CN115016353A - Monitoring management system for remote control equipment - Google Patents
Monitoring management system for remote control equipment Download PDFInfo
- Publication number
- CN115016353A CN115016353A CN202210689652.5A CN202210689652A CN115016353A CN 115016353 A CN115016353 A CN 115016353A CN 202210689652 A CN202210689652 A CN 202210689652A CN 115016353 A CN115016353 A CN 115016353A
- Authority
- CN
- China
- Prior art keywords
- evaluation index
- information
- control
- control parameter
- index value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 65
- 238000011156 evaluation Methods 0.000 claims abstract description 195
- 238000012216 screening Methods 0.000 claims abstract description 89
- 238000005457 optimization Methods 0.000 claims abstract description 70
- 230000006870 function Effects 0.000 claims description 47
- 238000000034 method Methods 0.000 claims description 38
- 238000012545 processing Methods 0.000 claims description 34
- 238000007726 management method Methods 0.000 claims description 33
- 238000004590 computer program Methods 0.000 claims description 9
- 239000002245 particle Substances 0.000 claims description 9
- 238000000605 extraction Methods 0.000 claims description 7
- 230000005484 gravity Effects 0.000 claims description 4
- 239000000284 extract Substances 0.000 abstract 2
- 230000008569 process Effects 0.000 description 17
- 238000004891 communication Methods 0.000 description 9
- 238000012549 training Methods 0.000 description 7
- 238000013528 artificial neural network Methods 0.000 description 6
- 230000001276 controlling effect Effects 0.000 description 6
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 238000003062 neural network model Methods 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 230000003213 activating effect Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 238000005314 correlation function Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 210000004556 brain Anatomy 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0428—Safety, monitoring
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24024—Safety, surveillance
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Selective Calling Equipment (AREA)
- Testing And Monitoring For Control Systems (AREA)
- Feedback Control In General (AREA)
Abstract
The invention provides a monitoring management system for remote control equipment, which comprises: the first acquisition unit acquires first monitoring scene information, including first control object information and first application object information; a first generation unit performs evaluation index matching on the first control object information to generate a first evaluation index set; the second generation unit extracts control parameters of the first control object information to generate a first control parameter set; the third generation unit traverses the first evaluation index set according to the first control parameter set to generate a first objective function set; the first obtaining unit extracts the state of the first application object information to obtain first real-time state information; the fourth generation unit inputs the first real-time state information and the first evaluation index set into the first screening model and generates an evaluation index value screening result; and the fifth generating unit is used for remotely controlling the optimization of the first control parameter set according to the first objective function set and the evaluation index value screening result information.
Description
Technical Field
The invention relates to the technical field of artificial intelligence correlation, in particular to a monitoring management system for remote control equipment.
Background
Remote control is the ability to operate remote devices using radio or electrical signals, i.e. the remote controlled device sends out control commands through the local control device and executes corresponding work processes after receiving the control commands. Monitoring of the state of remote controlled equipment in remote control is an important process for guaranteeing control accuracy.
The current monitoring management in remote control mainly monitors the state of the controlled equipment and depends on manual evaluation of the equipment state to match corresponding parameters, and the mode depends on manual decision.
In the prior art, the process of manual decision making has great instability, so that the technical problems of poor monitoring and management stability and low automation degree exist.
Disclosure of Invention
The embodiment of the application provides a monitoring management system for remote control equipment, and solves the technical problems that in the prior art, due to the fact that the process of manual decision making has great instability, monitoring management stability is poor and the automation degree is low.
In view of the foregoing, embodiments of the present application provide a monitoring management system for a remote control device.
In a first aspect, an embodiment of the present application provides a monitoring management system for a remote control device, where the system is in communication connection with a first control object, and includes: the system comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring first monitoring scene information, and the first monitoring scene information comprises first control object information and first application object information; a first generation unit, configured to perform evaluation index matching on the first control object information, and generate a first evaluation index set, where the evaluation index represents a control object state; a second generation unit, configured to perform control parameter extraction on the first control object information, and generate a first control parameter set; a third generating unit, configured to traverse the first evaluation index set according to the first control parameter set, and generate a first objective function set; the first obtaining unit is used for extracting the real-time state of the first application object information to obtain first real-time state information; a fourth generating unit, configured to input the first real-time status information and the first evaluation index set into a first screening model, and generate evaluation index value screening result information; and the fifth generating unit is used for optimizing the first control parameter set according to the first objective function set and the evaluation index value screening result information to generate an optimized control parameter set, and remotely controlling the first control object based on the optimized control parameter set.
In another aspect, an embodiment of the present application provides a monitoring management method for a remote control device, where the method applies a monitoring management system for a remote control device, the system is in communication connection with a first control object, and the method includes: acquiring first monitoring scene information, wherein the first monitoring scene information comprises first control object information and first application object information; carrying out evaluation index matching on the first control object information to generate a first evaluation index set, wherein the evaluation index represents a control object state; extracting control parameters of the first control object information to generate a first control parameter set; traversing the first evaluation index set according to the first control parameter set to generate a first objective function set; extracting the real-time state of the first application object information to obtain first real-time state information; inputting the first real-time state information and the first evaluation index set into a first screening model to generate evaluation index value screening result information; optimizing the first control parameter set according to the first objective function set and the evaluation index value screening result information to generate an optimization control parameter set, and remotely controlling the first control object based on the optimization control parameter set.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method according to any one of the first aspect when executing the program.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method of any one of the first aspect.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the control object and the application object information processed by the representation control object are determined by monitoring the scene information; evaluating an index set according to the matching state of the control object; extracting and evaluating control parameter information corresponding to the index sets one by one aiming at the control objects; generating an experience objective function set representing the incidence relation between the control parameters and the evaluation indexes which are in one-to-one correspondence; extracting real-time state information of the application object information, and matching and evaluating an index value by using an intelligent model according to the real-time state; the control parameter set is optimized based on the matching evaluation index value and the objective function set, the technical scheme that the control object is controlled by the control parameter optimization result suitable for the current scene is obtained, the evaluation index value suitable for the current scene is matched by using an intelligent model, and then the matching of the control parameters is realized through an optimization algorithm according to the objective function and the evaluation index value.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
Fig. 1 is a schematic flowchart of a monitoring management method for a remote control device according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a control parameter optimization method in a monitoring management method for a remote control device according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a monitoring management system for a remote control device according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: the system comprises a first acquisition unit 11, a first generation unit 12, a second generation unit 13, a third generation unit 14, a first obtaining unit 15, a fourth generation unit 16, a fifth generation unit 17, an electronic device 300, a memory 301, a processor 302, a communication interface 303 and a bus architecture 304.
