Resource scheduling method and device and storage medium

Abstract

The application provides a resource scheduling method and device and a storage medium, and relates to the technical field of smart cities. First meteorological information of a prediction duration is acquired, the first meteorological information comprising first time-sharing air temperature data and a first meteorological condition, the first time-sharing air temperature data is fitted to obtain a first air temperature change curve function of air temperature change over time within the prediction duration, in a preset corresponding relationship between meteorological conditions and key parameters, a first key parameter corresponding to the first meteorological condition is matched, based on the first air temperature change curve function, the first key parameter and a first equivalent total heat dissipation area of a heating building corresponding to a current heating season, a heating gas load of the prediction duration is determined, and resource scheduling within the prediction duration is performed according to the heating gas load. The application more accurately quantifies the equivalent total heat dissipation area of the heating building, comprehensively considers the climate condition factor, effectively improves the prediction accuracy of the heating gas load, and improves the accuracy of resource scheduling.

Description

Technical Field

[0001] This application relates to the field of smart city technology, and in particular to a resource scheduling method, device and storage medium. Background Technology

[0002] With the development of the gas industry and the increase in gas consumption, many cities face various issues such as pipeline planning, gas source conversion, or expansion of gas supply scale. Moreover, the uneven distribution of gas consumption is becoming increasingly prominent. Peak shaving and gas storage have become common, thorny, and urgent problems faced by all cities, and are essential information that pipeline dispatching and planning departments must possess. Urban gas load forecasting plays a crucial role in the planning, design, operation, and scheduling of gas transmission and distribution systems, and is of great significance to the economy, safety, and reliability of the gas supply system.

[0003] Existing urban gas load forecasting methods mostly rely on historical patterns of daily gas load changes for urban heating to predict future daily gas loads for urban heating. However, the forecast results are prone to deviations, leading to unreasonable phenomena such as resource waste. Summary of the Invention

[0004] This application provides a resource scheduling method, apparatus, and storage medium to solve the problem of inaccurate daily load forecasting results for urban gas heating.

[0005] In a first aspect, this application provides a resource scheduling method, comprising: acquiring first meteorological information for a predicted duration, the first meteorological information including first hourly temperature data and first meteorological conditions; performing fitting processing on the first hourly temperature data to obtain a first temperature change curve function of temperature changing with hourly variations within the predicted duration; matching a first key parameter corresponding to the first meteorological conditions in a preset correspondence between meteorological conditions and key parameters, the first key parameter being data affecting the heat dissipation of urban heating buildings; determining the heating gas load for the predicted duration based on the first temperature change curve function, the first key parameter, and the first equivalent total heat dissipation area of ​​the heating buildings corresponding to the current heating season; and performing resource scheduling within the predicted duration based on the heating gas load.

[0006] Optionally, the heating gas load for the predicted duration is determined based on the first temperature change curve function, the first key parameter, and the first equivalent total heat dissipation area of ​​the heating building corresponding to the current heating season. This includes: determining the first total heat dissipation of the heating building within the predicted duration based on the objective function, the first key parameter, and the first equivalent total heat dissipation area. The objective function is used to reflect the correlation between the temperature change curve function, the key parameter, the equivalent total heat dissipation area of ​​the heating building, and the total heat dissipation of the heating building; and determining the heating gas load as the ratio of the first total heat dissipation to the calorific value of natural gas.

[0007] Optionally, key parameters include the daily equivalent average convective heat transfer coefficient, the average absorption coefficient of the building envelope to solar radiation, and the solar radiation intensity. Based on the objective function, and according to the first temperature change curve function, the first key parameters, and the first equivalent total heat dissipation area, the first total heat dissipation of the heating building within the forecast period is determined, including: determining the first total heat dissipation of the heating building within the forecast period according to the following formula:

[0008]

[0009] Where Φ represents the first total heat dissipation of the heated building within the predicted time period, τ represents the time-of-use factor, h represents the daily equivalent average convective heat transfer coefficient, S represents the first equivalent total heat dissipation area of ​​the heated building, ρ represents the average absorption coefficient of solar radiation by the building envelope, and I s t represents the intensity of solar radiation. e t represents outdoor temperature. f The function representing the first daily temperature variation curve, t w It represents the equivalent average temperature of the outer surface of a heated building.

[0010] Optionally, resource scheduling within the predicted time frame is performed based on the heating gas load, including: determining the resource demand based on the heating gas load; and sending the resource demand to the resource provider to instruct the resource provider to provide resources according to the resource demand.

[0011] Optionally, the correspondence between meteorological conditions and key parameters is determined as follows: First sample data for each first set duration within at least one historical heating season is obtained. The first sample data includes historical meteorological information, which includes historical meteorological conditions. The first sample data is grouped according to the year of the historical heating season and the historical meteorological conditions to obtain a data group containing first sample data with the same historical meteorological conditions within the same historical heating season. For each historical meteorological condition, the following data processing is performed to establish the correspondence between meteorological conditions and key parameters: N first sample data points are selected from the data group corresponding to the historical meteorological conditions, where N is a natural number. Based on the N first sample data points, the key parameters corresponding to the historical meteorological conditions are determined by solving a system of equations.

