A device control method and apparatus, a computer device and a readable storage medium

Abstract

This application relates to a device control method, apparatus, computer device, and readable storage medium. The method includes: responding to a configuration operation by a target user on control software for a target device; the target device including a barcode reader; the configuration operation configuring at least one of a plurality of configurable functional items included in the control software; configuring the control software according to the configuration operation, and controlling the target device to operate through the configured control software, obtaining device operation data of the target device; evaluating the configuration operation and the device operation data to obtain a configuration success rate; determining the target user's level of understanding of the configuration progress of the control software based on the configuration success rate and the target user's historical configuration behavior data for the control software; and displaying the level of understanding of the configuration progress. Using this method, the configuration efficiency and accuracy of control software for devices such as barcode readers can be improved.

Description

Technical Field

[0001] This application relates to the field of equipment control technology, and in particular to an equipment control method, apparatus, computer equipment, and readable storage medium. Background Technology

[0002] With the rapid development of industrial automation and intelligent manufacturing technologies, industrial vision equipment, especially barcode readers, has become an indispensable core component in modern production lines, logistics warehousing, and quality traceability systems. To adapt to diverse application scenarios (such as different materials, lighting conditions, installation angles, and communication protocols), barcode reader manufacturers typically provide powerful and complex control software. This control software integrates numerous modules, including imaging configuration, algorithm parameter adjustment, communication settings, and output format definition. Each module contains dozens or even hundreds of adjustable function items, forming a parameter space that combines depth and breadth.

[0003] In related technologies, barcode reader users need to learn how to use the barcode reader control software to set the reader's operating parameters to meet their application needs. The barcode reader control software has many functional details, requiring a certain learning time. Often, understanding the meaning of many functions requires practical application in real-world scenarios. Many users, due to limited experience with barcode readers and a lack of professional guidance, do not achieve good results in learning the barcode reader control software. Summary of the Invention

[0004] Therefore, it is necessary to provide a device control method, apparatus, computer equipment, and readable storage medium to address the aforementioned technical problems, which can improve the configuration efficiency and accuracy of control software.

[0005] In a first aspect, this application provides a device control method, comprising:

[0006] In response to a configuration operation by a target user for the control software of a target device; the target device includes a barcode reader; the configuration operation is used to configure at least one of a plurality of configurable functional items included in the control software;

[0007] Configure the control software according to the configuration operation, and control the target device to operate through the configured control software to obtain the device operation data of the target device;

[0008] The success rate of the configuration operation is determined by evaluating the configuration operation based on the configuration operation and device operation data.

[0009] Based on the configuration success rate and the target user's historical configuration behavior data for the control software, determine the target user's level of control software configuration progress.

[0010] Secondly, this application provides a device control apparatus, comprising:

[0011] A response module is used to respond to a configuration operation by a target user on the control software of a target device; the target device includes a barcode reader; the configuration operation is used to configure at least one of a plurality of configurable function items included in the control software;

[0012] The operation module is used to configure the control software according to the configuration operation, and to control the target device to operate through the configured control software, thereby obtaining the device operation data of the target device;

[0013] The evaluation module is used to evaluate the configuration success rate of the configuration operation based on the configuration operation and device operation data.

[0014] The determination module is used to determine the target user's level of understanding of the configuration progress of the control software based on the configuration success rate and the target user's historical configuration behavior data.

[0015] The display module is used to show data on the progress of configuration.

[0016] Thirdly, this application provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the device control method described above.

[0017] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the device control method described above.

[0018] Fifthly, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps in the device control method described above.

[0019] The aforementioned equipment control methods, devices, computer equipment, computer-readable storage media, and computer program products, by accumulating each effective user operation and generating configuration progress mastery data, transform fragmented and aimless learning into a systematic process with clear goals and well-defined paths. Unlike traditional methods that rely on subjective feelings, this application embodiment uses intelligent evaluation based on real equipment operating data to output refined configuration success rates, providing accurate and interpretable quantitative feedback for each user operation, enabling scientific measurement of skill mastery quality. The combination of visualized progress goals and real-time operational feedback in this application embodiment forms a gamified positive interactive loop, effectively stimulating the user's learning motivation and desire to explore, transforming passive learning into active and continuous skill improvement, and enhancing the configuration efficiency and accuracy of the control software. Attached Figure Description

[0020] Figure 1 A schematic flowchart illustrating a device control method provided in an embodiment of this application;

[0021] Figure 2 A structural block diagram of a device control apparatus provided in an embodiment of this application;

[0022] Figure 3 An internal structural diagram of a computer device provided in an embodiment of this application;

[0023] Figure 4 This is an internal structural diagram of a computer-readable storage medium provided in an embodiment of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0025] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0026] The device control method provided in this application can be applied to the use and training scenarios of industrial vision equipment (such as barcode readers). The terminal can be a field industrial computer, engineer's workstation, or mobile operating terminal with barcode reader control software installed. The server can be an enterprise intranet server or a cloud service platform. In specific applications, engineers configure parameters on the barcode reader control software interface on the terminal. The software drives the barcode reader to perform trial runs according to the configuration and collects device operation data generated during imaging, decoding, and communication. This operation data and running data can be processed locally or uploaded to the server for intelligent evaluation. Based on the evaluation results (configuration success rate) and the engineer's historical learning records, the software updates the engineer's mastery progress of each function (such as exposure control, decoding parameter settings, etc.) and visualizes it in real time on the software interface using progress bars, color markers, etc.

[0027] like Figure 1 As shown, this application provides a device control method, which will be described using the aforementioned server as an example. The method includes the following steps:

[0028] Step 202, in response to a configuration operation by a target user for the control software of a target device; the target device includes a code reader; the configuration operation is used to configure at least one of a plurality of configurable functional items included in the control software.

[0029] The target users are operators learning or using the control software. They can log in to the system to manage their personalized learning progress. The target equipment refers to the physical device that requires parameter setting and control via the control software. Understandably, the control software for the target equipment may be feature-rich, detailed, and involve a complex learning process for the control methods. For example, in the field of intelligent industrial manufacturing, the target equipment could be a barcode reader, used to automatically identify and read one-dimensional and two-dimensional barcodes from object surfaces, widely used in production lines, logistics sorting, and quality traceability. The control software refers to the application running on a computer (such as an industrial PC) or embedded system, providing a graphical user interface (GUI) for configuring parameters, monitoring status, and viewing results of the barcode reader. Control software can include multiple configuration modules such as imaging, algorithms, communication, and output.

[0030] Configuration operations refer to the interactive behavior of users adjusting or setting configurable functions provided by the control software interface to achieve specific device control goals (such as clear imaging, stable decoding, and reliable communication of the code reader). Each configurable function can correspond to a specific configuration parameter or a set of related parameters in the software. For example, under the "Imaging Configuration" module, "Exposure Time," "Gain," and "Focus Position" are all independent function items; under the "Algorithm Configuration" module, "Code Type Selection," "Decoding Timeout," and "Code Reading Retry Count" are also independent function items. Configuration operations can be adjustments to a single function item (such as dragging the exposure time slider) or combined settings for multiple function items.