Detailed Description
The embodiment of the application provides a monitoring management system for remote control equipment, and solves the technical problems that in the prior art, due to the fact that the process of manual decision making has great instability, monitoring management stability is poor and the automation degree is low. The evaluation index value suitable for the current scene is matched by using an intelligent model, and the matching of the control parameters is realized by an optimization algorithm according to the objective function and the evaluation index value, so that the method is suitable for a real-time state, has high accuracy, and achieves the technical effects of reducing manual dependence and improving the stability of remote monitoring management.
Summary of the application
The mode commonly used in remote control monitoring management is to utilize monitoring software to acquire the real-time state of the controlled equipment and then to screen the control parameters by depending on a manual setting rule, although a scheme for performing remote monitoring control by automatically matching the control parameters also exists, due to the difficulty in fitting complex unstructured scene data, the technical problems of low accuracy and automation degree and poor stability exist in the prior art.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides a monitoring management system for remote control equipment, solves the technical problems of poor monitoring management stability and low automation degree caused by large instability of a manual decision process in the prior art, and determines a control object and represents application object information processed by the control object by monitoring scene information; evaluating an index set according to the matching state of the control object; extracting and evaluating control parameter information corresponding to the index sets one by one aiming at the control objects; generating an experience objective function set representing the incidence relation between the control parameters and the evaluation indexes which are in one-to-one correspondence; extracting real-time state information of the application object information, and matching and evaluating an index value by using an intelligent model according to the real-time state; the control parameter set is optimized based on the matching evaluation index value and the objective function set, the technical scheme that the control object is controlled by the control parameter optimization result suitable for the current scene is obtained, the evaluation index value suitable for the current scene is matched by using an intelligent model, and then the matching of the control parameters is realized through an optimization algorithm according to the objective function and the evaluation index value.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, an embodiment of the present application provides a monitoring management method for a remote control device, where the method applies a monitoring management system for a remote control device, the system is in communication connection with a first control object, and the method includes:
s100: acquiring first monitoring scene information, wherein the first monitoring scene information comprises first control object information and first application object information;
specifically, the first monitoring scenario information refers to real-time scenario information for monitoring the controlled device, including but not limited to: the system comprises the controlled equipment, the controlled equipment and the controlled equipment, wherein the controlled equipment comprises the controlled equipment quantity information, the controlled equipment working parameter information, the controlled equipment real-time working state information, the controlled equipment processing product real-time state information and the like. Monitoring means include, but are not limited to: the control terminal is in wired or wireless connection with the controlled equipment and receives the working parameter information of the controlled equipment; the monitoring is carried out by the modes of collecting real-time dynamic information and the like during work through the image collecting device.
Further, the first control object information refers to monitoring scene information directly related to the controlled device; the first application object information refers to monitoring scene information related to a controlled equipment processing product. Optionally, in any group of data of the first control object information and the first application object information, the collected information sets are stored according to a time sequence in the longitudinal direction, so that the information is sequentially processed according to the time sequence, and the collected information sets are stored in groups according to information types in the transverse direction, exemplarily: at the same time, the working parameters of each element during the processing of the controlled equipment are divided into different types of data to be stored in groups according to different elements.
The working state of the controlled equipment can be evaluated by collecting the first monitoring scene information, so that a reference datum is provided for adjusting the working parameters, and the accuracy of automatic adjustment of the working parameters is ensured by collecting the comprehensive monitoring scene information.
S200: carrying out evaluation index matching on the first control object information to generate a first evaluation index set, wherein the evaluation index represents a control object state;
specifically, the first evaluation index set refers to an index dimension representing the state information of the control object, and the factory does not set any limit to the processing of the processed product: the number of processing equipment, the time required for processing products, the energy loss in the processing process, the load factor of the current processing equipment and other dimensional index sets. Further, the determination manner of the first evaluation index set is not limited to: preferably, performance evaluation index information corresponding to a plurality of remote control devices in different fields is acquired based on big data, and the performance evaluation index information is stored as: the equipment type-performance evaluation index set database can be used for adjusting the performance evaluation index set through a user-defined panel, and the database is periodically updated based on big data, so that the timeliness of index information acquisition is guaranteed. When the performance evaluation index set needs to be matched, the first control object information is input into the device type-performance evaluation index set database, and the performance evaluation index set corresponding to the corresponding device can be retrieved, wherein the input mode includes but is not limited to voice, typing, copy and paste and other input modes which can be replaced conventionally.
The state of the control object is represented by the evaluation index, namely the index dimension for evaluating the performance of the control object, and the data island in the traditional remote control is broken through by fitting multi-party data based on big data, so that the comprehensiveness of the matched evaluation index is improved, and the accurate evaluation of the performance of the control object is further ensured.
