CN117762112B - On-line parameter adjusting system and method based on digital communication - Google Patents
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Abstract
The invention discloses an online parameter adjusting system and method based on digital communication, and belongs to the technical field of digital communication. The on-line parameter adjusting system based on digital communication comprises a data transmission module, a central control module, a parameter adjusting module, a circulation optimizing module and a training module, new system parameters are generated and transmitted to an actuator through a digital communication technology, the actuator distributes the new system parameters to each intelligent node in the system through a point-to-point communication mechanism among nodes, when each intelligent node receives the new system parameters, the parameter integrating module controls each node to be responsible for optimizing local parameters, so that each node can be responsible for optimizing the local parameters, the parallelism and adaptability of the system can be improved, the dependence on the central control unit can be reduced, the load rate of the parameter adjusting system can be reduced, and meanwhile the working efficiency of the parameter adjusting system can be improved.
Description
Technical Field
The invention relates to the field of digital communication, in particular to an online parameter adjusting system and method based on digital communication.
Background
Digital communication (digital telecommunications) is a communication mode in which a message is transmitted using a digital signal as a carrier, or a carrier is digitally modulated using a digital signal and then transmitted. It can transmit telegraph, digital data and other digital signals, and also can transmit digital processed speech and image and other analog signals.
The current on-line parameter adjusting system based on digital communication mainly generates new adjusting parameters through the system, and then adjusts the parameters of each node in the system by utilizing the central control unit, so that the central control unit can synchronously adjust each node in the process of adjusting the system parameters, thereby improving the load rate of the parameter adjusting system in the synchronous adjustment period and further reducing the working efficiency of the parameter adjusting system.
Disclosure of Invention
The invention aims to provide an online parameter adjusting system and method based on digital communication, which are used for solving the problems in the background technology.
An online parameter adjusting system based on digital communication comprises a data transmission module, a central control module, a parameter adjusting module, a circulation optimizing module and a training module;
the data transmission module is used for transmitting the acquired data to the central control unit through a digital communication technology;
the central control module is used for receiving and processing real-time data from the sensor, analyzing, storing and primarily analyzing the data and preparing for subsequent parameter adjustment;
The parameter adjusting module is used for making decisions according to the real-time data, the historical data and the parameter range information set by the user, generating new system parameter settings, transmitting the new parameter settings to the executor through a digital communication technology, distributing the generated new parameters to a plurality of intelligent nodes in the system by the executor, enabling each node to be responsible for optimizing local parameters, and adjusting the running state of the system by the parameters, wherein the parameters possibly comprise the settings of control equipment and the working mode of the system;
the cyclic optimization module is used for periodically optimizing parameters of the system;
the training module is used for training operators regularly and enabling the operators to be skilled in the operating system;
The data transmission module transmits the acquired data to the central control unit through a digital communication technology, the central control module analyzes, stores and primarily analyzes the received data, the real-time data, the historical data and the parameter range information set by a user after analysis by the parameter adjustment module are used for making decisions, new system parameter settings are generated, the generated new parameters are transmitted to the executor through the digital communication technology, parameter adjustment operation is carried out, and after the parameter adjustment operation, the parameter of the system is periodically optimized by the cycle optimization module, and meanwhile, operators are regularly trained by the training module.
Preferably, the data transmission module comprises a data acquisition module and a transmission module;
The data acquisition module is used for acquiring real-time data, such as temperature, pressure and humidity, of the system when the sensor node is in operation, and the frequency of data acquisition can be configured according to the system requirements;
The transmission module is used for encoding the acquired data, packaging the acquired data according to a predefined data format, wherein the common data format can be selected as JSON and XML, and then transmitting the packaged data to the central control unit.
Preferably, the central control module comprises a network layer module and a data processing module;
The network layer module is used for receiving the data transmitted by the sensor node by the network layer of the central control unit, then enabling the network layer to perform deblocking and decoding operations on the data, and storing the received data in the central control unit;
The data processing module is used for carrying out statistics, filtering and smoothing processing steps on the data subjected to the deblocking and decoding operation, and preparing for subsequent parameter adjustment.
