Factory simulation detection method applied to VCU controller of forklift

Abstract

The invention discloses a factory simulation detection method applied to a VCU controller of a forklift, and relates to the technical field of simulation detection, and the method comprises three steps: detection preparation, detection execution and result processing. In the detection preparation stage, the model of a VCU controller is determined through an automatic identification mechanism, a database is dynamically updated, meanwhile, the load of an execution unit is monitored, and the bandwidth is dynamically allocated; the detection execution stage relates to analog quantity output, fault injection and intelligent prediction comparison; in a result processing stage, the industrial personal computer receives the data and then carries out integrity verification and analysis, the test progress and the result are displayed in real time, and a report is generated after detection is finished; according to the invention, the intelligent level of fault detection is improved and the performance and reliability of the forklift VCU controller are ensured through the intelligent prediction comparison technology and the severity distinguishing of the audio alarm sound.

Description

Technical Field

[0001] The present invention relates to the technical field of simulation detection, and in particular to a factory simulation detection method for a forklift VCU controller. Background Art

[0002] In the field of factory inspection of forklift VCU (Vehicle Control Unit) controllers, traditional inspection methods often rely on manual operation and simple automated equipment. These methods have problems such as low efficiency, low accuracy, and difficulty in simulating complex working conditions. With the development of forklift technology and the improvement of safety requirements, existing inspection methods can no longer meet the growing demand for accuracy and efficiency. In particular, in terms of simulating various working conditions of forklifts in actual working scenarios, fault injection, and output signal detection, traditional methods lack flexibility and intelligence and cannot fully evaluate the performance and reliability of VCU controllers.

[0003] In order to improve the efficiency and accuracy of factory inspection of forklift VCU controllers, it is necessary to develop a new simulation inspection method. This method should be able to automatically identify the VCU controller model, dynamically update the database, and dynamically allocate bandwidth during the inspection process to ensure efficient and stable data transmission.

[0004] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0005] The purpose of the present invention is to solve the problems of low efficiency, low accuracy and difficulty in simulating complex working conditions in the existing forklift VCU controller factory detection method, and to propose a factory simulation detection method for a forklift VCU controller.

[0006] The purpose of the present invention can be achieved through the following technical solutions: The factory simulation detection method applied to the forklift VCU controller includes the following steps: S1: Test preparation, import the forklift VCU controller test related data on the industrial computer, determine the model and verify it through the automatic recognition mechanism, and dynamically update the database; then send the data to each execution unit and start the test process, while monitoring the execution unit load and dynamically allocating bandwidth to ensure efficient and stable data transmission; S2: Detection execution, the input execution unit outputs analog quantity and data quantity according to the logical timing, the fault injection unit injects fault signals according to the settings, and the output execution unit detects the output signal of the VCU controller. Each execution unit checks the data after each timing execution and sends it to the industrial computer. It also includes scenario simulation of analog quantity output, random and diversified combination of fault injection, and intelligent prediction and comparison of output signal detection; S3: Result processing. After receiving the data, the industrial computer performs integrity verification and analysis to determine whether the test results are qualified. It displays the test progress, data content and qualified or not in real time, and generates a report after the test is completed. In the marking of qualified and unqualified items, the severity will be determined based on the comprehensive analysis of the judgment parameters, and then different audio alarm frequencies and durations will be set.

[0007] Furthermore, the specific steps of S1 are as follows: First, import the project and index data of the forklift VCU controller that needs to be tested on the industrial computer; use the automatic recognition mechanism to automatically identify the imported forklift VCU controller model; The industrial computer sends data to the signal simulator processing unit, input execution unit, output execution unit and fault injection unit through the multi-channel CAN bus, and sends data instructions through the RS232 serial port to start the detection process; at the same time, it monitors the load of each execution unit and dynamically allocates bandwidth.

[0008] Furthermore, the process of the automatic identification mechanism in S1 is as follows: Parse the imported data file, including reading the file format and data structure information, and then extract characteristic information related to the VCU controller model from the data, including identification fields, data encoding rules, data volume and format characteristics; A database containing various forklift VCU controller models and their characteristic information is pre-established; the extracted characteristic information is matched with the records in the database; when a completely matching record is found, the model of the VCU controller is preliminarily determined; Further verification is then performed to verify other parts of the imported data according to the standard data structure of the model, including whether the number and type of data fields are consistent with the standard of the model in the database; if the verification passes, the model recognition is confirmed to be successful; If it fails, it will prompt that the data is wrong or perform fuzzy matching, and provide model options for manual confirmation; As the number of forklift VCU controller models detected continues to increase, the database is dynamically updated; when a new model is encountered, after manual confirmation of the correct model, the feature information of the model is automatically extracted and added to the database for subsequent automatic identification.