[0031] For example, user "Alice" needs to configure a barcode reader on a new production line to read laser-marked QR codes on metal parts. She opens the reader's control software, enters the "Imaging Configuration" interface, and manually adjusts the "Exposure Time" from the default 10 milliseconds to 25 milliseconds to obtain a brighter image. This action of "dragging the slider or entering a value" constitutes a configuration operation for the "Exposure Time" function.

[0032] Step 204: Configure the control software according to the configuration operation, and control the target device to operate through the configured control software to obtain the device operation data of the target device.

[0033] Here, "configured control software" refers to the software state where the user's configuration operation has taken effect. After responding to the user's operation, the new parameter values ​​are written to the reader's configuration file or sent directly to the reader to apply the new parameters.

[0034] Running refers to the process of actively or passively driving the barcode reader to execute one or more complete "trigger-imaging-decoding-output" work cycles according to the new parameters after the configuration takes effect, in order to verify the configuration effect. Running can be triggered by the user manually clicking the "test" button, or it can be executed automatically after detecting changes in key parameters.

[0035] Equipment operation data refers to multi-dimensional data generated by the barcode reader and its associated systems during operation, reflecting the actual effectiveness of configuration operations. This data is a key basis for objectively evaluating the success of the configuration.

[0036] For example, continuing from the previous example, when the user sets the exposure time to 25 milliseconds and clicks the "Test Photo" button, the control software sends the new exposure parameters to the barcode reader. The barcode reader takes a picture of the metal part on site based on these parameters, obtaining an image. The decoding algorithm inside the barcode reader attempts to decode this image and generates a series of process and result data. During this process, the device operation data, including at least one of the following, is simultaneously collected and recorded: image data captured by the barcode reader (i.e., the pixel information of the aforementioned metal part photo), decoding data output by the decoding algorithm (such as whether decoding was successful, decoding time, and reading confidence level), and if the configuration involves communication, communication status data with the host PLC (such as response delay and data packet correctness), and the final output result data (such as the read string content and output format). This provides the basis for subsequent intelligent and precise evaluation of whether the user configuration was successful, avoiding the limitations of judging solely based on the user's subjective feeling or a single result (such as "can read barcode" or "cannot read barcode").

[0037] Step 206: Evaluate the configuration success rate of the configuration operation based on the configuration operation and device operation data.

[0038] The configuration success rate is used to quantitatively evaluate the effectiveness of a single configuration operation. It can be a judgment of "success" or "failure" for the current configuration operation, or a continuous value (such as a score between 0 and 1) or a discrete level (such as "excellent," "good," or "needs improvement"), to characterize the extent to which the configuration achieved the expected goals, and whether the user's understanding and application of the functional items (i.e., learning how to use the control software) is correct and sufficient. The evaluation process is a comprehensive analytical process, the core of which lies in comparing and interpreting the user's intent (implied in the configuration operation) with the actual performance of the equipment (reflected in the equipment's operating data).

[0039] For example, taking the scenario of reading codes on metal parts as an example, we obtain Alice's configuration operation (setting exposure time = 25ms) and the corresponding device operating data (average image brightness improved to a reasonable range, successful decoding with high confidence). The evaluation process can analyze: whether the adjustment of exposure time significantly improved image quality (rather than overexposure), whether the improved image directly led to an improvement in decoding performance, and whether this adjustment reflects a correct understanding of the principle of the "exposure control" function (e.g., appropriately increasing exposure for reflective materials). Specifically, the above dimensions can be evaluated using preset or learned evaluation rules, thereby obtaining an evaluation conclusion of the configuration success rate, such as "Configuration success rate: 0.9 (Excellent)," indicating that the configuration is effective.

[0040] Step 208: Based on the configuration success rate and the target user's historical configuration behavior data for the control software, determine the target user's level of understanding of the configuration progress of the control software.

[0041] Historical configuration behavior data refers to a collection of records of all past configuration operations by the target user and their corresponding evaluation results, which can be stored in the user's personal learning progress database. Configuration progress mastery data serves as a quantitative indicator to comprehensively reflect the user's mastery of the entire control software. It is typically structured data and may include: mastery progress for each functional item (sub-configuration progress), summarized progress for each functional module (such as the imaging module and algorithm module), and overall mastery progress for the entire software.

[0042] For example, Alice's configuration history shows that she has successfully configured the "Exposure Control" function four times. This latest configuration also achieved a high success rate (0.9). Therefore, according to the rules (e.g., a cumulative total of five successful configurations is considered "mastered"), this success will be counted, bringing the cumulative total to five. Thus, Alice's sub-configuration progress for the "Exposure Control" function is determined to be 100% (mastered). Simultaneously, the progress of the "Imaging Configuration" module is updated (e.g., if this module has 10 functions and Alice has mastered 3, then the module progress is 30%), as well as Alice's overall software mastery progress.

[0043] Step 210 displays data on the level of control over the configuration progress.

[0044] The "display" aspect refers to presenting the calculated mastery level data of the configuration to the target user in an intuitive and easy-to-understand format through the user interface of the control software. For example, the control software interface could have a dedicated "Learning Center" or "My Progress" panel. In this panel, user Alice could see at least one of the following: a circular progress bar showing her overall software mastery level as 25%; a tree or list view listing all functional modules and their corresponding mastery levels (imaging configuration 30%, algorithm configuration 10%, etc.); and a corresponding status icon or color indicator next to each specific functional item (e.g., red for not mastered, yellow for in progress, green for mastered). When the user masters "Exposure Control," the item will change from yellow to green, and the progress number for the "Imaging Configuration" module will update in real time.

[0045] By transforming the calculation results inside the target device into external, visual feedback, it is possible to continuously stimulate users' learning motivation, guide them to actively tackle unmastered functionalities, and thus transform passive learning into active exploration.

[0046] This application embodiment constructs a complete "operation-evaluation-feedback-incentive" closed-loop system by responding to configuration operations, collecting equipment operation data, performing intelligent configuration success assessment, accumulating calculations, and visually displaying the configuration progress. This effectively solves the problems of vague feedback, lack of systematicity, and insufficient motivation faced by users when learning industrial control software in related technologies. It provides users with immediate and quantitative single-point operation feedback and panoramic learning progress, making the learning process systematic and goal-oriented, and improving the user's control efficiency and accuracy of the target equipment.

[0047] In some embodiments, the configuration success rate of a configuration operation is evaluated based on configuration operations and device operating data, including:

[0048] Based on the function type of the target function item targeted by the configuration operation, determine the multiple device operation indicators corresponding to the target function item and the influence weight of the configuration success rate of each device operation indicator.

[0049] Based on the equipment operation indicators, analyze the configuration operation and equipment operation data to obtain the indicator values ​​of the target user under each equipment operation indicator.

[0050] The configuration success rate is obtained by weighting the indicator values ​​according to the influence weight of the configuration success rate.

