Multi-temporal-spatial-scale energy visualization analysis method

Abstract

The invention discloses a multi-temporal-spatial-scale energy visualization analysis method, and relates to the technical field of energy analysis. The multi-temporal-spatial-scale energy visualization analysis method comprises the steps that a current to-be-analyzed area is divided into a plurality of to-be-analyzed sub-areas, the area type of each to-be-analyzed sub-area is determined, and the area types of the to-be-analyzed sub-areas are uploaded to a visualization platform to be displayed; determining the time scale of each to-be-analyzed sub-region, and uploading the comprehensive state index of each to-be-analyzed sub-region to a visual platform for display; uploading the energy data set corresponding to each to-be-analyzed sub-region to a visual platform for display; and determining the evaluation result of each to-be-analyzed sub-region, and uploading the evaluation result of each to-be-analyzed sub-region to a visualization platform for display, thereby solving the problem of poor visualization effect caused by fuzzy time scale determination and single analysis method in region division of energy visualization analysis in the prior art.

Description

Technical Field

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

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

[0003] The Chinese invention patent with announcement number CN112256789B discloses a method and device for intelligent data visualization analysis, which constructs a chart knowledge base, which contains knowledge structure information of multiple chart types; obtains a data set to be analyzed, extracts field information of samples in the data set and relationship information between fields; calculates the matching quality score between the data set to be analyzed and any chart type in the chart knowledge base based on the field information of samples in the data set and relationship information between fields; outputs the chart configuration of the data set to be analyzed based on the matching quality score, and by constructing a chart knowledge base, calculates the matching quality score between the data set to be analyzed and any chart type in the chart knowledge base, thereby outputting the chart configuration of the data set to be analyzed, data developers can quickly complete data visualization analysis of the data set to be analyzed, reduce the development cost of data visualization, and improve the development efficiency of data visualization.

[0004] However, existing technologies have problems such as vague time scale determination in regional division of energy visualization analysis and single analysis method, which leads to poor visualization effect. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a multi-temporal and spatial scale energy visualization analysis method, which solves the problems in the prior art of energy visualization analysis, such as fuzzy time scale determination in regional division and single analysis method, resulting in poor visualization effect.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-time and space scale energy visualization analysis method, comprising the following steps: obtaining a regional characteristic information data set of a current area to be analyzed, dividing the current area to be analyzed into multiple sub-areas to be analyzed, and determining the regional type of each sub-area to be analyzed, and uploading the regional type of the sub-area to be analyzed to a visualization platform for display; obtaining an energy data set of each sub-area to be analyzed, and based on the energy data set, obtaining a comprehensive status indicator of each sub-area to be analyzed, and determining the time scale of each sub-area to be analyzed, and uploading the comprehensive status indicator of each sub-area to be analyzed to a visualization platform for display, wherein the energy data set includes power generation data, distributed photovoltaic data, energy consumption data, and carbon emission data; based on the time scale of each sub-area to be analyzed, uploading the energy data set corresponding to each sub-area to be analyzed to the visualization platform for display; after determining the time scale of each sub-area to be analyzed, comprehensively analyzing the energy data set of each sub-area to be analyzed to determine the evaluation result of each sub-area to be analyzed; and uploading the evaluation result of each sub-area to be analyzed to the visualization platform for display according to the evaluation result.

[0007] Further, the current area to be analyzed is divided into multiple sub-areas to be analyzed, and the area type of each sub-area to be analyzed is determined, specifically including the following steps: comparing the area characteristic information data set with the reference area characteristic information data set of each reference area stored in the database to obtain each matching similarity, the area characteristic information data set includes area geographic information characteristic data, area energy characteristic data, and area socio-economic characteristic data, and the reference area characteristic information data set includes reference area geographic information characteristic data, reference area energy characteristic data, and reference area socio-economic characteristic data; arranging each matching similarity in ascending order to determine the maximum matching similarity: determining the reference area characteristic information data set of the corresponding reference area based on the maximum matching similarity, obtaining the sub-area division standard of the reference area and the area type corresponding to each sub-area from the database; dividing the current area to be analyzed based on the determined sub-area division standard and the area type corresponding to each sub-area to obtain multiple sub-areas to be analyzed, and determining the area type of each sub-area to be analyzed.

