A monitoring and management system for remote control devices.

Abstract

A monitoring and management system for a remote control device includes a first control object and a second control object. The first collecting unit 11 and the first collecting unit 11 collect the first control object information and the first application object information. The first generation unit 12 collects first surveillance scene information including: , performing evaluation index matching on the first control object information to generate a first evaluation index set; The evaluation index characterizes the state of the control object, and the second generation unit 13 and the second generation unit 13 extracts control parameters from the first control object information, and obtains a first control parameter set. The third generation unit 14 generates the first control parameter set. Based on the above, the first evaluation index set is ergodicized to generate a first target function set, and a first acquisition The first acquisition unit 15 receives real-time information of the first application object information. The fourth generating unit 16 extracts the state of the first real-time state information, and the fourth generating unit 17 extracts the state of the second real-time state information. The unit 16 selects the first real-time state information and the first set of evaluation indicators as a first selection. The fifth generation unit 17 inputs the evaluation index value selection result information into the evaluation index selection model to generate the evaluation index value selection result information. The fifth generating unit 17 receives the first objective function set and the evaluation index value selecting result information. The first control parameter set is optimized based on the information to generate an optimal control parameter set. and remotely controlling the first control object based on the optimal control parameter set. The application object information processed by determining the control object and characterizing the control object using determining information on the control object matching state, evaluating the set of indicators based on the control object matching state, and providing instructions to the control object; Extract and evaluate the one-to-one corresponding control parameter information of the target set, and A set of empirical objective functions that characterize the relationship between the parameters and the evaluation indexes is generated, and the application target information is Extract real-time status information from the information and create intelligent models based on the real-time status The evaluation index values ​​are matched using the model, and a set of objective functions is generated based on the matching evaluation index values. Optimize the control parameter set by using the optimization algorithm to obtain the optimal control parameter result for the current scene. The control object is controlled by the intelligent model, and the evaluation index suitable for the current scene is used. The values ​​are matched and then controlled by an optimization algorithm based on the objective function and the evaluation index values. It realizes matching of control parameters, suitable for real-time conditions, and has high accuracy. This will achieve the technical effect of reducing dependency on humans and improving the stability of remote monitoring management.

Description

[Technical field]

[0001] The present invention relates to the field of artificial intelligence-related technology, and in particular to a monitoring and management system for remote control devices. . [Background technology]

[0002] Remote control is the ability to operate a device at a distant end by radio or electrical signals. When a control device located at a local node issues a control command, the control device at the remote node receives the control command. A process of performing a corresponding operation after receiving a remote control device. Monitoring the state of the chair is a key process to ensure control accuracy. Currently, monitoring and management in remote control mainly involves monitoring the status of the controlled equipment. It relies on technically evaluating the condition of the equipment and matching the corresponding parameters. This method relies on human decision making. In conventional technology, the process of human decision-making is highly unstable, which makes it difficult to ensure the security of monitoring and management. There were technical problems with low quality and low degree of automation. Summary of the Invention

[0003] An embodiment of the present invention provides a monitoring and management system for a remote control device. In conventional technology, the human decision-making process is highly unstable, and the monitoring and management Solved the technical problems of low stability and low degree of automation. In view of the above problems, the present embodiment provides a monitoring and management system for a remote control device. .

[0004] The monitoring and management system for remote control devices includes: The system is in communication with a first control object; A first collection unit 11; The first collecting unit 11 collects a first monitoring scene including first control object information and first application object information. Collect information, A first generation unit 12; The first generation unit 12 performs evaluation index matching on the first control target information, A set of evaluation indices is generated, the evaluation indices characterizing the state of the control object; A second generation unit 13; The second generation unit 13 extracts control parameters from the first control object information. 1. Generate a set of control parameters; A third generation unit 14; The third generation unit 14 generates the first evaluation index set based on the first control parameter set. to generate a first set of objective functions, A first acquisition unit 15; The first acquisition unit 15 performs real-time state extraction on the first application object information; First, obtain real-time status information; A fourth generation unit 16; The fourth generating unit 16 outputs the first real-time state information and the first set of evaluation indexes to the first cell. Input the result of the evaluation index selection into the selecting model to generate the evaluation index value selecting result information; A fifth generating unit 17; A fifth generating unit 17 receives the first objective function set and the evaluation index value selecting result information. optimizing the first control parameter set based on the first control parameter set to generate an optimal control parameter set; The first control object is remotely controlled based on the optimal control parameter set.

[0005] In addition, a remote control device used in a monitoring and management system of a remote control device that is connected to the first control object through communication The monitoring and management method for the control equipment is as follows: S100: A step of collecting first monitoring scene information including first control object information and first application object information. Tep and S200: Matching an evaluation index characterizing a control object state with the first control object information. to generate a first evaluation index set; S300: Extracting a control parameter from the first control object information, and obtaining the first control parameter generating a set; S400: Ergodicating the first evaluation index set based on the first control parameter set. , generating a first objective function set; S500: Extracting a real-time state of the first application object information; obtaining state information; S600: The first real-time status information and the first set of evaluation indexes are selected as a first selecting model. inputting the evaluation index value selecting result information into the database to generate the evaluation index value selecting result information; S700: Select the second objective function based on the first objective function set and the evaluation index value selecting result information. 1. Optimize a control parameter set to generate an optimized control parameter set; remotely controlling the first control object based on a parameter set; Includes.

[0006] Also included is a processor, the processor coupled to the memory, the memory being programmed When the program is executed by the processor, An electronic device capable of executing the above system.

[0007] Also, a computer-readable storage medium, the storage medium storing a computer program The computer program, when executed by the processor, A computer-readable storage medium implementing the above system.

