Automatic calibration method, system, medium and device for controller AD / DA channel

Abstract

The present application provides a method, system, medium and equipment for automatic calibration of the AD / DA channel of a controller, and relates to the technical field of industrial controller calibration. The technical solution provided by the present application performs cluster analysis by extracting environmental characteristics and error type characteristics, and divides controllers with similar characteristics into the same test group. Then, by injecting test signals and collecting feedback signals, a comparative analysis is performed in combination with the standard feedback signal corresponding to the cluster center, and targeted adjustments are made to the calibration scheme. At the same time, the correlation influence between the controllers on the production line is taken into account, and the first influence value between the test groups and the second influence value between the controllers in the group are determined by the process nodes and the production rhythm, so that the target calibration scheme finally generated not only maintains the high efficiency of group calibration, but also ensures the calibration accuracy by considering the influence relationship between the controllers. Therefore, it can take into account the efficiency and accuracy of the controller calibration on the production line and improve the calibration effect.

Description

Technical Field

[0001] The present application relates to the technical field of industrial controller calibration, and in particular to a method, system, medium, and device for automatically calibrating an AD / DA channel of a controller. Background Art

[0002] In the field of industrial automation control, controllers are widely used as core execution units in various automated production lines. Controllers collect analog signals from various sensors, convert them into digital (AD) signals for data processing, and then convert the processed digital signals into analog (DA) signals for output to actuators, thereby achieving precise control of industrial production processes. However, in actual use, due to environmental factors such as temperature and humidity, as well as the aging and wear of the components themselves, the controller's AD / DA channels can experience problems such as zero drift and gain deviation. In particular, temperature fluctuations can cause temperature drift in the signals, making calibration parameters completed during shutdown inappropriate for the actual operating temperature of the equipment during operation, thus affecting the accuracy of signal acquisition and output. To ensure accurate industrial control, the controller's AD / DA channels must be calibrated online without stopping the production line.

[0003] In related technologies, the calibration method for the controller AD / DA channel mainly adopts single-point calibration or multi-point linear calibration method, that is, by injecting a standard voltage signal into a single controller, collecting feedback data, and then calculating the compensation parameters for calibration. However, when faced with a large number of controllers on the production line, this method often needs to be calibrated one by one according to the production rhythm. Not only is the calibration efficiency low, but also due to the signal influence and coupling relationship between the controllers on the production line, a single calibration cannot take into account the mutual influence between the controllers. Therefore, in large-scale automated production lines, the calibration method in related technologies is difficult to achieve efficient and accurate calibration, resulting in poor calibration effect. Summary of the Invention

[0004] The present application provides a method, system, medium and equipment for automatic calibration of the AD / DA channel of a controller, which can take into account the efficiency and accuracy of controller calibration on a production line, thereby improving the calibration effect.

[0005] In a first aspect, the present application provides a method for automatically calibrating an AD / DA channel of a controller, the method comprising:

[0006] Acquire historical environmental parameters and historical calibration data of multiple controllers on the production line, extract environmental features from the historical environmental parameters, and extract error type features from the historical calibration data;

[0007] performing cluster analysis on the controllers based on the environmental characteristics and the error type characteristics, and dividing the plurality of controllers into a plurality of test groups;

[0008] Injecting test signals into the controllers in the test group respectively, and collecting feedback signals from the controllers;

[0009] According to the comparison result of the feedback signal and the standard feedback signal corresponding to the cluster center of the test group, the standard calibration scheme corresponding to the cluster center is adjusted to obtain the initial calibration scheme corresponding to each controller in the test group;

[0010] Obtaining a process node and a production tact of each of the controllers on the production line, and determining a first impact value between the test groups and a second impact value between the controllers within the test group according to the process node and the production tact;

[0011] The initial calibration scheme is adjusted according to the first impact value and the second impact value to generate a target calibration scheme, where the target calibration scheme is used to calibrate the AD / DA channel of each of the controllers.

[0012] By adopting the above technical solution, the historical environmental parameters and historical calibration data of multiple controllers on the production line are obtained, and the environmental characteristics and error type characteristics are extracted for cluster analysis, and controllers with similar characteristics are divided into the same test group. Then, by injecting test signals and collecting feedback signals, combined with the standard feedback signal corresponding to the cluster center, a comparative analysis is performed to make targeted adjustments to the calibration scheme. At the same time, considering the correlation influence between the controllers on the production line, the first influence value between the test groups and the second influence value between the controllers in the group are determined by the process nodes and the production rhythm, and the initial calibration scheme is optimized and adjusted accordingly, so that the target calibration scheme finally generated not only maintains the high efficiency of group calibration, but also ensures the calibration accuracy by considering the influence relationship between controllers. Therefore, it can take into account the efficiency and accuracy of the controller calibration on the production line and improve the calibration effect.

[0013] Optionally, acquiring historical environmental parameters and historical calibration data of multiple controllers on the production line, extracting environmental features from the historical environmental parameters, and extracting error type features from the historical calibration data, includes:

[0014] Obtain historical environmental data from multiple controllers on a production line;

[0015] Determine the environmental interval in the historical environmental data where the controller has been located for the longest time, and determine the environmental tag corresponding to the environmental interval as the environmental feature of the controller;

[0016] Acquiring historical calibration data of each of the controllers, wherein the historical calibration data includes historical error types and historical error degrees;

[0017] Performing weighted calculation on the historical error type using the historical error degree to obtain a feature score corresponding to the historical error type;

[0018] The historical error type corresponding to the largest feature score is determined as the error type feature of the controller.

[0019] By adopting the above technical solution, the historical environmental data of the controller on the production line is obtained, and the environmental interval in which the controller has been located the longest is determined as the environmental feature, ensuring that the selected environmental feature has the strongest representativeness and stability. At the same time, by obtaining the historical error type and historical error degree information from the historical calibration data of the controller, the historical error type is weighted using the historical error degree to obtain a feature score, and the historical error type corresponding to the maximum feature score is selected as the error type feature of the controller, so that the extracted error feature can accurately reflect the most important error manifestations of the controller. By combining environmental interval screening and error weighted calculation, the accuracy of the feature can be guaranteed while improving the efficiency of feature extraction.

[0020] Optionally, performing cluster analysis on the controllers based on the environmental characteristics and the error type characteristics to divide the multiple controllers into a plurality of test groups includes:

[0021] Pre-classifying the controllers using environmental features, and dividing the plurality of controllers into a number of initial test groups corresponding to the environmental features;

[0022] Combining the environmental features with the error type features by performing feature vectors to construct a feature matrix of the controller;

[0023] The discreteness between the controllers is calculated according to the characteristic matrix, and the initial test group is divided into a plurality of test groups according to the discreteness.

[0024] By adopting the above technical solution, a two-step clustering approach is used to group controllers. First, pre-classification is performed based on environmental characteristics to obtain initial test groups, quickly achieving a rough division of controllers and improving classification efficiency. On this basis, a feature matrix is ​​constructed by combining the environmental characteristics and error type characteristics into feature vectors, and the degree of discreteness between controllers is calculated. This achieves a refined division of the initial test groups and obtains the final test groups. This coarse-to-fine clustering analysis method avoids directly performing complex multi-feature clustering calculations through the rapid pre-classification of environmental characteristics. At the same time, the accuracy of the grouping is guaranteed by the calculation of the feature matrix and the degree of discreteness, achieving a balance between efficiency and precision in the grouping process.

[0025] Optionally, adjusting the standard calibration scheme corresponding to the cluster center according to the comparison result of the feedback signal with the standard feedback signal corresponding to the cluster center of the test group to obtain the initial calibration scheme corresponding to each controller in the test group includes:

[0026] Comparing the feedback signal with a standard feedback signal of the cluster center of the test group to obtain a deviation compensation parameter of the feedback signal;

[0027] The deviation compensation parameters are used to replace the corresponding standard compensation parameters in the standard calibration scheme of the cluster center to obtain the initial calibration scheme corresponding to each of the controllers in the test group.