[0008] Furthermore, each matching similarity is obtained, which specifically includes the following steps: comparing the regional geographic information feature data with the reference regional geographic information feature data of each reference region to obtain each geographic matching similarity; comparing the regional energy feature data with the reference regional energy feature data of each reference region to obtain each energy matching similarity; comparing the regional socio-economic feature data with the reference regional socio-economic feature data of each reference region to obtain each socio-economic matching similarity; comprehensively analyzing each geographic matching similarity, energy matching similarity and socio-economic matching similarity to obtain each matching similarity.

[0009] Furthermore, a comprehensive state index of each sub-region to be analyzed is obtained, which specifically includes the following steps: obtaining power generation data of each sub-region to be analyzed, and obtaining power generation characteristic index of each sub-region to be analyzed, wherein the power generation data includes power generation fluctuation factor, power generation power stability factor, and unit operation state stability factor; obtaining distributed photovoltaic data of each sub-region to be analyzed, and obtaining photovoltaic distribution characteristic index of each sub-region to be analyzed, wherein the distributed photovoltaic data includes distribution quantity, installed capacity density, and installed capacity; obtaining energy consumption data of each sub-region to be analyzed, and obtaining energy consumption characteristic index of each sub-region to be analyzed, wherein the energy consumption data includes power consumption, power consumption factor, and average power consumption during peak hours; obtaining carbon emission data of each sub-region to be analyzed, and obtaining carbon emission characteristic index of each sub-region to be analyzed, wherein the carbon emission data includes total carbon emission, carbon emission intensity, and carbon emission peak value; comprehensively analyzing 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, and obtaining the comprehensive state index of each sub-region to be analyzed; and determining the time scale of each sub-region to be analyzed based on the comprehensive state index of each sub-region to be analyzed.

[0010] Furthermore, the time scale of each sub-region to be analyzed is determined, and the specific analysis steps are: based on the regional type of each sub-region to be analyzed, the minimum threshold value and the maximum threshold value of the comprehensive status indicator corresponding to each regional type are obtained from the database; the comprehensive status indicator of the sub-region to be analyzed is compared with the minimum threshold value of the comprehensive status indicator: if the comprehensive status indicator is less than the minimum threshold value of the comprehensive status indicator, the time scale of the sub-region to be analyzed is short-term time; if the comprehensive status indicator is not less than the minimum threshold value of the comprehensive status indicator, the comprehensive status indicator is compared with the maximum threshold value of the comprehensive status indicator: if the comprehensive status indicator is not greater than the maximum threshold value of the comprehensive status indicator, the time scale of the sub-region to be analyzed is medium-term time; if the comprehensive status indicator is greater than the maximum threshold value of the comprehensive status indicator, the time scale of the sub-region to be analyzed is long-term time.

[0011] Furthermore, the calculation formula of the power generation characteristic index is:

[0012]

[0013] In the formula, is the power generation characteristic index of the jth sub-region to be analyzed, Bd j is the power generation fluctuation factor of the jth sub-region to be analyzed, Gl j Yx is the power generation stability factor of the jth sub-region to be analyzed, j is the unit operating status stability factor of the jth sub-area to be analyzed.

[0014] Furthermore, the calculation formula of the photovoltaic distribution characteristic index is:

[0015]

[0016] In the formula, is the photovoltaic distribution characteristic index of the jth sub-region to be analyzed, Fb j is the distribution number of the jth sub-area 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.