[0051] The target function item refers to the specific configurable function item targeted in this configuration operation, such as "exposure time." Function type is a logical classification of complex functions in the control software based on their technical purpose and scope. For example, it can be divided into imaging configuration functions, decoding algorithm configuration functions, communication configuration functions, output data format configuration functions, etc. Equipment operation indicators refer to specific parameters extracted from raw equipment operation data, used to quantitatively evaluate a particular technical dimension. Considering that each function type focuses on different core technical effects, its corresponding key evaluation indicators also differ. The configuration success rate impact weight is used to characterize the relative importance of this equipment operation indicator in the final configuration success rate calculation. The higher the configuration success rate impact weight value, the greater the influence of this indicator on judging whether the configuration was successful and the degree of success.

[0052] For example, suppose user Alice's target function for this configuration operation is "gain control," which belongs to the imaging configuration function. Based on a pre-established "function type-indicator-weight" mapping table (which can be preset through expert experience or data analysis), the device operating indicators corresponding to "gain control" can include: average image grayscale value, image signal-to-noise ratio (SNR), and image contrast. Since "gain" mainly affects the overall brightness and noise level of the image, the success rate of image SNR configuration is set to have the highest weight (e.g., 0.5), followed by average image grayscale value (e.g., 0.3), and image contrast has the lowest weight (e.g., 0.2). Conversely, if the target function is "code scheme selection" (belonging to the decoding algorithm configuration function), its core indicators might be decoding success rate and decoding time, with the decoding success rate having a significantly higher weight than decoding time.

[0053] In this embodiment, different types of configuration operations have different definitions and evaluation criteria for success. For imaging configuration, "generating a high-quality image" is the core objective; for algorithm configuration, "accurate and fast decoding" is the core objective. Therefore, by dynamically determining the evaluation indicators and their weights according to the function type, the evaluation model is no longer a "one-size-fits-all" single standard. This ensures the technical rationality and credibility of the evaluation results of configuration success, providing a prerequisite for the subsequent incentive feedback mechanism to gain user approval.

[0054] Configuration operation data refers to structured records of original configuration operations (such as "set gain value = 1.8 times"), which may include parameter names, set values, operation timestamps, etc. Index value analysis is the calculation definition of the aforementioned equipment operation indicators, and the process of extracting or calculating the specific value of the indicator from raw, mixed equipment operation data.

[0055] Continuing with the previous example of "gain control," after execution, the original device operation data of the target device is acquired, such as a RAW format image data stream and related metadata. For the indicator image signal-to-noise ratio (SNR), the analysis process may include: dividing the image data into background and target regions, calculating the standard deviation (representing noise) and mean difference (representing signal) of their pixel values ​​respectively, and then calculating the SNR value using the formula SNR = signal / noise, such as 42dB. For the indicator image average grayscale value, the indicator value can be the arithmetic mean of the grayscale values ​​of all pixels in the entire image, such as 128 (assuming an 8-bit image). The calculated 42dB and 128 are the indicator values ​​for user Alice's operation in terms of SNR and average grayscale.

[0056] Weighted analysis is a multi-indicator decision-making method. It involves first normalizing or scoring the value of each indicator to convert it into a unified scoring scale (such as 0 to 1 points), then multiplying it by the weight of the configuration success rate of that indicator, and finally summing all the weighted scores to obtain the final configuration success rate total score.

[0057] Continuing the previous example, we obtain the weights (W_snr=0.5, W_gray=0.3, W_contrast=0.2) and values ​​(SNR=42dB, Gray=128, Contrast=60) for the three indicators, and then convert the indicator values ​​into scores. For example, we presuppose that an SNR greater than 40dB is excellent, with a score of 1.0; between 30-40dB is good, with a score of 0.7, and so on. Assuming 42dB maps to a score S_snr=1.0, a grayscale value of 128 maps to a score S_gray=0.8, and a contrast ratio of 60 maps to a score S_contrast=0.6. Then, a weighted sum is performed: Configuration success rate = (W_snr * S_snr) + (W_gray * S_gray) + (W_contrast * S_contrast) = (0.5 * 1.0) + (0.3 * 0.8) + (0.2 * 0.6) = 0.5 + 0.24 + 0.12 = 0.86. The final configuration success rate for this "gain control" operation is 0.86 (or converted to a "good" level).

[0058] By employing weighted analysis based on weights, the priority of core indicators under different functional types is respected (e.g., image quality has a high weight in imaging functions), while also taking into account the contributions of other auxiliary indicators. This results in a comprehensive, balanced, and interpretable configuration success rate. This quantitative result provides an accurate and reliable basis for subsequent progress updates and learning incentives.

[0059] This application embodiment obtains the matching success rate by defining indicators and weights according to functional type, analyzing data to obtain indicator values, and weighted summarization. This avoids the one-sidedness of a single judgment standard. The preset rules of weights and indicators make the entire evaluation process transparent and interpretable, and easy to adjust and optimize according to product iteration or customer needs, thereby improving the adaptability and robustness of the evaluation scheme for the configuration operation of control software.

[0060] In some embodiments, in order to adjust the importance of indicators representing the effects of different technologies according to the function type, so as to more accurately reflect the user's learning of specific skill points, the device operation data includes at least one of the following: image data collected by the barcode reader, decoding data of the barcode reader for the image data, communication status data between the barcode reader and external devices, and output data of the barcode reader; the function type includes at least one of the following: imaging configuration function, decoding algorithm configuration function, communication configuration function, and output data format configuration function.

[0061] Based on the function type of the target function item targeted by the configuration operation, determine multiple device operation indicators corresponding to the target function item and the weight of each device operation indicator's configuration success rate, including:

[0062] If the target function is an imaging configuration function, the weight of the configuration success rate of the image quality index characterizing the image data is increased; or,

[0063] When the target function item's function type is a decoding algorithm configuration function, the weight of the configuration success rate, which represents the decoding completion rate of the decoding data, is increased; or,

[0064] When the target function item's function type is a communication configuration function, the weight of the configuration success rate, which characterizes the communication stability index representing communication status data, is increased; or,

[0065] When the target function item is an output data format configuration function, the weight of the configuration success rate, which represents the correctness of the output data format, is increased.

[0066] The imaging configuration function refers to the set of functions directly related to controlling and optimizing the image quality acquired by the barcode reader. The learning objective of these functions is to help users understand how different parameters affect the imaging results and learn to adjust for different materials (such as reflective metals and light-absorbing plastics) to produce clear, moderately contrasted, and uniformly bright images. Image data refers to the raw or pre-processed image frames actually captured by the barcode reader's camera. Image quality metrics are indicators extracted from the image data to quantify the quality of the image, such as: average grayscale, image contrast, image sharpness (which can be measured by edge detection intensity), image signal-to-noise ratio, and illumination uniformity.

[0067] Increase processing refers to intentionally increasing the weight coefficients of the overall image quality indicators or core indicators, based on the preset baseline weight allocation for this function type. For example, the weights of "image signal-to-noise ratio" and "image contrast ratio" are set to be significantly higher than the weights of indicators in other function types (such as communication functions).

