Robot control method, device and substation operation assisting robot

Abstract

The application relates to a robot control method and device, a storage medium, a computer program product and a substation operation auxiliary robot. The method comprises the following steps: in response to a user's information input operation, a target control signal is acquired; the target control signal is input to an instruction fuzzy identification model to obtain the membership degree of the target control signal relative to each candidate instruction fuzzy category; the candidate instruction fuzzy category corresponding to the maximum membership degree is taken as a target instruction category corresponding to the target control signal; and an operation matched with a target instruction signal corresponding to the target instruction category is executed. The method can improve the intelligence of human-computer interaction in the robot inspection process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power, and in particular to a robot control method and device, a storage medium, a computer program product and a substation operation auxiliary robot. BACKGROUND

[0002] As a component in the electric power system, the substation plays an important role in adjusting voltage, converting power types and distributing power. In order to ensure the safe, stable and efficient operation of the substation equipment, the construction of the monitoring and management system in the substation has become the focus of current research. With the proposal of production automation demand and the progress of intelligent technology, more and more intelligent products and equipment are being widely used in substation inspection tasks, such as substation operation robots.

[0003] In order to let the substation operation robot better perform the inspection at the operation site, the user often needs to issue some movement instructions to the substation operation robot. However, in the related art, the substation operation robot cannot accurately recognize the movement instructions, resulting in poor human-computer interaction intelligence.

[0004] Therefore, the related art has the problem of poor human-computer interaction intelligence in the robot inspection process. SUMMARY

[0005] Therefore, it is necessary to provide a robot control method, device, computer readable storage medium, computer program product and substation operation auxiliary robot capable of improving human-computer interaction intelligence in the robot inspection process.

[0006] In a first aspect, the present application provides a robot control method. The method comprises:

[0007] In response to an information input operation of a user, a target control signal is obtained;

[0008] The target control signal is input into an instruction fuzzy recognition model to obtain the membership degree of the target control signal with respect to each candidate instruction fuzzy category;

[0009] The candidate instruction fuzzy category corresponding to the maximum membership degree is taken as a target instruction category corresponding to the target control signal;

[0010] An operation matching the target instruction signal corresponding to the target instruction category is performed.

[0011] In one of the embodiments, the instruction fuzzy recognition model comprises instruction fuzzy recognition models corresponding to each of the candidate instruction fuzzy categories; each of the instruction fuzzy recognition models is composed of a corresponding membership function; the inputting of the target control signal into the instruction fuzzy recognition model to obtain the membership degree of the target control signal with respect to each of the candidate instruction fuzzy categories comprises:

[0012] inputting the target control signal into the membership function corresponding to each of the candidate instruction fuzzy categories to obtain the membership degree output by each of the membership functions for the target control signal;

[0013] determining the membership degree of the target control signal with respect to each of the candidate instruction fuzzy categories according to the membership degree output by each of the membership functions for the target control signal.

[0014] In one of the embodiments, the obtaining of the target control signal in response to the information input operation of the user comprises:

[0015] obtaining a control signal in response to the information input operation of the user;

[0016] extracting a signal corresponding to the key information in the control signal as the target control signal.

[0017] In one of the embodiments, the extracting of the signal corresponding to the key information in the control signal as the target control signal comprises:

[0018] converting the control signal into a digital signal;

[0019] extracting a signal corresponding to the key information in the digital signal as the target control signal.

[0020] In one of the embodiments, the control signal comprises a voice signal; the digital signal comprises a first digital signal corresponding to the voice signal; the extracting of the signal corresponding to the key information in the digital signal as the target control signal comprises:

[0021] determining a digital signal corresponding to the mobile description word information in the first digital signal as a target voice signal;

[0022] taking the target voice signal as the target control signal corresponding to the voice signal.

[0023] In one of the embodiments, the control signal comprises a gesture video signal; the digital signal comprises a second digital signal corresponding to the gesture video signal; the extracting of the signal corresponding to the key information in the digital signal as the target control signal comprises:

[0024] determine a digital signal corresponding to image information belonging to a target region in the second digital signal as a target gesture signal;

[0025] take the target gesture signal as a target control signal corresponding to the gesture video signal.

