CN115992814A - Method and device for scheduling air compression system - Google Patents
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Abstract
The application discloses a method and a device for scheduling an air compressor system, which relate to the field of energy, and adopt the method for acquiring the power consumption of the air compressor system under a plurality of candidate operation state combinations and a plurality of candidate gas flow combinations, determine a target operation state combination and a target gas flow combination from the plurality of candidate operation state combinations and the plurality of candidate gas flow combinations based on the power consumption corresponding to the plurality of candidate operation state combinations and the plurality of candidate gas flow combinations, and adjust the operation state and the gas flow combination of each air compressor in the air compressor system according to the target operation state combination and the target gas flow combination.
Description
Technical Field
The application relates to the field of energy, in particular to a method and a device for scheduling an air compression system.
Background
Compressed air is the second largest power source next to electricity and is widely used in various fields. Currently, most air compression systems are controlled by a single programmable logic controller (programmable logic controller, PLC). However, in the control method, when multiple units are operated in parallel, the loading and unloading of each air compressor in the air compressor system are asynchronous, the load distribution is uneven, and the continuous emptying phenomenon of some units exists, so that energy waste is caused, and the operation optimization of the air compressor units becomes a focus of enterprises.
In the prior art, a method of dispatching an air compressor unit according to a dispatching instruction issued by a telephone and regulating and controlling by an operator according to personal experience is adopted. However, the air compressor unit is scheduled by the prior art, so that the defects of low resource utilization rate and high power consumption of the air compression system exist.
Disclosure of Invention
In view of this, the present application provides a method and apparatus for scheduling an air compression system, so as to achieve the purpose of reducing energy consumption of the air compression system.
The method for scheduling the air compression system is realized by the following steps:
acquiring power consumption of the air compression system under a plurality of candidate operation state combinations and a plurality of candidate gas flow combinations;
determining a target running state combination and a target gas flow combination from the plurality of candidate running state combinations and the plurality of candidate gas flow combinations based on power consumption corresponding to the plurality of candidate running state combinations and the plurality of candidate gas flow combinations, wherein the power consumption corresponding to the target running state combination and the target gas flow combination is minimum in the plurality of candidate running state combinations and the plurality of candidate gas flow combinations;
and adjusting the operation state and the gas flow combination of each air compressor in the air compression system according to the target operation state combination and the target gas flow combination.
Optionally, the plurality of candidate operating state combinations includes a first candidate operating state combination, the plurality of candidate gas flow combinations includes the first candidate gas flow combination, and obtaining power consumption of the air compression system under the plurality of candidate operating state combinations and the plurality of candidate gas flow combinations includes:
inputting the candidate gas production flow of the first air compressor in the first candidate gas production flow combination into a power consumption prediction model, wherein the first air compressor is any air compressor in an operation state in the first candidate gas production flow combination;
processing the suction pressure, the ambient temperature, the exhaust pressure and the candidate gas flow of the first air compressor through a power consumption prediction model, and outputting the power consumption of the first air compressor;
and based on the power consumption of the first air compressor, obtaining the power consumption of the air compression system under the first candidate operation state combination and the first candidate gas flow combination.
Optionally, the power consumption prediction model is trained based on a historical suction pressure, a historical ambient temperature, a historical discharge pressure, a historical gas flow rate and a historical power consumption of each air compressor in the air compression system.
Optionally, the power consumption prediction model is a neural network model.
Optionally, determining the target operating state combination and the target gas flow combination from the plurality of candidate operating state combinations and the plurality of candidate gas flow combinations based on the power consumption corresponding to the plurality of candidate operating state combinations and the plurality of candidate gas flow combinations includes:
and determining a target population from the plurality of candidate populations based on the fitness of each candidate population in the plurality of candidate populations, wherein each candidate population in the plurality of candidate populations represents an air compression system in a candidate running state combination, each individual in the candidate populations represents an air compressor in the air compression system, and the fitness represents power consumption corresponding to the candidate population.
Optionally, before determining the target population from the plurality of candidate populations based on the fitness of each candidate population of the plurality of candidate populations, the method further comprises:
and performing cross operation and mutation operation on the codes of each individual in the initial population to obtain a candidate population, wherein the codes of the individuals represent the running states of the air compressors corresponding to the individuals.