[0008] One or more technical aspects provided in the embodiments of the present application include at least the following: has a significant technical effect or advantage. The control object is determined using the monitored scene information and processed by characterizing the control object. Determine the application object information to be applied, evaluate the index set based on the control object matching state, and control Extract and evaluate the control parameter information that corresponds one-to-one to the index set for the target, and Generate a set of empirical objective functions that characterize the relevant relationships between the corresponding control parameters and performance indicators Extract real-time status information of application target information and perform intelligent analysis based on the real-time status. The evaluation index values ​​are matched using the regent model, and the matching evaluation index values ​​and the target relationship are Optimize the control parameter set based on the number set and select the control parameter suitable for the current scene. Obtain the optimization results, control the control object, and use the intelligent model to apply it to the current scene. Matching suitable evaluation index values ​​and then optimizing the algorithm based on the objective function and the evaluation index values Realize the matching of control parameters by rhythm. Suitable for real-time conditions. Moreover, it has achieved the technical effect of being highly accurate, reducing dependency on humans and improving the stability of remote monitoring management. do.

[0009] The above description is only an outline of the technical solution of the present application, and can allow the technical means of the present application to be more clearly understood. The present invention relates to a method and apparatus for producing a computer-readable recording medium, comprising: In order to make the features and advantages of the present application more clearly comprehensible, the following specific embodiments of the present application are given below. List. [Brief description of the drawings]

[0010] [Figure 1] FIG. 2 is a flowchart of a method for monitoring and managing a remote control device according to an embodiment of the present invention. [Diagram 2] FIG. 4 is a flowchart illustrating a control parameter optimization method in a remote control device monitoring and management method according to an embodiment of the present invention. [Diagram 3] 1 is a configuration diagram of a monitoring and management system for remote control devices according to an embodiment of the present invention; [Figure 4] FIG. 1 is a block diagram of an exemplary electronic device according to an embodiment of the present application.

[0011] [Explanation of symbols] First collection unit 11 First Generation Unit 12 Second generation unit 13 Third Generation Unit 14 1st Acquisition Unit 15 4th Generating Unit 16 5th Generating Unit 17 Electronic Devices 300 Memory 301 Processor 302 Communication Interface 303 Bus Architecture 304 DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] The embodiment of the present invention provides a monitoring and management system for a remote control device. There is a high degree of instability in the decision-making process by the system, and the stability of monitoring and management is low, The technical problem of low degree of intelligence has been solved. Matching the evaluation index values ​​suitable for the scene, and then optimizing based on the objective function and the evaluation index values The control parameters are matched by the optimization algorithm. This technology is highly accurate, reduces human dependency, and improves the stability of remote monitoring and management. The results were achieved.

[0013] Overview of the Invention A commonly used method for remote control, monitoring and management is to use monitoring software to monitor the equipment to be controlled. Collect real-time status of the system and filter the control parameters based on manually set rules. The technical solution for remote monitoring and control by automatic matching control parameters is also However, fitting complex unstructured scene data is difficult, and conventional techniques There are technical problems such as low precision, low degree of automation, and low stability. In response to the above technical problems, the overall concept of the technical solution provided by this application is as follows: . An embodiment of the present invention provides a monitoring and management system for a remote control device. do. In conventional technology, the human decision-making process is highly unstable, and the monitoring and management Solved the technical problems of low stability and low automation. Determine the control object and the application object information to be processed by characterizing the control object. The index set is evaluated based on the control object matching state, and the index set is set for the control object. Extract and evaluate the one-to-one corresponding control parameter information of A set of empirical objective functions is generated that characterize the relationship between the data and the evaluation index, and the application target information is retrieved. Extract real-time status information and create intelligent models based on real-time status The evaluation index value is matched using the above method, and the control is performed based on the matching evaluation index value and the objective function set. Optimize the parameter set and obtain the control parameter optimization result suitable for the current scene. Control the object and use the intelligent model to model the evaluation index suitable for the current scene. The control parameters are then adjusted by an optimization algorithm based on the objective function and the evaluation index. It realizes meter matching, is suitable for real-time conditions, has high accuracy, and is human-friendly. This will achieve the technological effect of reducing dependency on remote monitoring and improving the stability of remote monitoring management. After introducing the basic principles of the present application, various non-limiting embodiments of the present application will be described with reference to the specification and drawings. Let me give you some specific examples.