[0012] Optionally, historical meteorological information also includes historical hourly temperature data, and the first sample data also includes historical heating gas load. Based on N first sample data, the key parameters corresponding to historical meteorological conditions are determined by solving a system of equations, including: for each of the N first sample data, the following processing is performed: the historical hourly temperature data in the first sample data is fitted to determine the second temperature change curve function of the corresponding first set time period; based on the historical heating gas load, the second temperature change curve function, and the second equivalent total heat dissipation area of ​​the heating building corresponding to the historical heating season in the first sample data, the key parameters corresponding to historical meteorological conditions are determined by solving a system of equations.

[0013] Optionally, before determining the heating gas load for the predicted duration based on the first temperature change curve function, the first key parameter, and the first equivalent total heat dissipation area of ​​the heating building corresponding to the current heating season, the method further includes: obtaining second sample data corresponding to the second set duration within the current heating season, the second sample data including the second heating gas load and second meteorological information, the second meteorological information including second hourly temperature data and second meteorological conditions; fitting the second hourly temperature data to obtain a third temperature change curve function showing the temperature changing with the hour; matching the second key parameter corresponding to the second meteorological conditions in the correspondence; multiplying the second heating gas load with the calorific value of natural gas to obtain the second total heat dissipation of the heating building within the second set duration; and substituting the second total heat dissipation, the second key parameter, and the third temperature change curve function into the objective function to obtain the first equivalent total heat dissipation area.

[0014] Secondly, this application provides a resource scheduling device, comprising: an acquisition module for acquiring first meteorological information for a predicted duration, the first meteorological information including first hourly temperature data and first meteorological conditions; a first determination module for fitting the first hourly temperature data to obtain a first temperature change curve function of temperature changing with hourly variations within the predicted duration; a second determination module for matching a first key parameter corresponding to the first meteorological conditions in a preset correspondence between meteorological conditions and key parameters, the first key parameter being data affecting the heat dissipation of urban heating buildings; a third determination module for determining the heating gas load for the predicted duration based on the first temperature change curve function, the first key parameter, and the first equivalent total heat dissipation area of ​​the heating buildings corresponding to the current heating season; and a scheduling module for performing resource scheduling within the predicted duration based on the heating gas load.

[0015] Thirdly, this application provides an electronic device, including: a memory and a processor; the memory for storing program instructions; and the processor for invoking the program instructions to execute the resource scheduling method provided in the first aspect above.

[0016] Fourthly, this application provides a readable storage medium on which a computer program is stored; when the computer program is executed, it implements the resource scheduling method provided in the first aspect above.

[0017] Fifthly, this application provides a computer program, including a computer program; when the computer program is executed, it implements the resource scheduling method provided in the first aspect above.

[0018] The resource scheduling method, apparatus, and storage medium provided in this application acquire first meteorological information for a predicted duration, including first hourly temperature data and first meteorological conditions. The first hourly temperature data is fitted to obtain a first temperature change curve function representing the hourly temperature variation within the predicted duration. A first key parameter corresponding to the first meteorological conditions is matched within a preset correspondence between meteorological conditions and key parameters. This first key parameter is data affecting the heat dissipation of urban heating buildings. Based on the first temperature change curve function, the first key parameter, and the first equivalent total heat dissipation area of ​​the heating buildings corresponding to the current heating season, the heating gas load for the predicted duration is determined. Resource scheduling is then performed based on the heating gas load within the predicted duration. This application predicts the heating gas load for the predicted duration before resource scheduling, considering changes in the scale of heating users, more accurately quantifying the equivalent total heat dissipation area of ​​heating buildings, and integrating climatic factors (such as temperature, wind speed, solar radiation, etc.) during the prediction process, effectively improving the prediction accuracy of the heating gas load and thus enhancing the accuracy of resource scheduling. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] Figure 1 This is a schematic diagram illustrating an application scenario provided in the embodiments of this application;

[0021] Figure 2 A flowchart illustrating the resource scheduling method provided in the embodiments of this application. Figure 1 ;

[0022] Figure 3 A flowchart illustrating the resource scheduling method provided in the embodiments of this application. Figure 2 ;

[0023] Figure 4 A flowchart illustrating the resource scheduling method provided in the embodiments of this application. Figure 3 ;

[0024] Figure 5 This is a schematic diagram of the structure of the resource scheduling device provided in the embodiments of this application;

[0025] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0026] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0027] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0028] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0029] Figure 1 This is a schematic diagram of an application scenario provided in the embodiments of this application, with reference to... Figure 1 This application scenario may include a resource demand prediction unit 101 and a resource scheduling unit 102.

[0030] The resource demand forecasting unit 101 is used to forecast the city's gas load. It can be an electronic device, such as a PC, server, or terminal, capable of directly or indirectly acquiring the required forecast data through interaction with other servers or computers. Based on the required forecast data, the resource demand forecasting unit 101 calculates and outputs the city's gas load for the forecast day or forecast period (i.e., forecast duration), which is the city's gas demand for that forecast day or forecast period. The resource scheduling unit 102 is used to schedule resources such as gas to heating buildings with heating needs. It can refer to the pipeline network scheduling department. The resource scheduling unit 102 can acquire the forecast data output by the resource demand forecasting unit 101 and schedule resources accordingly.