S300: extracting control parameters of the first control object information to generate a first control parameter set;
specifically, the first control object refers to a controlled device; the first control parameter set refers to a real-time operation parameter set for controlling the controlled device to operate, and is still an example of a remote control factory product process without limitation: the feeding speed, the circulation sequence of the products among controlled devices, the operation geometric parameters of all the parts, the operation time sequence parameters of all the parts and other control parameter sets. The method comprises the steps of collecting a first control parameter set corresponding to a current first control object, providing subsequent parameter adjustment reference data, and simultaneously evaluating whether a current parameter is an optimal control parameter set, wherein the collection dimension of the control parameter is preferably a parameter collection dimension set by using the same type of equipment of the first control object in multiple ways according to big data statistics, so that the integrity and comprehensiveness of the collected data are ensured.
Based on the above content, the preferred extraction mode of the first control parameter set is to determine the control parameter acquisition dimension of the first control object through big data, and then perform real-time control parameter acquisition on the first control object according to the acquisition dimension, and upload the acquired data to the remote control terminal for processing, and the processed data at each time is stored as historical data, so as to facilitate the follow-up tracing.
S400: traversing the first evaluation index set according to the first control parameter set to generate a first objective function set;
specifically, the first objective function set refers to an empirical functional relationship that characterizes a data relationship between the first evaluation index set and the first control parameter set, that is, a result determined by the common influence of a plurality of control parameters by different evaluation indexes, and a preferred determination method is, by way of example and not limitation: grey correlation analysis was used based on historical data: taking the evaluation index value as a reference sequence, taking the control parameter set as a comparison sequence, changing the value of one control parameter as a variable once based on the idea of a comparison experiment, further analyzing the association degree of different control parameters to the evaluation index value, and performing weight distribution on different control parameters, namely the ratio of the association degree corresponding to the control parameter to the sum of the association degrees of all types of control parameters corresponding to the current evaluation index; further, a correlation function is constructed based on the historical data:
y m ＝f(w 1 x 1 ，w 2 x 2 ，w 3 x 3 ...w l x l ) Where, and, xl denotes the l-th control parameter, w l Representing the corresponding weight, y m Represents the m-th evaluation index, satisfies f (w) 1 x 1 ，w 2 x 2 ，w 3 x 3 ...w l x l ) There may be more than one solution for the control parameter, which means when y is m At a certain time, there may be multiple groups of combined values of the control parameters, which together form a solution set, that is, one evaluation index value corresponds to multiple groups of solution sets, and preferably, the two solution sets are stored in one-to-one correspondence, and the later calling is waited.
Furthermore, the intersection of the control parameters among the multiple evaluation index dimensions is recorded as a coupled control parameter, and the non-intersection is recorded as an independent control parameter. The incidence relation between the evaluation index dimension and the control parameter can be represented through the first objective function set, and then the selection of the unstructured equipment state information is converted into the adjustment of the equipment control parameter, so that the computer processing is facilitated, and the falling possibility of the automatic control is improved.
S500: extracting the real-time state of the first application object information to obtain first real-time state information;
specifically, the first real-time status information refers to real-time status information of the current processing object, including but not limited to: the number information of different process nodes waiting for processing, the number information of different process nodes in processing, the processed number information, the processing energy consumption requirement information, the processing cost information, the processing time limit information and the like can be set by a worker in a user-defined mode, and the acquisition mode can be an unlimited mode of acquiring real-time product processing information by using an image acquisition device and uploading the product processing state information by a machine. Wherein, the first application object is a processed product, and the product types are different under different scenes: in the group control elevator, the first application object is a crowd; in remote meter reading, a first application object is meter reading data; in a factory, the first application object is a processed product. By collecting the real-time state of the first application object, an information feedback basis is provided for the selection of the optimal equipment performance index in the subsequent determination of the current state.
S600: inputting the first real-time state information and the first evaluation index set into a first screening model to generate evaluation index value screening result information;
further, based on the inputting the first real-time status information and the first evaluation index set into a first screening model, generating evaluation index value screening result information, where step S600 includes:
s610: extracting an index value of the first control object according to a first evaluation index set to generate a first evaluation index value set;
s620: inputting the first real-time state information and the first evaluation index value set into a first matching network layer to obtain a first matching result;
s630: when the first matching result comprises unmatched information, activating a first screening network layer, inputting the first real-time state information and the first evaluation index set, and generating evaluation index value screening result information;
s640: when the first matching result includes matching information, setting the first evaluation index value set as the evaluation index value screening result information.
Specifically, the first screening model is an intelligent model which is constructed based on a deep neural network and used for evaluating first real-time state information and a first evaluation index set and further screening specific values of the evaluation index set suitable for the current first real-time state information, the neural network model is an intelligent model commonly used for fitting nonlinear data, the neural network constructed based on the simulation of the human brain has learning capacity and memory capacity, and after training, new input data can be trained. The first screening model comprises a first matching network layer and a first screening network layer, wherein the first matching network layer and the first screening network layer are independent sub-neural networks, and the first matching network layer and the first screening network layer are integrated and combined to generate the first screening model after independent training.
The first matching network layer is used for evaluating a neural network model of a matching relation between the first evaluation index value set and the first real-time state information. The first evaluation index value set refers to a specific value of the first evaluation index set of the current first control object; the first matching result refers to a matching result of evaluating the first evaluation index value set and the first real-time status information, and a matching relationship between the two, exemplarily: determining the processing efficiency according to the product processing time limit in the first real-time state information; and regulating and controlling the processing energy consumption and the selection of equipment according to the cost information of the processed product. The process is a complex and nonlinear processing process, so that a model can be constructed based on neural network training to generate a first matching result which can accurately represent matching information between the first real-time state information and the first evaluation index value set, and each evaluation index value set corresponds to one of the matching information or the mismatching information.
