Instrument data acquisition method, data processing method and device

Abstract

The embodiment of the present application provides an instrument data acquisition method, a data processing method and a device. Among them, the recognition model corresponding to the production equipment and the associated information related to the recognition model are determined; in response to the image acquisition operation, the dial image of the instrument of the production equipment is acquired according to the area indication information in the associated information; the instrument data in the dial image is identified by using the recognition model; the instrument data is verified according to the verification indication information in the associated information; and the instrument data is sent when the verification of the instrument data is successful. The technical solution provided by the embodiment of the present application improves the portability of instrument data acquisition and ensures the accuracy of instrument data.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of production and manufacturing technology, and in particular to an instrument data acquisition method, a data processing method and a device. Background Art

[0002] In a product manufacturing environment, production equipment is usually equipped with instruments to record the operating status of the production equipment. In order to monitor the operating status of the production equipment and respond to abnormal events, manufacturers currently use MES (manufacturing execution system) to manage production conditions. MES needs to collect instrument data from production equipment.

[0003] In the related technology, the production equipment is connected to the host computer through the serial port or the gateway is connected to the local area network. The MES collects the instrument data through the open interface, but this data acquisition method is relatively complicated. Summary of the invention

[0004] The embodiments of the present application provide an instrument data acquisition method, a data processing method and a device to solve the technical problem that the data acquisition method in the prior art is relatively complicated.

[0005] In a first aspect, an embodiment of the present application provides a method for acquiring instrument data, comprising:

[0006] Determine the identification model corresponding to the production equipment and the associated information related to the identification model;

[0007] In response to the image acquisition operation, acquiring a dial image of an instrument of the production equipment according to the area indication information in the association information;

[0008] Using the recognition model, identifying instrument data in the dial image;

[0009] Verifying the instrument data according to the verification instruction information in the associated information;

[0010] When the meter data verification is successful, the meter data is sent.

[0011] In a second aspect, an embodiment of the present application provides a data processing method, including:

[0012] Determine a plurality of sample images obtained by performing image acquisition on an instrument in a production device at a plurality of acquisition angles and / or in a plurality of acquisition environments;

[0013] Determine label data corresponding to each of the plurality of sample images;

[0014] Using the plurality of sample images and the corresponding label data, training a recognition model;

[0015] Determine the associated information corresponding to the production equipment;

[0016] Establish an association relationship between the recognition model, the association information and the production equipment; the recognition model is used to recognize the instrument data in the dial image of the instrument; the association information is used to instruct the extraction of the dial image and the verification of the instrument data.

[0017] In a third aspect, an electronic device is provided in an embodiment of the present application, including an image acquisition component, a storage component, and a processing component;

[0018] The storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement the instrument data acquisition method as described in the first aspect above.

[0019] In a fourth aspect, an embodiment of the present application provides a computing device, including a storage component and a processing component;

[0020] The storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement the data processing method as described in the second aspect above.

[0021] In the embodiment of the present application, the recognition model corresponding to the production equipment and the associated information related to the recognition model are determined; in response to the image acquisition operation, the dial image of the instrument of the production equipment is acquired according to the area indication information in the associated information; the instrument data in the dial image is identified using the recognition model; the instrument data is verified according to the verification indication information in the associated information; and the instrument data is sent if the verification of the instrument data is successful. By acquiring the dial image of the instrument, the automatic reading of the instrument data is realized by using the recognition model, which improves the portability of acquisition, and the verification of the instrument data is realized in combination with the associated information of the recognition model, which ensures the accuracy of the instrument data.

[0022] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 A schematic diagram showing a system structure in which the technical solution of an embodiment of the present application is applied;

[0025] Figure 2 A flow chart showing an embodiment of a method for acquiring instrument data provided by the present application is shown;

[0026] Figure 3 A flow chart showing an embodiment of a data processing method provided by the present application is shown;

[0027] Figure 4 A schematic diagram of scene interaction in a practical application of an embodiment of the present application is shown;

[0028] Figure 5 A schematic diagram showing the structure of an embodiment of a meter data device provided by the present application is shown;

[0029] Figure 6 A schematic structural diagram of an embodiment of an electronic device provided by the present application is shown;

[0030] Figure 7 A schematic diagram showing the structure of an embodiment of a data processing device provided by the present application is shown;

[0031] Figure 8 A schematic diagram of the structure of an embodiment of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0033] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0034] The technical solution of the embodiment of the present application is mainly applicable to the application scenario of instrument data acquisition, especially instrument data of production equipment.

[0035] As mentioned in the background technology, the traditional instrument data acquisition method is relatively complicated. In addition, there are certain requirements for production equipment, which requires the production equipment to have corresponding interfaces. Some production equipment may not provide collection interfaces, so it is necessary to customize and develop different production equipment. In addition, the manufacturing execution system (MES) needs to integrate multiple production equipment and adopt hardware integration. The integration workload is large and the data acquisition cost is also high.

