CN111639779B - Information processing method and related equipment - Google Patents
Information processing method and related equipment Download PDFInfo
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- CN111639779B CN111639779B CN201910159756.3A CN201910159756A CN111639779B CN 111639779 B CN111639779 B CN 111639779B CN 201910159756 A CN201910159756 A CN 201910159756A CN 111639779 B CN111639779 B CN 111639779B
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Abstract
The embodiment of the invention provides an information processing method and related equipment, which are used for helping an oil gas gathering and transportation joint station library to optimize production decisions. The method comprises the following steps: acquiring a first running state of equipment in an oil gas gathering and transportation joint station library; vectorizing the first running state; inputting the first running state after vectorization processing into a preset action model to determine a target action, wherein the target action is used for operating equipment in the oil-gas gathering and transportation joint station library, the preset action model is obtained after training a state space and an action space based on a random process, the state space comprises static parameters of the equipment in the oil-gas gathering and transportation joint station library, the action space comprises operation actions of the equipment in the oil-gas gathering and transportation joint station library, and the operation actions have an association relation with the static parameters.
Description
Technical Field
The present invention relates to the field of information processing, and in particular, to an information processing method and related device.
Background
The oil gas gathering and conveying system covers a gathering and conveying pipe network and an oilfield station warehouse, wherein the gathering and conveying pipe network gathers produced liquid of a single well into a station warehouse such as a joint station, and the joint station generally comprises an oil station, a sewage station and a water injection station.
The current corporate store of treatment measures manual for different states does not cover all situations that may occur. In this case, for safe production and efficient production, it is necessary that optimal handling measures in different production states can be found while avoiding danger. At the same time, the current manual of treatment measures only describes the treatment measures qualitatively, such as opening the valve. For this situation, it is desirable to be able to find quantitative treatment measures more accurately.
The production of the oil gas gathering and transportation combined station library has time hysteresis, and after related operation, the simulation needs to be continued for a certain time to see the result after the operation. The production of the oil gas gathering and transportation combined station library has a huge state space, the product space formed by information such as liquid level, flow rate, pressure and the like of different equipment has a remarkable dimension, and the intensive traversal to all states is basically impossible. Meanwhile, the operation scheme is quite a lot, different devices are provided with a plurality of controllable valves, and the product space formed by the controllable valves is quite huge. It is almost impossible to want to traverse all.
Disclosure of Invention
The embodiment of the invention provides an information processing method and related equipment, which are used for finding a decision scheme for optimizing the current state of equipment in an oil and gas gathering and transportation combined station library and helping the oil and gas gathering and transportation combined station library to optimize production decisions.
The first aspect of the embodiment of the invention provides an information processing method, which specifically comprises the following steps:
acquiring a first running state of equipment in an oil gas gathering and transportation joint station library;
vectorizing the first running state;
inputting the first running state after vectorization processing into a preset action model to determine a target action, wherein the target action is used for operating equipment in the oil-gas gathering and transportation joint station library, the preset action model is obtained after training a state space and an action space based on a random process, the state space comprises static parameters of the equipment in the oil-gas gathering and transportation joint station library, the action space comprises operation actions of the equipment in the oil-gas gathering and transportation joint station library, and the operation actions have an association relation with the static parameters.
Optionally, before the first operation state of the equipment in the oil gas gathering and transportation combination station library is obtained, the method further includes:
and constructing a simulation system of the oil and gas gathering and transportation combined station library, wherein the simulation system comprises a physical model corresponding to equipment in the oil and gas gathering and transportation combined station library.
Optionally, before the first operation state is input into a preset action model to determine a target action, the method further includes:
Determining the state space and the action space;
determining an initial action model corresponding to the oil and gas gathering and transportation joint station library, wherein the initial action model comprises a first module and a second module, and the first module and the second module are both composed of an artificial neural network;
based on the random process, training the state space and the action space according to the initial action model and the simulation system to determine the preset action model.
Optionally, the training the state space and the action space according to the initial action model and the simulation system based on the random process to determine the preset action model includes:
step 1, determining a target network according to the initial action model, wherein the target network comprises a third module and a fourth module, the first module and the third module have an association relationship, and the second module and the fourth module have an association relationship;
step 2, randomly selecting a second running state in the state space, inputting the second running state into the first module, and outputting a first action, wherein the first action is an action corresponding to the second running state;
Step 3, adding the first action into the random process to obtain the second action, wherein the random process is obtained randomly;
step 4, determining a third running state based on the second action and the simulation system;
step 5, determining a reward value of the third operation state, wherein the reward value of the third operation state indicates whether the simulation system is in the second operation state or not;
step 6, updating the parameter value of the first parameter of the first module and the parameter value of the second parameter of the second module through the second operation state, the second action, the third operation state and the rewarding value of the third operation state;
step 7, updating the parameter value of the first parameter of the third module and the parameter value of the second parameter of the fourth module through the first module after updating the parameter value and the second module after updating the parameter value based on incremental updating;
repeatedly executing the steps 2 to 7 until the preset iteration termination condition is met;
and determining the target network when the iteration is ended as the preset action model.