[0068] Decoding algorithm configuration refers to the set of functions related to setting and optimizing the behavior of the reader's internal decoding engine. Its learning objective is to enable users to master how to configure algorithm parameters based on barcode type (code system), print quality, and image conditions to achieve high-precision, high-speed decoding. Decoded data is the process and result information output by the reader's kernel after processing image data. The decoding completion rate metric is a measure of the core performance of the decoding algorithm, typically represented by the decoding success rate (number of successful decodings / number of decoding attempts). Other related metrics may include first-time decoding time, average decoding confidence, etc. Increased processing power means that when evaluating decoding algorithm configurations, metrics that directly reflect decoding effectiveness, such as the decoding success rate, are given the highest or highest weight.

[0069] Communication configuration functions refer to the set of functions related to setting how the barcode reader receives trigger signals and how it sends result data to the upper-level system (such as PLC or IPC). Its learning objective is to ensure that the barcode reader can be reliably integrated into the automated production line, achieving stable signal interaction and data flow. Communication status data records the health of the communication link, such as trigger signal response delay, data packet transmission / reception success rate, communication timeout count, and link stability duration. Communication stability indicators are comprehensive indicators reflecting communication reliability, and can be single data points or composite calculations of multiple data points. "Enhancing processing" refers to placing stability metrics at the core of the evaluation when assessing network or bus parameter configurations.

[0070] Output data format configuration refers to the set of functions related to defining and filtering the content and structure of the barcode reader's output. Its learning objective is to enable users to customize and output standardized, clean data according to the needs of backend systems (such as MES and WMS). Output data is the string or data structure that the barcode reader ultimately sends out. Format correctness metrics are used to determine whether the output content conforms to predetermined rules, such as: whether output fields completely match preset templates, whether duplicate reading result filtering is effective, and whether specific string concatenation is correct. Increased processing power means that, when evaluating output configuration, the accuracy and standardization of the output content become the most important evaluation dimensions.

[0071] For example, the production line requires the barcode reader to not only read the barcode content "ABC123" but also add the station identifier "S01:" before the result and filter out consecutive duplicate readings. Bob configured items related to output data format configuration functions, such as "output field selection," "string prefix," and "duplicate filtering interval." When evaluating the success of the configuration, the weight of format correctness indicators such as whether the output string strictly conforms to "S01:ABC123" and whether it outputs at intervals when the same barcode is presented consecutively will be increased. Even if the reading and decoding themselves are without problems, if the output format is incorrect or the duplicate filtering fails, the configuration learning is not considered successful.

[0072] In some embodiments, the data on the degree of mastery of configuration progress includes data on the degree of mastery of the target user's sub-configuration progress for each configurable function item; based on the configuration success rate and the target user's historical configuration behavior data for the control software, the data on the target user's degree of mastery of the configuration progress of the control software is determined, including:

[0073] When the configuration success rate indicates that the configuration is successful, the cumulative number of successes for the target function item targeted by the configuration operation is incremented by one in the preset configuration progress mastery database, so as to obtain the cumulative number of successes for the target user after updating the target function item; wherein, the configuration progress mastery database records the historical cumulative number of successes for each function item and the number of successes required to complete the configuration.

[0074] The data on the target user's understanding of the sub-configuration progress of the target function item is obtained by calculating the cumulative number of successful updates and the number of successful updates required to complete the configuration.

[0075] The database tracking configuration progress can be a structured table stored locally or on a server. Its internal fields may include: UserID, FeatureID, AccumulatedSuccessCount, and RequiredSuccessCount. The accumulated success count is dynamic, recording the number of valid configuration operations that have been judged as "successful" for that specific feature by the user. Each increment updates the accumulated success count to the historical accumulated success count plus one.

[0076] For example, user Bob is working on the "Trigger Delay Setting" feature. His first attempt involved setting a delay that was too short, causing the barcode reader to trigger before the material arrived, resulting in a success rate of 0.4 (failure). At this point, the cumulative success count for "Trigger Delay Setting" in the database remained at 0. After consulting learning materials, Bob adjusted the delay to a reasonable range, achieving a success rate of 0.85 (success). The database of configuration progress mastery is then used to locate the record for the "Trigger Delay Setting" feature under Bob's account, updating its cumulative success count from 0 to 1. Each increase in the cumulative success count signifies that the user has completed an approved, technically compliant operation.

[0077] The number of successful attempts required for configuration completion is a fixed threshold preset for each feature. This threshold can be set based on the complexity, importance, or learning curve of the feature. For example, a simple "image rotation" function may only require one successful attempt to be considered mastered, while a complex "custom communication protocol configuration" may require 3 to 5 successful attempts. Sub-configuration progress can be a percentage value, used to visually reflect how far the user is from fully mastering the feature. Its calculation formula is typically: Sub-configuration progress = MIN(100%, (cumulative successful attempts after update / number of successful attempts required for configuration completion) * 100%). The MIN function ensures that the progress does not exceed 100%.

[0078] For example, suppose the required number of successful configurations for the "Trigger Delay Setting" function is preset to 3 (this is because the parameter needs to be matched with the production line cycle time, requiring multiple practice sessions to understand its impact). After Bob's second successful operation, his cumulative success count after the update is 1. Therefore, the sub-configuration progress is calculated as: (1 / 3) * 100% ≈ 33.33%. At this point, the progress bar next to "Trigger Delay Setting" on Bob's learning progress interface will show approximately 33%. When Bob subsequently successfully completes two more delay configurations for different scenarios (such as for fast and slow production lines), reaching a cumulative success count of 3, the calculated sub-configuration progress is 100%, indicating that the function has been learned successfully.

[0079] In some embodiments, configuration operations are received through a preset function item configuration interface of the control software; multiple configurable function items belong to at least one functional module; and data on the progress of configuration is displayed, including:

[0080] On the preset function configuration interface, the function items whose mastery of the configuration progress has not reached the preset threshold are marked in a first-person visual style.

[0081] On the preset function configuration interface, the function modules in which all function items have reached the degree threshold are marked with a second visual style.

[0082] The method also includes:

[0083] In response to interactive actions on feature items marked with a first visual style, display the configuration interface of the feature item to which the interactive action is performed;

[0084] When the progress data of a feature item's sub-configuration reaches a certain threshold, a message indicating that the configuration of the current feature item is complete is displayed.

[0085] The function configuration interface serves as the main workspace for users to configure parameters. It can be presented as a tree list, menu bar, or panel, listing configurable functions or their respective modules. The level threshold is used to determine whether a function is considered "mastered" or "requires special attention." For example, if the mastery level of a sub-configuration is less than 100%, it can be considered that the level threshold has not been reached. The first visual style is a specific visual presentation method used to quickly attract the user's attention on interface elements, conveying a "not completed" or "needs learning" status. Specific forms may include: red borders or backgrounds, highlighted yellow flashing, an attached "not mastered" icon (such as an exclamation mark), and a progress bar displayed in red or orange.