[0036] In order to improve the portability of data acquisition and reduce the cost of data acquisition, the inventor has proposed the technical solution of the present application after a series of studies. In the embodiment of the present application, by collecting the dial image of the instrument and using the recognition model, the automatic reading of the instrument data is realized, thereby improving the portability of data acquisition and effectively reducing the cost of data acquisition without the need for hardware integration. In addition, the verification of the instrument data is realized by combining the associated information of the recognition model, thereby ensuring the accuracy of the instrument data.

[0037] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0038] Figure 1 A schematic diagram of a system architecture is shown in which the technical solution of the embodiment of the present application can be applied. The system architecture may include a client 101 and a server 102.

[0039] The client 101 and the server 102 may be connected via a network. The network provides a medium for a communication link between the client 101 and the server 102. The network may include various connection types, such as wired or wireless communication links or optical fiber cables.

[0040] Among them, the client 101 can be an APP (Application), or a web application such as H5 (HyperText Markup Language5, Hypertext Markup Language 5th Edition) application, or a light application (also known as a mini-program, a lightweight application) or a cloud application, etc. The client 101 is deployed in an electronic device and needs to rely on the device or certain APPs in the device to run. For example, the electronic device can have a display screen and support information browsing, such as a personal mobile terminal such as a mobile phone, a tablet computer, a personal computer, etc., and can also have an image acquisition function, etc. For ease of understanding, Figure 1The client is mainly represented by a device. Various other types of applications can usually be configured in electronic devices, such as human-computer dialogue applications, model training applications, text processing applications, web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc. Of course, the electronic device can also be a dedicated device that is configured with the client separately.

[0041] The server 102 may include servers that provide various services, such as a server for background training that provides support for the model used on the client 101.

[0042] In addition, the client 101 can use the image acquisition function of the electronic device to acquire images of the instruments in the production equipment 103 to obtain instrument data, etc. The client 101 can establish a network connection with the production execution system (MES) 104. Of course, in other embodiments of the present application, the production execution system 104 can be integrated in the server 102. In addition, in other embodiments of the present application, the production execution system 104 can be connected to the server 102 through the network, and the client 101 can exchange information with the production execution system 104 through the server 102.

[0043] It should be noted that the server 102 can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. The server can also be a server of a distributed system, or a server combined with a blockchain. The server can also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology.

[0044] It should be noted that the instrument data acquisition method provided in the embodiment of the present application is generally executed by the client 101, and the corresponding instrument data acquisition device is generally set in the client 101. However, in other embodiments of the present application, the server 102 may also have similar functions as the client 101, so as to execute the instrument data acquisition method provided in the embodiment of the present application. In other embodiments, the instrument data acquisition method provided in the embodiment of the present application may also be jointly executed by the client 101 and the server 102.

[0045] It should be noted that the data processing method provided in the embodiment of the present application is generally executed by the server 102, and the corresponding data processing device is generally arranged in the server 102. However, in other embodiments of the present application, the client 101 may also have similar functions as the server 102, so as to execute the data processing method provided in the embodiment of the present application. In other embodiments, the data processing method provided in the embodiment of the present application may also be jointly executed by the client 101 and the server 102.

[0046] It should be understood that Figure 1The number of clients and servers in the example is only for illustration. Any number of clients and servers may be used as required. Different staff members may use different clients to obtain instrument data, etc.

[0047] Figure 1 The number of production equipment in the figure is also only for illustration. It can be understood by those skilled in the art that in an actual production environment, a production workshop may deploy multiple production equipment, and the staff may use the client to obtain instrument data of different production equipment.

[0048] The implementation details of the technical solution of the embodiment of the present application are elaborated in detail below.

[0049] Figure 2 A flowchart of an embodiment of a method for acquiring instrument data provided in an embodiment of the present application, the method may include the following steps:

[0050] 201: Determine the identification model corresponding to the production equipment and the associated information related to the identification model.

[0051] The recognition model may be obtained by training based on sample images and label data of the instrument dial, wherein the label data includes instrument data corresponding to the sample images, etc.

[0052] The instrument in the embodiment of the present application may refer to an instrument used to measure the production status of a production device, which has a dial to display the measured data, i.e., instrument data. The instrument data may include, for example, temperature, air pressure, electricity, blood pressure, flow, output, fault indication, etc. The dial may include pointer-type data, which is represented by scale values, and may also include digital, text, or indicator light data.

[0053] Among them, the recognition models corresponding to different production equipment and their related association information can be pre-trained, and the server can save the equipment identification, recognition model and related information accordingly. The specific training method will be introduced in detail in the following embodiments.

[0054] There are multiple ways to determine the identification model corresponding to the production equipment and the associated information related to the identification model:

[0055] As an optional method, multiple model selection prompt information can be displayed; based on the user selection operation, the selected recognition model and the associated information related to the recognition model are determined.

