Energy business internet intelligent management system based on micro-service architecture

Abstract

The invention relates to the technical field of energy service management, in particular to an energy service internet intelligent management system based on a micro-service architecture. The system comprises a power consumption determination module which obtains power consumption data of a user and determines power consumption according to the power consumption data; the sensitivity determination module obtains the meteorological data and the geographic data and determines the power consumption sensitivity according to the meteorological data and the geographic data; the adjustment data determination module obtains the original electricity price and obtains electricity price adjustment data according to the electricity consumption sensitivity; the response time determination module determines the user response time through the electricity price adjustment data and the electricity consumption sensitivity; the electricity utilization information determination module determines response electricity utilization information according to the user response time; and the configuration scheme determination module obtains the power consumption data type, and determines a power resource configuration scheme according to the power consumption data type and the response power consumption information. According to the method, the power consumption of the user after the electricity price adjustment is acquired and analyzed, and the power resource configuration scheme is determined, so that the power resource utilization rate is improved, and idle and even waste of the power resources are avoided.

Description

Technical Field

[0001] The present application relates to the technical field of energy business management, and in particular, to an Internet intelligent management system for energy business based on a microservices architecture. Background Art

[0002] With the rapid development of the economic society, the demand for electricity is also increasing continuously. By exploring electricity data, it can help the electricity management department manage electricity resources and determine electricity prices, so as to maximize the utilization rate of electricity resources.

[0003] However, when managing electricity data, there will be a situation where the electricity data is relatively complex. This complex situation will lead to inaccurate management of electricity data in the system, thereby reducing the allocation efficiency of electricity resources and affecting the determination of electricity prices. Summary of the Invention

[0004] The present application provides an Internet intelligent management system for energy business based on a microservices architecture to solve the above problems.

[0005] In a first aspect, the present application provides an Internet intelligent management system for energy business based on a microservices architecture. The system includes a power consumption determination module, a sensitivity determination module, an adjustment data determination module, a response time determination module, a power consumption information determination module, and a configuration plan determination module. The adjustment data module, the response time determination module, the power consumption information determination module, and the configuration plan determination module are connected in sequence. The sensitivity determination module is connected to the response time determination module and the power consumption determination module respectively:

[0006] The power consumption determination module is used to obtain user power consumption data and determine the power consumption according to the user power consumption data;

[0007] The sensitivity determination module is used to obtain meteorological data and geographical data and determine the power consumption sensitivity according to the meteorological data and the geographical data;

[0008] The adjustment data determination module is used to obtain the original electricity price and obtain the electricity price adjustment data according to the power consumption sensitivity and the original electricity price;

[0009] The response time determination module is used to determine the user response time according to the electricity price adjustment data and the power consumption sensitivity;

[0010] The power consumption information determination module is used to determine the response power consumption and the response power consumption period according to the user response time;

[0011] A configuration scheme determination module, configured to obtain types of electricity consumption data, determine a power resource configuration scheme according to the types of electricity consumption data, the response electricity consumption, and the response electricity consumption period, and send the power resource configuration scheme to a power resource management system, so that the power resource management system configures power resources.

[0012] Through the above technical solution, by comprehensively considering multiple dimensions such as user electricity consumption data, meteorological and geographical data, and original electricity prices, the refinement and personalization of electricity price adjustment are realized, and the efficiency and fairness of power resource allocation are effectively improved. Accurately evaluate the sensitivity of electricity consumption to different factors, and dynamically adjust electricity prices accordingly to encourage users to reduce electricity consumption during peak hours or increase electricity consumption during off-peak hours, thereby balancing the grid load and reducing resource waste. At the same time, by predicting the user response time and formulating a power resource configuration scheme accordingly, the accurate matching of power supply and demand is ensured, the stability and reliability of the power system are improved, and ultimately the efficient utilization and sustainable development of energy are promoted.

[0013] Optionally, when determining the electricity consumption sensitivity according to the meteorological data and the geographical data, the sensitivity determination module is specifically configured to:

[0014] Determine the ambient temperature, ambient humidity, and atmospheric pressure according to the meteorological data;

[0015] Determine the altitude and terrain undulation degree of the electricity consumption area according to the geographical data;

[0016] Determine the historical ambient temperature, historical ambient humidity, and historical atmospheric pressure according to the power resource management system;

[0017] Determine the ambient temperature weight, ambient humidity weight, atmospheric pressure weight, altitude weight, and terrain weight according to the historical ambient temperature, the historical ambient humidity, the historical atmospheric pressure, the altitude, and the terrain undulation degree;

[0018] Determine the preliminary electricity consumption sensitivity according to the ambient temperature weight, the ambient humidity weight, the atmospheric pressure weight, the altitude weight, the terrain weight, the ambient temperature, the ambient humidity, the atmospheric pressure, the altitude, and the terrain undulation degree:

[0019] Q = w1T + w2H + w3P + w4A + w5R;

[0020] Among them, Q represents the preliminary electricity consumption sensitivity, T represents the environmental temperature, H represents the environmental humidity, P represents the atmospheric pressure, A represents the altitude, R represents the degree of terrain undulation, w1 represents the environmental temperature weight, w2 represents the environmental humidity weight, w3 represents the atmospheric pressure weight, w4 represents the altitude weight, and w5 represents the terrain weight;

[0021] Obtain user information, and determine the electricity consumption sensitivity according to the preliminary electricity consumption sensitivity and the user information.

[0022] Through the above technical solution, by introducing detailed meteorological data and geographical data, such as environmental temperature, humidity, atmospheric pressure, altitude and terrain undulation degree, and combining with user information, the preliminary electricity consumption sensitivity is accurately calculated using a comprehensive calculation formula, and then the specific impact on electricity consumption is determined. It more accurately reflects the actual impact of external environmental factors on users' electricity consumption behavior, makes the electricity price adjustment strategy more scientific and reasonable, helps guide users to use electricity rationally under different climate and geographical conditions, promotes the optimal allocation and efficient utilization of electric power resources, and at the same time improves the stability and reliability of the power grid.

[0023] Optionally, when the sensitivity determination module determines the altitude and terrain undulation degree of the electricity consumption area according to the geographical data, it is specifically used for:

[0024] Determine several sets of terrain curvature, altitude and terrain slope angle according to the geographical data;

[0025] Determine the standard deviation of terrain curvature according to the several terrain curvatures;

[0026] Determine the standard deviation of terrain slope angle according to the set of terrain slope angles;

[0027] Obtain the overall image of the electricity consumption area, and determine several building heights according to the overall image;

[0028] Determine the standard deviation of building height according to the several building heights;

[0029] Obtain several regional images according to the overall image;

[0030] Determine several altitudes according to the several regional images and the altitude;

[0031] Determine the standard deviation of altitude according to the several altitudes;

[0032] Determine the terrain curvature weight, altitude weight, terrain slope angle weight and building height weight according to the several terrain curvatures, the several altitudes, the set of terrain slope angles and the several building heights;

[0033] Determine the degree of terrain undulation according to the terrain curvature weight, the altitude weight, the terrain aspect angle weight, the building height weight, the altitude standard deviation, the terrain curvature standard deviation, the terrain aspect angle standard deviation, and the building height standard deviation:

[0034] R = k1·ΔA + k2·ΔC + k3·ΔH + k4·Δθ;

[0035] Wherein, R represents the degree of terrain undulation, ΔA represents the altitude standard deviation, k1 represents the altitude weight, k2 represents the terrain curvature weight, k3 represents the building height weight, ΔC represents the terrain curvature standard deviation, θ represents the set of terrain aspect angles, ΔH represents the building height standard deviation, k4 represents the terrain aspect angle weight, and Δθ represents the terrain aspect angle standard deviation.

[0036] Through the above technical solution, by comprehensively considering multiple factors such as terrain curvature, altitude, set of terrain aspect angles, building height change value, and building density in geographical data, the degree of terrain undulation is accurately calculated using a formula. It more comprehensively and accurately reflects the influence of the terrain complexity and building layout in the power consumption area on power demand and distribution. Based on the actual terrain and building characteristics, it provides a more refined basis for power resource allocation, helps optimize the power grid layout, improve power transmission efficiency, reduce energy losses, and enhance the power grid's ability to respond to emergencies such as natural disasters, thereby ensuring the stability and reliability of power supply.

[0037] Optionally, the user information includes the number of users, the reference power consumption temperature, the reference power consumption humidity, the user type, and the power consumption of each power consumption area. When the sensitivity determination module determines the power consumption sensitivity according to the preliminary power consumption sensitivity and the user information, it specifically is used for:

[0038] Determine the environmental temperature change amount and the environmental humidity change amount within a preset time period according to the meteorological data and the user type;

[0039] Determine the reference power consumption of users within a preset time period according to the power resource management system;

[0040] Obtain the historical environmental temperature, historical environmental humidity, and historical power consumption, and determine the temperature influence coefficient and the humidity influence coefficient according to the historical environmental temperature, the historical environmental humidity, and the historical power consumption;

[0041] Determine the electricity consumption sensitivity based on the number of users, the reference electricity consumption temperature, the reference electricity consumption humidity, the humidity influence coefficient, the temperature influence coefficient, the reference electricity consumption of users, the change in ambient temperature, the preliminary electricity consumption sensitivity, and the change in ambient humidity:

[0042]

[0043] Among them, E represents the electricity consumption sensitivity, Q represents the preliminary electricity consumption sensitivity, N represents the number of users, ΔT represents the change in ambient temperature, and T b represents the reference electricity consumption temperature, ɑ T represents the temperature influence coefficient, ΔH represents the change in ambient humidity, and H b represents the reference electricity consumption humidity, ɑ H represents the humidity influence coefficient, and U represents the reference electricity consumption of users.

[0044] Through the above technical solutions, by combining meteorological data, user types, historical data, and actual electricity consumption of users, a comprehensive evaluation model is constructed to determine the electricity consumption sensitivity. The changes in ambient temperature and humidity are captured in real time, and user types, historical electricity consumption habits, and reference conditions are considered to more accurately quantify the impact of these changes on electricity consumption. It helps power suppliers formulate more scientific and reasonable electricity price adjustment strategies, which can not only effectively encourage users to save electricity under suitable conditions, but also ensure the reasonable allocation and efficient utilization of power resources, and ultimately promote the sustainable development of energy and the energy conservation and emission reduction goals of society.