Preferably, the parameter adjusting module comprises an iteration module, a parameter generating module and an intelligent node module;
the iteration module performs optimization iteration according to the processed data on the basis of the data of the steps of statistics, filtering and smoothing of the real-time data, continuously adjusts system parameters according to the real-time data, and the optimization process involves minimization or maximization of an objective function;
The generation parameter module checks whether the optimization process is converged based on each iteration, and if so, generates new system parameter settings;
the intelligent node module is used for distributing the generated new system parameter setting to a plurality of intelligent nodes in the system, and enabling each node to be responsible for optimizing local parameters.
Preferably, the intelligent node module comprises a node communication module and a parameter integration module;
The node communication module is used for defining a data format of communication between nodes and realizing a communication interface on each node, and comprises a sending function and a receiving function, and then communication connection is established between each node, which can comprise a handshake protocol, so that the nodes can mutually identify and establish a safe communication channel, a point-to-point communication mechanism is arranged between each node, and new system parameter setting is generated and distributed to each intelligent node in the system through the point-to-point communication mechanism;
The parameter integration module is used for controlling each intelligent node in the system to realize optimization of local parameters.
Preferably, the circulation optimization module comprises a real-time monitoring module and a circulation module;
The real-time monitoring module is used for monitoring the performance of the system in real time after new parameter setting is applied, including sensor data, output results and system states, and allowing a user to provide feedback through an interface, possibly including adjusting a parameter range, proposing special requirements, dynamically adjusting system parameters according to the user feedback and the real-time monitoring results, and carrying out real-time monitoring again after the parameters are adjusted, so as to generate new monitoring information and reflect the adjusted effect;
The circulation module is used for periodically optimizing parameters of the system and recording historical data of each round of optimization, including parameter change and improvement of system performance, which is helpful for subsequent analysis and optimization.
Preferably, the training module comprises a guiding operation module, an examination module and a video demonstration module;
The guiding operation module is used for guiding an operator to operate the concrete steps of the parameter adjusting system;
The examination module is used for an operator to carry out a simulation operation examination;
the video demonstration module is used for demonstrating the correct operation steps of the operation parameter adjusting system.
Preferably, an online parameter adjusting method based on digital communication comprises the following steps:
S1, data transmission: firstly, acquiring real-time data of a system during operation by using a sensor node, coding the acquired data, packaging according to a JSON data format, and transmitting the packaged data to a central control unit through a digital communication technology;
S2, data processing: after the central control unit receives the packed data, the network layer in the central control unit is utilized to carry out deblocking and decoding operations on the data, and then the steps of statistics, filtering and smoothing processing of the real-time data are carried out;
s3, generating parameters: after the statistics, filtering and smoothing operations of the real-time data are carried out on the data, carrying out optimization iteration according to the processed data, continuously adjusting system parameters according to the real-time data, checking whether an optimization process is converged after the system parameters are adjusted, and generating new system parameter settings after the convergence;
S4, intelligent nodes: after new system parameter setting is generated, the generated new system parameters are transmitted to an actuator through a digital communication technology, then the actuator distributes the generated new system parameters to each intelligent node in the system through a point-to-point communication mechanism among the nodes, and when each intelligent node receives the new system parameters, the parameter integration module controls each node to be responsible for optimizing local parameters;
S5, cyclic optimization: when each node is subjected to parameter optimization, the real-time monitoring module monitors the performance of the system in real time, simultaneously allows a user to provide feedback through an interface, dynamically adjusts system parameters according to the feedback of the user and the real-time monitoring result, and then carries out real-time monitoring again after adjusting the parameters to generate new monitoring information and records the history data of each round of optimization;
S6, training operators: an operator can regularly guide the specific steps of the self-operation parameter adjusting system by using the guiding operation module, when the operator is skilled in the guiding operation module to operate the specific steps of the parameter adjusting system, the examination module is used for carrying out a simulated operation examination, if an operation error occurs in the operation examination, the system can prompt the operation error, and when the system prompts the operation error, the video demonstration module can pop up the demonstration step, so that the operator can know the action of the operation step until the operator passes through the examination module.
Compared with the prior art, the invention has the advantages that:
In the invention, the generated new system parameters are transmitted to the executor through the digital communication technology, then the executor distributes the generated new system parameters to each intelligent node in the system through the point-to-point communication mechanism among the nodes, and when each intelligent node receives the new system parameters, the parameter integration module controls each node to be responsible for optimizing the local parameters, so that each node can be responsible for optimizing the local parameters, thereby improving the parallelism and adaptability of the system, reducing the dependence on a central control unit, further reducing the load rate of the parameter adjusting system, and improving the working efficiency of the parameter adjusting system.