[0056] The model selection prompt information may include equipment information such as the equipment identification of the production equipment, which is used to facilitate users to find and confirm the production equipment, and then perform the selection operation. The identification model corresponding to the selected model selection prompt information and its related associated information are used as the identification model and associated information corresponding to the production equipment.

[0057] Optionally, based on the user selection operation, a model selection request including a device identifier may be sent to the server, so that the server determines the recognition model corresponding to the model selection prompt information operated by the user and associated information related to the recognition model, and sends them to the client.

[0058] As another optional method, the information code configured by the production equipment is scanned to obtain the equipment identification, and the corresponding recognition model and associated information related to the recognition model are searched based on the equipment identification.

[0059] The information code may be a graphic code such as a barcode, a QR code or a 3D code that carries the device identification. The information code may be scanned using an image acquisition component and decoded to obtain device information such as the device identification. Thus, the identification model and associated information corresponding to the device identification may be searched based on the device identification.

[0060] Optionally, a model acquisition request may be sent to the server based on the device identification, so that the server can search for the recognition model and associated information corresponding to the device identification, and send them to the client.

[0061] As another optional method, the radio frequency tag configured for the production equipment is read to obtain the equipment identification, and the corresponding recognition model and associated information related to the recognition model are searched based on the equipment identification.

[0062] The radio frequency tag can be an NFC (Near Field Communication) tag. By using the near field communication technology, a contactless method can be adopted. The NFC tag in the electronic device is brought close to the NFC tag configured on the production equipment. That is, the NFC tag configured on the production equipment can be read to obtain device information such as the device identification, so that the identification model and related information corresponding to the device identification can be found based on the device identification.

[0063] Optionally, a model acquisition request may be sent to the server based on the device identification, so that the server can search for the recognition model and associated information corresponding to the device identification, and send them to the client.

[0064] 202: In response to the image acquisition operation, obtain a dial image of an instrument of the production equipment according to the area indication information in the associated information.

[0065] The associated information may include area indication information, and the instrument image may be collected according to the area indication information, thereby improving the accuracy of extracting the target area from the instrument image. The target area may refer to the ROI area, that is, the dial area of ​​the instrument.

[0066] The area indication information may include an area restriction condition, and the area location condition may include, for example, a target area location, etc.

[0067] As an optional method, an image preview interface may be displayed in response to an image acquisition operation; area prompt information corresponding to the target area position may be displayed on the image preview interface according to the area indication information in the associated information; and a dial image corresponding to the target area position may be extracted from the acquired instrument image based on a confirmation operation on the image preview interface.

[0068] The image preview interface can present the currently captured image. The area prompt information may include, for example, an area box, etc., to prompt the user that they can move the instrument dial to the area box by moving the capture area. After that, they can perform a confirmation operation on the image presented in the image preview interface to generate an instrument image. Based on the target area position, the dial image can be accurately extracted from the instrument image.

[0069] The position of the target area may be determined based on the target area marked on the sample image of the production equipment.

[0070] 203: Using the recognition model, identify the instrument data in the dial image.

[0071] 204: Verify the instrument data according to the verification instruction information in the associated information.

[0072] The instrument data can be identified from the dial image using the recognition model. However, due to the influence of external environment such as light, the collected instrument image may not be able to accurately extract the dial image and accurately identify the instrument data from the dial image. The accuracy of dial image extraction can be improved through the area indication information in the associated information, thereby ensuring the recognition accuracy to a certain extent. In addition, the associated information can also include verification indication information to verify the identified instrument data to improve the recognition accuracy.

[0073] The verification prompt information may include a verification condition, and the meter data is verified by determining whether the meter data meets the verification condition.

[0074] The verification condition may include, for example, a predetermined data format and / or a predetermined data type, etc., and the meter data is verified by judging whether the meter data conforms to the predetermined data format and / or whether it conforms to the predetermined data type. Data format refers to the data arrangement format, which may include arrangement formats such as numerical values, characters or binary numbers. For example, %d indicates output according to the actual length of integer data; another example is %md, where m is the width of the specified output field. If the number of digits of the data is less than m, spaces are added to the left end. If it is greater than m, it is output according to the actual number of digits; another example is %ld, which outputs long integer data, etc. The data type may include, for example, integer type, real number type, Boolean type, character type and combination type, etc.

[0075] Assuming that the predetermined data format is %d, if the value 9 in the instrument data is recognized as P, since P is a letter, it indicates that the recognition is wrong and the instrument data verification fails.

[0076] The verification prompt information may be determined based on the instrument type, data attributes, etc. of the production equipment, and may also be obtained by user settings, etc.

[0077] 205: When the meter data verification is successful, the meter data is sent.