[0045] Optionally, when the response time determination module determines the user response time according to the electricity price adjustment data and the electricity consumption sensitivity, it is specifically used for:

[0046] Obtain the electricity price adjustment frequency, electricity price fluctuation range, and electricity price adjustment time according to the electricity price adjustment data;

[0047] Obtain the historical electricity consumption behavior of users, and determine the user sensitivity level according to the historical electricity consumption behavior and the user type;

[0048] Determine the user response time according to the user sensitivity level, the electricity price adjustment frequency, the electricity price fluctuation range, the electricity price adjustment time, and the electricity consumption sensitivity, and calculate according to the following formula:

[0049]

[0050] Among them, T r represents the user response time, S represents the user sensitivity level, F prepresents the electricity price adjustment frequency, ΔP represents the electricity price fluctuation range, and T a represents the electricity price adjustment time, and E represents the electricity consumption sensitivity.

[0051] Through the above technical solution, by comprehensively considering the electricity price adjustment data (including frequency, fluctuation range, and adjustment time) and user characteristics (such as historical electricity consumption behavior, user type, and sensitivity level), the user response time is accurately calculated using a formula. More accurately predict the reaction speed of users to electricity price changes, thereby helping power suppliers formulate more precise electricity price adjustment strategies. By understanding the response patterns of users in advance, power suppliers can more effectively guide users to increase electricity consumption when the electricity price is low and reduce electricity consumption when the electricity price is high, thereby balancing the grid load and improving the utilization efficiency of power resources. At the same time, this personalized electricity price adjustment strategy also helps to improve user satisfaction and promote the healthy development of the electricity market.

[0052] Optionally, when the response time determination module determines the user sensitivity level according to the historical electricity consumption behavior and the user type, it specifically is used for:

[0053] Obtain high-sensitivity users and low-sensitivity users according to the user type;

[0054] Determine the change range between the user's electricity consumption and the electricity price adjustment data according to the historical electricity consumption behavior;

[0055] Obtain historical electricity consumption data, and determine the electricity consumption of high-sensitivity users and the electricity consumption of low-sensitivity users according to the historical electricity consumption data;

[0056] Determine the user sensitivity level according to the change range, the electricity consumption of high-sensitivity users, and the electricity consumption of low-sensitivity users.

[0057] Through the above technical solution, by segmenting user types (high-sensitivity and low-sensitivity) and deeply analyzing the change relationship between their historical electricity consumption behavior and electricity price adjustment data, the user sensitivity level is more accurately evaluated. Enabling power suppliers to formulate differentiated electricity price strategies and service measures for different sensitivity user groups. For high-sensitivity users, more flexible and timely electricity price adjustment strategies can be adopted to quickly guide their electricity consumption behavior; while for low-sensitivity users, other incentive means or long-term strategies may be needed to gradually influence their electricity consumption habits. This not only helps to improve the utilization efficiency of power resources but also enhances user satisfaction and promotes the sustainable development of the electricity market.

[0058] Optionally, when the electricity consumption information determination module determines the response electricity consumption and the response electricity consumption period according to the user response time, it specifically is used for:

[0059] Determine a response time window based on the user response time;

[0060] Determine a response time level based on the response time window and the historical electricity consumption behavior;

[0061] Determine the response electricity consumption based on the response time level and the user type;

[0062] Determine key electricity-consuming equipment based on the historical electricity consumption behavior;

[0063] Determine the response electricity consumption period based on the key electricity-consuming equipment, the electricity price adjustment data, and the meteorological data.

[0064] Through the above technical solutions, by constructing a response time window, a response time level, and combining the user type and historical electricity consumption behavior, the response electricity consumption and the response electricity consumption period of the user are accurately predicted and determined. The actual electricity consumption habits and response speeds of users are fully considered, so as to formulate a more scientific and reasonable power resource allocation plan. At the same time, by identifying key electricity-consuming equipment and combining electricity price adjustment and meteorological data, the selection of the electricity consumption period is further optimized to ensure the accurate matching of power supply and demand, improve the overall efficiency and stability of the power system. This not only helps to reduce energy waste, but also enhances the user experience and promotes the healthy development of the power market.

[0065] Optionally, when the configuration plan determination module determines the power resource configuration plan according to the electricity consumption data type, the response electricity consumption, and the response electricity consumption period, it is specifically used for:

[0066] Determine the electricity consumption load curve and the electricity consumption peak according to the electricity consumption data type;

[0067] Determine the peak electricity consumption period and the off-peak electricity consumption period according to the electricity consumption peak and the response electricity consumption period;

[0068] Determine the electricity consumption distribution according to the electricity consumption load curve, the peak electricity consumption period, and the off-peak electricity consumption period;

[0069] Determine the power resource configuration plan according to the electricity consumption distribution and the response electricity consumption period.

[0070] Through the above technical solution, by deeply analyzing the types of electricity consumption data, combining the electricity consumption peak, the responsive electricity consumption period, and the electricity load curve, the peak and low peak electricity consumption periods are accurately divided, and a power resource allocation plan is formulated accordingly. It reflects the dynamic changes of electricity demand in real time, ensuring the reasonable allocation of power resources in terms of time and space. By optimizing the electricity consumption distribution, the power pressure during peak periods is reduced, and the power resources during low peak periods are fully utilized, which not only improves the operating efficiency of the power system, but also promotes the effective utilization of energy and energy conservation and emission reduction. In addition, it helps to reduce the power supply cost, enhance the competitiveness and sustainable development ability of the power market.

[0071] Optionally, when determining the power resource allocation plan according to the electricity consumption distribution and the responsive electricity consumption period, the configuration plan determination module is specifically configured to:

[0072] Determine the electricity consumption distribution of each region according to the electricity consumption distribution;

[0073] Determine the electricity consumption demand of the electrical equipment according to the responsive electricity consumption period;

[0074] Determine a demand response strategy according to the electricity consumption distribution, the electricity consumption demand, and the response time window;

[0075] Determine the power resource allocation plan according to the demand response strategy and the electricity consumption peak.

[0076] Through the above technical solution, by comprehensively considering the electricity consumption distribution, the electricity consumption demand of electrical equipment, the response time window, and the electricity consumption peak, an accurate demand response strategy is formulated, and a power resource allocation plan is determined accordingly. The power supply is adjusted in real time and dynamically to match the electricity consumption demands of different regions and equipment. Especially during peak periods, users' electricity consumption behaviors are guided through the demand response strategy to effectively relieve the pressure on the power grid. This refined resource allocation not only improves the flexibility and reliability of the power system, but also promotes the efficient utilization of energy and energy conservation and emission reduction. At the same time, it also enhances the response speed and adjustment ability of the power market, contributing to the balance and sustainable development of power supply and demand.

[0077] In a second aspect, the present application provides an energy business Internet intelligent management method based on a microservice architecture, and the method includes:

[0078] Obtain user electricity consumption data, and determine the electricity consumption according to the user electricity consumption data;

[0079] Obtain meteorological data and geographical data, and determine the electricity consumption sensitivity according to the meteorological data and the geographical data;

[0080] Obtain the original electricity price, and obtain electricity price adjustment data according to the electricity consumption sensitivity and the original electricity price;

[0081] Determine the user response time according to the electricity price adjustment data and the electricity consumption sensitivity;

[0082] Determine the responsive electricity consumption and the responsive electricity consumption period according to the user response time;

[0083] Obtain the type of electricity consumption data, determine the power resource allocation plan according to the type of electricity consumption data, the responsive electricity consumption and the responsive electricity consumption period, and send the power resource allocation plan to the power resource management system so that the power resource management system configures the power resources. Description of the Drawings

[0084] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0085] Figure 1 A schematic diagram of an application scenario provided by an embodiment of the present application;

[0086] Figure 2 A schematic diagram of the structure of an energy service Internet intelligent management system based on a microservice architecture provided by an embodiment of the present application;

[0087] Figure 3 A flowchart of an energy service Internet intelligent management method based on a microservice architecture provided by an embodiment of the present application. Detailed Embodiments

[0088] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0089] In addition, the term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after unless otherwise specified.

[0090] The following further describes the embodiments of the present application in detail with reference to the drawings in the specification.

[0091] When managing power data, there will be complex situations of power data. The emergence of complex situations will lead to inaccurate management of power data in the system, thus reducing the allocation efficiency of power resources and affecting the determination of power prices.

[0092] Based on this, the present application provides an Internet intelligent management system for energy business based on a microservices architecture. By obtaining and analyzing user power consumption data, meteorological data, and geographical data in real time, it accurately evaluates the power consumption and the degree to which it is affected by external factors, thereby flexibly adjusting the electricity price strategy. This not only promotes the rational allocation of power resources but also encourages users to adjust their power consumption behaviors at different times, realizing the response management of the power demand side. At the same time, the power resource allocation plan formulated according to the user response time and power consumption type further improves the flexibility and efficiency of the power supply system, ensures the stable operation of the power grid, effectively reduces energy waste and costs, and provides strong support for building a green and sustainable energy system.

[0093] Figure 1 This is a schematic diagram of an application scenario provided by the present application. When managing power resources, the method provided by the present application is applied to obtain and analyze the power consumption of users within the response time after the electricity price adjustment, thereby determining the power resource allocation plan.

[0094] Specifically, the method provided by the present application is applied to any server. The server interacts with the power resource management system. The server determines the power resource allocation plan by obtaining and analyzing the user power consumption information in the power resource management system and sends the power resource allocation plan to the power resource management system. The power resource management system can be a system for allocating and managing power resources. Among them, the power resource management system can include information such as user power consumption, the time period corresponding to the user power consumption, and peak power consumption periods. By comprehensively and accurately obtaining and analyzing the power consumption of users within the response time after the electricity price adjustment, the determination of the power resource allocation plan is made more scientific, reasonable, and effective, thus avoiding the idleness and even waste of power resources.

[0095] The specific implementation method can refer to the following embodiments.