In the invention, after parameter optimization is carried out through each node, the real-time monitoring module can monitor the performance of the system in real time, simultaneously allows a user to provide feedback through an interface, then dynamically adjusts system parameters according to the feedback of the user and the result of real-time monitoring, and then carries out real-time monitoring again after adjusting the parameters to generate new monitoring information and records the optimized historical data of each round, thus the running state of the system can be monitored in real time, the autonomy of the parameter adjusting system can be improved by periodically repeating the steps, thereby providing convenience for operators and further improving the working efficiency of the parameter adjusting system.
According to the invention, when an operator is guided to skillfully operate the parameter adjusting system in the operation module, the examination module is utilized to carry out a simulated operation examination, if an operation error occurs in the operation examination, the system prompts the operation error, and when the system prompts the operation error, the video demonstration module pops up the demonstration step, so that the operator knows the action of the operation step, and the operator can quickly use and operate the system until the operator passes through the examination module, thereby effectively shortening the time of the operator familiar with the parameter adjusting system and improving the working efficiency of the parameter adjusting system on a certain basis.
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FIG. 1 is a schematic diagram of an overall system flow of the present invention;
fig. 2 is a flow chart of an online parameter adjusting method based on digital communication according to the present invention.
Detailed Description
Examples: referring to fig. 1, an on-line parameter adjusting system based on digital communication includes a data transmission module, a central control module, a parameter adjusting module, a circulation optimizing module and a training module;
The data transmission module is used for transmitting the acquired data to the central control unit through a digital communication technology, so that the real-time performance and the reliability of the data are ensured;
The central control module is used for receiving and processing real-time data from the sensor, analyzing, storing and primarily analyzing the data and preparing for subsequent parameter adjustment;
The parameter adjusting module is used for making a decision according to the real-time data, the historical data and the parameter range information set by the user, generating new system parameter settings, transmitting the new parameter settings to the executor through a digital communication technology, distributing the generated new parameters to a plurality of intelligent nodes in the system by the executor, enabling each node to be responsible for optimizing local parameters, and adjusting the running state of the system by the parameters, wherein the parameters possibly comprise the settings of control equipment and the working mode of the system;
the cycle optimization module is used for periodically optimizing parameters of the system;
The training module is used for training operators regularly to enable the operators to be skilled in the operating system;
The data transmission module transmits the acquired data to the central control unit through a digital communication technology, the central control module analyzes, stores and primarily analyzes the received data, the real-time data, the historical data and the parameter range information set by a user after analysis by the parameter adjustment module are decided, new system parameter setting is generated, the generated new parameters are transmitted to the executor through the digital communication technology, parameter adjustment operation is carried out, and after the parameter adjustment operation, the parameter of the system is periodically optimized by the cycle optimization module, and meanwhile, operators are regularly trained by the training module.
Specifically, the generated new system parameters are transmitted to the executor through a digital communication technology, then the executor distributes the generated new system parameters to each intelligent node in the system through a point-to-point communication mechanism among the nodes, when each intelligent node receives the new system parameters, the parameter integration module controls each node to be responsible for optimizing local parameters, so that each node can be responsible for optimizing the local parameters, the parallelism and the adaptability of the system can be improved, the dependence on a central control unit can be reduced, the load rate of the parameter adjusting system can be reduced, and meanwhile, the working efficiency of the parameter adjusting system can be improved.
The data transmission module comprises a data acquisition module and a transmission module;
the data acquisition module is used for acquiring real-time data, such as temperature, pressure and humidity, of the system during operation of the sensor node, and the frequency of data acquisition can be configured according to the system requirements;
The transmission module is used for encoding the acquired data, packaging the acquired data according to a predefined data format, wherein the common data format can be selected as JSON and XML, and then transmitting the packaged data to the central control unit.
The central control module comprises a network layer module and a data processing module;
The network layer module is used for receiving the data transmitted by the sensor node by the network layer of the central control unit, then enabling the network layer to perform deblocking and decoding operations on the data, ensuring that the data can be correctly understood by the central control unit, and storing the received data in the central control unit;
The data processing module is used for carrying out statistics, filtering and smoothing processing steps on the data subjected to the deblocking and decoding operation and preparing for subsequent parameter adjustment.