[0028] By employing this technical solution, the controller's feedback signal is compared with the standard feedback signal from the test group cluster center to obtain the feedback signal's deviation compensation parameters. This cluster center-based comparison method avoids comparison with all possible standard signals, improving parameter acquisition efficiency. Furthermore, by directly replacing the corresponding standard compensation parameters in the cluster center standard calibration scheme with the deviation compensation parameters, an initial calibration scheme corresponding to each controller is quickly generated. This avoids the complex calculation process of reconstructing the calibration scheme while ensuring the accuracy of the calibration scheme through precise deviation compensation parameters.

[0029] Optionally, obtaining the process node and the production tact of each controller on the production line, and determining the first impact value between the test groups and the second impact value between the controllers in the test group according to the process node and the production tact, includes:

[0030] Obtaining the process nodes and production rhythm corresponding to each controller on the production line;

[0031] determining a first node association value between the target process nodes according to a first node distance between the target process nodes corresponding to the cluster centers;

[0032] Determine a first timing difference value between the target process nodes according to the production tact;

[0033] determining a first impact value between the test groups according to the first node association value and the first timing difference value;

[0034] determining a second node correlation value between the process nodes in the test group and the target process node according to a second node distance between the process nodes in the test group and the target process node;

[0035] Determine a second timing difference value between the process nodes in the test group and the target process node according to the production tact;

[0036] A second impact value between the controllers in the test group is determined according to the second node association value and the second timing difference value.

[0037] By adopting the above technical solution, the process node and production rhythm information of the controllers on the production line are obtained, and the influence relationship between the controllers is determined from the two dimensions of space and time. In the impact analysis between test groups, the first node association value is calculated based on the first node distance between the target process nodes corresponding to the cluster center, and combined with the first time difference value determined by the production rhythm, the first influence value reflecting the overall influence degree between the groups is obtained, thereby realizing a rapid assessment of the influence between the groups. At the same time, within the test group, the second node association value is obtained by calculating the second node distance between the process node and the target process node, and the second time difference value is determined based on the production rhythm, thereby obtaining the second influence value that characterizes the influence degree between the controllers within the group, thereby ensuring the accuracy of the impact assessment within the group.

[0038] Optionally, adjusting the initial calibration scheme according to the first impact value and the second impact value to generate a target calibration scheme includes:

[0039] constructing an interconnection matrix reflecting the physical connection relationship between each test group according to the first impact value;

[0040] Based on the signal transmission relationship represented in the interconnection matrix, inter-group adjustment is performed on the compensation parameters in the initial calibration scheme to obtain a first calibration scheme;

[0041] Constructing a coupling matrix reflecting the shared relationship between the actuators and sensors among the controllers in the test group according to the second impact value;

[0042] Based on the signal coupling relationship represented in the coupling matrix, the first calibration scheme is adjusted within the group to obtain a target calibration scheme.

[0043] By adopting the above technical solution, an interconnection matrix is ​​constructed based on the first influence value. The matrix reflects the physical connection relationship between the test groups. By analyzing the signal transmission relationship represented in the interconnection matrix, the inter-group adjustment of the compensation parameters in the initial calibration scheme is quickly completed to obtain the first calibration scheme. On this basis, the second influence value is used to construct a coupling matrix. The matrix reflects the shared relationship between the actuators and sensors between the controllers in the test group, and the first calibration scheme is finely adjusted within the group based on the signal coupling relationship represented in the coupling matrix, and finally the target calibration scheme is generated. This hierarchical calibration scheme adjustment method of first performing inter-group adjustment and then performing intra-group optimization simplifies the complexity of parameter adjustment and improves optimization efficiency through matrix processing. At the same time, the physical connection relationship and signal coupling characteristics are considered respectively with the help of the interconnection matrix and the coupling matrix, ensuring the accuracy of the calibration scheme adjustment and achieving the unity of efficiency and accuracy in the calibration scheme optimization process.

[0044] Optionally, after adjusting the initial calibration scheme according to the first impact value and the second impact value to generate a target calibration scheme, the method further includes:

[0045] Obtaining a target voltage point of each of the controllers in the test group, and setting a plurality of calibration voltage points at equal intervals according to the target voltage points;

[0046] generating a plurality of target test voltages according to the calibration voltage points, and synchronously outputting the plurality of target test voltages to the AD / DA channels of the controller in the test group;

[0047] Using an external ADC to collect a first feedback voltage of the controller, and using an internal ADC of the controller to collect a second feedback voltage of the controller;

[0048] calibrating the DA channel of the controller according to a first difference between the first feedback voltage and the target test voltage until an absolute value of the first difference is less than a preset error threshold;

[0049] The AD channel of the controller is calibrated according to a second difference between the second feedback voltage and the target test voltage until the absolute value of the first difference is smaller than a preset error threshold.

[0050] By adopting the above technical solution, the target voltage point of the controller in the test group is obtained and the calibration voltage point is set at equal intervals, which quickly determines the calibration test range and avoids the inefficient process of point-by-point testing. The multiple target test voltages generated according to the calibration voltage point are synchronously output to the AD / DA channel of the controller in the test group, thereby realizing parallel calibration of the controller in the group. In the specific calibration process, the first feedback voltage is first collected by an external ADC, and the first difference between it and the target test voltage is used for DA channel calibration. The accuracy of DA channel calibration is ensured by iterative adjustment until the absolute value of the first difference is less than the preset error threshold. After the DA channel calibration is completed, the second feedback voltage is collected by the internal ADC of the controller, and the second difference between it and the target test voltage is used for AD channel calibration. Similarly, the accuracy of AD channel calibration is ensured by iterative adjustment until the absolute value of the second difference is less than the preset error threshold. This orderly calibration method of DA first and AD later improves the overall calibration efficiency through group parallel processing.

[0051] In a second aspect, the present application provides an automatic calibration system for an AD / DA channel of a controller, the system comprising:

[0052] A feature extraction module is used to obtain historical environmental parameters and historical calibration data of multiple controllers on the production line, and extract environmental features from the historical environmental parameters and error type features from the historical calibration data;

[0053] A clustering module, configured to perform cluster analysis on the controllers based on the environmental characteristics and the error type characteristics, and divide the plurality of controllers into a plurality of test groups;

[0054] A preliminary test module, configured to inject test signals into the controllers in the test group respectively and collect feedback signals from the controllers;

[0055] A first scheme generating module is configured to adjust the standard calibration scheme corresponding to the cluster center according to a comparison result between the feedback signal and the standard feedback signal corresponding to the cluster center of the test group, so as to obtain an initial calibration scheme corresponding to each of the controllers in the test group;

[0056] a processing module, configured to obtain a process node and a production tact of each of the controllers on the production line, and determine, based on the process node and the production tact, a first impact value between the test groups and a second impact value between the controllers within the test group;

[0057] The second scheme generating module is configured to adjust the initial calibration scheme according to the first impact value and the second impact value to generate a target calibration scheme, wherein the target calibration scheme is used to calibrate the AD / DA channel of each controller.

[0058] In a third aspect, the present application provides a computer storage medium, which stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing any one of the above methods.

[0059] In a fourth aspect, the present application provides an electronic device comprising a processor, a memory and a transceiver, wherein the memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any one of the above methods.