[0031] Figure 2 A flowchart illustrating the resource scheduling method provided in the embodiments of this application. Figure 1 , refer to Figure 2 The method includes the following steps:

[0032] S201: Obtain the first meteorological information for the forecast duration, which includes the first hourly temperature data and the first meteorological conditions.

[0033] The first hourly temperature data refers to the temperature data corresponding to each hour within the forecast period. The first meteorological conditions may include meteorological data such as weather type and wind speed level. The first meteorological information can be obtained from weather forecasts, for example, from databases or through interaction with other devices or platforms (meteorological platforms, websites). Furthermore, the meteorological information obtained from the meteorological platform may be obtained through interaction with devices such as sensors.

[0034] S202: Fit the first hourly temperature data to obtain the first temperature change curve function of temperature changing with the hour within the predicted time period.

[0035] Specifically, the fitting process for the first time-of-day temperature data may include: establishing a relational function with the time-of-day as the independent variable and the first time-of-day temperature data corresponding to the time-of-day as the dependent variable; fitting the constant parameters in the relational function by substituting the time-of-day and first time-of-day temperature data to determine the first temperature change curve function that varies with the time-of-day within the predicted time period.

[0036] For example, the first temperature change curve function can be expressed using the following formula (Formula 1):

[0037]

[0038] Among them, t f Let τ represent the first temperature change curve function, where τ represents the time interval, and A, b1, b2, b3, b4, ... are constant parameters. These constant parameters can be obtained by fitting the first time interval temperature data. Since the first time interval temperature data generally differs for different dates, the resulting first temperature change curve function t... f The values ​​of the constant parameters are usually different.

[0039] S203: In the preset correspondence between meteorological conditions and key parameters, match the first key parameter corresponding to the first meteorological condition. The first key parameter is the data that affects the heat dissipation of buildings for urban heating.

[0040] For example, key parameters may include: daily equivalent average convective heat transfer coefficient, average absorption coefficient of solar radiation by the heating building envelope, and solar radiation intensity, wherein the daily equivalent average convective heat transfer coefficient is closely related to wind speed level, and the average absorption coefficient of solar radiation by the heating building envelope and solar radiation intensity are closely related to weather type.

[0041] In this embodiment, the preset correspondence between meteorological conditions and key parameters can be pre-established and directly obtainable. For example, a correspondence table between meteorological conditions and key parameters can be pre-established, and this embodiment finds the key parameters corresponding to the meteorological conditions by looking up the correspondence table. In other embodiments, the preset correspondence between meteorological conditions and key parameters can also be calculated in real time, and the specific method will be described below.

[0042] S204: Based on the first temperature change curve function, the first key parameter, and the first equivalent total heat dissipation area of ​​the heating building corresponding to the current heating season, determine the heating gas load for the predicted duration.

[0043] In this embodiment of the application, the first equivalent total heat dissipation area of ​​the heating building corresponding to the current heating season can be predetermined, directly obtained, or calculated in real time. The specific method will be described below.

[0044] Optionally, based on the objective function, the first total heat dissipation of the heating building within the predicted time period can be determined according to the first temperature change curve function, the first key parameter, and the first equivalent total heat dissipation area. The heating gas load can be determined as the ratio of the first total heat dissipation to the calorific value of natural gas. The objective function is used to reflect the correlation between the temperature change curve function, the key parameter, the equivalent total heat dissipation area of ​​the heating building, and the total heat dissipation of the heating building.

[0045] Specifically, taking the key parameters of daily equivalent average convective heat transfer coefficient, average absorption coefficient of solar radiation by the building envelope, and solar radiation intensity as an example, the first total heat dissipation of the heating building within the forecast period is determined according to the following formula (Formula 2):

[0046]

[0047] Where Φ represents the first total heat dissipation of the heated building within the predicted time period, τ represents the time-of-use factor, h represents the daily equivalent average convective heat transfer coefficient, S represents the first equivalent total heat dissipation area of ​​the heated building, ρ represents the average absorption coefficient of solar radiation by the building envelope, and I s t represents the intensity of solar radiation. e t represents outdoor temperature. f The function representing the first daily temperature variation curve, t w This represents the equivalent average temperature of the exterior surface of a heated building. In my country's urban heating systems, the indoor temperature design standard is no less than 18℃. Here, t... w It can be approximated as 18℃.

[0048] S205: Resource scheduling within the predicted time frame based on heating gas load.

[0049] The heating gas load with the predicted duration determined through S201 to S204 can be used for resource scheduling. For example, it can be achieved through... Figure 1 The resource scheduling unit 102 in the illustrated embodiment performs resource scheduling within the predicted timeframe. For example, it determines the gas resources required for urban heating by establishing a correspondence between gas resources and heating gas load. Alternatively, it can interact with other devices to notify them to supply resources based on the determined heating gas load for the predicted timeframe. Optionally, resource scheduling within the predicted timeframe based on the heating gas load specifically includes: determining the resource demand based on the heating gas load; and sending the resource demand to the resource provider (i.e., the resource scheduling unit) to instruct the resource provider to supply resources according to the demand.