Optionally, the method further comprises:
Judging whether the iteration times reach a preset value, if so, determining that the preset iteration termination condition is met;
or alternatively, the first and second heat exchangers may be,
judging whether the parameter value of the first parameter of the first module and/or the parameter value of the second parameter of the second module are converged, and if yes, determining that the preset iteration termination condition is met.
Optionally, the method further comprises:
adding the target action to the random process to determine a third action;
determining a fourth operating state based on the third action and the simulation system;
determining a prize value for the fourth operating state, the prize value for the fourth operating state indicating whether the simulation system is operating normally when in the fourth operating state;
and updating the parameter values of the preset action model through the reward values of the first running state, the third action, the fourth running state and the fourth running state.
A second aspect of an embodiment of the present invention provides an information processing apparatus including:
the acquisition unit is used for acquiring a first running state of equipment in the oil gas gathering and transportation combined station library;
the first determining unit is used for vectorizing the first running state;
The second determining unit is used for inputting the first running state after vectorization processing into a preset action model to determine a target action, wherein the target action is used for operating equipment in the oil-gas gathering and transportation combined station library, the preset action model is obtained after training a state space and an action space based on a random process, the state space comprises static parameters of the equipment in the oil-gas gathering and transportation combined station library, the action space comprises operation actions of the equipment in the oil-gas gathering and transportation combined station library, and the operation actions have association relation with the static parameters.
Optionally, the apparatus further comprises:
the building unit is used for building a simulation system of the oil and gas gathering and transportation combined station library, and the simulation system comprises a physical model corresponding to equipment in the oil and gas gathering and transportation combined station library.
Optionally, the apparatus further comprises: training unit, training unit is used for:
determining the state space and the action space;
determining an initial action model corresponding to the oil and gas gathering and transportation joint station library, wherein the initial action model comprises a first module and a second module, and the first module and the second module are both composed of an artificial neural network;
Based on the random process, training the state space and the action space according to the initial action model and the simulation system to determine the preset action model.
Optionally, the training unit trains the state space and the action space according to the initial action model and the simulation system based on the random process, so as to determine the preset action model includes:
step 1, determining a target network according to the initial action model, wherein the target network comprises a third module and a fourth module, the first module and the third module have an association relationship, and the second module and the fourth module have an association relationship;
step 2, randomly selecting a second running state in the state space, inputting the second running state into the first module, and outputting a first action, wherein the first action is an action corresponding to the second running state;
step 3, adding the first action into the random process to obtain the second action, wherein the random process is obtained randomly;
step 4, determining a third running state based on the second action and the simulation system;
step 5, determining a reward value of the third operation state, wherein the reward value of the third operation state indicates whether the simulation system is in the second operation state or not;
Step 6, updating the parameter value of the first parameter of the first module and the parameter value of the second parameter of the second module through the second operation state, the second action, the third operation state and the rewarding value of the third operation state;
step 7, updating the parameter value of the first parameter of the third module and the parameter value of the second parameter of the fourth module through the first module after updating the parameter value and the second module after updating the parameter value based on incremental updating;
repeatedly executing the steps 2 to 7 until the preset iteration termination condition is met;
and determining the target network when the iteration is ended as the preset action model.
Optionally, the second determining unit is further configured to:
judging whether the iteration times reach a preset value, if so, determining that the preset iteration termination condition is met;
or alternatively, the first and second heat exchangers may be,
judging whether the parameter value of the first parameter of the first module and/or the parameter value of the second parameter of the second module are converged, and if yes, determining that the preset iteration termination condition is met.
Optionally, the second determining unit is further configured to:
adding the target action to the random process to determine a third action;
Determining a fourth operating state based on the third action and the simulation system;
determining a prize value for the fourth operating state, the prize value for the fourth operating state indicating whether the simulation system is operating normally when in the fourth operating state;
and updating the parameter values of the preset action model through the reward values of the first running state, the third action, the fourth running state and the fourth running state.
A third aspect of the embodiments of the present invention provides a processor for running a computer program which, when run, performs the steps of the information processing method as described in the above aspects.
A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program is executed by a processor to perform the steps of the information processing method described in the above aspects.
In summary, it can be seen that in the embodiment provided by the present invention, after the state of the device in the oil and gas gathering and transportation joint station library at the current moment is vectorized, the target action of the device in the oil and gas gathering and transportation joint station library is output, and the device in the oil and gas gathering and transportation joint station library is operated by the target action. The method is characterized in that the method is obtained by training the state space and the action space of the oil and gas gathering and transportation combined station library based on a random process through a preset action model, so that when equipment in the oil and gas gathering and transportation combined station library is in a new state, the preset action model can find an optimal decision scheme, and the oil and gas gathering and transportation combined station library is helped to optimize production decisions.