The first screening network layer is used for screening the neural network model of the specific value of the first evaluation index set which is more suitable for the first real-time state information according to the first real-time state information and the first evaluation index set. The evaluation index value screening result information refers to a result obtained by activating a first screening network layer and inputting the first real-time state information and the first evaluation index set into the first screening network layer for matching when the first matching result comprises unmatched information; when the first matching result includes the matching information, the first evaluation index value set is set as the evaluation index value screening result information, and the first screening network layer does not need to be activated.
Therefore, the first matching network layer is a processing layer which needs to be activated for each data processing, and the first screening network layer is activated according to the processing result of the first matching network layer, so that the intelligent degree of remote control monitoring management is improved.
S700: optimizing the first control parameter set according to the first objective function set and the evaluation index value screening result information to generate an optimization control parameter set, and remotely controlling the first control object based on the optimization control parameter set.
Further, as shown in fig. 2, optimizing the first control parameter set based on the first objective function set and the evaluation index value screening result information to generate an optimized control parameter set, where step S700 includes:
s710: generating a first optimization space according to the first objective function set, wherein the dimension of the first optimization space is the same as that of the first objective function set;
s720: obtaining a first speed constraint parameter and a first position constraint parameter according to the first optimization space;
s730: inputting the information of the evaluation index value screening result into the first optimization space for initialization, and generating a first optimization instruction after the initialization is completed;
s740: receiving the first optimization instruction, inputting the first control parameter set into the first optimization space, and performing optimization based on the first speed constraint parameter and the first position constraint parameter to generate the optimized control parameter set.
Specifically, the optimization control parameter set refers to a result determined after optimization of the first control parameter set is performed according to the first objective function set and the evaluation index value screening result information after evaluation index value screening result information applicable to the first real-time state information is determined, and further, the first control object is remotely controlled according to the optimization control parameter set to ensure that the processing process of the first control object meets the evaluation index value screening result information.
The optimization process is preferably as follows:
generating a first optimization space: generating a general frame of an optimization space based on the first objective function set, namely determining a selection range determining frame of an optimization control parameter set according to the dimension number of the first objective function set, and determining the dimension number of the first optimization space; the first speed constraint parameter refers to a change step length when the characteristic control parameter is optimized and updated, namely a numerical value change amplitude; the first position constraint parameter refers to a parameter for representing a control parameter selection range, namely a selection range determination frame of an optimization control parameter set determined based on a first objective function set; the dimension corresponding to the correlation function with the coupling control parameter has a spatial position intersection at the coupling control parameter, the dimension corresponding to the correlation function with the independent control parameter has different spatial positions at the independent control parameter, and the different spatial positions correspond to different control parameter values.
Initializing the first optimization space, inputting the information of the screening result of the evaluation index value into a first target function set, namely, the information can be input into a target function of a corresponding dimension to obtain a solution set corresponding to each dimension, namely, a first position constraint parameter is determined, and then, a first speed constraint parameter is set by a user, namely, a single traversal step length can be determined, namely, the initialization process of the first optimization space is completed.
Starting optimization: and inputting the first control parameter set as an optimization initial position into a first optimization space after initialization is completed, selecting traversal points in the first optimization space based on the first position constraint parameter and the first speed constraint parameter, gradually evaluating the matching degree and the acceptance degree of each traversal point, and adding the data which do not meet the requirements into a culled data set. And setting the global optimal values of the matching degree and the acceptance degree as an optimization control parameter set until the global optimal values of the matching degree and the acceptance degree are finally obtained.
And performing global traversal on the solution set through an optimization algorithm to obtain a control parameter set which is more suitable for the current real-time state.
Further, the method step S600 includes S650:
s651: obtaining first historical data, wherein the first historical data comprises a plurality of groups: real-time state information, evaluation index value set and matching result identification information;
s652: according to the multiple groups: real-time state information, evaluation index value set and matching result identification information, and constructing the first matching network layer;
s653: obtaining second historical data, wherein the second historical data comprises a plurality of groups: real-time state information, an evaluation index set and evaluation index value identification information;
s654: according to the multiple groups: real-time state information, an evaluation index set and evaluation index value identification information are used for constructing the first screening network layer;
s655: and merging the first matching network layer and the first screening network layer to generate the first screening model.
Specifically, the first screening model is constructed as follows:
generating a first matching network layer: collecting first historical data: comprises a plurality of groups: the method comprises the steps of obtaining real-time state information, an evaluation index value set and matching result identification information, using the real-time state information and the evaluation index value set as input training data, using the matching result identification information as output identification information, carrying out supervised learning based on a neural network, constructing a first matching network layer, and stopping training when a model reaches convergence.
Further, generating a first screening network layer: collecting second historical data: comprises a plurality of groups: the method comprises the steps of obtaining real-time state information, an evaluation index set and evaluation index value identification information, using the real-time state information and the evaluation index set as input training data, using the evaluation index value identification information as output identification information, carrying out supervised learning based on a neural network, constructing a first screening network layer, and stopping training when a model reaches convergence.
Further, the input end of the first matching network layer is merged with the input end of the first screening model; combining the output end of the first matching network layer with the output end of the first screening model, and fully connecting the output end of the first matching network layer with the input end of the first screening network layer; and combining the input end of the first screening network layer with the input end of the first screening model, and combining the output end of the first screening network layer with the output end of the first screening model to generate the first screening model.
When the first real-time state information and the first evaluation index set are input into the first screening model, only the first matching network layer is activated to carry out matching relation evaluation on the first real-time state information and the first evaluation index set: when the first screening network layer receives the matching information output by the first matching network layer, rejecting the response and directly synchronizing the first evaluation index value set to the output end of the first screening model; when the first screening network layer receives the unmatched information output by the first matching network layer, responding, stopping the first matching network layer, activating the first screening network layer, receiving the first real-time state information and the first evaluation index set from the input end of the first screening model for evaluation, and synchronizing the output result to the output end of the first screening model.