[0096] Figure 2 This is a schematic diagram of the structure of an Internet intelligent management system for energy business based on a microservices architecture provided by an embodiment of the present application. The system of this embodiment can be applied to the server in the above scenario. As Figure 2As shown, the energy business Internet intelligent management system 200 based on the microservices architecture includes: a power consumption determination module 201, a sensitivity determination module 202, an adjustment data determination module 203, a response time determination module 204, a power consumption information determination module 205, and a configuration plan determination module 206.

[0097] The power consumption determination module 201 is used to obtain user power consumption data and determine the power consumption according to the user power consumption data.

[0098] The sensitivity determination module 202 is used to obtain meteorological data and geographical data and determine the power consumption sensitivity according to the meteorological data and geographical data.

[0099] The adjustment data determination module 203 is used to obtain the original electricity price and obtain the electricity price adjustment data according to the power consumption sensitivity and the original electricity price.

[0100] The response time determination module 204 is used to determine the user response time according to the electricity price adjustment data and the power consumption sensitivity.

[0101] The power consumption information determination module 205 is used to determine the response power consumption and the response power consumption period according to the user response time.

[0102] The configuration plan determination module 206 is used to obtain the power consumption data type and determine the power resource configuration plan according to the power consumption data type, the response power consumption, and the response power consumption period.

[0103] Optionally, the sensitivity determination module 202 is specifically used to: determine the ambient temperature, ambient humidity, and atmospheric pressure according to the meteorological data; determine the altitude and terrain undulation degree of the power consumption area according to the geographical data; determine the historical ambient temperature, historical ambient humidity, and historical atmospheric pressure according to the power resource management system; determine the ambient temperature weight, ambient humidity weight, atmospheric pressure weight, altitude weight, and terrain weight according to the historical ambient temperature, the historical ambient humidity, the historical atmospheric pressure, the altitude, and the terrain undulation degree; determine the preliminary power consumption sensitivity according to the ambient temperature weight, the ambient humidity weight, the atmospheric pressure weight, the altitude weight, the terrain weight, the ambient temperature, the ambient humidity, the atmospheric pressure, the altitude, and the terrain undulation degree:

[0104] Q = w1T + w2H + w3P + w4A + w5R;

[0105] Among them, Q represents the preliminary electricity consumption sensitivity, T represents the environmental temperature, H represents the environmental humidity, P represents the atmospheric pressure, A represents the altitude, R represents the degree of terrain undulation, w1 represents the environmental temperature weight, w2 represents the environmental humidity weight, w3 represents the atmospheric pressure weight, w4 represents the altitude weight, and w5 represents the terrain weight;

[0106] Obtain user information, and determine the electricity consumption sensitivity according to the preliminary electricity consumption sensitivity and the user information.

[0107] Optionally, the sensitivity determination module 202 is specifically configured to: determine a plurality of terrain curvature, altitude, and terrain slope angle sets according to the geographical data; determine the terrain curvature standard deviation according to the plurality of terrain curvatures; determine the terrain slope angle standard deviation according to the terrain slope angle set; obtain the overall image of the power consumption area, and determine a plurality of building heights according to the overall image; determine the building height standard deviation according to the plurality of building heights; obtain a plurality of regional images according to the overall image; determine a plurality of altitudes according to the plurality of regional images and the altitude; determine the altitude standard deviation according to the plurality of altitudes; determine the terrain curvature weight, altitude weight, terrain slope angle weight, and building height weight according to the plurality of terrain curvatures, the plurality of altitudes, the terrain slope angle set, and the plurality of building heights; determine the degree of terrain undulation according to the terrain curvature weight, the altitude weight, the terrain slope angle weight, the building height weight, the altitude standard deviation, the terrain curvature standard deviation, the terrain slope angle standard deviation, and the building height standard deviation:

[0108] R = k1·ΔA + k2·ΔC + k3·ΔH + k4·Δθ;

[0109] Among them, R represents the degree of terrain undulation, ΔA represents the altitude standard deviation, k1 represents the altitude weight, k2 represents the terrain curvature weight, k3 represents the building height weight, ΔC represents the terrain curvature standard deviation, θ represents the terrain slope angle set, ΔH represents the building height standard deviation, k4 represents the terrain slope angle weight, and Δθ represents the terrain slope angle standard deviation.

[0110] Optionally, the sensitivity determination module 202 is specifically configured to: determine the environmental temperature change amount and the environmental humidity change amount within a preset time period according to meteorological data and user types; determine the user reference power consumption within a preset time period according to the power resource management system; obtain historical environmental temperature, historical environmental humidity, and historical power consumption, and determine the temperature influence coefficient and the humidity influence coefficient according to the historical environmental temperature, historical environmental humidity, and historical power consumption; determine the power consumption sensitivity according to the number of users, the reference power consumption temperature, the reference power consumption humidity, the humidity influence coefficient, the temperature influence coefficient, the user power consumption, the environmental temperature change amount, the preliminary power consumption sensitivity, and the environmental humidity change amount, and calculate according to the following formula:

[0111]

[0112] Among them, E represents the power consumption sensitivity, Q represents the preliminary power consumption sensitivity, N represents the number of users, ΔT represents the environmental temperature change amount, T b represents the reference power consumption temperature, α T represents the temperature influence coefficient, ΔH represents the environmental humidity change amount, H b represents the reference power consumption humidity, α H represents the humidity influence coefficient, and U represents the user power consumption.

[0113] Optionally, the response time determination module 204 is specifically configured to: obtain the electricity price adjustment frequency, the electricity price fluctuation range, and the electricity price adjustment time according to the electricity price adjustment data; obtain the historical electricity consumption behavior of the user, and determine the user sensitivity according to the historical electricity consumption behavior and user types; determine the user response time according to the user sensitivity, the electricity price adjustment frequency, the electricity price fluctuation range, the electricity price adjustment time, and the power consumption sensitivity, and calculate according to the following formula:

[0114]

[0115] Among them, T r represents the user response time, S represents the user sensitivity, F p represents the electricity price adjustment frequency, ΔP represents the electricity price fluctuation range, T a represents the electricity price adjustment time, and E represents the power consumption sensitivity.

[0116] Optionally, the response time determination module 204 is specifically configured to: obtain high-sensitivity users and low-sensitivity users according to user types; determine the change range of the user power consumption and the electricity price adjustment data according to the historical electricity consumption behavior; obtain the historical electricity consumption data, and determine the power consumption of high-sensitivity users and the power consumption of low-sensitivity users according to the historical electricity consumption data; determine the user sensitivity according to the change range, the power consumption of high-sensitivity users, and the power consumption of low-sensitivity users.

[0117] Optionally, the electricity consumption information determination module 205 is specifically configured to: determine a response time window according to the user response time; determine a response time level according to the response time window and the historical electricity consumption behavior; determine the response electricity consumption according to the response time level and the user type; determine the key electricity consumption equipment according to the historical electricity consumption behavior; determine the response electricity consumption period according to the key electricity consumption equipment, the electricity price adjustment data, and the meteorological data.

[0118] Optionally, the configuration plan determination module 206 is specifically configured to: determine the electricity load curve and the electricity consumption peak according to the electricity consumption data type; determine the peak electricity consumption period and the low peak electricity consumption period according to the electricity consumption peak and the response electricity consumption period; determine the electricity consumption distribution according to the electricity load curve, the peak electricity consumption period, and the low peak electricity consumption period; determine the power resource configuration plan according to the electricity consumption distribution and the response electricity consumption period.

[0119] Optionally, the configuration plan determination module 206 is specifically configured to: determine the electricity consumption distribution of each region according to the electricity consumption distribution; determine the electricity consumption demand of the electricity consumption equipment according to the response electricity consumption period; determine the demand response strategy according to the electricity consumption distribution, the electricity consumption demand, and the response time window; determine the power resource configuration plan according to the demand response strategy and the electricity consumption peak.

[0120] Figure 3 The flowchart of a method for intelligent management of an energy business Internet based on a microservice architecture provided by an embodiment of the present application is as Figure 3 shown, and the method includes:

[0121] S301. Obtain user electricity consumption data, and determine the electricity consumption according to the user electricity consumption data.

[0122] The user electricity consumption data may be data related to user electricity consumption when performing power resource allocation.

[0123] Specifically, since when performing power resource allocation, it is necessary to perform power resource allocation according to the obtained electricity consumption to ensure the accuracy of power resource allocation.

[0124] Obtain the user electricity consumption data from the power resource management system, and extract the electricity consumption from the user electricity consumption data.

[0125] S302. Obtain meteorological data and geographical data, and determine the electricity consumption sensitivity according to the meteorological data and the geographical data.

[0126] The electricity consumption sensitivity may be the degree of influence of the meteorological data and the geographical data on the electricity consumption.

[0127] The meteorological data may be data related to the meteorology of the area where power resource allocation needs to be performed. The meteorological data may include the ambient temperature, the ambient humidity, and the atmospheric pressure.

[0128] Geographical data can be data about the geographical environment of the area where power resource allocation is to be carried out. The geographical data can include the altitude and terrain undulation degree of the power consumption area.

[0129] Specifically, during the power resource allocation process, meteorological factors and geographical factors will both affect the power consumption of users to a certain extent. Therefore, these factors need to be considered to better determine the impact of these factors on the power consumption of users.

[0130] Obtain meteorological data from the meteorological website of the area where power resource allocation is to be carried out, and obtain geographical data from the meteorological website of the area where power resource allocation is to be carried out. Extract the ambient temperature, ambient humidity, and atmospheric pressure from the meteorological data, and extract the altitude and terrain undulation degree of the power consumption area from the geographical data. Use mathematical analysis methods to analyze the ambient temperature, ambient humidity, atmospheric pressure, altitude, and terrain undulation degree of the power consumption area to determine the power consumption sensitivity.

[0131] S303. Obtain the original electricity price, and obtain the electricity price adjustment data based on the power consumption sensitivity and the original electricity price.

[0132] The original electricity price can be the price of power resources before the electricity price is adjusted.

[0133] The electricity price adjustment data can be data about power resources after the price of power resources is adjusted according to the power consumption of users. The electricity price adjustment data can include the electricity price and the time of electricity price adjustment.

[0134] Specifically, during the power resource allocation process, the power consumption sensitivity will affect the users' use of power resources to a certain extent, thus affecting the power consumption of users. Therefore, it is necessary to determine the electricity price adjustment data based on the power consumption sensitivity and the original electricity price, providing a basis for the subsequent determination of the user response time.