[0078] In the embodiment of the present application, the instrument data can be sent after the instrument data verification is successful, so as to ensure the accuracy of the sent instrument data.

[0079] Optionally, the instrument data may be sent to a production execution system. The production execution system may determine the production status of the production equipment based on the instrument data, and then make processing decisions, etc. The client may send the instrument data directly to the production execution system, or send it to the production execution system through the server, etc.

[0080] In addition, the associated information may further include reporting instruction information. When the instrument data verification is successful, the instrument data may be sent to the production execution system according to the reporting instruction information in the associated information.

[0081] Among them, the reporting indication information may include reporting methods, such as reporting data format, reporting data type, and / or reporting description method, etc. For example, if the instrument data is a temperature value, the reporting description method is "the current temperature value is %d℃ (degrees Celsius)", and %d represents the reporting data format, so that the instrument data can be converted into the reporting data format, etc., and then sent to the production execution system according to the reporting description method.

[0082] In this embodiment, by collecting the dial image of the instrument and using the recognition model, the instrument data is automatically read, which improves the portability of data acquisition and does not require hardware integration. The data acquisition cost can be effectively reduced. The instrument data is verified in combination with the associated information of the recognition model to ensure the accuracy of the instrument data.

[0083] In some embodiments, the association information may further include abnormal indication information, and the method may further include:

[0084] According to the abnormal indication information in the associated information, when it is determined that the instrument data is abnormal data, an early warning prompt information is output.

[0085] Early warning information can promptly alert users to abnormal conditions in production equipment, allowing for timely intervention and processing.

[0086] The abnormal indication information may include an abnormal condition, such as a data value range, and whether the instrument data is abnormal data can be determined by judging whether the instrument data is within the data value range.

[0087] The early warning prompt information can be used to prompt that the production equipment has an abnormality, which may include the instrument data. In addition, the corresponding processing method can be determined according to the instrument data, so that the early warning prompt information including the processing method can be generated to prompt the production equipment to be intervened according to the processing method.

[0088] In some embodiments, the method may further include:

[0089] When the instrument data verification fails, an error message is output.

[0090] As an optional method, the error prompt information can prompt the user to perform a re-capture operation to re-execute the image capture operation to perform re-recognition and the like.

[0091] As another optional method, the error prompt information may include update prompt information, which is used to prompt the user to check the instrument dial to determine the real data, and modify the instrument data based on the real data, so that the updated instrument data can be sent.

[0092] In some embodiments, in order to further improve the recognition accuracy, the method may further include:

[0093] In case the meter data verification fails, the dial image is used as a sample image;

[0094] Send the sample image to the server; the sample image is used to combine the label data corresponding to the sample image to retrain the recognition model.

[0095] Among them, the label data corresponding to the sample image can be obtained by manual annotation. After receiving the sample image, the server can prompt the user to manually annotate the sample image, such as annotating the target area position, annotating the corresponding real instrument data, etc.

[0096] Of course, the tag data may also be obtained in other ways. In some embodiments, the error prompt information is used to prompt the user to perform a re-collection operation. The method may also include:

[0097] In case the meter data verification fails, the dial image is used as a sample image;

[0098] Determine the re-identification data that has been successfully verified corresponding to the sample image; the re-identification data is obtained by using the recognition model to recognize the re-collected dial image;

[0099] Use the re-identification data as label data for the sample image;

[0100] Send sample images and label data to the server; sample images are used in combination with label data to retrain the recognition model.

[0101] In some embodiments, when the error prompt information includes update prompt information, the method may further include:

[0102] In response to an update operation on the meter data, updating the meter data;

[0103] Send the updated instrument data to the production execution system.

[0104] In some embodiments, when the error prompt information includes update prompt information, the method may further include:

[0105] The dial image is used as a sample image, and the updated instrument data is used as label data;

[0106] Send sample images and label data to the server; sample images are used in combination with label data to retrain the recognition model.

[0107] In some embodiments, before determining the recognition model corresponding to the production equipment and the associated information related to the recognition model, the method may further include:

[0108] Determine a plurality of sample images obtained by performing image acquisition on an instrument in a production device at a plurality of acquisition angles and / or in a plurality of acquisition environments;

[0109] Based on manual labeling operations, determine the label data corresponding to the multiple sample images;

[0110] Send multiple sample images and their corresponding label data to the server; the multiple sample images and their corresponding label data are used to train the recognition model.

[0111] Among them, the label data may include manually annotated instrument data and target areas, etc.

[0112] The plurality of sample images may be obtained by acquiring images of instruments in production equipment from a plurality of acquisition angles in a plurality of acquisition environments; wherein the plurality of acquisition environments may be acquisition environments with different light, and the like.

[0113] Of course, since different devices may also affect the image acquisition results, the multiple sample images used to train the recognition model may include sample images acquired by using multiple acquisition devices at multiple acquisition angles and / or multiple acquisition environments, etc.