[0017] Furthermore, the calculation formula of the energy consumption characteristic index is:

[0018]

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

[0020] Furthermore, the calculation formula of the carbon emission characteristic index is:

[0021]

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

[0023] Further, the evaluation results of each sub-region to be analyzed are determined, specifically including the following steps: obtaining characteristic index data of each sub-region to be analyzed, the characteristic index data including 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 largest matching similarity, obtaining the reference characteristic index data of each sub-region of the reference region from the database, the reference characteristic index data including reference power generation characteristic index, reference photovoltaic distribution characteristic index, reference energy consumption characteristic index and reference carbon emission characteristic index; processing the characteristic index data with the reference characteristic index data to obtain a comprehensive evaluation value of each sub-region to be analyzed; comparing 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, the sub-region to be analyzed corresponding to the comprehensive evaluation value is recorded as an area to be improved, and the result is uploaded 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, the sub-region to be analyzed corresponding to the comprehensive evaluation value is recorded as an advanced area, and the result is uploaded to the visualization platform for display.

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

[0025] This multi-time and space scale energy visualization analysis method can realize multi-dimensional comprehensive analysis, integrate geography, energy, socio-economic and other factors and multiple energy type data in regional division and energy data set considerations, avoid one-sidedness, and provide rich and accurate basis for decision-making, so that data can be visualized according to the corresponding time scale and space scale according to demand. It is intuitive, easy to understand and compare, and convenient for timely optimization management to ensure the safe and stable operation of the power grid. It can also accurately locate the areas that need to be improved, promote efficient and green energy development, and effectively improve the quality and effectiveness of energy analysis, management and optimization.

[0026] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a multi-temporal and spatial scale energy visualization analysis method of the present invention. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0029] See also Figure 1 The embodiment of the present invention provides a technical solution: a multi-temporal and spatial scale energy visualization analysis method, comprising the following steps:

[0030] Obtain a 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 type of each sub-area to be analyzed, and upload the regional type of the sub-area to be analyzed to the visualization platform for display.

[0031] The regional characteristic information dataset is compared 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 geographic information characteristic data, regional energy characteristic data, and regional socio-economic characteristic data, and the reference regional characteristic information dataset includes reference regional geographic information characteristic data, reference regional energy characteristic data, and reference regional socio-economic characteristic data.

[0032] Arrange the matching similarities in ascending order to determine the greatest matching similarity: determine the reference area characteristic information data set of the corresponding reference area based on the greatest matching similarity, obtain the sub-area division standard of the reference area and the area type corresponding to each sub-area from the database; divide the current area to be analyzed based on the determined sub-area division standard and the area type corresponding to each sub-area, obtain a plurality of sub-areas to be analyzed, and determine the area type of each sub-area to be analyzed.

[0033] The regional geographic information feature data are compared with the reference regional geographic information feature data of each reference area to obtain the geographical matching similarities; the regional energy feature data are compared with the reference regional energy feature data of each reference area to obtain the energy matching similarities; the regional socio-economic feature data are compared with the reference regional socio-economic feature data of each reference area to obtain the socio-economic matching similarities; the geographic matching similarities, energy matching similarities and socio-economic matching similarities are comprehensively analyzed to obtain the matching similarities.

[0034] The calculation formula for matching similarity is:

[0035]

[0036] In the formula, is the matching similarity between the current region to be analyzed and the i-th reference region, is the geographical matching similarity between the current area to be analyzed and the i-th reference area, is the energy matching similarity between the current area to be analyzed and the i-th reference area, is the socioeconomic matching similarity between the current area to be analyzed and the i-th reference area, α1 is the The weight factor of α2 is stored in the database The weight factor of α3 is stored in the database The weight factor of .

[0037] The geographic matching similarity, energy matching similarity and socio-economic matching similarity are all calculated using the cosine similarity method. For example, the calculation formula for geographic matching similarity is:

[0038]

[0039] In the formula, It is the regional geographic information feature vector representation of the current region to be analyzed, used to represent the regional geographic information feature data. is a reference area geographic information feature vector representation of the i-th reference area stored in the database, used to represent the reference area geographic information feature data of the reference area, for The model, for Model.