[0086] For example, after logging into the control software, user Cindy enters the main configuration interface. The left side of the main configuration interface is a navigation bar, listing modules such as "Imaging," "Algorithm," and "Communication" in a tree structure. Under the "Imaging" module, functions such as "Exposure," "Gain," and "Focus" are listed. A query of Cindy's personal configuration progress database reveals that her "Gain" function's sub-configuration progress is 45% (not reaching the 100% threshold). Therefore, the "Gain" entry is highlighted with a prominent red background on the interface, and a yellow warning icon is added before its text. Conversely, the "Exposure" function, which Cindy has mastered (100% progress), is displayed in plain gray or black.

[0087] This allows users to see a comprehensive overview of their skill mastery without having to actively access a dedicated "learning report" page, but rather within the core working interface of the software during their daily use.

[0088] Correspondingly, a second visual style is another visual presentation method used to convey a positive, completed state. Specific forms may include: a green border or background, an attached "mastered" icon (such as a checkmark or trophy), and the module name displayed in green or bold. A module is marked as having a second visual style only if the sub-configuration progress of all leaf nodes (functional items) under it reaches 100%.

[0089] For example, suppose that after a period of learning, Cindy has mastered all the functions (exposure, gain, focus, white balance, etc.) under the "Imaging" module to 100% mastery. In the configuration interface's navigation bar, the entire "Imaging Configuration" module name or its parent node will be marked with a green background and a gold checkmark icon. However, if there are still items under his "Communication Configuration" module that he hasn't mastered, that module will retain its default style or only partially highlight them.

[0090] By using a differentiated second visual style, users receive immediate positive feedback, similar to achievement unlocking in gamification. This effectively motivates users to tackle the next module, breaking down the long-term systematic learning process into a series of sub-goals with immediate rewards, thereby maintaining users' learning enthusiasm and focus.

[0091] Interactive operations refer to user attention to functional items with special visual markers, most typically mouse hover events, but can also be clicks. The configuration interface is the interface component that executes the specific configuration of this functional item; it may be a pop-up parameter panel, a slider control, or a detailed settings page after a jump. Continuing the previous example, suppose user Cindy sees the "Gain" item highlighted in red. She can move the mouse pointer over this item. In this embodiment, in response to the hover, a semi-transparent floating tooltip pops up next to the "Gain" text. The tooltip can directly contain a shortcut entry, such as: "'Gain' function not yet fully mastered. Click here [Start Practice] or [View Tutorial]." If user Cindy clicks this entry, in this embodiment, the user is directly redirected to or a dedicated learning and practice interface for the "Gain" function corresponding to the click operation pops up, saving the user the step of searching through menus.

[0092] A configuration completion notification is a proactive, congratulatory message pushed to a user when they have achieved 100% completion (mastery) of a specific feature's configuration through practice. It can take the form of a brief Toast notification, a modal dialog box in the center of the interface, or a combination of animation and sound effects, effectively shaping and maintaining the user's learning behavior through immediate and positive feedback.

[0093] In some embodiments, considering that the control software of the target device is typically used by multiple users (such as different engineers or team members) in an enterprise environment, and that the control software may be upgraded with product iterations, in order to securely isolate and manage the learning data of multiple users, and to ensure that users' valuable learning progress is intelligently inherited and migrated when software functions are added, deleted, or modified, thereby ensuring the continuity and fairness of long-term learning, in this embodiment, the mastery level data of the configuration progress is stored in association with the user account; the method further includes:

[0094] In response to control software version upgrades, obtain control software version change information;

[0095] Migrate the data on the level of control over configuration progress based on version change information;

[0096] Among them, for the first functional item that is the same as the current version in the new version, the mastery data of the original configuration progress corresponding to the first functional item will be retained;

[0097] For the new features in the new version, initialize the data on the mastery of the configuration progress corresponding to the new features;

[0098] For a second functional item whose operational logic has changed, the mastery data of the original configuration progress corresponding to the second functional item is converted according to preset rules to obtain the mastery data of the configuration progress under the new version.

[0099] Among them, the user account is the unique identifier for different operators in the system, which is usually achieved by logging into the system. The associated storage data record representing the mastery of each configuration progress (such as the sub-configuration progress of a certain function item) must contain a foreign key field pointing to the user account to which it belongs.

[0100] For example, engineer Alice, Bob, and production line operator Cindy share the same industrial PC with barcode reader control software installed, each with their own independent account. When Alice logs in and practices "exposure control," all increases in the cumulative number of successful attempts and updates to the progress percentage for that function are only written to the database record labeled "Alice." Bob and Cindy, after logging in, see their own individual progress views and historical records. Therefore, even though the three are operating on the same machine, their learning journeys are independent and do not interfere with each other.

[0101] Version upgrade refers to the process of updating software from an older version (denoted as Version_A) to a newer version (denoted as Version_B). Version change information is a structured description of the differences between two versions. Methods for obtaining version change information may include: parsing the manifest file included in the software upgrade package, querying cloud version management services, or comparing the configuration and feature manifests of Version_A and Version_B locally through static analysis.

[0102] For example, when upgrading the control software from V2.1 to V3.0, the upgrade program or system background service will obtain a detailed change log, which clearly indicates that V3.0 adds an "AI noise reduction" function (belonging to the imaging module), removes the outdated "serial port baud rate" configuration item (originally belonging to the communication module), and reconstructs the configuration interface of the "deep learning decoder" (its functional logic has changed). In this embodiment, different migration strategies are executed according to the type of functional change in the version transition, in order to preserve and continue the user's historical efforts to the greatest extent and guide the user to adapt to the new version.

[0103] Specifically, the first functional item refers to a functional item whose functional identifier, operational logic, and technical goals are completely consistent in Version_A and Version_B. For example, the configuration method of "exposure time" remains unchanged in both versions. Therefore, the entire record of this functional item in the user database (including the cumulative number of successes, sub-configuration progress, etc.) can be copied to the corresponding database structure in the new version. This ensures that the user's efforts to master the functional item are recognized and protected, and that the user's learning continuity is not interrupted by the upgrade.

[0104] New features are those introduced in Version B that do not exist in Version A, such as "AI noise reduction" in the previous example. For these features, a new record can be created in the user's progress database, and its cumulative success count can be initialized to 0, naturally setting the sub-configuration progress to 0%. This clearly informs the user that this is a new skill that needs to be learned from scratch. The progress bar starting from zero also stimulates the user's interest in exploring new features, promoting their adoption and use.

[0105] Correspondingly, the second feature item exists in both Version_A and Version_B, but its configuration method, parameter meaning, or underlying logic has undergone substantial changes. For example, in V2.1, "decoding sensitivity" was a global slider, while in V3.0 it is split into two independent parameters: "local contrast threshold" and "global confidence threshold." The default rule is a trade-off algorithm designed to balance "inheriting history" and "relearning." A typical and reasonable rule is "half-inheritance," that is, new version progress = old version progress * 50%. Assume that the user's mastery of "decoding sensitivity" in V2.1 is 80% (4 successful attempts, 5 attempts required to master it). After upgrading to V3.0, the initial progress of this feature item (or its split main related item) in the new database is calculated to be 80% * 50% = 40%. The corresponding cumulative number of successful attempts may be reduced to 4 * 50% = 2 (rounded down).

