Webgis display method and system for energy management

Abstract

The present application relates to the technical field of information display, in particular to a webgis display method and system for energy management, which receives energy use data of differentiated locations within a period of time based on a webgis platform, evaluates the instability of energy use of the differentiated locations, adjusts the data receiving frequency in combination with standard data receiving frequency, and obtains data receiving frequency adjustment results. Through the evaluation of the instability of energy use of the differentiated locations and the adjustment of the data receiving frequency, the present application improves the data quality and reaction speed, makes the monitoring of energy use trends and change rates more timely and accurate, adjusts the abnormal monitoring threshold by considering the influence of different time periods and equipment aging, enhances the sensitivity of abnormal state detection of energy use, realizes the hierarchical sorting display of energy information through the priority evaluation of differentiated information, improves the information display logic of the user interface, and enables decision makers to identify key issues more quickly and prioritize processing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of information display, and particularly relates to a webgis display method and system for energy management. BACKGROUND

[0002] The technical field of information display involves the visualization and display of data, aiming to convert data sets into visual formats that are easy to understand and analyze through various interfaces and graphical methods. Key technologies include graphical user interface design, data visualization, data display techniques in augmented reality and virtual reality, and efficient and dynamic information display using various software and hardware platforms. This enables users to intuitively understand and analyze complex data or information, supporting the decision-making process.

[0003] Among them, the webgis display method for energy management uses web-based geographic information system technology to provide visualization of spatial and dynamic information of energy assets such as power grids, renewable energy sites and their related infrastructure. The method integrates, analyzes and displays energy-related geographic and temporal data using a WebGIS platform, allowing users to view energy consumption, production and distribution in real time on a web page. Its main uses include energy monitoring, optimizing resource allocation, improving energy efficiency, and supporting the formulation and implementation of energy policies. Through this display method, energy managers can better understand energy flow and consumption patterns, and thus take more effective management and scheduling strategies.

[0004] Traditional display methods collect data at a fixed frequency when dealing with energy management data, failing to consider the differences in energy use between locations and the impact of time changes, resulting in incomplete or excessive data collection, affecting the efficiency and accuracy of data analysis, and lacking the function of dynamically adjusting data receiving frequency, which is slow in response to rapidly changing energy use situations. When displaying energy data, there is a lack of effective information priority determination mechanism, which cannot highlight the most critical energy use information, increasing the difficulty for decision-makers to identify important information in the vast amount of data, thereby affecting the speed and quality of decision-making. SUMMARY

[0005] The purpose of the present application is to solve the shortcomings in the prior art and to provide a webgis display method and system for energy management.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical scheme: a webgis display method for energy management, comprising the following steps:

[0007] S1: receiving energy use data of a differentiated place in a period of time based on a webgis platform, evaluating the instability of energy use of the differentiated place, adjusting the data receiving frequency in combination with standard data receiving frequency, obtaining data receiving frequency adjustment result;

[0008] S2: collecting energy use data of the differentiated place based on the data receiving frequency adjustment result, comparing with historical contemporaneous energy use data, evaluating the change rate of energy use, obtaining energy use change information;

[0009] S3: adjusting the abnormal monitoring threshold of the differentiated period based on the energy use change information, combining the time period influence and the equipment aging influence, implementing abnormal energy monitoring, and evaluating the energy use abnormality level according to the deviation of energy use data and abnormal monitoring threshold, obtaining abnormal energy use identification result;

[0010] S4: based on the abnormal energy use identification result, evaluating the display priority of differentiated information on the webgis platform according to the information type, performing hierarchical sorting on the energy use information display, obtaining energy information display result.

[0011] The application improves that the method for evaluating the instability of energy use of the differentiated place is:

[0012] S111: receiving energy use data of a differentiated place in a period of time based on a webgis platform, the energy use data including the recording time and energy use amount of each data point;

[0013] S112: based on the energy use data, calculating the instability B of energy data through the formula:

[0014]

[0015] , obtaining energy data instability information, wherein, x i is the energy use amount of the i th time point, μ B is the weighted average energy use amount, w i is the weight of the i th time point, n is the total number of time points, and B is the instability of energy data.

[0016] The application improves that the obtaining step of the data receiving frequency adjustment result is:

[0017] S121: based on the energy data instability information, collecting standard data receiving frequency data and benchmark instability, obtaining receiving frequency correlation information;

[0018] S122: based on the receiving frequency correlation information, through the formula:

[0019]

[0020] calculating an adjusted data receiving frequency f new , wherein f new is the adjusted data receiving frequency, f current is the current data receiving frequency, B is the instability of energy data, T base is the reference instability, C f is the adjustment coefficient;

[0021] S123: based on the adjusted data receiving frequency f new , adjust the receiving frequency of the webgis platform for energy data, to obtain a data receiving frequency adjustment result.