[0009] Furthermore, the specific operation steps of monitoring the load of each execution unit and dynamically allocating bandwidth in S1 are as follows: Regularly collect and preprocess the CPU usage, memory occupancy, and load indicator data of the number of currently running processes of the signal simulator processing unit, input execution unit, output execution unit, and fault injection unit, and perform normalization processing to unify different data within a numerical range; Based on the preprocessed data, the load status of each execution unit is evaluated; different load level thresholds are set to quantify the real-time load status of each execution unit into a status value; According to the detection process and functional requirements, the data transmission priority is set in advance for each execution unit; based on the load status evaluation results of each execution unit provided by the load monitoring module and the pre-set priority, the bandwidth allocation strategy algorithm calculates the bandwidth to be allocated to each execution unit; According to the calculated bandwidth allocation results, the data transmission paths of the CAN bus and the RS232 serial port are dynamically adjusted; for execution units that require a bandwidth greater than the preset standard, the CAN bus transmission path with high bandwidth and low transmission delay is preferentially selected; When the bandwidth demand is less than the preset standard, it is allocated to the RS232 serial port transmission path, or when the CAN bus load does not exceed the threshold, part of the bandwidth is allocated to the CAN bus to improve the overall transmission efficiency; at the same time, the status of the transmission path is monitored in real time, including the communication error rate of the CAN bus, the buffer occupancy of the RS232 serial port, etc. When a transmission abnormality is found, the path is switched or the bandwidth allocation strategy is adjusted to ensure stable and efficient data transmission.

[0010] Furthermore, the specific operation steps of S2 are as follows: The input execution unit reads the corresponding analog quantity and data quantity setting values ​​from its built-in storage unit according to the preset logic sequence; these setting values ​​are set based on the signal range and data characteristics that the sensor should generate under various actual working conditions of the forklift; During the output process, in order to ensure the accuracy and stability of the output signal, the input execution unit will perform real-time self-checking; through the internal detection circuit, the output analog quantity is sampled and compared with the preset output value; when it is found that the deviation exceeds the allowable range, self-correction is performed to adjust the output signal to meet the requirements; After receiving the startup command and related fault injection setting parameters from the industrial computer, the fault injection unit injects fault signals into the VCU controller according to the set fault mode and timing; the fault modes include sensor signal disconnection, short circuit, signal interference, and analog value mutation. The output execution unit monitors the output signals of the VCU controller in real time, including motor drive signals, indicator light control signals, and alarm signal outputs; for analog output signals, a sampling circuit is used for sampling to convert analog signals into digital signals for subsequent processing; for digital output signals, their logic level values ​​are directly read; after each timing execution, each execution unit quickly organizes and packages the detected data, adds timestamps and execution unit identification information, and then sends the detection results to the industrial computer through the communication interface; a data verification and retransmission mechanism is used during the sending process to ensure the accuracy and integrity of data transmission.

[0011] Furthermore, the specific operation steps of the scenario simulation of analog output, the random and diversified combination of fault injection, and the intelligent prediction comparison of output signal detection in S2 are as follows: When simulating the output, a database containing various typical working scene models of forklifts is constructed. Each scene model defines the changing rules of multiple related sensor signals at different time points. The input execution unit retrieves the corresponding scene model from the database according to the detection requirements, and generates analog output signals that meet the scene requirements in real time through interpolation algorithms and signal synthesis technology. At the same time, the scene model is continuously updated and optimized based on the collection and analysis of actual forklift operation data to improve the authenticity of the simulation. When injecting faults, a random number generation algorithm is used to determine various parameters of fault injection, including random selection of fault types from a preset fault type library, random distribution of fault occurrence time within the detection time period, and random setting of fault duration according to a reasonable range of different fault types. At the same time, a fault combination strategy algorithm is used to generate a variety of fault combination schemes based on rules to avoid injecting contradictory or incompatible faults at the same time. When injecting faults, the randomly generated parameters and combination schemes are followed to ensure that the fault injection situation of each detection is random and diverse. When outputting the signal, a prediction comparison is performed. Specifically: a mathematical model of the VCU controller is established, and the actual operation data is trained through a machine learning algorithm so that the model can simulate the output response of the VCU controller under different input conditions; during the detection process, the output execution unit inputs the current input analog quantity and data volume into the model to obtain the predicted output signal range; then the actual detected output signal is compared with the predicted range in real time. When the deviation exceeds the set warning threshold, the warning mechanism is triggered to notify the industrial computer and related operators; at the same time, as the detection data accumulates, the model parameters are continuously optimized to improve the accuracy of the prediction.