Optionally, determining the target operating state combination and the target gas flow combination from the plurality of candidate operating state combinations and the plurality of candidate gas flow combinations based on the power consumption corresponding to the plurality of candidate operating state combinations and the plurality of candidate gas flow combinations includes:
and determining a target running state combination and a target gas flow combination from the plurality of candidate running state combinations and the plurality of candidate gas flow combinations based on the power consumption, the required gas supply amount, the minimum gas yield of each air compressor and the maximum gas yield of each air compressor corresponding to the plurality of candidate gas flow combinations, wherein the power consumption corresponding to the target running state combination and the target gas flow combination is minimum in the plurality of candidate running state combinations and the plurality of candidate gas flow combinations, the target gas flow combination meets the required gas supply amount, and the gas yield of each air compressor in the target gas flow combination is in a range from the minimum gas yield of the corresponding air compressor to the maximum air amount.
The application also provides a device for scheduling the air compression system, which comprises: the device comprises an acquisition unit, a determination unit and an adjustment unit;
the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring the power consumption of the air compression system under a plurality of candidate running state combinations and a plurality of candidate gas flow combinations;
the acquisition unit is further used for acquiring the power consumption of the air compression system under the first candidate operation state combination and the first candidate gas flow combination based on the power consumption of the first air compressor, wherein the first air compressor is any air compressor in the operation state in the first candidate gas flow combination;
a determining unit configured to determine, based on power consumption corresponding to the plurality of candidate operation state combinations and the plurality of candidate gas flow combinations, a target operation state combination and a target gas flow combination from the plurality of candidate operation state combinations and the plurality of candidate gas flow combinations, the power consumption corresponding to the target operation state combination and the target gas flow combination being the smallest among the plurality of candidate operation state combinations and the plurality of candidate gas flow combinations;
the determining unit is further used for determining a target population from the candidate populations based on the fitness of each candidate population in the candidate populations, wherein each candidate population in the candidate populations represents an air compression system in a candidate running state combination, each individual in the candidate populations represents an air compressor in the air compression system, and the fitness represents power consumption corresponding to the candidate population;
and the adjusting unit is used for adjusting the running state and the gas flow combination of each air compressor in the air compression system according to the target running state combination and the target gas flow combination.
Optionally, the apparatus further comprises: an input unit, an output unit, or an encoding unit;
the input unit is used for inputting the suction pressure of the first air compressor, the ambient temperature, the exhaust pressure of the first air compressor and the candidate gas flow of the first air compressor in the first candidate gas flow combination into the power consumption prediction model;
the output unit is used for processing the suction pressure, the ambient temperature, the exhaust pressure and the candidate gas production flow of the first air compressor through the power consumption prediction model and outputting the power consumption of the first air compressor;
the coding unit is used for carrying out cross operation and mutation operation on the codes of each individual in the initial population to obtain a candidate population, and the codes of the individuals represent the running state of the air compressor corresponding to the individuals.
The present application also provides a computer device comprising: and the processor is coupled with the memory, at least one computer program instruction is stored in the memory, and the at least one computer program instruction is loaded and executed by the processor, so that the computer equipment realizes the method for scheduling the air compression system.
Therefore, the beneficial effects of this application are: the method comprises the steps of acquiring power consumption of an air compressor system under a plurality of candidate operation state combinations and a plurality of candidate gas flow combinations, determining a target operation state combination and a target gas flow combination from the plurality of candidate operation state combinations and the plurality of candidate gas flow combinations based on the power consumption corresponding to the plurality of candidate operation state combinations and the plurality of candidate gas flow combinations, enabling the power consumption corresponding to the target operation state combination and the target gas flow combination to be minimum in the plurality of candidate operation state combinations and the plurality of candidate gas flow combinations, adjusting the operation state of each air compressor in the air compressor system and the gas flow combination according to the target operation state combination and the target gas flow combination, selecting the combination with the minimum power consumption from the plurality of candidate operation state combinations and the plurality of candidate gas flow combinations, and dispatching the air compressor system according to the combination with the minimum power consumption, so as to achieve the effect of reducing energy consumption of the air compressor system, and achieve intelligent operation of the air compressor system.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings may be obtained according to the provided drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of a first embodiment of the present application;
FIG. 2 is a flow chart of a second embodiment of the present application;
FIG. 3 is a schematic illustration of a neural network model of the present application;
FIG. 4 is a flow chart of a third embodiment of the present application;
FIG. 5 is a flow chart of a fourth embodiment of the present application;
FIG. 6 is a schematic view of an apparatus of the present application;
fig. 7 is a schematic diagram of a computer device of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The inventor finds that the scheduling of the air compression system is performed through the prior art, the scheduling result is good or bad and depends on the service level and experience of operators, certain deviation and hysteresis exist in scheduling accuracy.
In embodiments of the present application, the device that schedules the air compression system may include, but is not limited to, a computer device.