[0022] The application improves that the step of obtaining energy use change information is:

[0023] S211: based on the data receiving frequency adjustment result, receive energy use data of differentiated locations, and extract corresponding historical energy use data of the same period, to obtain energy change correlation data;

[0024] S212: based on the energy change correlation data, through the formula:

[0025]

[0026] calculate the change rate V of energy use, wherein e c，i is the energy use at the i-th time point of the current period, e h，i is the energy use at the i-th time point of the historical same period, w V，i is the weight coefficient of the i-th time point, Δt V is the measurement interval time, n V is the total number of data points, and V represents the change rate of energy use;

[0027] S213: based on the change rate V of energy use, according to the size and positive and negative of the change rate V of energy use, evaluate the change rate of energy use, to obtain energy use change information.

[0028] The application improves that the method for adjusting the abnormal monitoring threshold of the differentiated period is:

[0029] S311: collect energy use records of the target period, and calculate the average value of energy use of the target period, to obtain average energy use information;

[0030] S312: based on the average energy use information and the energy use change information, combine device aging data, through the formula:

[0031] T = μ T + k T · σ T · (1 + r T · t T + α T · V)

[0032] anomaly monitoring threshold T of a target period is calculated, wherein μ T is the average energy consumption of the target period, σ T is the standard deviation, k T is the sensitivity coefficient, r T is the device aging influence factor, t T is the number of years since the device is installed, α T is the change rate influence coefficient, V is the change rate of energy consumption, and T is the anomaly monitoring threshold.

[0033] The application improves that the obtaining step of the abnormal energy consumption identification result is:

[0034] S321: based on the anomaly monitoring threshold T of the target period, real-time receiving energy consumption data, comparing the energy consumption with the anomaly monitoring threshold in the target period, if the energy consumption exceeds the anomaly monitoring threshold, triggering an abnormal alarm, and obtaining abnormal detection information;

[0035] S322: based on the abnormal detection information, calculating the energy consumption anomaly level L through the formula:

[0036]

[0037] wherein γ L is the proportion factor, δ L is the adjustment parameter, E c is the energy consumption data,

[0038] T is the anomaly monitoring threshold of the target period, θ L is the baseline threshold, and L is the energy consumption anomaly level;

[0039] S323: based on the energy consumption anomaly level L, according to the size of the energy consumption anomaly level L value, evaluating the abnormal degree of energy consumption, and obtaining the abnormal energy consumption identification result.

[0040] The application improves that the obtaining step of the energy information display result is:

[0041] S411: based on the abnormal energy consumption identification result, collecting energy information for each location, the energy information including energy consumption, change rate of energy consumption and energy consumption anomaly level, and obtaining energy display information association data;

[0042] S412: based on the energy display information association data, combining the preset priority of each location, through the formula:

[0043] S E =P·W E ·E c

[0044] and

[0045] S V =P·W V ·V

[0046] and

[0047]

[0048] The energy usage information display priority S E , the energy usage rate of change information display priority S V and the display priority S L of abnormal energy use information are calculated, wherein E c , V and L represent energy usage, energy usage rate of change and energy usage abnormality level respectively, P is the preset priority of the location, W U , W V and W L are the weights of energy usage, rate of change and abnormality level information respectively, ∈ S is a smoothing factor, λ S is a baseline threshold of abnormality level, S E is the energy usage information display priority, S V is the energy usage rate of change information display priority, and S L is the display priority of abnormal energy use information.

[0049] S413: based on the energy usage information display priority S E , the energy usage rate of change information display priority S V and the display priority S L of abnormal energy use information, the energy use information display is hierarchically sorted by comparing the values of S E , S V and S L , the display order of the energy use information on the webgis platform is adjusted, and the energy information display result is obtained.

[0050] A webgis display system for energy management, which is used to execute the above-mentioned webgis display method for energy management, the system comprising:

[0051] The data stability evaluation module is based on a webgis platform, receives energy use data of differentiated locations within a period of time, evaluates the instability of energy use of the differentiated locations, and obtains energy data instability information;

[0052] The receiving frequency adjustment module adjusts the data receiving frequency based on the energy data instability information and in combination with a standard data receiving frequency, obtains a data receiving frequency adjustment result;

[0053] The energy use change analysis module collects energy use data of the differentiated locations based on the data receiving frequency adjustment result, compares the energy use data with historical contemporaneous energy use data, evaluates the change rate of the energy use amount, and obtains energy use change information;

[0054] The energy use anomaly analysis module adjusts the anomaly monitoring threshold of the differentiated period based on the energy use change information in combination with time period influence and equipment aging influence, implements anomaly energy monitoring, and evaluates the energy use anomaly level according to the deviation of the energy use data from the anomaly monitoring threshold, and obtains an anomaly energy use identification result;

[0055] The display order adjustment module adjusts the display order of the energy use information based on the anomaly energy use identification result, according to the information type in combination with the preset priority of the differentiated locations, evaluates the display priority of the differentiated information on the webgis platform, performs hierarchical sorting on the energy use information display, and obtains an energy information display result.

