CN117829440A - Resource scheduling method, device and storage medium - Google Patents
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Abstract
The application provides a resource scheduling method, a resource scheduling device and a storage medium, and relates to the technical field of smart cities. The method comprises the steps of obtaining first weather information of a predicted time period, fitting the first time-sharing air temperature data to obtain a first air temperature change curve function of air temperature changing along with time sharing in the predicted time period, matching a first key parameter corresponding to the first weather condition in a corresponding relation between the preset weather condition and the key parameter, determining heating air load of the predicted time period 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, and scheduling resources in the predicted time period according to the heating air load. The method and the device more accurately quantify the equivalent total heat dissipation area of the heating building, comprehensively consider climate condition factors, effectively improve the prediction precision of the heating gas load and improve the accuracy of resource scheduling.
Description
Technical Field
The present application relates to the technical field of smart cities, and in particular, to a resource scheduling method, device and storage medium.
Background
With the development of gas industry and the increase of gas consumption, many cities face various problems such as pipe network planning, gas source conversion or gas supply scale expansion, and the problem of non-uniformity of gas consumption is more and more prominent, and the problems of peak shaving and gas storage of gas become a common, troublesome and urgent problem to be solved in each city, so that the method is one of basic information which a transmission and distribution pipe network scheduling department and a planning and design department must have. The urban gas load prediction has extremely important roles in the aspects of planning and designing, management, operation scheduling and the like of a gas transmission and distribution system, and has very important significance in economy, safety and reliability of a gas supply system.
Most of the existing urban gas load prediction methods utilize the historical law of daily load change of urban gas heating to predict daily load of urban gas heating at future time, and the prediction results are easy to deviate, so that unreasonable phenomena such as resource waste and the like are caused.
Disclosure of Invention
The application provides a resource scheduling method, a resource scheduling device and a storage medium, which are used for solving the problem that the daily gas load prediction result for urban gas heating is inaccurate.
In a first aspect, the present application provides a resource scheduling method, including: acquiring first weather information of a predicted time length, wherein the first weather information comprises first time-sharing air temperature data and first weather conditions; fitting the first time-sharing air temperature data to obtain a first air temperature change curve function of air temperature changing along with time sharing in the predicted time length; in the corresponding relation between the preset meteorological conditions and the key parameters, matching first key parameters corresponding to the first meteorological conditions, wherein the first key parameters are data affecting the heat dissipation capacity of the urban heating building; determining a heating gas load of a predicted duration 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 the current heating season; and carrying out resource scheduling in the predicted time period according to the heating gas load.
Optionally, determining the heating gas load of the predicted duration based on the first air 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 includes: determining a first total heat dissipation capacity of the heating building in the predicted time period according to a first air temperature change curve function, a first key parameter and a first equivalent total heat dissipation area based on an objective function, wherein the objective function is used for reflecting the association relation between the air temperature change curve function, the key parameter and the equivalent total heat dissipation area of the heating building and the total heat dissipation capacity of the heating building; and determining the heating gas load as the ratio of the first total heat dissipation capacity to the natural gas calorific value.
Optionally, the key parameters include a solar equivalent average convective heat transfer coefficient, an average absorption coefficient of solar radiation by the heating building enclosure structure, and a solar radiation intensity, and determining, based on the objective function, a first total heat dissipation of the heating building within the predicted time period according to the first air temperature change curve function, the first key parameter, and the first equivalent total heat dissipation area, including: determining a first total heat dissipation of the heating building for a predicted period of time according to the following formula:
wherein phi represents the first total heat dissipation capacity of the heating building in the predicted time period, tau represents time sharing, h represents solar equivalent average convective heat transfer coefficient, S represents the first equivalent total heat dissipation area of the heating building, ρ represents the average absorption coefficient of the heating building enclosure structure to solar radiation, I s Representing the intensity of solar radiation, t e Indicating the outdoor air temperature, t f Representing a first air temperature daily variation curve function, t w Indicating the equivalent average temperature of the exterior surface of the heating building.
Optionally, the resource scheduling in the predicted duration according to the heating gas load includes: determining the resource demand according to the heating gas load; the resource demand is sent to the resource provider to instruct the resource provider to provide the resource in accordance with the resource demand.
Optionally, the correspondence between the meteorological conditions and the key parameters is determined by: acquiring first sample data of each first set time length in at least one historical heating season, wherein the first sample data comprises historical weather information, and the historical weather information comprises historical weather conditions; grouping the first sample data according to the age of the historical heating season and the historical meteorological conditions to obtain a data set 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 is carried out to establish the corresponding relation between the meteorological condition and the key parameter: selecting N pieces of first sample data from a data set corresponding to historical meteorological conditions, wherein N is a natural number; and determining key parameters corresponding to the historical meteorological conditions by solving an equation set based on the N pieces of first sample data.