The computer device may include: and the processor is coupled with the memory, at least one computer program instruction is stored in the memory, and the at least one computer program instruction is loaded and executed by the processor, so that the computer equipment realizes the method for scheduling the air compression system.
Referring to fig. 1, the specific steps of the first embodiment of the present application are as follows:
s101: the computer obtains the power consumption of the air compression system under a plurality of candidate operation state combinations and a plurality of candidate gas production flow combinations.
The air compression system is an air compression system.
In some implementations, an optimal scheduling model is pre-built, and is solved based on a genetic algorithm to obtain a plurality of candidate operating state combinations and a plurality of candidate gas flow combinations.
In some implementations, a power consumption prediction model is pre-built, and power consumption of the air compression system under a plurality of candidate operation state combinations and a plurality of candidate production gas flow combinations is obtained through the power consumption prediction model. The power consumption prediction model may be constructed based on a neural network model. The power consumption prediction model is obtained by training based on the historical suction pressure, the historical environment temperature, the historical exhaust pressure, the historical gas production flow and the historical power consumption of each air compressor in the air compression system.
S102: the computer determines a target operating state combination and a target gas flow combination from the plurality of candidate operating state combinations and the plurality of candidate gas flow combinations based on power consumption corresponding to the plurality of candidate operating state combinations and the plurality of candidate gas flow combinations.
The power consumption corresponding to the target operating state combination and the target gas flow combination is minimized among the plurality of candidate operating state combinations and the plurality of candidate gas flow combinations.
S103: and the computer adjusts the running state and the gas flow combination of each air compressor in the air compression system according to the target running state combination and the target gas flow combination.
In some implementations, the computer sends the target operating state combination and the target gas flow combination to a self-contained control system of the air compression system, and the operating state and the gas flow combination of each air compressor in the air compression system are adjusted through the self-contained control system of the air compression system so that the energy consumption of the air compression system is minimized.
In the first embodiment of the application, the combination with the minimum power consumption is selected from the plurality of candidate operation state combinations and the plurality of candidate gas flow combinations, and the air compression system is scheduled according to the combination with the minimum power consumption, so that the energy consumption and the adjusting hysteresis of the air compression system are reduced, the energy saving and consumption reduction targets of enterprises are achieved, and the intelligent operation of the air compression system is realized.
In a second embodiment, please refer to fig. 2 and 3, a specific description is given of how to construct a power consumption prediction model, and specific steps of the second embodiment of the present application are as follows:
s201: the computer obtains historical data of the ambient temperature, the suction pressure, the gas production flow, the exhaust pressure and the current of the air compressor within a preset time threshold.
The preset time threshold may be 24 hours, may be 12 hours, may be 36 hours, or may be set according to actual requirements.
The environment temperature, the air compressor suction pressure, the gas production flow, the exhaust pressure and the air compressor current are parameters which have larger influence on the power consumption of the air compressor, and the computer can acquire other parameters which have larger influence on the power consumption of the air compressor according to actual requirements.
In some implementations, the computer collects historical data of ambient temperature, suction pressure, gas flow, discharge pressure, and current within a preset time threshold from a real-time database.
S202: and the computer determines the power consumption of the air compressor according to the current of the air compressor.
In some implementations, the power consumption of the air compressor is calculated according to the following formula:
wherein P is the power consumption of the air compressor, U is the voltage of the air compressor, I is the current of the air compressor,
is a power factor.
S203: the computer normalizes the input data set and the output data set.
The input data set is historical data of the ambient temperature, the air suction pressure, the gas production flow and the exhaust pressure of the air compressor within a preset time threshold value obtained by a computer.
The output data set is the power consumption of the air compressor, which is obtained by the computer according to the current determination of the air compressor.
In some implementations, the input data set and the output data set are normalized by mapping the input data set and the output data set to [0,1 ]]And (3) the following steps:
wherein x is i In order to normalize the pre-processed data,
to normalize the processed data, x max To normalize the maximum value, x, in the data set where the pre-processed data is located min Is the minimum value in the dataset where the pre-processed data is located.
If the environment temperature in the input data set is normalized, x i To normalize the first ambient temperature of the pre-processing (the first ambient temperature may be any ambient temperature in the input dataset),
to normalize the processed first ambient temperature, x max For maximum ambient temperature in the input dataset, x min Is the minimum value of the ambient temperature in the input dataset.
S204: the computer initializes the power consumption prediction model according to the first initial parameters.