[0060] In summary, the beneficial effects brought about by the technical solution of this application include:

[0061] By adopting the above method, the historical environmental parameters and historical calibration data of multiple controllers on the production line are obtained, and the environmental characteristics and error type characteristics are extracted for cluster analysis, and controllers with similar characteristics are divided into the same test group. Then, by injecting test signals and collecting feedback signals, combined with the standard feedback signal corresponding to the cluster center, a comparative analysis is performed to make targeted adjustments to the calibration scheme. At the same time, considering the correlation influence between the controllers on the production line, the first influence value between the test groups and the second influence value between the controllers in the group are determined by the process nodes and the production rhythm, and the initial calibration scheme is optimized and adjusted accordingly, so that the target calibration scheme finally generated not only maintains the high efficiency of group calibration, but also ensures the calibration accuracy by considering the influence relationship between controllers. Therefore, it can take into account the efficiency and accuracy of the controller calibration on the production line and improve the calibration effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 1 is a flow chart of a method for automatically calibrating an AD / DA channel of a controller according to an embodiment of the present application;

[0063] Figure 2 Schematic diagram of the structure of an automatic calibration system for AD / DA channels of a controller according to an embodiment of the present application;

[0064] Figure 3 This is a structural diagram of an electronic device provided in an embodiment of the present application.

[0065] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0066] In order to enable people skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0067] In the description of the embodiments of this application, words such as "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0068] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0069] See Figure 1 The following is a flow chart illustrating a method for automatically calibrating the AD / DA channels of a controller, as provided in an embodiment of the present application. This method can be implemented using a computer program, a single-chip microcomputer, or a von Neumann architecture-based automatic calibration system for the AD / DA channels of a controller. The computer program can be integrated into an application or run as a standalone tool. The specific steps of the method for automatically calibrating the AD / DA channels of a controller are described in detail below.

[0070] First, let's briefly introduce the application scenario of the embodiments of this application. In an intelligent manufacturing production line, multiple distributed controllers are responsible for controlling different process nodes. These controllers establish communication connections with a high-performance master control device via an industrial bus, forming a star-shaped control network. The master control device is equipped with a high-precision ADC reference module, which can serve as a standard reference during the calibration process.

[0071] In actual production, controllers may experience performance deviations due to factors such as long-term operation and environmental changes. To ensure control accuracy, the master control unit must regularly calibrate these controllers. During calibration, the master control unit first distributes a preset test signal to each controller, which then responds. The master control unit then samples each controller's response signal using its high-precision ADC reference module to obtain the actual feedback signal. By comparing the feedback signal with the theoretical expected value, it can detect any controller deviations and generate a corresponding calibration plan to achieve precise controller calibration.

[0072] S101: Acquire historical environmental parameters and historical calibration data of multiple controllers on the production line, extract environmental features from the historical environmental parameters, and extract error type features from the historical calibration data;

[0073] Among them, historical environmental parameters refer to operating environment data such as ambient temperature, humidity, vibration intensity, electromagnetic interference intensity, etc. recorded during the historical operation of the controller. In the embodiment of the present application, historical environmental parameters are used to characterize the operating environment characteristics of the controller. By analyzing these parameters, the main environmental factors affecting the performance of the controller can be identified, and the controller can be classified accordingly. For example, a controller in a high-temperature environment may have temperature drift, while a controller in a strong electromagnetic interference environment may have signal distortion. The differences in these environmental characteristics will cause the controller to exhibit different error characteristics.

[0074] Historical calibration data refers to various parameter information recorded during the controller's previous calibration process, including historical error types (such as zero drift, linearity error, gain error, etc.) and corresponding historical error levels (such as error amplitude and error frequency). In the embodiments of the present application, historical calibration data is used to reflect the typical error characteristics exhibited by the controller during operation. By weighted analysis of historical error types and historical error levels, the most prominent error manifestations of the controller can be identified, providing a reliable characteristic basis for subsequent group calibration.

[0075] In implementation, the system first acquires historical environmental data for each controller, including historical records of environmental parameters such as temperature, humidity, vibration, and electromagnetic interference. By analyzing this data, the system determines the environmental range in which each controller spends the most time. For example, if a controller spends the most time operating in the 25-30°C temperature range, the environmental tag corresponding to this temperature range is used as the environmental signature for that controller. This time-weighted feature extraction method ensures the representativeness of environmental signatures.

[0076] At the same time, historical controller calibration data is obtained, including historical error types (such as zero drift and linearity error) and their corresponding historical error severity. For each error type, its historical error severity is used as a weight to calculate a characteristic score. For example, if a controller has a high frequency and large amplitude of zero drift errors, this error type will have a high characteristic score. By comparing the characteristic scores of different error types, the historical error type corresponding to the highest characteristic score is determined as the error type characteristic of the controller.

[0077] Based on the above embodiment, as an optional implementation manner, the extraction method of the environmental features and the error type features can be specifically implemented through the following steps S201-S205.

[0078] S201: Acquire historical environmental data of multiple controllers on the production line;

[0079] In specific implementation, environmental sensors deployed in various areas of the production line collect environmental parameters such as temperature, humidity, vibration intensity, and electromagnetic interference intensity in real time. These parameters are transmitted to the main control device via the industrial bus and recorded and stored according to a preset sampling period (such as once every 10 minutes). For example, for a controller, its historical environmental data may record that over the past month, the temperature at the controller's location fluctuated between 25-35°C, the humidity varied between 45-60%, and there was a certain degree of vibration and electromagnetic interference.

[0080] S202: Determine the environmental interval in the historical environmental data where the controller has been located for the longest time, and determine the environmental tag corresponding to the environmental interval as the environmental feature of the controller;

[0081] In specific implementation, historical environmental data is first divided into intervals. For example, temperature is divided into multiple intervals at 5°C intervals (such as 20-25°C, 25-30°C, and 30-35°C), and humidity is divided into multiple intervals at 10% intervals (such as 40-50%, 50-60%, etc.). Vibration intensity and electromagnetic interference intensity are similarly divided into reasonable intervals. The cumulative operating time of each controller in each environmental interval is then calculated. For example, over the past month, a controller operated in the 25-30°C temperature range 60% of the time, in the 30-35°C range 20% of the time, and in other temperature ranges for the remainder of the time. By comparing the cumulative time in different environmental intervals, the longest interval is determined. The corresponding environmental parameter combination (such as 25-30°C temperature, 50-60% humidity, low vibration, and moderate electromagnetic interference) is used as an environmental label to determine the environmental characteristics of the controller.

[0082] S203: Acquire historical calibration data of each controller, where the historical calibration data includes historical error types and historical error degrees;

[0083] During specific implementation, the calibration data recorded during the historical calibration process of each controller is retrieved through the database of the main control device. This data includes historical error types (such as zero drift, linear error, gain error, etc.) and the corresponding historical error levels. The historical error level is characterized by error amplitude and error frequency. For example, in the past calibration records of a controller, the average amplitude of the zero drift error was 2% of the full scale and the frequency of occurrence was 3 times per week, and the average amplitude of the linear error was 1% of the full scale and the frequency of occurrence was 2 times per month. This error analysis method based on historical calibration data avoids the feature extraction bias that may be caused by relying on a single indicator by simultaneously recording information on error type and error level.

[0084] S204: Perform weighted calculation on the historical error type using the historical error degree to obtain a feature score corresponding to the historical error type;

[0085] In specific implementation, the error amplitude and error frequency in the historical error degree are used as weight factors for calculation. For the error amplitude, the ratio of the historical average amplitude of the error type to the full scale is used as the amplitude weight; for the error frequency, the ratio of the number of occurrences of the error type per unit time to the preset baseline frequency is used as the frequency weight. The error amplitude weight and error frequency weight are weighted together with the corresponding error type to obtain the characteristic score corresponding to each error type.

[0086] S205: Determine the historical error type corresponding to the maximum feature score as the error type feature of the controller.

[0087] In practice, all historical error types for each controller are first sorted according to their corresponding feature scores. The feature score directly reflects the degree to which the error type affects the controller's performance. By comparing the sorting results, the historical error type with the largest feature score is selected as the error type feature for that controller.