Optionally, the historical weather information further includes historical time-sharing air temperature data, the first sample data further includes a historical heating air load, and based on the N pieces of first sample data, determining key parameters corresponding to the historical weather conditions by solving the equation set includes: for each of the N pieces of first sample data, the following processing is performed: fitting the historical time-sharing air temperature data in the first sample data, and determining a second air temperature change curve function corresponding to the air temperature in the first set time period along with time sharing; and determining key parameters corresponding to the historical meteorological conditions by solving an equation set according to the historical heating gas load, the second air 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.
Optionally, before determining the heating gas load of the predicted duration based on the first air 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: acquiring second sample data corresponding to a second set time length in the current heating season, wherein the second sample data comprises a second heating gas load and second weather information, and the second weather information comprises second time-sharing air temperature data and second weather conditions; fitting the second time-sharing air temperature data to obtain a third air temperature change curve function of air temperature changing along with time sharing; in the corresponding relation, matching a second key parameter corresponding to a second meteorological condition; multiplying the second heating gas load by the natural gas heat value to obtain a second total heat dissipation capacity of the heating building within a second set time period; substituting the second total heat dissipation capacity, the second key parameter and the third air temperature change curve function into the objective function to obtain a first equivalent total heat dissipation area.
In a second aspect, the present application provides a resource scheduling apparatus, including: the acquisition module is used for acquiring first weather information of the predicted duration, wherein the first weather information comprises first time-sharing air temperature data and first weather conditions; the first determining module is used for carrying out fitting processing on the first time-sharing air temperature data to obtain a first air temperature change curve function of air temperature changing along with time sharing in the predicted duration; the second determining module is used for matching a first key parameter corresponding to the first meteorological condition in the corresponding relation between the preset meteorological condition and the key parameter, wherein the first key parameter is data affecting the heat dissipation capacity of the urban heating building; the third determining module is used for determining the heating gas load of the predicted duration based on the first air 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; and the scheduling module is used for scheduling the resources within the predicted time length according to the heating gas load.
In a third aspect, the present application provides an electronic device, comprising: a memory and a processor; a memory for storing program instructions; and a processor for calling program instructions to execute the resource scheduling method as provided in the first aspect.
In a fourth aspect, the present application provides a readable storage medium having a computer program stored thereon; when the computer program is executed, the resource scheduling method provided in the first aspect is implemented.
In a fifth aspect, the present application provides a computer program, comprising a computer program; when the computer program is executed, the resource scheduling method provided in the first aspect is implemented.
According to the resource scheduling method, the device and the storage medium, first weather information of a predicted time length is obtained, the first weather information comprises first time-sharing air temperature data and first weather conditions, fitting processing is conducted on the first time-sharing air temperature data to obtain a first air temperature change curve function of air temperature changing along with time sharing in the predicted time length, in a corresponding relation between preset weather conditions and key parameters, the first key parameters corresponding to the first weather conditions are matched, the first key parameters are data affecting heat dissipation capacity of urban heating buildings, heating air load of the predicted time length is determined based on the first air temperature change curve function, the first key parameters and a first equivalent total heat dissipation area of heating buildings corresponding to a current heating season, and resource scheduling in the predicted time length is conducted according to the heating air load. According to the method, the prediction of the heating gas load of the predicted duration is carried out before the resource scheduling, the change condition of the scale of a heating user is considered, the equivalent total heat dissipation area of a heating building is more accurately quantized, climate condition factors (such as air temperature, wind speed, solar radiation and the like) are integrated in the prediction process, the prediction precision of the heating gas load is effectively improved, and the accuracy of the resource scheduling is further improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application;
fig. 2 is a schematic flow chart of a resource scheduling method according to an embodiment of the present application;
fig. 3 is a second flowchart of a resource scheduling method according to an embodiment of the present application;
fig. 4 is a flowchart of a resource scheduling method according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a resource scheduling device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Specific embodiments thereof have been shown by way of example in the drawings and will herein be described in more detail. These drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application, and referring to fig. 1, the application scenario may include a resource demand prediction unit 101 and a resource scheduling unit 102.
The resource demand prediction unit 101 is used for predicting the urban gas load condition, and may be an electronic device, such as a PC, a server, a terminal, etc., and may directly obtain or indirectly interact with other servers or computers to obtain the required prediction data. The resource demand prediction unit 101 calculates, based on the required prediction data, the urban gas load of the prediction day or the prediction period (i.e., the prediction time period), that is, the urban gas demand of the prediction day or the prediction period. The resource scheduling unit 102 is used for scheduling resources such as fuel gas for a heating building with heating requirements, and may be referred to as a transmission pipe network scheduling department. The resource scheduling unit 102 can acquire the predicted data output from the resource demand predicting unit 101, and schedule resources according to the predicted data.
Fig. 2 is a flowchart of a resource scheduling method provided in an embodiment of the present application, and referring to fig. 2, the method includes the following steps:
S201: first weather information of a predicted time period is acquired, wherein the first weather information comprises first time-sharing air temperature data and first weather conditions.