[0050] It should be noted that τ shown in Formulas 1 and 2 provided in the embodiments of this application preferably represents time-sharing, ensuring sufficient calculation accuracy while controlling the computational load to avoid excessive complexity. In other embodiments, τ can also represent time units with higher or lower precision, such as seconds or hours, depending on different precision requirements. Correspondingly, the first temperature change curve function and the first total heat dissipation can be obtained by fitting and calculating based on other time units.

[0051] The resource scheduling method provided in this application embodiment obtains first meteorological information for the predicted duration, including first hourly temperature data and first meteorological conditions. The first hourly temperature data is fitted to obtain a first temperature change curve function that reflects the hourly temperature variation within the predicted duration. In a preset correspondence between meteorological conditions and key parameters, a first key parameter corresponding to the first meteorological conditions is matched. This first key parameter is data affecting the heat dissipation of urban heating buildings. Based on the first temperature change curve function, the first key parameter, and the first equivalent total heat dissipation area of ​​the heating buildings corresponding to the current heating season, the heating gas load for the predicted duration is determined. Resource scheduling is then performed based on the heating gas load within the predicted duration. This application embodiment predicts the heating gas load for the predicted duration before resource scheduling, considering changes in the scale of heating users, more accurately quantifying the equivalent total heat dissipation area of ​​heating buildings, and comprehensively incorporating climatic factors (such as temperature, wind speed, solar radiation, etc.) during the prediction process, effectively improving the prediction accuracy of the heating gas load and thus enhancing the accuracy of resource scheduling.

[0052] Figure 3 A flowchart illustrating the resource scheduling method provided in the embodiments of this application. Figure 2 This paper provides an exemplary method for determining the correspondence between preset meteorological conditions and key parameters, with reference to... Figure 3 The method includes:

[0053] S301: Obtain first sample data for each first set duration within at least one historical heating season, the first sample data including historical meteorological information, the historical meteorological information including historical meteorological conditions.

[0054] In this embodiment of the application, a first sample data of a first set duration within a historical heating season can be selected to establish the correspondence between meteorological conditions and key parameters. Here, the heating season usually refers to November 15 to March 15 of the following year. Since a historical heating season may not include all meteorological conditions, in order to increase the number of entries in the correspondence table between meteorological conditions and key parameters, first sample data of a first set duration within multiple historical heating seasons can also be selected to establish the correspondence between meteorological conditions and key parameters. This embodiment of the application does not limit this.

[0055] The first set duration can be limited as needed, such as every day within the entire historical heating season, or a specific duration selected within the entire historical heating season as the first set duration. The first set duration can be one day or multiple days (e.g., 16 days), for example, collecting the first sample data of the year from November 15th to November 30th (16 days). After the correspondence between meteorological conditions and key parameters is determined, a correspondence table can be established and stored in a readable storage medium, which can be retrieved by the resource demand prediction unit of this application embodiment.

[0056] S302: Group the first sample data according to the year and historical meteorological conditions of the historical heating season to obtain a data group containing the first sample data with the same historical meteorological conditions within the same historical heating season.

[0057] It is generally believed that the equivalent total heat dissipation area of ​​heated buildings in a city remains unchanged within the same heating season, and the same meteorological conditions correspond to the same key parameters. Therefore, for the same set of first sample data, the equivalent total heat dissipation area of ​​heated buildings corresponding to each set of first sample data is the same, and the key parameters corresponding to each set of first sample data are also the same.

[0058] Therefore, the first sample data with the same historical meteorological conditions within the same historical heating season are grouped together to obtain a data group with the same equivalent total heat dissipation area of ​​heated buildings and the same meteorological conditions. For each historical meteorological condition, the following data processing is performed to establish the correspondence between meteorological conditions and key parameters.

[0059] S303: For each historical meteorological condition, select N first sample data from the data set corresponding to the historical meteorological condition, where N is a natural number; S304: Based on the N first sample data, determine the key parameters corresponding to the historical meteorological condition by solving the system of equations.

[0060] For each data set of the first sample data with the same historical meteorological conditions within the same historical heating season, that is, for each data set based on a certain historical meteorological condition, N first sample data are selected as the basic data for determining the correspondence between meteorological conditions and key parameters.

[0061] Optionally, in the step of determining the key parameters corresponding to historical meteorological conditions by solving a system of equations based on N first sample data, the historical meteorological information also includes historical hourly temperature data, and the first sample data also includes historical heating gas load. Determining the key parameters corresponding to historical meteorological conditions by solving a system of equations based on N first sample data includes: performing the following processing on each of the N first sample data:

[0062] The historical hourly temperature data in the first sample data are fitted to determine the second daily temperature variation curve function corresponding to the first set time period. Based on the historical heating gas load, the second daily temperature variation curve function, and the second equivalent total heat dissipation area of ​​the internally heated building corresponding to the historical heating season in the first sample data, the key parameters corresponding to the historical meteorological conditions are determined by solving the equation system.

