CN113805544B - Method, device, computer equipment and storage medium for determining operation parameters - Google Patents
Method, device, computer equipment and storage medium for determining operation parameters Download PDFInfo
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Abstract
The application relates to a method, a device, a computer device and a storage medium for determining an operation parameter. The method comprises the following steps: acquiring a first operation parameter acquired by target equipment at the last moment; acquiring a digital twin model matched with target equipment; inputting the first operation parameters into a digital twin model to obtain target operation parameters; and determining a second operation parameter of the target equipment at the next moment according to the first operation parameter and the target operation parameter. It can be appreciated that the application determines the target operating parameter of the target device by processing the first operating parameter based on a digital twin model adapted to the environmental parameter at the previous time of the target device, and further determines the second operating parameter at the next time of the target device, and then the target device can operate according to the second operating parameter at the next time. Therefore, a large number of sensors and negative feedback regulating circuits are not required to be installed in the equipment, the occupied space and the hardware cost are saved, and the control cost of the equipment is reduced.
Description
Technical Field
The present application relates to the field of device control technologies, and in particular, to a method and apparatus for determining an operating parameter, a computer device, and a storage medium.
Background
With the development of industrial technology, the degree of control of automation equipment is becoming higher and higher. In the process of controlling an automation device, a built-in negative feedback adjustment system is often adopted to adjust the operation parameters of the device so as to maintain the device in a normal operation state.
In the conventional art, a negative feedback adjustment system generally includes a sensor and a negative feedback adjustment circuit, and an operation parameter of a device is detected in real time by the sensor. When the operation parameter deviates from the preset value, the operation parameter is regulated by the negative feedback regulating circuit.
However, as the control method becomes more and more complex, a large number of sensors and negative feedback adjustment circuits are required to be installed when the conventional technology is adopted, resulting in higher control costs of the apparatus.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, apparatus, computer device, and storage medium for determining an operation parameter that can reduce the control cost of the device.
A method of determining an operating parameter, the method comprising:
acquiring a first operation parameter acquired by target equipment at the last moment;
Acquiring a digital twin model matched with the target equipment;
inputting the first operation parameters into the digital twin model to obtain target operation parameters;
and determining a second operation parameter of the target equipment at the next moment according to the first operation parameter and the target operation parameter.
In one embodiment, the obtaining a digital twin model that matches the target device includes:
acquiring a digital twin model matched with a production batch to which the target equipment belongs and an environment parameter corresponding to the target equipment;
the step of inputting the first operation parameter to the digital twin model to obtain a target operation parameter includes:
and inputting the first operation parameter and the environment parameter into the digital twin model to obtain a target operation parameter.
In one embodiment, the determining, according to the first operation parameter and the target operation parameter, a second operation parameter of the target device at a next moment includes:
judging whether the first operation parameter and the target operation parameter meet preset conditions or not;
if not, calculating the deviation between the first operation parameter and the target operation parameter, and adjusting the first operation parameter according to the deviation to obtain an adjusted operation parameter;
Inputting the adjusted operation parameters into the digital twin model to obtain adjusted target operation parameters;
and if the adjusted operation parameter and the adjusted target operation parameter meet the preset condition, determining the adjusted operation parameter as a second operation parameter of the target equipment at the next moment.
In one embodiment, the acquiring the first operation parameter acquired by the target device at the previous time includes:
receiving an operation parameter sample set sent by a plurality of target devices;
the method further comprises the steps of:
receiving environment parameter sample sets sent by the plurality of target devices;
classifying the operation parameter sample set according to the environment types matched with the environment parameter samples in the environment parameter sample set to obtain operation parameter samples of various environment types;
and training the plurality of initial digital twin models respectively by taking the operation parameter sample of each environment type in the operation parameter samples of the plurality of environment types as input data and taking the target operation parameter sample of the target equipment as output data to obtain the digital twin model corresponding to each environment type.
In one embodiment, the classifying the operation parameter sample set according to the environment types matched with the environment parameter samples in the environment parameter sample set to obtain operation parameter samples with multiple environment types includes:
classifying the operation parameter sample set according to the production batches to which the target equipment belongs to obtain operation parameter sample subsets of a plurality of production batches;
classifying the operation parameter sample subsets of each production lot in the operation parameter sample subsets of the plurality of production lots according to the environment types matched with the environment parameter samples in the environment parameter sample sets to obtain operation parameter samples of a plurality of environment types in each production lot;
taking an operation parameter sample of each environment type in the operation parameter samples of the plurality of environment types as input data, and taking a target operation parameter sample of target equipment as output data, respectively training a plurality of initial digital twin models to obtain digital twin models corresponding to each environment type, wherein the method comprises the following steps:
and training the plurality of initial digital twin models respectively by taking the operation parameter sample of each environment type in the operation parameter samples of the plurality of environment types of each production batch as input data and taking the target operation parameter sample of the target equipment as output data to obtain the digital twin model corresponding to each environment type of each production batch.