[0088] S102: performing cluster analysis on the controllers based on environmental characteristics and error type characteristics, and dividing the multiple controllers into a number of test groups;

[0089] Among them, the test group refers to a collection of controllers with similar environmental characteristics and error type characteristics on the production line. In an embodiment of the present application, the test group is used to group controllers with similar environmental conditions and similar error performance into a group for unified calibration. This grouping method can avoid the efficiency loss caused by independent calibration of each controller. Since the controllers in the same test group have similar operating characteristics, a unified calibration strategy can be formulated based on the cluster center of the group, and local adjustments can be made to adapt to individual differences within the group, which ensures both calibration efficiency and calibration accuracy.

[0090] In specific implementation, the controllers are first pre-classified using environmental characteristics, dividing multiple controllers into initial test groups corresponding to the environmental characteristics, quickly achieving a rough classification of the controllers. Subsequently, the environmental characteristics and error type characteristics are combined into a feature vector to construct a controller feature matrix. This feature matrix is ​​used to calculate the degree of discreteness between controllers. The initial test groups are then further divided according to the degree of discreteness to obtain the final test groups.

[0091] Based on the above embodiment, as an optional implementation, the controller is divided into test groups by cluster analysis, which can be specifically implemented through the following steps S301-S303.

[0092] S301: Pre-classify the controllers using environmental features, and divide multiple controllers into initial test groups corresponding to a number of environmental features;

[0093] In implementation, feature extraction is first performed on all controllers' environmental tags. These tags include combinations of environmental parameters such as temperature range, humidity range, vibration intensity, and electromagnetic interference intensity. By setting a similarity threshold for these environmental parameters, controllers with environmental parameter differences within this threshold are grouped together in the same initial test group. For example, if two controllers have a temperature range difference of within 5°C, a humidity range difference of within 10%, and the same vibration and electromagnetic interference intensity levels, they can be grouped together in the same initial test group.

[0094] S302: Combining the environmental features and the error type features into feature vectors to construct a feature matrix of the controller;

[0095] In specific implementation, the environmental characteristics of each controller are first quantized. This includes converting temperature ranges into interval center values, humidity ranges into interval center values, and vibration and electromagnetic interference intensities into corresponding quantized levels. Error type characteristics are also quantized by mapping different error types (such as zero drift, linearity error, and gain error) to corresponding characteristic values. The quantized environmental and error type characteristic values ​​are then arranged according to pre-defined combination rules to form a characteristic vector corresponding to each controller.

[0096] After obtaining the eigenvectors for all controllers, we then combine and arrange them in row vectors to construct a feature matrix that reflects the characteristics of the entire production line controller. Each row of the feature matrix represents the complete feature information for a controller, while the columns of the matrix correspond to different feature dimensions.

[0097] S303: Calculate the discreteness between the controllers according to the characteristic matrix, and divide the initial test group into several test groups according to the discreteness.

[0098] In specific implementation, the degree of dispersion between controllers within the initial test group is first calculated based on the feature matrix. For each initial test group, the eigenvectors corresponding to all controllers in that group are extracted. The Euclidean distance between these eigenvectors is calculated to obtain a dispersion value that reflects the characteristic differences between the controllers. Larger dispersion values ​​indicate significant characteristic differences between the controllers, while smaller dispersion values ​​indicate similar characteristic performance among the controllers.

[0099] After obtaining the discreteness value, a discreteness threshold is set as the grouping criterion. When the discreteness value between controllers within the initial test group exceeds the preset threshold, it indicates that the characteristics of the controllers within the group are too different and further division is required. By iteratively calculating the intra-group discreteness under different division schemes, the division scheme that minimizes the discreteness within each sub-group is selected, and the initial test group is split into several more refined test groups. This discreteness-based grouping optimization process continues until the internal discreteness of all test groups is less than the preset threshold or the preset minimum group size is reached.

[0100] S103: injecting test signals into the controllers in the test group respectively, and collecting feedback signals from each controller;

[0101] During implementation, the master control device first generates a sequence of preset test signals. These test signals cover the controller's primary operating range and include standard waveforms of varying amplitudes and frequencies. To improve testing efficiency, the master control device employs a grouped parallel approach, simultaneously injecting the same test signal into all controllers within the same test group. The test signals are transmitted to each controller via an industrial bus, where they are received and processed by the controller's signal processing unit.

[0102] As the test signal is injected, the master control unit initiates a signal acquisition program, sampling the response output of each controller using a high-precision ADC reference module to obtain feedback signals. During the acquisition process, the system records the complete response waveform of each controller under different test signals, including characteristic parameters such as signal amplitude, phase, and response time.

[0103] S104: adjusting the standard calibration scheme corresponding to the cluster center according to the comparison result of the feedback signal and the standard feedback signal corresponding to the cluster center of the test group to obtain the initial calibration scheme corresponding to each controller in the test group;

[0104] During implementation, the collected feedback signal is first compared with the standard feedback signal corresponding to the cluster center of the test group. The standard feedback signal, representing the ideal response characteristic for that test group, includes parameters such as signal amplitude and phase under standard test conditions. By calculating the deviation between the actual feedback signal and the standard feedback signal, deviation compensation parameters are derived, reflecting the degree of deviation in the controller's current performance. These deviation compensation parameters comprehensively describe the controller's response error under different test signals, encompassing multiple dimensions such as zero offset, gain deviation, and linearity error.

[0105] Based on the obtained deviation compensation parameters, the system makes targeted adjustments to the standard calibration scheme corresponding to the cluster center. The standard calibration scheme includes preset standard compensation parameters, which are ideal calibration values ​​predetermined based on the characteristics of the cluster center. By replacing the corresponding standard compensation parameters in the standard calibration scheme with the deviation compensation parameters obtained from actual tests, the calibration scheme is transformed from standardized to personalized. This parameter replacement process based on measured deviations ensures that the calibration scheme accurately reflects the actual performance status of the controller.

[0106] Based on the above embodiment, as an optional implementation, step S104 of generating an initial calibration solution specifically further includes S401 - S402 .

[0107] S401: Compare the feedback signal with the standard feedback signal of the cluster center of the test group to obtain a deviation compensation parameter of the feedback signal;

[0108] Among them, the cluster center of the test group refers to the center point that can best represent the characteristics of all controllers in the test group in the feature matrix space, and its environmental characteristics and error type characteristics are at the average level of the group. In an embodiment of the present application, the cluster center is used as a standard reference point for the test group, and its corresponding standard feedback signal and standard calibration scheme can be used as a benchmark for the calibration of the controllers in the group. Since the cluster center has the typical characteristics of the group, adjusting the calibration scheme based on the cluster center not only ensures the targeted calibration, but also avoids the computational burden of redesigning the calibration scheme for each controller, thereby improving the calibration efficiency while ensuring the calibration accuracy.

[0109] The deviation compensation parameter refers to a correction value reflecting the controller's current performance deviation, obtained by comparing the controller's actual feedback signal with the standard feedback signal corresponding to the test group cluster center. It includes quantitative indicators such as zero offset compensation value, gain error compensation coefficient, and linearity correction parameter. In the embodiments of the present application, the deviation compensation parameter is used to replace and correct the standard compensation parameters in the standard calibration scheme, so that the calibration scheme can accurately match the actual performance status of the controller.

[0110] In practice, the system first obtains the standard feedback signal corresponding to the test group cluster center. This standard feedback signal represents the ideal response characteristics of this type of controller. The feedback signals collected by the controller under different test signals are then compared and analyzed with the corresponding standard feedback signals. During this comparison, the system calculates the deviation of the feedback signal from the standard feedback signal at zero, full-scale, and multiple intermediate-scale points.

[0111] By analyzing the deviation of the zero position, we can obtain the zero compensation value that reflects the zero drift of the controller. By analyzing the deviation ratio of the full-scale point, we can obtain the gain compensation coefficient that reflects the change of the controller gain. By analyzing the deviation distribution of the mid-scale point, we can obtain the linear correction parameter that reflects the change of the controller linearity.

[0112] S402: Using the deviation compensation parameters to replace the corresponding standard compensation parameters in the standard calibration scheme of the cluster center, to obtain an initial calibration scheme corresponding to each controller in the test group.