[0014] Example 1 As shown in FIG. 1, the present invention is used in a monitoring and management system for a remote control facility that is connected to a first control object. The method for monitoring and managing a remote control device includes the following steps. S100: Collect first monitoring scene information including first control object information and first application object information. The first monitoring scene information is real-time scene information for monitoring the controlled equipment, Number of equipment, operating parameter information of controlled equipment, real-time operating status information of controlled equipment Real-time status information of the processed products of the controlled equipment, etc. The monitoring method is a method of monitoring the operation parameters of the controlled equipment by a wired or wireless connection between the control terminal and the controlled equipment. Receiving data information, monitoring by collecting real-time dynamic information during operation using image collection devices, etc. The methods include, but are not limited to, The first control object information is monitoring scene information directly related to the controlled equipment, and the first application object information is The information is monitoring scene information directly related to the processing product of the controlled equipment. Optionally, the first control Either one of the groups of target information and first application target information can be conveniently arranged in chronological order. The information set collected vertically is stored in chronological order for sequential processing, but The collected information groups are grouped according to the type of information and stored. At this time, the operating parameters of each element during processing of the controlled equipment are The data is then divided into different types and grouped for storage. The first monitoring scene information can be collected to evaluate the operating status of the controlled equipment; and It can provide a basis for implementing adjustments to operating parameters. By collecting this information, the accuracy of the automatic adjustment of operating parameters can be ensured. S200: Matching an evaluation index characterizing a control object state with the first control object information. to generate a first set of evaluation indicators. The first evaluation index set is an index dimension that characterizes the control object state information, and For example, in the case of product processing, there is no limit to the number of processing equipment, the time required for processing products, and the processing process. There are a number of indicators such as energy loss due to the use of electricity, the load coefficient of processing equipment at that time, etc. The first evaluation index set determination method is preferably, for example, Based on the data, performance evaluation index information corresponding to remote control devices in different fields is collected and Preparation type - Performance evaluation indexes are stored as a collection database. The performance evaluation index set can be adjusted through the To ensure reliability, the database can be periodically updated based on big data. However, the determination method is not limited to this. The first control object information is input into the equipment type-performance evaluation index collection database, and the corresponding setting The input method can be voice, key input, Including, but not limited to, copy and paste, and other commonly substituted units. stomach. The evaluation index characterizes the state of the controlled object, i.e., it is an index for evaluating the performance of the controlled object. Based on big data, multi-dimensional data fitting is used to improve the conventional remote control. Break down data silos in the data center, improve the comprehensiveness of matching evaluation indicators, and gain better control. This ensures accurate evaluation of the target's performance. S300: Extracting a control parameter from the first control object information, and obtaining the first control parameter Generate a set. The first control object is a controlled facility, and the first control parameter set is a parameter set that controls the operation of the controlled facility. It is a real-time set of operating parameters for controlling the product manufacturing process in a remote controlled factory. For example, in a manufacturing process, the material supply speed, the order of product transfer between controlled devices, the geometry of each part operation, The control parameters include the parameters, timing parameters for each part operation, etc. However, the present invention is not limited to these. At that time, a first set of control parameters corresponding to the first control object is collected. This provides reference data for adjusting post-process parameters and optimizes the parameters at the time. It is possible to evaluate whether the parameter set is a good one. Preferably, the first control target in the multiple use of big data statistics is set by statistically analyzing the same equipment. The parameter collection dimension is adjusted to ensure the completeness and comprehensiveness of data collection. Based on the above, the preferred extraction method of the first set of control parameters is to Therefore, the control parameter collection dimension of the first control object is determined, and the first control object is further determined based on the collection dimension. Collect control parameters for the control object in real time and upload them to the remote control terminal. The data processed each time is then downloaded and processed, and the data is stored as a historical data for easy future tracking. The data is stored as S400: Ergodicating the first evaluation index set based on the first control parameter set. , generate a first objective function set. The first objective function set characterizes a data relationship between the first evaluation index set and the first control parameter set. In other words, different evaluation indices are the joint functions of multiple control parameters. The results are determined by the impact. A preferred determination method is, for example, based on historical data. Using the gray relevance analysis method, the evaluation index value is taken as the reference sequence, and the control parameter set is taken as the contrast sequence. In accordance with the concept of controlled experiments, only the values ​​of the control parameters are changed at one time. By this, the relevance of different control parameters to the evaluation index values, The weight distribution for the data, i.e., the relevance of the control parameters to the corresponding evaluation index at that time Analyzing the ratio of the sum of all kinds of control parameter relevance, based on historical data Related Functions TIFF0007657397000001.tif949 is constructed, but TIFF0007657397000002.tif76 TIFF0007657397000003.tif106th control parameter, TIFF0007657397000004.tif1010 is the corresponding weight, TIFF0007657397000005.tif1012 TIFF0007657397000006.tif11The 10th evaluation index, meet TIFF0007657397000007.tif940 TIFF0007657397000008.tifThere may be multiple numerical solutions for the 106 control parameters, i.e. If TIFF0007657397000009.tif119 is constant, TIFF0007657397000010.tif106 control parameter combinations may have multiple groups, and can be combined to solve A set of evaluation indices corresponds to a set of solutions for multiple groups, and the two are in one-to-one correspondence. Examples of suitable methods include, but are not limited to, storing the call in a call queue and waiting for subsequent calls. The intersection of the control parameters between multiple evaluation index dimensions is called the joint control parameter, and the non-intersection are denoted as independent control parameters. The first objective function set is the relationship between the index dimension and the control parameters. The relationships can be characterized, so that the selection of unstructured equipment status information can be mediated by equipment control parameters. This allows the shift to computer-based adjustment, facilitating computer processing and enabling automation control. This is improving the capabilities of the system. S500: Extracting a real-time state of the first application object information; Get status information. The first real-time status information is the real-time status information of the processing object at that time, and Quantity information waiting for processing in a process node, progress of processing in different process nodes Processing quantity information, processed quantity information, processing energy consumption requirement information, processing cost information This includes, but is not limited to, information on the collection status, processing time limit information, etc. The capture method can be customized and set, and the capture method can be selected from the real-time capture device. Real-time collection of product processing information and uploading of product processing status information by machine The first application is a product to be processed, and the second application is a product to be processed. The product types are different for different scenes. For group control elevators, the primary application is crowds. In remote meter reading, the first application is the meter reading data, and in the factory, the first response is The application object is a processed product. The real-time status of the first application object is collected, and the Provides a basis for information feedback for selection of optimal equipment performance indices at given times . S600: The first real-time status information and the first set of evaluation indexes are selected as a first selecting model. The data is input to the data processor 100 to generate evaluation index value selection result information. The first real-time status information and the first set of evaluation indexes are used as a first selecting model. Step S600: The method includes the following steps: S610: Extracting index values ​​for the first control object based on the first evaluation index set; A set of evaluation index values ​​is generated. S620: The first real-time state information and the first set of evaluation index values ​​are matched with a first matching network. Input to the work layer and obtain the first matching result. S630: If the first matching result includes non-matching information, the first selector activating a network layer and inputting the first real-time state information and the first set of evaluation indexes; Then, the evaluation index value selecting result information is generated. S640: if the first matching result includes matching information, The result is regarded as the evaluation index value selecting result information. The first selecting model is constructed based on deep neural networks. The real-time state information and the first evaluation index set are evaluated, and the first real-time state information at that time is also evaluated. It is an intelligent model that selects specific values ​​of a set of evaluation indicators that adapt to information, and uses neural Network models are intelligent models that are often used for fitting nonlinear data. Neural networks built on the basis of mimicking the human brain have the potential to improve learning and memory capabilities. After training, the training process can be performed on newly input data. The selecting model consists of the first matching network layer and the first selecting network layer. The first matching network layer and the first selecting network layer are Both are independent sub-neural networks, and they are trained independently and then integrated. A first selecting model is generated by: The first matching network layer matches a first set of evaluation index values ​​with first real-time state information. This is a neural network model for evaluating matching network relationships. The first evaluation index value set is the specific value of the first evaluation index set of the first control object at that time, and the first match The matching result is a matching result of evaluating the first evaluation index value set and the first real-time state information. The matching relationship between the two is, for example, Determining the processing efficiency based on the product processing time limit, and processing based on the cost information of the processed product This includes controlling energy consumption and equipment selection. This process is complex and Considering it to be a nonlinear processing process, we trained a neural network to By constructing a model, a matching between the first real-time state information and the first set of evaluation index values ​​is established. A first matching result characterizing the matching information of each group can be generated. The evaluation index value set is matching information or non-matching information. The first selecting network layer receives first real-time state information and a first set of evaluation indicators. Based on the result, a specific value of the first evaluation index set to be applied to the first real-time state information is selected. The neural network model is a neural network model that performs the evaluation index value selection. If the first matching result contains non-matching information, the first selecting network Activate the network layer and transmit the first real-time state information and the first set of evaluation indicators to the first selecting network. This is the result obtained by inputting and matching into the network layer, and it matches the first matching result. If the evaluation index value selection information is included, the first evaluation index value set is used as the evaluation index value selection result information. In this case, there is no need to activate the first selecting network layer. In other words, the first matching network layer is activated for each data processing. The first selecting network layer is the first matching network layer. It is activated according to the processing results of each layer, thereby improving the intelligence of remote control, monitoring and management. . S700: Select the second objective function based on the first objective function set and the evaluation index value selecting result information. 