[0012] Furthermore, the specific operation steps of S3 are as follows: The industrial computer continuously receives the test result data sent back by each execution unit through the CAN bus or RS232 serial port; it contains information such as execution unit identification, timing number, test data and timestamp; Perform integrity check on the received data, using checksum or cyclic redundancy check methods to ensure that there are no errors in the data transmission process; then, parse the values ​​of each field according to the data format for subsequent processing; For each time series of detection data, the industrial computer makes a qualification judgment based on the standard indicator range of the VCU controller of this model pre-stored in the database; Real-time display of test sequence progress: The industrial computer calculates the proportion of completed sequences to the total sequence according to the received sequence number, and displays the test progress on the human-machine interface in the form of a progress bar; at the same time, the name and number of the sequence currently being executed are displayed next to the progress bar, making it easier for operators to understand the test progress; Display data content: Display the received detection data in the form of a list or chart in the preset area of ​​the interface; for analog data, draw a real-time change curve to show its change trend over time; for digital data, display its logic level status in binary or hexadecimal form; Marking of qualified and unqualified items: For qualified test items, the test item name, data value and qualified word are displayed in green font or green background frame on the interface; for unqualified items, they are displayed in red font or red background frame, and flashing prompts, and audio alarm sounds to attract the attention of operators; the frequency and duration of the audio alarm sound are differentiated according to the severity of the unqualified items; After the test is completed, all test result data are summarized and a test report is generated; including the basic information of the forklift VCU controller, test time, the name of the test item for each time sequence, the standard indicator range, the actual test data, and the qualified or unqualified judgment results; the report adopts a standardized format.

[0013] Furthermore, the specific operation steps for distinguishing the frequency and duration of the audio alarm sound in S3 according to the severity of the unqualified items are as follows: Comprehensive analysis is conducted through the grading parameters of unqualified items, which include: Deviation degree parameter: For analog detection items, such as motor drive current, voltage, etc., calculate the deviation degree between the actual detection value D and the standard index range; set the standard lower limit value to L, the standard upper limit value to U, and the deviation degree E is calculated by the formula Calculate; calculate the sum of the deviations of all analog quantity detection items and record it as the bias evaluation value; Fault duration parameter: When an unqualified item has an intermittent fault, record the fault duration. The sum of all time intervals from the first occurrence of the fault to recovery to normal is the fault duration, which is recorded as the fault duration value. Fault frequency parameter: For repetitive faults that occur during the detection process, count their occurrence frequency within the total detection time and record it as the fault frequency value; The obtained partial evaluation value, time value and frequency value are calibrated as pp, gs and gp respectively, and after normalization, they are substituted into the following formula: To get the level judgment value JPZ, where are the preset weight coefficients of partial evaluation value, failure time value and failure frequency value respectively, and the obtained grade judgment value JPZ is used to measure the severity of the unqualified items; The obtained level judgment value JPZ is compared with the preset three level judgment intervals, and the preset three level judgment intervals correspond to three severity levels, namely the first severity level, the second severity level and the third severity level; when the level judgment interval to which the level judgment value JPZ belongs is determined, the corresponding severity level is determined. When the severity of the unqualified item is the first severity level, an 800Hz audio alarm sound frequency is used and lasts for 0.5 seconds to 1 second; when the severity of the unqualified item is the second severity level, an 1500Hz audio alarm sound frequency is used and lasts for 1 second to 2 seconds; when the severity of the unqualified item is the third severity level, an 2500Hz audio alarm sound frequency is used and lasts for 2 seconds to 3 seconds.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention significantly improves the efficiency and accuracy of factory inspection of forklift VCU controllers by integrating automatic identification mechanism and dynamic database update; the automatic identification mechanism can quickly and accurately determine the VCU controller model, reducing errors and time consumption in manual operation. At the same time, the dynamically updated database ensures that the detection method can adapt to the ever-changing controller models and maintain the timeliness and applicability of the detection process; The present invention can simulate the operating environment of a forklift under various actual working conditions more realistically through scenario simulation of analog output, random and diversified combination of fault injection, and intelligent prediction comparison of output signal detection; this simulation not only improves the coverage of fault detection, but also can evaluate the performance of the VCU controller under different conditions, ensuring its reliability and stability in actual use; The present invention adopts intelligent prediction and comparison technology, which realizes accurate prediction of the output response of the VCU controller by establishing a mathematical model of the VCU controller and using a machine learning algorithm to train actual operation data; it not only improves the accuracy of fault detection, but also can compare the actual output signal with the prediction range in real time, promptly discover deviations and trigger an early warning mechanism, thereby improving the intelligence level of fault detection and providing a more efficient and reliable solution for factory inspection of forklift VCU controllers. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0016] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0018] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations.