[0063] Optionally, the second daily temperature variation curve function can be determined based on Formula 1 provided in the above embodiment, and the equation set can be established based on Formula 2 (i.e., the objective function) provided in the above embodiment. In each equation set, since the equivalent total heat dissipation area of ​​the heated building is the same as the meteorological conditions, N-1 key parameters corresponding to the historical meteorological conditions in each data set can be solved by N first sample data.

[0064] Specifically, when determining the key parameters of a certain set of data, four first sample data points are selected from the data set corresponding to the meteorological conditions. Based on the historical heating gas load, the second temperature change curve function fitted by the above formula 1, and the calorific value of natural gas in these four first sample data points, a set of equations is established using formula 2 to solve for the three key parameters corresponding to the meteorological conditions.

[0065] In some embodiments, the second temperature change curve function can be determined by fitting the hourly temperature data of each day throughout the entire historical heating season. In other embodiments, it can also be determined by fitting the first sample data selected from the data set.

[0066] In some embodiments, before selecting N first sample data points from the data set corresponding to historical meteorological conditions, the method further includes deleting first sample data sets with fewer than N data points. For example, if N = 4, and the number of first sample data points in a certain first sample data set is less than 4, then that first sample data set is deleted. This is because if the number of first sample data points in a certain first sample data set is less than N, the N-1 key parameters corresponding to the meteorological condition cannot be determined by solving the system of equations.

[0067] To further reduce errors, in some embodiments, it is possible to... Figure 2 The first equivalent total heat dissipation area is determined in advance in the embodiment shown.

[0068] Figure 4 A flowchart illustrating the resource scheduling method provided in the embodiments of this application. Figure 3 This method exemplarily illustrates a method for determining the first equivalent total heat dissipation area, referring to... Figure 4 The method includes:

[0069] S401: Obtain second sample data corresponding to the second set duration within the current heating season. The second sample data includes the second heating gas load and the second meteorological information. The second meteorological information includes the second hourly temperature data and the second meteorological conditions.

[0070] S402: Fit the second time-of-day temperature data to obtain the third temperature change curve function that varies with time of day;

[0071] S403: In the correspondence, match the second key parameter corresponding to the second meteorological condition;

[0072] S404: Multiply the second heating gas load with the calorific value of natural gas to obtain the second total heat dissipation of the heating building within the second set time period;

[0073] S405: Substitute the second total heat dissipation, the second key parameter, and the third temperature change curve function into the objective function to obtain the first equivalent total heat dissipation area.

[0074] Selecting the second sample data from the current heating season to determine the equivalent total heat dissipation area can more closely approximate the first equivalent total heat dissipation area for the predicted day. The second set duration can be determined according to requirements and may vary in different embodiments. The third temperature change curve function can be determined according to Formula 1 in the above embodiments, and the objective function can be determined according to Formula 2 in the above embodiments.

[0075] Optionally, multiple candidate first equivalent total heat dissipation areas can be determined based on multiple sets of second sample data, and the average of the multiple candidate first equivalent total heat dissipation areas can be determined as the final first equivalent total heat dissipation area.

[0076] In some specific embodiments, the correspondence between meteorological conditions and key parameters and the determination of the first equivalent total heat dissipation area are performed continuously in the same embodiment. The method includes the following steps:

[0077] Step 1: Obtain first sample data for each first set duration within at least one historical heating season. The first sample data includes historical meteorological information, which includes historical meteorological conditions.

[0078] Step 2: Group the first sample data according to the years and historical meteorological conditions of the historical heating seasons to obtain data groups containing the first sample data with the same historical meteorological conditions within the same historical heating season.

[0079] Step 3: For each historical meteorological condition, select N first sample data points from the data set corresponding to the historical meteorological condition, where N is a natural number; based on the N first sample data points, determine the key parameters corresponding to the historical meteorological condition by solving the system of equations;

[0080] Step 4: Obtain the second sample data corresponding to the second set duration within the current heating season. The second sample data includes the second heating gas load and the second meteorological information. The second meteorological information includes the second hourly temperature data and the second meteorological conditions.

[0081] Step 5: Fit the second time-of-day temperature data to obtain the third temperature change curve function that shows the temperature variation with time of day;

[0082] Step Six: In the correspondence, match the second key parameter corresponding to the second meteorological condition;

[0083] Step 7: Multiply the second heating gas load by the calorific value of natural gas to obtain the second total heat dissipation of the heating building within the second set time period;

[0084] Step 8: Substitute the second total heat dissipation, the second key parameter, and the third temperature change curve function into the objective function to obtain the first equivalent total heat dissipation area;

[0085] Step 9: Obtain the first meteorological information for the forecast duration, which includes the first hourly temperature data and the first meteorological conditions;

[0086] Step 10: Fit the first hourly temperature data to obtain the first temperature change curve function of temperature variation with time intervals within the predicted time period;

[0087] Step 11: In the preset correspondence between meteorological conditions and key parameters, match the first key parameter corresponding to the first meteorological condition. The first key parameter is the data that affects the heat dissipation of buildings for urban heating.