The parameter adjusting module comprises an iteration module, a parameter generating module and an intelligent node module;
The iteration module performs optimization iteration according to the processed data on the basis of the data of the steps of statistics, filtering and smoothing of the real-time data, continuously adjusts system parameters according to the real-time data, and the optimization process involves minimization or maximization of an objective function so as to realize optimal adjustment of the system;
The generation parameter module checks whether the optimization process is converged based on each iteration, and if so, generates new system parameter settings;
The intelligent node module is used for distributing the generated new system parameter setting to a plurality of intelligent nodes in the system, and enabling each node to be responsible for optimizing local parameters.
The intelligent node module comprises a node communication module and a parameter integration module;
The node communication module is used for defining a data format of communication between nodes, realizing a communication interface on each node, comprising a sending function and a receiving function, ensuring that the interface can pack and unpack data correctly, processing errors possibly occurring in communication, then establishing communication connection between each node, which can comprise a handshake protocol, ensuring that the nodes can mutually identify and establish a safe communication channel, setting a point-to-point communication mechanism between each node, and distributing new system parameter setting to each intelligent node in the system through the point-to-point communication mechanism;
the parameter integration module is used for controlling each intelligent node in the system to realize optimization of local parameters.
The circulation optimization module comprises a real-time monitoring module and a circulation module;
The real-time monitoring module is used for monitoring the performance of the system in real time after the new parameter setting is applied, including sensor data, output results and system states, and allowing a user to provide feedback through an interface, possibly including adjusting the parameter range and providing special requirements, dynamically adjusting the system parameters according to the user feedback and the real-time monitoring results, and carrying out real-time monitoring again after the parameters are adjusted, so as to generate new monitoring information and reflect the adjustment effect;
the loop module is used for periodically optimizing parameters of the system and recording historical data of each round of optimization, including parameter change and improvement of system performance, which facilitates subsequent analysis and optimization.
Specifically, after parameter optimization is performed through each node, the real-time monitoring module can monitor the performance of the system in real time, meanwhile, a user is allowed to provide feedback through an interface, then, system parameters are dynamically adjusted according to user feedback and real-time monitoring results, after the parameters are adjusted, real-time monitoring is performed again, new monitoring information is generated, and history data of optimization of each round is recorded, so that the running state of the system can be monitored in real time, the optimization operation can be performed through periodically repeating the steps, the autonomy of the parameter adjusting system can be improved, convenience can be provided for operators, and the working efficiency of the parameter adjusting system is improved.
The training module comprises a guiding operation module, an examination module and a video demonstration module;
the guiding operation module is used for guiding an operator to operate the specific steps of the parameter adjusting system;
the examination module is used for an operator to carry out a simulation operation examination;
the video demonstration module is used for demonstrating the correct operation steps of the operation parameter adjusting system.
Specifically, the operator can regularly guide the specific steps of the self-operation parameter adjusting system by using the guiding operation module, and when the operator is skilled in the guiding operation module to operate the specific steps of the parameter adjusting system, the examination module is used for carrying out a simulation operation examination, if an operation error occurs in the operation examination, the system can prompt the operation error, and when the system prompts the operation error, the video demonstration module can pop up the demonstration step, so that the operator can know the action of the operation step until the operator passes through the examination module, the operator can quickly use and operate the system, further the time that the operator is familiar with the parameter adjusting system is effectively shortened, and meanwhile, the working efficiency of the parameter adjusting system is improved on a certain basis.