In one embodiment, the method further comprises:
obtaining a production batch to which the target equipment belongs;
obtaining the identification of other devices belonging to the production batch;
and sharing the second operation parameters to the other equipment according to the identification so as to instruct the other equipment to operate according to the second operation parameters at the next moment.
In one embodiment, the operating parameters include operating parameters of the target component;
the method further comprises the steps of:
acquiring the identification of other devices containing the target component;
and sharing the second operation parameters to the other equipment according to the identification so as to instruct a target component in the other equipment to operate according to the second operation parameters at the next moment.
An apparatus for determining an operating parameter, the apparatus comprising:
the parameter acquisition module is used for acquiring a first operation parameter acquired by the target equipment at the previous moment;
the model acquisition module is used for acquiring a digital twin model matched with the target equipment;
the parameter input module is used for inputting the first operation parameter into the digital twin model to obtain a target operation parameter;
And the parameter determining module is used for determining a second operation parameter of the target equipment at the next moment according to the first operation parameter and the target operation parameter.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring a first operation parameter acquired by target equipment at the last moment;
acquiring a digital twin model matched with the target equipment;
inputting the first operation parameters into the digital twin model to obtain target operation parameters;
and determining a second operation parameter of the target equipment at the next moment according to the first operation parameter and the target operation parameter.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring a first operation parameter acquired by target equipment at the last moment;
acquiring a digital twin model matched with the target equipment;
inputting the first operation parameters into the digital twin model to obtain target operation parameters;
and determining a second operation parameter of the target equipment at the next moment according to the first operation parameter and the target operation parameter.
According to the method, the device, the computer equipment and the storage medium for determining the operation parameters, the first operation parameters of the target equipment at the moment are processed based on the digital twin model matched with the target equipment, the target operation parameters of the target equipment are determined, the second operation parameters of the target equipment at the next moment are further determined, and then the target equipment can operate according to the second operation parameters at the next moment. Therefore, a large number of sensors and negative feedback regulating circuits are not required to be installed in the equipment, the occupied space and the hardware cost are saved, and the control cost of the equipment is reduced.
Drawings
FIG. 1 is an application environment diagram of a method of determining operating parameters in one embodiment;
FIG. 2 is a flow chart of a method of determining operating parameters in one embodiment;
FIG. 3 is a flow diagram of sharing a second operating parameter in one embodiment;
FIG. 4 is a block diagram of an apparatus for determining operating parameters in one embodiment;
fig. 5 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The method for determining the operation parameters provided by the application can be applied to an application environment shown in fig. 1. Wherein the target device 102 communicates with the server 104 via a network. The server 104 obtains the first operating parameter collected by the target device 102 at the last time. The server 104 then obtains a digital twin model that matches the target device. The server 104 then inputs the first operating parameter to the digital twin model to obtain the target operating parameter. Finally, the server 104 determines a second operating parameter of the target device at a next time according to the first operating parameter and the target operating parameter. The target device 102 may be, but not limited to, an industrial device and a home device, and the server 104 may be implemented as a stand-alone server or a server cluster composed of a plurality of servers.
In one embodiment, as shown in fig. 2, a method for determining an operation parameter is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:
step S202, acquiring a first operation parameter acquired by a target device at the last moment.
The operation parameters refer to parameter information generated when the target equipment operates. For example, the operating parameters may include operating speed, operating power, rotational speed, and the like.
Specifically, the target device may collect a first operation parameter of itself at a previous time through a built-in sensor, and upload the first operation parameter to the server. The server thus obtains the first operating parameter collected by the target device at the last moment. Optionally, the server uses distributed streaming computing to monitor log data of the target device in real time, where the log data includes the first operating parameter. In one embodiment, a server obtains a first operating parameter and an environmental parameter collected by a target device at a previous time.
Step S204, a digital twin model matched with the target device is obtained.
Specifically, the server obtains a digital twin model that matches the target device. Optionally, the server obtains a digital twin model that matches the identity of the target device. Optionally, the server obtains a digital twinning model that matches the production lot to which the target device belongs. Optionally, the server obtains a digital twin model that matches the environmental parameters corresponding to the target device. Specifically, the server determines an environment type from the environment parameters, and obtains a digital twin model associated with the environment type. Optionally, the server matches the environmental parameters with parameter intervals corresponding to the plurality of environmental types, thereby obtaining the environmental type successfully matched with the environmental parameters. The environment type may be, for example, a strong electromagnetic operating environment, a high temperature operating environment, a low temperature operating environment, and the like. Each environment type is provided with an associated digital twin model. Thus, after determining the type of environment for which the environmental parameter match was successful, a digital twin model that matches the environmental parameter may be further determined.
Step S206, inputting the first operation parameters into the digital twin model to obtain target operation parameters.