The first time-sharing air temperature data refers to air temperature data corresponding to each time sharing in the prediction duration. The first weather condition may include weather data such as weather type and wind speed level. The method of obtaining the first weather information may be obtained from weather forecasts, for example, from databases or by interaction with other devices or platforms (weather platforms, websites). Further, the weather information obtained by the weather platform can be obtained by interaction with devices such as sensors.
S202: fitting the first time-sharing air temperature data to obtain a first air temperature change curve function of the air temperature changing along with time sharing in the predicted time length.
The fitting process performed on the first time-sharing air temperature data may specifically include: and using the time division as an independent variable, using first time division air temperature data corresponding to the time division as a dependent variable, establishing a relation function, and carrying out fitting determination on constant parameters in the relation function through bringing the time division and the first time division air temperature data, thereby determining a first air temperature change curve function for determining the air temperature change along with the time division in the prediction duration.
By way of example, the first air temperature change curve function may be expressed using the following formula (formula 1):
wherein t is f Represents a first air temperature change curve function, τ represents time sharing, A, b 1 、b 2 、b 3 、b 4 … are constant parameters that can be solved by fitting calculation from the first time-sharing air temperature data. For different dates, the first time-sharing air temperature data is generally different, so that the obtained first air temperature change curve function t f The values of the constant parameters are also typically different.
S203: and matching a first key parameter corresponding to the first meteorological condition in a corresponding relation between the preset meteorological condition and the key parameter, wherein the first key parameter is data affecting the heat dissipation capacity of the urban heating building.
By way of example, key parameters may include: solar equivalent average convection heat exchange coefficient, average absorption coefficient of heating building enclosure structure to solar radiation and solar radiation intensity, wherein, the solar equivalent average convective heat transfer coefficient is closely related to the wind speed level, and the average absorption coefficient of the heating building enclosure structure to solar radiation and the solar radiation intensity are closely related to the weather type.
In this embodiment of the present application, the preset correspondence between the meteorological conditions and the key parameters may be pre-established, and may be directly obtained, for example, a correspondence table between the meteorological conditions and the key parameters is pre-established. In other embodiments, the correspondence between the preset weather conditions and the key parameters may be calculated in real time, and the specific method will be described below.
S204: and determining the heating gas load of the predicted duration based on the first air 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.
In this embodiment of the present application, the first equivalent total heat dissipation area of the heating building corresponding to the current heating season may be determined in advance, may be directly obtained, or may be obtained by calculation in real time, and a specific method will be described below.
Optionally, the first total heat dissipation capacity of the heating building in the predicted period of time may be determined based on an objective function according to the first air temperature change curve function, the first key parameter and the first equivalent total heat dissipation area, and the heating air load is determined to be a ratio of the first total heat dissipation capacity to the natural gas heat value, where the objective function is used to reflect the association relationship between the air temperature change curve function, the key parameter and the equivalent total heat dissipation area of the heating building and the total heat dissipation capacity of the heating building.
Specifically, taking a key parameter as a daily equivalent average convective heat transfer coefficient, an average absorption coefficient of the heating building enclosure structure to solar radiation and solar radiation intensity as examples, determining a first total heat dissipation of the heating building within a predicted time period according to the following formula (formula 2):
Wherein phi represents the first total heat dissipation capacity of the heating building in the predicted time period, tau represents time sharing, h represents solar equivalent average convective heat transfer coefficient, S represents the first equivalent total heat dissipation area of the heating building, ρ represents the average absorption coefficient of the heating building enclosure structure to solar radiation, I s Representing the intensity of solar radiation, t e Indicating the outdoor air temperature, t f Representing a first air temperature daily variation curve function, t w Indicating the equivalent average temperature of the exterior surface of the heating building. Wherein, in urban heat supply in China, the indoor temperature heat supply design standard is not lower than 18 ℃, t is the temperature of the indoor temperature heat supply w Can be considered approximately 18 ℃.
S205: and carrying out resource scheduling in the predicted time period according to the heating gas load.
The heating gas load of the predicted time period determined by the steps S201 to S204 can be used for resource scheduling. Illustratively, scheduling of resources for a predicted duration may be performed by the resource scheduling unit 102 in the embodiment shown in fig. 1. For example, the gas resource required for urban heating is determined by the correspondence between the gas resource and the heating gas load, and for example, the determined heating gas load of the predicted duration may be interacted with other devices to notify them of resource supply. Optionally, the resource scheduling in the predicted duration according to the heating gas load specifically includes: determining the resource demand according to the heating gas load; the resource demand is sent to a resource provider (i.e., a resource scheduling unit) to instruct the resource provider to provide resources in accordance with the resource demand.