1. Optimize a control parameter set to generate an optimized control parameter set; The first control object is remotely controlled based on the parameter set. As shown in FIG. 2, the first objective function set and the evaluation index value selecting result information optimizing the first control parameter set based on the Based on this, step 700 includes the following steps. S710: Generate a first optimization space based on a first objective function set, and determine a first optimization space dimension. is homothetic to the first objective function set dimension. S720: Based on the first optimization space, a first velocity constraint parameter and a first position constraint parameter are calculated. Obtain the data. S730: inputting the evaluation index value selecting result information into the first optimization space for initialization; Once initialization is complete, a first optimization command is generated. S740: Receive a first optimization command and input the first set of control parameters into a first optimization space. and optimizing the first velocity constraint parameter and the first position constraint parameter. A control parameter set is generated. The optimization control parameter set is a set of evaluation index values ​​to be applied to the first real-time state information. After determining the result information of the selection, the first objective function set and the evaluation index value selection result information are The first control parameter set is optimized based on the result determined, and the optimized control parameters are A first control object is remotely controlled based on the meter set, and the process of the first control object is evaluated based on an evaluation index value. Ensure that the selecting result information is filled. The optimization process preferably comprises: A first optimization space is generated, and a basic framework of the optimization space is generated based on the first objective function set. , i.e., based on the number of dimensions of the first objective function set, the selection range of the optimized control parameter set A determination framework is determined, and a first optimization space dimension is determined. The first speed constraint parameter is the control It characterizes the change step length, i.e., the range of numerical change, when optimizing and updating the control parameters. The first position constraint parameter is a parameter that characterizes the control parameter selection range. That is, the selection range of the optimization control parameter set determined based on the first objective function set is determined. The corresponding dimension of the related function with the joint control parameters is The corresponding dimension of the related function with the spatial location intersection and independent control parameters is the independent control parameter. The meter has different spatial locations, each of which corresponds to a different control parameter value. . Initialize the first optimization space and input the evaluation index value selecting result information into the first objective function set. Then, we can input the corresponding dimension into the objective function and obtain the corresponding solution for each dimension. That is, the first position constraint parameter is determined, and the first speed constraint parameter is customized. By setting the length of the single ergodic step, the length of the single ergodic step can be determined, i.e. The initialization process of the first optimization space can be completed. When optimization begins, the first set of control parameters is used as the optimization initial position, and the first set of control parameters after initialization is completed is used as the optimization initial position. The first position constraint parameter and the first velocity constraint parameter are input to the first optimization space. Based on the data, we select ergodic points, and then calculate the matching and acceptance of each ergodic point. Evaluate step by step, adding those that do not meet the requirements to the cull data group, and so on. Finally, the global optimum values ​​of the matching degree and the acceptance degree are obtained, and the optimization control parameters are The data set is The optimization algorithm globally ergodicizes the solution and obtains the real-time state at that time. Obtain the control parameter group that applies to Step S600 of the method includes the following step S650. S651: Real-time status information, evaluation index value set and matching results for multiple groups First historical data including the identification information is obtained. S652: Identifying the real-time status information, evaluation index value set and matching result of the multiple groups The first matching network layer is constructed based on the other information. S653: Real-time status information of multiple groups, evaluation index set and evaluation index value identification information The second historical data includes the following information: S654: real-time status information, evaluation index set, and evaluation index value identification information of the multiple groups Based on this, the first selecting network layer is constructed. S655: Integrate the first matching network layer and the first selecting network layer to generate the first selecting model. The construction process of the first selecting model is as follows: Generate the first matching network layer, and obtain real-time status information and evaluation guidelines for multiple groups. First history data including the set of benchmarks and matching result identification information is collected, and real-time status information is The training data is the set of evaluation index values, and the matching result classification information is the output classification information. ,Supervised learning is performed based on neural networks, and the first matching network layer is Build and stop training when the model converges. Generate the first selecting network layer to collect real-time status information and evaluation of multiple groups. Collect second history data including a value index set and evaluation index value identification information, and The information, the evaluation index set is the input training data, the evaluation index value discrimination information is the output discrimination information, Based on the neural network, supervised learning is performed, and the first selecting network layer is Build and stop training when the model converges. The input terminal of the first matching network layer is merged with the input terminal of the first selecting model. The output terminal of the first matching network layer and the output terminal of the first selecting model are marked. The output terminal of the first matching network layer and the first selecting network layer are connected to each other. The input terminal of the first selecting network layer is connected to the input terminal of the first selecting network layer. The input terminal of the selecting model is merged, and the output terminal of the first selecting network layer is merged with the output terminal of the second selecting network layer. The output ends of the first selecting model are merged to generate the first selecting model. The first real-time state information and the first set of evaluation indexes are input to a first selecting model. In this case, only the first selecting network layer is activated to obtain the first real-time state information. The first evaluation index value set is evaluated for matching relationship, and the first selecting network layer is When receiving the matching information output from the matching network layer, the response is rejected; The first evaluation index value set is directly synchronized with the output end of the first selecting model. The selecting network layer outputs the mismatching the first selecting network layer halting operation in response to receiving the selecting information; Activate the first selecting network layer and input the first selecting model Receive and evaluate the first real-time status information and the first set of evaluation indexes, and output the output result to the first selector. The output of the timing model is synchronized. Using a neural network model that mimics the way the human brain thinks, we can extract empirical knowledge from big data. The first real-time state information and the first set of evaluation indicators are fitted to the first real-time state information and the first ... It is possible to perform evaluation and generate relatively comprehensive and accurate evaluation index value selection result information. can. Regarding the above "inputting the evaluation index value selecting result information into the first optimization space for initialization" Then, step S730 includes the following steps. S731 inputs the selecting result information of the evaluation index value into the first objective function set, and controls Generate a group of control parameter particles. S732: A first matching degree evaluation formula and a first acceptance degree evaluation formula are obtained. S734: The control parameter particle swarm, the first matching degree evaluation formula, and the first acceptance degree evaluation formula The first optimization space is initialized based on a value expression. The initialization process is as follows: The control parameter particle swarm inputs the evaluation index value selecting result information into the first optimization space. The numerical solution of each dimension control parameter is generated by the The set is the order of the evaluation index along the dimensional space direction in which each first objective function set exists. The directional influence relationships are arranged in order. The forward influence relationships are the relationships in which the control performance increases as the evaluation index increases. The direction of change of the parameter is the direction of change of the control parameter. Then, a virtual coordinate value is generated to represent the same position of the same control parameter. The position values ​​are the same, but may be the intersection of multiple dimensions. A first matching degree evaluation formula and a first acceptability evaluation formula are constructed. The first matching degree evaluation formula is Evaluate the matching degree between the group's control parameter set and the evaluation index value selecting result information. It is a formula for evaluating the The first acceptability evaluation formula adjusts the control parameters based on the degree of matching at different positions. This is an evaluation formula for updating the location of a data set. Control the parameter particle group to determine the particle selection range, and determine the first matching degree evaluation formula and the first acceptance The ergodic rule is set through the degree evaluation formula, and the iterative update is performed according to the above-mentioned position parameters. The new step length is determined and further initialization of the first optimization space is completed. Step S732 of the method includes the following steps. S7321: The first matching degree evaluation formula is TIFF0007657397000011.tif1538, where TIFF0007657397000012.tif89 is the selection of the nth control parameter set in the manufacturer's historical control data where the first control object exists. Indicates frequency, TIFF0007657397000013.tif129 is the manufacturer history control data of the nth control parameter set in which the same type of equipment as the first control object exists. The frequency of selection in TIFF0007657397000014.tif1013 is TIFF0007657397000015.tif107 and TIFF0007657397000016.tif117 Characterize the specific gravity. S7322: The first acceptability evaluation formula is TIFF0007657397000017.tif1444, where TIFF0007657397000018.tif1115 is the acceptability of the n+1th control parameter set. Step S7322 of the method includes the following steps. S73221: If the first acceptance is 1, select the nth control parameter set. Then, the n+1 control parameter set is added to the group and the optimization continues based on the n+1 control parameter set. S73222: If the first tolerance is 0, select the n+1th control parameter set. group and continue optimization based on the nth control parameter set. The first matching degree evaluation formula may be, optionally, TIFF0007657397000019.tif1439, where TIFF0007657397000020.tif99 is the selection of the nth control parameter set in the manufacturer's history control data where the first control object exists. Indicates frequency, TIFF0007657397000021.tif128 is the manufacturer history control data of the nth control parameter set in which the same type of equipment as the first control object exists. The frequency of selection in TIFF0007657397000022.tif1014 is TIFF0007657397000023.tif1110 and Characterize the specific gravity of TIFF0007657397000024.tif128, TIFF0007657397000025.tif1110 characterizes the degree of matching. The first acceptability evaluation formula may optionally be: TIFF0007657397000026.tif1545, where TIFF0007657397000027.tif914 is the acceptability of the n+1th control parameter set. If the first acceptance degree is 1, the nth control parameter set is added to the selection data group; Continue optimizing based on the (n+1)th control parameter set. If the first acceptance is 0, Add the (n+1)th control parameter set to the selected data set; and based on the (n)th control parameter set The optimization continues based on the selected data group. It is a selection data group that is updated to the same data set, and it skips and duplicates the data. The first acceptability ensures that the selected control parameter set is optimal. It is also possible to ensure that the control parameter set is highly matched, and the remote control device Realize automatic monitoring data processing for monitoring management and improve system stability. As described above, the present invention provides a monitoring and management system for remote control devices. In conventional technology, the human decision-making process is highly unstable, and the monitoring and management Solved the technical problems of low stability and low automation. Determine the control object and the application object information to be processed by characterizing the control object. The index set is evaluated based on the control object matching state, and the index set is set for the control object. Extract and evaluate the one-to-one corresponding control parameter information of A set of empirical objective functions is generated that characterize the relationship between the data and the evaluation index, and the application target information is retrieved. Extract real-time status information and create intelligent models based on real-time status The evaluation index value is matched using the above method, and the control is performed based on the matching evaluation index value and the objective function set. Optimize the parameter set and obtain the control parameter optimization result suitable for the current scene. Control the object and use the intelligent model to model the evaluation index suitable for the current scene. The control parameters are then adjusted by an optimization algorithm based on the objective function and the evaluation index. It realizes meter matching, is suitable for real-time conditions, has high accuracy, and is human-friendly. This will achieve the technological effect of reducing dependency on remote monitoring and improving the stability of remote monitoring management.