The digital twin model is obtained by training an initial digital twin model by taking operation parameter samples of a plurality of devices as input data and target operation parameter samples of the plurality of devices as output data.
Specifically, the server inputs the first operating parameter to a digital twin model, which outputs the target operating parameter. Alternatively, the target operating parameter may be an optimal operating parameter.
Step S208, determining a second operation parameter of the target equipment at the next moment according to the first operation parameter and the target operation parameter.
Specifically, the server determines a second operation parameter of the target device at a next moment according to the first operation parameter and the target operation parameter. Optionally, the server adjusts the first operation parameter according to the deviation between the first operation parameter and the target operation parameter until the proximity between the first operation parameter and the target operation parameter meets a preset condition. And then, the server generates a remote instruction according to the second operation parameter and sends the remote instruction to the target equipment. And the target equipment receives the remote instruction and analyzes the remote instruction to obtain a second operation parameter, and operates according to the second operation parameter at the next moment. Thereby achieving the purpose of autonomous regulation.
In the method for determining the operation parameters, the first operation parameters of the target equipment at the previous moment are processed based on the digital twin model matched with the target equipment, the target operation parameters of the target equipment are determined, the second operation parameters of the target equipment at the next moment are further determined, and then the target equipment can operate according to the second operation parameters at the next moment. Therefore, a large number of sensors and negative feedback regulating circuits are not required to be installed in the equipment, the occupied space and the hardware cost are saved, and the control cost of the equipment is reduced.
In one embodiment, one possible implementation of step S204 "acquire digital twin model matched to target device" described above is involved. On the basis of the above embodiment, step S204 may be specifically implemented by the following steps:
step S2042, obtaining a digital twin model matched with the production batch to which the target equipment belongs and the environmental parameters corresponding to the target equipment.
The environmental parameter refers to parameter information of the surrounding environment where the target device is located. For example, environmental parameters may include air humidity, air ph, temperature, electromagnetic strength, equipment current stability, and the like.
Wherein, the digital twin model establishes an association relation with the production batch (such as equipment model) and the environment type in advance.
Specifically, the environmental parameters around the target device at the previous time may be monitored by the environmental monitor, and uploaded to the server. The server determines an environment type based on the environment parameter, and obtains a digital twin model associated with the environment type and the production lot.
Based on this, in one embodiment, one possible implementation of the above-described step S206 "input the first operating parameter to the digital twin model resulting in the target operating parameter" is referred to. On the basis of the above embodiment, step S206 may be specifically implemented by the following steps:
step S2062, inputting the first operating parameter and the environmental parameter to the digital twin model to obtain the target operating parameter.
The digital twin model is obtained by training an initial digital twin model by taking operation parameter samples and environment parameter samples of a plurality of devices as input data and taking target operation parameter samples of the plurality of devices as output data.
Specifically, the server inputs the first operating parameter and the environmental parameter to a digital twin model, which outputs the target operating parameter. Alternatively, the target operating parameter may be an optimal operating parameter.
In this embodiment, the digital twin model is determined based on the production lot and the environmental parameters, so that the accuracy of the target operation parameters can be improved.
In one embodiment, the method further comprises the steps of:
step S212, obtaining a production batch to which the target equipment belongs;
step S214, obtaining the identification of other devices belonging to the same production batch;
step S216, sharing the second operation parameters to other devices according to the identification to instruct the other devices to operate according to the second operation parameters at the next moment.
Specifically, the server obtains the production lot to which the target device belongs, searches for the identifiers of other devices belonging to the production lot, and shares the second operation parameters to the other devices. And the other equipment receives the second operation parameters and operates according to the second operation parameters at the next moment.
In this embodiment, considering that the hardware structures of the devices in the same batch are the same or close to each other, the operation parameters can be shared for use, so that when modeling, a corresponding digital twin model is built for each production batch, and after determining the second operation parameters based on the digital twin model, the second operation parameters can be shared for use with other devices in the same batch, so that the calculation resources can be saved.
In one embodiment, the operating parameters include operating parameters of the target component. Based on this, in one embodiment, the method further comprises the steps of:
step S222, obtaining the identification of other devices containing the target component;
step S224, sharing the second operation parameter to other devices according to the identification, so as to instruct the target component in the other devices to operate according to the second operation parameter at the next moment.
Wherein the component is an assembly unit in the device. For example, the components may include motors, rotating components, and the like.
Specifically, the server obtains the identifier of the other device including the target component, and then shares the second operation parameter to the other device according to the identifier. And the other equipment receives the second operation parameters and controls the target component to operate according to the second operation parameters at the next moment.
In this embodiment, the operating parameters of the devices having the same components may be shared, so that after determining the second operating parameters of the target device based on the digital twin model, the second operating parameters may be shared with other devices having the same components, which is beneficial to saving computing resources.