Drawings
Fig. 1 is a schematic diagram of an embodiment of an information processing method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a training process of a preset motion model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an embodiment of an information processing apparatus according to an embodiment of the present invention;
fig. 4 is a schematic hardware structure of a server according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides an information processing method and related equipment, which are used for finding a decision scheme for optimizing the current state of equipment in an oil and gas gathering and transportation combined station library and helping the oil and gas gathering and transportation combined station library to optimize production decisions.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The information processing method of the present invention will be described in terms of an information processing apparatus, which may be a server or a service unit in the server, and is not particularly limited.
Referring to fig. 1, fig. 1 is a schematic diagram of an embodiment of an information processing method according to an embodiment of the present invention, including:
101. and acquiring a first running state of equipment in the oil gas gathering and transportation combination station library.
In this embodiment, in the process that the device in the library is operating, the information processing apparatus may acquire the first operating state of the device in the library of the oil and gas gathering and transportation unit at the current moment. The first operation state is a state of equipment in the oil-gas gathering and transportation combination station warehouse at the current moment, such as a liquid level of a water tank in the oil-gas gathering and transportation combination station warehouse, a switching state of a valve in the oil-gas gathering and transportation combination station warehouse, and the like.
Before the first operation state of the equipment in the oil-gas gathering and transportation combined station library is obtained, a simulation system of the oil-gas gathering and transportation combined station library can be further constructed, and the simulation system comprises a physical model corresponding to the equipment in the oil-gas gathering and transportation combined station library, and the following specific description is given: first, determining a plurality of devices in a library of oil and gas gathering and transportation combined stations and static parameters of the plurality of devices, wherein the static parameters are used for representing inherent properties of the plurality of devices, and the plurality of devices can comprise but are not limited to the following: three-phase separator, settling tank, walnut shell filter, fiber ball filter, etc. The above-mentioned static parameters represent inherent properties of the plurality of devices themselves, and taking a three-phase separator as an example, the static parameters of the three-phase separator may include, but are not limited to, the following: equivalent length of the three-phase separator, radius of the three-phase separator, total volume in a three-phase separator tank, cross-sectional area of a water chamber of the three-phase separator, cross-sectional area of an oil chamber of the three-phase separator, height of an oil spilling baffle of the three-phase separator, water density, oil density, gas average molecular mass, gas constant, gravity acceleration and the like.
Second, a physical model of each of the plurality of devices is constructed based on the static parameters of the plurality of devices. That is, parameters in the physical model of each device may be randomly initialized according to information of each device in the plurality of devices, and a secondary loss function between predicted data and actual production data at each moment is calculated according to historical operation parameters of each device, and the parameters in each device are estimated using a random gradient descent algorithm.
And determining the connection relation between each device in the plurality of devices of the oil and gas gathering and transportation combined station library. For example, the connection mode of the physical models of the plurality of devices can be determined according to the connection mode between the devices in the oil gas gathering and transportation combination station library. Specifically, each of the plurality of devices may be used as a node, and the node corresponding to each of the plurality of devices may be connected in an edge manner according to the connection manner between the devices in the oil and gas gathering and transportation joint station library. For example, in real production, the water outlet of the three-phase separator is connected with the water inlet of the settling tank, and then the node of the three-phase separator is connected with the node of the settling tank. The connection of the physical model to each of the plurality of devices is achieved in the same manner. Specifically, edges can be built according to information such as valves, water pumps and the like which can be controlled and adjusted among all the devices, and the edges are connected into a station library system according to nodes.
And finally, connecting the physical models of each device in the plurality of devices based on the connection relation to construct a simulation system of the oil gas gathering and transportation joint station library. Specifically, the following procedure may be repeated for system simulation: updating the information of each side according to the controllable information; for each node, integrating information of all edges flowing to the node, and updating information of each node according to information of each node and information of the integrated edges.
102. And carrying out vectorization processing on the first running state.
In this embodiment, after the information processing apparatus acquires the first running state, the first running state may be vectorized, for example, the first running state may be vectorized by a word2rvec vectorization tool, or may be vectorized by other means, which is not limited specifically.
103. And inputting the first running state after vectorization processing into a preset action model to determine a target action.