[0135] Obtain the original electricity price from the power resource management system. Extract the tiered electricity price and the electricity price structure for each time period from the original electricity price. The electricity price structure can be the percentage of increase or decrease in electricity price adjustment before determining the current electricity price adjustment data. Extract the tiered electricity price levels and the electricity prices corresponding to each tiered electricity price level from the tiered electricity price. Obtain the electricity price and the electricity consumption limit corresponding to each tiered electricity price level from the tiered electricity price levels. The electricity consumption limit can be a range divided based on the numerical value of electricity consumption. Obtain the time corresponding to each time period and the percentage of increase or decrease in electricity price for each time period from the electricity price structure of each time period. Based on the electricity price and the electricity consumption limit corresponding to each tiered electricity price level obtained from the above steps, adjust the percentage of increase or decrease in electricity price for each time period according to the electricity consumption sensitivity to obtain the adjusted percentage of increase or decrease. Multiply the adjusted percentage of increase or decrease by the electricity price corresponding to each tiered electricity price level to obtain the adjusted electricity price. Determine the adjusted electricity price as the electricity price adjustment data.

[0136] For example, the tiered electricity price is: the first tier (0 - 100 kWh): 0.5 yuan / kWh, the second tier (100 - 200 kWh): 0.6 yuan / kWh, the third tier (above 200 kWh): 0.8 yuan / kWh. The time period electricity price structure is: peak period (8:00 - 20:00): electricity price increases by 20%, valley period (20:00 - 24:00): no adjustment, off-peak period (0:00 - 8:00): electricity price decreases by 10%.

[0137] The electricity consumption sensitivity is divided into three levels: high electricity consumption sensitivity, medium electricity consumption sensitivity, and low electricity consumption sensitivity.

[0138] Based on the above situation, the determined electricity price adjustment data is: when the electricity consumption sensitivity is high: increase the increase percentage in the peak period from 20% to 30%, that is, adjust to: the first tier: 0.65 yuan / kWh (originally 0.6 yuan / kWh), the second tier: 0.78 yuan / kWh (originally 0.72 yuan / kWh), the third tier: 1.04 yuan / kWh (originally 0.96 yuan / kWh). When the electricity consumption sensitivity is medium: adjust the decrease percentage in the valley period from 10% to 15%, that is, adjust to: the first tier: 0.425 yuan / kWh (originally 0.45 yuan / kWh), the second tier: 0.51 yuan / kWh (originally 0.54 yuan / kWh), the third tier: 0.68 yuan / kWh (originally 0.72 yuan / kWh). When the electricity consumption sensitivity is low, the electricity price remains unchanged.

[0139] S304. Determine the user response time according to the electricity price adjustment data and the electricity consumption sensitivity.

[0140] The user response time can be the time from when the electricity price is adjusted to when the user makes an adjustment to their electricity consumption behavior.

[0141] Specifically, after the electricity price is adjusted, different users have different reactions to the electricity price. At this time, it can be understood that the response time of users to the electricity price is different. The different response times of users determine that the electricity consumption within the response time of users is also different, which will directly affect the subsequent allocation of power resources.

[0142] Therefore, obtain the time of electricity price adjustment from the electricity price adjustment data obtained in the above steps. Based on the electricity consumption sensitivity level obtained in the above steps, obtain the electricity consumption of users within a certain period of time after the electricity price adjustment from the power resource management system. Obtain the historical electricity consumption before the electricity price adjustment from the power resource management system. According to the historical electricity consumption, predict the electricity consumption of users within a certain period of time in the future. Subtract the historical electricity consumption from the electricity consumption of users within a certain period of time in the future to obtain the subtracted value of electricity consumption, and take the absolute value of the subtracted value of electricity consumption to obtain the electricity consumption threshold. Subtract the electricity consumption of users within a certain period of time after the electricity price adjustment from the electricity consumption of users within a certain period of time in the future to obtain the subtracted value, and take the absolute value of the subtracted value to obtain the electricity consumption difference. Compare the electricity consumption difference with the electricity consumption threshold. If the electricity consumption difference is not within the range of the electricity consumption threshold, then determine the start time corresponding to a certain period of time after the electricity price adjustment as the time when the user adjusts their own electricity consumption behavior after the electricity price adjustment. Subtract the time when the user adjusts their own electricity consumption behavior after the electricity price adjustment from the electricity price adjustment time to obtain the user response time.

[0143] S305. Determine the response electricity consumption and the response electricity consumption period according to the user response time.

[0144] The response electricity consumption can be the electricity consumption of the user within the user response time.

[0145] The response electricity consumption period can be the period from the time corresponding to the electricity price adjustment to the time when the user changes their own electricity consumption behavior after the electricity price adjustment. For example, if the electricity price adjustment time is 8:00 and the time when the user changes their own electricity consumption behavior is 20:00, then the response electricity consumption period can be 8:00 - 20:00 at this time.

[0146] Specifically, since the user response times are not exactly the same, therefore, the response times corresponding to different types of users or each user in the same user type are also different. It is necessary to determine the response electricity consumption and the corresponding electricity consumption period according to the user response time.

[0147] Since the power resource management system needs to determine the power resource allocation plan according to the user's response power consumption and response power consumption period, it is necessary to obtain the power consumption of the user during the response time and the period corresponding to the power consumption during the response time from the power resource management system. The power consumption of the user during the response time is determined as the response power consumption. The period corresponding to the power consumption during the response time is determined as the response power consumption period.

[0148] S306. Obtain the type of power consumption data. According to the type of power consumption data, response power consumption, and response power consumption period, determine the power resource allocation plan, and send the power resource allocation plan to the power resource management system for execution.

[0149] The type of power consumption data can be the type corresponding to the power consumption data of different users. The type of power consumption data can be industrial power consumption data and commercial power consumption data.

[0150] The power resource allocation plan can be a plan for adjusting the electricity price and a plan for adjusting the electricity supply according to the user type.

[0151] Specifically, obtain the type of power consumption data, the real-time power consumption of the user, and the power consumption of the user within a certain period before reacting to the electricity price adjustment data from the power resource management system. According to the power consumption of the user within a certain period before reacting to the electricity price adjustment data, predict the power consumption of the user within a certain period in the future, and determine the power consumption of the user within a certain period in the future as the power consumption demand of the user. According to the type of power consumption data, response power consumption, and response power consumption period, determine the peak shaving measures for power resources. The peak shaving measures for power resources can be to deliver the power resources other than those that meet the power demand of the user to other power consumption areas. Determine the peak shaving measures for power resources determined in the above steps as the power resource allocation plan. For example, the type of power consumption data of a certain factory is industrial power consumption, and the response power consumption is: 2000 kWh per hour during peak hours and 1000 kWh per hour during off-peak hours. The response power consumption period is from 10 pm to 6 am. At this time, the power resource allocation plan is: Peak shaving measure: Under the condition of meeting the power consumption required by the factory, deliver the remaining power resources supplied to the factory to other power consumption areas.

[0152] Through the method provided in this embodiment, by comprehensively considering multiple dimensions such as user electricity consumption data, meteorological and geographical data, and original electricity prices, the refinement and personalization of electricity price adjustment are achieved, effectively improving the efficiency and fairness of power resource allocation. Accurately evaluate the sensitivity of electricity consumption to different factors, and dynamically adjust the electricity price accordingly to encourage users to reduce electricity consumption during peak hours or increase electricity consumption during off-peak hours, thereby balancing the grid load and reducing resource waste. At the same time, by predicting the user response time and formulating a power resource allocation plan accordingly, the accurate matching of power supply and demand is ensured, improving the stability and reliability of the power system, and ultimately promoting the efficient utilization and sustainable development of energy.

[0153] In some embodiments, according to meteorological data, determine the ambient temperature, ambient humidity, and atmospheric pressure; according to geographical data, determine the altitude and terrain undulation degree of the electricity consumption area; according to the power resource management system, determine the historical ambient temperature, historical ambient humidity, and historical atmospheric pressure; according to the historical ambient temperature, historical ambient humidity, historical atmospheric pressure, altitude, and terrain undulation degree, determine the ambient temperature weight, ambient humidity weight, atmospheric pressure weight, altitude weight, and terrain weight; according to the ambient temperature weight, ambient humidity weight, atmospheric pressure weight, altitude weight, terrain weight, ambient temperature, ambient humidity, atmospheric pressure, altitude, and terrain undulation degree, calculate according to formula (1) to determine the preliminary electricity consumption sensitivity; obtain user information, and according to the preliminary electricity consumption sensitivity and user information, determine the electricity consumption sensitivity:

[0154] Q = w1T + w2H + w3P + w4A + w5R (1)

[0155] Wherein, Q represents the preliminary electricity consumption sensitivity, T represents the ambient temperature, H represents the ambient humidity, P represents the atmospheric pressure, A represents the altitude, R represents the terrain undulation degree, w1 represents the ambient temperature weight, w2 represents the ambient humidity weight, w3 represents the atmospheric pressure weight, w4 represents the altitude weight, and w5 represents the terrain weight.

[0156] Since the electricity consumption sensitivity is also affected by other factors in addition to the above environmental and geographical factors, if only the environmental and geographical factors are considered, there will be a certain error in the determination of the electricity consumption sensitivity. Therefore, during the calculation process, the impact of environmental and geographical factors on electricity consumption can be calculated first, and this impact is called the preliminary electricity consumption sensitivity. In the specific implementation method, in addition to considering the impact of the above environmental and geographical factors on the preliminary electricity consumption, the impact of other factors on electricity consumption can also be considered, such as building height, building density, and building area, so as to further accurately determine the electricity consumption sensitivity.

[0157] The preliminary electricity consumption sensitivity can be the preliminary impact degree of environmental and geographical factors on the user's electricity consumption.

[0158] Specifically, the environment and terrain altitude factors in the power consumption area will affect the amount of power resources used by users to a certain extent. Therefore, it is necessary to consider the influence of environmental and terrain altitude factors on power consumption to make the determination of the influence of power consumption more accurate and reasonable.