It should be noted that τ shown in equation 1 and equation 2 provided in the embodiments of the present application is preferably expressed in a time-sharing manner, and the amount of calculation is controlled not to be excessively large on the premise of ensuring sufficient calculation accuracy. In other embodiments, τ may represent time units with higher or lower accuracy, such as seconds or hours, according to different accuracy requirements, and accordingly, the first air temperature change curve function and the first total heat dissipation may be fitted and calculated based on other time units.
According to the resource scheduling method provided by the embodiment of the application, first weather information of a predicted time period is obtained, the first weather information comprises first time-sharing air temperature data and first weather conditions, fitting processing is carried out on the first time-sharing air temperature data to obtain a first air temperature change curve function of air temperature changing along with time sharing in the predicted time period, in a corresponding relation between preset weather conditions and key parameters, first key parameters corresponding to the first weather conditions are matched, the first key parameters are data affecting heat dissipation capacity of urban heating buildings, heating air load of the predicted time period is determined based on the first air temperature change curve function, the first key parameters and a first equivalent total heat dissipation area of the heating buildings corresponding to a current heating season, and resource scheduling in the predicted time period is carried out according to the heating air load. According to the embodiment of the application, the prediction of the heating gas load of the predicted duration is performed before the resource scheduling, the change condition of the scale of a heating user is considered, the equivalent total heat dissipation area of a heating building is more accurately quantized, climate condition factors (such as air temperature, wind speed and solar radiation) are integrated in the prediction process, the prediction precision of the heating gas load is effectively improved, and the accuracy of the resource scheduling is further improved.
Fig. 3 is a second flowchart of a resource scheduling method provided in the embodiment of the present application, and an exemplary method for determining a correspondence between preset weather conditions and key parameters is provided, and referring to fig. 3, the method includes:
s301: first sample data of each first set duration in at least one historical heating season are obtained, the first sample data comprises historical weather information, and the historical weather information comprises historical weather conditions.
In this embodiment of the present application, a first sample data of a set duration of historical heating Ji Nadi may be selected to establish a correspondence between a meteorological condition and a key parameter, where a heating season generally refers to 11 months 15 days to 3 months 15 days of the next year, and since a historical heating season may not include all meteorological conditions, in order to increase a table entry of a correspondence between a meteorological condition and a key parameter, a plurality of first sample data of a set duration of historical heating Ji Nadi may also be selected to establish a correspondence between a meteorological condition and a key parameter, which is not limited in this embodiment of the present application.
The first set duration may be defined according to needs, for example, each day in the whole historical heating season, and then, for example, a certain duration is selected as the first set duration in the whole historical heating season. The first set period may be 1 day or may be multiple days (for example, 16 days), for example, the first sample data of the current year is collected for 16 days, such as 11 months 15 days to 11 months 30 days. After the correspondence between the meteorological conditions and the key parameters is determined, a correspondence table may be established and stored in a readable storage medium, and may be obtained by the resource demand prediction unit in the embodiment of the present application.
S302: and grouping the first sample data according to the age of the historical heating season and the historical meteorological conditions to obtain a data set containing the first sample data with the same historical meteorological conditions in the same historical heating season.
The equivalent total heat dissipation area of the heating buildings in the cities in the same heating season is generally considered to be unchanged, and the same weather conditions correspond to the same key parameters, so that for the first sample data of the same group, the equivalent total heat dissipation area of the heating buildings corresponding to the first sample data in the group is the same, and the key parameters corresponding to the first sample data in the group are also the same.
Therefore, the first sample data with the same historical meteorological conditions in the same historical heating season are divided into a group to obtain the data group with the same equivalent total heat dissipation area and meteorological conditions of the heating building, and the following data processing is performed for each historical meteorological condition to establish the corresponding relation between the meteorological conditions and the key parameters.
S303: for each historical meteorological condition, selecting N pieces of first sample data from a data set corresponding to the historical meteorological condition, wherein N is a natural number; s304: and determining key parameters corresponding to the historical meteorological conditions by solving an equation set based on the N pieces of first sample data.
For each data set of first sample data with the same historical meteorological conditions in the same historical heating season, namely for each data set based on the historical meteorological conditions, N pieces of first sample data are selected to be used as basic data for determining the corresponding relation between the meteorological conditions and the key parameters.
Optionally, in the step of determining the key parameter corresponding to the historical meteorological condition by solving the equation set based on the N pieces of first sample data, the historical meteorological information further includes historical time-sharing air temperature data, the first sample data further includes historical heating air load, and determining the key parameter corresponding to the historical meteorological condition by solving the equation set based on the N pieces of first sample data includes: for each of the N pieces of first sample data, the following processing is performed:
fitting the historical time-sharing air temperature data in the first sample data, and determining a second air temperature daily change curve function corresponding to the air temperature in the first set time period along with time sharing; and determining key parameters corresponding to the historical meteorological conditions by solving an equation set according to the historical heating gas load, the second air temperature daily change curve function and the second equivalent total heat dissipation area of the internal heating building corresponding to the corresponding historical heating season in the first sample data.