[0019] like Figure 1 As shown, the factory simulation detection method applied to the forklift VCU controller includes the following steps: During the test preparation, the forklift VCU controller test-related data is imported into the industrial computer, the model is determined and verified through the automatic identification mechanism, and the database is dynamically updated. After that, the industrial computer sends the data to each execution unit and starts the test process. During this period, the execution unit load is monitored and the bandwidth is dynamically allocated to ensure efficient and stable data transmission. First, import the project and index data of the forklift VCU controller that needs to be tested on the industrial computer; use the automatic recognition mechanism to automatically identify the imported forklift VCU controller model. The automatic recognition mechanism process is as follows: Parse the imported data file, including reading the file format and data structure information, and then extract the characteristic information related to the VCU controller model from the data, including identification fields, data encoding rules, data volume and format characteristics; pre-establish a database containing a variety of forklift VCU controller models and their detailed characteristic information; match the extracted characteristic information with the records in the database. If a completely matching record is found, the model of the VCU controller is preliminarily determined; then further verification is performed, and other key parts of the imported data are verified according to the standard data structure of the model, such as whether the number and type of data fields are consistent with the standards of the model in the database; if the verification passes, the model recognition is confirmed to be successful; if it fails, it prompts that the data is wrong or attempts to perform fuzzy matching (if the system supports it), and provides model options for manual confirmation; as the number of forklift VCU controller models detected continues to increase, the database is dynamically updated; when a new model is encountered, after the correct model is manually confirmed, the characteristic information of the model is automatically extracted and added to the database so that it can be automatically identified later; The industrial computer sends data to the signal simulator processing unit, input execution unit, output execution unit and fault injection unit through the multi-channel CAN bus, and sends data instructions through the RS232 serial port to start the detection process; at the same time, it monitors the load of each execution unit and dynamically allocates bandwidth. The process is as follows: Collect the CPU usage, memory occupancy, and the number of currently running processes of each execution unit (signal simulator processing unit, input execution unit, output execution unit, and fault injection unit) periodically (for example, every 100 milliseconds) and pre-process the load index data, and normalize them so that different data are unified within a numerical range; evaluate the load status of each execution unit based on the pre-processed data. Set different load level thresholds. For example, when the CPU usage exceeds 80% and the memory occupancy exceeds 70%, it is judged as a high load state; when the CPU usage is between 50%-80% and the memory occupancy is between 50%-70%, it is a medium load state; below these ranges is a low load state; in this way, the real-time load status of each execution unit is quantified into comparable state values; according to the detection process and functional requirements, pre-set the data transmission priority for each execution unit; based on the load status evaluation results of each execution unit provided by the load monitoring module and the pre-set priority, the bandwidth allocation strategy algorithm calculates the bandwidth to be allocated to each execution unit; According to the calculated bandwidth allocation results, the data transmission paths of the CAN bus and the RS232 serial port are dynamically adjusted. For execution units that require a larger bandwidth, the CAN bus transmission path with higher bandwidth and lower transmission delay is preferred (if the CAN bus has multiple channels, it can be further allocated to the channel with lighter load); for execution units with smaller bandwidth requirements and relatively low real-time requirements, they can be allocated to the RS232 serial port transmission path, or when the CAN bus load is light, part of the bandwidth can be allocated to the CAN bus to improve the overall transmission efficiency. At the same time, the system will monitor the status of the transmission path in real time, such as the communication error rate of the CAN bus, the buffer occupancy of the RS232 serial port, etc. Once a transmission abnormality is found, the path will be switched in time or the bandwidth allocation strategy will be adjusted to ensure stable and efficient data transmission.

