region to be analyzed to obtain the comprehensive status index of each sub-region to be analyzed; based on the comprehensive status index of each sub-region to be analyzed, determine the time scale of each sub-region to be analyzed.

[0069] The calculation formula for the comprehensive status index is:

[0070]

[0071] In the formula, Z j is the comprehensive status index of the jth sub-region to be analyzed, β1 is the weight factor of stored in the database, β2 is the weight factor of stored in the database, β3 is the weight factor of stored in the database, β4 is the weight factor of stored in the database.

[0072] Obtain the minimum threshold and maximum threshold of the comprehensive status index corresponding to each region type from the database based on the region type of each sub-region to be analyzed; compare the comprehensive status index of the sub-region to be analyzed with the minimum threshold of the comprehensive status index: if the comprehensive status index is less than the minimum threshold of the comprehensive status index, the time scale of the sub-region to be analyzed is short-term; if the comprehensive status index is not less than the minimum threshold of the comprehensive status index, then compare the comprehensive status index with the maximum threshold of the comprehensive status index: if the comprehensive status index is not greater than the maximum threshold of the comprehensive status index, the time scale of the sub-region to be analyzed is medium-term; if the comprehensive status index is greater than the maximum threshold of the comprehensive status index, the time scale of the sub-region to be analyzed is long-term.

[0073] The time scale is determined based on the comparison between the comprehensive status index and the reference sub-region threshold, realizing the adaptive division of the time scale. Different comprehensive status indexes correspond to different time scales, and the appropriate analysis time scale is automatically matched according to the complexity and dynamic change characteristics of the regional energy system, avoiding the limitations and errors that may be brought by the fixed time scale analysis, and improving the accuracy and effectiveness of energy analysis. With the adaptive determination of the time scale, a personalized analysis time frame is provided for different regional energy systems, serving the multi-faceted needs such as energy planning, management decision-making, energy market operation, and energy policy formulation, and promoting the scientific management and sustainable development of the regional energy system.

[0074] The comprehensive status index provides a unified quantitative evaluation result for the regional energy situation and is one of the core data inputs for subsequent multi-temporal and multi-spatial scale analysis. The determination of the time scale provides a basic basis for the time dimension for carrying out energy characteristic analysis, energy trend prediction, energy system optimization, and energy policy evaluation at different temporal and spatial scales, ensuring that the entire multi-temporal and multi-spatial scale energy visualization analysis method can conduct accurate and dynamic analysis and decision support according to the actual situation of the regional energy.

[0075] Upload and display the time scale of the sub-region to be analyzed, enabling analysts to intuitively know the analysis scale of different regional energy data in the time dimension, facilitating the grasp of the time characteristic differences of the data from a macro perspective and the comparison between different regions. At the same time, display the energy data corresponding to the time scale to help explore the variation law of the energy situation in each region over time and the horizontal comparison between regions.

[0076] After determining the time scale for each sub-region to be analyzed, comprehensively analyze the energy data sets of each sub-region to be analyzed to determine the evaluation results of each sub-region to be analyzed; according to the evaluation results, upload the evaluation results of each sub-region to be analyzed to the visualization platform for display.

[0077] Obtain the characteristic index data of each sub-region to be analyzed, where the characteristic index data includes power generation characteristic indexes, photovoltaic distribution characteristic indexes, energy consumption characteristic indexes, and carbon emission characteristic indexes; based on the reference region corresponding to the maximum matching similarity, obtain the reference characteristic index data of each sub-region of this reference region from the database, where the reference characteristic index data includes reference power generation characteristic indexes, reference photovoltaic distribution characteristic indexes, reference energy consumption characteristic indexes, and reference carbon emission characteristic indexes; process the characteristic index data and the reference characteristic index data to obtain the comprehensive evaluation value of each sub-region to be analyzed.

[0078] Compare the comprehensive evaluation value with the corresponding comprehensive evaluation threshold stored in the database for the reference sub-region to be analyzed: If the comprehensive evaluation value is less than the comprehensive evaluation threshold corresponding to the reference sub-region to be analyzed, mark the sub-region to be analyzed corresponding to this comprehensive evaluation value as the area to be improved, and upload the result to the visualization platform for display; If the comprehensive evaluation value is not less than the comprehensive evaluation threshold corresponding to the reference sub-region to be analyzed, mark the sub-region to be analyzed corresponding to this comprehensive evaluation value as the advanced area, and upload the result to the visualization platform for display.

[0079] The calculation formula for the comprehensive evaluation value is:

[0080]

[0081] In the formula, B j is the comprehensive evaluation value of the j-th sub-region to be analyzed, is the reference power generation characteristic index corresponding to the j-th sub-region to be analyzed, is the reference photovoltaic distribution characteristic index corresponding to the j-th sub-region to be analyzed, is the reference energy consumption characteristic index corresponding to the j-th sub-region to be analyzed, is the reference carbon emission characteristic index corresponding to the j-th sub-region to be analyzed, γ1 is the weight factor of the ratio of stored in the database, γ2 is the weight factor of the ratio of stored in the database, γ3 is the weight factor of the ratio of stored in the database, γ4 is the weight factor of the ratio of stored in the database.

[0082] Integrate the characteristic indexes in aspects such as power generation, photovoltaic distribution, energy consumption, and carbon emission with a specific formula, considering the relative relationship between each index and the reference value and weighting, to evaluate the overall energy performance of each sub-region to be analyzed. Determining the evaluation result based on the comparison between the comprehensive evaluation value and the threshold can clearly distinguish the advanced areas and the areas to be improved in the sub-regions to be analyzed, intuitively present the relative advantages and disadvantages of each region in the entire energy system, and provide a basis for targeted management decisions.

[0083] Upload the evaluation result to the visualization platform for display. Relevant parties such as energy management departments, enterprises, and research institutions can conveniently obtain the overall energy status information of each region, promote the rapid dissemination and sharing of information among different entities, and based on this, better carry out collaborative work. Improve the refined level of energy management, promote the coordinated development of regional energy, and guide the optimal allocation of resources, helping to optimize the overall energy system and achieve sustainable development.

[0084] In a specific embodiment, the regional feature information dataset of the currently to-be-analyzed area is compared with the reference feature information dataset of the i-th reference area stored in the database. To simplify the calculation, it is obtained that is 0.92, is 0.83, and it is obtained that is 0.79, α1 is 0.3, α2 is 0.4, α3 is 0.3, and it is calculated that is 0.85. Similar calculations are performed for other reference areas, and the matching similarity corresponding to the largest reference area is 0.94. The regional division standard corresponding to the reference feature information dataset of the corresponding reference area is the regional division standard of the currently to-be-analyzed area. Based on this regional division standard, multiple sub-areas to be analyzed are divided.

[0085] In the power generation data, Bd in the j-th sub-area to be analyzed j is 0.2, Gl j is 0.8, Yx j is 0.9, and it is calculated that is 0.85.

[0086] In the distributed photovoltaic data, Fb in the j-th sub-area to be analyzed j is 50 units, Zj j is 0.3 units per square kilometer, Rl j is 100 MW, and it is calculated that is 0.17.

[0087] In the energy consumption data, Yd in the j-th sub-area to be analyzed j is 500 MWh, Xh j is 0.95, Pj j is 200 MWh, and it is calculated that is 0.67.

[0088] In the carbon emission data, Tp in the j-th sub-area to be analyzed j is 1000 tCO2, Qd j is 0.5 tCO2 / MWh, Fz j is 300 tCO2, and it is calculated that is 0.28, β1 is 0.2, β2 is 0.2, β3 is 0.25, β4 is 0.15, and it is calculated that Z jis 0.41. The minimum threshold of the comprehensive status index of the reference sub-region of the corresponding region type stored in the database corresponding to the j-th sub-region to be analyzed is 1. If the comprehensive status index of the j-th sub-region to be analyzed is less than the minimum threshold of the comprehensive status index of the reference sub-region of the corresponding region type stored in the database, the time scale of this sub-region to be analyzed is the short-term time. Similar calculations are performed for the remaining sub-regions to be analyzed to obtain the time scales corresponding to each sub-region to be analyzed.

[0089] is 0.8, is 0.1, is 0.7, is 0.2, γ1 is 0.2, γ2 is 0.15, γ3 is 0.2, γ4 is 0.25, and B is calculated j is 2.52. The comprehensive evaluation threshold corresponding to the j-th sub-region to be analyzed is 2. B j is greater than the comprehensive evaluation threshold corresponding to the j-th sub-region to be analyzed, then B j The corresponding sub-region to be analyzed is recorded as an advanced region, and the result is uploaded to the visualization platform for display. Similar calculations are performed for other sub-regions to be analyzed. Multiple sub-regions to be analyzed are divided into advanced regions and regions to be improved, and the results are uploaded to the visualization platform for display. And the sub-regions to be analyzed recorded as regions to be improved are improved.

[0090] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology make various modifications or supplements to the described specific embodiments or use similar methods to replace them. As long as they do not deviate from the structure of the invention or exceed the scope defined by this claims, they should fall within the protection scope of the present invention.

[0091] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0092] The present invention is described with reference to the flowcharts and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general purpose computers, special purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in the Figure 1 single flow or multiple flows and / or blocks Figure 1 single block or multiple blocks.

[0093] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in the Figure 1 single flow or multiple flows and / or blocks Figure 1 single block or multiple blocks.

[0094] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the Figure 1 single flow or multiple flows and / or blocks Figure 1 single block or multiple blocks.

[0095] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0096] 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 equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A multi - spatio - temporal scale energy visualization analysis method, characterized in that Including the following steps: Obtain the regional characteristic information dataset of the current area to be analyzed, divide the current area to be analyzed into multiple sub-areas to be analyzed, determine the regional types of each sub-area to be analyzed, and upload the regional types of the sub-areas to be analyzed to the visualization platform for display; Obtain the energy datasets of each sub-area to be analyzed. Based on the energy datasets, obtain the comprehensive status indicators of each sub-area to be analyzed, and determine the time scales of each sub-area to be analyzed. Upload the comprehensive status indicators of each sub-area to be analyzed to the visualization platform for display. The energy datasets include power generation data, distributed photovoltaic data, energy consumption data, and carbon emission data; Based on the time scales of each sub-area to be analyzed, upload the corresponding energy datasets of each sub-area to be analyzed to the visualization platform for display; After determining the time scales of each sub-area to be analyzed, comprehensively analyze the energy datasets of each sub-area to be analyzed to determine the evaluation results of each sub-area to be analyzed; According to the evaluation results, upload the evaluation results of each sub-area to be analyzed to the visualization platform for display; Obtain the comprehensive status indicators of each sub-area to be analyzed, which specifically include the following steps: Obtain the power generation data of each sub-area to be analyzed to obtain the power generation characteristic indicators of each sub-area to be analyzed. The power generation data includes power generation volatility factor, power generation power stability factor, and unit operation status stability factor; Obtain the distributed photovoltaic data of each sub-area to be analyzed to obtain the photovoltaic distribution characteristic indicators of each sub-area to be analyzed. The distributed photovoltaic data includes distribution quantity, installed capacity density, and installed capacity; Obtain the energy consumption data of each sub-area to be analyzed to obtain the energy consumption characteristic indicators of each sub-area to be analyzed. The energy consumption data includes electricity consumption, consumption power factor, and average electricity consumption during peak hours; Obtain the carbon emission data of each sub-area to be analyzed to obtain the carbon emission characteristic indicators of each sub-area to be analyzed. The carbon emission data includes total carbon emissions, carbon emission intensity, and carbon emission peak; Comprehensively analyze the power generation characteristic indicators, photovoltaic distribution characteristic indicators, energy consumption characteristic indicators, and carbon emission characteristic indicators corresponding to each sub-area to be analyzed to obtain the comprehensive status indicators of each sub-area to be analyzed; Based on the comprehensive status indicators of each sub-area to be analyzed, determine the time scales of each sub-area to be analyzed; The calculation formula of the power generation characteristic indicator is: ； Wherein, is the power generation characteristic index of the th sub-region to be analyzed, is the power generation volatility factor of the th sub-region to be analyzed, is the power generation power stability factor of the th sub-region to be analyzed, is the unit operation state stability factor of the th sub-region to be analyzed; The calculation formula of the photovoltaic distribution characteristic indicator is: ； In the formula, is the photovoltaic distribution characteristic index of the th sub-region to be analyzed, is the distribution quantity of the th sub-region to be analyzed, is the installed capacity density of the th sub-region to be analyzed, is the installed capacity of the th sub-region to be analyzed; The calculation formula of the energy consumption characteristic indicator is: ； In the formula, is the energy consumption characteristic index of the th sub-region to be analyzed, is the electricity consumption of the th sub-region to be analyzed, is the consumption power factor of the th sub-region to be analyzed, is the average consumption electricity during the peak period of the th sub-region to be analyzed; The calculation formula of the carbon emission characteristic indicator is: ； Wherein, is the carbon emission characteristic index of the th sub-region to be analyzed, is the total carbon emission of the th sub-region to be analyzed, is the carbon emission intensity of the th sub-region to be analyzed, is the carbon emission peak value of the th sub-region to be analyzed.

2. The multi-temporal and multi-spatial scale energy visualization analysis method according to claim 1, characterized in that Divide the current area to be analyzed into multiple sub-areas to be analyzed, and determine the regional types of each sub-area to be analyzed. Specifically, it includes the following steps: Compare the regional characteristic information dataset with the reference regional characteristic information datasets of each reference area stored in the database to obtain each matching similarity. The regional characteristic information dataset includes regional geographical information characteristic data and regional energy characteristic data. The reference regional characteristic information dataset includes reference regional geographical information characteristic data and reference regional energy characteristic data; Arrange each matching similarity in ascending order and determine the maximum matching similarity: Determine the reference regional feature information dataset corresponding to the reference region based on the maximum matching similarity, and obtain the sub-region division criteria of the reference region and the corresponding regional types of each sub-region from the database; Based on the determined sub-region division criteria and the corresponding regional types of each sub-region, divide the current region to be analyzed to obtain multiple sub-regions to be analyzed, and determine the regional types of each sub-region to be analyzed.

3. A multi-temporal and multi-spatial scale energy visualization analysis method according to claim 2, characterized in that Obtain each matching similarity, which specifically includes the following steps: Compare the regional geographic information feature data with the reference regional geographic information feature data of each reference region to obtain each geographic matching similarity; Compare the regional energy feature data with the reference regional energy feature data of each reference region to obtain each energy matching similarity; Conduct a comprehensive analysis of each geographic matching similarity and energy matching similarity to obtain each matching similarity.

4. A multi - spatio - temporal scale energy visualization analysis method according to claim 1, characterized in that: Determine the time scale of each sub-region to be analyzed. The specific analysis steps are as follows: Based on the regional types of each sub-region to be analyzed, obtain the minimum threshold and maximum threshold of the comprehensive status index corresponding to each regional type from the database; Compare the comprehensive status index of the sub-region to be analyzed with the minimum threshold of the comprehensive status index: If the comprehensive status index is less than the minimum threshold of the comprehensive status index, the time scale of the sub-region to be analyzed is the short-term time; If the comprehensive status index is not less than the minimum threshold of the comprehensive status index, then compare the comprehensive status index with the maximum threshold of the comprehensive status index: If the comprehensive status index is not greater than the maximum threshold of the comprehensive status index, the time scale of the sub-region to be analyzed is the medium-term time; If the comprehensive status index is greater than the maximum threshold of the comprehensive status index, the time scale of the sub-region to be analyzed is the long-term time.

5. A multi-temporal and multi-spatial scale energy visualization analysis method according to claim 1, characterized in that: Determine the evaluation results of each sub-region to be analyzed, which specifically includes the following steps: Obtain the characteristic index data of each sub-region to be analyzed, and the characteristic index data includes power generation characteristic index, photovoltaic distribution characteristic index, energy consumption characteristic index and carbon emission characteristic index; Based on the reference region corresponding to the maximum matching similarity, obtain the reference characteristic index data of each sub-region of the reference region from the database. The reference characteristic index data includes reference power generation characteristic index, reference photovoltaic distribution characteristic index, reference energy consumption characteristic index and reference carbon emission characteristic index; Process the characteristic index data and the reference characteristic index data to obtain the comprehensive evaluation value of each sub-region to be analyzed; Compare the comprehensive evaluation value with the comprehensive evaluation threshold corresponding to the corresponding reference sub-region to be analyzed stored in the database: If the comprehensive evaluation value is less than the comprehensive evaluation threshold corresponding to the reference sub-region to be analyzed, mark the sub-region to be analyzed corresponding to the comprehensive evaluation value as the region to be improved, and upload the result to the visualization platform for display; If the comprehensive evaluation value is not less than the comprehensive evaluation threshold corresponding to the reference sub-region to be analyzed, mark the sub-region to be analyzed corresponding to the comprehensive evaluation value as the advanced region, and upload the result to the visualization platform for display.

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