In one embodiment, one possible implementation of the step S208 "determining the second operation parameter of the target device at the next moment according to the first operation parameter and the target operation parameter" is referred to above. On the basis of the above embodiment, step S208 may be specifically implemented by the following steps:
Step S2082, judging whether the first operation parameter and the target operation parameter meet preset conditions;
step S2084, if not, calculating the deviation between the first operation parameter and the target operation parameter, and adjusting the first operation parameter according to the deviation to obtain the adjusted operation parameter;
step S2086, the adjusted operation parameters are input into a digital twin model to obtain the adjusted target operation parameters;
in step S2088, if the adjusted operation parameter and the adjusted target operation parameter meet the preset condition, the adjusted operation parameter is determined as the second operation parameter of the target device at the next moment.
Specifically, the server determines whether the first operation parameter and the target operation parameter satisfy a preset condition. The preset condition is used to ensure proximity between the operating parameters. Alternatively, the preset condition may be that the deviation between the operating parameters is less than a deviation threshold. The deviation threshold may be, for example, 0.95. The preset condition may also be that the ratio between the operating parameters lies within a ratio interval. The ratio interval may be, for example, [0.99,1.01].
And then, if the server judges that the first operation parameter and the target operation parameter meet the preset conditions, determining the first operation parameter as a second operation parameter of the target equipment at the next moment. If the server judges that the first operation parameter and the target operation parameter do not meet the preset condition, calculating the deviation between the first operation parameter and the target operation parameter, and adjusting the first operation parameter according to the deviation to obtain the adjusted operation parameter. Optionally, the first operation parameter may be adjusted according to the deviation by adding the first operation parameter to the deviation, or adding the first operation parameter to a preset adjustment value, or multiplying the deviation by a preset coefficient, and then adding the first operation parameter to the multiplication result.
And then, the server inputs the adjusted operation parameters into the digital twin model to obtain the adjusted target operation parameters.
And finally, the server judges whether the adjusted operation parameters and the adjusted target operation parameters meet preset conditions. If yes, the adjusted operation parameters are determined to be second operation parameters of the target equipment at the next moment. If not, the step S2086 is executed again until the adjusted operating parameters and the adjusted target operating parameters meet the preset conditions. I.e. until all the indexes of the target equipment are in the optimal state.
In this embodiment, by continuously adjusting the operation parameters, it is ensured that the target device and other devices are always in an optimal operation state.
In one embodiment, the training process involves a digital twin model. On the basis of the above embodiment, step S202 may be specifically implemented by the following steps:
step S2022, receiving an operation parameter sample set sent by a plurality of target devices;
based on this, the method further comprises the steps of:
step S232, receiving environment parameter sample sets sent by a plurality of target devices; step S234, classifying the operation parameter sample set according to the environment types matched with the environment parameter samples in the environment parameter sample set to obtain operation parameter samples of various environment types;
Step S236, taking the operation parameter sample of each environment type in the operation parameter samples of the plurality of environment types as input data, taking the target operation parameter sample of the target equipment as output data, and respectively training a plurality of initial digital twin models to obtain the digital twin model corresponding to each environment type.
Wherein the set of operating parameter samples is a set of at least one operating parameter sample. An environmental parameter sample set is a set of at least one environmental parameter sample. The operation parameter samples and the environment parameter samples collected by the same target equipment at the same time have a corresponding relationship.
Specifically, each of the plurality of target devices uploads the operating parameter sample set and the environmental parameter sample set to a big data center of the server through an internet of things (Internet of Things, IOT) protocol. And then the server classifies the operation parameter samples corresponding to the environment parameter samples according to the environment types matched with the environment parameter samples in the environment parameter sample set to obtain operation parameter samples of various environment types. And finally, aiming at each environment type, taking a corresponding operation parameter sample as input data, taking a corresponding target operation parameter sample of target equipment as output data, and training at least one initial digital twin model to obtain a digital twin model corresponding to each environment type. It is understood that one environment type corresponds to at least one digital twinning model.
In this embodiment, all devices may realize information sharing through an internet of things protocol, and then through deep learning on training data generated by the devices, different digital twin models are built according to different environments, so that the digital twin models have environmental diversity, and meanwhile, digital twin models corresponding to the devices in different environments are continuously optimized, so as to create and perfect an operation growth model belonging to the devices. The feedback mechanism of the equipment control is continuously perfected through deep learning, and the equipment is energized.
In one embodiment, the step S234 is related to a possible implementation manner of "classifying the operation parameter sample set according to the environment types matched by each environment parameter sample in the environment parameter sample set to obtain operation parameter samples of multiple environment types". On the basis of the above embodiment, step S234 may be specifically implemented by the following steps:
step S2342, classifying the operation parameter sample set according to the production batches to which the target equipment belongs to obtain operation parameter sample subsets of a plurality of production batches;
step S2344, classifying the operation parameter sample subsets of each production lot from the operation parameter sample subsets of the production lots according to the environment types matched by the environment parameter samples in the environment parameter sample set, so as to obtain the operation parameter samples of the plurality of environment types in each production lot.