[0106] This application's embodiments address data management issues in multi-user environments and the challenge of learning continuity during long-term software evolution through two main mechanisms: "account-isolated storage" and "intelligent migration based on version change information." By employing differentiated migration strategies (retention, clearing, and compromise), it maximizes the protection of users' learning outcomes during software upgrades and intelligently guides users to adapt to new versions. In some embodiments, considering that users may face real-world obstacles such as missing materials, unavailable external devices, or real-world environmental risks when learning specific types of functions, this application's embodiments introduce a simulated operation mode. This aims to lower the learning threshold, enhance learning security and convenience, and ensure a smooth learning process under any conditions. Specifically, the control software is configured according to the configuration operation, and the configured control software controls the target device's operation to obtain the target device's operating data, including:

[0107] If the function type of the target function item is the decoding algorithm configuration function, the simulated operation data obtained after processing the preset simulated material image is used as the equipment operation data;

[0108] If the target function is a communication configuration function, the communication simulator built into the control software is used to simulate interaction with the target device, and the data generated during the simulation interaction is used as the device operation data.

[0109] The learning objective of the decoding algorithm configuration function is primarily to understand the impact of different algorithm parameters on the decoding process, rather than relying on a specific, real-time physical imaging result. Therefore, the preset simulated material images refer to a high-quality, feature-defined image library pre-stored within the software or on a cloud server. These images can cover various typical scenarios: highly reflective metals, low-contrast paper, curved surfaces, partial occlusion, and different printing qualities, etc. The simulated running data refers to the result data generated by the reader software's kernel decoding engine after receiving the user's configuration parameters and decoding this preset simulated image. This process is entirely closed-loop at the software level and does not involve actual camera photography.

[0110] For example, a new user named Emma needs to learn how to configure "filter preprocessing" and "code recognition priority" to handle blurry QR codes. However, she doesn't have a suitable blurry QR code on her workbench. In this case, she can select "algorithm simulation learning mode" in the software. A pop-up interface will display a preset image of a "slightly motion-blurred Datamatrix code." After Emma adjusts the filter and code parameters, she clicks "simulate decoding." The software's internal decoder will process this fixed image based on her current configuration. It can also generate and provide simulation data, including: decoding success or failure, decoding time, the filtering algorithm log used, and the order in which different code types were tried. This data is structurally and semantically identical to the data generated by actual decoding, sufficient for subsequent configuration success evaluation.

[0111] Correspondingly, the learning objective for communication configuration functions is generally to understand communication protocols, data frame formats, and handshake timings. This typically requires establishing a real connection with an external controller (such as a PLC), which is often difficult to achieve in the early stages of learning. The communication simulator built into the control software is a virtual instance of an external device that can completely simulate the behavior of the target protocol (such as PROFINET, EtherNet / IP, Modbus TCP). Simulated interaction refers to the user's configuration operations (such as setting IP addresses, station names, and I / O mappings) being sent to this built-in simulator, rather than the real physical network. The simulator responds to the correctness of the configuration according to the protocol specifications. The data generated during the simulated interaction is the communication status data fed back by the simulator, such as: virtual connection establishment success / failure, simulated trigger signal timings, virtual data transmission and reception logs, simulated communication delays, etc.

[0112] This application's embodiments revolutionize the way skills are learned by providing a built-in, high-fidelity simulated learning environment for two highly externally dependent functionalities: decoding algorithms and communication configuration. By constructing a secure sandbox in a virtual space that allows for trial and error, abstract communication protocols and algorithmic logic become visible and interactive, improving the user's learning efficiency in configuring control software.

[0113] In some embodiments, before evaluating the configuration success rate of a configuration operation based on configuration operation data and device operation data, the method further includes:

[0114] Analyze the historical configuration operations of multiple historical users to construct multiple sets of training samples; each set of training samples includes historical interaction operation samples, historical device operation samples corresponding to the historical interaction operation samples, and configuration success labels.

[0115] Using historical interaction operation samples and historical device operation samples from multiple training samples as input, and the configuration success labels in the corresponding training samples as output, a preset deep learning model is trained to obtain an evaluation model for the configuration status of the control software.

[0116] The success rate of the configuration operation is evaluated based on the configuration operation and device operation data, including:

[0117] The configuration operation data and device operation data are input into the configuration status evaluation model for processing, and the configuration success rate of the configuration operation is output.

[0118] Historical configuration operations can be a massive amount of real operations collected and anonymized during the actual deployment and use of the software. Each operation record documents a configuration event. Historical interaction operation samples are structured and vectorized representations of a specific configuration operation. For example, for an "exposure time" configuration, its sample might be a feature vector: [Function item code='exposure', Set value=25, Adjustment direction='increase', Initial value before operation=10, ...].

[0119] Historical equipment operation samples are multi-dimensional equipment operation data generated after the operation corresponding to that specific operation, and they have also undergone cleaning and feature engineering processing. For example, it may be a feature vector: [average image brightness = 128, image contrast = 0.6, decoding success rate = 1.0, decoding time = 150ms, ...].

[0120] The configuration success rate label is a manual or semi-automatic annotation result of the final effect of this historical configuration operation. Its sources can be: retrospective scoring by senior experts based on the improvement of equipment performance and the rationality of parameter adjustment before and after the operation; a comprehensive score automatically generated by combining the stable operation performance of the equipment over a subsequent period; or a label formed after expert review and correction based on the early rule evaluation system.

[0121] For example: Extract 1 million historical configuration records from a cloud database. For one record where a user adjusts the "gain value" from 1.0 to 2.0, use this as a training sample:

[0122] Historical interactive operation sample X_op: [Function: Gain control, Operation type: Numerical adjustment, Original value: 1.0, New value: 2.0, Change range: +1.0];

[0123] Historical device running sample X_run: [Image SNR change: +3dB, Image average grayscale change: +30, Decoding success rate change: 0%, Decoding time change: +5ms];

[0124] Configuration success rating Y: 0.7 (score given by experts based on "SNR improved but decoding efficiency not improved, judged as partially effective").

[0125] The pre-defined deep learning model can be a selected machine learning model architecture, such as a deep neural network, a gradient boosting decision tree, or a more complex fusion model. Its input layer is designed to simultaneously receive features from both operational and operational samples. Internally, the placement assessment model learns a highly non-linear mapping function from input features to output scores through multiple layers of non-linear transformations. Specifically, the training process of the placement assessment model can employ a supervised learning paradigm, using optimization methods such as backpropagation to continuously adjust the model's internal parameters to minimize the loss (e.g., mean squared error) between the model's predicted placement success rate and the true label.

[0126] For example, a neural network with three fully connected hidden layers can be used as a deep learning model. The million training samples constructed in the previous step are input into this model. During training, the model attempts to learn the following pattern: when the operation sample shows "significantly increased gain for low-contrast scenes," and the corresponding running sample shows "significantly improved image contrast and significantly improved decoding success rate," its predicted output value should be close to the label 1.0; conversely, if the running sample shows "image overexposure and decreased SNR," the predicted value should be close to 0.2. After several rounds of iterative training, the model gradually masters the complex pattern of evaluating the effectiveness of different configuration operations, converging into a stable and accurate configuration evaluation model.