[0114] Optionally, each time a sample image is obtained, corresponding annotation prompt information may be output, and the user may annotate the target area in the sample image and input corresponding instrument data and the like.

[0115] Figure 3 This is a flowchart of an embodiment of a data processing method provided in an embodiment of the present application. This embodiment mainly introduces the technical solution of the present application from the perspective of model generation. The method may include the following steps:

[0116] 301: Determine a plurality of sample images obtained by performing image acquisition on an instrument in a production device at a plurality of acquisition angles and / or a plurality of acquisition environments.

[0117] The plurality of sample images may be acquired by a plurality of acquisition devices, which may be electronic devices equipped with the above-mentioned client or other devices with image acquisition functions.

[0118] 302: Determine label data corresponding to a plurality of sample images respectively.

[0119] Optionally, the target areas and corresponding instrument data in multiple sample images can be determined based on manual annotation operations. The target areas and corresponding instrument data in the sample images can be annotated manually. After the acquisition device acquires the sample images, it can output annotation prompt information, and the user can annotate the target areas and instrument data accordingly.

[0120] 303: Train a recognition model using multiple sample images and their corresponding label data.

[0121] Each sample image and its corresponding label data can be used as a training sample, and a recognition model can be trained and generated using a large number of training samples.

[0122] The recognition model may include a region extraction module and a data recognition module. During the training process, the region extraction module of the recognition model may be trained according to the marked target area, and the data recognition module may be trained according to the instrument data.

[0123] 304: Determine the associated information corresponding to the production equipment.

[0124] 305: Establish the relationship between the identification model, associated information and production equipment.

[0125] For each production equipment Figure 3 The implementation method of the illustrated embodiment is executed to obtain the identification models and associated information corresponding to different production equipment.

[0126] The recognition model is used to recognize the instrument data in the dial image of the instrument; the associated information is used to instruct to extract the dial image and verify the instrument data.

[0127] The recognition model, the associated information and the equipment identification of the production equipment may be stored in correspondence. The production equipment may be provided with an information code or a radio frequency tag containing the equipment identification, from which the equipment identification may be obtained, and the corresponding recognition model and associated information may be obtained based on the equipment identification.

[0128] The associated information may include area indication information for indicating the extraction of the dial image, and may also include verification indication information for verifying the instrument data, etc.

[0129] In addition, the associated information may also include reporting instruction information for instructing to send the meter data.

[0130] In addition, the associated information may also include abnormal indication information, which is used to determine whether the instrument data is abnormal data.

[0131] In some embodiments, the associated information of the production equipment can be obtained according to the equipment type of the production equipment, the data requirements of the production execution system, etc.

[0132] Of course, the associated information can also be obtained through manual setting operations on the production equipment, etc.

[0133] In some embodiments, the method may further include:

[0134] Send multiple model selection prompts to the client;

[0135] Based on the model selection request sent by the client, the selected recognition model and corresponding associated information are sent to the client.

[0136] In some embodiments, the method may further include:

[0137] Receive the device identification sent by the client, and search for the identification model and associated information corresponding to the device identification;

[0138] Send the recognition model and associated information to the client.

[0139] The client can scan the information code set by the production equipment or read the equipment identification from the radio frequency tag set by the production equipment.

[0140] The client responds to the image acquisition operation, that is, it can obtain the dial image of the instrument of the production equipment according to the area indication information in the associated information, and use the recognition model to identify the instrument data in the dial image, and verify the instrument data according to the verification indication information in the associated information, and send the instrument data if the instrument data verification is successful.

[0141] For ease of understanding, the following Figure 4 The scene interaction diagram shown in the figure explains the technical solution of this application. Figure 4 As shown in

[0142] The first user can use the client 401 configured on the first electronic device to acquire images of the instrument dial from multiple acquisition angles under multiple acquisition environments for any production equipment 400 in the production workshop to obtain multiple sample images, and respectively mark the target areas and corresponding instrument data in the multiple sample images, as well as relevant information of the production equipment corresponding to the multiple sample images, such as equipment identification, equipment type, etc.; and send the multiple sample images and the manually labeled data to the server 402.

[0143] The server 402 can train a recognition model based on the multiple sample images, as well as the target areas and instrument data manually annotated on the multiple sample images, so as to obtain recognition models of different production equipment, and determine the associated information corresponding to the production equipment based on the relevant information of the production equipment; and then save the recognition model in correspondence with the equipment identification and associated information.

[0144] The second user can use the client 403 configured in the second electronic device to obtain the device identification for any production device in the production workshop by scanning the QR code set on the production device or reading the NFC tag set on the production device, and based on the device identification, request to obtain the corresponding recognition model and the associated information related to the recognition model from the server 402. The first user and the second user can be the same user or different users, and the first electronic device can be the same as or different from the second electronic device.