The first initial parameters include learning efficiency, maximum convergence number, and minimum error. The learning efficiency may be set to any value of 0.01 to 0.25, for example, the learning efficiency is selected to be 0.075, or the learning efficiency may be set according to actual situations. The maximum convergence number may be set to 200, or may be set according to actual conditions. The minimum error may be set to 0.000001, or may be set according to actual conditions.
In some implementations, the computer also determines the number of output layer nodes, the number of hidden layer nodes (the number of hidden layer nodes is typically less than the number of output layer nodes minus one), and the number of output layer nodes based on the input and output of the model. In this embodiment, the number of output layer nodes is 4, the number of hidden layer nodes is 2, and the number of output layer nodes is 1.
S205: the computer divides the input data set and the output data set into a training set, a testing set, and a validation set.
The dividing ratio of the training set, the testing set and the verification set can be set according to actual requirements.
S206: the computer determines whether the maximum convergence number is reached.
If the maximum convergence number is not reached, S207-S208 are executed; if the maximum convergence number is reached, S209 is executed.
S207: the computer randomly selects a set of data from the training set, determines the output of the hidden layer and the output layer, and determines the error of the output layer output and the expected output.
The desired output is data of an output dataset corresponding to data randomly selected by the computer from the training dataset.
The error of the output layer output and the expected output can be root mean square difference of the output layer output and the expected output, or the error of the output layer output and the expected output can be obtained according to other algorithms.
S208: the computer judges whether the error is smaller than or equal to the minimum error.
If the error is less than or equal to the minimum error, executing S209; if the error is greater than the minimum error, the process returns to S206.
S209: and the computer adopts a test set to test, adopts a verification set to verify, and completes the construction of the power consumption prediction model.
In some implementations, the computer further updates real-time data collected in real-time from the real-time database into the dataset of the power consumption prediction model, and dynamically updates the power consumption prediction model in real-time.
In the second embodiment of the application, the power consumption of each air compressor in the air compression system under different loads can be predicted by constructing the power consumption prediction model, and then the power consumption of each air compressor is added to obtain the total power consumption of the air compression system, so that data support is provided for the scheduling control of the air compression system, and the optimization direction is guided for scheduling optimization.
The third embodiment describes how the scheduling of the air compression system is implemented in connection with constructing and using an optimized scheduling model.
Referring to fig. 4, the specific steps of the third embodiment of the present application are as follows:
s401: and initializing an optimized dispatching model by the computer according to the second initial parameters.
The second initial parameters include population size, crossover probability, mutation probability, and maximum number of iterations. The population size can be set to 100, and other settings can be performed according to actual requirements. The crossover probability can be set to any value of 0.4-0.99, can be set to 0.65, and can be set to other values according to actual requirements. The variation probability may be set to any value of 0.001 to 0.1, may be set to 0.002, or may be set to another value according to actual demands. The maximum iteration number can be set to 100, or other settings can be made for the maximum iteration number according to the actual requirements.
In some implementations, in the optimal scheduling model, an objective function targeting the total power consumption of the air compression system is set, where the objective function is as follows:
in the method, PC is the total power consumption of the air compression system, and P i The power consumption of the ith air compressor is calculated, and n is the total number of air compressors in the air compressor system.
In other implementations, three constraints are provided in the optimized scheduling model.
The first constraint condition is an air supply balance constraint:
in which Q i Is the gas flow rate of the ith air compressor, Q N The total air supply amount of the system is required.
The second constraint condition is a gas production constraint: q (Q) min <Q i <Q max 。
In which Q min Minimum gas yield of the ith air compressor, Q max The maximum gas production rate of the ith air compressor is obtained.
The third constraint condition is start-stop constraint:
in the formula, k is the running state of the air compressor, 0 is the closing state, and 1 is the running state.
S402: the computer encodes the population individuals to obtain an initial population.
The population individuals are air compressors in the air compression system, and one population individual is one air compressor in the air compression system.
The codes represent the running states of the air compressors corresponding to the population individuals. The coding can be carried out in a binary coding mode, and population individuals can also be coded in other modes. When the binary coding mode is adopted to code the population individuals, the air compressors corresponding to the population individuals are in a closed state when the codes are 0, and the air compressors corresponding to the population individuals are in an operating state when the codes are 1.
S403: and the computer performs crossover operation and mutation operation on the codes of each individual in the initial population to obtain candidate populations.
In some implementations, the interleaving operation uses a single point interleaving method and the mutation operation uses polynomial mutation.
S404: the computer determines whether the maximum number of iterations is reached.
If the maximum iteration number is not reached, S405 is executed; if the maximum number of iterations is reached, S406 is performed.