In this embodiment, the information processing apparatus may train in advance an action output model, that is, a preset action model, which is used to output actions corresponding to the running states according to vectors of the running states, then, the first running state after vectorization processing may be input into the preset action model to determine a target action, where the target action is used to operate the devices in the oil and gas collection and transportation joint station library, where the preset action model is obtained by training a state space and an action space based on a random process (that is, a random process is added in the training process of the preset action model, so that the preset action model obtained by training may perform effective learning when new states occur in the simulation system and devices in the oil and gas collection and transportation joint station library, where the state space includes static parameters of the devices in the oil and gas collection and transportation joint station library, for example, where the liquid level of a water tank in the oil and gas collection and transportation joint station library is from 0m to 10m, then, where a plurality of continuous spaces of different states such as liquid level of the water tank in the liquid level height are obtained (that is 0m to 10 m) are used as a continuous space of liquid level height, that is formed by taking all the devices in the same state as a continuous state, that are all the devices in the joint station, that have a state, and all the devices in the oil and gas collection and transportation joint station have a continuous state form, and a continuous state is formed by a continuous state, and a continuous state is formed by a continuous state of the operation state, for example, a certain valve or a certain water pump of equipment in the oil-gas gathering and transportation joint station library is adjusted, for example, the continuous space of a single valve is the space formed by a plurality of different actions in the { minimum to maximum }, and then the continuous action spaces of all the equipment in the oil-gas gathering and transportation joint station library are spliced to form an action space (all the operation actions in the action space exist in the form of vectors). The state space has an association relation with the action space, that is, each state vector in the state space can find an operation action corresponding to the state vector in the action space.
After determining the target action probability distribution through the preset action model, the target action probability distribution may be displayed to the user, so that the user may operate the device in the oil and gas gathering and transportation joint station library according to the target action with the highest probability in the target action probability distribution, or the information processing device may directly operate the device in the oil and gas gathering and transportation joint station library based on the target action with the highest probability in the target action probability distribution, for example, adjust the height of the liquid level, and the like, which is not limited in detail.
In summary, it can be seen that in the embodiment provided by the present invention, after the state of the device in the oil and gas gathering and transportation joint station library at the current moment is vectorized, the target action of the device in the oil and gas gathering and transportation joint station library is output, and the device in the oil and gas gathering and transportation joint station library is operated by the target action. The method is characterized in that the method is obtained by training the state space and the action space of the oil and gas gathering and transportation combined station library based on a random process through a preset action model, so that when equipment in the oil and gas gathering and transportation combined station library is in a new state, the preset action model can find an optimal decision scheme, and the oil and gas gathering and transportation combined station library is helped to optimize production decisions.
The training of the preset motion model is described below with reference to fig. 2.
Referring to fig. 2, fig. 2 is a schematic diagram of a training flow of a preset motion model according to an embodiment of the present invention, including:
201. a state space and an action space are determined.
In this embodiment, a state space and an action space may be determined, where the state space includes static parameters of devices in the oil and gas gathering and transportation joint station library, for example, the liquid level of a water tank in the oil and gas gathering and transportation joint station library is from 0m to 10m, then a continuous space of a plurality of different states of (0 m-10 m) such as the liquid level of the water tank can be obtained as a space of the liquid level, and similarly, dynamic parameters such as pressure, flow and the like and static parameters such as specific heat capacity, length, density and the like of all devices in the oil and gas gathering and transportation joint station library also include a space formed by adopting the above manner, then the space of all devices in the oil and gas gathering and transportation joint station library is taken as a product space, and a state space (the states of all devices in the state space exist in the form of vectors) includes an operation action of the devices in the oil and transportation joint station library, that is considered to be formed by splicing all individual operations, that is, for example, an operation is performed after the operation is considered to be performed at the same time, for a certain valve or a water pump of the devices in the oil and transportation joint station library, for example, the continuous space of a single valve is { maximum } is formed by the largest } and the operation space is formed by the operation of the largest. The state space has an association relation with the action space, that is, each state vector in the state space can find an operation action corresponding to the state vector in the action space.
The state space is represented by a vector formed by splicing vectors formed by state information such as all flow, pressure, liquid level height and the like of all equipment in the oil gas gathering and transportation combined station library and vectors formed by action information such as valve opening, water pump pressure, flow and the like; the action space is represented by vectors formed by splicing all operable valve openings, water pump pressures and the like in the oil and gas gathering and transportation combined station library, wherein the operation is divided into two types, one type is operation with only limited options, such as that the valves can be opened or closed, in this case, a two-dimensional vector representation is used, wherein at most one position is 1, and the rest is 0; the other is a continuously controllable operation, such as the adjustment of the pump pressure to within a specified range of values, in this case represented by a three-dimensional vector, representing an increase of 10%, a constant decrease of 10%, respectively, where at most one position is 1 and the rest is 0.
It is to be understood that the above-described values are merely illustrative and are not meant to be limiting.
202. And determining an initial action model corresponding to the oil and gas gathering and transportation joint station library.
