Dynamic collaborative management and control system and method for multi-model mixed-line production

Abstract

This invention provides a dynamic collaborative management and control system and method for multi-vehicle mixed-line production, relating to the field of automotive intelligent manufacturing technology. The method includes: acquiring vehicle model information of the production vehicles and performing template matching to obtain a vehicle model detection template corresponding to the target vehicle model; distributing the vehicle model detection template to the production equipment at each production station on the production line; determining the station status data of each production station on the production line to assign a corresponding target production station to the target vehicle model, with the production equipment at the target production stations sharing operational status data; and collecting vehicle fault data from each target production station in real time, performing fault classification processing to obtain fault levels, and distributing the fault handling strategy corresponding to each fault level to the production equipment at each target production station. This invention addresses the problems of slow vehicle model switching, poor station collaboration, and inefficient fault handling in multi-vehicle mixed-line production management.

Description

Technical Field

[0001] This invention relates to the field of intelligent automotive manufacturing technology, specifically to a dynamic collaborative management and control system and method for multi-model mixed-line production. Background Technology

[0002] With the increasing demand for personalization in the automotive market, multi-model mixed-line production has become the mainstream production mode for automobiles. However, in the scenario of multi-model mixed-line production, existing vehicle production management technologies still face some problems. First, the model changeover is slow, mainly due to the rigidity of model adaptation. The testing system is generally bound to a specific model, and when multiple models are produced on a mixed line, manual replacement of testing programs or adjustment of equipment parameters is required. The changeover time for each model can be as long as 30-60 minutes, causing production cycle interruptions and failing to meet the "minute-level" model changeover requirements. Second, there is poor workstation collaboration, mainly due to the lack of workstation coordination. Workstations such as EOL testing, voice testing, and vehicle system verification operate independently, lacking a dynamic resource allocation mechanism across workstations. When equipment fails or tasks are backlogged at one workstation, other workstations cannot coordinate to fill the gap, reducing overall production efficiency. Third, fault handling is inefficient. Faults found during testing (such as minor abnormalities in electronic control components or vehicle system function lag) are all treated as "stopping for repair" without distinguishing the fault level. This results in non-critical faults occupying a lot of production time, extending the delivery cycle of qualified vehicles. Summary of the Invention

[0003] In view of this, it is necessary to provide a dynamic collaborative management and control system and method for multi-model mixed production lines to solve the problems of slow model switching, poor workstation coordination, and inefficient fault handling in multi-model mixed production line management.

[0004] To address the aforementioned problems, this invention provides a dynamic collaborative management and control system for multi-model mixed-line production, the system comprising: The vehicle model adaptive detection module is used to acquire vehicle model information of the production vehicle, perform template matching based on the vehicle model information to obtain the vehicle model detection template corresponding to the target vehicle model of the production vehicle, and send the vehicle model detection template to the production equipment of each production station in the production line. The cross-workstation dynamic collaboration module is used to determine the workstation status data of each production workstation in the production line, and to allocate the corresponding target production workstation to the target vehicle model according to the workstation status data. The production equipment of the target production workstations share the operating status data. The fault classification and processing module is used to collect vehicle fault data of production vehicles in each of the target production workstations in real time, perform fault classification processing on the vehicle fault data to obtain fault levels, and send the fault handling strategy corresponding to each fault level to the production equipment of each of the target production workstations.

[0005] In one possible implementation, the vehicle model adaptive detection module is further configured to obtain a preset vehicle model detection template library, which includes vehicle model detection templates for multiple vehicle models, and each vehicle model detection template records the detection items for the corresponding vehicle model. The vehicle model information is matched with the vehicle model detection template to obtain the vehicle model detection template, and the matched vehicle model is determined as the target vehicle model for production. The inspection items in the vehicle inspection template of the target vehicle model are distributed to the production equipment at each production station on the production line.

[0006] In one possible implementation, the cross-workstation dynamic collaboration module is further used to acquire the inspection items of the production equipment in each production workstation in the production line, and determine the inspection time of the inspection items; Determine the load in the operating status data and the target production station corresponding to a load less than the load threshold; Detection items whose detection time exceeds the time threshold are assigned to the production equipment of the target production station.

[0007] In one possible implementation, the cross-workstation dynamic collaboration module is further configured to, when a fault is detected in a production device at a target production workstation, assign the detection items of the faulty production device to other target production workstations. Among them, the inspection items of the production equipment in the other target production stations are the same as those of the equipment that has experienced equipment failure, and the load is less than the load threshold.

[0008] In one possible implementation, the fault classification processing module is further configured to calculate fault indicators based on the vehicle fault data, the fault indicators including fault impact degree indicators and repair difficulty indicators. When the fault indicator is not less than the first indicator threshold, the fault level is determined to be a critical vehicle fault. When the fault indicator is less than the first indicator threshold and not less than the second indicator threshold, the fault level is determined to be a major vehicle fault. When the fault indicator is less than the second indicator threshold, the fault level is determined as either a major vehicle fault or a minor vehicle fault. The severity of the major vehicle malfunction is greater than the severity of the minor vehicle malfunction, but less than the severity of the critical vehicle malfunction.