S405: and the computer predicts the adaptability of the candidate population through the power consumption prediction model.
The candidate population is an air compression system under a candidate running state combination. Each individual in the candidate population represents an air compressor in the air compression system.
The fitness represents the power consumption corresponding to the candidate population, and the smaller the fitness is, the smaller the total power consumption of the air compression system is.
After the completion of S405 execution, the process returns to S404.
In some implementations, the computer inputs the suction pressure of the first air compressor, the ambient temperature, the discharge pressure of the first air compressor, and the candidate gas production flow of the first air compressor in the first candidate gas production flow combination to a power consumption prediction model, processes the suction pressure of the first air compressor, the ambient temperature, the discharge pressure of the first air compressor, and the candidate gas production flow of the first air compressor through the power consumption prediction model, outputs the power consumption of the first air compressor, and obtains the power consumption of the air compression system in the first candidate operation state combination and the first candidate gas production flow combination based on the power consumption of the first air compressor.
The first candidate operating state combination is any one of a plurality of candidate operating state combinations.
The first candidate gas flow rate combination is any one of a plurality of candidate gas flow rate combinations.
The first air compressor is any air compressor in an operation state in the first candidate gas flow combination. In a specific implementation, the total power consumption of the air compression system is obtained by carrying out power consumption prediction on all air compressors in the air compression system.
S406: the computer determines a target population from the plurality of candidate populations based on the fitness of each candidate population in the plurality of candidate populations.
In some implementations, the target operating state combination and the target gas production flow combination are determined from the plurality of candidate operating state combinations and the plurality of candidate gas production flow combinations based on power consumption, a required gas supply amount, a minimum gas production amount per air compressor, and a maximum gas production amount per air compressor corresponding to the plurality of candidate operating state combinations and the plurality of candidate gas production flow combinations.
The power consumption corresponding to the target running state combination and the target gas flow combination is minimum in the candidate running state combinations and the candidate gas flow combinations, the target gas flow combination meets the required gas supply amount, and the gas flow of each air compressor in the target gas flow combination is in the range from the minimum gas yield to the maximum air quantity of the corresponding air compressor.
In other implementations, the computer also uses a selection strategy for roulette to determine the target population from a plurality of candidate populations.
S407: and the computer adjusts the running state and the gas flow combination of each air compressor in the air compression system according to the target population.
In some implementations, the computer adjusts the operating states and the gas flow combinations of the individual air compressors in the air compression system based on the target operating state combinations and the target gas flow combinations.
In the third embodiment of the application, the target population can be rapidly determined by optimizing the scheduling model, so that the load distribution condition of each air compressor in the air compression system is known, the population control of the air compression system is realized on the basis of not changing the original PLC control system, and the closed-loop control of the on-line stability adjustment and optimization of the air compression system is realized.
How to realize the dispatching of the air compression system is described below in combination with specific scenes, taking 3 air compressors in the air compression system as an example, and rated gas flow of the air compressors is 95m 3 Per min, the required flow of the air compression system is 180m 3 /min。
Referring to fig. 5, the steps of the fourth embodiment of the present application are as follows:
s501: the computer initializes the optimized dispatching model, the population scale is 100, the crossover probability is 0.65, the variation probability is 0.002, and the maximum iteration number is 100.
The objective function of the optimal scheduling model is:
the first constraint is: />
The second constraint is: 0m 3 /min<Q i <95m 3 /min。
S502: the computer encodes the population individuals to obtain an initial population.
S503: and the computer performs single-point cross operation and polynomial mutation operation on the codes of each individual in the initial population to obtain candidate populations.
S504: the computer determines whether the maximum number of iterations is reached.
If the maximum number of iterations is not reached, S505 is executed; if the maximum number of iterations is reached, S506 is performed.
S505: and the computer predicts the adaptability of the candidate population through the power consumption prediction model.
After the completion of S505 execution, the flow returns to S504.
S506: the computer determines a target population from the plurality of candidate populations based on fitness of each of the plurality of candidate populations according to a selection strategy for roulette.
In combination with the example in the fourth embodiment, the target group is that the first air compressor is in an operating state and the load is 90m 3 The power consumption is 580.7kW, the second air compressor is in a closed state, and the load is 0m 3 The power consumption is 0kW per minute, the third air compressor is in an operation state, and the load is 90m 3 And/min, the power consumption is 566.3kW, the total power consumption of the air compression system of the target population is 1147kW, and the total power consumption of the air compression system in the candidate population is the lowest.