[0056] Compared with the prior art, the present application has the following advantages and positive effects:

[0057] In the present application, the data receiving frequency is adjusted by evaluating the instability of energy use of the differentiated locations, the data quality and reaction speed are improved, the monitoring of the energy use trend and change rate is more timely and accurate, the anomaly monitoring threshold is adjusted by considering the influence of different time periods and equipment aging, the sensitivity of the anomaly state detection of the energy use is enhanced, the anomaly can be identified earlier, the hierarchical sorting display of the energy information is realized by evaluating the priority of the differentiated information, the information display logic of the user interface is improved, and the decision maker can identify the key problems more quickly and handle them in priority. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 The method flowchart of the present application is shown in the figure;

[0059] Figure 2 The flowchart of evaluating the instability of energy use of the differentiated locations of the present application is shown in the figure;

[0060] Figure 3 The flowchart of obtaining the data receiving frequency adjustment result of the present application is shown in the figure;

[0061] Figure 4 Flowchart for obtaining energy use change information for the present application;

[0062] Figure 5 Flowchart for adjusting abnormal monitoring threshold of differentiated period for the present application;

[0063] Figure 6 Flowchart for obtaining abnormal energy use identification result for the present application;

[0064] Figure 7 Flowchart for obtaining energy information display result for the present application. DETAILED DESCRIPTION

[0065] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0066] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0067] Please refer to Figure 1 The present application provides a technical solution: a webgis display method for energy management, comprising the following steps:

[0068] S1: Based on the webgis platform, receiving energy use data of differentiated places within a period of time, evaluating the instability of energy use of differentiated places, adjusting the data receiving frequency in combination with the standard data receiving frequency, obtaining data receiving frequency adjustment result;

[0069] S2: Based on the data receiving frequency adjustment result, collecting energy use data of differentiated places, comparing with historical contemporaneous energy use data, evaluating the change rate of energy use, obtaining energy use change information;

[0070] S3: Based on the energy use change information, combining the time period influence and the equipment aging influence, adjusting the abnormal monitoring threshold of differentiated period, implementing abnormal energy monitoring, and evaluating the energy use abnormality level according to the deviation of energy use data and abnormal monitoring threshold, obtaining abnormal energy use identification result;

[0071] S4: Based on the abnormal energy use identification result, according to the information type including energy use amount information, energy use amount change rate information and abnormal energy use information, combined with the preset priority of the differentiated place, the display priority of the differentiated information on the webgis platform is evaluated, the energy use information display is hierarchically sorted, and an energy information display result is obtained;

[0072] The data receiving frequency adjustment result includes the adjusted receiving time interval and the target place identifier, the energy use change information includes the energy consumption increase / decrease value and the time comparison index, the abnormal energy use identification result includes the abnormal level information, the associated place and the alarm sending time, and the energy information display result includes the display level and the preferentially displayed data type.

[0073] Please refer to Figure 2 , the method for evaluating the instability of energy use of the differentiated place is:

[0074] S111: Based on the webgis platform, receiving energy use data of the differentiated place within a period of time, the energy use data including the recording time and the energy use amount of each data point;

[0075] S112: Based on the energy use data, calculating the instability B of the energy data through the formula:

[0076]

[0077] The energy data instability B is obtained, wherein, x i is the energy use amount of the i-th time point, μ B is the weighted average energy use amount, w i is the weight of the i-th time point, n is the total number of time points, and B is the instability of the energy data.

[0078] The formula is:

[0079]

[0080] The meanings and acquisition methods of the parameters are as follows:

[0081] x i : represents the energy use amount of the i-th time point, which is usually collected by the energy monitoring system at a fixed time interval.

[0082] μ B : the weighted average energy use amount, which is calculated based on the weight w i and the energy use amount x i . The average value takes into account the importance or reliability of data at different time points, thereby providing a more accurate average energy use amount reflecting the actual situation.

[0083] w i : The weight associated with time point i is set based on the reliability or importance of the time point, and can also be set based on the freshness of the data. For example, during a period of time, data during peak hours will be given a higher weight because the energy usage data during these hours is more critical to the overall analysis.