Wherein the subset of operating parameter samples is a set of at least one operating parameter sample.
Specifically, the server classifies the operation parameter sample sets according to production batches to which the equipment belongs, and obtains operation parameter sample subsets of a plurality of production batches. Optionally, the server performs an Extract-Transform-Load (ETL) process on the set of operating parameter samples to obtain a subset of operating parameter samples for the plurality of production lots. Then, the server classifies the operation parameter sample subset of each production lot in the operation parameter sample subsets of a plurality of production lots again according to the environment types matched by the environment parameter samples in the environment parameter sample set to obtain operation parameter samples of a plurality of environment types in each production lot, and the operation parameter samples are classified.
Further, the server classifies the operation parameter sample set and the environment parameter sample set according to the production batches to which the equipment belongs, so as to obtain operation parameter sample subsets and environment parameter sample subsets of a plurality of production batches. And then, the server classifies the operation parameter sample subset and the environment parameter sample subset of each production lot in the operation parameter sample subsets of a plurality of production lots again according to the environment types matched by the environment parameter samples in the environment parameter sample set to obtain operation parameter samples and environment parameter samples of a plurality of environment types under each production lot. Then, using a distributed computing engine spark to take the 'production batch-monitoring component' as a unique identifier, and acquiring the operation parameters of each dimension of the identifier, such as: days of operation, etc. Each dimension name and dimension value serves as a label.
Based on this, in one embodiment, the step S236 "takes the operation parameter sample of each environmental type of the operation parameter samples of the plurality of environmental types as input data, and takes the target operation parameter sample of the target device as output data, and trains the plurality of initial digital twin models respectively, so as to obtain a possible implementation manner of the digital twin model corresponding to each environmental type". On the basis of the above embodiment, step S236 may be specifically implemented by the following steps:
step S2362, taking the operation parameter sample of each environment type in the operation parameter samples of the plurality of environment types in each production batch as input data, taking the target operation parameter sample of the target equipment as output data, and respectively training the plurality of initial digital twin models to obtain the digital twin model corresponding to each environment type in each production batch.
Specifically, the server trains at least one initial digital twin model by taking a corresponding operation parameter sample of each environment type in each production batch as input data and taking a target operation parameter sample of target equipment as output data, so as to obtain the digital twin model corresponding to each environment type in each production batch. It will be appreciated that in each production lot, one environment type corresponds to at least one digital twinning model.
Further, the server trains at least one initial digital twin model by taking the corresponding operation parameter sample and the environment parameter sample of each production batch as input data and taking the target operation parameter sample of the equipment as output data to obtain the digital twin model corresponding to each environment type in each production batch.
In this embodiment, when a digital twin model is established, for each production lot, a corresponding digital twin model is established in different environmental scenes, and the model receives and feeds back target operation parameters of each device in the same environmental scene of the same production lot, thereby realizing information sharing between the devices.
It should be understood that, although the steps in the flowcharts of fig. 2-3 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2-3 may include multiple steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the steps or stages in other steps or other steps.
In one embodiment, as shown in fig. 4, there is provided an apparatus for determining an operation parameter, including:
the parameter obtaining module 302 is configured to obtain a first operation parameter collected by the target device at a previous time;
a model acquisition module 304, configured to acquire a digital twin model matched with a target device;
the parameter input module 306 is configured to input a first operation parameter to the digital twin model to obtain a target operation parameter;
the parameter determining module 308 is configured to determine a second operation parameter of the target device at a next moment according to the first operation parameter and the target operation parameter.
In the above-mentioned operation parameter determining device, based on the digital twin model matched with the target device, the first operation parameter of the target device at the previous moment is processed, the target operation parameter of the target device is determined, and then the second operation parameter of the target device at the next moment is determined, and then the target device can operate according to the second operation parameter at the next moment. Therefore, a large number of sensors and negative feedback regulating circuits are not required to be installed in the equipment, the occupied space and the hardware cost are saved, and the control cost of the equipment is reduced.
In one embodiment, the model obtaining module 304 is specifically configured to obtain a digital twin model that matches the production lot to which the target device belongs and the environmental parameter corresponding to the target device; the parameter input module 306 is specifically configured to input the first operation parameter and the environmental parameter to the digital twin model, so as to obtain the target operation parameter.
In one embodiment, the apparatus further comprises:
the batch acquisition module is used for acquiring the production batch of the target equipment;
the identification acquisition module is used for acquiring the identification of other equipment belonging to the same production batch;
and the parameter sharing module is used for sharing the second operation parameters to other equipment according to the identification so as to instruct the other equipment to operate according to the second operation parameters at the next moment.