[0020] Detection execution: the input execution unit outputs analog quantity and data quantity according to the logical timing, the fault injection unit injects fault signals according to the settings, and the output execution unit detects the output signal of the VCU controller. Each execution unit checks the data after each timing execution and sends it to the industrial computer. This process also includes innovative operations such as scenario simulation of analog quantity output, random and diversified combination of fault injection, and intelligent prediction and comparison of output signal detection; The input execution unit outputs analog quantity and data quantity according to the logical timing, the fault injection unit injects fault signals according to the settings, and the output execution unit detects the output signal of the VCU controller; after each timing is executed, each execution unit checks the data back and sends the test results to the industrial computer; the process is as follows: The input execution unit reads the corresponding analog quantity and data quantity setting values ​​from its built-in storage unit according to the preset logical timing; these setting values ​​are set based on the signal range and data characteristics that the sensor should generate under various actual working conditions of the forklift; during the output process, in order to ensure the accuracy and stability of the output signal, the input execution unit will perform real-time self-test. Through the internal detection circuit, the output analog quantity is sampled and compared with the preset output value. If it is found that the deviation exceeds the allowable range, self-correction is performed to adjust the output signal to meet the requirements; after receiving the startup instruction of the industrial computer and the relevant fault injection setting parameters, the fault injection unit injects fault signals into the VCU controller according to the set fault mode and timing. Fault modes include but are not limited to sensor signal disconnection, short circuit, signal interference, analog value mutation and other types; the output execution unit monitors the output signals of the VCU controller in real time, including various types of outputs such as motor drive signals, indicator light control signals, and alarm signals. For analog output signals, such as motor drive current and voltage signals, sampling circuits are used for sampling, and analog signals are converted into digital signals for subsequent processing. For digital output signals, such as the level status of indicator light control signals, directly read their logic level values; after each sequence execution, each execution unit quickly organizes and packages the detected data, adds necessary information such as timestamps and execution unit identification, and then sends the detection results to the industrial computer through a high-speed communication interface (such as CAN bus or RS232 serial port, select the appropriate interface according to the data volume and real-time requirements). During the transmission process, a data check and retransmission mechanism is used to ensure the accuracy and integrity of data transmission.

[0021] When simulating output, a database containing various typical working scenario models of forklifts is constructed. Each scenario model defines the changing rules of multiple related sensor signals at different time points. The input execution unit retrieves the corresponding scenario model from the database according to the detection requirements, and generates analog output signals that meet the scenario requirements in real time through interpolation algorithms and signal synthesis technology. At the same time, the scenario model can be continuously updated and optimized based on the collection and analysis of actual forklift operation data to improve the authenticity of the simulation; When injecting faults, a random number generation algorithm is used to determine the various parameters of fault injection, including the random selection of fault types from a preset fault type library, the random distribution of fault occurrence time within the detection time period, and the random setting of fault duration according to the reasonable range of different fault types. At the same time, a fault combination strategy algorithm is developed to generate a variety of fault combination schemes according to certain rules (such as avoiding the simultaneous injection of contradictory or non-existent faults). When injecting faults, the randomly generated parameters and combination schemes are strictly followed to ensure that the fault injection situation of each detection is random and diverse; When outputting the signal, a prediction comparison is performed. Specifically, a mathematical model of the VCU controller is established, and a large amount of actual operation data is trained through a machine learning algorithm so that the model can accurately simulate the output response of the VCU controller under different input conditions. During the detection process, the output execution unit inputs the current input analog quantity and data volume into the model to obtain the predicted output signal range. Then the actual detected output signal is compared with the predicted range in real time. When the deviation exceeds the set warning threshold, the warning mechanism is triggered to notify the industrial computer and relevant operators. At the same time, with the accumulation of detection data, the model parameters are continuously optimized to improve the accuracy of the prediction.