[0084] n: The total number of time points, obtained directly from the dataset. For example, if the monitoring system records data once an hour, there will be 24 data points in a day.

[0085] Calculation example:

[0086] The following data points and weights are set to calculate weighted average and volatility:

[0087] Data points x: [100, 150, 200, 250] kWh, weights w: [1, 2, 1, 2], total number of time points n: 4.

[0088] Calculate the weighted average energy usage μ B :

[0089]

[0090] Calculate the instability of energy data B:

[0091]

[0092] The calculated result B≈54.07 represents the weighted volatility of energy use in the entire time series. This value is large, indicating that the energy use data has high instability.

[0093] See also Figure 3 , the steps for obtaining the data receiving frequency adjustment result are:

[0094] S121: Based on the energy data instability information, collect standard data receiving frequency data and benchmark instability to obtain receiving frequency related information;

[0095] S122: Based on the received frequency association information, the formula:

[0096]

[0097] Calculate the adjusted data receiving frequency f new , where f new is the adjusted data receiving frequency, f current is the current data receiving frequency, B is the instability of energy data, T base is the benchmark instability, C f is the adjustment factor;

[0098] S123: Based on the adjusted data receiving frequency f new , adjust the frequency of receiving energy data on the webgis platform and obtain the result of data receiving frequency adjustment.

[0099] formula:

[0100]

[0101] The meaning and acquisition method of the parameters:

[0102] f current : The current data receiving frequency is usually obtained directly from the settings or configuration files of the data collection system. It is pre-set by the system administrator or automated script. It can also be set to the minimum data receiving frequency that can meet the energy monitoring needs.

[0103] B: Instability of energy data. The instability of energy data at the current location is calculated using the previous method.

[0104] T base : Baseline instability, determined based on analysis of long-term energy use data, defines the desired level of energy use stability and can also be set by selecting the minimum value of instability across all locations.

[0105] C f : Adjustment coefficient, a coefficient manually set according to the system's response sensitivity and data reception requirements, used to control the adjustment sensitivity of the receiving frequency and achieve a balance of fine adjustments.

[0106] Calculation example:

[0107] Set the following parameters:

[0108] F current =1Hz, indicating that the current data receiving frequency is once per second.

[0109] B=75, the current instability is 75.

[0110] T base =50, the baseline instability is 50.

[0111] C f =10, the adjustment coefficient is set to 10 to balance the adjustment range of the receiving frequency.

[0112] Calculation process:

[0113] Calculate the absolute deviation of volatility from the benchmark:

[0114] |BT base |=|75-50|=25.

[0115] Calculate the frequency adjustment factor:

[0116] ln(|B-T base |+1)

[0117] = ln(25+1)

[0118] = ln(26)≈3.26

[0119] Calculate the adjusted receiving frequency:

[0120]

[0121]

[0122] The calculation result f new = 1.326 Hz, indicating that due to the current volatility exceeding the reference threshold, the data receiving frequency should be increased from 1 per second to about 1.326 per second, such adjustment is to monitor and respond to the volatility of energy use more frequently.

[0123] Please refer to Figure 4 , the acquisition step of energy use change information is:

[0124] S211: Based on the data receiving frequency adjustment result, receive the differentiated site energy use data, and extract the corresponding historical energy use data of the same period to obtain energy change correlation data;

[0125] S212: Based on the energy change correlation data, calculate the change rate V of energy use amount through the formula:

[0126]

[0127] , where e c，i is the energy use amount at the i-th time point of the current period, e h，i is the energy use amount at the i-th time point of the same period in history, w V，i is the weight coefficient of the i-th time point, Δt V is the measurement interval time, n V is the total number of data points, and V represents the change rate of energy use amount.

[0128] S213: Based on the change rate V of energy use amount, according to the size and positive and negative of the change rate V of energy use amount, evaluate the change rate of energy use amount to obtain energy use change information.

[0129] Formula:

[0130]

[0131] Meaning and acquisition method of parameters:

[0132] e c,i and e h,i : Current and historical energy usage at the i-th time point, respectively, obtained directly from received data and data records.

[0133] w V，i : Weighting factor at the i-th time point, set according to the importance of economic or environmental impact at the time point. In practical applications, the weight needs to be set according to the demand peak or valley period of the time point.

[0134] Δt V : This is the measurement interval time, which is directly related to the data receiving frequency, determined by system settings or data collection strategies.

[0135] n V : This is the total number of data points, i.e., the total number of time points participating in the calculation.

[0136] Calculation example:

[0137] Set four data points of energy usage recorded every 10 minutes within the same period:

[0138] Current energy usage data is [100, 105, 110, 120] kWh.