[0145] Afterwards, the client 403 can display an image preview interface based on the image acquisition operation triggered by the user, and display area prompt information based on the target area position in the image preview interface according to the area indication information in the associated information; then, according to the confirmation operation on the image preview interface, the dial image corresponding to the target area position can be extracted from the acquired image.

[0146] The client 403 can use the recognition model to identify the instrument data in the dial image; verify the instrument data according to the verification instruction information in the associated information; if the instrument data verification is successful, the instrument data can be sent to the MES404 according to the reporting instruction information in the associated information.

[0147] In addition, the client 403 may also output warning information when determining that the instrument data is abnormal data according to the abnormal indication information in the associated information.

[0148] Among them, when the meter data verification fails, the client 403 can also output an error prompt message to prompt the user to re-collect the dial image, etc.

[0149] When the meter data verification fails, the client 403 may also use the dial image as a sample image and send the sample image to the server 402 . The server 402 may retrain the recognition model based on the sample image and the label data.

[0150] The label data may be re-identified data that has been successfully verified, or may be obtained through manual annotation, or may be updated instrument data, etc.

[0151] Through the technical solution of the embodiment of the present application, there is no need to make any modifications to the production equipment. Only an electronic device with an image acquisition function, such as a mobile phone or a PDA, is needed. By simply acquiring the dial image of the instrument on the production equipment, the instrument data can be automatically identified and reported to the MES system, thereby realizing automatic reading of the instrument data and improving the portability of instrument data acquisition. In addition, the instrument data can be verified in combination with the associated information of the recognition model to ensure the accuracy of the instrument data. In the event that the instrument data verification fails, the corresponding dial image can be used as a sample image to retrain the recognition model, thereby improving the applicability of the recognition model and the recognition accuracy, so as to further ensure the accuracy of the instrument data. In addition, the instrument data can also be detected for abnormalities, so as to output warning prompt information when it is abnormal data, so as to achieve the purpose of timely warning, so that the user can be promptly prompted with abnormal conditions in the production equipment, so that timely intervention and processing can be carried out.

[0152] Figure 5 A schematic diagram of a structure of an embodiment of an instrument data acquisition device provided in an embodiment of the present application, the device may include:

[0153] The first determination module 501 is used to determine the recognition model corresponding to the production equipment and the associated information related to the recognition model;

[0154] An image acquisition module 502 is used to acquire a dial image of an instrument of the production equipment according to the area indication information in the associated information in response to the image acquisition operation;

[0155] The recognition module 503 is used to recognize the instrument data in the dial image by using the recognition model;

[0156] Verification module 504, used to verify the instrument data according to the verification instruction information in the associated information;

[0157] The data sending module 505 is used to send the instrument data when the instrument data verification is successful.

[0158] In some embodiments, the device may further include:

[0159] The first processing module is used to output an error prompt message when the instrument data verification fails.

[0160] In some embodiments, the device may further include:

[0161] The second processing module is used to use the dial image as a sample image when the meter data verification fails; send the sample image to the server; the sample image is used to combine the label data corresponding to the sample image to retrain the recognition model.

[0162] In some embodiments, the error prompt information is used to prompt the user to perform a re-collection operation;

[0163] The first processing module may be used to use the dial image as a sample image when the meter data verification fails;

[0164] Determine the re-identification data corresponding to the sample image that has been successfully verified; the re-identification data is obtained by using the recognition model to recognize the re-collected image;

[0165] Use the re-identification data as label data for the sample image;

[0166] Send sample images and label data to the server; sample images are used in combination with label data to retrain the recognition model.

[0167] In some embodiments, the error prompt information includes update prompt information.

[0168] The first processing module may also be configured to update the instrument data in response to an update operation on the instrument data; and send the updated instrument data to the production execution system.

[0169] In some embodiments, the first processing module can also be used to use the dial image as a sample image and the updated instrument data as label data; send the sample image and label data to the server; the sample image is used to combine with the label data to train the recognition model.

[0170] In some embodiments, the first acquisition module can be specifically used to respond to an image acquisition operation, display an image preview interface, and display acquisition prompt information corresponding to the target area position in the image preview interface according to the area indication information in the associated information; and extract the dial image corresponding to the target area position from the acquired image based on the confirmation operation on the image preview interface.

[0171] In some embodiments, the data sending module is specifically used to send the instrument data to the production execution system according to the reporting instruction information in the associated information.

[0172] In some embodiments, the device may further include:

[0173] The exception handling module is used to output warning prompt information when determining that the instrument data is abnormal data according to the abnormal indication information in the associated information.

[0174] In some embodiments, the first determination module may be specifically used to display a plurality of model selection prompt information; based on the user selection operation, determine the selected recognition model and the associated information related to the recognition model;

[0175] Alternatively, scan the information code configured by the production equipment to obtain the equipment identification, and search for the corresponding recognition model and the associated information related to the recognition model based on the equipment identification;

[0176] Alternatively, the radio frequency tag configured for the production equipment is read to obtain the equipment identification, and the corresponding recognition model and associated information related to the recognition model are searched based on the equipment identification.