In this embodiment, the information processing apparatus may determine an initial motion model, where the initial motion model includes a first module and a second module, and the first module and the second module are both formed by an artificial neural network, and the first module is herein described as an Actor module, and the second module is illustrated as a Critic module. The Actor module consists of an MLP (Multi-Layer Perceptron) with a hidden Layer of 5 layers, wherein the input is a vector for simulating the running state of a system in a state space, namely all static parameters of each device in an oil-gas gathering and transportation combined station library and each controllable operation information, and the output is a vector with all controllable information, namely each position expresses an operation mode of the system; the Critic module consists of an MLP with a hidden layer of 3 layers, the input is identical to the Actor module, and the output is a numerical value which indicates the value corresponding to the input state.
It should be noted that, the state space and the action space may be determined by the step 201, and the initial action model corresponding to the oil and gas gathering and transportation joint station library may be determined by the step 202, however, there is no limitation of the execution sequence between the two steps, and the step 201 may be executed first, the step 202 may be executed first, or the steps may be executed simultaneously, which is not limited in particular.
203. Based on the random process, training the state space and the action space according to the initial action model and the simulation system to determine a preset action model.
In this embodiment, after obtaining the state space and the action space of the oil and gas gathering and transportation joint station library, the information processing apparatus may train the state space and the action space of the oil and gas gathering and transportation joint station library based on a random process in combination with an initial action model and a simulation system to determine a preset action model, where the simulation system is a preset system for simulating the state and the action of equipment in the oil and gas gathering and transportation joint station library, and the following is specifically described:
and step 1, determining a target network according to the initial action model.
In this embodiment, the information processing apparatus may determine, according to the initial action model, a target network including a third module and a fourth module, where the first module has an association relationship with the third module, and the second module has an association relationship with the fourth module, that is, the first module in the initial action model may be copied to obtain the third module, and the second module in the initial action model may be copied to obtain the fourth module.
And 2, randomly selecting a second running state in the state space to input the first module so as to output a first action, wherein the first action is an action of which the action space corresponds to the second running state.
That is, the information processing apparatus may randomly select a second state vector, for example, a state vector of a liquid level, from a state space, input to the first module, and output the first action, that is, a state in the state space is an input to the first module.
And step 3, adding the first action into a random process to obtain a second action.
In this embodiment, the information processing apparatus may randomly select a random process (OU), and then add the OU process to the first action to obtain the second action.
And 4, determining a third operation state based on the second action and the simulation system.
In this embodiment, after the second action is obtained, the information processing apparatus may simulate the third action through the second action and a simulation system, where the state of each device after the simulation system inputs the second action is the third running state, and the simulation system is a preset system for simulating the state and action of the device in the oil and gas gathering and transportation joint station library.
And 5, determining the rewarding value of the third running state.
In this embodiment, a prize value of the second operation state may be determined, where the prize value of the second operation state indicates whether the simulation system is in the second operation state and is normal, specifically, a prize function R of the simulation system may be defined in advance, that is, the prize value of the prize function R is 0.01 when the simulation system is in normal operation, the prize value of the prize function R is-1 when the simulation system gives an early warning, where it may be determined whether the simulation system is in normal operation according to the third operation state, the prize value of the third operation state is 0.01 when the simulation system is in normal operation, and the prize value of the third operation state is-1 when the simulation system gives an abnormal early warning, where the prize function R is merely illustrative and is not meant to be limiting.
After obtaining the prize value of the third operating state, the second action, the third operating state, and the prize value of the third operating state may be stored in the buffer as a four-point group.
And step 6, updating the parameter value of the first parameter of the first module and updating the parameter value of the second parameter of the second module through the second operation state, the second action, the third operation state and the rewarding value of the third operation state.
In this embodiment, the parameter value of the first parameter of the first module may be updated by a policy gradient descent method based on the prize values of the second operation state, the second action, the third operation state, and the parameter value of the second parameter of the second module may be updated by minimizing the quadratic time-series differential loss function based on the prize values of the second operation state, the second action, the third operation state, and the third operation state.
It should be noted that the time-series differential loss may be calculated by the value of the second operating state, the value of the third operating state, and the prize value of the third operating state, where the value of the second operating state and the value of the third operating state may be determined by the second module.
And 7, updating the parameter value of the first parameter of the third module and the parameter value of the second parameter of the fourth module through the first module after updating the parameter value and the second module after updating the parameter value based on incremental updating.
In this embodiment, after updating the parameter values of the first parameter of the first module and the parameter values of the second parameter of the second module, the parameter values of the first parameter of the third module may be updated by using the first module after updating the parameter values in an incremental update manner, and at the same time, the parameter values of the second parameter of the fourth module may be updated by using the second module after updating the parameter values in an incremental update manner.
And 8, repeatedly executing the steps 2 to 7 until a preset iteration termination condition is met.
In this embodiment, after each iteration, whether the number of iterations reaches a preset value may be determined, if yes, it is determined that a preset iteration termination condition is satisfied; or judging whether the parameter value of the first parameter of the first module and/or the parameter value of the second parameter of the second module are converged, if yes, determining that the preset iteration termination condition is met.