In some implementations, the air-conditioning system is manually scheduled, wherein the manual scheduling scheme is that the first air compressor is in an operation state and the load is 92m 3 The power consumption is 582.37kW per minute, the second air compressor is in an operating state, and the load is 88m 3 The power consumption is 614.24kW, the third air compressor is in a closed state, and the load is 0m 3 And the power consumption per min is 0kW, and the total power consumption of the air system in the manual scheduling scheme is 1196.61kW, which is higher than the total power consumption of the scheduling scheme in the optimal scheduling model.
S507: and the computer adjusts the running state and the gas flow combination of each air compressor in the air compression system according to the target population.
In combination with the example in the fourth embodiment, the computer adjusts the first air compressor to be in an operation state according to the target population, and the gas flow rate is 90m 3 And/min, regulating the second air compressor to be in a closed state, and enabling the gas flow to be 0m 3 And/min, regulating the third air compressor to be in an operation state, wherein the gas flow is 90m 3 /min。
In the fourth embodiment of the application, the air compression system is scheduled by optimizing the scheduling model, so that the energy consumed by the air compression system is minimum, the time for manually making a scheduling scheme and adjusting and controlling is shortened, the energy consumption of an enterprise is reduced, and the production benefit and the energy utilization rate of the enterprise are improved.
Referring to fig. 6, the present application provides an apparatus 600 for scheduling an air compression system, including: an acquisition unit 601, a determination unit 602, and an adjustment unit 603.
Acquisition unit 601: the method is used for acquiring the power consumption of the air compression system under the plurality of candidate operation state combinations and the plurality of candidate gas flow rate combinations.
Alternatively, the acquisition unit 601: and the first air compressor is used for acquiring the power consumption of the air compression system in the first candidate operation state combination and the first candidate gas flow combination based on the power consumption of the first air compressor, and the first air compressor is any air compressor in the operation state in the first candidate gas flow combination.
The determination unit 602: and determining a target operating state combination and a target gas flow combination from the plurality of candidate operating state combinations and the plurality of candidate gas flow combinations based on the power consumption corresponding to the plurality of candidate operating state combinations and the plurality of candidate gas flow combinations, wherein the power consumption corresponding to the target operating state combination and the target gas flow combination is the smallest in the plurality of candidate operating state combinations and the plurality of candidate gas flow combinations.
Alternatively, the determining unit 602: and the method is also used for determining a target population from the plurality of candidate populations based on the fitness of each candidate population in the plurality of candidate populations, wherein each candidate population in the plurality of candidate populations represents an air compression system under a candidate running state combination, each individual in the candidate populations represents an air compressor in the air compression system, and the fitness represents the power consumption corresponding to the candidate population.
An adjusting unit 603: the system is used for adjusting the running state and the gas flow combination of each air compressor in the air compression system according to the target running state combination and the target gas flow combination.
Optionally, the apparatus further comprises: an input unit 604, an output unit 605, or an encoding unit 606.
An input unit 604: and the candidate gas production flow of the first air compressor in the first candidate gas production flow combination is input to the power consumption prediction model.
The output unit 605: and the power consumption prediction model is used for processing the suction pressure, the ambient temperature, the exhaust pressure and the candidate gas flow of the first air compressor and outputting the power consumption of the first air compressor.
Encoding unit 606: and the method is used for carrying out cross operation and mutation operation on the codes of each individual in the initial population to obtain a candidate population, wherein the codes of the individuals represent the running states of the air compressors corresponding to the individuals.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
It should be noted that: in the device for scheduling an air-compressing system according to the above embodiment, when the function of scheduling an air-compressing system is implemented, only the division of the above functional modules is used for illustration, and in practical application, the above functional allocation may be performed by different functional modules according to needs, that is, the internal structure of the device for scheduling an air-compressing system is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the device for scheduling the air compression system provided in the foregoing embodiment and the method embodiment for scheduling the air compression system belong to the same concept, and the specific implementation process of the device is detailed in the method embodiment, which is not described herein again.
Fig. 7 is a schematic structural diagram of a computer device 700 according to an embodiment of the present application.
The computer device 700 includes at least one processor 701, memory 702, and at least one network interface 703.
The processor 701 is, for example, a general-purpose central processing unit (central processing unit, CPU), a network processor (network processer, NP), a graphics processor (graphics processing unit, GPU), a neural-network processor (neural-network processing units, NPU), a data processing unit (data processing unit, DPU), a microprocessor, or one or more integrated circuits for implementing the aspects of the present application. For example, the processor 701 includes an application-specific integrated circuit (ASIC), a programmable logic device (programmable logic device, PLD), or a combination thereof. PLDs are, for example, complex programmable logic devices (complex programmable logic device, CPLD), field-programmable gate arrays (field-programmable gate array, FPGA), general-purpose array logic (generic array logic, GAL), or any combination thereof.