[0088] Step 12: Based on the first temperature change curve function, the first key parameter, and the first equivalent total heat dissipation area of ​​the heating building corresponding to the current heating season, determine the heating gas load for the predicted duration;

[0089] Step 13: Perform resource scheduling within the predicted time frame based on the heating gas load.

[0090] Based on the same technical concept, embodiments of this application also provide a resource scheduling device, an electronic device, and a readable storage medium, which will be described in detail below.

[0091] Figure 5 This is a schematic diagram of the structure of the resource scheduling device provided in an embodiment of this application. (Refer to...) Figure 5 The device 500 includes:

[0092] The acquisition module 501 is used to acquire the first meteorological information for the predicted duration, which includes the first hourly temperature data and the first meteorological conditions.

[0093] The first determining module 502 is used to fit the first time-sharing temperature data to obtain the first temperature change curve function of temperature changing with time within the predicted time period;

[0094] The second determining module 503 is used to match the first key parameter corresponding to the first meteorological condition in the preset correspondence between meteorological conditions and key parameters. The first key parameter is data that affects the heat dissipation of urban heating buildings.

[0095] The third determining module 504 is used to determine the heating gas load for the predicted duration based on the first temperature change curve function, the first key parameter and the first equivalent total heat dissipation area of ​​the heating building corresponding to the current heating season.

[0096] The scheduling module 505 is used to schedule resources within a predicted time period based on the heating gas load.

[0097] Optionally, the third determining module 504 can be specifically used to determine the first total heat dissipation of the heating building within the predicted time period based on the objective function, the first temperature change curve function, the first key parameter, and the first equivalent total heat dissipation area. The objective function is used to reflect the correlation between the temperature change curve function, the key parameter, the equivalent total heat dissipation area of ​​the heating building, and the total heat dissipation of the heating building; and to determine the heating gas load as the ratio of the first total heat dissipation to the calorific value of natural gas.

[0098] Optionally, the third determining module 504 can also be specifically used to determine the first total heat dissipation of the heated building within the predicted time period according to the following formula:

[0099]

[0100] Where Φ represents the first total heat dissipation of the heated building within the predicted time period, τ represents the time-of-use factor, h represents the daily equivalent average convective heat transfer coefficient, S represents the first equivalent total heat dissipation area of ​​the heated building, ρ represents the average absorption coefficient of solar radiation by the building envelope, and I s t represents the intensity of solar radiation. e t represents outdoor temperature. f The function representing the first daily temperature variation curve, t w It represents the equivalent average temperature of the outer surface of a heated building.

[0101] Optionally, the scheduling module 505 can be specifically used to determine the resource demand based on the heating gas load; and send the resource demand to the resource provider to instruct the resource provider to provide resources according to the resource demand.

[0102] Optionally, the resource scheduling device 500 further includes a fourth determining module, which can be specifically used to acquire first sample data for each first set duration within at least one historical heating season. The first sample data includes historical meteorological information, which includes historical meteorological conditions. The first sample data is grouped according to the year of the historical heating season and the historical meteorological conditions to obtain a data group containing first sample data with the same historical meteorological conditions within the same historical heating season. For each historical meteorological condition, the following data processing is performed to establish the correspondence between meteorological conditions and key parameters: N first sample data are selected from the data group corresponding to the historical meteorological conditions, where N is a natural number. Based on the N first sample data, the key parameters corresponding to the historical meteorological conditions are determined by solving a system of equations.

[0103] Optionally, the fourth determining module can also be specifically used to perform the following processing on each of the N first sample data: fit the historical time-sharing temperature data in the first sample data to determine the second temperature change curve function of the corresponding first set time period; and determine the key parameters corresponding to the historical meteorological conditions by solving the equation system based on the historical heating gas load, the second temperature change curve function and the second equivalent total heat dissipation area of ​​the heating building corresponding to the historical heating season in the first sample data.

[0104] Optionally, the resource scheduling device 500 further includes a fifth determining module, which can be specifically used to acquire second sample data corresponding to a second set time period within the current heating season. The second sample data includes a second heating gas load and second meteorological information. The second meteorological information includes second hourly temperature data and second meteorological conditions. The second hourly temperature data is fitted to obtain a third temperature change curve function that shows the temperature changing with the hour. In the correspondence, a second key parameter corresponding to the second meteorological conditions is matched. The second heating gas load is multiplied by the calorific value of natural gas to obtain the second total heat dissipation of the heating building within the second set time period. The second total heat dissipation, the second key parameter, and the third temperature change curve function are substituted into the objective function to obtain the first equivalent total heat dissipation area.

[0105] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. (Refer to...) Figure 6 The electronic device 600 includes:

[0106] Processor 601, memory 602, communication interface 603 and system bus 604.