In one embodiment, the operating parameters include operating parameters of the target component; the apparatus further comprises:
the identification acquisition module is used for acquiring the identification of other equipment containing the target component;
and the parameter sharing module is used for sharing the second operation parameters to other equipment according to the identification so as to instruct the target component in the other equipment to operate according to the second operation parameters at the next moment.
In one embodiment, the parameter determining module 308 is specifically configured to determine whether the first operating parameter and the target operating parameter meet a preset condition; if not, calculating the deviation between the first operation parameter and the target operation parameter, and adjusting the first operation parameter according to the deviation to obtain an adjusted operation parameter; inputting the adjusted operation parameters into a digital twin model to obtain adjusted target operation parameters; and if the adjusted operation parameters and the adjusted target operation parameters meet the preset conditions, determining the adjusted operation parameters as second operation parameters of the target equipment at the next moment.
In one embodiment, the apparatus further comprises:
the model training module is used for receiving an operation parameter sample set and an environment parameter sample set which are sent by a plurality of target devices through the Internet of things; classifying the operation parameter sample set according to the environment types matched with the environment parameter samples in the environment parameter sample set to obtain operation parameter samples of various environment types; taking an operation parameter sample of each environment type in the operation parameter samples of the plurality of environment types as input data, taking a target operation parameter sample of target equipment as output data, and respectively training a plurality of initial digital twin models to obtain digital twin models corresponding to each environment type.
In one embodiment, the model training module is specifically configured to classify the operation parameter sample set according to production lots to which the target device belongs, so as to obtain operation parameter sample subsets of a plurality of production lots; classifying the operation parameter sample subsets of each production lot in the operation parameter sample subsets of a plurality of production lots according to the environment types matched with the environment parameter samples in the environment parameter sample sets to obtain operation parameter samples of a plurality of environment types in each production lot; taking an operation parameter sample of each environment type in operation parameter samples of a plurality of environment types in each production batch as input data, taking a target operation parameter sample of target equipment as output data, and respectively training a plurality of initial digital twin models to obtain digital twin models corresponding to each environment type in each production batch.
For specific limitations of the determination means of the operation parameters, reference is made to the above limitations of the determination method of the operation parameters, and no further description is given here. The respective modules in the above-described determination means of the operation parameters may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of determining an operating parameter.
It will be appreciated by those skilled in the art that the structure shown in FIG. 5 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring a first operation parameter acquired by target equipment at the last moment;
acquiring a digital twin model matched with target equipment;
inputting the first operation parameters into a digital twin model to obtain target operation parameters;
and determining a second operation parameter of the target equipment at the next moment according to the first operation parameter and the target operation parameter.
In the above computer device, based on the digital twin model matched with the target device, the first operation parameter of the target device at the previous moment is processed, the target operation parameter of the target device is determined, and then the second operation parameter of the target device at the next moment is determined, and then the target device can operate according to the second operation parameter at the next moment. Therefore, a large number of sensors and negative feedback regulating circuits are not required to be installed in the equipment, the occupied space and the hardware cost are saved, and the control cost of the equipment is reduced.
In one embodiment, the processor when executing the computer program further performs the steps of:
acquiring a digital twin model matched with production batches to which target equipment belongs and environmental parameters corresponding to the target equipment; and inputting the first operation parameters and the environment parameters into the digital twin model to obtain target operation parameters.
In one embodiment, the processor when executing the computer program further performs the steps of:
obtaining a production batch to which target equipment belongs; obtaining the identification of other devices belonging to the same production batch; and sharing the second operation parameters to other devices according to the identification to instruct the other devices to operate according to the second operation parameters at the next moment.
In one embodiment, the processor when executing the computer program further performs the steps of:
acquiring the identification of other devices containing the target component; and sharing the second operation parameters to other devices according to the identification to instruct the target components in the other devices to operate according to the second operation parameters at the next moment.
In one embodiment, the processor when executing the computer program further performs the steps of:
judging whether the first operation parameter and the target operation parameter meet preset conditions or not; if not, calculating the deviation between the first operation parameter and the target operation parameter, and adjusting the first operation parameter according to the deviation to obtain an adjusted operation parameter; inputting the adjusted operation parameters into a digital twin model to obtain adjusted target operation parameters; and if the adjusted operation parameters and the adjusted target operation parameters meet the preset conditions, determining the adjusted operation parameters as second operation parameters of the target equipment at the next moment.
In one embodiment, the processor when executing the computer program further performs the steps of:
receiving an operation parameter sample set and an environment parameter sample set which are sent by a plurality of target devices through the Internet of things; classifying the operation parameter sample set according to the environment types matched with the environment parameter samples in the environment parameter sample set to obtain operation parameter samples of various environment types; taking an operation parameter sample of each environment type in the operation parameter samples of the plurality of environment types as input data, taking a target operation parameter sample of target equipment as output data, and respectively training a plurality of initial digital twin models to obtain digital twin models corresponding to each environment type.