[0113] During implementation, a standard calibration scheme corresponding to the test group cluster centers is first obtained. This scheme includes preset standard compensation parameters, which are ideal calibration values ​​pre-set based on the characteristics of the cluster centers. The standard compensation parameters in the standard calibration scheme have a one-to-one correspondence with the previously obtained deviation compensation parameters, including zero point compensation value, gain compensation coefficient, linearity correction parameters, and so on.

[0114] By replacing the deviation compensation parameters obtained from actual testing with the corresponding standard compensation parameters in the standard calibration scheme, the calibration scheme can be personalized. During the replacement process, the system maintains the basic framework and compensation logic of the calibration scheme and only updates the specific compensation parameter values.

[0115] S105: Obtaining the process node and production tact of each controller on the production line, and determining a first impact value between test groups and a second impact value between controllers within the test group based on the process node and production tact;

[0116] Among them, the process node of the controller refers to the key process locations and processing links involved in the production and manufacturing process of the controller, including specific locations such as component welding points, signal processing units, power output units, etc. that may affect the performance of the controller. In the embodiment of the present application, the process node is used to characterize the process correlation between controllers, and the calibration scheme is further optimized by analyzing the mutual influence that may be generated by controllers sharing the same process node. For example, when multiple controllers use components from the same batch or are processed at the same workstation, these controllers have a process node correlation, and their performance may show similar deviation characteristics.

[0117] Among them, the first impact value between test groups refers to a quantitative indicator of the degree of performance correlation between controllers in different test groups due to shared process nodes. This value is obtained by calculating the number of shared process nodes, the importance of process nodes, and the similarity of controller performance. In an embodiment of the present application, the first impact value is used to evaluate the degree of mutual influence between different test groups due to process correlation, and provide a basis for the overall optimization of the calibration scheme. Through the calculation of the first impact value, it is possible to identify which test groups have a strong process correlation, so that the impact of this correlation can be considered when formulating the calibration scheme, thereby improving the overall effect of the calibration.

[0118] The second impact value is a quantitative indicator of the degree of performance correlation caused by shared process nodes between controllers within the same test group. This value is calculated by weighting the number of shared process nodes between controllers within the group, the node importance weights, and the controller performance similarity. In the present embodiment, the second impact value is used to assess the strength of process correlation between controllers within the same test group, providing a reference for optimizing calibration schemes within the group.

[0119] During specific implementation, the detailed process information of each controller is first extracted from the manufacturing execution system (MES) of the production line, including process node information such as component sources, processing stations, operators, and the production time rhythm corresponding to each process node. Based on the acquired process information, the system first analyzes the process associations between different test groups. By counting the number of process nodes shared by controllers in different groups and combining the importance weights of each process node, the first impact value between the test groups is calculated. The importance weight of the process node is determined according to the degree of influence of the node on the performance of the controller. For example, the welding points of core components have a higher weight. At the same time, the system also takes into account the time correlation of the production rhythm. When controllers in different groups pass through the same process node at a similar time, their impact values ​​will increase accordingly.

[0120] While calculating the inter-group primary impact value, the system also analyzes the process dependencies between controllers within the same test group. By identifying shared process nodes within the group and combining node importance with production timing, the system calculates the secondary impact value between controllers. This intra-group impact value calculation specifically focuses on critical process nodes that could cause systematic deviations in controller performance, such as when using components from the same batch or when processing continuously at the same workstation.

[0121] It should be understood that in the same test group, when there are differences in the production timing of the controller, the calculation of the impact value needs to be adjusted through the time weight function. In specific implementation, a time decay model is first established based on the production beat information. This model reflects the characteristic that the process impact gradually weakens as the time interval increases. For example, an exponential decay function can be used so that the larger the time interval, the smaller the impact weight of the process node. Therefore, when calculating the second impact value, the system will multiply the time weight by the basic weight of the process node to obtain a corrected weight that takes into account the timing difference. For controllers with similar time, the impact weight of their shared process node is close to the original value; for controllers with larger time intervals, even if they share the same process node, their impact weight will decrease as the time difference increases. This timing-based weight adjustment method ensures that the impact value calculation can accurately reflect the time-dependent characteristics of the process correlation strength.

[0122] Optionally, the process of determining the impact value first classifies the weights of the process nodes. For example, the weight of the core component soldering point is 0.8-1.0, the weight of the signal processing unit assembly point is 0.6-0.8, the weight of the peripheral circuit soldering point is 0.4-0.6, and the weight of the mechanical assembly point is 0.2-0.4. When calculating the first impact value between different test groups, the number of process nodes shared by the two groups is counted, the weight value of each node is added, and then the weight value is corrected based on the proximity of the production timing. When the controllers of two groups pass through the same process node at a similar time, their impact values ​​will increase accordingly.

[0123] The calculation method for the second impact value between controllers within the same test group is similar, focusing on the shared process node weight and production timing. For example, if two controllers in the group use core components from the same batch (weight 0.9) and their production times differ by less than 2 hours, their second impact value may reach above 0.8. However, if they only share peripheral circuit soldering stations (weight 0.5) and their production times differ by more than 8 hours, their second impact value may drop below 0.3.

[0124] When adjusting the calibration scheme, the calibration parameters of groups with higher first impact values ​​(e.g., greater than 0.7) need to be adjusted collaboratively. For example, if two groups experience zero-point drift with consistent trends, the compensation parameters should be adjusted to maintain similar ratios. For controllers with higher second impact values ​​(e.g., greater than 0.6), their calibration parameters should be adjusted similarly to avoid significant compensation discrepancies.

[0125] Based on the above embodiment, as an optional implementation manner, the method for determining the first impact value and the second impact value can be specifically implemented through the following steps S501-S507.

[0126] S501: Obtain the process nodes and production rhythm corresponding to each controller on the production line;

[0127] S502: determining a first node association value between target process nodes according to a first node distance between target process nodes corresponding to cluster centers;

[0128] In implementation, the first step is to determine the first node distance between two target process nodes. This distance reflects the relative position of the process nodes in the production process. The node distance is measured by the number of steps in the shortest path between the nodes in the process flow diagram. This distance value directly reflects the spatial correlation characteristics of the process nodes during the manufacturing process.

[0129] Based on the calculated first-node distances, the system uses a weighted calculation method to determine the first-node correlation value between target process nodes. This calculation takes into account multiple influencing factors: First, the closer the process nodes are physically, the greater their correlation value, as adjacent process nodes are more susceptible to shared environmental factors. Second, the correlation value increases for process nodes that are close together in the process flow, as process continuity can lead to the transfer of process influences. Third, the correlation value increases further for process nodes that use the same or similar processing equipment, as equipment characteristics can lead to systematic process deviations.

[0130] S503: Determine a first timing difference value between target process nodes according to the production tact;

[0131] During implementation, detailed production time information for each target process node is first extracted from the production line's manufacturing execution system. This includes time series data such as processing start time, completion time, and process duration. This time series data reflects the temporal relationships between different process nodes and provides a basis for assessing the timeliness of process impacts.

[0132] Based on the acquired production takt time information, the system calculates the first-order timing difference between target process nodes. This calculation focuses on the following timing characteristics: First, the time interval between process nodes is analyzed; shorter time intervals indicate a greater likelihood of process impact transmission; second, the degree of overlap between process durations is considered; the longer the overlap, the more significant the mutual impact between process nodes; and third, the continuity of production batches is evaluated; process nodes within the same batch have stronger timing correlations.

[0133] The system combines these timing characteristics into a first timing difference value through a weighted calculation method. A smaller timing difference value indicates that the process nodes are more closely related in time, and their process impacts are more easily transmitted to each other.