[0139] Historical energy usage data in the same period is [90, 100, 105, 115] kWh.

[0140] The weight w V，i of each time point = [1, 1.5, 1, 1.5].

[0141] The measurement interval Δt V = 10 minutes.

[0142] The total number of data points n V = 4.

[0143] Calculation process:

[0144] Calculate the energy usage difference at each time point and apply the weight:

[0145] (1 x (100-90)) + (1.5 x (105-100)) + (1 x (110-105)) + (1.5 x (120-115))

[0146] = 10 + 7.5 + 5 + 7.5

[0147] = 30

[0148] Calculate the energy usage change rate v:

[0149]

[0150] The calculation result V=0.755 kWh / minute indicates that the average energy usage per minute in the given period has increased by 0.75 kWh under the condition of considering the importance of the time point, and the energy usage shows a low growth trend.

[0151] Please refer to Figure 5 , the method for adjusting the abnormal monitoring threshold of the differentiated period is:

[0152] S311: Collect the energy usage records of the target period and calculate the average value of the energy usage of the target period to obtain average energy usage information;

[0153] S312: Based on the average energy usage information and the energy usage change information, combined with the device aging data, the abnormal monitoring threshold T of the target period is calculated by the formula:

[0154] T=μ T +k T ·σ T ·(1+r T ·t T +α T ·V)

[0155] , wherein μ T is the average energy usage of the target period, σ T is the standard deviation, k T is the sensitivity coefficient, r T is the device aging influence factor, t T is the number of years since the device was installed, α T is the change rate influence coefficient, V is the change rate of energy usage, and T is the abnormal monitoring threshold.

[0156] The formula is:

[0157] T=μ T +k T ·σ T ·(1+r T ·t T +α T ·V)

[0158] The meanings and acquisition methods of the parameters are as follows:

[0159] μ T : represents the average energy usage of the target period in the target period. This is obtained by collecting historical data and averaging the energy usage data in the specified period.

[0160] σ T: represents the standard deviation, i.e. the standard dispersion of energy usage at this site, used to measure the volatility of energy usage at this site. It is calculated based on historical data. The larger the standard deviation, the greater the volatility of energy usage.

[0161] k T : sensitivity coefficient, set according to the importance of the equipment or risk tolerance. It is a preset parameter, usually adjusted based on the potential impact of equipment failure and past experience.

[0162] r T : equipment aging impact factor, reflecting the impact of equipment aging on energy usage. Based on the service life of the equipment and maintenance records.

[0163] t T : the number of years since the installation of the equipment, obtained from the equipment archives. The parameter is used to quantify the service life of the equipment, affecting the calculation of the aging factor.

[0164] α T : change rate impact coefficient, set based on historical change trends and expected risk levels. This parameter is used to adjust the threshold to reflect the impact of the rate of change of energy usage.

[0165] V: the rate of change of energy usage, calculated by the previous method.

[0166] Calculation example:

[0167] Set the following parameters:

[0168] Energy usage record gives μ T = 150 kWh, σ T = 15 kWh, set sensitivity coefficient k T = 1.5, equipment aging impact factor r T = 0.05 (equipment used for more than 20 years, relatively old). Equipment usage time t T = 20 years, change rate impact coefficient α T = 0.1, rate of change of energy usage V = 2 kWh / d.

[0169] Calculate the anomaly monitoring threshold T:

[0170] T = 150 + 1.5 x 15 x (1 + 0.05 x 20 + 0.1 x 2)

[0171] = 150 + 22.5 x (1 + 1 + 0.2)

[0172] = 150 + 22.5 x 2.2

[0173] = 150 + 49.5 = 199.5 kWh

[0174] The calculation result T = 199.5 kWh indicates that the abnormal monitoring threshold is adjusted to 199.5 kWh based on the aging state of the equipment and the recent energy use change rate. This means that if the energy use at the site exceeds this threshold, the system will trigger an abnormal alarm. This dynamic adjustment method can more accurately reflect the actual operating state and potential risks, improving the warning ability and accuracy of the energy monitoring system.

[0175] Please refer to Figure 6 , the acquisition step of the abnormal energy use identification result is:

[0176] S321: Based on the abnormal monitoring threshold T of the target period, real-time receive energy use data, in the target period, compare the energy use amount with the abnormal monitoring threshold, if the energy use amount exceeds the abnormal monitoring threshold, trigger an abnormal alarm, get the abnormal detection information;

[0177] S322: Based on the abnormal detection information, through the formula:

[0178]

[0179] Calculate the energy use abnormality level L, where γ L is the proportional factor, δ L is the adjustment parameter, E c is the energy use amount data,

[0180] T is the abnormal monitoring threshold of the target period, θ L is the baseline threshold, L is the energy use abnormality level;

[0181] S323: Based on the energy use abnormality level L, according to the size of the energy use abnormality level L value, evaluate the abnormality degree of energy use, get the abnormal energy use identification result.