It should be noted that after the multiple iterations are performed, after the multiple four-point groups are obtained, the parameter value of the second parameter of the second module is updated by minimizing the quadratic time sequence differential loss function, and meanwhile, the parameter value of the first parameter of the first module is updated by the strategy gradient descent, based on the multiple four-point groups (for example, 3 or 4, which may be specifically set according to the actual situation).
It should be noted that, the first parameter is a parameter of a state determining action in the first module, and the second parameter is a parameter of a state determining value in the second module.
And 9, determining the target network when the iteration is ended as a preset action model.
It should be noted that, in the actual operation process, after the device in the oil gas gathering and transportation joint station library is operated based on the target action, the parameter value of the preset action model may be updated, which specifically includes the following steps: adding the target action to a random process to determine a third action; determining a fourth operating state based on the third action and the simulation system; determining a reward value of a fourth operating state, wherein the reward value of the fourth operating state indicates whether the simulation system operates normally when in the fourth operating state; and updating the parameter values of the preset action model through the first running state, the third action, the fourth running state and the rewarding value of the fourth running state. The parameter values in the updated preset motion model may be described in the above steps 2 to 7, and detailed descriptions thereof are omitted herein.
In summary, it can be seen that, in the training process of the preset action model, the parameter value of the first parameter of the first module and the parameter value of the second parameter of the second module are continuously updated through iteration, and the parameter value of the first parameter of the third module and the parameter value of the second parameter of the fourth module are continuously updated through the first module after updating the parameter value and the second module after updating the parameter value in an incremental updating manner, so that the trained preset action model is more fit to the state of each device in the oil-gas collection-transmission joint station library and the operation corresponding to the state, and meanwhile, due to the addition of a random process in the training process, the preset action model can find an optimal decision scheme to help the oil-gas collection-transmission joint station library optimize the production decision when the device in the oil-gas collection-transmission joint station library has a new state.
The information processing method provided by the embodiment of the present invention is described above, and the information processing apparatus provided by the embodiment of the present invention is described below with reference to fig. 3.
Referring to fig. 3, fig. 3 is a schematic diagram of an embodiment of an information processing apparatus according to an embodiment of the present invention, where the information processing apparatus includes:
An obtaining unit 301, configured to obtain a first operation state of equipment in a library of the oil and gas gathering and transportation combination station;
a first determining unit 302, configured to perform vectorization processing on the first operation state;
the second determining unit 303 is configured to input the first operation state after vectorization processing into a preset action model to determine a target action, where the target action is used to operate equipment in the oil and gas gathering and transportation joint station library, the preset action model is obtained after training a state space and an action space based on a random process, the state space includes static parameters of the equipment in the oil and gas gathering and transportation joint station library, the action space includes operation actions of the equipment in the oil and gas gathering and transportation joint station library, and the operation actions have an association relationship with the static parameters.
Optionally, the apparatus further comprises:
the construction unit 304 is configured to construct a simulation system of the oil and gas gathering and transportation combination station library, where the simulation system includes a physical model corresponding to a device in the oil and gas gathering and transportation combination station library.
Optionally, the apparatus further comprises: a training unit 305, the training unit 305 being configured to:
determining the state space and the action space;
Determining an initial action model corresponding to the oil and gas gathering and transportation joint station library, wherein the initial action model comprises a first module and a second module, and the first module and the second module are both composed of an artificial neural network;
based on the random process, training the state space and the action space according to the initial action model and the simulation system to determine the preset action model.
Optionally, the training unit 305 trains the state space and the action space according to the initial action model and the simulation system based on the random process, so as to determine the preset action model includes:
step 1, determining a target network according to the initial action model, wherein the target network comprises a third module and a fourth module, the first module and the third module have an association relationship, and the second module and the fourth module have an association relationship;
step 2, randomly selecting a second running state in the state space, inputting the second running state into the first module, and outputting a first action, wherein the first action is an action corresponding to the second running state;
step 3, adding the first action into the random process to obtain the second action, wherein the random process is obtained randomly;
Step 4, determining a third running state based on the second action and the simulation system;
step 5, determining a reward value of the third operation state, wherein the reward value of the third operation state indicates whether the simulation system is in the second operation state or not;
step 6, updating the parameter value of the first parameter of the first module and the parameter value of the second parameter of the second module through the second operation state, the second action, the third operation state and the rewarding value of the third operation state;
step 7, updating the parameter value of the first parameter of the third module and the parameter value of the second parameter of the fourth module through the first module after updating the parameter value and the second module after updating the parameter value based on incremental updating;
repeatedly executing the steps 2 to 7 until the preset iteration termination condition is met;
and determining the target network when the iteration is ended as the preset action model.