The Memory 702 is, for example, but not limited to, a read-only Memory (ROM) or other type of static storage device that can store static information and instructions, as well as a random access Memory (random access Memory, RAM) or other type of dynamic storage device that can store information and instructions, as well as an electrically erasable programmable read-only Memory (electrically erasable programmable read-only Memory, EEPROM), compact disc read-only Memory (compact disc read-only Memory) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media, or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Alternatively, the memory 702 is independent and is coupled to the processor 701 via an internal connection 704. Alternatively, memory 702 and processor 701 may be integrated.
The network interface 703 uses any transceiver-like device for communicating with other devices or communication networks. The network interface 703 includes, for example, at least one of a wired network interface or a wireless network interface. The wired network interface is, for example, an ethernet interface. The ethernet interface is, for example, an optical interface, an electrical interface, or a combination thereof. The wireless network interface is, for example, a wireless local area network (wireless local area networks, WLAN) interface, a cellular network interface, a combination thereof, or the like.
In some embodiments, processor 701 includes one or more CPUs, such as CPU0 and CPU1 shown in fig. 7.
In some embodiments, computer device 700 optionally includes multiple processors, such as processor 701 and processor 705 shown in fig. 7. Each of these processors is, for example, a single-core processor (single-CPU), and is, for example, a multi-core processor (multi-CPU). A processor herein may optionally refer to one or more devices, circuits, and/or processing cores for processing data (e.g., computer program instructions).
In some embodiments, computer device 700 also includes internal connection 704. The processor 701, the memory 702 and the at least one network interface 703 are connected by an internal connection 704. The internal connections 704 include pathways that communicate information between the components described above. Optionally, internal connection 704 is a board or bus. Optionally, the internal connections 704 are divided into address buses, data buses, control buses, etc.
In some embodiments, computer device 700 also includes an input-output interface 706. An input-output interface 706 is connected to the internal connection 704.
In some embodiments, the input-output interface 706 is configured to interface with an input device, and receive commands or data related to the above-described embodiments that are input by a user via the input device. Input devices include, but are not limited to, a keyboard, touch screen, microphone, mouse or sensing device, and the like.
In some embodiments, the input-output interface 706 is also used to connect with an output device. The input-output interface 706 outputs intermediate and/or final results produced by the processor 701 executing the above-described method embodiments via an output device. Output devices include, but are not limited to, displays, printers, projectors, and so forth.
Alternatively, the processor 701 implements the method in the above embodiment by reading the program code stored in the memory 702, or the processor 701 implements the method in the above embodiment by internally storing the program code. In the case where the processor 701 implements the method in the above embodiment by reading the program code stored in the memory 702, the program code 710 implementing the method provided in the embodiment of the present application is stored in the memory 702.
For more details on the implementation of the above-described functions by the processor 701, reference is made to the description of the previous method embodiments, which is not repeated here.
Finally, it is further noted that 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 the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of scheduling an air compression system, the method comprising:
acquiring power consumption of the air compression system under a plurality of candidate operation state combinations and a plurality of candidate gas flow combinations;
determining a target operating state combination and a target gas flow combination from the plurality of candidate operating state combinations and the plurality of candidate gas flow combinations based on the power consumption corresponding to the plurality of candidate operating state combinations and the plurality of candidate gas flow combinations, wherein the power consumption corresponding to the target operating state combination and the target gas flow combination is the smallest in the plurality of candidate operating state combinations and the plurality of candidate gas flow combinations;
and adjusting the running state and the gas flow combination of each air compressor in the air compression system according to the target running state combination and the target gas flow combination.
2. The method of claim 1, wherein the plurality of candidate operating state combinations includes a first candidate operating state combination, the plurality of candidate gas flow combinations includes the first candidate gas flow combination, and the obtaining the power consumption of the air compression system at the plurality of candidate operating state combinations and the plurality of candidate gas flow combinations includes:
inputting the suction pressure, the ambient temperature, the exhaust pressure of a first air compressor and the candidate gas flow of the first air compressor in the first candidate gas flow combination into a power consumption prediction model, wherein the first air compressor is any air compressor in an operation state in the first candidate gas flow combination;
processing the suction pressure of the first air compressor, the ambient temperature, the exhaust pressure of the first air compressor and the candidate gas flow of the first air compressor through the power consumption prediction model, and outputting the power consumption of the first air compressor;
and based on the power consumption of the first air compressor, obtaining the power consumption of the air compression system under the first candidate running state combination and the first candidate gas production flow combination.