[0026] In a second aspect, the present application further provides a robot control device. The device comprises:

[0027] an acquisition module configured to acquire a target control signal in response to an information input operation of a user;

[0028] an input module configured to input the target control signal into an instruction fuzzy recognition model to obtain a membership degree of the target control signal with respect to each candidate instruction fuzzy category;

[0029] a category determination module configured to take a candidate instruction fuzzy category corresponding to the maximum membership degree as a target instruction category corresponding to the target control signal;

[0030] an execution module configured to execute an operation matched with a target instruction signal corresponding to the target instruction category.

[0031] In a third aspect, the present application further provides a substation operation auxiliary robot. The substation operation auxiliary robot comprises a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0032] acquire a target control signal in response to an information input operation of a user;

[0033] input the target control signal into an instruction fuzzy recognition model to obtain a membership degree of the target control signal with respect to each candidate instruction fuzzy category;

[0034] take a candidate instruction fuzzy category corresponding to the maximum membership degree as a target instruction category corresponding to the target control signal;

[0035] execute an operation matched with a target instruction signal corresponding to the target instruction category.

[0036] In a fourth aspect, the present application further provides a computer readable storage medium. The computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0037] acquire a target control signal in response to an information input operation of a user;

[0038] input the target control signal into an instruction fuzzy recognition model to obtain a membership degree of the target control signal with respect to each candidate instruction fuzzy category;

[0039] a candidate instruction fuzzy category corresponding to the maximum membership degree is taken as a target instruction category corresponding to the target control signal;

[0040] an operation corresponding to a target instruction signal matching the target instruction category is executed.

[0041] In a fifth aspect, the present application further provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the following steps:

[0042] a target control signal is acquired in response to an information input operation of a user;

[0043] the target control signal is input into an instruction fuzzy recognition model to obtain a membership degree of the target control signal with respect to each candidate instruction fuzzy category;

[0044] a candidate instruction fuzzy category corresponding to the maximum membership degree is taken as a target instruction category corresponding to the target control signal;

[0045] an operation corresponding to a target instruction signal matching the target instruction category is executed.

[0046] The above robot control method, device, storage medium, computer program product and substation operation auxiliary robot acquire a target control signal in response to an information input operation of a user, input the target control signal into an instruction fuzzy recognition model to obtain a membership degree of the target control signal with respect to each candidate instruction fuzzy category, take a candidate instruction fuzzy category corresponding to the maximum membership degree as a target instruction category corresponding to the target control signal, and execute an operation corresponding to a target instruction signal matching the target instruction category. In this way, the target control signal input by the user is recognized fuzzily, the membership degree of the target control signal with respect to each candidate instruction fuzzy category is determined, and a candidate instruction fuzzy category corresponding to the maximum membership degree is taken as a target instruction category meeting the demand of the user, so that the instruction input by the user can be accurately recognized, and the intelligence of human-machine interaction in the robot inspection process is effectively improved. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 a flowchart of a robot control method in one embodiment;

[0048] Figure 2 a logic connection diagram of each module of a substation operation auxiliary robot in one embodiment;

[0049] Figure 3 a control logic diagram of an input module in one embodiment;

[0050] Figure 4A flowchart of a robot control method in another embodiment;

[0051] Figure 5 A structural block diagram of a robot control device in an embodiment;

[0052] Figure 6 An internal structural diagram of a substation operation auxiliary robot in an embodiment. DETAILED DESCRIPTION

[0053] In order to make the purposes, technical solutions and advantages of the present application clearer, further detailed description will be made to the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0054] It should be noted that the terms "first", "second", and the like in the description and claims of the present disclosure and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Rather, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0055] In one embodiment, as shown in Figure 1 A power operation monitoring and alarming method is provided, and the present embodiment is exemplified by the method applied to a substation operation auxiliary robot. It should be understood that the method can also be applied to a terminal, a server, a system including a terminal and a server, and can be realized through the interaction of the terminal and the server. In the present embodiment, the method comprises the following steps:

[0056] Step S110, in response to the information input operation of the user, acquiring a target control signal.

[0057] The substation operation auxiliary robot comprises an input module (1), a connection module (2), a control module (3), a movement module (4), a receiving module (5), a storage module (6), and a collection module (7).

[0058] The control signal is input through the input module (1), the input module (1) establishes a wireless connection with the receiving module (5) through the connection module (2), the input module (1) converts the control signal into an instruction signal, and inputs the control signal into the control module (3) through the connection module (2) and the receiving module (5), and the control module (3) sends a movement instruction to the movement module (4) according to the instruction signal;

[0059] The acquisition module (7) video shoots the substation operation scene, and then the shot operation site video is input to the storage module (6) for storage, and the control module (3) calls the stored video in the storage module (6).

[0060] The control module (3) is used to control the opening and closing of the acquisition module (7).

[0061] In order to facilitate those skilled in the art to understand, Figure 2 A logical connection diagram of each module of the substation operation auxiliary robot is provided.

[0062] In a specific implementation, a user can input a movement instruction to the substation operation auxiliary robot according to the operation situation of the substation operation site. Specifically, the user can perform an information input operation on the substation operation auxiliary robot, and the input module of the substation operation auxiliary robot responds to the information input operation to enable the substation operation auxiliary robot to obtain a target control signal.

[0063] The input module includes an input unit, which can respond to the information input operation.

[0064] In step S120, the target control signal is input to the instruction fuzzy recognition model to obtain the membership degree of the target control signal with respect to each candidate instruction fuzzy category.

[0065] The input module further includes an identification unit.

[0066] The candidate instruction fuzzy categories include movement instruction categories such as forward movement, backward movement, left turn, right turn, and the like.

[0067] In a specific implementation, the input unit can send the target control signal to the identification unit of the input module, and the identification unit of the input module can input the target control signal to the instruction fuzzy recognition model to obtain the membership degree of the target control signal with respect to each candidate instruction fuzzy category.

[0068] In step S130, the candidate instruction fuzzy category corresponding to the maximum membership degree is taken as the target instruction category corresponding to the target control signal.

[0069] In a specific implementation, the identification unit can take the candidate instruction fuzzy category corresponding to the maximum membership degree as the target instruction category corresponding to the target control signal.

[0070] In step S140, an operation matching the target instruction signal corresponding to the target instruction category is performed.

[0071] The input module further includes a processing unit.

[0072] In specific implementation, after identifying the target instruction category, the identification unit can generate a target instruction signal matching the target instruction category and input the target instruction signal to the processing unit of the input module. The processing unit inputs the target instruction signal to the control module through the connection module. The control module sends a movement instruction to the movement module according to the target instruction signal. The movement module controls the substation operation auxiliary robot to perform a movement operation matching the target instruction signal in response to the movement instruction, so as to perform inspection and monitoring on the operation link process.

[0073] In the above robot control method, the target control signal is obtained in response to the information input operation of the user. The target control signal is input to the instruction fuzzy identification model to obtain the membership degrees of the target control signal with respect to each candidate instruction fuzzy category. The candidate instruction fuzzy category corresponding to the maximum membership degree is taken as the target instruction category corresponding to the target control signal. An operation matching the target instruction signal corresponding to the target instruction category is performed. In this way, the target control signal input by the user is subjected to fuzzy identification, the membership degrees of the target control signal with respect to each candidate instruction fuzzy category are determined, and the candidate instruction fuzzy category corresponding to the maximum membership degree is taken as the target instruction category meeting the user demand, so that the instruction input by the user can be accurately identified, and the intelligence of human-machine interaction in the robot inspection process is effectively improved.

[0074] In one embodiment, the instruction fuzzy identification model includes instruction fuzzy identification models corresponding to each candidate instruction fuzzy category. Each instruction fuzzy identification model is composed of a corresponding membership function. The input of the target control signal to the instruction fuzzy identification model to obtain the membership degrees of the target control signal with respect to each candidate instruction fuzzy category includes: inputting the target control signal to the membership function corresponding to each candidate instruction fuzzy category to obtain the membership degrees output by each membership function for the target control signal; and determining the membership degrees of the target control signal with respect to each candidate instruction fuzzy category according to the membership degrees output by each membership function for the target control signal.

[0075] In specific implementation, in the process of inputting the target control signal to the instruction fuzzy identification model to obtain the membership degrees of the target control signal with respect to each candidate instruction fuzzy category, the identification unit can input the target control signal to the membership function corresponding to each candidate instruction fuzzy category to obtain the membership degrees output by each membership function for the target control signal. Then, the identification unit can determine the membership degrees of the target control signal with respect to each candidate instruction fuzzy category according to the membership degrees output by each membership function for the target control signal.

[0076] The technical scheme of the embodiment comprises: the instruction fuzzy recognition model comprises instruction fuzzy recognition models corresponding to each candidate instruction fuzzy category; each instruction fuzzy recognition model is composed of a corresponding membership function; the membership degrees output by each membership function for the target control signal are obtained by inputting the target control signal into the membership function corresponding to each candidate instruction fuzzy category; and the membership degrees of the target control signal relative to each candidate instruction fuzzy category are determined according to the membership degrees output by each membership function for the target control signal. In this way, the membership degrees of the target control signal relative to each candidate instruction fuzzy category can be accurately determined, so that the target instruction category matched with the target control signal can be more accurately determined through the membership degrees.

[0077] In one embodiment, in response to the information input operation of the user, the target control signal of the user is acquired, comprising: in response to the information input operation of the user, a control signal is acquired; and a signal corresponding to key information in the control signal is extracted as the target control signal.

[0078] In a specific implementation, in the process in which the input unit acquires the target control signal of the user in response to the information input operation of the user, the input unit can acquire a control signal in response to the information input operation of the user, and then the input unit can extract a signal corresponding to key information in the control signal as the target control signal.

[0079] The technical scheme of the embodiment comprises: the instruction fuzzy recognition model comprises instruction fuzzy recognition models corresponding to each candidate instruction fuzzy category; each instruction fuzzy recognition model is composed of a corresponding membership function; the membership degrees output by each membership function for the target control signal are obtained by inputting the target control signal into the membership function corresponding to each candidate instruction fuzzy category; and the membership degrees of the target control signal relative to each candidate instruction fuzzy category are determined according to the membership degrees output by each membership function for the target control signal. In this way, the membership degrees of the target control signal relative to each candidate instruction fuzzy category can be accurately determined, so that the target instruction category matched with the target control signal can be more accurately determined through the membership degrees.

[0080] In one embodiment, the signal corresponding to the key information in the control signal is extracted as the target control signal, comprising: the control signal is converted into a digital signal; and the signal corresponding to the key information in the digital signal is extracted as the target control signal.

[0081] In a specific implementation, in the process in which the input unit extracts the signal corresponding to the key information in the control signal as the target control signal, the input unit can convert the control signal into a digital signal; and then the signal corresponding to the key information in the digital signal is extracted as the target control signal.

[0082] The control signal comprises a voice signal; the input unit comprises a voice input unit; and the digital signal comprises a first digital signal corresponding to the voice signal.

[0083] The signal corresponding to the key information in the digital signal is extracted as a target control signal, including: determining the digital signal corresponding to the mobile description vocabulary information in the first digital signal as a target voice signal; and taking the target voice signal as a target control signal corresponding to the voice signal.

[0084] The mobile description vocabulary information includes, but is not limited to, direction and distance.

[0085] In a specific implementation, the voice input unit can collect a voice signal in response to a voice information input operation of a user, convert the voice signal into a digital signal, and obtain a first digital signal. Due to the difference between spoken language and pronunciation, as well as speaking habits, there is a certain error rate in recognition. In order to improve the accuracy of recognition in work, only mobile description vocabulary information can be used to issue voice information to a substation operation auxiliary robot, such as language information including direction and distance, for example, 3 meters forward or 4 meters left or turn left by 45° or 1 meter forward.

[0086] Therefore, the voice input unit can convert the voice signal into the first digital signal, capture the digital signal corresponding to the mobile description vocabulary information in the first digital signal as a target voice signal, and take the target voice signal as a target control signal corresponding to the voice signal. In this way, the voice input unit can input the target voice signal to the recognition unit, the recognition unit recognizes the control voice content corresponding to the target voice signal, determines the target instruction type, converts the target instruction type into an instruction signal, obtains a target instruction signal, and inputs the target instruction signal to the processing unit.

[0087] The control signal further includes a gesture video signal; the input unit includes an image input unit; and the digital signal includes a second digital signal corresponding to the gesture video signal.

[0088] The signal corresponding to the key information in the digital signal is extracted as a target control signal, including: determining the digital signal corresponding to the mobile description vocabulary information in the first digital signal as a target voice signal; and taking the target voice signal as a target control signal corresponding to the voice signal.

[0089] In a specific implementation, the image input unit can collect the hand gesture video signal in response to a user gesture information input operation, convert the hand gesture video signal into a digital signal, and obtain a second digital signal. Since gestures are different each time and everyone's hands are different, in order to improve the accuracy of gesture action recognition, the gesture information needs to be simplified, such as using the gesture to guide a certain forward direction, maintaining the gesture to continue to move forward, and changing the gesture to stop moving forward. The image input unit can convert the hand gesture video signal into a second digital signal, such as using freescale platform to support adv7180 to complete the conversion of the AV signal into YUV data. By capturing the digital signal corresponding to the image information of the target region (for example, a specific region) in the second digital signal as a target gesture signal, the key gesture action therein can be recognized.

[0090] In this way, the target gesture signal is a target control signal corresponding to the hand gesture video signal. Then, the image input unit can input the target gesture signal to the recognition unit, the recognition unit recognizes the control gesture content corresponding to the target gesture signal, determines the corresponding target instruction category, converts the target instruction category into an instruction signal to obtain a target instruction signal, and inputs the target instruction signal to the processing unit.

[0091] In this way, by extracting the signals corresponding to the key information in the voice signal and the hand gesture video signal as target control signals, the target instruction category recognition is performed by inputting the target control signals to the instruction fuzzy recognition model, which realizes the use of standard speech language commands and hand gesture commands to improve the accuracy of the instruction fuzzy recognition model, and has greater generality and flexibility.

[0092] In some embodiments, the method further includes establishing an instruction fuzzy recognition model. Specifically, it is assumed that there are n instruction fuzzy categories corresponding to the voice signal (considering the difference between spoken language and pronunciation, and the large number of categories caused by speaking habits)

[0093] They are respectively represented as n fuzzy subsets A1,..., An of a certain domain X, X0∈X, representing the recognized object, if i∈(1, 2,..., n), such that

[0094] Let the fuzzy subsets A1,..., An of the domain X represent n fuzzy patterns (i.e., corresponding to n candidate instruction fuzzy categories), and the recognized object can be represented as a subset B of X, if i∈(1, 2,..., n), such that σ(B, A i )=max{σ(B, A1), σ(B, A2),...σ(B, A n ),

[0095] Then, it is considered that B is more consistent with A. In the specific application of fuzzy pattern recognition, the key is the construction of the fuzzy set of the pattern or the object to be recognized, that is, how to establish the fuzzy set of the pattern or the object.

[0096] That is, increasing the closeness B can improve the probability of recognition.

[0097] Let X be a real number field, and the membership function of the fuzzy set A on X is

[0098] A is called a normal fuzzy set.

[0099]

[0100] Where, α is the mathematical expectation, and σ is the mean square deviation. The membership function of the normal fuzzy set is Therefore, the normal fuzzy set can be regarded as a fuzzy set on X induced by the normal probability distribution.

[0101] Suppose there is a pattern 4, which can be characterized by a real number x∈X. A set of samples x1, x2, x3 consistent with the pattern A is known. They can usually be understood as a set of observations of a certain random variable E(4). If E(4) obeys the normal distribution, the sample mean α and sample variance of (A) can be calculated by using mathematical statistics method, and then the membership function of A is established, and the instruction fuzzy recognition model for the speech signal is obtained.

[0102] Similarly, for the gesture video signal, the corresponding instruction fuzzy recognition model can be established.

[0103] In this way, the recognition unit can include a speech recognition unit and an image recognition unit. After the recognition unit receives the target speech signal input by the speech input unit, the speech recognition unit can recognize the corresponding target instruction category through the instruction fuzzy recognition model for the speech signal. After the recognition unit receives the target gesture signal input by the image input unit, the image recognition unit can recognize the corresponding target instruction category through the instruction fuzzy recognition model for the gesture video signal.

[0104] In practical applications, the user can input voice information or gesture information through the voice input unit or the image input unit according to the working environment. For example, if the working environment noise is relatively large, the gesture information can be input, and if the working environment light is insufficient, the voice information can be input.

[0105] In order to facilitate those skilled in the art to understand, Figure 3An input module control logic diagram is provided. The input module (1) comprises an input unit (11), the input unit (11) comprising a voice input unit (111) and an image input unit (112); the input module (1) further comprises an identification unit (12), the identification unit (12) comprising a voice identification unit (121) and an image identification unit (122); the input module (1) further comprises a processing unit (13).

[0106] In another embodiment, as shown in Figure 4 A robot control method is provided, which is described by taking the substation operation auxiliary robot as an example, and comprises the following steps:

[0107] In step S402, a control signal is acquired in response to a user's information input operation.

[0108] In step S404, a signal corresponding to key information in the control signal is extracted as a target control signal.

[0109] In step S406, the target control signal is input into a membership function corresponding to each candidate instruction fuzzy category, to obtain a membership degree output by each membership function for the target control signal.

[0110] In step S408, the membership degree of the target control signal relative to each candidate instruction fuzzy category is determined according to the membership degrees output by each membership function for the target control signal.

[0111] In step S410, a candidate instruction fuzzy category corresponding to the maximum membership degree is taken as a target instruction category corresponding to the target control signal.

[0112] In step S412, an operation matched with a target instruction signal corresponding to the target instruction category is executed.

[0113] It should be noted that the specific definition of the above steps can refer to the specific definition of the robot control method described above.

[0114] It should be understood that although each step in the flowchart involved in each embodiment described above is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise stated herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each embodiment described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0115] Based on the same inventive concept, the embodiments of the present application also provide a robot control device for implementing the robot control method described above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more robot control device embodiments provided below can refer to the limitations of the robot control method described above, which will not be repeated here.

[0116] In one embodiment, as shown in Figure 5 A robot control device is provided, comprising: an acquisition module 510, an input module 520, a category determination module 530, and an execution module 540, wherein:

[0117] The acquisition module 510 is configured to acquire a target control signal in response to a user's information input operation.

[0118] The input module 520 is configured to input the target control signal into an instruction fuzzy recognition model to obtain the membership degree of the target control signal with respect to each candidate instruction fuzzy category.

[0119] The category determination module 530 is configured to determine the candidate instruction fuzzy category corresponding to the maximum membership degree as the target instruction category corresponding to the target control signal.

[0120] The execution module 540 is configured to execute an operation matching the target instruction signal corresponding to the target instruction category.

[0121] In one embodiment, the instruction fuzzy recognition model includes an instruction fuzzy recognition model corresponding to each candidate instruction fuzzy category; each instruction fuzzy recognition model is composed of a corresponding membership function; the input module 520 is specifically configured to input the target control signal into the membership function corresponding to each candidate instruction fuzzy category to obtain the membership degree output by each membership function for the target control signal; and the membership degree output by each membership function for the target control signal is used to determine the membership degree of the target control signal with respect to each candidate instruction fuzzy category.

[0122] In one embodiment, the acquisition module 510 is specifically configured to acquire a control signal in response to a user's information input operation; and extract a signal corresponding to key information in the control signal as the target control signal.

[0123] In one embodiment, the acquisition module 510 is specifically configured to convert the control signal into a digital signal; and extract a signal corresponding to key information in the digital signal as the target control signal.

[0124] In one of the embodiments, the control signal comprises a voice signal; the digital signal comprises a first digital signal corresponding to the voice signal; the obtaining module 510 is specifically configured to determine a digital signal corresponding to mobile description vocabulary information in the first digital signal as a target voice signal; and take the target voice signal as a target control signal corresponding to the voice signal.

[0125] In one of the embodiments, the control signal comprises a gesture video signal; the digital signal comprises a second digital signal corresponding to the gesture video signal; the obtaining module 510 is specifically configured to determine a digital signal corresponding to image information belonging to a target region in the second digital signal as a target gesture signal; and take the target gesture signal as a target control signal corresponding to the gesture video signal.

[0126] The above-mentioned various modules in the robot control device can be realized by software, hardware and combinations thereof in whole or in part. The above-mentioned various modules can be embedded in or independent of the processor in the transformer substation operation auxiliary robot in hardware form, or can be stored in the memory in the transformer substation operation auxiliary robot in software form, so as to be called and executed by the processor to perform the operations corresponding to the above-mentioned various modules.

[0127] In one of the embodiments, a transformer substation operation auxiliary robot is provided, which can be a terminal, and the internal structure diagram thereof can be as shown in Figure 6The substation operation auxiliary robot includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the substation operation auxiliary robot is configured to provide computing and control capabilities. The memory of the substation operation auxiliary robot includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the substation operation auxiliary robot is configured to exchange information between the processor and external devices. The communication interface of the substation operation auxiliary robot is configured to perform wired or wireless communication with external terminals. The wireless communication can be achieved through WIFI, mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by the processor to implement a robot control method. The display unit of the substation operation auxiliary robot is configured to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the substation operation auxiliary robot can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the substation operation auxiliary robot, or an external keyboard, touchpad or mouse, etc.

[0128] Those skilled in the art can understand that Figure 6 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the substation operation auxiliary robot to which the scheme of the present application is applied. The specific substation operation auxiliary robot can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0129] In one embodiment, a substation operation auxiliary robot is also provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in each of the method embodiments.

[0130] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the steps in each of the method embodiments.

[0131] In one embodiment, a computer program product is provided, including a computer program, and the computer program is executed by a processor to implement the steps in each of the method embodiments.

[0132] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of the country and region.

[0133] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments of each method. In the embodiments provided in the present application, any reference to memory, database or other medium can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetic variable memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0134] The technical features of the above embodiments can be combined in any way. In order to make the description simple, not all possible combinations of the technical features in the above embodiments are described, but as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0135] The above-described embodiments are merely illustrative of several embodiments of the present application, which are described in more detail and in a specific manner, but should not be construed as limiting the scope of the patent of the present application. It should be noted that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A robot control method characterized by, The method comprises: In response to a user's information input operation, obtaining a target control signal, comprising: in response to a user's information input operation, obtaining a control signal; converting the control signal into a digital signal; extracting the signal corresponding to the key information in the digital signal as the target control signal; wherein the control signal includes a voice signal; the digital signal includes a first digital signal corresponding to the voice signal; the control signal includes a gesture video signal; the digital signal includes a second digital signal corresponding to the gesture video signal; specifically including: determining the digital signal corresponding to the moving description vocabulary information in the first digital signal as the target voice signal; taking the target voice signal as the target control signal corresponding to the voice signal; determining the digital signal corresponding to the image information belonging to the target area in the second digital signal as the target gesture signal; taking the target gesture signal as the target control signal corresponding to the gesture video signal; Input the target control signal into the instruction fuzzy recognition model to obtain the membership degree of the target control signal with respect to each candidate instruction fuzzy category; each candidate instruction fuzzy category includes a moving instruction category in forward, backward, left turn and right turn; The candidate instruction fuzzy category corresponding to the maximum membership degree is taken as the target instruction category corresponding to the target control signal; Perform an operation matching the target instruction signal corresponding to the target instruction category.

2. The method of claim 1, wherein, The instruction fuzzy recognition model comprises an instruction fuzzy recognition model corresponding to each candidate instruction fuzzy category; each instruction fuzzy recognition model is composed of a corresponding membership function; the input of the target control signal into the instruction fuzzy recognition model to obtain the membership degree of the target control signal with respect to each candidate instruction fuzzy category comprises: Input the target control signal into the membership function corresponding to each candidate instruction fuzzy category to obtain the membership degree output by each membership function for the target control signal; According to the membership degree output by each membership function for the target control signal, determine the membership degree of the target control signal with respect to each candidate instruction fuzzy category.

3. A robot control device characterized by comprising: The device comprises: An acquisition module configured to obtain a target control signal in response to a user's information input operation; The acquisition module is specifically configured to: in response to a user's information input operation, acquire a control signal; convert the control signal into a digital signal; extract a signal corresponding to key information in the digital signal as a target control signal; wherein the control signal includes a voice signal; the digital signal includes a first digital signal corresponding to the voice signal; the control signal includes a gesture video signal; the digital signal includes a second digital signal corresponding to the gesture video signal; and specifically configured to: determine a digital signal corresponding to mobile description vocabulary information in the first digital signal as a target voice signal; take the target voice signal as a target control signal corresponding to the voice signal; determine a digital signal corresponding to image information belonging to a target area in the second digital signal as a target gesture signal; and take the target gesture signal as a target control signal corresponding to the gesture video signal. The input module is configured to input the target control signal into an instruction fuzzy identification model to obtain the membership degree of the target control signal with respect to each candidate instruction fuzzy category; and each candidate instruction fuzzy category includes a mobile instruction category in forward, backward, left turn, and right turn. The category determination module is configured to take a candidate instruction fuzzy category corresponding to the maximum membership degree as a target instruction category corresponding to the target control signal. The execution module is configured to execute an operation matched with a target instruction signal corresponding to the target instruction category.

4. A substation work assisting robot comprising a memory and a processor, the memory storing a computer program, characterized by, The processor executes the computer program to implement the steps of the method in any one of claims 1 to 2.

5. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 2.

6. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 2.

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