[0134] S504: Determine a first impact value between the test groups according to the first node association value and the first timing difference value;

[0135] During implementation, the system first analyzes the first-node correlation value, which reflects the strength of the spatial correlation between process nodes. When process nodes are physically close, have closely connected processes, or use the same equipment, the first-node correlation value is higher, indicating strong process influence transmission between these nodes.

[0136] At the same time, the system uses the first timing difference value as the evaluation basis for the time dimension. A smaller timing difference value indicates that the time interval between process nodes is shorter, and their process impact is easier to transmit to each other. For example, when the processing time of two process nodes is similar or there is time overlap, the process impact between them will be more significant. When determining the first impact value between test groups, the system performs a weighted calculation on the first node association value and the first timing difference value by reasonably allocating weights. When the first node association value is higher and the first timing difference value is smaller, the obtained first impact value is larger, indicating that there is a strong process correlation between these test groups.

[0137] S505: Determine a second node correlation value between the process nodes in the test group and the target process node according to the second node distance between the process nodes in the test group and the target process node;

[0138] In practice, the second node distance between each process node in the group and the target process node is first calculated. This distance calculation not only considers the actual physical distance but also the degree of process connectivity within the process flow. For example, if process nodes use the same processing equipment, share the same batch of raw materials, or are operated by the same operator, their second node distance will be reduced accordingly.

[0139] Based on the calculated second-node distance, the system determines the second-node association value between the process node and the target process node through weighted calculation. The calculation focuses on the following factors: When the process node and the target process node are physically adjacent, their association value increases due to their susceptibility to the same environmental factors; when the process node and the target process node are directly connected in terms of process steps, their association value also increases due to the direct transmission of process influences; and when the process node and the target process node share key resources, their association value is further enhanced due to the consistency of resource characteristics.

[0140] S506: Determine a second timing difference value between the process nodes in the test group and the target process node according to the production tact;

[0141] During implementation, the production execution system extracts detailed time information for each process node, including processing start and end times, process duration, and other data. This time series information reflects the temporal distribution characteristics of process nodes in the production process and is crucial for assessing the timeliness of process impacts.

[0142] Based on the acquired production cycle information, the second timing difference between the process nodes within the test group and the target process node is calculated. The calculation process focuses on the temporal connection characteristics between the process nodes: when the processing time interval between the process node and the target process node is short, the timing difference between the two is small because the process influence has not yet decayed. When the processing time of the process node overlaps with the target process node, the timing difference is further reduced because they are affected by the process conditions. When the process nodes belong to the same production batch, their timing difference is also reduced accordingly.

[0143] S507: Determine a second impact value between the controllers in the test group according to the second node association value and the second timing difference value.

[0144] In practice, the second-node correlation value is first analyzed. This value reflects the strength of the spatial correlation between process nodes within a group and the target process node. When process nodes are physically close, have close process connections, or share the same resources, the second-node correlation value is large, indicating strong process influence transmission between these nodes.

[0145] The second timing difference value is also used as the evaluation basis for the time dimension. Smaller timing difference values ​​indicate a stronger temporal correlation between process nodes, making their process impacts more easily transferred to each other. For example, when the processing times of controllers are similar or overlap, the process impact between them will be more significant.

[0146] S106: Adjusting the initial calibration scheme according to the first impact value and the second impact value to generate a target calibration scheme, where the target calibration scheme is used to calibrate the AD / DA channels of each controller.

[0147] During implementation, the process correlation between test groups, as reflected by the first impact value, is analyzed. If the first impact values ​​between different groups are significant, the calibration schemes for these groups need to be adjusted in a coordinated manner. For example, if two test groups share a common key process node and therefore have a high first impact value, the system will adjust their calibration parameters accordingly to ensure consistent compensation.

[0148] At the same time, in-depth optimization is performed based on the process correlation between controllers within a group, as reflected by the Second Impact Value. When the Second Impact Values ​​between certain controllers within a group are large, more detailed coordination of the calibration parameters of these controllers is required. For example, when multiple controllers have high Second Impact Values ​​due to using components from the same batch, the system will coordinate and adjust parameters such as their zero point compensation values ​​and gain compensation coefficients to avoid inconsistent compensation.

[0149] Based on the impact values ​​at these two levels, the system comprehensively optimizes the initial calibration plan to generate the final target calibration plan. During the optimization process, for controllers with significant process dependencies, the calibration parameters are adjusted to account for their mutual influence. For controllers with weaker process dependencies, the initial calibration plans are maintained independent of each other.

[0150] Optionally, step S106 specifically also includes steps S601-S604.

[0151] S601: Constructing an interconnection matrix reflecting the physical connection relationship between each test group according to the first impact value;

[0152] During implementation, each test group is first numbered and a corresponding matrix structure is established. The rows and columns of the matrix represent different test groups, and the value of each element in the matrix is ​​determined by the first impact value between the corresponding test groups. For example, the first impact value between test group i and test group j is entered in the i-th row and j-th column of the matrix. When filling the interconnection matrix, the system directly uses the first impact value as the matrix element value. A larger first impact value is represented as a larger element value in the matrix, reflecting a stronger process correlation between the two test groups; a smaller first impact value is represented as a smaller element value, indicating a weaker process correlation between the two test groups.

[0153] S602: Based on the signal transmission relationship represented in the interconnection matrix, the compensation parameters in the initial calibration scheme are adjusted between groups to obtain a first calibration scheme;

[0154] In practice, the distribution of element values ​​in the interconnect matrix is ​​first analyzed to identify test pairs with strong process dependencies. For pairs with larger element values ​​in the interconnect matrix, compensation parameter adjustments must account for their mutual impact. For pairs with smaller element values, parameter adjustments can be made relatively independently.

[0155] When adjusting compensation parameters between groups, the system focuses on key parameters such as zero-point compensation value and gain compensation coefficient. When the values ​​of the interconnection matrix elements between two test groups are large, their compensation parameters need to be adjusted in a coordinated manner. For example, if the controllers of these two groups use the same key components, then their zero-point compensation values ​​should maintain a certain degree of correlation to avoid significant differences in compensation effects. Similarly, the adjustment of the gain compensation coefficient also needs to consider the degree of process correlation between groups. During the adjustment of compensation parameters, the system determines the weight of the parameter adjustment based on the size of the interconnection matrix element values. For group pairs with strong process correlation, their parameters will be given a larger correlation weight when adjusting, to ensure that the adjusted parameters can reflect the transfer characteristics of process influences.

[0156] S603: Constructing a coupling matrix reflecting the shared relationship between actuators and sensors among controllers in the test group according to the second impact value;

[0157] In specific implementation, each controller within a group is numbered and a corresponding matrix structure is constructed. The rows and columns of the matrix represent different controllers, and the value of each matrix element is determined by the second influence value between the corresponding controllers. For example, the second influence value between controllers i and j is entered in the i-th row and j-th column of the matrix. When populating the coupling matrix, the system directly uses the second influence value as the matrix element value. Larger second influence values ​​appear as larger element values ​​in the matrix, indicating a strong process connection between the two controllers due to shared actuators or sensors; smaller second influence values ​​appear as smaller element values, indicating a weaker process connection between the two controllers. Because the process influence between controllers is bidirectional, the constructed coupling matrix also has symmetry. This coupling matrix intuitively displays the process coupling network between controllers within the group. Each row or column in the matrix reflects the strength of the process connection between a controller and all other controllers, particularly reflecting the systemic influence caused by shared actuators and sensors.

[0158] S604: Based on the signal coupling relationship represented in the coupling matrix, perform intra-group adjustment on the first calibration scheme to obtain a target calibration scheme.

[0159] In practice, the distribution of element values ​​in the coupling matrix is ​​first analyzed to identify controller pairs with strong process coupling. Controller pairs with large element values ​​in the coupling matrix indicate significant process dependencies due to shared actuators or sensors, necessitating coordinated adjustment of their calibration parameters.