In one embodiment, the processor when executing the computer program further performs the steps of:
classifying the operation parameter sample sets according to the production batches to which the target equipment belongs to obtain operation parameter sample subsets of a plurality of production batches; classifying the operation parameter sample subsets of each production lot in the operation parameter sample subsets of a plurality of production lots according to the environment types matched with the environment parameter samples in the environment parameter sample sets to obtain operation parameter samples of a plurality of environment types in each production lot; taking an operation parameter sample of each environment type in operation parameter samples of a plurality of environment types in each production batch as input data, taking a target operation parameter sample of target equipment as output data, and respectively training a plurality of initial digital twin models to obtain digital twin models corresponding to each environment type in each production batch.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring a first operation parameter acquired by target equipment at the last moment;
acquiring a digital twin model matched with target equipment;
inputting the first operation parameters into a digital twin model to obtain target operation parameters;
and determining a second operation parameter of the target equipment at the next moment according to the first operation parameter and the target operation parameter.
In the above computer-readable storage medium, the first operation parameter of the target device at a previous time is processed based on the digital twin model matched with the target device, the target operation parameter of the target device is determined, and then the second operation parameter of the target device at a next time is determined, and then the target device can operate according to the second operation parameter at a next time. Therefore, a large number of sensors and negative feedback regulating circuits are not required to be installed in the equipment, the occupied space and the hardware cost are saved, and the control cost of the equipment is reduced.
In one embodiment, the computer program when executed by the processor further performs the steps of:
Acquiring a digital twin model matched with production batches to which target equipment belongs and environmental parameters corresponding to the target equipment; and inputting the first operation parameters and the environment parameters into the digital twin model to obtain target operation parameters.
In one embodiment, the computer program when executed by the processor further performs the steps of:
obtaining a production batch to which target equipment belongs; obtaining the identification of other devices belonging to the same production batch; and sharing the second operation parameters to other devices according to the identification to instruct the other devices to operate according to the second operation parameters at the next moment.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring the identification of other devices containing the target component; and sharing the second operation parameters to other devices according to the identification to instruct the target components in the other devices to operate according to the second operation parameters at the next moment.
In one embodiment, the computer program when executed by the processor further performs the steps of:
judging whether the first operation parameter and the target operation parameter meet preset conditions or not; if not, calculating the deviation between the first operation parameter and the target operation parameter, and adjusting the first operation parameter according to the deviation to obtain an adjusted operation parameter; inputting the adjusted operation parameters into a digital twin model to obtain adjusted target operation parameters; and if the adjusted operation parameters and the adjusted target operation parameters meet the preset conditions, determining the adjusted operation parameters as second operation parameters of the target equipment at the next moment.
In one embodiment, the computer program when executed by the processor further performs the steps of:
receiving an operation parameter sample set and an environment parameter sample set which are sent by a plurality of target devices through the Internet of things; classifying the operation parameter sample set according to the environment types matched with the environment parameter samples in the environment parameter sample set to obtain operation parameter samples of various environment types; taking an operation parameter sample of each environment type in the operation parameter samples of the plurality of environment types as input data, taking a target operation parameter sample of target equipment as output data, and respectively training a plurality of initial digital twin models to obtain digital twin models corresponding to each environment type.
In one embodiment, the computer program when executed by the processor further performs the steps of:
classifying the operation parameter sample sets according to the production batches to which the target equipment belongs to obtain operation parameter sample subsets of a plurality of production batches; classifying the operation parameter sample subsets of each production lot in the operation parameter sample subsets of a plurality of production lots according to the environment types matched with the environment parameter samples in the environment parameter sample sets to obtain operation parameter samples of a plurality of environment types in each production lot; taking an operation parameter sample of each environment type in operation parameter samples of a plurality of environment types in each production batch as input data, taking a target operation parameter sample of target equipment as output data, and respectively training a plurality of initial digital twin models to obtain digital twin models corresponding to each environment type in each production batch.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A method of determining an operating parameter, the method comprising:
acquiring a first operation parameter acquired by target equipment at the last moment; the first operation parameter refers to parameter information generated when the target equipment operates;
acquiring a digital twin model matched with the target equipment; the digital twin model is obtained by training an initial digital twin model by taking operation parameter samples of a plurality of devices as input data and taking target operation parameter samples of the plurality of devices as output data;
the obtaining the first operation parameter acquired by the target device at the last moment includes:
receiving an operation parameter sample set sent by a plurality of target devices;
The method further comprises the steps of: classifying the operation parameter sample set according to the production batches to which the target equipment belongs to obtain operation parameter sample subsets of a plurality of production batches; classifying the operation parameter sample subsets of each production lot in the operation parameter sample subsets of the plurality of production lots according to the environment types matched with the environment parameter samples in the environment parameter sample sets to obtain operation parameter samples of a plurality of environment types in each production lot; the operation parameter samples of the plurality of environment types under each production batch are used for obtaining a digital twin model corresponding to each environment type under each production batch;
inputting the first operation parameters into the digital twin model to obtain target operation parameters; the target operation parameter is the optimal operation parameter of the target equipment;
and determining a second operation parameter of the target equipment at the next moment according to the first operation parameter and the target operation parameter.