[0159] Obtain historical environmental temperature, historical environmental humidity, and historical atmospheric pressure from the power resource management system. Use the linear regression algorithm to analyze historical environmental temperature, historical environmental humidity, historical atmospheric pressure, altitude, and terrain undulation degree to obtain environmental temperature weight, environmental humidity weight, atmospheric pressure weight, altitude weight, and terrain weight. Extract environmental temperature, environmental humidity, and atmospheric pressure from meteorological data, and extract altitude and terrain undulation degree from geographical data. Use the mathematical analysis algorithm to analyze environmental temperature weight, environmental humidity weight, atmospheric pressure weight, altitude weight, terrain weight, environmental temperature, environmental humidity, atmospheric pressure, altitude, and terrain undulation degree, and calculate according to formula (1) to determine the preliminary power consumption sensitivity.

[0160] Among them, in formula (1): w1, w2, w3, w4, and w5 respectively reflect the importance and relative contribution of environmental temperature, environmental humidity, atmospheric pressure, altitude, and terrain undulation degree to the influence of power consumption. Through the weighted sum method, the influence of different factors can be integrated to make the calculation of the preliminary power consumption sensitivity more comprehensive.

[0161] Based on the power consumption obtained in the above steps, select the power consumption within the first preset time period. Divide the first preset time period to obtain several time periods and the power consumption corresponding to each time period. Divide the power consumption corresponding to each time period obtained in the above steps by the power consumption within the preset time period to obtain the power consumption sensitivity of each time period. Divide the power consumption sensitivity of each time period by the preliminary power consumption sensitivity to obtain the power consumption sensitivity.

[0162] Through the method provided in this embodiment, by introducing detailed meteorological data and geographical data, such as environmental temperature, humidity, atmospheric pressure, altitude, and terrain undulation degree, and combining user information, the preliminary power consumption sensitivity is accurately calculated using the comprehensive calculation formula, and then the specific influence on power consumption is determined. It more accurately reflects the actual influence of external environmental factors on users' electricity consumption behavior, makes the electricity price adjustment strategy more scientific and reasonable, helps to guide users to use electricity reasonably under different climate and geographical conditions, promotes the optimal allocation and efficient utilization of power resources, and at the same time improves the stability and reliability of the power grid.

[0163] In some embodiments, according to geographical data, a number of sets of terrain curvature, altitude, and terrain aspect angle are determined; according to the number of terrain curvatures, the standard deviation of terrain curvature is determined; according to the set of terrain aspect angles, the standard deviation of terrain aspect angles is determined; an overall image of the power consumption area is obtained, and according to the overall image, a number of building heights are determined; according to the number of building heights, the standard deviation of building heights is determined; according to the overall image, a number of regional images are obtained; according to the number of regional images and altitude, a number of altitudes are determined; according to the number of altitudes, the standard deviation of altitude is determined; according to the number of terrain curvatures, the number of altitudes, the set of terrain aspect angles, and the number of building heights, the terrain curvature weight, altitude weight, terrain aspect angle weight, and building height weight are determined; according to the terrain curvature weight, altitude weight, terrain aspect angle weight, building height weight, altitude standard deviation, terrain curvature standard deviation, terrain aspect angle standard deviation, and building height standard deviation, calculate according to formula (2) to determine the degree of terrain undulation:

[0164] R = k1·ΔA + k2·ΔC + k3·ΔH + k4·Δθ (2)

[0165] Wherein, R represents the degree of terrain undulation, ΔA represents the altitude standard deviation, k1 represents the altitude weight, k2 represents the terrain curvature weight, k3 represents the building height weight, ΔC represents the terrain curvature standard deviation, θ represents the set of terrain aspect angles, ΔH represents the building height standard deviation, k4 represents the terrain aspect angle weight, and Δθ represents the terrain aspect angle standard deviation.

[0166] Specifically, to a certain extent, the degree of terrain undulation will be affected by building and geographical factors. On the basis of the same altitude within a certain range, if the variation range of the building heights within this range is too large, the building height will affect the determination of the degree of terrain undulation within this range at this time. At the same time, factors such as altitude and terrain curvature will also affect the determination of the degree of terrain undulation to a certain extent. Therefore, these factors need to be considered to make the determination of the degree of terrain undulation more accurate and reasonable.

[0167] Extract the terrain curvature, altitude, and terrain aspect angle set from the geographical data. Obtain the overall image of the power consumption area from the power resource management system, and use image analysis algorithms to analyze the overall image to determine several building heights. Since the area for power resource allocation is large, there will also be significant differences in altitude within a large area. If the image of the entire area is directly analyzed, the degree of altitude change in the entire area obtained will be difficult to accurately reflect the altitude change in the entire area. Therefore, it is necessary to use an image segmentation algorithm to divide the overall image into equal-area segments to obtain several regional images. Based on the altitude obtained from the above steps, use image analysis algorithms to analyze the several regional images to determine the altitude of each region. Subtract the altitude of adjacent regions to obtain the subtraction result, and take the absolute value of the subtraction result to obtain the altitude change value. Take the arithmetic mean of several terrain curvatures to obtain the average terrain curvature. Subtract each of the several terrain curvatures from the average terrain curvature to obtain the subtraction results of several terrain curvatures. Take the arithmetic square value of the subtraction results of several terrain curvatures to obtain several terrain curvature square values. Add up several terrain curvature square values to obtain the terrain curvature square value. Perform an arithmetic square root calculation on the terrain curvature square value to obtain the terrain curvature standard deviation. Use the above calculation logic for calculating the terrain curvature standard deviation to calculate the altitude standard deviation, building height standard deviation, and terrain aspect angle standard deviation.

[0168] Use a linear analysis algorithm to analyze several terrain curvatures, several altitudes, the terrain aspect angle set, and several building heights to obtain the terrain curvature weight, altitude weight, terrain aspect angle weight, and building height weight.

[0169] Use a mathematical analysis algorithm to analyze the terrain curvature weight, altitude weight, terrain aspect angle weight, building height weight, altitude standard deviation, terrain curvature standard deviation, terrain aspect angle standard deviation, and building height standard deviation, and calculate according to formula (2) to determine the degree of terrain undulation.

[0170] Among them, in formula (2): By comprehensively considering the standard deviations of altitude, terrain curvature, building height, and aspect angle, the degree of terrain undulation is quantified. The standard deviation of each factor represents the degree of change of the corresponding feature, reflecting multiple aspects related to the terrain within the region. By introducing weight coefficients (k1, k2, k3, k4), the formula allows for flexible adjustment according to the terrain characteristics of a specific region. For example, in some areas, altitude may be more important than building height, and in this case, the value of k1 can be appropriately increased while reducing other weights. At the same time, the formula includes geographical features in multiple dimensions, making the evaluation of terrain undulation more comprehensive. By combining the standard deviations of these different features, the complexity of the terrain can be more accurately described.

[0171] Through the method provided in this embodiment, by comprehensively considering multiple factors such as terrain curvature, altitude, terrain aspect angle set, building height change value, and building density in geographical data, the degree of terrain undulation is accurately calculated using a formula. It can more comprehensively and accurately reflect the influence of the terrain complexity and building layout in the power consumption area on power demand and distribution. Based on the actual terrain and building characteristics, it provides a more refined basis for power resource allocation, helps optimize the power grid layout, improves power transmission efficiency, reduces energy loss, and enhances the ability of the power grid to respond to emergencies such as natural disasters, thereby ensuring the stability and reliability of power supply.

[0172] In some embodiments, according to meteorological data and user types, the environmental temperature change amount and environmental humidity change amount within a preset time period are determined; according to the power resource management system, the user reference power consumption within the preset time period is determined; historical environmental temperature, historical environmental humidity, and historical power consumption are obtained, and based on the historical environmental temperature, historical environmental humidity, and historical power consumption, the temperature influence coefficient and humidity influence coefficient are determined; according to the number of users, reference power consumption temperature, reference power consumption humidity, humidity influence coefficient, temperature influence coefficient, user reference power consumption, environmental temperature change amount, preliminary power consumption sensitivity, and environmental humidity change amount, calculate according to formula (3) to determine the power consumption sensitivity:

[0173]

[0174] Among them, E represents the power consumption sensitivity, Q represents the preliminary power consumption sensitivity, N represents the number of users, ΔT represents the environmental temperature change amount, T b represents the reference power consumption temperature, α T represents the temperature influence coefficient, ΔH represents the environmental humidity change amount, H b represents the reference power consumption humidity, α H represents the humidity influence coefficient, and U represents the user reference power consumption.

[0175] The preset time period can be a time period set to obtain the user reference power consumption. The preset time period can be one day or one week. Within the preset time period, the environmental temperature and environmental humidity in the area where the user is located change little, so as to reduce the influence of environmental factors on the determination process of the user reference power consumption when determining the user reference power consumption.

[0176] The reference power consumption temperature can be the environmental temperature when the user does not need additional electrical equipment. The additional electrical equipment can be equipment that needs additional cooling or heating in a comfortable environment.

[0177] The reference power consumption humidity can be the environmental humidity when the user does not need additional electrical equipment.

[0178] The user's baseline power consumption can be the power consumption of the user under the conditions of the baseline power consumption temperature and the baseline power consumption humidity within a preset period.

[0179] Specifically, in the process of determining the power consumption sensitivity, environmental factors and user types will affect the determination of power consumption sensitivity to a certain extent. These factors need to be considered to ensure the accuracy of the determination of power consumption sensitivity.