[0160] When making intra-group adjustments to calibration parameters, the system focuses on the systematic impact caused by the sharing of actuators and sensors. When the coupling matrix element values ​​between two controllers are large, their compensation parameters need to be adjusted in a coordinated manner. For example, if the two controllers share the same sensor, the adjustment of their zero-point compensation value and gain compensation coefficient should take into account the consistency of sensor characteristics. Similarly, for controllers that share actuators, the adjustment of their compensation parameters also needs to maintain corresponding correlation. During the adjustment of compensation parameters, the system determines the priority and amplitude of parameter adjustment based on the size of the coupling matrix element values. For controller pairs with strong process coupling, more coupling factors will be considered when adjusting their parameters to ensure that the adjusted parameters can reflect the systematic impact brought by shared resources.

[0161] Optionally, a specific calibration method is as follows: obtaining a target voltage point of each controller in the test group, and setting a plurality of calibration voltage points at equal intervals according to the target voltage points;

[0162] Generate multiple target test voltages based on the calibration voltage points, and synchronously output the multiple target test voltages to the AD / DA channels of the controllers within the test group;

[0163] An external ADC is used to collect a first feedback voltage of the controller, and an internal ADC of the controller is used to collect a second feedback voltage of the controller;

[0164] Calibrate the DA channel of the controller according to a first difference between the first feedback voltage and the target test voltage until the absolute value of the first difference is less than a preset error threshold;

[0165] The AD channel of the controller is calibrated according to a second difference between the second feedback voltage and the target test voltage until the absolute value of the first difference is smaller than a preset error threshold.

[0166] During implementation, the target voltage points for each controller within the test group are first determined. These voltage points typically include key values ​​such as zero-point voltage and full-scale voltage. Based on these target voltage points, the system then sets multiple calibration voltage points at equal intervals. The distribution of these calibration voltage points must cover the entire operating range of the controllers while ensuring uniform calibration accuracy.

[0167] To achieve synchronized calibration, the system generates multiple target test voltages based on the set calibration voltage points and uses a multi-channel signal generator to synchronously output these voltages to the AD / DA channels of all controllers within the test group. This synchronized testing method not only improves calibration efficiency but also ensures consistent test conditions, facilitating the detection and resolution of systematic deviations caused by shared resources.

[0168] During testing, the system uses a high-precision external ADC to acquire the controller's first feedback voltage, which reflects the output characteristics of the controller's DA channel. Simultaneously, the controller's internal ADC acquires the second feedback voltage, which reflects the sampling characteristics of the controller's AD channel. This dual acquisition method allows the system to independently evaluate the performance of both the DA and AD channels.

[0169] To calibrate the DA channel, the system calculates the first difference between the first feedback voltage and the target test voltage. When the absolute value of this first difference exceeds a preset error threshold, the system adjusts the DA channel based on the compensation parameters in the target calibration scheme. This adjustment process is iterative, with the difference remeasured and recalculated after each adjustment until the difference meets the accuracy requirements.

[0170] Similarly, for AD channel calibration, the system calculates the second difference between the second feedback voltage and the target test voltage. When the absolute value of this second difference exceeds a preset error threshold, the system adjusts the AD channel based on the compensation parameters in the target calibration scheme. This process is also iterative, ensuring that the AD channel sampling accuracy meets the requirements through multiple adjustments.

[0171] The following are system embodiments of the present application, which can be used to implement the method embodiments of the present application. For details not disclosed in the system embodiments of the present application, please refer to the method embodiments of the present application.

[0172] See Figure 2 , which shows a schematic diagram of the structure of an automatic calibration system for controller AD / DA channels provided by an exemplary embodiment of the present application. This system can be implemented as all or part of a system through software, hardware, or a combination of both. The automatic calibration system for controller AD / DA channels includes:

[0173] A feature extraction module is used to obtain historical environmental parameters and historical calibration data of multiple controllers on the production line, extract environmental features from the historical environmental parameters, and extract error type features from the historical calibration data;

[0174] Clustering module, used to perform cluster analysis on controllers based on environmental characteristics and error type characteristics, and divide multiple controllers into several test groups;

[0175] A preliminary test module is used to inject test signals into the controllers in the test group and collect feedback signals from each controller;

[0176] A first scheme generating module is configured to adjust the standard calibration scheme corresponding to the cluster center according to the comparison result between the feedback signal and the standard feedback signal corresponding to the cluster center of the test group, so as to obtain the initial calibration scheme corresponding to each controller in the test group;

[0177] A processing module, configured to obtain a process node and a production tact of each controller on a production line, and determine a first impact value between test groups and a second impact value between controllers within a test group based on the process node and the production tact;

[0178] The second scheme generating module is used to adjust the initial calibration scheme according to the first impact value and the second impact value to generate a target calibration scheme, and the target calibration scheme is used to calibrate the AD / DA channels of each controller.

[0179] Based on the above embodiments, as an optional embodiment, the feature extraction module is also used to obtain historical environmental data of multiple controllers on the production line; determine the environmental interval in which the controller has been located for the longest time in the historical environmental data, and determine the environmental label corresponding to the environmental interval as the environmental feature of the controller; obtain historical calibration data of each controller, the historical calibration data includes historical error types and historical error degrees; use the historical error degrees to perform weighted calculations on the historical error types to obtain feature scores corresponding to the historical error types; and determine the historical error type corresponding to the largest feature score as the error type feature of the controller.

[0180] Based on the above embodiment, as an optional embodiment, the clustering module is also used to pre-classify the controllers using environmental features, and divide multiple controllers into several initial test groups corresponding to the environmental features; combine the environmental features and the error type features into feature vectors to construct a feature matrix of the controller; calculate the degree of discreteness between the controllers based on the feature matrix, and divide the initial test group into several test groups according to the degree of discreteness.

[0181] Based on the above embodiment, as an optional embodiment, the first scheme generation module is also used to compare the feedback signal with the standard feedback signal of the cluster center of the test group to obtain the deviation compensation parameter of the feedback signal; use the deviation compensation parameter to replace the corresponding standard compensation parameter in the standard calibration scheme of the cluster center to obtain the initial calibration scheme corresponding to each controller in the test group.

[0182] Based on the above embodiment, as an optional embodiment, the processing module is further configured to obtain the process nodes and production tact corresponding to each controller on the production line; determine the first node correlation value between the target process nodes based on the first node distance between the target process nodes corresponding to the cluster centers; and determine the first timing difference value between the target process nodes based on the production tact;

[0183] Based on the first node association value and the first timing difference value, the first impact value between the test groups is determined; based on the second node distance between the process node in the test group and the target process node, the second node association value between the process node in the test group and the target process node is determined; based on the production rhythm, the second timing difference value between the process node in the test group and the target process node is determined; based on the second node association value and the second timing difference value, the second impact value between the controllers in the test group is determined.

[0184] Based on the above embodiment, as an optional embodiment, the second scheme generation module is further used to construct an interconnection matrix reflecting the physical connection relationship between each test group based on the first influence value; based on the signal transmission relationship represented in the interconnection matrix, the compensation parameters in the initial calibration scheme are adjusted between groups to obtain the first calibration scheme; based on the second influence value, a coupling matrix reflecting the shared relationship between the actuators and sensors between the controllers in the test group is constructed; based on the signal coupling relationship represented in the coupling matrix, the first calibration scheme is adjusted within the group to obtain the target calibration scheme.

[0185] Based on the above embodiments, as an optional embodiment, the second scheme generation module is also used to obtain the target voltage points of each controller in the test group, and set multiple calibration voltage points at equal intervals according to the target voltage points; generate multiple target test voltages according to the calibration voltage points, and synchronously output the multiple target test voltages to the AD / DA channels of the controllers in the test group; use an external ADC to acquire the first feedback voltage of the controller, and use the internal ADC of the controller to acquire the second feedback voltage of the controller; calibrate the DA channel of the controller according to the first difference between the first feedback voltage and the target test voltage until the absolute value of the first difference is less than a preset error threshold; calibrate the AD channel of the controller according to the second difference between the second feedback voltage and the target test voltage until the absolute value of the first difference is less than a preset error threshold.