[0040] Collect regional characteristic information data sets, covering geography, energy, and socio-economic information, understand the actual situation of the area to be analyzed, and provide basic data support for subsequent analysis and regional division. Divide the region into multiple sub-regions and determine their types. Among them, regional types include cross-sectional areas, administrative areas, substation areas, etc., which can achieve targeted analysis and management of different spatial areas, make the analysis granularity finer, and avoid the inaccuracy caused by general analysis of the entire region. Uploading regional types to the visualization platform for display allows users (such as energy managers, decision makers, researchers, etc.) to intuitively see the classification of different spatial areas, quickly grasp the overall layout of the area to be analyzed and the general characteristics of the region, and facilitate macro control and subsequent specific data viewing and comparison.

[0041] Through data comparison, we find the reference area that is most similar to the current area to be analyzed in many aspects. Using this as a bridge, we use the existing relevant standards, division conditions and other information of the reference area to assist in determining the key elements such as the sub-regional division and regional type of the current area, and reasonably and efficiently determine the sub-regional division standards and regional types of the current area to be analyzed. Ensure that the division results can not only fit the actual characteristics of the current area, but also be consistent with past analysis experience and standards, so as to build a standardized and accurate spatial regional framework for subsequent energy visualization analysis and improve analysis efficiency and accuracy. It provides an important intermediate transition basis for further refinement and accurate acquisition of relevant information of each sub-region in the current region and the implementation of energy visualization analysis in line with actual conditions.

[0042] The above-mentioned α1, α2, and α3 are obtained from the database and are calculated based on historical data. as well as Establish And the mapping set of its corresponding weight factors are obtained to obtain the current α1, α2, and α3. The β1, β2, β3, β4, γ1, γ2, γ3, and γ4 mentioned below 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 set of each sub-area to be analyzed, obtain the comprehensive status index of each sub-area to be analyzed based on the energy data set, determine the time scale of each sub-area to be analyzed, and upload the comprehensive status index of each sub-area to be analyzed to the visualization platform for display. The energy data set includes power generation data, distributed photovoltaic data, energy consumption data, and carbon emission data; based on the time scale of each sub-area to be analyzed, upload the energy data set corresponding to each sub-area to be analyzed to the visualization platform for display.

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

[0045] The power generation data of each sub-area to be analyzed is obtained to obtain the power generation characteristic index of each sub-area to be analyzed. The power generation data includes a power generation fluctuation factor, a power generation stability factor, and a unit operation state stability factor.

[0046] The calculation formula of power generation characteristic index is:

[0047]

[0048] In the formula, is the power generation characteristic index of the jth sub-region to be analyzed, Bd j is the power generation fluctuation factor of the jth sub-region to be analyzed, Gl j Yx is the power generation stability factor of the jth sub-region to be analyzed, j is the unit operating status stability factor of the jth sub-area to be analyzed.

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

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

[0051] The calculation formula of 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 number of the jth sub-area 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 equipment (such as solar panels) installed in the jth sub-region to be analyzed, reflecting the degree of development of photovoltaic resources in the region. The installed capacity density represents the number of photovoltaic equipment per unit area in the jth sub-region to be analyzed. The installed capacity refers to the total installed capacity of all photovoltaic equipment in the jth sub-region to be analyzed, usually calculated in kilowatts (kW) or megawatts (MW).

[0055] Integrate the distribution quantity, installed capacity density and installed capacity to evaluate the overall layout of distributed photovoltaics in each sub-region to be analyzed, including scale, 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 longer-term monitoring method can be adopted. On the contrary, the corresponding monitoring time interval is shorter.

[0056] The energy consumption data of each sub-area to be analyzed is obtained, and the energy consumption characteristic index of each sub-area to be analyzed is obtained. The energy consumption data includes power consumption, power consumption factor, and average power consumption during peak hours.

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

[0058]

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

[0060] Electricity consumption is usually expressed in kilowatt-hours (kWh), reflecting the energy demand of the area. The power factor is used to measure the efficiency of electricity use, calculated as the ratio of active power to apparent power, with an ideal value of 1. A low power factor (such as close to 0) means that the power is not used sufficiently, there are problems with aging equipment or poor load. Peak-time average power consumption is used to average power consumption during peak power consumption periods, usually measuring the maximum load during a specific time period.