[0127] The model can automatically discover and utilize complex features and relationships hidden in massive amounts of data that are difficult to enumerate manually. For example, it may automatically learn that "when the ambient light is low, a slight increase in exposure combined with a significant increase in gain is usually an effective combination strategy" and reflect this in the evaluation, so that the evaluation system is freed from dependence on fixed rules and has the potential to be adaptive and become more accurate with use.

[0128] For example, consider a user named Gina attempting to configure a barcode reader for a brand-new mirrored metal part. She performs an unusual combination of operations: drastically reducing exposure to suppress reflections while simultaneously enabling a special "mirror suppression" filter algorithm. For such rare scenarios, the pre-defined rule base might lack explicit rules. Gina's operation data and the resulting device operation data are processed into feature vectors and input into a pre-trained configuration evaluation model. The model, based on its extensive patterns learned from massive amounts of data (potentially including various successful and unsuccessful reflection suppression cases), performs comprehensive reasoning and ultimately outputs a score of 0.65. This score is not only based on successful decoding but also incorporates evaluations of complex strategies such as "reducing exposure is a reasonable approach under strong reflections" and "combining with a specific filter algorithm yields better results."

[0129] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0130] Based on the same inventive concept, this application also provides a device control apparatus. The solution provided by this apparatus is similar to the solution described in the above method. Therefore, the specific limitations of one or more device control apparatus embodiments provided below can be found in the limitations of the device control method above, and will not be repeated here.

[0131] like Figure 2 As shown, this application embodiment provides a device control apparatus 300, including:

[0132] The response module 302 is configured to respond to a configuration operation by a target user on the control software of a target device; the target device includes a barcode reader; the configuration operation is configured to configure at least one of a plurality of configurable function items included in the control software.

[0133] The running module 304 is used to configure the control software according to the configuration operation, and to control the target device to run through the configured control software, thereby obtaining the device operation data of the target device;

[0134] Evaluation module 306 is used to evaluate the configuration success rate of the configuration operation based on the configuration operation and device operation data.

[0135] The determination module 308 is used to determine the target user's level of understanding of the configuration progress of the control software based on the configuration success rate and the target user's historical configuration behavior data for the control software.

[0136] Display module 310 is used to display data on the progress of configuration.

[0137] In some embodiments, in evaluating the configuration success rate of a configuration operation based on configuration operations and device operating data, the evaluation module 306 is specifically used for:

[0138] Based on the function type of the target function item targeted by the configuration operation, determine the multiple device operation indicators corresponding to the target function item and the influence weight of the configuration success rate of each device operation indicator.

[0139] Based on the equipment operation indicators, analyze the configuration operation and equipment operation data to obtain the indicator values ​​of the target user under each equipment operation indicator.

[0140] The configuration success rate is obtained by weighting the indicator values ​​according to the influence weight of the configuration success rate.

[0141] In some embodiments, the device operation data includes at least one of the following: image data acquired by the barcode reader, decoding data of the image data by the barcode reader, communication status data between the barcode reader and external devices, and output data of the barcode reader; the function type includes at least one of the following: imaging configuration function, decoding algorithm configuration function, communication configuration function, and output data format configuration function.

[0142] In determining the multiple device operation indicators corresponding to the target function item and the weight of each device operation indicator's configuration success rate based on the function type of the target function item targeted by the configuration operation, the evaluation module 306 is specifically used for:

[0143] If the target function is an imaging configuration function, the weight of the configuration success rate of the image quality index characterizing the image data is increased; or,

[0144] When the target function item's function type is a decoding algorithm configuration function, the weight of the configuration success rate, which represents the decoding completion rate of the decoding data, is increased; or,

[0145] When the target function item's function type is a communication configuration function, the weight of the configuration success rate, which characterizes the communication stability index representing communication status data, is increased; or,

[0146] When the target function item is an output data format configuration function, the weight of the configuration success rate, which represents the correctness of the output data format, is increased.

[0147] In some embodiments, the data on the degree of mastery of configuration progress includes data on the degree of mastery of the target user's sub-configuration progress for each configurable function item; in determining the data on the target user's degree of mastery of the configuration progress of the control software based on the configuration success rate and the target user's historical configuration behavior data of the control software, the determining module 308 is specifically used for:

[0148] When the configuration success rate indicates that the configuration is successful, the cumulative number of successes for the target function item targeted by the configuration operation is incremented by one in the preset configuration progress mastery database, so as to obtain the cumulative number of successes for the target user after updating the target function item; wherein, the configuration progress mastery database records the historical cumulative number of successes for each function item and the number of successes required to complete the configuration.

[0149] The data on the target user's understanding of the sub-configuration progress of the target function item is obtained by calculating the cumulative number of successful updates and the number of successful updates required to complete the configuration.

[0150] In some embodiments, the configuration operation is received through a preset function item configuration interface of the control software; multiple configurable function items belong to at least one function module; regarding the display module 310 for showing the progress of configuration mastery data, it is specifically used for:

[0151] On the preset function configuration interface, the function items whose mastery of the configuration progress has not reached the preset threshold are marked in a first-person visual style.

[0152] On the preset function configuration interface, the function modules in which all function items have reached the degree threshold are marked with a second visual style.

[0153] In addition, in response to interactive actions on functional items marked with a first visual style, display the configuration interface of the functional item to which the interactive action is targeted;

[0154] When the progress data of a feature item's sub-configuration reaches a certain threshold, a message indicating that the configuration of the current feature item is complete is displayed.

[0155] In some embodiments, the data on the level of control over the configuration progress is stored in association with the user account; the determination module 308 is also used for:

[0156] In response to a control software version upgrade, the system acquires version change information of the control software; it then migrates the configuration progress mastery data based on the version change information; specifically, for the first functional item that is the same as the current version in the new version, the original configuration progress mastery data corresponding to the first functional item is retained; for newly added functional items in the new version, the configuration progress mastery data corresponding to the newly added functional item is initialized; for second functional items whose operation logic has changed, the original configuration progress mastery data corresponding to the second functional item is recalculated according to preset rules to obtain the configuration progress mastery data under the new version.

[0157] In some embodiments, in configuring the control software according to the configuration operation and controlling the target device to operate through the configured control software to obtain the device operation data of the target device, the operation module 304 is specifically used for:

[0158] If the function type of the target function item is the decoding algorithm configuration function, the simulated operation data obtained after processing the preset simulated material image is used as the equipment operation data;

[0159] If the target function is a communication configuration function, the communication simulator built into the control software is used to simulate interaction with the target device, and the data generated during the simulation interaction is used as the device operation data.

[0160] In some embodiments, the evaluation module 306 is further configured to:

[0161] The historical configuration operations of multiple historical users are analyzed to construct multiple sets of training samples. Each set of training samples includes historical interaction operation samples, historical device operation samples corresponding to the historical interaction operation samples, and configuration success labels. The historical interaction operation samples and historical device operation samples in the multiple sets of training samples are used as inputs, and the configuration success labels in the corresponding training samples are used as outputs to train the preset deep learning model to obtain an evaluation model for the configuration status of the control software.

[0162] In assessing the success rate of configuration operations based on configuration operations and device operating data, the evaluation module 306 is specifically used for:

[0163] The configuration operation data and device operation data are input into the configuration status evaluation model for processing, and the configuration success rate of the configuration operation is output.

[0164] Each module in the aforementioned equipment control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0165] In some embodiments, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements the steps in the device control method described above. The display unit of the computer device forms a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0166] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0167] In one exemplary embodiment, a computer device is provided, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the embodiments of the above-described device control methods.

[0168] In one embodiment, such as Figure 4 As shown, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps in the embodiments of the above-described device control methods.

[0169] In one embodiment, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the steps in the embodiments of the above-described device control methods.

[0170] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0171] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0172] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0173] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A device control method characterized by, include: Responding to the configuration operations of the target user on the control software of the target device; The target device includes a barcode reader; the configuration operation is used to configure at least one of the multiple configurable function items included in the control software; The control software is configured according to the configuration operation, and the target device is controlled to run through the configured control software to obtain the device operation data of the target device; The success rate of the configuration operation is determined by evaluating the configuration operation based on the configuration operation and the device operation data. Based on the configuration success rate and the target user's historical configuration behavior data for the control software, determine the target user's level of understanding of the configuration progress of the control software; This displays data on the level of understanding of the configuration progress.

2. The method of claim 1, wherein, The step of evaluating the configuration success rate of the configuration operation based on the configuration operation and the device operating data includes: Based on the function type of the target function item targeted by the configuration operation, determine multiple device operation indicators corresponding to the target function item and the influence weight of the configuration success rate of each device operation indicator. Based on the device operation indicators, the configuration operation and the device operation data are analyzed to obtain the indicator values ​​of the target user under each of the device operation indicators. The configuration success rate of the configuration operation is obtained by weighting the indicator value according to the influence weight of the configuration success rate.

3. The method of claim 2, wherein, The device operation data includes at least one of the following: image data acquired by the barcode reader, decoding data of the image data by the barcode reader, communication status data between the barcode reader and external devices, and output data of the barcode reader; the function type includes at least one of the following: imaging configuration function, decoding algorithm configuration function, communication configuration function, and output data format configuration function. The step of determining multiple device operation indicators corresponding to the target function item and the configuration success rate impact weight of each device operation indicator based on the function type of the target function item targeted by the configuration operation includes: If the function type of the target function item is the imaging configuration function, the weight of the configuration success rate of the image quality index characterizing the image data is increased; or, When the function type of the target function item is the decoding algorithm configuration function, the weight of the configuration success rate of the decoding completion rate indicator representing the decoding data is increased; or, When the function type of the target function item is the communication configuration function, the weight of the configuration success rate, which characterizes the communication stability index representing the communication status data, is increased; or... When the function type of the target function item is the output data format configuration function, the influence weight of the configuration success rate of the index representing the format correctness of the output data is increased.

4. The method of claim 1, wherein, The data on the degree of mastery of the configuration progress includes the data on the degree of mastery of the target user's sub-configuration progress for each of the configurable function items; the step of determining the target user's degree of mastery of the configuration progress of the control software based on the configuration success rate and the target user's historical configuration behavior data of the control software includes: When the configuration success rate indicates successful configuration, the cumulative success count of the target function item targeted by the configuration operation is incremented by one in the preset configuration progress mastery database to obtain the cumulative success count of the target user for the target function item after the update; wherein, the configuration progress mastery database records the historical cumulative success count and the number of success counts required to complete the configuration for each function item. The data on the target user's mastery of the sub-configuration progress of the target function item is obtained by calculating the cumulative number of successful updates and the number of successful updates required to complete the configuration.

5. The method of claim 4, wherein, The configuration operation is received through the preset function item configuration interface of the control software; the multiple configurable function items belong to at least one function module; The data demonstrating the level of understanding of the configuration progress includes: On the preset function item configuration interface, the function items whose mastery of the sub-configuration progress data has not reached the preset threshold are marked with a first visual style; On the preset function item configuration interface, the function modules in which all function items have reached the degree threshold are marked with a second visual style; The method further includes: In response to an interaction with the functional item marked with the first visual style, a configuration interface for the functional item to which the interaction is directed is displayed; When the progress data of the sub-configuration of the function item reaches the threshold, a configuration completion prompt message is displayed for the current function item.

6. The method of claim 1, wherein, The data on the level of control over the configuration progress is stored in association with the user account; the method further includes: In response to a version upgrade of the control software, obtain the version change information of the control software; The data on the level of control over the configuration progress is migrated based on the version change information; Among them, for the first functional item that is the same as the current version in the new version, the mastery data of the original configuration progress corresponding to the first functional item is retained; For the new features in the new version, initialize the mastery data of the configuration progress corresponding to the new features; For a second functional item whose operation logic has changed, the mastery data of the original configuration progress corresponding to the second functional item is converted according to a preset rule to obtain the mastery data of the configuration progress under the new version.

7. The method of claim 3, wherein, The step of configuring the control software according to the configuration operation, and controlling the target device to operate through the configured control software, to obtain the device operation data of the target device, includes: If the function type of the target function item is the decoding algorithm configuration function, the simulated operation data obtained after processing the preset simulated material image is used as the equipment operation data; If the function type of the target function item is the communication configuration function, the control software uses a built-in communication simulator to simulate interaction with the target device and obtains the data generated during the simulation interaction as the device's operating data.

8. The method of claim 1, wherein, Before evaluating the configuration success rate of the configuration operation based on the configuration operation and the device operating data, the method further includes: Analyze the historical configuration operations of multiple historical users to construct multiple sets of training samples; each set of training samples includes historical interaction operation samples, historical device operation samples corresponding to the historical interaction operation samples, and configuration success labels. Using the historical interaction operation samples and historical device operation samples from the multiple sets of training samples as inputs, and the configuration success labels from the corresponding training samples as outputs, a preset deep learning model is trained to obtain a configuration evaluation model for the control software. The step of evaluating the configuration success rate of the configuration operation based on the configuration operation and the device operating data includes: The configuration operation data and the device operation data are input into the configuration status evaluation model for processing, and the configuration success rate of the configuration operation is output.

9. An apparatus control device characterized by comprising: include: The response module is used to respond to configuration operations performed by the target user on the control software of the target device; The target device includes a barcode reader; the configuration operation is used to configure at least one of the multiple configurable function items included in the control software; The operation module is used to configure the control software according to the configuration operation, and control the target device to run through the configured control software, so as to obtain the device operation data of the target device; An evaluation module is used to evaluate the configuration operation based on the configuration operation and the device operation data to obtain the configuration success rate of the configuration operation. The determination module is used to determine the target user's level of understanding of the configuration progress of the control software based on the configuration success rate and the target user's historical configuration behavior data for the control software; The display module is used to show data on the progress of the configuration.

10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

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