[0009] In one possible implementation, the fault classification processing module is further configured to trigger a shutdown command for the production vehicle when the fault level is determined to be a critical vehicle fault. When a fault is determined to be a critical vehicle fault, the fault information is marked, and after the inspection items of the production equipment in each target production station are completed, the faulty vehicle is triggered to be repaired based on the fault information. When the fault level is determined to be a minor vehicle fault, the fault information is recorded. After the inspection items of the production equipment in each target production station are completed, an upgrade package for the fault information is generated, and the faulty vehicle is remotely repaired through the upgrade package.

[0010] In one possible implementation, the system further includes a resource optimization and management module, which is used to collect the operating status data of the production equipment in each target production station, and calculate the equipment operation indicators based on the operating status data. The equipment operation indicators include the production equipment utilization rate, the number of staff, and the number of inspection items. Perform the following operations based on the aforementioned equipment operating parameters: Schedule the production equipment at the target production station; Configure the number of workers in the target production station; Screening is performed on the inspection items in the production equipment at the target production station.

[0011] This invention also provides a dynamic collaborative management and control method for multi-model mixed-line production, including: Obtain the vehicle model information of the production vehicle, perform template matching based on the vehicle model information to obtain the vehicle model detection template corresponding to the target vehicle model of the production vehicle, and send the vehicle model detection template to the production equipment of each production station in the production line; The status data of each production station in the production line is determined, and the target production station is assigned to the target vehicle model according to the status data. The production equipment of the target production station shares the operating status data. Real-time vehicle fault data of production vehicles in each of the target production workstations is collected, the vehicle fault data is processed to obtain fault levels, and the fault handling strategy corresponding to each fault level is distributed to the production equipment of each of the target production workstations.

[0012] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a program; the processor is coupled to the memory and is used to execute the program stored in the memory to realize the steps of the above-described dynamic collaborative management and control method for multi-model mixed-line production.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described dynamic collaborative management and control method for mixed production lines of multiple vehicle models.

[0014] The beneficial effects of adopting the above implementation method are as follows: The dynamic collaborative management and control system and method for multi-model mixed-line production provided by this invention first obtains a model detection template through template matching based on model information and directly distributes it to each production station on the production line. This allows for rapid switching of production models, eliminating the need to modify the model detection parameters of each production device individually, thus improving model switching efficiency. Furthermore, by allocating target production stations based on the station status data of each production station on the production line, station allocation is achieved from a global production line perspective, facilitating the rational utilization of station resources. Moreover, the production equipment at each allocated target production station shares operational status data, improving the coordination between production stations and preventing the entire production line from shutting down due to a single station failure. In addition, by classifying vehicle failures to differentiate failure levels and issuing failure handling strategies to the production line, production line stoppages caused by simple failures can be avoided, reducing the handling time for non-critical failures and improving the operating efficiency of the production line. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the framework of the dynamic collaborative management and control system for multi-model mixed-line production provided by the present invention. Figure 2 This is a schematic diagram of vehicle model information configuration provided by the present invention; Figure 3 A layout example diagram of a multi-model mixed-line production line provided in this application; Figure 4 Example diagram of the configuration of the production workstation provided in this application; Figure 5 This is one of the schematic diagrams illustrating the configuration of the detection items provided by the present invention; Figure 6 This is the second schematic diagram of the detection item configuration provided by the present invention; Figure 7 The third schematic diagram of the detection item configuration provided by the present invention; Figure 8 This is a flowchart illustrating the dynamic collaborative management and control method for multi-model mixed-line production provided by the present invention. Figure 9 A schematic diagram of an embodiment of the electronic device provided by the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0018] In the description of the embodiments of this application, unless otherwise stated, "a plurality of" means two or more.

[0019] In this embodiment of the invention, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such process, method, product or device.

[0020] The naming or numbering of steps in the embodiments of the present invention does not mean that the steps in the method flow must be executed in the time / logical order indicated by the naming or numbering. The execution order of the named or numbered process steps can be changed according to the technical purpose to be achieved, as long as the same or similar technical effect can be achieved.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] The dynamic collaborative management and control system and method for multi-model mixed-line production provided by this invention can be applied to vehicle production line scenarios involving multi-model mixed-line production. The executing entities can be various terminals, servers, or remote cloud devices. The hardware architecture of the dynamic collaborative management and control system for multi-model mixed-line production can be designed as a five-layer architecture: access layer, business layer, service interface layer, control layer, and device layer. It supports network protocols such as UDP, TCP, MQTT, and HTTP, supports horizontal scaling, and features a service-oriented architecture design to achieve unified service registration and release management, canary release, and supports relational databases, in-memory databases, and distributed databases. It also supports standard and customized data interfaces, enabling data integration with other third-party information systems and multi-device data access. By monitoring the vehicle production line scenario in real time, the system collects vehicle model information, workstation status data, and vehicle fault data from various target workstations, uploading them to the dynamic collaborative management and control system for multi-model mixed-line production. Each module of the system then executes control actions such as model adaptive detection, cross-workstation dynamic collaboration, and fault classification processing, achieving dynamic collaborative management and control during the multi-model mixed-line production process.

[0023] The following section details the dynamic collaborative management and control system for multi-model mixed-line production provided by this invention. Unless otherwise stated, the term "system" as used below refers to the dynamic collaborative management and control system for multi-model mixed-line production.

[0024] Figure 1 This is a schematic diagram of the architecture of the dynamic collaborative management and control system for multi-model mixed-line production provided by the present invention. Figure 1 As shown, the dynamic collaborative management and control system for multi-model mixed-line production specifically includes: a model adaptive detection module, a cross-workstation dynamic collaboration module, and a fault classification and processing module, which are described in detail below.

[0025] The vehicle model adaptive detection module is used to obtain the vehicle model information of the production vehicle, perform template matching based on the vehicle model information, obtain the vehicle model detection template corresponding to the target vehicle model of the production vehicle, and send the vehicle model detection template to the production equipment of each production station in the production line.

[0026] The multi-model mixed-line production line in this embodiment of the invention mainly involves the quality inspection stage of vehicle production. This production stage requires vehicle quality inspection for different vehicle models. In the quality inspection stage, the system first needs to be adapted to different vehicle models. After the production vehicle passes through the inspection area, when vehicle model adaptation begins immediately, the VIN code of the production vehicle can be obtained through a high-definition barcode scanning device deployed in the production line, such as "LZWADAGA14XXXXXXXXX". Figure 2This is a schematic diagram of vehicle model information configuration provided by the present invention. During the vehicle production process, each vehicle model name is configured with a unique VIN code, parking space code, and equipment code. Based on the VIN code encoding rules (e.g., the 10th digit is the production year, the 11th digit is the assembly plant, and the 12th-17th digits are the serial number), the vehicle model and configuration information (such as power type, electronic control component model, and vehicle system version) can be obtained.

[0027] Furthermore, the vehicle model information is used to match the vehicle model inspection template, thereby determining the corresponding inspection items for that vehicle model. Finally, the vehicle model inspection template is distributed to the production equipment at each production station on the production line, so that the generating equipment receives the corresponding inspection items and performs quality inspection on the production vehicles entering the inspection area.

[0028] In one possible implementation, the vehicle model adaptive detection module is also used to obtain a preset vehicle model detection template library, which includes vehicle model detection templates for multiple vehicle models, and each vehicle model detection template records the detection items for the corresponding vehicle model.

[0029] Here, the vehicle inspection template library is constructed according to vehicle type classification. Specifically, it includes vehicle inspection templates for multiple vehicle types. Each template records the inspection items for the corresponding vehicle type, including inspection items (SUV models add panoramic sunroof electronic control inspection, and new energy vehicle models add battery pack inspection), parameter thresholds (such as the acceptable range of braking pressure for different vehicle types), inspection order, etc.

[0030] Of course, each vehicle model has multiple inspection items, each requiring different production stations. For example, the inspection items for a certain 710S and 720S model include EOL (End of Service) inspection, voice inspection, and vehicle infotainment system inspection. Each inspection item needs to be pre-configured, for example... Figure 5 As shown, Figure 5 It displays information about the EOL testing project, including the testing number, testing status, testing details, and testing duration. For example... Figure 6 As shown, Figure 6 It displays information about the speech detection project, including the detection number, detection status, detection details, and test duration, etc. For example... Figure 7 As shown, Figure 7 The system displays information about the vehicle inspection items, including VIN code, number of executions, inspection status, test duration, and parking space code. Once each inspection item for a vehicle model is configured, a vehicle model inspection template can be generated and stored in the vehicle model inspection template library for retrieval.

[0031] Furthermore, the vehicle model information is matched with the vehicle model detection template to obtain the vehicle model detection template, and the matched vehicle model is determined as the target vehicle model for production.

[0032] Here, the vehicle model information is used to retrieve the vehicle model inspection template library, thereby finding the corresponding vehicle model inspection template. For example, searching the vehicle model inspection template library based on "1.5T SUV model" will retrieve "1.5T SUV inspection template," which shows that the inspection items for this model include 28 EOL inspection items (including panoramic sunroof electronic control inspection), 15 voice commands, and 8 vehicle infotainment function inspections. A successful search indicates that the vehicle model has been matched, and the matched model is identified as the target model for production vehicles.

[0033] If no vehicle inspection template is found in the vehicle inspection template library using the vehicle information, it means that the vehicle model is not included in the production line plan and the search item cannot be executed. The current inspection process can be terminated.

[0034] Finally, the inspection items in the vehicle inspection template for the target vehicle model are distributed to the production equipment at each production station on the production line.

[0035] After retrieving the vehicle inspection template corresponding to the target vehicle model, the inspection items in the vehicle inspection template can be distributed to the production equipment at each production station on the production line, so that the production equipment can inspect the production vehicles of that vehicle model according to the inspection items.

[0036] In this embodiment of the invention, a vehicle model adaptive detection module is used to achieve flexible vehicle model detection. Based on vehicle model information, a vehicle model detection template is obtained through template matching, and then directly sent to each production station on the production line. This allows for rapid switching of production vehicle models, eliminating the need to change the vehicle model detection parameters of each production device individually. Since the time spent on template matching and sending is much less than the time spent changing the vehicle model detection parameters of the production devices, the efficiency of vehicle model switching is improved.

[0037] See also Figure 1 The cross-workstation dynamic collaboration module is used to determine the workstation status data of each production workstation in the production line, and to allocate the corresponding target production workstation to the target vehicle model based on the workstation status data.

[0038] Figure 3 This diagram illustrates the layout of a multi-model mixed-line production line provided in this application. Such a line typically consists of multiple production stations, each responsible for different testing items. Figure 3 As shown, taking a certain vehicle model as an example, the production workstations include: an EOL (End-of-Life) inspection workstation area, a voice testing area, a voice verification area, an engineering inspection workstation area, and a vehicle system verification point area. Of course, there may be multiple identical production workstations; for example, there may be two EOL inspection workstation areas, each responsible for the same inspection items.

[0039] Various status sensors and other monitoring devices are installed at each production station to monitor the station's status data, including equipment status (normal, fault, standby), the current inspection progress, and the remaining inspection time. The monitoring frequency is set to once per second to ensure real-time performance.

[0040] The workstation status data collected by the monitoring equipment is uploaded to the cross-workstation dynamic collaboration module in real time. Furthermore, the cross-workstation dynamic collaboration module analyzes this workstation status data to determine the load and idle status of each production workstation, thereby allocating the appropriate target production workstation to the production vehicles corresponding to the target vehicle model. The allocation standard adopts a strategy of "minimum load priority combined with time matching," meaning that vehicles with the most inspection time are assigned to the production workstations with the least load whenever possible.

[0041] Furthermore, the production equipment at the target production station shares operational status data. A cross-station task collaboration protocol is pre-established in each production equipment. Once a testing item for a certain vehicle model is issued and the production station is assigned, this cross-station task collaboration protocol will take effect, establishing network communication between production stations and enabling the sharing of operational status data between production equipment.

[0042] For example, the production workstations allocated for the testing items of a certain vehicle model are: EOL (End-of-Life) testing station, voice testing area, voice verification area, and vehicle infotainment system verification point area. After the EOL testing station completes its testing items, the status data of the electronic control components is automatically synchronized to the voice testing station through a data sharing mechanism. After the voice verification station completes its testing items, the status data is synchronized to the voice verification area, avoiding duplicate testing. Once the voice verification area begins its testing items, it automatically triggers the vehicle infotainment system verification point area to execute the testing items in parallel through the data sharing mechanism. Finally, the testing results are uniformly summarized, which can significantly shorten testing time and improve vehicle production efficiency.

[0043] In one possible implementation, the cross-workstation dynamic collaboration module is also used to acquire the inspection items of the production equipment at each production station in the production line and determine the inspection time for the inspection items.

[0044] When the cross-workstation dynamic collaboration module starts executing workstation allocation, it obtains the inspection items of the production equipment in each production workstation in the production line through edge nodes, and then calculates the inspection time of the inspection items, that is, the time required to complete the inspection items.

[0045] Furthermore, the system identifies the load in the operational status data and the target production workstations whose loads are below the load threshold. After receiving the workstation status data uploaded by the monitoring equipment, the cross-workstation dynamic collaboration module performs unified analysis to determine the load and fault conditions of each workstation, specifically as follows: Figure 4 As shown, Figure 4The system displays the operating status of a production station for a certain vehicle model, indicating the load and faults of the production station. For example, the EOL inspection area has two production stations, EOL1 and EOL, both of which perform EOL inspections. EOL1 shows a positive load of 40%, indicating a load rate of 40%, while EOL shows a fault rate of 60%, indicating a fault rate of 60% for vehicles of this model.

[0046] Therefore, based on the operating status data, the equipment load can be determined, and target production stations with loads below the load threshold can be identified for allocation. The load threshold can be reasonably set, for example, 50%. Figure 4 As shown, the voice testing station includes two stations: Voice 1 and Voice 2. Voice 2 has a load of 30%, which is less than the load threshold of 50%. Therefore, the station corresponding to Voice 2 is determined as the target production station.

[0047] Finally, the inspection items with inspection time exceeding the time threshold are assigned to the production equipment of the target production station.

[0048] Here, when assigning testing items, a time threshold is used to determine the testing time. This time threshold can be reasonably set based on the total time span of the production workstations, for example, 45 minutes. If the testing time exceeds the time threshold, i.e., exceeds 45 minutes, it indicates that the testing item requires a long time and is considered a high-time testing item. Therefore, high-time testing items are assigned to the target production workstation. For example, if the testing time for a certain voice testing workstation is 50 minutes, exceeding 45 minutes, it will be assigned to the workstation corresponding to Voice 2. This achieves the goal of assigning testing items with a testing time greater than 45 minutes to target production workstations with a load of less than 50%.

[0049] The cross-workstation dynamic collaboration module of this invention adopts a strategy of "minimum load priority combined with time matching" when allocating the corresponding target production workstation to the production vehicle corresponding to the target model. This allows the detection items that take longer to detect to be allocated to the production workstation with the lower load as much as possible, thereby realizing the rational scheduling of production workstation allocation and improving the utilization rate of production workstation resources.

[0050] In one possible implementation, the cross-workstation dynamic collaboration module is also used to assign the detection items of the production equipment with equipment failure to other target production stations when a equipment failure is detected in the production equipment of the target production station.

[0051] Here, the cross-workstation dynamic collaboration module will perform fault identification after acquiring the operating status data, that is, identify whether there are equipment faults in the production equipment at each target production workstation.

[0052] For example Figure 4As shown, during the identification process, equipment failure was detected at the EOL2 workstation in the EOL detection area, such as hardware failure or network offline, with a failure rate reaching 60%. Therefore, it can be considered that the EOL2 workstation is no longer suitable for continued production. The detection items for the faulty production equipment are then reassigned to other target production workstations. This reassignment process requires meeting certain fault allocation conditions. Specifically, the detection items for the production equipment in other target production workstations must be the same as those for the faulty equipment, and the load must be less than the load threshold. In other words, it is necessary to ensure that the detection items for the reassigned target production workstations are the same as those for the faulty production equipment, and also to ensure that the load of the other target production workstations is less than the load threshold.

[0053] For example Figure 4 As shown, there is a equipment fault at station EOL2 in the EOL testing area. The testing items at station EOL2 are the same as those at station EOL1, and the load at station EOL1 is 40%, which is less than the load threshold of 50%. Therefore, the fault allocation conditions are met, and the testing items at station EOL2 can be allocated to station EOL1.

[0054] In this embodiment of the invention, fault allocation conditions are set through a cross-workstation dynamic collaboration module, which can quickly transfer and allocate testing items when production equipment fails, without the need for manual handling of the transfer, thus avoiding production line disruption and affecting production line efficiency.

[0055] See also Figure 1 The fault classification and processing module is used to collect vehicle fault data of production vehicles in each target production station in real time, classify the vehicle fault data to obtain fault level, and send the fault handling strategy corresponding to each fault level to the production equipment of each target production station.

[0056] Here, the fault classification and processing module implements a fault handling process from fault identification to fault processing and fault decision-making. During fault identification, vehicle fault data from production vehicles at each target production station is collected in real time, such as fault codes from EOL (End of Service) diagnostics, recognition errors from voice tests, and crash logs from vehicle infotainment system tests. Corresponding fault indicators are then calculated to determine the fault level, thus achieving fault classification and processing. Based on the fault level, a fault feature library can be used to determine the fault handling strategy, and the fault handling strategy corresponding to each fault level is then distributed to the production equipment at each target production station.

[0057] In one possible implementation, the fault classification processing module is also used to calculate fault indicators based on vehicle fault data. The fault indicators include indicators of the degree of fault impact and indicators of repair difficulty.

[0058] Here, the impact and repair difficulty of a fault can be assessed based on vehicle fault data. This can be achieved by calling a machine learning model, using a fault impact index and a repair difficulty index to measure the impact and repair difficulty respectively. The greater the impact, the higher the fault impact index; the greater the repair difficulty, the higher the repair difficulty index. Finally, the fault index is obtained by weighting and summing the fault impact index and repair difficulty index with equal weights.

[0059] When the fault indicator is not less than the first indicator threshold, the fault level is determined to be a critical vehicle fault; when the fault indicator is less than the first indicator threshold but not less than the second indicator threshold, the fault level is determined to be a significant vehicle fault; when the fault indicator is less than the second indicator threshold, the fault level is determined to be a minor vehicle fault.

[0060] Furthermore, fault classification is based on fault indicators. Specifically, two threshold indicators are preset, including a first threshold and a second threshold, with the first threshold being greater than the second threshold. If the fault indicator is not less than the first threshold, it indicates that the severity of the fault and the difficulty of repair are the highest, and the fault level can be determined as the first fault. This is generally a critical vehicle fault, which usually seriously affects the use of the vehicle and may pose potential dangers, such as abnormal braking system signals or excessive battery pack voltage.

[0061] If the fault indicator is less than the first indicator threshold and not less than the second indicator threshold, it indicates that the fault is more serious and difficult to repair. The fault level can be determined as the second fault, which is generally a major vehicle fault. Although these faults do not affect the use of the vehicle, they affect the user experience, such as unstable Bluetooth connection of the vehicle system and poor wireless network.

[0062] When the fault indicator is below the second indicator threshold, it indicates that the severity of the fault and the difficulty of repair are relatively low, and the fault level is determined to be the third fault. This is generally a minor vehicle fault. Although these faults do not affect the use of the vehicle or the user experience, they require optimization, such as a 0.5-second delay in voice command recognition or unstable voice volume adjustment. Furthermore, the severity of a major vehicle fault is greater than that of a minor vehicle fault but less than that of a critical vehicle fault, thus achieving a standard fault classification.

[0063] In this embodiment of the invention, a fault classification processing module calculates the fault indicators of production vehicles to measure the different degrees of severity of faults, so as to classify and process vehicle faults in order to take corresponding fault handling strategies to make improvements and improve the production quality of the production line.

[0064] Furthermore, in one possible implementation, the fault classification processing module is also used to trigger a shutdown command for the production vehicle when the fault level is determined to be a critical vehicle fault.

[0065] Here, the fault classification and processing module can adaptively generate corresponding fault handling strategies based on the fault level. When the fault level is a critical vehicle fault, it indicates the highest severity of the fault, and the faulty vehicle can no longer proceed with subsequent production work. In this case, a stop command is issued for the production vehicle. The stop command instructs the faulty vehicle to be removed from the production line, and the fault location and repair plan are simultaneously pushed to the maintenance terminal. After the repair is completed, a new inspection is performed.

[0066] When a fault is determined to be a critical vehicle fault, the fault information is marked, and after the inspection items of the production equipment in each target production station are completed, the faulty vehicle is repaired based on the fault information.

[0067] When the fault level is classified as a critical vehicle fault, it indicates that although the fault is severe, it does not affect the subsequent production operation of the faulty vehicle and does not require shutdown. Therefore, the fault information is first marked, and after the inspection items of the production equipment in each target production station are completed, the repair operation for the faulty vehicle is triggered based on the fault information, and the faulty vehicle is guided to a dedicated repair station for repair.

[0068] When the fault level is determined to be a minor vehicle fault, the fault information is recorded, and the fault information is triggered to be repaired after the inspection items of the production equipment in each target production station are completed.

[0069] When the fault level is classified as a minor vehicle fault, it indicates a low severity that does not affect subsequent production operations and requires no downtime. Therefore, the fault information is pre-recorded, and an upgrade package is generated after the inspection items on the production equipment at each target production station are completed. Then, after production operations on the production line are completed, or even after the faulty vehicle has been delivered, the upgraded package can be used to remotely repair the vehicle, for example, using over-the-air (OTA) technology.

[0070] In this embodiment of the invention, a fault classification processing module generates corresponding real-time fault repair strategies for different fault levels. This enables timely handling of faulty vehicles in complex production line environments, ensuring the completion of inspection items for production equipment at each target production station, shortening the processing time for non-critical faults, and avoiding production line downtime caused by faulty vehicles.

[0071] See also Figure 1In one possible implementation, the system also includes a resource optimization and management module, which mainly optimizes and manages the resources of each production station, including the optimization and management of resources such as production equipment, station staff, and testing hours, as explained below.

[0072] The resource optimization and management module is used to collect operational status data of production equipment in each target production station and determine equipment operation indicators based on the operational status data.

[0073] Here, the resource optimization and management module still obtains the operating status data of the production equipment through the monitoring equipment of each production workstation, and then determines the equipment operation indicators from the operating status data, specifically including the production equipment utilization rate, the number of staff, and the number of inspection items.

[0074] The utilization rate of production equipment can be determined based on its runtime, failure frequency, and idle time. Longer runtime, lower failure frequency, and shorter idle time result in higher equipment utilization, and vice versa. Based on these relationships, runtime, failure frequency, and idle time can be mapped to production equipment utilization. The number of staff assigned to a specific production workstation is updated in real-time according to the production line's status. The number of inspection items is the number of inspection items allocated to the corresponding production equipment at that workstation; this number directly determines the inspection time.

[0075] Furthermore, based on the equipment operation indicators, the following operations are performed: scheduling the production equipment at the target production station; configuring the number of workers at the target production station; and screening the testing items in the production equipment at the target production station.

[0076] Based on the three equipment operation indicators determined above, three scheduling operations can be performed accordingly, which will be explained in detail below.

[0077] The first type is equipment scheduling, which involves scheduling the production equipment at the target production station.

[0078] For example, production equipment with high utilization rates can be moved to other production stations with low utilization rates. Alternatively, production equipment with different utilization rates at the same production station can be swapped to meet the demand during peak periods.

[0079] The second type is staffing, which involves allocating the number of workers in the target production workstation.

[0080] For example, staff at production workstations with low equipment utilization can be reassigned to those at production workstations with high equipment utilization, or additional staff can be added to production workstations with high equipment utilization. Alternatively, a skills matrix can be established for staff, and staff with the corresponding skills can be matched to the corresponding production workstations. For example, staff proficient in EOL testing can be assigned to EOL testing workstations as much as possible.

[0081] The third type is inspection item optimization, which involves screening the inspection items in the production equipment at the target production station. For example, during EOL inspection, the inspection parameters are dynamically adjusted to reduce the number of repeated tests for non-critical inspection items, thereby optimizing the inspection during peak periods.

[0082] In this embodiment of the invention, by designing a resource optimization and management module, the production equipment, personnel, and testing items can be scheduled and optimized based on the operating status data of the production equipment in each target production station. This effectively addresses peak production periods on the production line. Through the scheduling of production equipment, the allocation of personnel, and the screening of testing items, the peak demand of certain production equipment can be effectively met. During peak production periods, the production process can be optimized, production efficiency can be improved, and production vehicle backlog can be avoided.

[0083] The dynamic collaborative management and control system for multi-model mixed-line production, as described in this invention, enables collaborative management and control of production line operations in four aspects: model inspection, workstation collaboration, fault handling, and resource scheduling. Taking a vehicle quality inspection scenario in multi-model mixed-line production as an example, theoretical indicators were pre-designed for the above five aspects. Then, the dynamic collaborative management and control system provided by this invention was deployed in the vehicle quality inspection scenario for testing and verification, determining the actual test values ​​for the five indicators. Details are shown in Table 1 below.

[0084] Table 1:

[0085] Based on the data shown in Table 1 above, the dynamic collaborative management and control system for multi-model mixed production line provided by this invention has achieved effective results in five indicators: flexible adaptation, collaborative efficiency, fault handling, detection efficiency, and quality control. Its actual test values ​​are all better than the theoretical design indicators, proving that the dynamic collaborative management and control system for multi-model mixed production line can meet the actual needs of multi-model mixed production line.

[0086] In summary, this embodiment of the invention, by matching vehicle model information to obtain a vehicle model detection template, directly distributes it to each production station on the production line. This allows for rapid switching between production vehicle models, eliminating the need to modify the vehicle model detection parameters of each production device individually, thus improving vehicle model switching efficiency. Furthermore, allocating target production stations based on the station status data of each production station on the production line achieves a global allocation of stations, facilitating the rational utilization of station resources. Moreover, the production equipment at each allocated target production station shares operational status data, improving the coordination between production stations and preventing a single station failure from causing a complete production line shutdown. In addition, by classifying vehicle malfunctions to differentiate fault levels and issuing fault handling strategies to the production line, production line stoppages due to simple faults can be avoided, reducing the processing time for non-critical faults and improving the overall operating efficiency of the production line.

[0087] The following describes in detail the dynamic collaborative management and control method for multi-model mixed-line production provided by this invention. This method can be applied to the dynamic collaborative management and control system for multi-model mixed-line production provided in the above embodiments.

[0088] Figure 8 This is a flowchart illustrating the dynamic collaborative management and control method for multi-model mixed-line production provided by the present invention, as shown below. Figure 8 As shown, the dynamic collaborative management and control method for multi-model mixed-line production can be implemented through the following steps 801 to 803, which are explained in detail below.

[0089] Step 801: Obtain the vehicle model information of the production vehicle, perform template matching based on the vehicle model information to obtain the vehicle model detection template corresponding to the target vehicle model of the production vehicle, and send the vehicle model detection template to the production equipment of each production station in the production line.

[0090] Step 802: Determine the status data of each production station in the production line, and assign the corresponding target production station to the target vehicle model based on the status data.

[0091] Among them, the production equipment at the target production station shares operational status data.

[0092] Step 803: Collect vehicle fault data of production vehicles in each target production station in real time, perform fault classification processing on the vehicle fault data to obtain fault level, and send the fault handling strategy corresponding to each fault level to the production equipment of each target production station.

[0093] In one possible implementation, the dynamic collaborative management and control method for multi-model mixed-line production also includes step 804.

[0094] Step 804: Collect the operating status data of the production equipment in each target production station, and calculate the equipment operation indicators based on the operating status data. The equipment operation indicators include the production equipment utilization rate, the number of staff, and the number of inspection items. Perform the following operations according to the equipment operation indicators: schedule the production equipment in the target production station; configure the number of staff in the target production station; and screen the inspection items in the production equipment in the target production station.

[0095] Step 804 can be executed after step 803, or it can be executed in parallel with steps 801, 802, and 803. This embodiment of the invention does not impose any limitations.

[0096] The dynamic collaborative management and control method for multi-model mixed-line production provided in the above embodiments can realize the technical solutions described in the embodiments of the dynamic collaborative management and control system for multi-model mixed-line production. The specific implementation principles of each step can be found in the corresponding contents of the embodiments of each module in the dynamic collaborative management and control system for multi-model mixed-line production. Specifically, the specific implementation principles of steps 801, 802, 803, and 804 can be found in the corresponding embodiments of the model adaptive detection module, the cross-workstation dynamic collaboration module, the fault classification and processing module, and the resource optimization and management module. Their technical effects can also be referred to accordingly, and will not be elaborated further here.

[0097] like Figure 9 As shown, the present invention also provides an electronic device 900. The electronic device 900 includes a processor 901, a memory 902, and a display 903. Figure 9 Only some components of the electronic device 900 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0098] In some embodiments, memory 902 may be an internal storage unit of electronic device 900, such as a hard disk or memory of electronic device 900. In other embodiments, memory 902 may also be an external storage device of electronic device 900, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 900.

[0099] Furthermore, the memory 902 may include both internal storage units of the electronic device 900 and external storage devices. The memory 902 is used to store application software and various types of data installed on the electronic device 900.

[0100] In some embodiments, processor 901 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 902 or process data, such as the dynamic collaborative control method for multi-model mixed-line production in this invention.

[0101] In some embodiments, display 903 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 903 is used to display information from electronic device 900 and to display a visual user interface. Components 901-903 of electronic device 900 communicate with each other via a system bus.

[0102] In some embodiments of the present invention, when the processor 901 executes the computer program in the memory 902, the following steps can be implemented: acquiring vehicle model information of the production vehicle, performing template matching based on the vehicle model information to obtain a vehicle model detection template corresponding to the target vehicle model of the production vehicle, and sending the vehicle model detection template to the production equipment of each production station in the production line; determining the station status data of each production station in the production line, and assigning a corresponding target production station to the target vehicle model according to the station status data, wherein the production equipment of the target production station shares the operating status data; collecting vehicle fault data of the production vehicle in each target production station in real time, performing fault classification processing on the vehicle fault data to obtain a fault level, and sending the fault handling strategy corresponding to each fault level to the production equipment of each target production station.

[0103] It should be understood that when the processor 901 executes the computer program in the memory 902, in addition to the functions described above, it can also perform other functions, as can be found in the description of the corresponding method embodiments above.

[0104] Furthermore, the embodiments of the present invention do not specifically limit the type of electronic device 900 mentioned. Electronic device 900 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, electronic device 900 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0105] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a dynamic collaborative management and control method for multi-model mixed-line production provided by the methods described above. The method includes: acquiring vehicle model information of the production vehicles; performing template matching based on the vehicle model information to obtain a vehicle model detection template corresponding to the target vehicle model of the production vehicles; and distributing the vehicle model detection template to the production equipment at each production station in the production line; determining the station status data of each production station in the production line; and assigning a corresponding target production station to the target vehicle model according to the station status data, wherein the production equipment at the target production stations shares operating status data; and collecting vehicle fault data of the production vehicles at each target production station in real time; performing fault classification processing on the vehicle fault data to obtain fault levels; and distributing the fault handling strategy corresponding to each fault level to the production equipment at each target production station.

[0106] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0107] The dynamic collaborative management and control system and method for multi-model mixed-line production provided by the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A dynamic collaborative control system for multi-model mixed-line production, characterized in that, The system includes: The vehicle model adaptive detection module is used to acquire vehicle model information of the production vehicle, perform template matching based on the vehicle model information to obtain the vehicle model detection template corresponding to the target vehicle model of the production vehicle, and send the vehicle model detection template to the production equipment of each production station in the production line. The cross-workstation dynamic collaboration module is used to determine the workstation status data of each production workstation in the production line, and to allocate the corresponding target production workstation to the target vehicle model according to the workstation status data. The production equipment of the target production workstations share the operating status data. The fault classification and processing module is used to collect vehicle fault data of production vehicles in each of the target production workstations in real time, perform fault classification processing on the vehicle fault data to obtain fault levels, and send the fault handling strategy corresponding to each fault level to the production equipment of each of the target production workstations.

2. The dynamic collaborative control system for multi-model mixed-line production according to claim 1, characterized in that, The vehicle model adaptive detection module is also used to obtain a preset vehicle model detection template library, which includes vehicle model detection templates for multiple vehicle models, and each vehicle model detection template records the detection items for the corresponding vehicle model. The vehicle model information is matched with the vehicle model detection template to obtain the vehicle model detection template, and the matched vehicle model is determined as the target vehicle model for production. The inspection items in the vehicle inspection template of the target vehicle model are distributed to the production equipment at each production station on the production line.

3. The dynamic collaborative control system for multi-model mixed-line production according to claim 1, characterized in that, The cross-workstation dynamic collaboration module is also used to acquire the inspection items of the production equipment in each production workstation in the production line and determine the inspection time of the inspection items. Determine the load in the operating status data and the target production station corresponding to a load less than the load threshold; Detection items whose detection time exceeds the time threshold are assigned to the production equipment of the target production station.

4. The dynamic collaborative control system for multi-model mixed-line production according to claim 3, characterized in that, The cross-workstation dynamic collaboration module is also used to assign the detection items of the production equipment with equipment failure to other target production stations when a equipment failure is detected in the production equipment in the target production station. Among them, the inspection items of the production equipment in the other target production stations are the same as those of the equipment that has experienced equipment failure, and the load is less than the load threshold.

5. The dynamic collaborative control system for multi-model mixed-line production according to claim 1, characterized in that, The fault classification processing module is also used to calculate fault indicators based on the vehicle fault data. The fault indicators include fault impact indicators and repair difficulty indicators. When the fault indicator is not less than the first indicator threshold, the fault level is determined to be a critical vehicle fault. When the fault indicator is less than the first indicator threshold and not less than the second indicator threshold, the fault level is determined to be a major vehicle fault. When the fault indicator is less than the second indicator threshold, the fault level is determined as either a major vehicle fault or a minor vehicle fault. The severity of the major vehicle malfunction is greater than the severity of the minor vehicle malfunction, but less than the severity of the critical vehicle malfunction.

6. The dynamic collaborative control system for multi-model mixed-line production according to claim 5, characterized in that, The fault classification processing module is also used to trigger a shutdown command for the production vehicle when the fault level is determined to be a critical vehicle fault. When a fault is determined to be a critical vehicle fault, the fault information is marked, and after the inspection items of the production equipment in each target production station are completed, the faulty vehicle is triggered to be repaired based on the fault information. When the fault level is determined to be a minor vehicle fault, the fault information is recorded. After the inspection items of the production equipment in each target production station are completed, an upgrade package for the fault information is generated, and the faulty vehicle is remotely repaired through the upgrade package.

7. The dynamic collaborative control system for multi-model mixed-line production according to claim 1, characterized in that, The system also includes a resource optimization and management module, which is used to collect the operating status data of the production equipment in each target production station, and calculate the equipment operation indicators based on the operating status data. The equipment operation indicators include the production equipment utilization rate, the number of staff, and the number of inspection items. Perform the following operations based on the aforementioned equipment operating parameters: Schedule the production equipment at the target production station; Configure the number of workers in the target production station; Screening is performed on the inspection items in the production equipment at the target production station.

8. A dynamic collaborative management and control method for multi-model mixed-line production, characterized in that, include: Obtain the vehicle model information of the production vehicle, perform template matching based on the vehicle model information to obtain the vehicle model detection template corresponding to the target vehicle model of the production vehicle, and send the vehicle model detection template to the production equipment of each production station in the production line; The status data of each production station in the production line is determined, and the target production station is assigned to the target vehicle model according to the status data. The production equipment of the target production station shares the operating status data. Real-time vehicle fault data of production vehicles in each of the target production workstations is collected, the vehicle fault data is processed to obtain fault levels, and the fault handling strategy corresponding to each fault level is distributed to the production equipment of each of the target production workstations.

9. An electronic device, characterized in that, Including memory and processor, among which, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps of the dynamic collaborative management and control method for multi-model mixed-line production as described in claim 8.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the dynamic collaborative management and control method for multi-model mixed-line production as described in claim 8.

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