[0180] Extract the ambient temperature, the ambient humidity at the start, the ambient temperature at the end, and the ambient humidity at the end within the preset period from the meteorological data obtained in the above steps. Subtract the ambient temperature at the start from the ambient temperature at the end to obtain the temperature subtraction result. Take the absolute value of the temperature subtraction result to obtain the ambient temperature change. Subtract the ambient humidity at the start from the ambient humidity at the end to obtain the humidity subtraction result, and take the absolute value of the humidity subtraction result to obtain the ambient humidity change. Obtain the historical ambient temperature, historical ambient humidity, and historical power consumption from the meteorological website of the area where power resource allocation is required, and use the linear regression method to analyze the historical ambient temperature, historical ambient humidity, and historical power consumption to determine the temperature influence coefficient and the humidity influence coefficient. Obtain the power consumption under the comfortable environment within the preset period from the power resource management system. Obtain several ambient temperatures and several ambient humidities corresponding to the power consumption under the comfortable environment within the preset period from the meteorological website of the area where power resource allocation is required. Use the linear regression method to analyze the power consumption under the comfortable environment within the preset period, several ambient temperatures and several ambient humidities corresponding to the power consumption under the comfortable environment within the preset period to obtain several ambient temperature weights corresponding to several ambient temperatures. Select the ambient temperature corresponding to the minimum ambient temperature weight from several ambient temperature weights as the baseline power consumption temperature. Select the ambient humidity corresponding to the minimum ambient humidity weight from several ambient humidity weights as the baseline power consumption humidity. Use a mathematical analysis algorithm to analyze the number of users, the baseline power consumption temperature, the baseline power consumption humidity, the humidity influence coefficient, the temperature influence coefficient, the user power consumption, the ambient temperature change, the preliminary sensitivity, and the ambient humidity change, and calculate according to formula (3) to determine the power consumption sensitivity of the current season.

[0181] Among them, in formula (3): The preliminary power consumption sensitivity Q is the initial estimate of the power consumption sensitivity, indicating the power consumption when factors such as the number of users and temperature and humidity changes are not considered. Q and N are combined by multiplication because they are amplification or reduction factors for the overall power consumption impact. Indicates the relative impact of temperature and humidity changes on power consumption, which is the core part of the power consumption sensitivity. This part determines the comprehensive impact of temperature and humidity on power consumption by calculating the relative changes of temperature and humidity and the influence coefficients. Represents the relative proportion of temperature change, reflecting the difference degree between the current temperature and the baseline temperature. It reflects the degree of influence brought about by temperature changes. Similar to temperature changes, the impact of humidity changes on electricity consumption also needs to consider the sensitivity differences of users to humidity changes. It reflects the different sensitivities of different users to humidity changes. Humidity changes will affect the use of equipment such as air conditioners, dehumidifiers, or humidifiers, thereby affecting electricity demand. U, as a base number, reflects the electricity consumption level of users under the reference humidity and reference temperature conditions for electricity consumption. Multiplying by U aims to reflect the additional electricity demand caused by environmental factors based on the electricity consumption under reference conditions. Each factor plays a different role in the sensitivity of the final electricity consumption, and these factors are not independent. They jointly determine the overall change in electricity consumption.

[0182] Through the method provided in this embodiment, by combining meteorological data, user types, historical data, and the actual electricity consumption of users, a comprehensive evaluation model is constructed to determine the sensitivity of electricity consumption. The changes in environmental temperature and humidity are captured in real time, and user types, historical electricity consumption habits, and reference conditions are considered, so as to more accurately quantify the impact of these changes on electricity consumption. It helps power suppliers formulate more scientific and reasonable electricity price adjustment strategies, which can not only effectively encourage users to save electricity under suitable conditions, but also ensure the reasonable allocation and efficient utilization of power resources, and ultimately promote the sustainable development of energy and the energy conservation and emission reduction goals of society.

[0183] In some embodiments, according to the electricity price adjustment data, the electricity price adjustment frequency, the electricity price fluctuation range, and the electricity price adjustment time are obtained; the historical electricity consumption behavior of users is acquired, and according to the historical electricity consumption behavior and user types, the user sensitivity level is determined; according to the user sensitivity level, the electricity price adjustment frequency, the electricity price fluctuation range, the electricity price adjustment time, and the electricity consumption sensitivity, calculate according to formula (4) to determine the user response time:

[0184]

[0185] Among them, T r represents the user response time, S represents the user sensitivity level, F p represents the electricity price adjustment frequency, ΔP represents the electricity price fluctuation range, T a represents the electricity price adjustment time, and E represents the electricity consumption sensitivity.

[0186] The user sensitivity level can be a value that measures the sensitivity of users to electricity price adjustment data.

[0187] Specifically, the adjusted electricity price, the number of electricity price adjustments in the current season, and the number of electricity price adjustments throughout the year are extracted from the electricity price adjustment data. Divide the number of electricity price adjustments in the current season by the number of electricity price adjustments throughout the year to obtain the electricity price adjustment frequency. Subtract the original electricity price obtained in the above steps from the adjusted electricity price to get the electricity price subtraction value, and take the absolute value of the electricity price subtraction value to obtain the electricity price fluctuation range. Extract the moments corresponding to each electricity price adjustment from the time corresponding to the number of electricity price adjustments, and determine the moments corresponding to each electricity price adjustment as the electricity price adjustment moments. Obtain the user's historical electricity consumption behavior from the power resource management system, extract the historical electricity consumption, historical electricity consumption moments, and historical electricity consumption frequency corresponding to the user type from the historical electricity consumption behavior, and use the linear regression algorithm to analyze the historical electricity consumption and historical electricity consumption frequency to obtain the historical electricity consumption weight. Multiply the historical electricity consumption weight by the historical electricity consumption, historical electricity consumption frequency, and historical electricity consumption moments respectively to get the multiplication results, and add up the multiplication results to obtain the user sensitivity. Use the mathematical analysis algorithm to analyze the user sensitivity, electricity price adjustment frequency, electricity price fluctuation range, electricity price adjustment moments, and electricity consumption sensitivity, and calculate according to formula (4) to determine the user response time.

[0188] Among them, in formula (4): represents the reciprocal of the sensitivity, which means that the higher the sensitivity, the shorter the user response time, that is, the easier it is for users to quickly respond to electricity price adjustments. For example, sensitive users will adjust their electricity consumption behavior faster, while insensitive users will respond slower. represents the mutual relationship between the electricity price adjustment frequency, fluctuation range, and adjustment moments, as well as the comprehensive impact on the user response time. This part reflects the impact of the characteristics of electricity price changes on the user response time by combining the electricity price adjustment frequency, fluctuation range, and adjustment moments.

[0189] (the reciprocal of the electricity consumption sensitivity): E represents the electricity consumption sensitivity, which depends on the electricity consumption changes brought about by factors such as environmental temperature and humidity. The greater the electricity consumption change, the faster the user's response to electricity price changes. Therefore, E is inversely proportional to the user response time. The introduction of is because when the electricity consumption changes greatly, users may adjust their electricity consumption habits more quickly to cope with electricity price changes. For example, when the temperature rises, it may increase the use of air conditioners, resulting in a sharp increase in electricity consumption. At this time, users will be more sensitive to the change in electricity price and thus quickly adjust their electricity consumption behavior.

[0190] Formula (4) adopts a multiplicative form, reflecting the combined effect of various factors on the user response time. The user's response time is not determined by a single factor, but by the combined effect of multiple factors. The multiplicative design method can capture the complex interactions between these factors.

[0191] Through the method provided in this embodiment, by comprehensively considering the electricity price adjustment data (including frequency, fluctuation range, and adjustment time) and user characteristics (such as historical electricity consumption behavior, user type, and sensitivity), the user response time is accurately calculated using a formula. More accurately predict the user's reaction speed to electricity price changes, thereby helping power suppliers formulate more precise electricity price adjustment strategies. By understanding the user's response pattern in advance, power suppliers can more effectively guide users to increase electricity consumption when the electricity price is low and reduce electricity consumption when the electricity price is high, thus balancing the power grid load and improving the utilization efficiency of power resources. At the same time, this personalized electricity price adjustment strategy also helps to improve user satisfaction and promote the healthy development of the power market.

[0192] In some embodiments, according to the user type, high-sensitivity users and low-sensitivity users are obtained; according to the historical electricity consumption behavior, the change range of the user's electricity consumption and the electricity price adjustment data is determined; historical electricity consumption data is obtained, and according to the historical electricity consumption data, the electricity consumption of high-sensitivity users and the electricity consumption of low-sensitivity users are determined; according to the change range, the electricity consumption of high-sensitivity users, and the electricity consumption of low-sensitivity users, the user sensitivity is determined.

[0193] High-sensitivity users can be users who adjust their electricity consumption within a relatively short time after the electricity price adjustment. The relatively short time can be 1 - 3 days.

[0194] Low-sensitivity users can be users who adjust their electricity consumption within a relatively long time after the electricity price adjustment. The relatively long time can be 4 - 7 days.

[0195] Specifically, after the electricity price is adjusted, different users have different sensitivities to the adjusted electricity price. At this time, the electricity consumption corresponding to different users is also different, and the electricity consumption of different users also reflects the user's sensitivity to a certain extent. Therefore, it is necessary to determine the user's sensitivity according to some information of the user.

[0196] Obtain the response time corresponding to the user type from the power resource management system, and determine high-sensitivity users and low-sensitivity users according to the time corresponding to the user type. For example, household users with a response time within one week are determined as high-sensitivity users, and household users with a response time of more than 7 days are determined as low-sensitivity users. Obtain the change range of the historical user electricity consumption and the electricity price adjustment data from the historical electricity consumption behavior. Based on the high-sensitivity users and low-sensitivity users determined in the above steps, extract the electricity consumption of high-sensitivity users and the electricity consumption of low-sensitivity users from the historical electricity consumption data obtained in the above steps. Use a mathematical analysis algorithm to analyze the change range, the electricity consumption of high-sensitivity users, and the electricity consumption of low-sensitivity users to determine the user sensitivity.

[0197] Through the method provided in this embodiment, by segmenting user types (high-sensitivity and low-sensitivity) and deeply analyzing the change relationship between their historical electricity consumption behaviors and electricity price adjustment data, the sensitivity of users can be evaluated more precisely. This enables power suppliers to formulate differentiated electricity price strategies and service measures for user groups with different sensitivities. For high-sensitivity users, more flexible and timely electricity price adjustment strategies can be adopted to quickly guide them to adjust their electricity consumption behaviors; while for low-sensitivity users, other incentive means or long-term strategies may be needed to gradually influence their electricity consumption habits. This not only helps improve the utilization efficiency of power resources, but also enhances user satisfaction and promotes the sustainable development of the power market.

[0198] In some embodiments, according to the user response time, a response time window is determined; according to the response time window and historical electricity consumption behaviors, a response time level is determined; according to the response time level and user type, the response electricity consumption is determined; according to historical electricity consumption behaviors, key electricity-consuming devices are determined; according to the key electricity-consuming devices, electricity price adjustment data, and meteorological data, the response electricity consumption period is determined.