Referring to fig. 2, an online parameter adjustment method based on digital communication includes the following steps:
S1, data transmission: firstly, acquiring real-time data of a system during operation by using a sensor node, coding the acquired data, packaging according to a JSON data format, and transmitting the packaged data to a central control unit through a digital communication technology;
S2, data processing: after the central control unit receives the packed data, the network layer in the central control unit is utilized to carry out deblocking and decoding operations on the data, and then the steps of statistics, filtering and smoothing processing of the real-time data are carried out;
s3, generating parameters: after the statistics, filtering and smoothing operations of the real-time data are carried out on the data, carrying out optimization iteration according to the processed data, continuously adjusting system parameters according to the real-time data, checking whether an optimization process is converged after the system parameters are adjusted, and generating new system parameter settings after the convergence;
S4, intelligent nodes: after new system parameter setting is generated, the generated new system parameters are transmitted to an actuator through a digital communication technology, then the actuator distributes the generated new system parameters to each intelligent node in the system through a point-to-point communication mechanism among the nodes, and when each intelligent node receives the new system parameters, the parameter integration module controls each node to be responsible for optimizing local parameters;
S5, cyclic optimization: when each node is subjected to parameter optimization, the real-time monitoring module monitors the performance of the system in real time, simultaneously allows a user to provide feedback through an interface, dynamically adjusts system parameters according to the feedback of the user and the real-time monitoring result, and then carries out real-time monitoring again after adjusting the parameters to generate new monitoring information and records the history data of each round of optimization;
S6, training operators: an operator can regularly guide the specific steps of the self-operation parameter adjusting system by using the guiding operation module, when the operator is skilled in the guiding operation module to operate the specific steps of the parameter adjusting system, the examination module is used for carrying out a simulated operation examination, if an operation error occurs in the operation examination, the system can prompt the operation error, and when the system prompts the operation error, the video demonstration module can pop up the demonstration step, so that the operator can know the action of the operation step until the operator passes through the examination module.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (5)
1. An online parameter adjusting system based on digital communication is characterized in that: the on-line parameter adjusting system based on digital communication comprises a data transmission module, a central control module, a parameter adjusting module, a circulation optimizing module and a training module;
the data transmission module is used for transmitting the acquired data to the central control unit through a digital communication technology;
the central control module is used for receiving and processing real-time data from the sensor, analyzing, storing and primarily analyzing the data and preparing for subsequent parameter adjustment;
The parameter adjusting module is used for making a decision according to the real-time data, the historical data and the parameter range information set by the user, generating new system parameter settings, transmitting the new system parameter settings to the executor through a digital communication technology, distributing the generated new parameters to a plurality of intelligent nodes in the system by the executor, and enabling each node to be responsible for optimizing local parameters;
the cyclic optimization module is used for periodically optimizing parameters of the system;
the training module is used for training operators regularly and enabling the operators to be skilled in the operating system;
The data transmission module transmits the acquired data to the central control unit through a digital communication technology, the central control module analyzes, stores and primarily analyzes the received data, the real-time data, the historical data and the parameter range information set by a user after analysis by the parameter adjustment module are decided, new system parameter setting is generated, the generated new parameters are transmitted to the executor through the digital communication technology, parameter adjustment operation is carried out, and after the parameter adjustment operation, the parameter of the system is periodically optimized by the cycle optimization module, and meanwhile, operators are regularly trained by the training module;
the parameter adjusting module comprises an iteration module, a parameter generating module and an intelligent node module;
the intelligent node module comprises a node communication module and a parameter integration module;
The node communication module is used for defining a data format of communication between nodes and realizing a communication interface on each node, and comprises a sending function and a receiving function, then communication connection is established between each node, a point-to-point communication mechanism is arranged between each node, and new system parameter setting is generated and distributed to each intelligent node in the system through the point-to-point communication mechanism;
the parameter integration module is used for controlling each intelligent node in the system to realize optimization of local parameters;
the circulation optimization module comprises a real-time monitoring module and a circulation module;
The real-time monitoring module is used for monitoring the performance of the system in real time after the new parameter setting is applied, allowing a user to provide feedback through an interface, dynamically adjusting the system parameters according to the user feedback and the real-time monitoring result, and carrying out real-time monitoring again after the parameters are adjusted to generate new monitoring information and reflect the adjustment effect;
The circulation module is used for periodically optimizing parameters of the system and recording history data of each round of optimization;
The training module comprises a guiding operation module, an examination module and a video demonstration module;
The guiding operation module is used for guiding an operator to operate the concrete steps of the parameter adjusting system;
The examination module is used for an operator to carry out a simulation operation examination;
the video demonstration module is used for demonstrating the correct operation steps of the operation parameter adjusting system.