Through a neural network model simulating human brain thinking and fitting experience knowledge of big data, the first real-time state information and the first evaluation index set can be evaluated more accurately, and more comprehensive and accurate evaluation index value screening result information is generated.
Further, the step S730 of inputting the information of the result of screening the evaluation index value into the first optimization space for initialization includes:
s731: inputting the information of the screening result of the evaluation index value into the first objective function set to generate a control parameter particle swarm;
s732: obtaining a first matching degree evaluation formula and a first receptivity evaluation formula;
s734: initializing the first optimization space according to the control parameter particle swarm, the first matching degree evaluation formula and the first acceptance degree evaluation formula.
Specifically, the initialization procedure is as follows:
the control parameter particle swarm is that after evaluation index value screening result information is input into a first optimization space, generated dimensional control parameter value solution sets are sequentially arranged along the dimensional space direction of each first objective function set on the forward influence relation of the evaluation index, the forward influence relation refers to the change direction of the control parameter along with the increase of the evaluation index and generates a virtual coordinate value, and the numerical values of the same control parameter at the same position are the same but can be a plurality of dimensional intersection points.
Constructing a first matching degree evaluation formula and a first acceptability evaluation formula, wherein the first matching degree evaluation formula is a formula for evaluating the matching degree of each group of control parameter set and evaluation index value screening result information, and can be constructed based on experience, and the specific form is not limited; the first acceptability assessment formula is an assessment formula for updating the positions of the control parameter sets according to the matching degree of different positions.
Determining a particle selection range through the control parameter particle swarm, realizing the setting of a traversal rule through a first matching degree evaluation formula and a first receptivity evaluation formula, determining and updating an iteration step length through the position parameters, and further completing the initialization of a first optimization space.
Further, the method step S732 includes:
s7321: the first matching degree evaluation formula is as follows:
wherein,
representing a selected frequency of the nth set of control parameters in historical control data for the plant in which the first control object is located,
representing the selected frequency of the nth control parameter set in the historical control data of the manufacturer of the same type of equipment of the first control object, and representing the alpha and beta characteristics
And
specific gravity;
s7322: the first acceptability assessment formula is:
wherein,
the acceptability of the parameter set is controlled for the (n + 1) th.
Still further, the method step S7322 includes:
s73221: when the first acceptance is 1, adding the nth control parameter set into a obsolete data set, and continuing optimization based on the (n + 1) th control parameter set;
s73222: and when the first acceptance is 0, adding the (n + 1) th control parameter set into the eliminated data set, and continuing optimization based on the nth control parameter set.
Specifically, the first matching degree evaluation formula may be selected as
Wherein,
representing a selected frequency of the nth set of control parameters in historical control data for the plant in which the first control object is located,
representing the selected frequency of the nth control parameter set in the historical control data of the manufacturer of the same type of equipment of the first control object, and representing the alpha and beta characteristics
And
the specific gravity of the mixture is higher than that of the mixture,
and characterizing the matching degree.
The first acceptability assessment formula is optionally:
wherein,
the acceptability of the parameter set is controlled for the (n + 1) th.
When the first acceptance is 1, adding the nth control parameter set into a obsolete data set, and continuing optimization based on the (n + 1) th control parameter set; and when the first acceptance is 0, adding the (n + 1) th control parameter set into the eliminated data set, and continuing optimization based on the nth control parameter set. And skipping to avoid repeated traversal when the eliminated data group is updated to the same data set. The selected control parameter sets can be guaranteed to be the control parameter set with the highest matching degree through the first acceptance, so that automatic monitoring data processing of monitoring management of the remote control equipment is realized, and the system stability is improved.
To sum up, the monitoring management system for the remote control device provided by the embodiment of the application has the following technical effects:
1. the control object and the application object information processed by the representation control object are determined by monitoring the scene information; evaluating an index set according to the matching state of the control object; extracting and evaluating control parameter information corresponding to the index sets one by one aiming at the control objects; generating an experience objective function set representing the incidence relation between the control parameters and the evaluation indexes which are in one-to-one correspondence; extracting real-time state information of the application object information, and matching and evaluating an index value by using an intelligent model according to the real-time state; the control parameter set is optimized based on the matching evaluation index value and the objective function set, the technical scheme that the control object is controlled by the control parameter optimization result suitable for the current scene is obtained, the evaluation index value suitable for the current scene is matched by using an intelligent model, and then the matching of the control parameters is realized through an optimization algorithm according to the objective function and the evaluation index value.
Example two
Based on the same inventive concept as the monitoring management method for the remote control device in the foregoing embodiment, as shown in fig. 3, an embodiment of the present application provides a monitoring management system for the remote control device, where the system is in communication connection with a first control object, and includes:
the system comprises a first acquisition unit 11, a second acquisition unit, a first monitoring unit and a second monitoring unit, wherein the first acquisition unit is used for acquiring first monitoring scene information, and the first monitoring scene information comprises first control object information and first application object information;
a first generating unit 12, configured to perform evaluation index matching on the first control object information, and generate a first evaluation index set, where the evaluation index represents a control object state;
a second generating unit 13, configured to perform control parameter extraction on the first control object information, and generate a first control parameter set;
a third generating unit 14, configured to traverse the first evaluation index set according to the first control parameter set, and generate a first objective function set;
a first obtaining unit 15, configured to perform real-time state extraction on the first application object information to obtain first real-time state information;
a fourth generating unit 16, configured to input the first real-time status information and the first evaluation index set into a first screening model, and generate evaluation index value screening result information;
a fifth generating unit 17, configured to optimize the first control parameter set according to the first objective function set and the evaluation index value screening result information, generate an optimized control parameter set, and perform remote control on the first control object based on the optimized control parameter set.