[0015] Example 2 Based on the same inventive concept as the monitoring and management method for remote control devices in the above embodiment, As shown in FIG. 3, the present embodiment provides a monitoring and management system for a remote control device. The system is in communication connection with a first controlled object and includes the following units: The first collecting unit 11 collects first monitoring scene information including first control object information and first application object information. Collect information. The first generation unit 12 performs evaluation index matching on the first control target information, and generates a first A set of performance indices is generated, and the performance indices characterize the controlled object state. The second generation unit 13 extracts control parameters from the first control object information, A control parameter set is generated. The third generation unit 14 generates the first evaluation index set based on the first control parameter set. Ergodicate to generate the first set of objective functions. The first acquisition unit 15 performs real-time state extraction on the first application object information; 1. Get real-time status information. The fourth generating unit 16 converts the first real-time state information and the first set of evaluation indexes into a first selection. Input the results of the evaluation index selection into the selecting model to generate the evaluation index selection results. A fifth generating unit 17 receives the first objective function set and the evaluation index value selecting result information. Optimizing the first control parameter set based on the The first control object is remotely controlled based on the optimal control parameter set. The system further comprises the following units: The sixth generation unit extracts index values ​​for the first control object based on the first evaluation index set. to generate a first set of evaluation index values. The second acquisition unit outputs the set of the first real-time status information and the first evaluation index value to a first master. The first matching result is obtained by inputting the first matching result into the matching network layer. The seventh generating unit generates a first cell when the first matching result includes non-matching information. Activating a reflecting network layer, the first real-time state information and the first evaluation index A set is input, and the evaluation index value selecting result information is generated. The first processing unit, when the first matching result includes matching information, The set of evaluation index values ​​is regarded as the evaluation index value selecting result information. The system further comprises the following units: The third acquisition unit acquires real-time status information of multiple groups, a set of evaluation index values ​​and a match First history data including the matching result identification information is obtained. The first construction unit is a system that can match real-time status information, evaluation index value sets, and Based on the matching result identification information, a first matching network layer is constructed. The fourth acquisition unit is for acquiring real-time status information of multiple groups, evaluation index sets, and evaluation index values. Second historical data including the identification information is obtained. The second building unit is a set of real-time status information, evaluation indexes and evaluation indicators for multiple groups. Based on the target value identification information, a first selecting network layer is constructed. The eighth generation unit is the first matching network layer and the first selecting network layer. The layers are merged to generate the first selecting model. The system further comprises the following units: A ninth generating unit generates a first optimization space based on the first objective function set. The dimension of one optimization space is the same as the dimension of the first objective function set. The fifth acquisition unit acquires a first velocity constraint parameter and a first position based on the first optimization space. Obtain the constraint parameters. A tenth generation unit inputs the evaluation index value selecting result information into the first optimization space. After the initialization is completed, the first optimization command is generated. An eleventh generation unit receives the first optimization command and converts the first set of control parameters into the first a first velocity constraint parameter and a first position constraint parameter based on the first velocity constraint parameter and the first position constraint parameter; and generating an optimized control parameter set. The system further comprises the following units: A 12th generation unit inputs the evaluation index value screening result information into the first objective function set. Then, a swarm of control parameter particles is generated. The sixth obtaining unit obtains the first matching degree evaluation formula and the first acceptability evaluation formula. The second processing unit is a processor for processing the control parameter particle swarm, the first fitness evaluation formula, and the first acceptance degree. The first optimization space is initialized based on an evaluation formula. The system further comprises the following units: The first setting unit sets the first matching degree evaluation formula, which is: TIFF0007657397000028.tif1533, where TIFF0007657397000029.tif109 is the selection of the nth control parameter set in the manufacturer's history control data where the first control object exists. Indicates frequency, TIFF0007657397000030.tif128 is the manufacturer history control data of the nth control parameter set in which the same type of equipment as the first control object exists. The frequency of selection in TIFF0007657397000031.tif1014 is TIFF0007657397000032.tif1110 and The relative weight of TIFF0007657397000033.tif128. The second setting unit sets the first acceptability evaluation formula, which is: TIFF0007657397000034.tif1443, where TIFF0007657397000035.tif914 is the acceptability of the n+1th control parameter set.