Alternatively, the second air temperature daily change curve function may be determined based on the formula 1 provided in the foregoing embodiment, and the equation sets may be established based on the formula 2 (i.e., objective function) provided in the foregoing embodiment, where in each equation set, since the equivalent total heat dissipation area of the heating building is the same as the weather condition, N-1 key parameters corresponding to the historical weather condition in each data set may be solved by N pieces of first sample data.
Specifically, when determining key parameters of a certain group of data sets, 4 pieces of first sample data are selected from the data sets corresponding to the meteorological conditions, and based on historical heating gas loads in the 4 pieces of first sample data, a second air temperature change curve function fitted through the formula 1 and a natural gas heat value, an equation set is established by using the formula 2, so that 3 key parameters corresponding to the meteorological conditions are solved.
In some embodiments, the second air temperature change curve function may be determined by fitting according to time-sharing air temperature data of each day in the whole historical heating season, and in other embodiments, may be determined by fitting according to first sample data selected from the data set.
In some embodiments, before selecting the N pieces of first sample data from the data set corresponding to the historical weather condition, the method further includes: and deleting each group of first sample data with the number smaller than N in the first sample data. For example, if n=4, the number of the first sample data counted in a certain set of the first sample data is smaller than 4, the reorganized first sample data is deleted. This is because if the number of first sample data in a certain set of first sample data is smaller than N, N-1 key parameters corresponding to the meteorological condition cannot be determined by solving the equation set.
To further reduce the error, in some embodiments, a look-ahead determination of the first equivalent total heat dissipation area may be made prior to the embodiment shown in FIG. 2.
Fig. 4 is a flowchart third of a resource scheduling method provided in the embodiment of the present application, where the method exemplarily illustrates a method for determining a first equivalent total heat dissipation area, and referring to fig. 4, the method includes:
s401: acquiring second sample data corresponding to a second set time length in the current heating season, wherein the second sample data comprises a second heating gas load and second weather information, and the second weather information comprises second time-sharing air temperature data and second weather conditions;
s402: fitting the second time-sharing air temperature data to obtain a third air temperature change curve function of air temperature changing along with time sharing;
s403: in the corresponding relation, matching a second key parameter corresponding to a second meteorological condition;
s404: multiplying the second heating gas load by the natural gas heat value to obtain a second total heat dissipation capacity of the heating building within a second set time period;
s405: substituting the second total heat dissipation capacity, the second key parameter and the third air temperature change curve function into the objective function to obtain a first equivalent total heat dissipation area.
And selecting second sample data of the current heating season to determine the equivalent total heat dissipation area, so that the first equivalent total heat dissipation area can be closer to the predicted day. The second set period of time may be determined according to requirements and may be different in different embodiments. Wherein the third air temperature change curve function may be determined according to formula 1 in the above embodiment, and the objective function may be determined according to formula 2 in the above embodiment.
Alternatively, a plurality of alternative first equivalent total heat dissipation areas may be determined according to the plurality of sets of second sample data, and an average value of the plurality of alternative first equivalent total heat dissipation areas is determined as a final first equivalent total heat dissipation area.
In some specific embodiments, the process of determining the correspondence between the meteorological conditions and the key parameters and the first equivalent total heat dissipation area is performed continuously in the same embodiment, and the method includes the following steps:
step one: acquiring first sample data of each first set time length in at least one historical heating season, wherein the first sample data comprises historical weather information, and the historical weather information comprises historical weather conditions;
step two: grouping the first sample data according to the age of the historical heating season and the historical meteorological conditions to obtain a data set containing the first sample data with the same historical meteorological conditions in the same historical heating season;
Step three: for each historical meteorological condition, selecting N pieces of first sample data from a data set corresponding to the historical meteorological condition, wherein N is a natural number; determining key parameters corresponding to historical meteorological conditions by solving an equation set based on the N pieces of first sample data;
step four: acquiring second sample data corresponding to a second set time length in the current heating season, wherein the second sample data comprises a second heating gas load and second weather information, and the second weather information comprises second time-sharing air temperature data and second weather conditions;
step five: fitting the second time-sharing air temperature data to obtain a third air temperature change curve function of air temperature changing along with time sharing;
step six: in the corresponding relation, matching a second key parameter corresponding to a second meteorological condition;
step seven: multiplying the second heating gas load by the natural gas heat value to obtain a second total heat dissipation capacity of the heating building within a second set time period;
step eight: substituting the second total heat dissipation capacity, the second key parameters and the third air temperature change curve function into the objective function to obtain a first equivalent total heat dissipation area;
step nine: acquiring first weather information of a predicted time length, wherein the first weather information comprises first time-sharing air temperature data and first weather conditions;
Step ten: fitting the first time-sharing air temperature data to obtain a first air temperature change curve function of air temperature changing along with time sharing in the predicted time length;
step eleven: in the corresponding relation between the preset meteorological conditions and the key parameters, matching first key parameters corresponding to the first meteorological conditions, wherein the first key parameters are data affecting the heat dissipation capacity of the urban heating building;
step twelve: determining a heating gas load of a predicted duration 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 the current heating season;
step thirteen: and carrying out resource scheduling in the predicted time period according to the heating gas load.