[0022] Result processing: after receiving the data, the industrial computer will perform integrity verification and analysis to determine whether the test results are qualified, and display the test progress, data content and qualified or not in real time. After the test is completed, a detailed report will be generated; in the marking of qualified and unqualified items, the severity will be determined based on the comprehensive analysis of the grading parameters, and then different audio alarm frequencies and durations will be set; The industrial computer determines whether the test results are qualified based on the received data, and displays the test timing progress, data content, qualified and unqualified items in real time; after the test is completed, the industrial computer automatically generates a test report and records the test results of the VCU controller in detail; The industrial computer continuously receives the test result data sent back by each execution unit through the CAN bus or RS232 serial port. These data contain information such as the execution unit identification, timing number, test data (such as the actual measurement value of the analog quantity, the logic level state of the digital quantity, the response data of the VCU during fault injection, etc.) and timestamp. The industrial computer first performs an integrity check on the received data, and uses the checksum or cyclic redundancy check (CRC) method to ensure that there are no errors in the data transmission process; then, the value of each field is parsed according to the data format for subsequent processing; for each timing test data, the industrial computer makes a qualification judgment based on the standard indicator range of the VCU controller of this model pre-stored in the database; Real-time display of test sequence progress: The industrial computer calculates the proportion of completed sequences to the total sequence according to the received sequence number, and displays the test progress on the human-machine interface in the form of a progress bar; at the same time, the name and number of the sequence currently being executed are displayed next to the progress bar, making it easier for operators to understand the test progress; Display data content: Display the received test data in the form of a list or chart in the specified area of ​​the interface. For analog data, a real-time change curve can be drawn to intuitively display its change trend over time; for digital data, its logic level status is displayed in binary or hexadecimal form; Marking of qualified and unqualified items: For qualified test items, the test item name, data value and the word "qualified" are displayed in green font or green background frame on the interface; for unqualified items, they are displayed in red font or red background frame, and flashing prompts, and an audio alarm sound (such as a short beep) is issued to attract the attention of the operator; the frequency and duration of the audio alarm sound are differentiated according to the severity of the unqualified item; the process is as follows: Comprehensive analysis is conducted through the grading parameters of unqualified items, which include: Deviation degree parameter: For analog detection items, such as motor drive current, voltage, etc., calculate the deviation degree between the actual detection value D and the standard index range; set the standard lower limit value to L, the standard upper limit value to U, and the deviation degree E is calculated by the formula Calculate; Calculate the sum of the deviations of all analog test items and record it as the bias value; Fault duration parameter: When an unqualified item has an intermittent fault, record the fault duration. The sum of all time intervals from the first occurrence of the fault to the restoration of normal is the fault duration, and is recorded as the fault duration value; Fault frequency parameter: For repetitive faults that occur during the test process, count their occurrence frequency within the total test time and record it as the fault frequency value; The obtained partial evaluation value, time value and frequency value are calibrated as pp, gs and gp respectively, and after normalization, they are substituted into the following formula: To get the level judgment value JPZ, where The preset weight coefficients are the bias value, the time value and the frequency value, and the obtained grade judgment value JPZ is used to measure the severity of the unqualified item; the obtained grade judgment value JPZ is compared with the preset three grade judgment value intervals, and the preset three grade judgment value intervals correspond to three severity levels, namely the first severity level, the second severity level and the third severity level; when the grade judgment value interval to which the grade judgment value JPZ belongs is determined, the corresponding severity level is determined, when the severity of the unqualified item is the first severity level, an 800Hz audio alarm sound frequency is used and lasts for 0.5 seconds to 1 second; when the severity of the unqualified item is the second severity level, an 1500Hz audio alarm sound frequency is used and lasts for 1 second to 2 seconds; when the severity of the unqualified item is the third severity level, an 2500Hz audio alarm sound frequency is used and lasts for 2 seconds to 3 seconds.

[0023] After the test is completed, the industrial computer summarizes all the test result data and generates a detailed test report. The report content includes the basic information of the forklift VCU controller (such as model, serial number, etc.), test time, the name of the test item for each time sequence, the standard indicator range, the actual test data, the qualified or unqualified judgment results, etc. The report adopts a standardized format.

[0024] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The factory simulation detection method applied to forklift VCU controller is characterized by: The following steps are involved: S1: Test preparation, import the forklift VCU controller test related data on the industrial computer, determine the model and verify it through the automatic recognition mechanism, and dynamically update the database; then send the data to each execution unit and start the test process, while monitoring the execution unit load and dynamically allocating bandwidth to ensure efficient and stable data transmission; S2: Detection execution, the input execution unit outputs analog quantity and data quantity according to the logical timing, the fault injection unit injects fault signals according to the settings, and the output execution unit detects the output signal of the VCU controller. Each execution unit checks the data after each timing execution and sends it to the industrial computer. It also includes scenario simulation of analog quantity output, random and diversified combination of fault injection, and intelligent prediction and comparison of output signal detection; S3: Result processing: After receiving the data, the industrial computer performs integrity verification and analysis to determine whether the test results are qualified. It displays the test progress, data content and qualification in real time, and generates a report after the test is completed. When marking qualified and unqualified items, the severity will be determined based on a comprehensive analysis of the grading parameters, and different audio alarm frequencies and durations will be set accordingly.

2. The method for factory simulation detection of forklift VCU controller according to claim 1 is characterized in that: The specific steps of S1 are as follows: First, import the project and index data of the forklift VCU controller that needs to be tested on the industrial computer; use the automatic recognition mechanism to automatically identify the imported forklift VCU controller model; The industrial computer sends data to the signal simulator processing unit, input execution unit, output execution unit and fault injection unit through the multi-channel CAN bus, and sends data instructions through the RS232 serial port to start the detection process; at the same time, it monitors the load of each execution unit and dynamically allocates bandwidth.