[0177] In some embodiments, the device may further include:

[0178] A training trigger module is used to acquire multiple sample images by performing image acquisition on instruments in production equipment at multiple acquisition angles and / or multiple acquisition environments;

[0179] Based on manual labeling operations, the label data corresponding to the multiple sample images are determined;

[0180] Send multiple sample images and their corresponding label data to the server; the multiple sample images and their corresponding label data are used to train the recognition model.

[0181] Figure 5 The instrument data acquisition device can execute Figure 2The implementation principle and technical effect of the instrument data acquisition method described in the embodiment are not described in detail. The specific way in which each module and unit performs operations in the instrument data acquisition device in the above embodiment has been described in detail in the embodiment of the method, and will not be described in detail here.

[0182] The present application also provides an electronic device, such as Figure 6 As shown, the electronic device may include an image acquisition component 601, a storage component 602 and a processing component 603;

[0183] The storage component 602 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component to implement the following steps: Figure 2 The instrument data acquisition method shown.

[0184] Of course, the electronic device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0185] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc. The communication component is configured to facilitate wired or wireless communication between the computing device and other devices.

[0186] The processing component may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0187] The storage component is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0188] The image acquisition component may be, for example, a component including an image sensor such as a camera. The image sensor may include, for example, a CCD (charge-coupled device) or a CMOS (complementary metal oxide semiconductor).

[0189] The display component may be an electroluminescent (EL) element, a liquid crystal display or a micro display having a similar structure, or a retinal direct display or a similar laser scanning display.

[0190] The present application also provides a computer-readable storage medium storing a computer program, which can achieve the above-mentioned Figure 2 The instrument data acquisition method of the illustrated embodiment. The computer readable medium may be included in the electronic device described in the above embodiment; or it may exist independently without being assembled into the electronic device.

[0191] The present application also provides a computer program product, which includes a computer program carried on a computer-readable storage medium. When the computer program is executed by a computer, the computer program can achieve the above-mentioned Figure 2 The instrument data acquisition method of the illustrated embodiment. In such an embodiment, the computer program can be downloaded and installed from a network, and / or installed from a removable medium. When the computer program is executed by a processor, various functions defined in the system of the present application are executed.

[0192] Figure 7 A schematic diagram of a data processing device according to an embodiment of the present application is provided. The device may include:

[0193] The second determination module 701 is used to determine a plurality of sample images obtained by performing image acquisition on an instrument in a production device at a plurality of acquisition angles and / or a plurality of acquisition environments; and to determine label data corresponding to each of the plurality of sample images;

[0194] A training module 702 is used to train a recognition model using a plurality of sample images and their corresponding label data;

[0195] The third determination module 703 is used to determine the associated information corresponding to the production equipment;

[0196] The association module 704 is used to establish an association relationship between the recognition model, the association information and the production equipment; the recognition model is used to recognize the instrument data in the dial image of the instrument; the association information is used to instruct to verify the instrument data.

[0197] Figure 7 The data processing device can execute Figure 3 The implementation principle and technical effect of the data processing method described in the illustrated embodiment will not be described in detail. The specific manner in which each module and unit performs operations in the instrument data acquisition device in the above embodiment has been described in detail in the embodiment of the method, and will not be described in detail here.

[0198] The present application also provides a computing device, which can be implemented as follows: Figure 1 The server side shown in Figure 8 As shown, the computing device may include a storage component 801 and a processing component 802;

[0199] The storage component 801 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component to implement the following steps: Figure 3 The data processing method shown.

[0200] Of course, a computing device may also include other components, such as input / output interfaces, communication components, etc.

[0201] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc. The communication component is configured to facilitate wired or wireless communication between the computing device and other devices.

[0202] The processing component may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0203] The storage component is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0204] It should be noted that the above computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. It can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or as a single server or a single terminal device.

[0205] The present application also provides a computer-readable storage medium storing a computer program, which can achieve the above-mentioned Figure 3The data processing method of the illustrated embodiment. The computer readable medium may be included in the electronic device described in the above embodiment; or it may exist independently without being assembled into the electronic device.

[0206] The present application also provides a computer program product, which includes a computer program carried on a computer-readable storage medium. When the computer program is executed by a computer, the computer program can achieve the above-mentioned Figure 3 The data processing method of the illustrated embodiment. In such an embodiment, the computer program can be downloaded and installed from a network, and / or installed from a removable medium. When the computer program is executed by a processor, various functions defined in the system of the present application are executed.