2. The method of claim 1, wherein the obtaining a digital twin model that matches the target device comprises:
acquiring a digital twin model matched with a production batch to which the target equipment belongs and an environment parameter corresponding to the target equipment;
The step of inputting the first operation parameter to the digital twin model to obtain a target operation parameter includes:
and inputting the first operation parameter and the environment parameter into the digital twin model to obtain a target operation parameter.
3. The method of claim 1, wherein determining a second operating parameter of the target device at a next time based on the first operating parameter and the target operating parameter comprises:
judging whether the first operation parameter and the target operation parameter meet preset conditions or not;
if not, calculating the deviation between the first operation parameter and the target operation parameter, and adjusting the first operation parameter according to the deviation to obtain an adjusted operation parameter;
inputting the adjusted operation parameters into the digital twin model to obtain adjusted target operation parameters;
and if the adjusted operation parameter and the adjusted target operation parameter meet the preset condition, determining the adjusted operation parameter as a second operation parameter of the target equipment at the next moment.
4. The method according to claim 2, wherein the method further comprises:
Receiving environment parameter sample sets sent by the plurality of target devices;
classifying the operation parameter sample set according to the environment types matched with the environment parameter samples in the environment parameter sample set to obtain operation parameter samples of various environment types;
and training the plurality of initial digital twin models respectively by taking the operation parameter sample of each environment type in the operation parameter samples of the plurality of environment types as input data and taking the target operation parameter sample of the target equipment as output data to obtain the digital twin model corresponding to each environment type.
5. The method according to claim 4, wherein the training the plurality of initial digital twin models to obtain the digital twin model corresponding to each environmental type using the operating parameter sample of each environmental type of the plurality of environmental type operating parameter samples as input data and the target operating parameter sample of the target device as output data includes:
and training the plurality of initial digital twin models respectively by taking the operation parameter sample of each environment type in the operation parameter samples of the plurality of environment types of each production batch as input data and taking the target operation parameter sample of the target equipment as output data to obtain the digital twin model corresponding to each environment type of each production batch.
6. The method of claim 1, 4 or 5, further comprising:
obtaining a production batch to which the target equipment belongs;
obtaining the identification of other devices belonging to the production batch;
and sharing the second operation parameters to the other equipment according to the identification so as to instruct the other equipment to operate according to the second operation parameters at the next moment.
7. The method of claim 1, 4 or 5, wherein the operating parameter comprises an operating parameter of a target component;
the method further comprises the steps of:
acquiring the identification of other devices containing the target component;
and sharing the second operation parameters to the other equipment according to the identification so as to instruct a target component in the other equipment to operate according to the second operation parameters at the next moment.
8. An apparatus for determining an operating parameter, the apparatus comprising:
the parameter acquisition module is used for acquiring a first operation parameter acquired by the target equipment at the previous moment; the first operation parameter refers to parameter information generated when the target equipment operates;
the model acquisition module is used for acquiring a digital twin model matched with the target equipment; the digital twin model is obtained by training an initial digital twin model by taking operation parameter samples of a plurality of devices as input data and taking target operation parameter samples of the plurality of devices as output data;
The parameter input module is used for inputting the first operation parameter into the digital twin model to obtain a target operation parameter; the target operation parameter is the optimal operation parameter of the target equipment;
the parameter determining module is used for determining a second operation parameter of the target equipment at the next moment according to the first operation parameter and the target operation parameter;
the parameter acquisition module is used for receiving operation parameter sample sets sent by a plurality of target devices;
the apparatus further comprises:
the model training module is used for classifying the operation parameter sample set according to the production batches to which the target equipment belongs to obtain operation parameter sample subsets of a plurality of production batches; classifying the operation parameter sample subsets of each production lot in the operation parameter sample subsets of the plurality of production lots according to the environment types matched with the environment parameter samples in the environment parameter sample sets to obtain operation parameter samples of a plurality of environment types in each production lot; the operation parameter samples of the plurality of environment types under each production batch are used for obtaining a digital twin model corresponding to each environment type under each production batch.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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| CN202111074353.2A CN113805544B (en) | 2021-09-14 | 2021-09-14 | Method, device, computer equipment and storage medium for determining operation parameters |
| CN202311715654.8A CN117724419A (en) | 2021-09-14 | 2021-09-14 | Method for constructing digital twin model, computer device and storage medium |
| US17/932,283 US20230082761A1 (en) | 2021-09-14 | 2022-09-14 | Systems and methods for device monitoring |
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