[0199] The response time window can be the time range corresponding to the user's electricity consumption reaction to the electricity price adjustment data.

[0200] The response time level can be the level determined after dividing the response time.

[0201] The response electricity consumption period can be the period corresponding to the user's response time.

[0202] Historical electricity consumption behaviors can be the electricity consumption patterns and habits of users within a preset period. Historical electricity consumption behaviors can include changes in electricity consumption, distribution of electricity consumption time, and responses to different electricity prices.

[0203] Specifically, the time period from when a user receives electricity price adjustment data to when the user adjusts their own electricity consumption behavior or electricity consumption habit is different for different users, that is, the user response time corresponding to different users is also different. At this time, it is necessary to determine the user response time to provide a reference basis for the subsequent determination of the response electricity consumption period.

[0204] Determine the user response time obtained in the above steps as the response time window. Since the user response time determined in the above steps is determined in real time, the response time window level is also determined in real time. Taking the time corresponding to the response time window as the benchmark, divide the time corresponding to the response time window to obtain the corresponding time level, and determine the corresponding time level as the response time window level. For example, set the response time window of 3 days as level one, the response time window of 6 days as level two, and the case without a response time window (when the user does not make any adjustments to electricity consumption after receiving the electricity price adjustment data) as level three. According to the response time level, obtain the electricity consumption of the user within the response time level, and determine the electricity consumption of the user within the response time level as the response electricity consumption. For example, when the response time is level one, obtain the electricity consumption of the user within these three days, and determine the electricity consumption of the user within these three days as the response electricity consumption.

[0205] Obtain the electricity load curve, electricity consumption, electricity peak period, and electricity valley period corresponding to the historical electricity consumption behavior within a preset period from the power resource management system. Analyze the electricity consumption, electricity peak period, and electricity valley period corresponding to the historical electricity consumption behavior to obtain the user's electricity consumption pattern. Based on the user's electricity consumption pattern, analyze the electricity load curve to obtain the fluctuation law of electricity consumption. According to the fluctuation law of electricity consumption and the user type, determine the key electricity-consuming equipment. For example, it is determined from the historical electricity consumption behavior that in a certain high-temperature and high-humid environment, the electricity consumption of users in a certain community surges. At this time, determine the air conditioners and other refrigeration equipment of the users as the key equipment. According to the key electricity-consuming equipment, electricity price adjustment data, and meteorological data, determine the response electricity consumption period.

[0206] Through the method provided in this embodiment, by constructing a response time window, a response time level, and combining the user type and historical electricity consumption behavior, accurately predict and determine the user's response electricity consumption and response electricity consumption period. Fully consider the actual electricity consumption habits and response speed of users, so as to formulate a more scientific and reasonable power resource allocation plan. At the same time, by identifying the key electricity-consuming equipment and combining the electricity price adjustment and meteorological data, further optimize the selection of the electricity consumption period to ensure the accurate matching of power supply and demand, and improve the overall efficiency and stability of the power system. This not only helps to reduce energy waste, but also enhances the user experience and promotes the healthy development of the power market.

[0207] In some embodiments, according to the type of electricity consumption data, determine the electricity load curve and electricity peak; according to the electricity peak and the response electricity consumption period, determine the electricity peak period and the electricity valley period; according to the electricity load curve, the electricity peak period, and the electricity valley period, determine the electricity distribution; according to the electricity distribution and the response electricity consumption period, determine the power resource allocation plan.

[0208] The electricity distribution can be the electricity distribution situation in a certain period of the area where power resource allocation is required.

[0209] Specifically, during the power resource allocation process, the allocation of power resources is affected by the specific electricity consumption of users at different times. Therefore, it is necessary to allocate power resources according to specific electricity consumption factors to maximize the utilization of power resources.

[0210] Extract the maximum electricity consumption of users within the response time from the power resource management system, and determine the maximum electricity consumption of users within the response time as the electricity peak. According to the electricity peak, user response time, and response electricity consumption period, determine the peak electricity consumption period and the off-peak electricity consumption period. For example, the user response time of a commercial building is 1 day. The electricity peak of this day is: morning peak: from 8 am to 12 noon. Midday peak: from 12 noon to 3 pm. Evening peak: from 5 pm to 6 pm. Considering comprehensively, the peak electricity consumption period can be defined as from 8 am to 6 pm, when the electricity demand in the building is the largest and the power load is close to the maximum value. The electricity consumption of the building drops significantly between 6 pm and 8 am on weekdays, especially between 10 pm and 6 am. Therefore, the off-peak electricity consumption period can be determined as from 10 pm to 6 am, when the power load is the lowest and the pressure on the power grid is also the smallest.

[0211] Since the area for power supply allocation by the power resource management system for power resources is fixed, it is necessary to obtain and based on the electricity consumption characteristics of a certain power supply allocation area, provide a reference basis for the determination of subsequent electricity consumption distribution. Obtain the electricity consumption of a certain power supply allocation area within the response time from the power resource management system, divide the period corresponding to the response time into several equal periods, and obtain the electricity consumption of several equal periods. Compare the electricity consumption of several equal periods to obtain the period corresponding to the highest electricity consumption and the period corresponding to the lowest electricity consumption. Determine the period corresponding to the highest electricity consumption and the period corresponding to the lowest electricity consumption as the peak electricity consumption period and the off-peak electricity consumption period respectively, and determine the peak electricity consumption period and the off-peak electricity consumption period as the electricity consumption distribution.

[0212] Based on the user types obtained from the above steps, determine the time periods corresponding to the electricity consumption load and the peak electricity consumption load according to the electricity consumption distribution and the responsive electricity consumption time periods. According to the electricity consumption load and the peak value corresponding to the electricity consumption load, determine the power resource allocation plan. For example, there are two types of users, namely the industrial park and the commercial area in a certain city. Among them, the industrial park in a certain city has a large electricity consumption load, and the commercial area has a large electricity consumption load between 9 am and 5 pm on weekdays. The electricity consumption load in the residential area reaches the peak in the early morning and evening. At this time, for the peak electricity consumption demands of the commercial area and the industrial area, the power resource allocation plan formulated is: time-of-use electricity price policy to encourage users to reduce electricity consumption during peak hours. During the low electricity consumption period, the load distribution can be optimized. The power resource allocation plan for the residential area is: when the electricity consumption load in the residential area is low at night, while meeting the electricity consumption needs of the residential area, the remaining power resources are transmitted to other areas with higher electricity consumption demands at night.

[0213] Through the method provided in this embodiment, by deeply analyzing the types of electricity consumption data, combining the electricity consumption peak, the responsive electricity consumption time period, and the electricity load curve, accurately divide the peak and low electricity consumption time periods, and formulate a power resource allocation plan accordingly. Reflect the dynamic changes in electricity demand in real time to ensure the reasonable allocation of power resources in terms of time and space. By optimizing the electricity consumption distribution, reduce the power pressure during peak hours, and at the same time make full use of the power resources during low hours, which not only improves the operation efficiency of the power system, but also promotes the effective utilization of energy and energy conservation and emission reduction. In addition, it helps to reduce the power supply cost, enhance the competitiveness and sustainable development ability of the power market.

[0214] In some embodiments, according to the electricity consumption distribution, determine the electricity consumption distribution of each region; according to the responsive electricity consumption time period, determine the electricity consumption demand of the electrical equipment; according to the electricity consumption distribution, electricity consumption demand, and response time window, determine the demand response strategy; according to the demand response strategy and the electricity consumption peak, determine the power resource allocation plan.

[0215] The electricity consumption demand can be the required amount of electricity consumption of the user's electrical equipment in daily production and life.

[0216] Specifically, based on the several regions divided by the above steps, obtain the peak and low electricity consumption time periods of the several regions within a day from the power resource management system, and determine the electricity consumption distribution of each region based on the peak and low electricity consumption time periods of the several regions within a day. Obtain the corresponding electricity consumption during the responsive electricity consumption time period from the power resource management system, and predict the electricity consumption demand of the electrical equipment based on the corresponding electricity consumption during the response period and the user types obtained from the above steps.

[0217] According to the electricity consumption demands of electrical equipment, determine the electricity consumption distribution of specific electrical equipment. According to the response time window, determine the peak electricity consumption period and the low electricity consumption period within the response time window. According to the electricity consumption distribution of specific electrical equipment and the peak and low electricity consumption periods within the response time window, determine the demand response strategy. For example, the electricity consumption demands of a certain community are for facilities such as air conditioners, water heaters, lighting, and electric vehicle chargers. The electricity consumption distribution is in two periods: from 6:00 to 8:00 in the morning and from 6:00 to 10:00 in the evening. The low consumption period appears from 11:00 at night to 6:00 in the early morning of the next day. The response time window is the peak electricity consumption period (from 6:00 to 10:00 in the evening). At this time, the determined demand response strategy is: during the peak electricity consumption period (such as from 6:00 to 10:00 in the evening), dispatch power resources through large-scale power infrastructure (such as hydropower stations and wind power stations) to reduce the power load during the peak period. The response strategy for the low consumption period: during the low electricity consumption period (such as from 11:00 at night to 6:00 in the early morning of the next day), while meeting the electricity consumption during the low period, allocate the remaining power resources to other regions with higher electricity consumption demands to meet the electricity consumption needs of high-electricity-consumption regions, thereby improving the overall load rate of the power grid and reducing power waste.

[0218] According to the demand response strategy, determine the electricity consumption characteristics corresponding to different user types. According to the electricity consumption characteristics corresponding to different user types, determine the electricity consumption characteristics during the peak electricity consumption period and the electricity consumption characteristics during the low electricity consumption period. According to the electricity consumption characteristics during the peak electricity consumption period, the electricity consumption characteristics during the low electricity consumption period, and the electricity consumption peak value, determine the maximum electricity consumption value during the peak electricity consumption period and the minimum electricity consumption value during the low electricity consumption period. Obtain the peak electricity consumption frequency of each region during the current season's electricity consumption from the power resource management system. According to the maximum electricity consumption value during the peak electricity consumption period, the minimum electricity consumption value during the low electricity consumption period, and the peak electricity consumption frequency of each region during the current season's electricity consumption obtained from the above steps, predict the electricity consumption demands of each electricity consumption region within a certain period in the future. According to the electricity consumption demands of each electricity consumption region within a certain period in the future, determine the power resource allocation plan.