[0107] The memory 602 and the communication interface 603 are connected to the processor 601 via the system bus 604 and communicate with each other. The memory 602 is used to store computer execution instructions, the communication interface 603 is used to communicate with other devices, and the processor 601 is used to execute the computer execution instructions to execute the resource scheduling method scheme as described in the above method embodiment.

[0108] Specifically, processor 601 may include one or more processing units, such as a central processing unit (CPU), a digital signal processing unit (DSP), an application-specific integrated circuit (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in this application can be directly embodied in the execution of a hardware processor, or executed by a combination of hardware and software modules within the processor. The processing and logic flow described in this specification can be executed by one or more programmable computers executing one or more computer programs to perform corresponding functions by operating on input data and generating output. The processing and logic flow can also be executed by dedicated logic circuits—such as a field-programmable gate array (FPGA) or an ASIC—and the device can also be implemented as dedicated logic circuits.

[0109] The memory 602 can be used to store program instructions. The memory 602 may include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback), etc. The data storage area may store data created during the use of the electronic device 600 (such as audio data), etc. Furthermore, the memory 602 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, Universal Flash Storage (UFS), etc. The processor 601 executes various functional applications and data processing of the electronic device 600 by running the program instructions stored in the memory 602.

[0110] Communication interface 603 can provide solutions for wireless communication, including 2G / 3G / 4G / 16G, applied to electronic device 600. Communication interface 603 can receive electromagnetic waves via an antenna, and perform filtering, amplification, and other processing on the received electromagnetic waves before transmitting them to a modem processor for demodulation. Communication interface 603 can also amplify the signal modulated by the modem processor and radiate it as electromagnetic waves via the antenna. In some embodiments, at least some functional modules of communication interface 603 can be housed in processor 601. In some embodiments, at least some functional modules of communication interface 603 and at least some modules of processor 601 can be housed in the same device.

[0111] The system bus 604 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This system bus 604 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, it is represented by only one thick line in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0112] It should be noted that the number of memory units 602 and processor units 601 is not limited in this embodiment; each can be one or more. Figure 6 The illustration shows an example; the memory 602 and the processor 601 can be connected via wired or wireless means, such as a bus connection. In practical applications, the electronic device 600 can be various forms of computers or mobile terminals. Computers include, for example, laptops, desktop computers, workbenches, servers, blade servers, mainframe computers, etc.; mobile terminals include, for example, personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0113] The electronic device in this embodiment can be used to execute the technical solutions in the above method embodiments. Its implementation principle and technical effect are similar, and will not be repeated here.

[0114] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the resource scheduling method provided in the above embodiments.

[0115] This application also provides a computer program product, including a computer program; when the computer program is executed, it implements the resource scheduling method provided in the above embodiments.

[0116] The embodiments of the methods and functions described in this application can be implemented using: digital electronic circuits, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this application and their structural equivalents, or combinations thereof. The method embodiments described in this application can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by a data processing device or for controlling the operation of a data processing device. Alternatively or additionally, the program instructions can be encoded on artificially generated propagation signals, such as machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information and transmit it to a suitable receiving device for execution by the data processing device. The computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or combinations thereof.

[0117] Computers suitable for executing computer programs include, for example, general-purpose and / or special-purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit receives instructions and data from read-only memory and / or random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as disks, magneto-optical disks, or optical disks, or the computer will be operatively coupled to such mass storage devices to receive data from or transfer data to them, or both. However, a computer is not required to have such devices. Furthermore, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device such as a Universal Serial Bus (USB) flash drive, to name a few.

[0118] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, such as semiconductor memory devices, magnetic disks, magneto-optical disks, etc. Processors and memory may be supplemented by or incorporated into dedicated logic circuitry.

[0119] While this specification contains numerous specific implementation details, these should not be construed as limiting the scope of any invention or the scope of the claims, but rather are primarily used to describe features of specific embodiments of a particular invention. Certain features described in the various embodiments herein may also be implemented in combination in a single embodiment. Conversely, various features described in a single embodiment may also be implemented separately in various embodiments or in any suitable sub-combination. Furthermore, while features may function in certain combinations as described above and even initially claimed in this way, one or more features from a claimed combination may be removed from that combination in some cases, and a claimed combination may refer to a sub-combination or a variation thereof.

[0120] Similarly, although the operations are depicted in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order shown or sequentially, or requiring all illustrated operations to be performed to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0121] Therefore, specific embodiments of the technical solution of this application have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings are not necessarily shown in a specific order or sequence to achieve the desired result. In some implementations, multitasking and parallel processing may be advantageous.