[0207] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0208] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0209] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0210] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for acquiring instrument data, It is characterized in that Applied to a client, the client is deployed in an electronic device with an image acquisition function, and includes: Determine the recognition model corresponding to the production equipment and the associated information related to the recognition model; different production equipment corresponds to different recognition models; In response to an image acquisition operation, acquiring a dial image of an instrument of the production equipment according to the area indication information in the association information; wherein the image acquisition operation is implemented based on the image acquisition function; Using the recognition model, identifying instrument data in the dial image; Verifying the instrument data according to the verification instruction information in the associated information; the verification instruction information includes a predetermined data format and / or a predetermined data type; If the instrument data verification is successful, sending the instrument data to the production execution system according to the reporting instruction information in the associated information; In the case where the meter data verification fails, the dial image is used as a sample image and an error prompt message is output; the error prompt message is used to prompt the user to perform a re-collection operation; Determine the verified re-identification data corresponding to the sample image; the re-identification data is obtained by using the recognition model to recognize the re-collected dial image; The re-identification data is used as label data of the sample image, and the sample image and the label data are sent to a server; the sample image is used to combine with the label data to retrain the recognition model.

2. The method according to claim 1, It is characterized in that Also includes: In case the meter data verification fails, taking the dial image as a sample image; The sample image is sent to a server; the sample image is used to combine with label data corresponding to the sample image to retrain the recognition model.

3. The method according to claim 1, It is characterized in that The error prompt information includes update prompt information, and the method further includes: In response to an update operation on the meter data, updating the meter data; Send the updated instrument data to the production execution system.

4. The method according to claim 3, It is characterized in that Also includes: Using the dial image as a sample image and the updated instrument data as label data; The sample image and the label data are sent to a server; the sample image is used in combination with the label data to retrain the recognition model.

5. The method according to claim 1, It is characterized in that In response to the image acquisition operation, extracting the dial image of the instrument of the production equipment according to the area indication information in the association information includes: In response to the image acquisition operation, an image preview interface is displayed, According to the area indication information in the association information, displaying area prompt information corresponding to the target area position in the image preview interface; According to the confirmation operation on the image preview interface, a dial image corresponding to the target area position is extracted from the collected instrument image.

6. The method according to claim 1, It is characterized in that Also includes: According to the abnormal indication information in the associated information, when it is determined that the instrument data is abnormal data, an early warning prompt information is output.

7. The method according to claim 1, It is characterized in that The determination of the identification model corresponding to the production equipment and the associated information related to the identification model includes: Display multiple model selection prompt information; determine the selected recognition model and related information related to the recognition model based on the user selection operation; Alternatively, scanning the information code configured by the production equipment to obtain the equipment identification, and searching for the corresponding recognition model and the associated information related to the recognition model based on the equipment identification; Alternatively, a radio frequency tag configured for the production equipment is read to obtain an equipment identification, and a corresponding recognition model and associated information related to the recognition model are searched based on the equipment identification.

8. The method according to claim 1, It is characterized in that Before determining the recognition model corresponding to the production equipment and the associated information related to the recognition model, the method further includes: Determine a plurality of sample images obtained by performing image acquisition on an instrument in the production equipment at a plurality of acquisition angles and / or a plurality of acquisition environments; Based on the manual labeling operation, determining the label data corresponding to each of the plurality of sample images; The plurality of sample images and the corresponding label data are sent to a server; the plurality of sample images and the corresponding label data are used to train the recognition model.

9. A data processing method, It is characterized in that include: Determine a plurality of sample images obtained by performing image acquisition on an instrument in a production device at a plurality of acquisition angles and / or in a plurality of acquisition environments; Determine label data corresponding to each of the plurality of sample images; Using the plurality of sample images and the corresponding label data, training a recognition model; Determine the associated information corresponding to the production equipment; the associated information includes verification indication information, and the verification indication information includes a predetermined data format and / or a predetermined data type; Establishing an association relationship between the recognition model, the association information and the production equipment; the recognition model is used to recognize the instrument data in the dial image of the instrument; The associated information is used to instruct extraction of the dial image and verification of the instrument data, wherein the dial image is acquired by the client using an image acquisition function in an electronic device; if the instrument data verification is successful, the client sends the instrument data to the production execution system according to the reporting instruction information in the associated information; In the event that the meter data verification fails, the sample image and label data sent by the client are obtained, and the recognition model is retrained; wherein the sample image includes the dial image, the label data includes the successfully verified re-identification data corresponding to the sample image, the re-identification data is obtained by using the recognition model to recognize the re-collected dial image, and the re-collected dial image is obtained by the client outputting an error prompt message to prompt the user to perform a re-collection operation.

10. An electronic device, It is characterized in that It includes image acquisition component, storage component and processing component; The storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement the instrument data acquisition method according to any one of claims 1 to 8.

11. A computing device, It is characterized in that including storage components and processing components; The storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement the data processing method as claimed in claim 9.

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