[0219] Through the method provided in this embodiment, by comprehensively considering the electricity consumption distribution, the demands of electrical equipment, the response time window, and the electricity consumption peak value, a precise demand response strategy is formulated, and based on this, the power resource allocation plan is determined. Adjust the power supply in real-time and dynamically to match the electricity consumption demands of different regions and equipment. Especially during the peak period, guide users to adjust their electricity consumption behaviors through the demand response strategy to effectively relieve the pressure on the power grid. This refined resource allocation not only improves the flexibility and reliability of the power system but also promotes the efficient utilization of energy and energy conservation and emission reduction. At the same time, it also enhances the response speed and regulation ability of the power market, contributing to the balance and sustainable development of power supply and demand.

Claims

1. An energy business Internet intelligent management system based on a microservices architecture, characterized in that, It includes a power consumption determination module, a sensitivity determination module, an adjustment data determination module, a response time determination module, a power consumption information determination module, and a configuration plan determination module. The adjustment data module, the response time determination module, the power consumption information determination module, and the configuration plan determination module are connected in sequence. The sensitivity determination module is connected to the response time determination module and the power consumption determination module respectively: The power consumption determination module is used to obtain user power consumption data and determine the power consumption according to the user power consumption data; The sensitivity determination module is used to obtain meteorological data and geographical data and determine the power consumption sensitivity according to the meteorological data and the geographical data; The adjustment data determination module is used to obtain the original electricity price and obtain the electricity price adjustment data according to the power consumption sensitivity and the original electricity price; The response time determination module is used to determine the user response time according to the electricity price adjustment data and the power consumption sensitivity; The power consumption information determination module is used to determine the responsive power consumption and the responsive power consumption period according to the user response time; The configuration plan determination module is used to obtain the type of power consumption data, determine the power resource configuration plan according to the type of power consumption data, the responsive power consumption, and the responsive power consumption period, and send the power resource configuration plan to the power resource management system so that the power resource management system configures the power resources.

2. The system according to claim 1, characterized in that When the power consumption sensitivity determination module determines the power consumption sensitivity according to the meteorological data and the geographical data, it is used to: Determine the ambient temperature, ambient humidity, and atmospheric pressure according to the meteorological data; Determine the altitude and terrain undulation degree of the power consumption area according to the geographical data; Determine the historical ambient temperature, historical ambient humidity, and historical atmospheric pressure according to the power resource management system; Determine the ambient temperature weight, ambient humidity weight, atmospheric pressure weight, altitude weight, and terrain weight according to the historical ambient temperature, historical ambient humidity, historical atmospheric pressure, altitude, and terrain undulation degree; Determine the preliminary power consumption sensitivity according to the ambient temperature weight, ambient humidity weight, atmospheric pressure weight, altitude weight, terrain weight, ambient temperature, ambient humidity, atmospheric pressure, altitude, and terrain undulation degree: Q = w1T + w2H + w3P + w4A + w5R; Wherein, Q represents the preliminary power consumption sensitivity, T represents the ambient temperature, H represents the ambient humidity, P represents the atmospheric pressure, A represents the altitude, R represents the terrain undulation degree, w1 represents the ambient temperature weight, w2 represents the ambient humidity weight, w3 represents the atmospheric pressure weight, w4 represents the altitude weight, and w5 represents the terrain weight; Obtain user information and determine the power consumption sensitivity according to the preliminary power consumption sensitivity and user information.

3. The system according to claim 2, wherein When the sensitivity determination module determines the altitude and terrain undulation degree of the power consumption area according to the geographical data, it is used to: Determine a number of sets of terrain curvature, altitude, and terrain aspect angle based on the geographical data; Determine the standard deviation of terrain curvature according to the number of terrain curvatures; Determine the standard deviation of terrain aspect angle according to the set of terrain aspect angles; Obtain the overall image of the power consumption area, and determine a number of building heights according to the overall image; Determine the standard deviation of building height according to the number of building heights; Obtain a number of regional images according to the overall image; Determine a number of altitudes according to the number of regional images and the altitude; Determine the standard deviation of altitude according to the number of altitudes; Determine the terrain curvature weight, altitude weight, terrain aspect angle weight, and building height weight according to the number of terrain curvatures, the number of altitudes, the set of terrain aspect angles, and the number of building heights; Determine the degree of terrain undulation according to the terrain curvature weight, the altitude weight, the terrain aspect angle weight, the building height weight, the standard deviation of altitude, the standard deviation of terrain curvature, the standard deviation of terrain aspect angle, and the standard deviation of building height: R = k1·ΔA + k2·ΔC + k3·ΔH + k4·Δθ; Wherein, R represents the degree of terrain undulation, ΔA represents the standard deviation of altitude, k1 represents the altitude weight, k2 represents the terrain curvature weight, k3 represents the building height weight, ΔC represents the standard deviation of terrain curvature, θ represents the set of terrain aspect angles, ΔH represents the standard deviation of building height, k4 represents the terrain aspect angle weight, and Δθ represents the standard deviation of terrain aspect angle.

4. The system according to claim 2, wherein The user information includes the number of users, power consumption reference temperature, power consumption reference humidity, user type, and user power consumption of each power consumption area. When determining the power consumption sensitivity according to the preliminary power consumption sensitivity and the user information, the sensitivity determination module is used for: Determine the environmental temperature change amount and environmental humidity change amount within a preset time period according to the meteorological data and the user type; Determine the user reference power consumption within a preset time period according to the power resource management system; Obtain historical environmental temperature, historical environmental humidity, and historical power consumption, and determine the temperature influence coefficient and humidity influence coefficient according to the historical environmental temperature, the historical environmental humidity, and the historical power consumption; Determine the power consumption sensitivity according to the number of users, the power consumption reference temperature, the power consumption reference humidity, the humidity influence coefficient, the temperature influence coefficient, the user reference power consumption, the environmental temperature change amount, the preliminary power consumption sensitivity, and the environmental humidity change amount; Among them, E represents the electricity consumption sensitivity, Q represents the preliminary electricity consumption sensitivity, N represents the number of users, ΔT represents the environmental temperature change, and T b represents the electricity consumption reference temperature, and α T represents the temperature influence coefficient, ΔH represents the environmental humidity change, and H b represents the electricity consumption reference humidity, and α H represents the humidity influence coefficient, and U represents the user reference electricity consumption.

5. The system according to claim 4, wherein When determining the user response time according to the electricity price adjustment data and the power consumption sensitivity, the response time determination module is used for: Obtain the electricity price adjustment frequency, electricity price fluctuation range, and electricity price adjustment time according to the electricity price adjustment data; Obtain the historical electricity consumption behavior of the user, and determine the user sensitivity according to the historical electricity consumption behavior and the user type; Determine the user response time according to the user sensitivity level, the electricity price adjustment frequency, the electricity price fluctuation range, the electricity price adjustment time, and the electricity consumption sensitivity, and calculate according to the following formula: Among them, T r represents the user response time, S represents the user sensitivity, F p represents the electricity price adjustment frequency, ΔP represents the electricity price fluctuation range, T a represents the electricity price adjustment time, and E represents the electricity consumption sensitivity.

6. The system according to claim 5, wherein When determining the user sensitivity level according to the historical electricity consumption behavior and the user type, the response time determination module is used for: Obtain high-sensitivity users and low-sensitivity users according to the user type; Determine the change range between the user's electricity consumption and the electricity price adjustment data according to the historical electricity consumption behavior; Obtain historical electricity consumption data, and determine the electricity consumption of high-sensitivity users and the electricity consumption of low-sensitivity users according to the historical electricity consumption data; Determine the user sensitivity level according to the change range, the electricity consumption of high-sensitivity users, and the electricity consumption of low-sensitivity users.

7. The system according to claim 6, wherein When determining the response electricity consumption and the response electricity consumption period according to the user response time, the electricity consumption information determination module is used for: Determine the response time window according to the user response time; Determine the response time level according to the response time window and the historical electricity consumption behavior; Determine the response electricity consumption according to the response time level and the user type; Determine the key electricity-consuming equipment according to the historical electricity consumption behavior; Determine the response electricity consumption period according to the key electricity-consuming equipment, the electricity price adjustment data, and the meteorological data.

8. The system according to claim 7, wherein When determining the power resource allocation plan according to the electricity consumption data type, the response electricity consumption, and the response electricity consumption period, the configuration plan determination module is used for: Determine the electricity consumption load curve and the electricity consumption peak according to the electricity consumption data type; Determine the peak electricity consumption period and the low electricity consumption period according to the electricity consumption peak and the response electricity consumption period; Determine the electricity consumption distribution according to the electricity consumption load curve, the peak electricity consumption period, and the low electricity consumption period; Determine the power resource allocation plan according to the electricity consumption distribution and the response electricity consumption period.

9. The system according to claim 8, wherein When determining the power resource allocation plan according to the electricity consumption distribution and the response electricity consumption period, the configuration plan determination module is used for: Determine the electricity consumption distribution of each region according to the electricity consumption distribution; Determine the electricity consumption demand of the electricity-consuming equipment according to the response electricity consumption period; Determine the demand response strategy according to the electricity consumption distribution, the electricity consumption demand, and the response time window; Determine the power resource allocation plan according to the demand response strategy and the electricity consumption peak.

10. An intelligent management method for the energy business Internet based on a microservices architecture, characterized in that Include: Obtain the user's electricity consumption data, and determine the electricity consumption according to the user's electricity consumption data; Obtain meteorological data and geographical data, and determine the electricity consumption sensitivity according to the meteorological data and the geographical data; Obtain the original electricity price, and obtain the electricity price adjustment data according to the electricity consumption sensitivity and the original electricity price; Determine the user response time according to the electricity price adjustment data and the electricity consumption sensitivity; Determine the response electricity consumption and the response electricity consumption period according to the user response time; Obtain the electricity consumption data type, determine the power resource allocation plan according to the electricity consumption data type, the response electricity consumption amount, and the response electricity consumption period, and send the power resource allocation plan to the power resource management system so that the power resource management system can allocate power resources.

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