3. The factory simulation detection method for forklift VCU controller according to claim 2 is characterized in that: The process of the automatic identification mechanism in S1 is as follows: Parse the imported data file, including reading the file format and data structure information, and then extract characteristic information related to the VCU controller model from the data, including identification fields, data encoding rules, data volume and format characteristics; A database containing various forklift VCU controller models and their characteristic information is pre-established; the extracted characteristic information is matched with the records in the database; when a completely matching record is found, the model of the VCU controller is preliminarily determined; Further verification is then performed to verify other parts of the imported data according to the standard data structure of the model, including whether the number and type of data fields are consistent with the standard of the model in the database; When the verification passes, it confirms that the model has been successfully identified; If it fails, it will prompt that the data is wrong or perform fuzzy matching, and provide model options for manual confirmation; As the number of forklift VCU controller models detected continues to increase, the database is dynamically updated; when a new model is encountered, after manual confirmation of the correct model, the feature information of the model is automatically extracted and added to the database for subsequent automatic identification.

4. The factory simulation detection method for forklift VCU controller according to claim 1 is characterized in that: The specific operation steps of monitoring the load of each execution unit and dynamically allocating bandwidth in S1 are as follows: Regularly collect and preprocess the CPU usage, memory occupancy, and load indicator data of the number of currently running processes of the signal simulator processing unit, input execution unit, output execution unit, and fault injection unit, and perform normalization processing to unify different data within a numerical range; Based on the preprocessed data, the load status of each execution unit is evaluated; different load level thresholds are set to quantify the real-time load status of each execution unit into a status value; According to the detection process and functional requirements, the data transmission priority is set in advance for each execution unit; based on the load status evaluation results of each execution unit provided by the load monitoring module and the pre-set priority, the bandwidth allocation strategy algorithm calculates the bandwidth to be allocated to each execution unit; According to the calculated bandwidth allocation results, the data transmission path of CAN bus and RS232 serial port is dynamically adjusted; When the bandwidth demand is less than the preset standard, it is allocated to the RS232 serial port transmission path, or when the CAN bus load does not exceed the threshold, part of the bandwidth is allocated to the CAN bus to improve the overall transmission efficiency; at the same time, the status of the transmission path is monitored in real time, including the communication error rate of the CAN bus and the buffer occupancy of the RS232 serial port. When a transmission abnormality is found, the path is switched or the bandwidth allocation strategy is adjusted to ensure stable and efficient data transmission.

5. The factory simulation detection method for forklift VCU controller according to claim 1 is characterized in that: The specific operation steps of S2 are as follows: The input execution unit reads the corresponding analog quantity and data quantity setting values ​​from its built-in storage unit according to the preset logic sequence; these setting values ​​are set based on the signal range and data characteristics that the sensor should generate under various actual working conditions of the forklift; During the output process, in order to ensure the accuracy and stability of the output signal, the input execution unit will perform real-time self-checking; through the internal detection circuit, the output analog quantity is sampled and compared with the preset output value; when it is found that the deviation exceeds the allowable range, self-correction is performed to adjust the output signal to meet the requirements; After receiving the startup command and related fault injection setting parameters from the industrial computer, the fault injection unit injects fault signals into the VCU controller according to the set fault mode and timing; the fault modes include sensor signal disconnection, short circuit, signal interference, and analog value mutation. The output execution unit monitors the output signals of the VCU controller in real time, including motor drive signals, indicator light control signals, and alarm signal outputs; for analog output signals, a sampling circuit is used for sampling to convert analog signals into digital signals for subsequent processing; for digital output signals, their logic level values ​​are directly read; after each timing execution, each execution unit quickly organizes and packages the detected data, adds timestamps and execution unit identification information, and then sends the detection results to the industrial computer through the communication interface; a data verification and retransmission mechanism is used during the sending process to ensure the accuracy and integrity of data transmission.