[0182] Formula:

[0183]

[0184] The meanings and acquisition methods of the parameters are as follows:

[0185] γ L : Proportional factor, adjust the mapping intensity of deviation to abnormality level. The coefficient is set according to historical data and expected abnormal sensitivity, the purpose is to ensure that even small energy use changes can reflect the corresponding abnormality level changes.

[0186] δ L : Adjustment parameter, used to measure the threshold T close to the set baseline threshold θ Lsensitivity of the anomaly level calculation. The parameter reflects the criticality of the threshold value, i.e. the closer the threshold value is to the baseline value, the more sensitive the impact on the anomaly level should be.

[0187] θ L : set baseline threshold value, which could be an average energy usage based on long-term observation or a specific value set due to special operating conditions. The value serves as a reference point to help determine when an anomaly alert should be triggered.

[0188] T: anomaly monitoring threshold value for the target period, obtained through previous means.

[0189] E c : energy usage data, directly obtained from the energy monitoring system.

[0190] Calculation example:

[0191] Set energy usage E c = 250 kWh, anomaly monitoring threshold value T = 200 kWh, set baseline threshold value θ L = 180 kWh, proportion factor γ L = 2.5, adjustment parameter δ L = 0.1.

[0192] Calculation process:

[0193] Calculate deviation: E c -T = 250-200 = 50 kWh

[0194] Calculate L:

[0195]

[0196] The calculation result L = 4, indicating that the anomaly level of the current energy usage is 4, which is a moderately high level, suggesting that there are signs of significant deviation from the normal pattern of energy usage, which should attract the attention of relevant operation and maintenance departments.

[0197] Please refer to Figure 7 , the steps to obtain the energy information display result are:

[0198] S411: Based on the anomaly energy usage identification result, for each location, collect energy information, including energy usage, energy usage change rate and energy usage anomaly level, to obtain energy display information association data;

[0199] S412: Based on the energy display information association data, combined with the preset priority of each location, through the formula:

[0200] S E = P·W E ·E c

[0201] and

[0202] S V = P·W V ·V

[0203] and

[0204]

[0205] the display priority S of the energy consumption information E , the display priority S of the energy consumption rate information V and the display priority S of the abnormal energy consumption information L , wherein E c , V and L represent the energy consumption, the energy consumption rate and the energy consumption abnormal level respectively, P is the preset priority of the location, W U , W V and W L are the weights of the energy consumption, the rate and the abnormal level information respectively, ∈ S is the smoothing factor, λ S is the reference threshold of the abnormal level, S E is the display priority of the energy consumption information, S V is the display priority of the energy consumption rate information, and S L is the display priority of the abnormal energy consumption information

[0206] S413: based on the display priority S of the energy consumption information E , the display priority S of the energy consumption rate information V and the display priority S of the abnormal energy consumption information L , the energy consumption information display is hierarchically sorted by comparing the values of S E , S V and S L , the display order of the energy consumption information on the webgis platform is adjusted, and the energy information display result is obtained.

[0207] Formula:

[0208] S E = P·W E ·E c

[0209] and

[0210] S V = P·W V ·V

[0211] and

[0212]

[0213] Meaning and acquisition method of parameters:

[0214] P: Preset priority of the site, usually based on strategic importance or historical risk sensitivity. The numerical value reflects the priority of the site in the overall network, usually determined by management or based on historical data and risk analysis.

[0215] W E , W V , and W L : The weights of energy consumption, rate of change, and abnormal level information, respectively. The weights are determined by energy management strategies to ensure that the importance of the data displayed matches the operational needs.

[0216] ∈ S : Smoothing factor, used to adjust the priority increase speed when the abnormal level is high. The parameter helps balance the influence of abnormal level on priority, preventing excessive adjustment of priority due to extreme data deviation.

[0217] λ S : Reference threshold of abnormal level, used to determine the starting point of priority adjustment. Based on historical abnormal data and potential risk assessment, it is used to determine when to start significantly adjusting the display priority.

[0218] Calculation example:

[0219] Set the following parameters:

[0220] Preset priority of the site P = 10, energy consumption E c = 500kWh, rate of change V = 20kWh / d, abnormal level L = 5, energy consumption information weight W E = 0.008, rate of change information weight W V = 0.3, abnormal information weight W L = 2, smoothing factor ∈ S = 0.05, abnormal level reference threshold λ S = 3.