[0016] Example 3 The embodiment of the present application is an invention similar to the monitoring and management method of the remote control device in the above embodiment. Based on the above idea, a computer-readable storage medium is provided. The storage medium stores a computer program, and the computer program The program, when executed by a processor, implements the method according to any one of the embodiments. .

[0017] Exemplary Electronic Devices Hereinafter, an electronic device according to an embodiment of the present invention will be described with reference to FIG. The embodiment of the present application is an invention similar to the monitoring and management method of the remote control device in the above-mentioned embodiment. Based on the above idea, an electronic device is also provided. When the program is executed by the processor, A memory operable to cause a system to perform the method according to any one of the first aspects. are combined. The electronic device 300 includes a processor 302, a communication interface 303, and a memory 301. Optionally, the electronic device 300 may include a bus architecture 304. Here, the communication interface 303, the processor 302, and the memory 301 can be connected to each other via a bus architecture 304, 304 is a peripheral component interconnect target (PCI abbreviated Extent interconnect bus or extended industry standard structure integrated industry standard architecture, EISA and The bus architecture 304 may include an address bus, a data bus, etc. For ease of illustration, Figure 4 shows a single bus. Although it is shown with only a thick line, it only shows one bus or type of bus. Not only that. The processor 302 may be a CPU, a microprocessor, an ASIC, or a program of the present application. There may be one or more integrated circuits for controlling the execution. The communication interface 303 is an Ethernet, a radio access network (RAD access network, RAN), wireless local area network (wire less local area networks, WLAN), wired access network The use of any transceiver to communicate with other devices or communication networks, such as a Use the class system. Memory 301 may be a ROM or other type of memory capable of storing static information and instructions. A static memory device, RAM or other type of dynamic memory capable of storing information and instructions. The memory device may be an electrically erasable programmable read-only memory (EERAM). ically erasable Programmable read-only m emory, EEPROM), read-only optical disk (compact disk d-only memory, CD-ROM) or other optical disk storage, disk storage Devices (compressed discs, laser discs, discs, digital general-purpose discs, Blu-ray discs) any disk storage medium or other magnetic storage device, or any instruction or data storage device A desired program code in the form of a data structure may be carried or stored and executed by a computer. The present invention may be used in any other medium accessible via a computer, including, but not limited to, a USB flash drive. The memory was a separate entity connected to the processor via a bus architecture 304. The memory can also be integrated with the processor. Here, the memory 301 stores computer-executable instructions for carrying out the present invention, and the processor The processor 302 controls the execution of the memory 301. , and is used to execute computer-executable instructions stored in the The remote control device monitoring and management method provided by the above embodiment is realized. Optionally, the computer-executable instructions in the present embodiment may be an application program. The embodiments of the present application are not specifically limited thereto. The embodiment of the present invention provides a monitoring and management system for a remote control device. Application object information processed by determining and characterizing the control object using Determine a matching state of the control object, evaluate the index set based on the control object matching state, and set the index set for the control object. Extract and evaluate the one-to-one corresponding control parameter information for each A set of empirical objective functions is generated that characterize the relationship between the data and the evaluation index, and the information of the application target is calculated. Extract real-time status information and create intelligent models based on the real-time status The evaluation index values ​​are matched using the Optimize the control parameter set, obtain the control parameter optimization result suitable for the current scene, and execute the control. Control the object and use the intelligent model to generate evaluation index values ​​suitable for the current scene. The control performance is then determined by the optimization algorithm based on the objective function and the evaluation index value. It realizes parameter matching, is suitable for real-time conditions, and has high accuracy. Achieve the technical effect of reducing human dependency and improving the stability of remote monitoring management.

[0018] Those skilled in the art will understand that the various numerals such as "1", "2" and the like in this application are merely for the convenience of explanation. The above-mentioned examples are not intended to limit the scope of the present application, but rather to indicate a sequential order. It is understood that "and / or" does not necessarily imply any Describes an association relationship and indicates that three relationships can exist. For example, A and / or B represents three cases: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates an OR relationship between the related objects before and after it. "At least one" means one or more. At least two means two or more. "At least one," "any one," or similar expressions mean These items, including any combination of items or multiple items For example, at least one of a, b, or c is , a, b, c, ab, ac, bc, or abc, where a, b , c may be single or multiple.

[0019] In the above embodiments, software, hardware, firmware, or any of them may be used. The software may be implemented in whole or in part by any combination of the above. When implemented using, implemented in whole or in part in the form of a computer program product The computer program product may include one or more computer instructions. When the computer program instructions are loaded and executed on a computer, The flows or functions described in the embodiments are generated in whole or in part by a computer. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or another It may be a programmable system.

[0020] The computer instructions may be stored on a computer readable storage medium or may be The information may be transmitted from a computer-readable storage medium to another computer-readable storage medium. Computer instructions are delivered from one website, computer, server, or data center. , wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless via other websites, computers, or other means (for example, infrared, radio, microwave, etc.) The computer-readable storage medium may transmit the information to a computer, a server, or a data center. The body may be any available medium accessible to a computer, and may include one or more The available media includes integrated servers, data centers, and other data storage devices. Usable media include magnetic media (e.g., floppy disks, hard disks, etc.) , magnetic tape), optical media (e.g., DVD), or solid-state media (e.g., SSD (So It can be a hard disk, a lid state disk, etc.

[0021] The various exemplary logic units and circuits described in the embodiments of this application may be implemented as general purpose processors. Digital signal processors, application specific integrated circuits (ASICs), field programs Programmable Gate Arrays (FPGAs) or other programmable logic systems, gate or transistor logic, discrete hardware components, or Any combination of the design can be used to implement or operate the described functionality. The general purpose processor may be a microprocessor, and optionally the general purpose processor may be any A conventional processor, controller, microcontroller, or state machine. The processor may be a digital signal processor, a microprocessor, or a plurality of microprocessors. microprocessor, one or more microprocessors and a digital signal processor A computing system that combines multiple cores, or any other similar configuration. It can also be implemented by combining

[0022] The steps of the method or algorithm described in the embodiments of the present application may be implemented using hardware, processor, directly embedding software units executed by the processor, or a combination of both The software unit can be embedded in RAM memory, flash memory, RO M memory, EPROM memory, EEPROM memory, register, hard disk, removable on a bubble disk, CD-ROM, or any other form of storage medium known in the art. For example, the storage medium may be a storage medium that allows a processor to read information from the storage medium. a storage medium connected to the processor such that information can be written to the storage medium; Optionally, the storage medium can be integrated into the processor. The processor and the storage medium may reside in an ASIC, which may in turn reside in a terminal. In the alternative, the processor and the storage medium may be implemented as separate components within a These computer program instructions may also be provided in a computer The program may be loaded into a computer or other programmable data processing device, or other programmable device to execute a series of operational steps The processing of the device can then be generated by a computer or other programmable The instructions executed on the device may be implemented as one or more of the flows and / or steps of the flowchart. is a function that implements a specified function in one or more blocks of a block diagram. Provide steps.

[0023] Although the present application has been described with a combination of specific features and embodiments, it is to be understood that the present application is not limited to the above and is not intended to depart from the spirit and scope of the present application. It is apparent that various modifications and combinations are possible without departing from the spirit and scope of the present invention. The specification and drawings are merely illustrative of the present application as defined herein and are within the scope of the present application. Any and all modifications, variations, combinations, or equivalents of the Obviously, those skilled in the art can make various modifications and variations of the present application without departing from the scope of the present application. Thus, it is intended that these modifications and variations of the present application be included within the scope of the present application and its equivalents. It is intended that this application include these modifications and variations if they come within the scope of the art.

Claims

1. A monitoring and management system for a remote control device, comprising: The system is in communication with a first control object; A first collection unit 11; The first collecting unit 11 collects a first monitoring scene including first control object information and first application object information. Collect information, and the first control object information is monitoring scene information directly related to the controlled equipment; The first application object information is monitoring scene information directly related to the processed product of the controlled equipment; A first generation unit 12; The first generating unit 12 performs evaluation index matching on the first control target information, A set of evaluation indices is generated, the evaluation indices characterizing the controlled object state; A second generation unit 13; The second generation unit 13 extracts control parameters from the first control object information. Generate a set of control parameters; A third generation unit 14; and The third generation unit 14 generates the first evaluation index set based on the first control parameter set. Conveniently, generate a first set of objective functions, A first acquisition unit 15; The first acquisition unit 15 performs real-time state extraction on the first application object information; Obtaining first real-time status information; A fourth generation unit 16; and The fourth generating unit 16 outputs the first real-time state information and the first set of evaluation indexes to the first cell. Input the result of the evaluation index selection into the selecting model to generate the evaluation index value selecting result information; A fifth generation unit 17; and A fifth generating unit 17 receives the first objective function set and the evaluation index value selecting result information. Optimizing the first control parameter set based on the first control parameter set to generate an optimized control parameter set. remotely controlling the first control object based on the optimized control parameter set; The "selecting the second objective function based on the first objective function set and the evaluation index value selecting result information" 1. Optimize the control parameter set and generate the optimized control parameter set. The steps include the following: generating a first optimization space based on the first objective function set; The space dimension is the same as the first objective function set dimension, Based on the first optimization space, a first velocity constraint parameter and a first position constraint parameter are calculated. and obtaining The evaluation index value selecting result information is input to the first optimization space for initialization; generating a first optimization command once the optimization is complete; receiving a first optimization command and inputting the first set of control parameters into a first optimization space; Optimizing the control based on the first velocity constraint parameter and the first position constraint parameter; generating a parameter set; A ninth generating unit; and A ninth generating unit generates a first optimization space based on the first objective function set, a first optimization space dimension is homogeneous with the first objective function set dimension; A fifth acquisition unit; and The fifth acquisition unit acquires a first velocity constraint parameter and a first position based on the first optimization space. Obtain the placement constraint parameters, A tenth generation unit; and A tenth generation unit inputs the evaluation index value selecting result information into the first optimization space. After the initialization is completed, a first optimization command is generated; An eleventh generation unit; and an eleventh generating unit receives the first optimization command and converts the first set of control parameters into the second optimization command; a first velocity constraint parameter and a first position constraint parameter. generating an optimized control parameter set; The monitoring and management system for the remote control device further comprises: A twelfth generation unit; and A twelfth generation unit inputs the evaluation index value screening result information into the first objective function set. to generate a control parameter particle swarm; A sixth acquisition unit; and The sixth obtaining unit obtains a first matching degree evaluation formula and a first acceptance degree evaluation formula; A second processing unit; and The second processing unit is configured to process the control parameter particle group, the first matching degree evaluation formula, and the second matching degree evaluation formula. initializing the first optimization space based on an acceptance evaluation formula; The monitoring and management system for the remote control device further comprises: A first setting unit; The first setting unit sets the first matching degree evaluation formula, the formula being: where is the selection of the n-th control parameter set in the manufacturer history control data in which the first control object exists. Indicates frequency, is the manufacturer history control data of the n-th control parameter set in which the same type of equipment as the first control object exists. The frequency of selection in are respectively and is the weighting parameter of A second setting unit; The second setting unit sets the first acceptance evaluation formula, the formula being: where is the acceptance of the (n+1)th control parameter set, A monitoring and management system for a remote control device, comprising:

2. The monitoring and management system for the remote control device further comprises: A sixth generation unit; and A sixth generation unit extracts an index value for the first control object based on the first evaluation index set. to generate a first evaluation index value set; A second acquisition unit; The second acquisition unit receives the first real-time status information and the first evaluation index value as a first Input the matching network layer to obtain the first matching result; A seventh generation unit; and The seventh generating unit generates a first matching result when the first matching result includes non-matching information. Activating a selecting network layer and receiving the first real-time status information and the first evaluation index. Input a set of evaluation indices and generate the evaluation index value selecting result information; A first processing unit; The first processing unit, when the first matching result includes matching information, One evaluation index value set is regarded as the evaluation index value selecting result information.

2. The monitoring and management system for a remote control device according to claim 1, further comprising:

3. The monitoring and management system for the remote control device further comprises: A third acquisition unit; and The third acquisition unit acquires real-time status information of multiple groups, a set of evaluation index values, and obtaining first history data including the switching result identification information; A first building unit; The first construction unit matches the real-time status information of multiple groups and the evaluation index value set. constructing a first matching network layer based on the matching result identification information; A fourth acquisition unit; and The fourth acquisition unit acquires real-time status information of multiple groups, a set of evaluation indexes, and an evaluation index. Obtaining second history data including value identification information; A second building unit; and The second construction unit is for providing real-time status information of multiple groups, a set of evaluation indexes, and an evaluation Construct a first selecting network layer based on the index value identification information; An eighth generation unit; and The eighth generation unit is a first matching network layer and a first selecting network layer. and generate the first selecting model.

3. The monitoring and management system for a remote control device according to claim 2, further comprising:

4. The monitoring and management system for the remote control device further comprises: A third processing unit; and The third processing unit selects the n-th control parameter set when the first acceptance degree is 1. add the n+1 control parameter set to the data group and continue optimizing based on the n+1 control parameter set; A fourth processing unit; and The fourth processing unit selects the (n+1)th control parameter set when the first acceptance degree is 0. adding the nth control parameter set to the selected data group and continuing the optimization based on the nth control parameter set; 2. The monitoring and management system for a remote control device according to claim 1, further comprising:

5. Remote control equipment for use in a monitoring and management system for remote control equipment communicatively connected to a first controlled object A monitoring and management method for S100: A step of collecting first monitoring scene information including first control object information and first application object information. Tep and S200: Matching an evaluation index characterizing a control object state with the first control object information to generate a first evaluation index set; S300: Extracting a control parameter from the first control object information, and obtaining the first control parameter generating a set; S400: Based on the first set of control parameters, the first set of evaluation indexes is conveniently classified as a first item. generating a set of characteristic functions; S500: performing real-time state extraction for the first application object information; obtaining state information; S600: The first real-time status information and the first set of evaluation indexes are selected as a first selecting model. inputting the evaluation index value selecting result information into the database to generate the evaluation index value selecting result information; S700: Select the second evaluation index value based on the first objective function set and the evaluation index value selecting result information. Optimizing a control parameter set to generate an optimized control parameter set; remotely controlling the first control object based on a parameter set; A method for monitoring and managing a remote control facility, comprising:

6. a processor coupled to a memory, the memory storing a program; When the program is executed by the processor, 5. An electronic device capable of executing the system according to any one of claims 1 to 4.

7. A computer-readable storage medium, the storage medium storing a computer program. and wherein the computer program, when executed by the processor, A computer-implemented method for implementing the system according to any one of claims 1 to 4. Reading storage medium.

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