3. The method of claim 2, wherein the power consumption prediction model is trained based on a historical suction pressure, a historical ambient temperature, a historical discharge pressure, a historical gas flow rate, and a historical power consumption of each air compressor in the air compression system.
4. A method according to claim 2 or 3, wherein the power consumption prediction model is a neural network model.
5. The method of claim 1, wherein the determining a target operating state combination and a target gas flow combination from the plurality of candidate operating state combinations and the plurality of candidate gas flow combinations based on the power consumption corresponding to the plurality of candidate operating state combinations and the plurality of candidate gas flow combinations comprises:
and determining a target population from the plurality of candidate populations based on the fitness of each candidate population in the plurality of candidate populations, wherein each candidate population in the plurality of candidate populations represents an air compression system in a candidate running state combination, each individual in the candidate populations represents an air compressor in the air compression system, and the fitness represents the power consumption corresponding to the candidate population.
6. The method of claim 5, wherein prior to determining the target population from the plurality of candidate populations based on the fitness of each candidate population in the plurality of candidate populations, the method further comprises:
and performing cross operation and mutation operation on the codes of each individual in the initial population to obtain the candidate population, wherein the codes of the individuals represent the running state of the air compressor corresponding to the individuals.
7. The method of claim 1, wherein the determining a target operating state combination and a target gas flow combination from the plurality of candidate operating state combinations and the plurality of candidate gas flow combinations based on the power consumption corresponding to the plurality of candidate operating state combinations and the plurality of candidate gas flow combinations comprises:
and determining a target operation state combination and a target gas flow combination from the candidate operation state combinations and the candidate gas flow combinations based on the power consumption, the required gas supply amount, the minimum gas yield of each air compressor and the maximum gas yield of each air compressor corresponding to the candidate gas flow combinations, wherein the power consumption corresponding to the target operation state combination and the target gas flow combination is minimum in the candidate operation state combinations and the candidate gas flow combinations, the target gas flow combination meets the required gas supply amount, and the gas yield of each air compressor in the target gas flow combination is in a range from the minimum gas yield of the corresponding air compressor to the maximum air amount.
8. An apparatus for scheduling air compression systems, the apparatus comprising: the device comprises an acquisition unit, a determination unit and an adjustment unit;
the acquisition unit is used for acquiring the power consumption of the air compression system under a plurality of candidate running state combinations and a plurality of candidate gas flow combinations;
the acquiring unit is further configured to obtain power consumption of the air compression system under the first candidate operation state combination and the first candidate gas flow combination based on power consumption of a first air compressor, where the first air compressor is any air compressor in an operation state in the first candidate gas flow combination;
the determining unit is configured to determine, based on the plurality of candidate operation state combinations and power consumption corresponding to the plurality of candidate gas flow combinations, a target operation state combination and a target gas flow combination from the plurality of candidate operation state combinations and the plurality of candidate gas flow combinations, where power consumption corresponding to the target operation state combination and the target gas flow combination is smallest among the plurality of candidate operation state combinations and the plurality of candidate gas flow combinations;
the determining unit is further configured to determine a target population from the multiple candidate populations based on fitness of each candidate population in the multiple candidate populations, where each candidate population in the multiple candidate populations represents an air compression system in a candidate operation state combination, each individual in the candidate populations represents an air compressor in the air compression system, and the fitness represents power consumption corresponding to the candidate population;
and the adjusting unit is used for adjusting the running state and the gas flow combination of each air compressor in the air compression system according to the target running state combination and the target gas flow combination.
9. The apparatus of claim 8, wherein the apparatus further comprises: an input unit, an output unit, or an encoding unit;
the input unit is configured to input, to a power consumption prediction model, a suction pressure of the first air compressor, an ambient temperature, an exhaust pressure of the first air compressor, and a candidate gas production flow of the first air compressor in the first candidate gas production flow combination;
the output unit is used for processing the suction pressure of the first air compressor, the ambient temperature, the exhaust pressure of the first air compressor and the candidate gas production flow of the first air compressor through the power consumption prediction model and outputting the power consumption of the first air compressor;
the coding unit is used for carrying out cross operation and mutation operation on the codes of each individual in the initial population to obtain candidate populations, and the codes of the individual represent the running states of the air compressors corresponding to the individual.
10. A computer device, the computer device comprising: a processor coupled to a memory having stored therein at least one computer program instruction that is loaded and executed by the processor to cause the computer arrangement to implement the method of any of claims 1-7.
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