6. The factory simulation detection method for forklift VCU controller according to claim 1 is characterized in that: The specific operation steps of the scenario simulation of analog output, random and diversified combination of fault injection, and intelligent prediction comparison of output signal detection in S2 are as follows: When simulating the output, a database containing various typical working scene models of forklifts is constructed. Each scene model defines the changing rules of multiple related sensor signals at different time points. The input execution unit retrieves the corresponding scene model from the database according to the detection requirements, and generates analog output signals that meet the scene requirements in real time through interpolation algorithms and signal synthesis technology. At the same time, the scene model is continuously updated and optimized based on the collection and analysis of actual forklift operation data to improve the authenticity of the simulation. When injecting faults, a random number generation algorithm is used to determine various parameters of fault injection, including random selection of fault types from a preset fault type library, random distribution of fault occurrence time within the detection time period, and random setting of fault duration according to a reasonable range of different fault types. At the same time, a fault combination strategy algorithm is used to generate a variety of fault combination schemes based on rules to avoid injecting contradictory or incompatible faults at the same time. When injecting faults, the randomly generated parameters and combination schemes are followed to ensure that the fault injection situation of each detection is random and diverse. When outputting signals, prediction and comparison are performed. Specifically, a mathematical model of the VCU controller is established, and actual operation data is trained through a machine learning algorithm so that the model can simulate the output response of the VCU controller under different input conditions. During the detection process, the output execution unit inputs the current input simulation quantity and data quantity into the model to obtain the predicted output signal range. The actual detected output signal is then compared with the predicted range in real time. When the deviation exceeds the set warning threshold, the warning mechanism is triggered to notify the industrial computer and relevant operators. At the same time, as the detection data accumulates, the model parameters are continuously optimized to improve the accuracy of the prediction.

7. The factory simulation detection method for forklift VCU controller according to claim 1 is characterized in that: The specific operation steps of S3 are as follows: The industrial computer continuously receives the test result data sent back by each execution unit through the CAN bus or RS232 serial port; it includes the execution unit identification, timing number, test data and timestamp information; Perform integrity check on the received data, using checksum or cyclic redundancy check methods to ensure that there are no errors in the data transmission process; Then, parse the values ​​of each field according to the data format for subsequent processing; For each time series of detection data, the industrial computer makes a qualification judgment based on the standard indicator range of the VCU controller of this model pre-stored in the database; Real-time display of test sequence progress: The industrial computer calculates the proportion of completed sequences to the total sequence according to the received sequence number, and displays the test progress on the human-machine interface in the form of a progress bar; at the same time, the name and number of the sequence currently being executed are displayed next to the progress bar, making it easier for operators to understand the test progress; Display data content: Display the received detection data in the form of a list or chart in the preset area of ​​the interface; for analog data, draw a real-time change curve to show its change trend over time; for digital data, display its logic level status in binary or hexadecimal form; Marking of qualified and unqualified items: For qualified test items, the test item name, data value and qualified word are displayed in green font or green background frame on the interface; for unqualified items, they are displayed in red font or red background frame, and flashing prompts, and audio alarm sounds to attract the attention of operators; the frequency and duration of the audio alarm sound are differentiated according to the severity of the unqualified items; After the test is completed, all test result data are summarized and a test report is generated; It includes the basic information of the forklift VCU controller, detection time, the name of the detection item for each time sequence, the standard indicator range, the actual detection data, and the qualified or unqualified judgment results; the report adopts a standardized format.

8. The factory simulation detection method for forklift VCU controller according to claim 7 is characterized in that: The specific operation steps for distinguishing the frequency and duration of the audio alarm in S3 according to the severity of the unqualified items are as follows: Comprehensive analysis is conducted through the grading parameters of unqualified items, which include: Deviation degree parameter: For analog detection items, calculate the deviation degree between the actual detection value D and the standard index range; set the standard lower limit value to L, the standard upper limit value to U, and the deviation degree E is calculated by the formula Calculate; calculate the sum of the deviations of all analog quantity detection items and record it as the bias evaluation value; Fault duration parameter: When an unqualified item has an intermittent fault, record the fault duration. The sum of all time intervals from the first occurrence of the fault to recovery to normal is the fault duration, which is recorded as the fault duration value. Fault frequency parameter: For repetitive faults that occur during the detection process, count their occurrence frequency within the total detection time and record it as the fault frequency value; The obtained partial evaluation value, time value and frequency value are calibrated as pp, gs and gp respectively, and after normalization, they are substituted into the following formula: To get the level judgment value JPZ, where are the preset weight coefficients of partial evaluation value, failure time value and failure frequency value respectively, and the obtained grade judgment value JPZ is used to measure the severity of the unqualified items; The obtained level judgment value JPZ is compared with the preset three level judgment intervals, and the preset three level judgment intervals correspond to three severity levels, namely the first severity level, the second severity level and the third severity level; when the level judgment interval to which the level judgment value JPZ belongs is determined, the corresponding severity level is determined. When the severity of the unqualified item is the first severity level, an 800Hz audio alarm sound frequency is used and lasts for 0.5 seconds to 1 second; when the severity of the unqualified item is the second severity level, an 1500Hz audio alarm sound frequency is used and lasts for 1 second to 2 seconds; when the severity of the unqualified item is the third severity level, an 2500Hz audio alarm sound frequency is used and lasts for 2 seconds to 3 seconds.

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