Optionally, the second determining unit 303 is further configured to:
judging whether the iteration times reach a preset value, if so, determining that the preset iteration termination condition is met;
Or alternatively, the first and second heat exchangers may be,
judging whether the parameter value of the first parameter of the first module and/or the parameter value of the second parameter of the second module are converged, and if yes, determining that the preset iteration termination condition is met.
Optionally, the second determining unit 303 is further configured to:
adding the target action to the random process to determine a third action;
determining a fourth operating state based on the third action and the simulation system;
determining a prize value for the fourth operating state, the prize value for the fourth operating state indicating whether the simulation system is operating normally when in the fourth operating state;
and updating the parameter values of the preset action model through the reward values of the first running state, the third action, the fourth running state and the fourth running state.
The interaction manner between the units of the information processing apparatus in this embodiment is described in the embodiments shown in fig. 1 and fig. 2, and is not described here in detail.
In summary, it can be seen that in the embodiment provided by the present invention, after the state of the device in the oil and gas gathering and transportation joint station library at the current moment is vectorized, the target action of the device in the oil and gas gathering and transportation joint station library is output, and the device in the oil and gas gathering and transportation joint station library is operated by the target action. The method is characterized in that the method is obtained by training the state space and the action space of the oil and gas gathering and transportation combined station library based on a random process through a preset action model, so that when equipment in the oil and gas gathering and transportation combined station library is in a new state, the preset action model can find an optimal decision scheme, and the oil and gas gathering and transportation combined station library is helped to optimize production decisions.
Referring to fig. 4, fig. 4 is a schematic diagram of a server according to an embodiment of the present invention, where the server 400 may have a relatively large difference due to different configurations or performances, and may include one or more central processing units (central processing units, CPU) 422 (e.g., one or more processors) and a memory 432, and one or more storage media 430 (e.g., one or more mass storage devices) storing application programs 442 or data 444. Wherein memory 432 and storage medium 430 may be transitory or persistent storage. The program stored on the storage medium 430 may include one or more modules (not shown), each of which may include a series of instruction operations on a server. Still further, the central processor 422 may be configured to communicate with the storage medium 430 and execute a series of instruction operations in the storage medium 430 on the server 400.
The server 400 may also include one or more power supplies 426, one or more wired or wireless network interfaces 450, one or more input/output interfaces 458, and/or one or more operating systems 441, such as Windows ServerTM, mac OS XTM, unixTM, linuxTM, freeBSDTM, and the like.
The steps performed by the information processing apparatus in the above-described embodiments may be based on the server structure shown in fig. 4.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The embodiment of the invention also provides a storage medium on which a program is stored, which when executed by a processor, implements the information processing method.
The embodiment of the invention also provides a processor for running a program, wherein the information processing method is executed when the program runs.
The embodiment of the invention also provides equipment, which comprises a processor, a memory and a program stored on the memory and capable of running on the processor, wherein the processor realizes the following steps when executing the program:
acquiring a first running state of equipment in an oil gas gathering and transportation joint station library;
vectorizing the first running state;
inputting the first running state after vectorization processing into a preset action model to determine a target action, wherein the target action is used for operating equipment in the oil-gas gathering and transportation joint station library, the preset action model is obtained after training a state space and an action space based on a random process, the state space comprises static parameters of the equipment in the oil-gas gathering and transportation joint station library, the action space comprises operation actions of the equipment in the oil-gas gathering and transportation joint station library, and the operation actions have an association relation with the static parameters.
In a specific implementation process, any implementation manner of the embodiments corresponding to fig. 1 and fig. 2 may be implemented when a processor executes a program.
The device herein may be a server, PC, PAD, cell phone, etc.
The invention also provides a computer program product adapted to perform, when executed on a data processing device, a program initialized with the method steps of:
acquiring a first running state of equipment in an oil gas gathering and transportation joint station library;
vectorizing the first running state;
inputting the first running state after vectorization processing into a preset action model to determine a target action, wherein the target action is used for operating equipment in the oil-gas gathering and transportation joint station library, the preset action model is obtained after training a state space and an action space based on a random process, the state space comprises static parameters of the equipment in the oil-gas gathering and transportation joint station library, the action space comprises operation actions of the equipment in the oil-gas gathering and transportation joint station library, and the operation actions have an association relation with the static parameters.
In a specific implementation, any of the embodiments corresponding to fig. 1 and fig. 2 may be implemented when the computer program product is executed.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that 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.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing is merely exemplary of the present invention and is not intended to limit the present invention. Various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are to be included in the scope of the claims of the present invention.