[0186] An embodiment of the present application also provides a computer storage medium, which can store multiple instructions. The instructions are suitable for being loaded by a processor and executed by the automatic calibration method of the controller AD / DA channel as in the above embodiment. The specific execution process can be found in the specific description of the embodiment and will not be repeated here.

[0187] See Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0188] The communication bus 302 is used to implement the connection and communication between these components.

[0189] The user interface 303 may include a display screen (Display) and a camera (Camera).

[0190] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0191] The processor 301 may include one or more processing cores. Using various interfaces and circuits, the processor 301 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 305, as well as accesses data stored in the memory 305, to perform various server functions and process data. Optionally, the processor 301 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 301 but implemented as a separate chip.

[0192] Among them, the memory 305 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be optionally at least one storage device located away from the aforementioned processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for an automatic calibration method of a controller AD / DA channel.

[0193] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call an application program stored in the memory 305 for an automatic calibration method of the controller AD / DA channel. When executed by one or more processors, the electronic device executes one or more methods in the above-mentioned embodiments.

[0194] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more methods in the above embodiments.

[0195] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0196] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0197] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0198] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0199] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0200] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0201] The above are merely exemplary embodiments of the present disclosure and are not intended to limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any variations, uses or adaptive changes of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the field of the present disclosure that are not recorded in the present disclosure.

Claims

1. A method for automatic calibration of a controller AD / DA channel, characterized in that: The method comprises: Acquire historical environmental parameters and historical calibration data of multiple controllers on the production line, extract environmental features from the historical environmental parameters, and extract error type features from the historical calibration data; performing cluster analysis on the controllers based on the environmental characteristics and the error type characteristics, and dividing the plurality of controllers into a plurality of test groups; Injecting test signals into the controllers in the test group respectively, and collecting feedback signals from the controllers; According to the comparison result of the feedback signal and the standard feedback signal corresponding to the cluster center of the test group, the standard calibration scheme corresponding to the cluster center is adjusted to obtain the initial calibration scheme corresponding to each controller in the test group; Obtaining a process node and a production tact of each of the controllers on the production line, and determining a first impact value between the test groups and a second impact value between the controllers within the test group according to the process node and the production tact; The initial calibration scheme is adjusted according to the first impact value and the second impact value to generate a target calibration scheme, where the target calibration scheme is used to calibrate the AD / DA channel of each of the controllers.

2. The method according to claim 1, characterized in that The acquiring of historical environmental parameters and historical calibration data of multiple controllers on the production line, extracting environmental features from the historical environmental parameters, and extracting error type features from the historical calibration data, includes: Obtain historical environmental data from multiple controllers on a production line; Determine the environmental interval in the historical environmental data where the controller has been located for the longest time, and determine the environmental tag corresponding to the environmental interval as the environmental feature of the controller; Acquiring historical calibration data of each of the controllers, wherein the historical calibration data includes historical error types and historical error degrees; Performing weighted calculation on the historical error type using the historical error degree to obtain a feature score corresponding to the historical error type; The historical error type corresponding to the largest feature score is determined as the error type feature of the controller.

3. The method according to claim 1, characterized in that The cluster analysis of the controllers based on the environmental characteristics and the error type characteristics is performed to divide the plurality of controllers into a plurality of test groups, including: Pre-classifying the controllers using environmental features, and dividing the plurality of controllers into a number of initial test groups corresponding to the environmental features; Combining the environmental features with the error type features by performing feature vectors to construct a feature matrix of the controller; The discreteness between the controllers is calculated according to the characteristic matrix, and the initial test group is divided into a plurality of test groups according to the discreteness.

4. The method according to claim 1, wherein The step of adjusting the standard calibration scheme corresponding to the cluster center according to the comparison result between the feedback signal and the standard feedback signal corresponding to the cluster center of the test group to obtain the initial calibration scheme corresponding to each controller in the test group includes: Comparing the feedback signal with a standard feedback signal of the cluster center of the test group to obtain a deviation compensation parameter of the feedback signal; The deviation compensation parameters are used to replace the corresponding standard compensation parameters in the standard calibration scheme of the cluster center to obtain the initial calibration scheme corresponding to each of the controllers in the test group.

5. The method according to claim 1, characterized in that The obtaining of the process node and the production tact of each controller on the production line, and determining, based on the process node and the production tact, a first impact value between the test groups and a second impact value between the controllers in the test group, includes: Obtaining the process nodes and production rhythm corresponding to each controller on the production line; determining a first node association value between the target process nodes according to a first node distance between the target process nodes corresponding to the cluster centers; Determine a first timing difference value between the target process nodes according to the production tact; determining a first impact value between the test groups according to the first node association value and the first timing difference value; determining a second node correlation value between the process nodes in the test group and the target process node according to a second node distance between the process nodes in the test group and the target process node; Determine a second timing difference value between the process nodes in the test group and the target process node according to the production tact; A second impact value between the controllers in the test group is determined according to the second node association value and the second timing difference value.

6. The method according to claim 1, characterized in that The adjusting the initial calibration scheme according to the first impact value and the second impact value to generate a target calibration scheme includes: constructing an interconnection matrix reflecting the physical connection relationship between each test group according to the first impact value; Based on the signal transmission relationship represented in the interconnection matrix, inter-group adjustment is performed on the compensation parameters in the initial calibration scheme to obtain a first calibration scheme; Constructing a coupling matrix reflecting the shared relationship between the actuators and sensors among the controllers in the test group according to the second impact value; Based on the signal coupling relationship represented in the coupling matrix, the first calibration scheme is adjusted within the group to obtain a target calibration scheme.

7. The method according to claim 1, characterized in that After adjusting the initial calibration scheme according to the first impact value and the second impact value to generate a target calibration scheme, the method further includes: Obtaining a target voltage point of each of the controllers in the test group, and setting a plurality of calibration voltage points at equal intervals according to the target voltage points; generating a plurality of target test voltages according to the calibration voltage points, and synchronously outputting the plurality of target test voltages to the AD / DA channels of the controller in the test group; Using an external ADC to collect a first feedback voltage of the controller, and using an internal ADC of the controller to collect a second feedback voltage of the controller; calibrating the DA channel of the controller according to a first difference between the first feedback voltage and the target test voltage until an absolute value of the first difference is less than a preset error threshold; The AD channel of the controller is calibrated according to a second difference between the second feedback voltage and the target test voltage until the absolute value of the first difference is smaller than a preset error threshold.

8. An automatic calibration system for controller AD / DA channels, characterized in that: The system comprises: A feature extraction module is used to obtain historical environmental parameters and historical calibration data of multiple controllers on the production line, and extract environmental features from the historical environmental parameters and error type features from the historical calibration data; A clustering module, configured to perform cluster analysis on the controllers based on the environmental characteristics and the error type characteristics, and divide the plurality of controllers into a plurality of test groups; A preliminary test module, configured to inject test signals into the controllers in the test group respectively and collect feedback signals from the controllers; A first scheme generating module is configured to adjust the standard calibration scheme corresponding to the cluster center according to a comparison result between the feedback signal and the standard feedback signal corresponding to the cluster center of the test group, so as to obtain an initial calibration scheme corresponding to each of the controllers in the test group; a processing module, configured to obtain a process node and a production tact of each of the controllers on the production line, and determine, based on the process node and the production tact, a first impact value between the test groups and a second impact value between the controllers within the test group; The second scheme generating module is configured to adjust the initial calibration scheme according to the first impact value and the second impact value to generate a target calibration scheme, wherein the target calibration scheme is used to calibrate the AD / DA channel of each controller.

9. A computer storage medium, characterized in that The computer storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The electronic device comprises a processor, a memory and a transceiver, wherein the memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 7.

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