Further, the system further comprises:
a sixth generating unit, configured to perform index value extraction on the first control object according to the first evaluation index set, and generate a first evaluation index value set;
a second obtaining unit, configured to input the first real-time status information and the first evaluation index value set into a first matching network layer, so as to obtain a first matching result;
a seventh generating unit, configured to activate a first screening network layer when the first matching result includes mismatch information, input the first real-time status information and the first evaluation index set, and generate the evaluation index value screening result information;
a first processing unit configured to set the first evaluation index value set as the evaluation index value screening result information when the first matching result includes matching information.
Further, the system further comprises:
a third obtaining unit, configured to obtain first history data, where the first history data includes multiple sets: real-time state information, evaluation index value set and matching result identification information;
a first building unit to, according to the plurality of sets: real-time state information, evaluation index value set and matching result identification information, and constructing the first matching network layer;
a fourth obtaining unit, configured to obtain second history data, where the second history data includes multiple sets: real-time state information, an evaluation index set and evaluation index value identification information;
a second building unit for, according to the plurality of sets: real-time state information, an evaluation index set and evaluation index value identification information are used for constructing the first screening network layer;
an eighth generating unit, configured to combine the first matching network layer and the first screening network layer, and generate the first screening model.
Further, the system further comprises:
a ninth generating unit, configured to generate a first optimization space according to the first objective function set, where a dimension of the first optimization space is the same as a dimension of the first objective function set;
a fifth obtaining unit, configured to obtain a first speed constraint parameter and a first position constraint parameter according to the first optimization space;
a tenth generating unit, configured to input the evaluation index value screening result information into the first optimization space for initialization, and generate a first optimization instruction after initialization is completed;
an eleventh generating unit, configured to receive the first optimization instruction, input the first control parameter set into the first optimization space, perform optimization based on the first speed constraint parameter and the first position constraint parameter, and generate the optimized control parameter set.
Further, the system further comprises:
a twelfth generating unit, configured to input the evaluation index value screening result information into the first objective function set, and generate a control parameter particle swarm;
a sixth obtaining unit, configured to obtain a first matching degree evaluation formula and a first receptivity evaluation formula;
and the second processing unit is used for initializing the first optimization space according to the control parameter particle swarm, the first matching degree evaluation formula and the first acceptance degree evaluation formula.
Further, the system further comprises:
a first setting unit, configured to set the first matching degree evaluation formula as:
wherein,
showing the history of the nth control parameter set in the manufacturer of the first control objectThe selected frequency in the control data is,
representing selected frequency of the nth control parameter set in historical control data of manufacturers of the same type of equipment of the first control object, and representing alpha and beta
And
specific gravity;
a second setting unit configured to set the first receptivity evaluation formula as:
wherein,
the acceptability of the parameter set is controlled for the (n + 1) th.
EXAMPLE III
Based on the same inventive concept as the monitoring management method for the remote control device in the foregoing embodiments, the present application also provides a computer-readable storage medium having a computer program stored thereon, where the computer program is executed by a processor to implement the method in any one of the embodiments.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to fig. 4.
Based on the same inventive concept as the monitoring management method for the remote control device in the foregoing embodiment, an embodiment of the present application further provides an electronic device, including: a processor coupled to a memory, the memory for storing a program that, when executed by the processor, causes a system to perform the method of any of the first aspects.
The electronic device 300 includes: processor 302, communication interface 303, memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. Wherein, the communication interface 303, the processor 302 and the memory 301 may be connected to each other through a bus architecture 304; the bus architecture 304 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 4, but this does not indicate only one bus or one type of bus.
The communication interface 303 is a system using any transceiver or the like, and is used for communicating with other devices or communication networks, such as ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), wired access network, and the like.
The memory 301 may be, but is not limited to, a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, an electrically erasable Programmable read-only memory (EEPROM), a compact-read-only-memory (CD-ROM) or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor through a bus architecture 304. The memory may also be integral to the processor.
The memory 301 is used for storing computer-executable instructions for executing the present application, and is controlled by the processor 302 to execute. The processor 302 is configured to execute computer-executable instructions stored in the memory 301, so as to implement a monitoring management method for a remote control device provided by the above-mentioned embodiment of the present application.
Optionally, the computer-executable instructions in the embodiments of the present application may also be referred to as application program codes, which are not specifically limited in the embodiments of the present application.
The embodiment of the application provides a monitoring management system for remote control equipment, which adopts application object information processed by determining a control object and representing the control object through monitoring scene information; evaluating an index set according to the matching state of the control object; extracting and evaluating control parameter information corresponding to the index sets one by one aiming at the control objects; generating an experience objective function set for representing the incidence relation between the control parameters and the evaluation indexes which are in one-to-one correspondence; extracting real-time state information of the application object information, and matching and evaluating an index value by using an intelligent model according to the real-time state; the control parameter set is optimized based on the matching evaluation index value and the objective function set, the technical scheme that the control object is controlled by the control parameter optimization result suitable for the current scene is obtained, the evaluation index value suitable for the current scene is matched by using an intelligent model, and then the matching of the control parameters is realized through an optimization algorithm according to the objective function and the evaluation index value.
Those of ordinary skill in the art will understand that: various numbers of the first, second, etc. mentioned in this application are only for convenience of description and distinction, and are not used to limit the scope of the embodiments of this application, nor to indicate a sequence order. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one" means one or more. At least two means two or more. "at least one," "any," or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one (one ) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.