[0061] The smaller the energy consumption characteristic index is, the more energy is consumed, and it is necessary to monitor it in a shorter time scale to ensure the stability of the system so as to find out whether there is a problem in time. The larger the energy consumption characteristic index is, the longer the time scale can be used for monitoring.

[0062] The carbon emission data of each sub-region to be analyzed is obtained, and the carbon emission characteristic indicators of each sub-region to be analyzed are obtained. The carbon emission data include the total carbon emission, carbon emission intensity, and carbon emission peak.

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

[0064]

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

[0066] Total carbon emissions represent the total carbon dioxide emissions in a specific period of time, usually in tons. This indicator reflects the impact of a region on the environment. Carbon emission intensity refers to the carbon emissions generated per unit of energy consumption or power generation. Carbon emission peak represents the highest carbon emissions in a specific period of time and is often used to assess the environmental impact of a certain period of time.

[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 is required at shorter time intervals to ensure the effectiveness of emission reduction measures, assess the adaptability of policies, analyze changes in economic activities, and respond to climate change needs. The larger the carbon emission characteristic index, the longer the time scale can be used for monitoring.

[0068] 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 are comprehensively 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, the time scale of each sub-area to be analyzed is determined.

[0069] The calculation formula of 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 The weight factor of The weight factor of The weight factor of β4 is stored in the database The weight factor of .

[0072] Based on the regional type of each sub-region to be analyzed, the minimum threshold value and the maximum threshold value of the comprehensive status indicator corresponding to each regional type are obtained from the database; the comprehensive status indicator of the sub-region to be analyzed is compared with the minimum threshold value of the comprehensive status indicator: if the comprehensive status indicator is less than the minimum threshold value of the comprehensive status indicator, the time scale of the sub-region to be analyzed is short-term time; if the comprehensive status indicator is not less than the minimum threshold value of the comprehensive status indicator, the comprehensive status indicator is compared with the maximum threshold value of the comprehensive status indicator: if the comprehensive status indicator is not greater than the maximum threshold value of the comprehensive status indicator, the time scale of the sub-region to be analyzed is medium-term time; if the comprehensive status indicator is greater than the maximum threshold value of the comprehensive status indicator, the time scale of the sub-region to be analyzed is long-term time.

[0073] The time scale is determined based on the comparison between the comprehensive state index and the reference sub-region threshold, and the adaptive division of the time scale is realized. Different comprehensive state indicators correspond to different time scales. 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 caused by fixed time scale analysis, and improving the accuracy and effectiveness of energy analysis. With the help of adaptive determination of time scale, a personalized analysis time frame is provided for different regional energy systems, serving the needs of energy planning, management decision-making, energy market operation, and energy policy formulation, and promoting the scientific management and sustainable development of regional energy systems.

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

[0075] The time scale of the sub-region to be analyzed is uploaded and displayed, so that analysts can intuitively know the analysis scale of energy data in different regions in the time dimension, which is convenient for grasping the differences in the time characteristics of data and the comparison between different regions from a macro perspective. At the same time, the energy data of the corresponding time scale is displayed to help explore the changes in the energy situation of each region over time and the horizontal comparison between regions.

[0076] After determining the time scale of each sub-area to be analyzed, the energy data sets of each sub-area to be analyzed are comprehensively analyzed to determine the assessment results of each sub-area to be analyzed; based on the assessment results, the assessment results of each sub-area to be analyzed are uploaded to the visualization platform for display.

[0077] Acquire characteristic indicator data of each sub-region to be analyzed, the characteristic indicator data include power generation characteristic indicators, photovoltaic distribution characteristic indicators, energy consumption characteristic indicators and carbon emission characteristic indicators; based on the reference region corresponding to the largest matching similarity, acquire the parameterized characteristic indicator data of each sub-region of the reference region from the database, the parameterized characteristic indicator data include parameterized power generation characteristic indicators, parameterized photovoltaic distribution characteristic indicators, parameterized energy consumption characteristic indicators and parameterized carbon emission characteristic indicators; process the characteristic indicator data and the parameterized characteristic indicator data to obtain a comprehensive evaluation value of each sub-region to be analyzed.

[0078] The comprehensive assessment value is compared with the comprehensive assessment threshold corresponding to the reference sub-area to be analyzed stored in the database: if the comprehensive assessment value is less than the comprehensive assessment threshold corresponding to the reference sub-area to be analyzed, the sub-area to be analyzed corresponding to the comprehensive assessment value is recorded as an area to be improved, and the result is uploaded to the visualization platform for display; if the comprehensive assessment value is not less than the comprehensive assessment threshold corresponding to the reference sub-area to be analyzed, the sub-area to be analyzed corresponding to the comprehensive assessment value is recorded as an advanced area, and the result is uploaded 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 jth sub-region to be analyzed, is the reference power generation characteristic index corresponding to the jth sub-region to be analyzed, is the reference photovoltaic distribution characteristic index corresponding to the jth sub-region to be analyzed, is the reference energy consumption characteristic index corresponding to the jth sub-area to be analyzed, is the reference carbon emission characteristic index corresponding to the jth sub-region to be analyzed, and γ1 is the and The weight factor of the ratio, γ2 is stored in the database and The weight factor of the ratio, γ3 is stored in the database and The weight factor of the ratio, γ4 is stored in the database and Weighting factor for the ratio.

[0082] The characteristic indicators of power generation, photovoltaic distribution, energy consumption and carbon emissions are integrated with a specific formula, and the relative relationship between each indicator and the reference value is considered and weighted to evaluate the comprehensive energy performance of each sub-region to be analyzed. The evaluation results are determined based on the comparison between the comprehensive evaluation value and the threshold value, which can clearly distinguish the advanced areas and 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] The assessment results will be uploaded to the visualization platform for display. All parties, including energy management departments, enterprises and research institutions, can easily obtain comprehensive information on energy status in each region, promote the rapid dissemination and sharing of information among different entities, and thus better carry out collaborative work. This will improve the level of refined energy management, promote regional energy coordinated development, guide resource optimization allocation, and help the overall optimization and sustainable development of the energy system.

[0084] In a specific embodiment, the regional characteristic information dataset of the current region to be analyzed is compared with the reference characteristic information dataset of the i-th reference region stored in the database. To simplify the calculation, is 0.92, is 0.83, so is 0.79, α1 is 0.3, α2 is 0.4, α3 is 0.3, and the calculated The matching similarity of the other reference areas is 0.85, and the matching similarity of the largest reference area is 0.94. The regional division standard corresponding to the reference feature information data set of the corresponding reference area is the regional division standard of the current area to be analyzed. Based on the regional division standard, multiple sub-areas to be analyzed are divided.

[0085] In the power generation data, Bd in the jth sub-area to be analyzed j is 0.2, Gl j is 0.8, Yx j is 0.9, and we calculate is 0.85.

[0086] In the distributed photovoltaic data, Fb in the jth sub-area to be analyzed j For 50 units, Zj j 0.3 units / km2, Rl j For 100MW, we calculate is 0.17.

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

[0088] In the carbon emission data, Tp in the jth sub-region to be analyzed j For 1000tCO2, Qd j 0.5tCO2 / MWh, Fz j For 300tCO2, we calculate is 0.28, β1 is 0.2, β2 is 0.2, β3 is 0.25, β4 is 0.15, and Z is calculated. jis 0.41, the minimum threshold of the comprehensive status index of the reference sub-region of the corresponding regional type stored in the database corresponding to the j-th sub-region to be analyzed is 1, and 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 regional type stored in the database, then the time scale of the sub-region to be analyzed is short-term, and the remaining sub-regions to be analyzed are calculated similarly to obtain the time scale 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, and γ4 is 0.25. Calculate B j is 2.52, the comprehensive assessment threshold corresponding to the jth sub-region to be analyzed is 2, B j If it is greater than the comprehensive assessment threshold corresponding to the jth sub-region to be analyzed, then B j The corresponding sub-area to be analyzed is recorded as an advanced area, and the results are uploaded to the visualization platform for display. Similar calculations are performed on other sub-areas to be analyzed, and multiple sub-areas to be analyzed are divided into advanced areas and areas to be improved. The results are uploaded to the visualization platform for display, and the sub-areas to be analyzed recorded as areas to be improved are improved.

[0090] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0091] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may 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 flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0093] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0094] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0095] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall 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 equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A multi-temporal and spatial scale energy visualization analysis method, characterized in that: The following steps are involved: Obtain a regional characteristic information dataset of the current region to be analyzed, divide the current region to be analyzed into multiple sub-regions to be analyzed, determine the regional type of each sub-region to be analyzed, and upload the regional type of the sub-region to be analyzed to the visualization platform for display; Obtain energy data sets of each sub-area to be analyzed, obtain comprehensive status indicators of each sub-area to be analyzed based on the energy data sets, determine the time scale of each sub-area to be analyzed, and upload the comprehensive status indicators of each sub-area to be analyzed to the visualization platform for display, wherein the energy data sets include power generation data, distributed photovoltaic data, energy consumption data, and carbon emission data; Based on the time scale of each sub-region to be analyzed, the energy data set corresponding to each sub-region to be analyzed is uploaded to the visualization platform for display; After the time scale is determined for each sub-region to be analyzed, the energy data sets of each sub-region to be analyzed are comprehensively analyzed to determine the evaluation results of each sub-region to be analyzed; According to the evaluation results, the evaluation results of each sub-area to be analyzed are uploaded to the visualization platform for display.

2. The multi-temporal and spatial scale energy visualization analysis method according to claim 1 is characterized in that: The current area to be analyzed is divided into multiple sub-areas to be analyzed, and the area type of each sub-area to be analyzed is determined, specifically including the following steps: Comparing the regional characteristic information dataset with the reference regional characteristic information datasets of each reference area stored in the database to obtain respective matching similarities, wherein the regional characteristic information dataset includes regional geographic information characteristic data, regional energy characteristic data, and regional socio-economic characteristic data, and the reference regional characteristic information dataset includes reference regional geographic information characteristic data, reference regional energy characteristic data, and reference regional socio-economic characteristic data; Arrange the matching similarities from small to large and determine the maximum matching similarity: Determine the reference region characteristic information dataset of the corresponding reference region based on the maximum matching similarity, and obtain the sub-region division standard of the reference region and the region type corresponding to each sub-region from the database; Based on the determined sub-region division standard and the region type corresponding to each sub-region, the current area to be analyzed is divided to obtain a plurality of sub-regions to be analyzed, and the region type of each sub-region to be analyzed is determined.

3. The multi-temporal and spatial scale energy visualization analysis method according to claim 2 is characterized in that: Obtaining each matching similarity 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 the similarity of each geographic match; Compare the regional energy characteristic data with the reference regional energy characteristic data of each reference region to obtain the matching similarity of each energy source; Compare the socioeconomic characteristic data of the region with the socioeconomic characteristic data of the reference region of each reference region to obtain the similarity of each socioeconomic match; The geographical matching similarities, energy matching similarities and socio-economic matching similarities are comprehensively analyzed to obtain the matching similarities.

4. The multi-temporal and spatial scale energy visualization analysis method according to claim 1 is characterized in that: Obtaining the comprehensive status index of each sub-area to be analyzed specifically includes the following steps: Acquire power generation data of each sub-area to be analyzed, and obtain power generation characteristic indicators of each sub-area to be analyzed, wherein the power generation data includes a power generation fluctuation factor, a power generation stability factor, and a unit operation state stability factor; Obtain distributed photovoltaic data of each sub-area to be analyzed, and obtain photovoltaic distribution characteristic indicators of each sub-area to be analyzed, wherein the distributed photovoltaic data includes distribution quantity, installed capacity density, and installed capacity; Obtaining energy consumption data of each sub-area to be analyzed, and obtaining energy consumption characteristic indicators of each sub-area to be analyzed, wherein the energy consumption data includes power consumption, power consumption factor, and average power consumption during peak hours; Acquire carbon emission data of each sub-region to be analyzed, and obtain carbon emission characteristic indicators of each sub-region to be analyzed, wherein the carbon emission data includes total carbon emission, carbon emission intensity, and carbon emission peak value; 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, and obtain the comprehensive status index of each sub-region to be analyzed; Based on the comprehensive status indicators of each sub-region to be analyzed, the time scale of each sub-region to be analyzed is determined.