Claims (6)
1. An information processing method, characterized by comprising:
constructing a simulation system of an oil and gas gathering and transportation combined station library, wherein the simulation system comprises a physical model corresponding to equipment in the oil and gas gathering and transportation combined station library;
determining a state space and an action space;
determining an initial action model corresponding to the oil and gas gathering and transportation joint station library, wherein the initial action model comprises a first module and a second module, and the first module and the second module are both composed of an artificial neural network;
Step 1, determining a target network according to the initial action model, wherein the target network comprises a third module and a fourth module, the first module and the third module have an association relationship, and the second module and the fourth module have an association relationship;
step 2, randomly selecting a second running state in the state space, inputting the second running state into the first module, and outputting a first action, wherein the first action is an action corresponding to the second running state;
step 3, adding the first action into a random process to obtain a second action, wherein the random process is obtained randomly;
step 4, determining a third running state based on the second action and the simulation system;
step 5, determining a reward value of the third operation state, wherein the reward value of the third operation state indicates whether the simulation system is in the second operation state or not;
step 6, updating the parameter value of the first parameter of the first module and the parameter value of the second parameter of the second module through the second operation state, the second action, the third operation state and the rewarding value of the third operation state;
step 7, updating the parameter value of the first parameter of the third module and the parameter value of the second parameter of the fourth module through the first module after updating the parameter value and the second module after updating the parameter value based on incremental updating;
Repeatedly executing the steps 2 to 7 until the preset iteration termination condition is met;
determining the target network when iteration is terminated as a preset action model;
acquiring a first running state of equipment in the oil gas gathering and transportation combined station library;
vectorizing the first running state;
inputting the first running state after vectorization processing into a preset action model to determine a target action, wherein the target action is used for operating equipment in the oil-gas gathering and transportation joint station library, the preset action model is obtained after training the state space and the action space based on the random process, the state space comprises static parameters of the equipment in the oil-gas gathering and transportation joint station library, the action space comprises operation actions of the equipment in the oil-gas gathering and transportation joint station library, and the operation actions have association relation with the static parameters.
2. The method according to claim 1, wherein the method further comprises:
judging whether the iteration times reach a preset value, if so, determining that the preset iteration termination condition is met;
or alternatively, the first and second heat exchangers may be,
judging whether the parameter value of the first parameter of the first module and/or the parameter value of the second parameter of the second module are converged, and if yes, determining that the preset iteration termination condition is met.
3. The method according to claim 1 or 2, characterized in that the method further comprises:
adding the target action to the random process to determine a third action;
determining a fourth operating state based on the third action and the simulation system;
determining a prize value for the fourth operating state, the prize value for the fourth operating state indicating whether the simulation system is operating normally when in the fourth operating state;
and updating parameters of the preset action model through the first running state, the third action, the fourth running state and the rewarding value of the fourth running state.
4. An information processing apparatus, characterized by comprising:
the construction unit is used for constructing a simulation system of the oil and gas gathering and transportation combined station library, and the simulation system comprises a physical model corresponding to equipment in the oil and gas gathering and transportation combined station library;
the training unit is used for determining a state space and an action space;
the training unit is further used for determining an initial action model corresponding to the oil and gas gathering and transportation joint station library, the initial action model comprises a first module and a second module, and the first module and the second module are both composed of an artificial neural network;
Step 1, determining a target network according to the initial action model, wherein the target network comprises a third module and a fourth module, the first module and the third module have an association relationship, and the second module and the fourth module have an association relationship;
step 2, randomly selecting a second running state in the state space, inputting the second running state into the first module, and outputting a first action, wherein the first action is an action corresponding to the second running state;
step 3, adding the first action into a random process to obtain a second action, wherein the random process is obtained randomly;
step 4, determining a third running state based on the second action and the simulation system;
step 5, determining a reward value of the third operation state, wherein the reward value of the third operation state indicates whether the simulation system is in the second operation state or not;
step 6, updating the parameter value of the first parameter of the first module and the parameter value of the second parameter of the second module through the second operation state, the second action, the third operation state and the rewarding value of the third operation state;
step 7, updating the parameter value of the first parameter of the third module and the parameter value of the second parameter of the fourth module through the first module after updating the parameter value and the second module after updating the parameter value based on incremental updating;
The training unit is further configured to repeatedly execute the steps 2 to 7 until a preset iteration termination condition is met;
the training unit is further configured to determine the target network when iteration is terminated as a preset action model;
the acquisition unit is used for acquiring a first running state of equipment in the oil gas gathering and transportation combined station library;
the first determining unit is used for vectorizing the first running state;
the second determining unit is used for inputting the first running state after vectorization processing into a preset action model to determine a target action, wherein the target action is used for operating equipment in the oil-gas gathering and transportation joint station library, the preset action model is obtained after training the state space and the action space based on the random process, the state space comprises static parameters of the equipment in the oil-gas gathering and transportation joint station library, the action space comprises operation actions of the equipment in the oil-gas gathering and transportation joint station library, and the operation actions have association relation with the static parameters.
5. A processor for running a computer program, which when run performs the steps of the method according to any one of claims 1 to 3.
6. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program implementing the steps of the method according to any one of claims 1 to 3 when executed by a processor.
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