Based on the same technical concept, the embodiment of the application also provides a resource scheduling device, an electronic device and a readable storage medium, and the detailed description is given below.
Fig. 5 is a schematic structural diagram of a resource scheduling device according to an embodiment of the present application. Referring to fig. 5, the apparatus 500 includes:
an obtaining module 501, configured to obtain first weather information of a predicted duration, where the first weather information includes first time-sharing air temperature data and a first weather condition;
the first determining module 502 is configured to perform fitting processing on the first time-sharing air temperature data to obtain a first air temperature change curve function of air temperature changing along with time sharing in a predicted duration;
A second determining module 503, configured to match a first key parameter corresponding to a first weather condition in a preset correspondence between the weather condition and the key parameter, where the first key parameter is data affecting heat dissipation of the urban heating building;
a third determining module 504, configured to determine a heating air load of a predicted duration 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;
the scheduling module 505 is configured to schedule resources within a predicted duration according to the heating air load.
Optionally, the third determining module 504 may be specifically configured to determine the first total heat dissipation of the heating building in the predicted period according to the first air temperature change curve function, the first key parameter, and the first equivalent total heat dissipation area based on an objective function, where the objective function is configured to reflect an association relationship between the air temperature change curve function, the key parameter, and 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 capacity to the natural gas calorific value.
Optionally, the third determining module 504 may be further specifically configured to determine the first total heat dissipation of the heating building within the predicted time period according to the following formula:
Wherein phi represents the first total heat dissipation capacity of the heating building in the predicted time period, tau represents time sharing, h represents solar equivalent average convective heat transfer coefficient, S represents the first equivalent total heat dissipation area of the heating building, ρ represents the average absorption coefficient of the heating building enclosure structure to solar radiation, I s Representing the intensity of solar radiation, t e Indicating the outdoor air temperature, t f Representing a first air temperature daily variation curve function, t w Indicating the equivalent average temperature of the exterior surface of the heating building.
Alternatively, the scheduling module 505 may be specifically configured to determine the resource demand according to the heating air load; the resource demand is sent to the resource provider to instruct the resource provider to provide the resource in accordance with the resource demand.
Optionally, the resource scheduling device 500 further includes a fourth determining module, which may be specifically configured to obtain first sample data of each first set duration in at least one historical heating season, where the first sample data includes historical weather information, and the historical weather information includes historical weather conditions; grouping the first sample data according to the age of the historical heating season and the historical meteorological conditions to obtain a data set 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 is carried out to establish the corresponding relation between the meteorological condition and the key parameter: selecting N pieces of first sample data from a data set corresponding to historical meteorological conditions, wherein N is a natural number; and determining key parameters corresponding to the historical meteorological conditions by solving an equation set based on the N pieces of first sample data.
Optionally, the fourth determining module may be further specifically configured to, for each piece of the N pieces of first sample data, perform the following processing: fitting the historical time-sharing air temperature data in the first sample data, and determining a second air temperature change curve function corresponding to the air temperature in the first set time period along with time sharing; and determining key parameters corresponding to the historical meteorological conditions by solving an equation set according to the historical heating gas load, the second air 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.
Optionally, the resource scheduling device 500 further includes a fifth determining module, where the fifth determining module may be specifically configured to obtain second sample data corresponding to a second set duration in the current heating season, where the second sample data includes a second heating air load and second weather information, and the second weather information includes second time-sharing air temperature data and second weather conditions; fitting the second time-sharing air temperature data to obtain a third air temperature change curve function of air temperature changing along with time sharing; in the corresponding relation, matching a second key parameter corresponding to a second meteorological condition; multiplying the second heating gas load by the natural gas heat value to obtain a second total heat dissipation capacity of the heating building within a second set time period; substituting the second total heat dissipation capacity, the second key parameter and the third air temperature change curve function into the objective function to obtain a first equivalent total heat dissipation area.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 6, the four-case device 600 includes:
a processor 601, a memory 602, a communication interface 603 and a system bus 604.
The memory 602 and the communication interface 603 are connected to the processor 601 through the system bus 604 and complete communication with each other, the memory 602 is used for storing computer execution instructions, the communication interface 603 is used for communicating with other devices, and the processor 601 is used for executing the computer execution instructions to execute the scheme of the resource scheduling method in the method embodiment.
In particular, the processor 601 may include one or more processing units, such as: the processor 601 may be a central processing unit (Central Processing Unit, CPU for short), digital signal processing (Digital Signal Processing, DSP for short), application specific integrated circuit (Application Specific Integrated Circuit, ASIC for short), or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present application may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in a processor for execution. The processes and logic flows described in this specification can be performed 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 processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., a field programmable gate array (Field Programmable Gate Array, FPGA) or ASIC.
Memory 602 may be used to store program instructions. The memory 602 may include a stored program area and a stored data area. The storage program area may store an application program (such as a sound playing function, etc.) required for at least one function of the operating system, and the like. The storage data area may store data created during use of the electronic device 600 (e.g., audio data, etc.), and so on. In addition, the memory 602 may include high-speed random access memory, and may also include nonvolatile memory, such as at least one magnetic disk storage device, flash memory device, universal flash memory (Universal Flash Storage, abbreviated UFS), and the like. The processor 601 performs various functional applications and data processing of the electronic device 600 by executing program instructions stored in the memory 602.
The communication interface 603 may provide a solution for wireless communication, including 2G/3G/4G/16G, as applied to the electronic device 600. The communication interface 603 may receive electromagnetic waves from an antenna, filter, amplify, and the like the received electromagnetic waves, and transmit the electromagnetic waves to a modem processor for demodulation. The communication interface 603 may also amplify the signal modulated by the modem processor, and convert the signal into electromagnetic waves through an antenna to radiate. In some embodiments, at least some of the functional modules of the communication interface 603 may be provided in the processor 601. In some embodiments, at least some of the functional modules of the communication interface 603 may be provided in the same device as at least some of the modules of the processor 601.
The system bus 604 may be a Peripheral Component Interconnect (PCI) bus, an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, or the like. The system bus 604 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
It should be noted that, the number of the memory 602 and the number of the processors 601 are not limited in this embodiment, and may be one or more, and fig. 6 illustrates one example; the memory 602 and the processor 601 may be connected by various means, such as wired or wireless, for example, via a bus. In practice, the electronic device 600 may be a computer or a mobile terminal in various forms. Examples of the computer include a laptop computer, a desktop computer, a workstation, a server, a blade server, and a mainframe computer; mobile terminals are, for example, personal digital assistants, cellular telephones, smart phones, wearable devices, and other similar computing devices.
The electronic device of the present embodiment may be used to execute the technical solution in the foregoing method embodiment, and its implementation principle and technical effects are similar, and are not described herein again.
The embodiment of the application also provides a computer readable storage medium, in which computer executable instructions are stored, where the computer executable instructions are used to implement the resource scheduling method provided in the above embodiment when executed.
The present application also provides a computer program product comprising a computer program; when the computer program is executed, the resource scheduling method provided by the above embodiment is implemented.
Embodiments of the method and functional operations described in the embodiments of the present application may be implemented in the following: digital electronic circuitry, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this application and structural equivalents thereof, or a combination of one or more of them. The method embodiments described in this application may 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, or to control the operation of, data processing apparatus. Alternatively or additionally, the program instructions may be encoded on a manually-generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode and transmit information to suitable receiver apparatus for execution by data processing apparatus. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.
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 will receive instructions and data from a read only memory and/or a random access memory. The essential elements of a computer include a central processing unit for carrying out or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks, etc. However, a computer does not have to have such a device. Furthermore, the computer may be embedded in another device, such as a mobile phone, a personal digital assistant (Personal Digital Assistant, PDA for short), a mobile audio or video player, a game console, a global positioning system (Global Positioning System, GPS for short) receiver, or a portable storage device such as a universal serial bus (Universal Serial Bus, USB for short) flash drive, to name a few.
Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices including, for example, semiconductor memory devices, magnetic disks, magneto-optical disks, and the like. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any invention or of what may be claimed, but rather as descriptions of features of specific embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. On the other hand, the various features described in the individual embodiments may also be implemented separately in the various embodiments or in any suitable subcombination. Furthermore, although features may be acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.
Similarly, although operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In some cases, multitasking and parallel processing may be advantageous. Moreover, the separation of various system modules and components in the embodiments described above should not be understood 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.
Thus, specific embodiments of the present application have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. Furthermore, the processes depicted in the accompanying drawings are not necessarily required to be in the particular order shown, or sequential order, to achieve desirable results. In some implementations, multitasking and parallel processing may be advantageous.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A method for scheduling resources, comprising:
acquiring first weather information of a predicted time length, wherein the first weather information comprises first time-sharing air temperature data and first weather conditions;
fitting the first time-sharing air temperature data to obtain a first air temperature change curve function of air temperature changing along with time sharing in the predicted duration;
matching a first key parameter corresponding to a first meteorological condition in a corresponding relation between a preset meteorological condition and the key parameter, wherein the first key parameter is data affecting heat dissipation capacity of a city heating building;
Determining the heating air load of the predicted duration 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 the current heating season;
and carrying out resource scheduling in the predicted time length according to the heating gas load.
2. The resource scheduling method according to claim 1, wherein the determining the heating air load for the predicted duration 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 includes:
determining a first total heat dissipation capacity of the heating building in the predicted time period based on an objective function according to the first air temperature change curve function, the first key parameter and the first equivalent total heat dissipation area, wherein the objective function is used for reflecting the association relation between the air temperature change curve function, the key parameter, the equivalent total heat dissipation area of the heating building and the total heat dissipation capacity of the heating building;
and determining the heating gas load as the ratio of the first total heat dissipation capacity to the natural gas heat value.
3. The method of claim 2, wherein the key parameters include a daily equivalent average convective heat transfer coefficient, an average absorption coefficient of solar radiation by the heating building enclosure, and a solar radiation intensity, and wherein the determining, based on the objective function, the first total heat dissipation of the heating building within the predicted time period according to the first air temperature change curve function, the first key parameter, and the first equivalent total heat dissipation area comprises:
Determining a first total heat dissipation of the heating building within the predicted time period according to the following formula:
wherein phi represents the first total heat dissipation capacity of the heating building in the predicted time period, tau represents time sharing, h represents solar equivalent average convective heat transfer coefficient, S represents the first equivalent total heat dissipation area of the heating building, ρ represents the average absorption coefficient of the heating building enclosure structure to solar radiation, I s Representing the intensity of solar radiation, t e Indicating the outdoor air temperature, t f Representing a first air temperature daily variation curve function, t w Indicating the equivalent average temperature of the exterior surface of the heating building.
4. A resource scheduling method according to any one of claims 1 to 3, wherein said scheduling of resources within said predicted period of time in accordance with said heating gas load comprises:
determining the resource demand according to the heating gas load;
and sending the resource demand to a resource provider to instruct the resource provider to provide resources according to the resource demand.
5. A resource scheduling method according to any one of claims 1 to 3, wherein the correspondence of the meteorological conditions to key parameters is determined by:
acquiring first sample data of each first set time length in at least one historical heating season, wherein the first sample data comprises historical weather information, and the historical weather information comprises historical weather conditions;
Grouping the first sample data according to the age of the historical heating season and the historical meteorological conditions to obtain a data set 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 is carried out to establish the corresponding relation between the meteorological condition and the key parameter:
selecting N pieces of first sample data from the data group corresponding to the historical meteorological conditions, wherein N is a natural number;
and determining key parameters corresponding to the historical meteorological conditions by solving an equation set based on the N pieces of first sample data.
6. The resource scheduling method according to claim 5, wherein the historical weather information further includes historical time-sharing air temperature data, the first sample data further includes a historical heating air load, and the determining key parameters corresponding to the historical weather conditions by solving an equation set based on the N pieces of first sample data includes:
for each first sample data in the N pieces of first sample data, performing the following processing:
fitting the historical time-sharing air temperature data in the first sample data, and determining a second air temperature change curve function corresponding to the air temperature in the first set time period along with time sharing;
And determining key parameters corresponding to the historical meteorological conditions by solving an equation set according to the historical heating gas load, the second air 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.
7. The resource scheduling method according to any one of claims 1 to 3, wherein before determining the heating air load for the predicted duration based on the first air 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, further comprises:
acquiring second sample data corresponding to a second set time length in the current heating season, wherein the second sample data comprises a second heating gas load and second weather information, and the second weather information comprises second time-sharing air temperature data and second weather conditions;
fitting the second time-sharing air temperature data to obtain a third air temperature change curve function of air temperature changing along with time sharing;
in the corresponding relation, matching a second key parameter corresponding to the second meteorological condition;
multiplying the second heating gas load with the natural gas heating value to obtain a second total heat dissipation capacity of the heating building within the second set time period;
Substituting the second total heat dissipation capacity, the second key parameter and the third air temperature change curve function into the objective function to obtain the first equivalent total heat dissipation area.
8. A resource scheduling apparatus, comprising:
the system comprises an acquisition module, a prediction module and a prediction module, wherein the acquisition module is used for acquiring first weather information of predicted duration, and the first weather information comprises first time-sharing air temperature data and first weather conditions;
the first determining module is used for carrying out fitting processing on the first time-sharing air temperature data to obtain a first air temperature change curve function of air temperature changing along with time sharing in the predicted duration;
the second determining module is used for matching a first key parameter corresponding to the first weather condition in the corresponding relation between the preset weather condition and the key parameter, wherein the first key parameter is data affecting the heat dissipation capacity of the urban heating building;
the third determining module is used for determining the heating gas load of the predicted duration based on the first gas 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;
and the scheduling module is used for scheduling the resources in the predicted duration according to the heating gas load.
9. An electronic device, comprising: a memory and a processor;
the memory is used for storing program instructions;
the processor being configured to invoke the program instructions to perform the resource scheduling method of any of claims 1 to 7.
10. A readable storage medium having a computer program stored thereon; the computer program, when executed, implements the resource scheduling method of any one of claims 1 to 7.
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