In the above embodiments, all or part of the implementation may be realized by software, hardware, firmware, or any combination thereof. When implemented in software, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable system. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The various illustrative logical units and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic system, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing systems, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in the embodiments herein may be embodied directly in hardware, in a software element executed by a processor, or in a combination of the two. The software cells may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be disposed in a terminal. In the alternative, the processor and the storage medium may reside in different components within the terminal. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the present application has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations can be made thereto without departing from the spirit and scope of the application. Accordingly, the specification and figures are merely exemplary of the application as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and its equivalent technology, it is intended that the present application include such modifications and variations.
Claims (10)
1. A monitoring and management system for a remote control device, the system communicatively coupled to a first control object, comprising:
the system comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring first monitoring scene information, and the first monitoring scene information comprises first control object information and first application object information;
a first generation unit, configured to perform evaluation index matching on the first control object information, and generate a first evaluation index set, where the evaluation index represents a control object state;
a second generation unit, configured to perform control parameter extraction on the first control object information, and generate a first control parameter set;
a third generating unit, configured to traverse the first evaluation index set according to the first control parameter set, and generate a first objective function set;
the first obtaining unit is used for extracting the real-time state of the first application object information to obtain first real-time state information;
a fourth generating unit, configured to input the first real-time status information and the first evaluation index set into a first screening model, and generate evaluation index value screening result information;
and a fifth generating unit, configured to optimize the first control parameter set according to the first objective function set and the evaluation index value screening result information, generate an optimized control parameter set, and perform remote control on the first control object based on the optimized control parameter set.
2. The system of claim 1, wherein the system further comprises:
a sixth generating unit, configured to perform index value extraction on the first control object according to the first evaluation index set, and generate a first evaluation index value set;
a second obtaining unit, configured to input the first real-time status information and the first evaluation index value set into a first matching network layer, so as to obtain a first matching result;
a seventh generating unit, configured to activate a first screening network layer when the first matching result includes mismatch information, input the first real-time status information and the first evaluation index set, and generate the evaluation index value screening result information;
a first processing unit configured to set the first evaluation index value set as the evaluation index value screening result information when the first matching result includes matching information.
3. The system of claim 2, wherein the system further comprises:
a third obtaining unit, configured to obtain first history data, where the first history data includes multiple sets: real-time state information, evaluation index value set and matching result identification information;
a first building unit to, according to the plurality of sets: real-time state information, evaluation index value set and matching result identification information, and constructing the first matching network layer;
a fourth obtaining unit, configured to obtain second history data, where the second history data includes multiple sets: real-time state information, an evaluation index set and evaluation index value identification information;
a second building unit for, according to the plurality of sets: real-time state information, an evaluation index set and evaluation index value identification information are used for constructing the first screening network layer;
an eighth generating unit, configured to combine the first matching network layer and the first screening network layer, and generate the first screening model.
4. The system of claim 1, wherein the system further comprises:
a ninth generating unit, configured to generate a first optimization space according to the first objective function set, where a dimension of the first optimization space is the same as a dimension of the first objective function set;
a fifth obtaining unit, configured to obtain a first speed constraint parameter and a first position constraint parameter according to the first optimization space;
a tenth generating unit, configured to input the evaluation index value screening result information into the first optimization space for initialization, and generate a first optimization instruction after initialization is completed;
an eleventh generating unit, configured to receive the first optimization instruction, input the first control parameter set into the first optimization space, perform optimization based on the first speed constraint parameter and the first position constraint parameter, and generate the optimized control parameter set.
5. The system of claim 4, wherein the system further comprises:
a twelfth generating unit, configured to input the evaluation index value screening result information into the first objective function set, and generate a control parameter particle swarm;
a sixth obtaining unit, configured to obtain a first matching degree evaluation formula and a first receptivity evaluation formula;
and the second processing unit is used for initializing the first optimization space according to the control parameter particle swarm, the first matching degree evaluation formula and the first acceptance degree evaluation formula.
6. The system of claim 5, wherein the system further comprises:
a first setting unit, configured to set the first matching degree evaluation formula as:
wherein,
indicating that the nth control parameter set is in the first controlThe selected frequency in the historical control data for the vendor where the object is located,
representing the selected frequency of the nth control parameter set in the historical control data of the manufacturer of the same type of equipment of the first control object, wherein alpha and beta are characteristics
And
a parameter of specific gravity;
a second setting unit, configured to set the first acceptability assessment formula as:
wherein,
the acceptability of the parameter set is controlled for the (n + 1) th.
7. The system of claim 6, wherein the system further comprises:
a third processing unit, configured to add the nth control parameter set into an obsolete data set when the first acceptability is 1, and continue optimization based on the (n + 1) th control parameter set;
and a fourth processing unit, configured to add the n +1 th control parameter set to the obsolete data set when the first acceptability is 0, and continue optimization based on the n-th control parameter set.
8. A monitoring management method for a remote control apparatus, characterized in that the method applies a monitoring management system for a remote control apparatus, the system being communicatively connected to a first control object, the method comprising:
acquiring first monitoring scene information, wherein the first monitoring scene information comprises first control object information and first application object information;
carrying out evaluation index matching on the first control object information to generate a first evaluation index set, wherein the evaluation index represents a control object state;
extracting control parameters of the first control object information to generate a first control parameter set;
traversing the first evaluation index set according to the first control parameter set to generate a first objective function set;
extracting the real-time state of the first application object information to obtain first real-time state information;
inputting the first real-time state information and the first evaluation index set into a first screening model to generate evaluation index value screening result information;
optimizing the first control parameter set according to the first objective function set and the evaluation index value screening result information to generate an optimization control parameter set, and remotely controlling the first control object based on the optimization control parameter set.