[0221] Calculation process:

[0222] Display priority of energy consumption information S E :

[0223] S E = P·W E ·E c = 10·0.008·500 = 40

[0224] Display priority of energy consumption rate of change information S V :

[0225] S V = P · W V · V = 10 · 0.3 · 20 = 60

[0226] Display priority of abnormal energy use information S L :

[0227]

[0228] Calculation result energy use information S U = 40, with a lower display priority, change rate information S V = 60, showing a medium-high priority, indicating that its change is not smooth enough and needs immediate attention, abnormal information S L = 91, with the highest value, which needs to be displayed first, indicating that it is a key display information, for the site, in the webgis platform, the abnormal information needs to be displayed first, followed by the change rate information, and finally the energy use information, ensuring that the display priority of the data matches its operational importance.

[0229] A webgis display system for energy management, the webgis display system for energy management is used to execute the above-mentioned webgis display method for energy management, the system comprises:

[0230] The data stability evaluation module is based on the webgis platform, receives the energy use data of the differentiated site within a period of time, evaluates the instability of the energy use of the differentiated site, and obtains energy data instability information;

[0231] The receiving frequency adjustment module adjusts the data receiving frequency based on the energy data instability information and in combination with the standard data receiving frequency, obtains a data receiving frequency adjustment result;

[0232] The energy use change analysis module collects the energy use data of the differentiated site based on the data receiving frequency adjustment result, compares it with the historical contemporaneous energy use data, evaluates the change rate of the energy use, and obtains energy use change information;

[0233] The energy use abnormality analysis module adjusts the abnormal monitoring threshold of the differentiated period based on the energy use change information, in combination with the time period influence and the equipment aging influence, implements abnormal energy monitoring, and evaluates the energy use abnormality level according to the deviation of the energy use data from the abnormal monitoring threshold, and obtains an abnormal energy use identification result;

[0234] The display sequence adjustment module evaluates display priority of the differentiated information on the webgis platform based on the abnormal energy use identification result, according to the information type, in combination with preset priorities of the differentiated places, performs hierarchical sorting on the energy use information display, and obtains an energy information display result.

[0235] The above merely describes the preferred embodiments of the present application, but the present application is not limited to the above. Any person skilled in the art can make modifications or changes to the above disclosed technical contents to obtain equivalent embodiments applied to other fields. However, any simple modification, equivalent change and modification made to the above embodiments without departing from the technical solution of the present application and according to the technical essence of the present application still falls within the protection scope of the present application.

Claims

1. A webgis display method for energy management, characterized in that: The following steps are involved: Based on the WebGIS platform, energy usage data of different locations over a period of time is received, the instability of energy usage at different locations is evaluated, and the data receiving frequency is adjusted based on the standard data receiving frequency to obtain the data receiving frequency adjustment result; Based on the data reception frequency adjustment result, energy usage data of different locations are collected, compared with historical energy usage data of the same period, and the rate of change of energy usage is evaluated to obtain energy usage change information; Based on the energy usage change information, combined with the impact of time periods and equipment aging, the abnormal monitoring thresholds for differentiated time periods are adjusted to implement abnormal energy monitoring. Based on the deviation between the energy usage data and the abnormal monitoring thresholds, the abnormal level of energy usage is evaluated to obtain abnormal energy usage identification results. Based on the abnormal energy usage identification results, the display priority of differentiated information on the WebGIS platform is evaluated according to the information type, and the energy usage information display is hierarchically sorted to obtain the energy information display results.

2. The webgis display method for energy management according to claim 1, characterized in that: The method for evaluating the instability of energy use at different locations is: Based on the WebGIS platform, energy usage data of different locations over a period of time is received. The energy usage data includes the recording time and energy usage of each data point. Based on the energy usage data, the formula: Calculate the instability B of energy data and obtain the instability information of energy data, where x i is the energy usage at the i-th time point, μ B is the weighted average energy usage, w i is the weight of the i-th time point, n is the total number of time points, and B is the instability of energy data.

3. The webgis display method for energy management according to claim 2, characterized in that: The steps for obtaining the data receiving frequency adjustment result are: Based on the energy data instability information, collecting standard data receiving frequency data and benchmark instability to obtain receiving frequency correlation information; Based on the received frequency association information, the formula: Calculate the adjusted data receiving frequency f new , where f new is the adjusted data receiving frequency, f current is the current data receiving frequency, B is the instability of energy data, T base is the benchmark instability, C f is the adjustment factor; Based on the adjusted data receiving frequency f new , adjust the frequency of receiving energy data on the webgis platform and obtain the result of data receiving frequency adjustment.

4. The webgis display method for energy management according to claim 1, characterized in that: The steps for obtaining the energy usage change information are as follows: Based on the data reception frequency adjustment result, receiving energy usage data of different locations, and extracting historical energy usage data corresponding to the same period to obtain energy change related data; Based on the energy change related data, the formula: Calculate the rate of change of energy usage V, where e c，i is the energy usage at the i-th time point in the current period, e h，i is the energy usage at the i-th time point in the same historical period, w V，i is the weight coefficient at the i-th time point, Δt V is the measurement interval, n V is the total number of data points, V represents the rate of change of energy usage; Based on the energy usage change rate V, the energy usage change rate is evaluated according to the magnitude and positive / negative value of the energy usage change rate V to obtain energy usage change information.

5. The webgis display method for energy management according to claim 1, characterized in that: The method for adjusting the abnormality monitoring threshold of the differentiated time period is: Collect energy usage records during the target period and calculate the average energy usage during the target period to obtain average energy usage information; Based on the average energy usage information and energy usage change information, combined with equipment aging data, the formula: T=μ T ·k T ·s T ·(1+r T ·t T +a T ·V) Calculate the abnormal monitoring threshold T of the target period, where μ T is the average energy usage during the target period, σ T is the standard deviation, k T is the sensitivity coefficient, r T is the equipment aging factor, t T is the time in years since the equipment was installed, α T is the change rate influence coefficient, V is the change rate of energy usage, and T is the abnormal monitoring threshold.

6. The webgis display method for energy management according to claim 5, characterized in that: The steps for obtaining the abnormal energy usage identification result are: Based on the abnormality monitoring threshold T of the target period, energy usage data is received in real time. During the target period, the energy usage is compared with the abnormality monitoring threshold. If the energy usage exceeds the abnormality monitoring threshold, an abnormality alarm is triggered to obtain abnormality detection information; Based on the anomaly detection information, the formula: Calculate the energy usage abnormality level L, where γ L is the scaling factor, δ L is the adjustment parameter, E c is the energy usage data, T is the abnormal monitoring threshold of the target period, θ L is the baseline threshold, L is the abnormal level of energy use; Based on the energy usage abnormality level L, the abnormality degree of energy usage is evaluated according to the value of the energy usage abnormality level L, and an abnormal energy usage identification result is obtained.

7. The webgis display method for energy management according to claim 1, characterized in that: The steps for obtaining the energy information display result are: Based on the abnormal energy usage identification results, energy information is collected for each location, the energy information including energy usage, rate of change of energy usage, and abnormal level of energy usage, to obtain energy display information related data; Based on the energy display information association data, combined with the preset priority of each location, the formula is: S E =P·W E ·E c and S V =P·W V ·V and Calculate energy usage information display priority S E , Energy usage change rate information display priority S V and display priority S for abnormal energy usage information L , where E c , V and L represent energy usage, energy usage change rate and energy usage abnormality level respectively, P is the preset priority of the location, W E 、W V and W L are the weights of energy usage, change rate and abnormal level information, ∈ S is the smoothing factor, λ S is the baseline threshold of abnormal level, S E Display priority for energy usage information, S V Display priority for energy usage change rate information, S L Prioritize the display of abnormal energy usage information; Display priority S based on the energy usage information E , Energy usage change rate information display priority S V and display priority S for abnormal energy usage information L , by comparing S E 、S V and S L The energy usage information is hierarchically sorted based on the value, and the display order of the energy usage information on the WebGIS platform is adjusted to obtain the energy information display results.

8. A webgis display system for energy management, characterized in that: The WebGIS display method for energy management according to any one of claims 1 to 7, wherein the system comprises: The data stability assessment module is based on the WebGIS platform, receives energy usage data of different locations over a period of time, assesses the instability of energy usage at different locations, and obtains energy data instability information; The receiving frequency adjustment module adjusts the data receiving frequency based on the energy data instability information and the standard data receiving frequency to obtain a data receiving frequency adjustment result; The energy usage change analysis module collects energy usage data of different locations based on the data reception frequency adjustment result, compares the data with the energy usage data of the same period in history, evaluates the rate of change of energy usage, and obtains energy usage change information; The energy usage anomaly analysis module adjusts the anomaly monitoring thresholds for differentiated time periods based on the energy usage change information, combined with the impact of time periods and equipment aging, implements abnormal energy monitoring, and evaluates the energy usage anomaly level based on the deviation between the energy usage data and the anomaly monitoring thresholds to obtain abnormal energy usage identification results. The display order adjustment module evaluates the display priority of differentiated information on the WebGIS platform based on the abnormal energy usage identification result, according to the information type and the preset priority of the differentiated locations, and hierarchically sorts the energy usage information display to obtain the energy information display result.

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