[0122] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0123] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0124] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A resource scheduling method, characterized in that, include: Obtain first meteorological information for the predicted duration, the first meteorological information including first hourly temperature data and first meteorological conditions; The first time-sharing temperature data is fitted to obtain a first temperature change curve function that shows the temperature change with time within the predicted duration. In the preset correspondence between meteorological conditions and key parameters, the first key parameter corresponding to the first meteorological condition is matched. The first key parameter is data that affects the heat dissipation of urban heating buildings. Based on the objective function, according to the first temperature change curve function, the first key parameters, and the first equivalent total heat dissipation area of ​​the heating building corresponding to the current heating season, the first total heat dissipation of the heating building within the predicted time period is determined. The objective function is used to reflect the correlation between the temperature change curve function, the key parameters, the equivalent total heat dissipation area of ​​the heating building, and the total heat dissipation of the heating building. The key parameters include the daily equivalent average convective heat transfer coefficient, the average absorption coefficient of the building envelope to solar radiation, and the solar radiation intensity. The heating gas load for the predicted duration is determined as the ratio of the first total heat dissipation to the calorific value of natural gas; Resource scheduling is performed within the predicted time period based on the heating gas load. The step of determining the first total heat dissipation of the heating building within the predicted time period based on the objective function, the first temperature change curve function, the first key parameter, and the first equivalent total heat dissipation area includes: The first total heat dissipation of the heated building during the predicted duration is determined according to the following formula: ， in, This represents the first total heat dissipation of a heated building within the predicted timeframe. Indicates time division, Indicates the daily equivalent average convective heat transfer coefficient. This represents the first equivalent total heat dissipation area of ​​a heated building. This represents the average absorption coefficient of solar radiation by the building envelope of a heated building. Indicates the intensity of solar radiation. Indicates outdoor temperature. The function representing the daily temperature variation curve. It represents the equivalent average temperature of the outer surface of a heated building.

2. The resource scheduling method according to claim 1, characterized in that, The resource scheduling within the predicted time period based on the heating gas load includes: Based on the heating gas load, determine the resource demand; Send the resource demand to the resource provider to instruct the resource provider to provide resources according to the resource demand.

3. The resource scheduling method according to claim 1, characterized in that, The correspondence between the meteorological conditions and the key parameters was determined in the following way: Acquire first sample data for each first set duration within at least one historical heating season, wherein the first sample data includes historical meteorological information, and the historical meteorological information includes historical meteorological conditions. The first sample data is grouped according to the years and historical meteorological conditions of the historical heating seasons to obtain a data group containing the first sample data with the same historical meteorological conditions in the same historical heating season. For each historical meteorological condition, the following data processing was performed to establish the correspondence between the meteorological conditions and key parameters: N first sample data points are selected from the data set corresponding to the historical meteorological conditions, where N is a natural number; Based on the N first sample data, the key parameters corresponding to the historical meteorological conditions are determined by solving a system of equations.

4. The resource scheduling method according to claim 3, characterized in that, The historical meteorological information also includes historical hourly temperature data, and the first sample data also includes historical heating gas load. The process of determining the key parameters corresponding to the historical meteorological conditions based on the N first sample data by solving a system of equations includes: For each of the N first sample data points, the following processing is performed: The historical time-sharing temperature data in the first sample data are fitted to determine the second temperature change curve function corresponding to the time-sharing within the first set time period; Based on the historical heating gas load, the second temperature change curve function, and the second equivalent total heat dissipation area of ​​the heating buildings corresponding to the historical heating season in the first sample data, the key parameters corresponding to the historical meteorological conditions are determined by solving the equation set.

5. The resource scheduling method according to claim 1, characterized in that, Before determining the heating gas load for the predicted duration based on the first temperature change curve function, the first key parameter, and the first equivalent total heat dissipation area of ​​the heating building corresponding to the current heating season, the method further includes: Obtain second sample data corresponding to the second set duration within the current heating season. The second sample data includes the second heating gas load and the second meteorological information. The second meteorological information includes the second hourly temperature data and the second meteorological conditions. The second time-of-day temperature data is fitted to obtain a third temperature change curve function that varies with time of day; In the correspondence, the second key parameter corresponding to the second meteorological condition is matched; The second heating gas load is multiplied by the calorific value of natural gas to obtain the second total heat dissipation of the heating building within the second set time period; Substituting the second total heat dissipation, the second key parameter, and the third temperature change curve function into the objective function, the first equivalent total heat dissipation area is obtained.

6. A resource scheduling device, characterized in that, The resource scheduling device is used to implement the resource scheduling method according to any one of claims 1-5, and the device includes: The acquisition module is used to acquire the first meteorological information for the forecast duration, the first meteorological information including the first hourly temperature data and the first meteorological conditions; The first determining module is used to fit the first time-sharing temperature data to obtain a first temperature change curve function of temperature changing with time within the predicted time period; The second determining module is used to match the first key parameter corresponding to the first meteorological condition in the preset correspondence between meteorological conditions and key parameters. The first key parameter is data that affects the heat dissipation of urban heating buildings. The third determining module is used to determine the heating gas load for the predicted duration based on the first temperature change curve function, the first key parameter and the first equivalent total heat dissipation area of ​​the heating building corresponding to the current heating season. The scheduling module is used to schedule resources within the predicted time period based on the heating gas load.

7. An electronic device, characterized in that, include: Memory and processor; The memory is used to store program instructions; The processor is configured to invoke the program instructions to execute the resource scheduling method as described in any one of claims 1 to 5.

8. A readable storage medium, characterized in that, The readable storage medium stores a computer program; when the computer program is executed, it implements the resource scheduling method as described in any one of claims 1 to 5.

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