2. The digital communication-based on-line parameter tuning system of claim 1, wherein: the data transmission module comprises a data acquisition module and a transmission module;
the data acquisition module is used for acquiring real-time data of the sensor node during the running of the system;
The transmission module is used for coding the acquired data, packaging the acquired data according to a predefined data format, and transmitting the packaged data to the central control unit.
3. The digital communication-based on-line parameter tuning system of claim 1, wherein: the central control module comprises a network layer module and a data processing module;
The network layer module is used for receiving the data transmitted by the sensor node by the network layer of the central control unit, then enabling the network layer to perform deblocking and decoding operations on the data, and storing the received data in the central control unit;
The data processing module is used for carrying out statistics, filtering and smoothing processing steps on the data subjected to the deblocking and decoding operation, and preparing for subsequent parameter adjustment.
4. The digital communication-based on-line parameter tuning system of claim 1, wherein:
The iteration module performs optimization iteration according to the processed data on the basis of the data of the real-time data statistics, filtering and smoothing processing steps, and continuously adjusts system parameters according to the real-time data;
The generation parameter module checks whether the optimization process is converged based on each iteration, and if so, generates new system parameter settings;
the intelligent node module is used for distributing the generated new system parameter setting to a plurality of intelligent nodes in the system, and enabling each node to be responsible for optimizing local parameters.
5. An online parameter adjusting method based on digital communication relates to an online parameter adjusting system based on digital communication as set forth in any one of claims 1-4, which is characterized by comprising the following steps:
S1, data transmission: firstly, acquiring real-time data of a system during operation by using a sensor node, coding the acquired data, packaging according to a JSON data format, and transmitting the packaged data to a central control unit through a digital communication technology;
S2, data processing: after the central control unit receives the packed data, the network layer in the central control unit is utilized to carry out deblocking and decoding operations on the data, and then the steps of statistics, filtering and smoothing processing of the real-time data are carried out;
s3, generating parameters: after the statistics, filtering and smoothing operations of the real-time data are carried out on the data, carrying out optimization iteration according to the processed data, continuously adjusting system parameters according to the real-time data, checking whether an optimization process is converged after the system parameters are adjusted, and generating new system parameter settings after the convergence;
S4, intelligent nodes: after new system parameter setting is generated, the generated new system parameters are transmitted to an actuator through a digital communication technology, then the actuator distributes the generated new system parameters to each intelligent node in the system through a point-to-point communication mechanism among the nodes, and when each intelligent node receives the new system parameters, the parameter integration module controls each node to be responsible for optimizing local parameters;
S5, cyclic optimization: when each node is subjected to parameter optimization, the real-time monitoring module monitors the performance of the system in real time, simultaneously allows a user to provide feedback through an interface, dynamically adjusts system parameters according to the feedback of the user and the real-time monitoring result, and then carries out real-time monitoring again after adjusting the parameters to generate new monitoring information and records the history data of each round of optimization;
S6, training operators: an operator can regularly guide the specific steps of the self-operation parameter adjusting system by using the guiding operation module, when the operator is skilled in the guiding operation module to operate the specific steps of the parameter adjusting system, the examination module is used for carrying out a simulated operation examination, if an operation error occurs in the operation examination, the system can prompt the operation error, and when the system prompts the operation error, the video demonstration module can pop up the demonstration step, so that the operator can know the action of the operation step until the operator passes through the examination module.
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| CN114424167A (en) * | 2019-05-06 | 2022-04-29 | 强力物联网投资组合2016有限公司 | Platform for promoting intelligent development of industrial Internet of things system |
| CN112581821A (en) * | 2020-12-02 | 2021-03-30 | 中国石油大学(华东) | Simulation training and examination system, method, medium and equipment for wet steam generator special for oil field |
| CN114046493A (en) * | 2021-11-02 | 2022-02-15 | 国家能源集团华北电力有限公司廊坊热电厂 | Boiler combustion optimization system and terminal |
| CN117291004A (en) * | 2023-08-25 | 2023-12-26 | 三峡大学 | Transformer parameter optimization design method based on MSPBO algorithm |
| CN117132228A (en) * | 2023-08-29 | 2023-11-28 | 长沙宏略信息科技有限公司 | Government affair fusion message platform method and system |
| CN117552774A (en) * | 2023-11-29 | 2024-02-13 | 四川省威沃敦石油科技股份有限公司 | Intelligent yield increase testing system based on quantum dot tracing technology |
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