5. The multi-temporal and spatial scale energy visualization analysis method according to claim 4 is characterized by: Determine the time scale of each sub-area to be analyzed. The specific analysis steps are: Based on the area type of each sub-area to be analyzed, the minimum threshold value and the maximum threshold value of the comprehensive status indicator corresponding to each area type are obtained from the database; Compare the comprehensive status index of the sub-area to be analyzed with the minimum threshold of the comprehensive status index: If the comprehensive state index is less than the minimum threshold of the comprehensive state index, the time scale of the sub-region to be analyzed is short-term; If the comprehensive status indicator is not less than the minimum threshold of the comprehensive status indicator, the comprehensive status indicator is compared with the maximum threshold of the comprehensive status indicator: If the comprehensive state index is not greater than the maximum threshold of the comprehensive state index, the time scale of the sub-region to be analyzed is the medium-term time; If the comprehensive state index is greater than the maximum threshold of the comprehensive state index, the time scale of the sub-region to be analyzed is long-term time.

6. The multi-temporal and spatial scale energy visualization analysis method according to claim 4 is characterized by: The calculation formula of the power generation characteristic index is: In the formula, is the power generation characteristic index of the jth sub-region to be analyzed, Bd j is the power generation fluctuation factor of the jth sub-region to be analyzed, Gl j Yx is the power generation stability factor of the jth sub-region to be analyzed, j is the unit operating status stability factor of the jth sub-area to be analyzed.

7. The multi-temporal and spatial scale energy visualization analysis method according to claim 4, characterized in that: The calculation formula of the photovoltaic distribution characteristic index is: In the formula, is the photovoltaic distribution characteristic index of the jth sub-region to be analyzed, Fb j is the distribution number of the jth sub-area 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.

8. The multi-temporal and spatial scale energy visualization analysis method according to claim 4 is characterized by: The calculation formula of the energy consumption characteristic index is: In the formula, is the energy consumption characteristic index of the jth sub-region to be analyzed, Yd j is the power consumption of the jth sub-area to be analyzed, Xh j is the power consumption factor of the jth sub-area to be analyzed, Pj j is the average power consumption during the peak period of the jth sub-area to be analyzed.

9. The multi-temporal and spatial scale energy visualization analysis method according to claim 4 is characterized by: The calculation formula of the carbon emission characteristic index is: In the formula, is the carbon emission characteristic index of the jth sub-region to be analyzed, Tp j is the total carbon emissions of the jth sub-region to be analyzed, Qd j is the carbon emission intensity of the jth sub-region to be analyzed, Fz j is the peak carbon emission value of the jth sub-region to be analyzed.

10. The multi-temporal and spatial scale energy visualization analysis method according to claim 4, characterized in that: Determine the evaluation results of each sub-area to be analyzed, specifically including the following steps: Acquire characteristic index data of each sub-area to be analyzed, wherein 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 area corresponding to the largest matching similarity, the parameterized characteristic index data of each sub-area of ​​the reference area are obtained from the database, wherein the parameterized characteristic index data include parameterized power generation characteristic index, parameterized photovoltaic distribution characteristic index, parameterized energy consumption characteristic index and parameterized carbon emission characteristic index; Process the characteristic index data and the parameter characteristic index data to obtain the comprehensive evaluation value of each sub-area to be analyzed; Compare the comprehensive assessment value with the comprehensive assessment 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, the sub-region to be analyzed corresponding to the comprehensive evaluation value is recorded as an area to be improved, and the result is uploaded to the visualization platform for display; If the comprehensive assessment value is not less than the comprehensive assessment threshold corresponding to the reference sub-region to be analyzed, the sub-region to be analyzed corresponding to the comprehensive assessment value is recorded as an advanced region, and the result is uploaded to the visualization platform for display.

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