9. An electronic device, comprising: a processor coupled to a memory for storing a program, wherein the program, when executed by the processor, causes a system to perform the system of any of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, implements the system according to any one of claims 1 to 7.
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210689652.5A CN115016353A (en) | 2022-06-17 | 2022-06-17 | Monitoring management system for remote control equipment |
| PCT/CN2022/110397 WO2023240769A1 (en) | 2022-06-17 | 2022-08-05 | Monitoring management system for remote control device |
| JP2022576569A JP2024524784A (en) | 2022-06-17 | 2022-08-05 | Supervisory management system for remote control devices |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210689652.5A CN115016353A (en) | 2022-06-17 | 2022-06-17 | Monitoring management system for remote control equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115016353A true CN115016353A (en) | 2022-09-06 |
Family
ID=83074345
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210689652.5A Pending CN115016353A (en) | 2022-06-17 | 2022-06-17 | Monitoring management system for remote control equipment |
Country Status (3)
| Country | Link |
|---|---|
| JP (1) | JP2024524784A (en) |
| CN (1) | CN115016353A (en) |
| WO (1) | WO2023240769A1 (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115327930A (en) * | 2022-10-17 | 2022-11-11 | 青岛艾德森物联科技有限公司 | Visual energy-saving management and control method and system |
| CN117330507A (en) * | 2023-10-12 | 2024-01-02 | 苏州星帆华镭光电科技有限公司 | Remote test control method for handheld laser instrument |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117724429B (en) * | 2023-12-29 | 2024-05-24 | 粤海永顺泰(宝应)麦芽有限公司 | Multimode analysis-based intelligent control method and system for barley soaking equipment |
| CN118368320B (en) * | 2024-06-19 | 2024-08-16 | 广东技术师范大学 | Remote automatic control method and system based on Internet |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP7044153B2 (en) * | 2018-03-30 | 2022-03-30 | 日本電気株式会社 | Evaluation system, evaluation method and evaluation program |
| WO2020160252A1 (en) * | 2019-01-30 | 2020-08-06 | Google Llc | Task-aware neural network architecture search |
| CN112944450B (en) * | 2021-02-03 | 2022-06-10 | 大唐吉林发电有限公司热力分公司 | Monitoring method and system for remotely and autonomously controlling heat exchange station equipment |
| CN113779496B (en) * | 2021-09-24 | 2022-04-26 | 广州健新科技有限责任公司 | Power equipment state evaluation method and system based on equipment panoramic data |
| CN114281044B (en) * | 2021-12-24 | 2023-06-06 | 工业云制造(四川)创新中心有限公司 | Industrial robot remote monitoring method and system based on cloud manufacturing |
-
2022
- 2022-06-17 CN CN202210689652.5A patent/CN115016353A/en active Pending
- 2022-08-05 JP JP2022576569A patent/JP2024524784A/en active Pending
- 2022-08-05 WO PCT/CN2022/110397 patent/WO2023240769A1/en unknown
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115327930A (en) * | 2022-10-17 | 2022-11-11 | 青岛艾德森物联科技有限公司 | Visual energy-saving management and control method and system |
| CN117330507A (en) * | 2023-10-12 | 2024-01-02 | 苏州星帆华镭光电科技有限公司 | Remote test control method for handheld laser instrument |
| CN117330507B (en) * | 2023-10-12 | 2024-04-05 | 苏州星帆华镭光电科技有限公司 | Remote test control method for handheld laser instrument |
Also Published As
| Publication number | Publication date |
|---|---|
| JP2024524784A (en) | 2024-07-09 |
| WO2023240769A1 (en) | 2023-12-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115016353A (en) | Monitoring management system for remote control equipment | |
| US11650968B2 (en) | Systems and methods for predictive early stopping in neural network training | |
| CN114037673B (en) | Hardware connection interface monitoring method and system based on machine vision | |
| CN113642938B (en) | Intelligent production management method and system | |
| CN111260255A (en) | Power distribution network operation parameter key factor analysis method, system and equipment | |
| CN115168027A (en) | Calculation power resource measurement method based on deep reinforcement learning | |
| CN109063818B (en) | Thermal process model online identification method and device | |
| CN113569457A (en) | Demand function model construction method and system based on digital twin | |
| CN113971089A (en) | Method and device for selecting equipment nodes of federal learning system | |
| CN112965903A (en) | Test method, test device, electronic equipment and computer readable storage medium | |
| CN117110748A (en) | Transformer substation main equipment operation state abnormality detection method based on fusion terminal | |
| CN114283023A (en) | Manufacturing management method and system based on cloud manufacturing support technology | |
| CN115392592A (en) | Storage product parameter configuration recommendation method, device, equipment and medium | |
| CN113703506B (en) | Building material production workshop environment control and regulation method and system | |
| CN116539994A (en) | Substation main equipment operation state detection method based on multi-source time sequence data | |
| CN110377769A (en) | Modeling Platform system, method, server and medium based on graph data structure | |
| CN114115150B (en) | Online modeling method and device for heat pump system based on data | |
| CN115412591A (en) | Video processing method and system under cloud edge fusion | |
| CN113591398A (en) | Intelligent operation batching method and device based on deep reinforcement learning and electronic equipment | |
| CN113822441A (en) | Decision model training method and device, terminal equipment and storage medium | |
| CN113705957A (en) | User cluster baseline load estimation method and device and terminal equipment | |
| CN115576205B (en) | Feedback control method, universal feedback controller, training method, readable storage medium, computer program product and system | |
| CN115482419B (en) | Data acquisition and analysis method and system for marine fishery products | |
| CN117474128B (en) | Distributed online machine learning model construction method based on multi-cloud server | |
| US20240177063A1 (en) | Information processing apparatus, information processing method, and non-transitory recording medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |