An assembling and processing management method and system for automobile lamp production

Abstract

The application provides an assembling and processing management method and system for automobile lamp production, and relates to the technical field of intelligent production management.The method comprises the following steps: preparing multiple resolution paths and their time factor for the decision game event of assembling and processing management in the automobile lamp production scene; and resolving the decision game event that develops in the future based on the resolution path of the time factor that triggers first when the decision game event develops in the future.The application prepares multiple resolution paths and their time factor for the decision game event of assembling and processing management in the automobile lamp production scene, resolves the decision game event that develops in the future based on the resolution path of the time factor that triggers first when the decision game event develops in the future, realizes the preparatory resolution of the decision game event, ensures the appropriate resolution of the decision game event at the appropriate time, improves the efficiency of assembling and processing management of the automobile lamp, and avoids affecting the production efficiency of the automobile lamp.

Description

Technical Field

[0001] This invention relates to the field of intelligent production management technology, and in particular to an assembly and processing management method and system for automotive lamp production. Background Technology

[0002] Currently, in automotive lighting production scenarios, due to the complex and ever-changing on-site conditions, computer systems frequently encounter decision-making game events when managing the assembly and processing of automotive lights. These decision-making game events typically stem from competition and coordination among multiple participants regarding resources, time, and processes during production. For example, when multiple stages of automotive lighting production need to compete for limited production resources, or when multiple processes in automotive lighting production are interdependent and conflicting, game-like decision-making is required when faced with different choices.

[0003] Existing technologies lack a way to preemptively resolve these decision-making game events, resulting in the inability to resolve them appropriately at the right time, reducing the efficiency of automotive lamp assembly and processing management, and further affecting automotive lamp production efficiency.

[0004] Therefore, a solution is urgently needed. Summary of the Invention

[0005] One of the objectives of this invention is to provide an assembly and processing management method for automotive lamp production, in order to solve the problems mentioned in the background.

[0006] This invention provides an assembly and processing management method for automotive lamp production, comprising:

[0007] For decision-making game events in the assembly and processing management of automotive lighting production scenarios, multiple resolution paths and their timing factors are prepared;

[0008] Based on the resolution path of the timing factor that is first triggered in the future development of the decision game event, the decision game event that will develop in the future is resolved.

[0009] Optional, the preparatory steps for multiple resolution paths and their timing factors include:

[0010] Based on the first historical development information of decision-making game events, predict multiple possible development events;

[0011] Matching the best solution knowledge to each of multiple possible development events;

[0012] For multiple possible development events, multiple resolution paths and their timing factors are determined by using the best resolution knowledge that matches each one; where each resolution path and its timing factor corresponds to one possible development event.

[0013] Optional, optimal knowledge matching steps include:

[0014] Iterate through each possible development event in turn, and each time it is encountered:

[0015] Represent the possible development events encountered in the traversal as an event development timeline;

[0016] Obtain the matching weights of multiple event development intervals on the event development timeline;

[0017] Knowledge matching constraints are generated based on the matching weights of multiple event development intervals.

[0018] Based on knowledge matching constraints, the best solution knowledge is matched for the traversed possible development events according to the first feature of each of the multiple event development intervals.

[0019] Among them, knowledge matching constraints include:

[0020] When at least two mutually replaceable first-part knowledge are matched, the first-part knowledge with the largest matching weight of the event development interval from which the first feature originates is retained, and the remaining first-part knowledge is discarded.

[0021] Furthermore, when at least two first features are jointly matched to the second part of knowledge, if the mean of the matching weights of the event development intervals from which the at least two first features originate exceeds the mean threshold, the second part of knowledge is retained; otherwise, it is discarded.

[0022] The first characteristic includes: factors influencing the development of the event, the time period of its development, and the order of its development.

[0023] Optionally, the steps for obtaining the matching weights include:

[0024] Based on the knowledge matching contribution allocation table, the knowledge matching contribution of each event development interval is determined according to the second feature of each of the multiple event development intervals on the event development timeline.

[0025] When the prediction confidence of an event development interval exceeds the confidence threshold, the matching weight of the corresponding event development interval is calculated as a weighted sum of its prediction confidence and the contribution of knowledge matching.

[0026] Otherwise, if the prediction confidence of other event development intervals that are adjacent to the corresponding event development interval on the event development timeline exceeds the confidence threshold, and the contribution types of the knowledge matching contributions of the three are the same, the matching weight of the corresponding event development interval is calculated as the weighted sum of its prediction confidence and the contribution of its knowledge matching contribution.

[0027] Otherwise, the matching weight of the corresponding event development interval is calculated as the corresponding value of its prediction confidence in the weight table;

[0028] The second characteristic includes: the type of event development.

[0029] Optionally, if the future development of a decision game event exceeds a threshold duration and the timing factor is not triggered, multiple triggering degree curves are plotted for the decision game event to approach the triggering of different timing factors during the future development process; wherein, one triggering degree curve corresponds to one timing factor.

[0030] Based on the triggering curves, select the inducing triggering timing factors from the timing factors;

[0031] Inducing the current decision-making game event to develop into a triggering timing factor;

[0032] Based on the resolution path of induced trigger timing factors, decision-making game events for future development are resolved.

[0033] Optionally, the steps for selecting the induction triggering timing factor include:

[0034] When there is a triggering curve with at least two peaks, the timing factor corresponding to the triggering curve with at least two peaks and the largest average peak value is used as the induction triggering timing factor.

[0035] Otherwise, if the first peak in each triggering curve is the same as the maximum peak, the timing factor corresponding to the triggering curve with the corresponding peak will be used as the induction triggering timing factor.

[0036] Otherwise, the peak evaluation value for each triggering curve is calculated by weighting the peak generation time order, the minimum generation time interval between peaks and other peaks, and the peak value size.

[0037] The timing factor corresponding to the triggering curve of the peak with the maximum peak evaluation value is used as the induction triggering timing factor.

[0038] Optionally, the steps to induce the current decision-making game event to develop to the point of triggering the inducing timing factor include:

[0039] Based on the second historical development information of the decision-making game event in the future development process and the induced triggering timing factors, match the induced triggering knowledge;

[0040] Based on the knowledge of induced triggers, the current decision-making game event is induced to develop into a triggering timing factor.

[0041] An assembly and processing management system for automotive lamp production provided in this embodiment of the invention includes:

[0042] The preparation module is used to prepare multiple resolution paths and their timing factors for decision-making game events in the assembly and processing management of automotive lighting production scenarios.

[0043] The resolution module is used to resolve future decision-making events based on the resolution path of the timing factor that was first triggered in the future development of the decision-making game event.

[0044] The present invention provides a computer-readable storage medium storing a computer program, wherein a processor executes the computer program to implement the method described in any of the above embodiments.

[0045] An electronic device provided by an embodiment of the present invention includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in any of the above embodiments.

[0046] The present invention has achieved the following beneficial effects:

[0047] This invention addresses decision-making game events in the assembly and processing management of automotive lighting production scenarios. It pre-prepares multiple resolution paths and their timing factors. Based on the resolution path of the timing factor that is triggered first in the future development of the decision-making game event, it resolves the decision-making game events that will develop further in the future. This achieves pre-emptive resolution of decision-making game events, ensuring that decision-making game events are resolved appropriately at the right time, thereby improving the efficiency of automotive lighting assembly and processing management and avoiding impacting automotive lighting production efficiency.

[0048] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0049] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0050] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0051] Figure 1 This is a flowchart of an assembly and processing management method for automotive lamp production according to an embodiment of the present invention;

[0052] Figure 2 This is another flowchart of an assembly and processing management method for automotive lamp production according to an embodiment of the present invention;

[0053] Figure 3 This is a schematic diagram of an assembly and processing management system for automotive lamp production according to an embodiment of the present invention. Detailed Implementation

[0054] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0055] Example 1:

[0056] This invention provides an assembly and processing management method for automotive lamp production, such as... Figure 1 As shown, it includes:

[0057] S1. For decision-making game events in the assembly and processing management of automotive lighting production scenarios, prepare multiple resolution paths and their timing factors;

[0058] S2. Based on the resolution path of the timing factor that is first triggered in the future development of the decision game event, resolve the decision game event that will develop in the future.

[0059] The resolution path is the execution path for resolving decision-making game events. Its timing factor is the appropriate time for the path execution. For example, if the installation of light sources and the assembly of shells on a temporary assembly line require the use of the same high-precision robot, and the decision-making game event is to decide which process should use the high-precision robot first, then resolution path 1 is to decide that the shell assembly process should use the high-precision robot first, and its timing factor is that the shell assembly process has an urgent need. Resolution path 2 is to comprehensively decide which process should use the robot first based on process priority, time requirements, and resource utilization, and its timing factor is when the decision is made to consider any one of the factors of process priority, time requirements, and resource utilization to decide which process should use the robot first.

[0060] Once multiple resolution paths and their timing factors are prepared, the decision-making game event will continue to develop. When it develops to the point where it meets the timing factor, the timing factor will be triggered. Based on the resolution path of the first triggered timing factor, the decision-making game event that develops further in the future will be resolved.

[0061] This invention addresses decision-making game events in the assembly and processing management of automotive lighting production scenarios. It pre-prepares multiple resolution paths and their timing factors. Based on the resolution path of the timing factor that is triggered first in the future development of the decision-making game event, it resolves the decision-making game events that will develop further in the future. This achieves pre-emptive resolution of decision-making game events, ensuring that decision-making game events are resolved appropriately at the right time, thereby improving the efficiency of automotive lighting assembly and processing management and avoiding impacting automotive lighting production efficiency.

[0062] Example 2:

[0063] In embodiments of the present invention, such as Figure 2 As shown, in S1, the preparatory steps for multiple resolution paths and their timing factors include:

[0064] S11. Based on the first historical development information of decision-making game events, predict multiple possible development events;

[0065] S12. Match the best solution knowledge for each of the multiple possible development events;

[0066] S13. For multiple possible development events, use the best resolution knowledge that matches each event to determine multiple resolution paths and their timing factors; where each resolution path and its timing factor corresponds to one possible development event.

[0067] First-hand historical development information includes at least the changes in the development process of a decision-making game event from its occurrence to the preparation of multiple resolution paths and timing factors, such as changes in demand and changes in the influence of external factors. Based on first-hand historical development information, multiple possible development events of a decision-making game event can be predicted. When making predictions, an artificial intelligence model obtained by machine learning training based on a large number of historical decision-making game events as training samples can be used to make predictions based on first-hand historical development information.

[0068] Optimal resolution knowledge indicates how and when it is appropriate to resolve potential development events in a game. Based on optimal resolution knowledge, the resolution path and timing factors of potential development events can be determined. Potential development events are possible future developments of decision-making game events. Their resolution paths and timing factors are prepared as reserves, waiting to be triggered by the actual development of the decision-making game event. Executing the corresponding resolution path achieves a preparatory resolution of the decision-making game event.

[0069] Overall, it has greatly improved the accuracy, comprehensiveness, and applicability of multiple resolution paths and their timing factors.

[0070] Example 3:

[0071] In this embodiment of the invention, the optimal knowledge matching step includes:

[0072] Iterate through each possible development event in turn, and each time it is encountered:

[0073] Represent the possible development events encountered in the traversal as an event development timeline;

[0074] Obtain the matching weights of multiple event development intervals on the event development timeline;

[0075] Knowledge matching constraints are generated based on the matching weights of multiple event development intervals.

[0076] Based on knowledge matching constraints, the best solution knowledge is matched for the traversed possible development events according to the first feature of each of the multiple event development intervals.

[0077] Among them, knowledge matching constraints include:

[0078] When at least two mutually replaceable first-part knowledge are matched, the first-part knowledge with the largest matching weight of the event development interval from which the first feature originates is retained, and the remaining first-part knowledge is discarded.

[0079] Furthermore, when at least two first features are jointly matched to the second part of knowledge, if the mean of the matching weights of the event development intervals from which the at least two first features originate exceeds the mean threshold, the second part of knowledge is retained; otherwise, it is discarded.

[0080] The first characteristic includes: factors influencing the development of the event, the time period of its development, and the order of its development.

[0081] When representing the timeline of an event's development, the possible events are divided into multiple local events according to their development stages. Each local event is set in a corresponding time interval on the timeline according to its development period. The local events within their set time intervals form the event development interval.

[0082] The matching weight of an event development interval represents its importance in matching optimal resolution knowledge. When matching optimal resolution knowledge, matching is performed based on the event development factors, development time periods, and development order of each of the multiple event development intervals. In the first feature, event development factors refer to factors in the event development process, development time periods refer to event segments, and development order refers to the sequence of events. Pre-set first-part knowledge for different first features (e.g., if the event development factor in the first feature is an urgent need for shell assembly, then the corresponding preset first-part knowledge is to prioritize using the high-precision robot in the shell assembly process) and second-part knowledge for joint matching of different first features (e.g., if the event development factors in the first feature are considering process priority and time requirements, with adjacent development time periods and sequential development orders, then the corresponding preset second-part knowledge is to comprehensively determine which process prioritizes using the robot based on process priority, time requirements, and resource utilization). During matching, the first-part knowledge and the second-part knowledge retained after knowledge matching constraints are combined to obtain the optimal resolution knowledge.

[0083] Therefore, when constraining the matching process using knowledge matching constraints, if at least two mutually substitutable first-part knowledge pieces are matched, the first-part knowledge piece with the largest matching weight for the event development interval from which each first feature originates is retained. This ensures that the retained first-part knowledge piece is most suitable for generating the optimal solution knowledge combination after comprehensively considering its importance as the best solution knowledge matching. When at least two first features jointly match the second-part knowledge, if the average of the matching weights for the event development intervals from which these at least two first features originate exceeds the average threshold, it indicates that the second-part knowledge is suitable for generating the optimal solution knowledge combination after comprehensively considering its importance as the best solution knowledge matching, and thus it can be retained.

[0084] In matching optimal solution knowledge, this invention represents possible development events as an event development timeline. Based on the matching weights of multiple event development intervals, knowledge matching constraints are generated. These constraints match the optimal solution knowledge according to the first features of each of the multiple event development intervals. This ensures that the importance of matching the optimal solution knowledge for each event development interval is fully considered during the matching process. When at least two mutually replaceable first parts of knowledge are matched, or when at least two first features are jointly matched to the second part of knowledge, the most appropriate choice is made. This greatly improves the accuracy of optimal solution knowledge matching and enhances the applicability of the system.

[0085] Example 4:

[0086] In this embodiment of the invention, the step of obtaining the matching weight includes:

[0087] Based on the knowledge matching contribution allocation table, the knowledge matching contribution of each event development interval is determined according to the second feature of each of the multiple event development intervals on the event development timeline.

[0088] When the prediction confidence of an event development interval exceeds the confidence threshold, the matching weight of the corresponding event development interval is calculated as a weighted sum of its prediction confidence and the contribution of knowledge matching.

[0089] Otherwise, if the prediction confidence of other event development intervals that are adjacent to the corresponding event development interval on the event development timeline exceeds the confidence threshold, and the contribution types of the knowledge matching contributions of the three are the same, the matching weight of the corresponding event development interval is calculated as the weighted sum of its prediction confidence and the contribution of its knowledge matching contribution.

[0090] Otherwise, the matching weight of the corresponding event development interval is calculated as the corresponding value of its prediction confidence in the weight table;

[0091] The second characteristic includes: the type of event development.

[0092] The knowledge matching contribution refers to the contribution of the event development interval to matching the best solution knowledge. It has a contribution degree, representing the magnitude of the contribution. For example, if the event development interval includes the event development factor that the shell assembly has an urgent need, then the knowledge matching contribution is used to directly determine the solution path (determining that the shell assembly process prioritizes the use of the high-precision robot) and its timing factor (the shell assembly has an urgent need), corresponding to a larger contribution degree of 10. The knowledge matching contribution allocation table pre-sets the knowledge matching contribution corresponding to the second feature of different event development intervals.

[0093] The prediction confidence level of an event development interval is the degree to which the AI ​​model is certain of the occurrence of a local event during prediction. The confidence threshold represents a higher prediction confidence level. When the prediction confidence level of an event development interval exceeds the confidence threshold, it indicates a high degree of certainty of its occurrence. When quantifying the importance of using the event development interval for optimal knowledge matching, the contribution of its prediction confidence and the contribution of knowledge matching should be considered together. Therefore, the matching weight of the corresponding event development interval is calculated as a weighted sum of the contributions of its prediction confidence and knowledge matching (each multiplied by a pre-set weight and then summed).

[0094] Otherwise, it indicates that the degree of certainty of its occurrence is relatively small. However, if the prediction confidence of the other event development intervals before and after it exceeds the confidence threshold and the contribution type of the knowledge matching contribution of the three (the event development interval and the other event development intervals before and after it) is the same, it indicates that the corresponding event development interval has a greater impact on making the same knowledge matching contribution. When quantifying the importance of the event development interval as the best solution for knowledge matching, its prediction confidence and the contribution of knowledge matching should also be considered. The matching weight of the corresponding event development interval is calculated as the weighted sum of its prediction confidence and the contribution of knowledge matching.

[0095] Otherwise, it indicates that the likelihood of its occurrence is low and its impact on making the same contribution to knowledge matching is small. When quantifying the importance of knowledge matching in the event development interval, quantification is performed only based on the prediction confidence, and the corresponding value in the weight table is used as the matching weight. The higher the prediction confidence in the weight table, the larger the corresponding value.

[0096] This invention introduces prediction confidence and contribution when obtaining matching weights, and quantifies the importance of using the event development interval as the best solution knowledge matching in three different cases, which greatly improves the accuracy and comprehensiveness of obtaining matching weights.

[0097] Example 5:

[0098] In this embodiment of the invention, if the future development of a decision game event exceeds a threshold duration and the timing factor is not triggered, multiple triggering degree curves are plotted for the decision game event to approach triggering different timing factors during the future development process; wherein, one triggering degree curve corresponds to one timing factor.

[0099] Based on the triggering curves, select the inducing triggering timing factors from the timing factors;

[0100] Inducing the current decision-making game event to develop into a triggering timing factor;

[0101] Based on the resolution path of induced trigger timing factors, decision-making game events for future development are resolved.

[0102] The threshold duration represents a relatively long timeframe. If the future development of a decision-making game event exceeds the threshold duration and the timing factor is still not triggered, it indicates that the decision-making game event has not been resolved using a suitable resolution path for a considerable period of time. In this case, multiple triggering degree curves are plotted as the decision-making game event approaches different triggering timing factors during its future development. When plotting, based on the triggering degree (compliance with timing factors) and compliance time of the process changes of the decision-making game event in the second historical development information of its future development process, multiple corresponding coordinate points are found in the template curve (the horizontal axis is time, and the vertical axis is the magnitude of triggering degree) and connected sequentially to obtain the triggering degree curve.

[0103] Multiple trigger curves reflect the situation where decision-making game events are close to triggering different timing factors in the future development process. Based on this, appropriate induction trigger timing factors can be selected and induction can be implemented. After induction, the decision-making game events in the future development can be resolved based on the resolution path of the induction trigger timing factors.

[0104] In this embodiment of the invention, when a decision-making game event has not been resolved using a suitable resolution path for a relatively long period of time, multiple triggering degree curves are plotted for the decision-making game event to approach the triggering of different triggering timing factors in the future development process. Based on this, a suitable induction triggering timing factor is selected and induction is implemented. After the induction is completed, the decision-making game event in the future development is resolved based on the resolution path of the induction triggering timing factor, which further improves the resolution efficiency and timeliness of decision-making game events and enhances the applicability of the system.

[0105] Example 6:

[0106] In this embodiment of the invention, the step of selecting the induction triggering timing factor includes:

[0107] When there is a triggering curve with at least two peaks, the timing factor corresponding to the triggering curve with at least two peaks and the largest average peak value is used as the induction triggering timing factor.

[0108] Otherwise, if the first peak in each triggering curve is the same as the maximum peak, the timing factor corresponding to the triggering curve with the corresponding peak will be used as the induction triggering timing factor.

[0109] Otherwise, the peak evaluation value for each triggering curve is calculated by weighting the peak generation time order, the minimum generation time interval between peaks and other peaks, and the peak value size.

[0110] The timing factor corresponding to the triggering curve of the peak with the maximum peak evaluation value is used as the induction triggering timing factor.

[0111] When a peak appears in the triggering curve, it indicates that the decision-making game event continuously approaches and then moves away from triggering a timing factor during its future development. When there are triggering curves with at least two peaks, the triggering timing factor corresponding to the triggering curve with at least two peaks and the largest average peak value is the one that the decision-making game event repeatedly approaches and triggers during its future development, with the highest average degree of final approach each time. This timing factor can be used as an induction target.

[0112] Otherwise, it means that only one peak appears in each triggering degree curve. If the first peak in each triggering degree curve is the same as the largest peak, it means that the decision game event is approaching the corresponding timing factor for the first time in the future development process, and the final degree of approach of this approach is the highest. Its corresponding timing factor can be used as an induction target.

[0113] Otherwise, the peak evaluation value of each triggering curve is calculated by weighting the peak generation time order (the order in which peaks in different curves are generated; the larger the peak generation time order, the more recent the triggering process, and the more correlated it is with the peak evaluation value), the minimum generation time interval between peaks (the larger the time interval, the less the peaks are simultaneously approaching other timing factors, and the more correlated it is with the peak evaluation value), and the peak value size (the larger the peak value, the higher the degree of approach, and the more correlated it is with the peak evaluation value). The timing factor corresponding to the triggering curve of the peak with the largest peak evaluation value is used as the induced triggering timing factor.

[0114] In selecting the induction triggering timing factor, the embodiments of the present invention accurately determine the most suitable timing factor as the induction triggering timing factor based on the peak conditions of each triggering degree curve, which greatly improves the accuracy, comprehensiveness and efficiency of the selection of the induction triggering timing factor.

[0115] Example 7:

[0116] In this embodiment of the invention, the step of inducing the current decision-making game event to develop to the point of triggering the induction timing factor includes:

[0117] Based on the second historical development information of the decision-making game event in the future development process and the induced triggering timing factors, match the induced triggering knowledge;

[0118] Based on the knowledge of induced triggers, the current decision-making game event is induced to develop into a triggering timing factor.

[0119] Similarly, the second historical development information includes information on process changes in the future development process. Different second historical development information and induced triggering knowledge are pre-set to match the induced triggering timing factors. The induced triggering knowledge indicates how to induce the current decision-making game event (a decision-making game event where future development exceeds a threshold time but has not yet triggered the timing factor) to develop to the point of triggering the induced triggering timing factor. For example, if the second historical development information is a decision to prioritize the use of a process based on the communication results with the process manager, and the induced triggering timing factor is a decision to prioritize the use of a process based on any one of process priority, time requirements, and resource utilization, then the induced triggering knowledge would be to provide the computer system with ideas considering process priority, time requirements, etc.

[0120] In this embodiment of the invention, when the current decision-making game event is induced to develop to the point of triggering the induction timing factor, the induction triggering knowledge is matched based on the second historical development information of the decision-making game event in the future development process and the induction triggering timing factor. Based on the induction triggering knowledge, the induction is implemented, thereby improving the accuracy and efficiency of the induction.

[0121] Example 8:

[0122] This invention provides an assembly and processing management system for automotive lamp production, such as... Figure 3 As shown, it includes:

[0123] Preparatory Module 1 is used to prepare multiple resolution paths and their timing factors for decision-making game events in the assembly and processing management of automotive lighting production scenarios.

[0124] Resolution Module 2 is used to resolve future decision-making events based on the resolution path of the timing factor that was first triggered in the future development of the decision-making game event.

[0125] This invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, and a processor executes the computer program to implement the method described in any of the above embodiments.

[0126] This invention provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in any of the above embodiments.

[0127] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for assembly and processing management in automotive lamp production, characterized in that, include: For decision-making game events in the assembly and processing management of automotive lighting production scenarios, multiple resolution paths and their timing factors are prepared; Based on the resolution path of the timing factor that is first triggered in the future development of the decision game event, the decision game event that will develop in the future is resolved. The preparatory steps for multiple resolution paths and their timing factors include: Based on the first historical development information of decision-making game events, predict multiple possible development events; Matching the best solution knowledge to each of multiple possible development events; For multiple possible development events, multiple resolution paths and their timing factors are determined by using the best resolution knowledge that matches each one; where each resolution path and its timing factor corresponds to one possible development event. The optimal steps for resolving knowledge matching include: Iterate through each possible development event in turn, and each time it is encountered: Represent the possible development events encountered in the traversal as an event development timeline; Obtain the matching weights of multiple event development intervals on the event development timeline; Knowledge matching constraints are generated based on the matching weights of multiple event development intervals. Based on knowledge matching constraints, the best solution knowledge is matched for the traversed possible development events according to the first feature of each of the multiple event development intervals. Among them, knowledge matching constraints include: When at least two mutually replaceable first-part knowledge are matched, the first-part knowledge with the largest matching weight of the event development interval from which the first feature originates is retained, and the remaining first-part knowledge is discarded. Furthermore, when at least two first features are jointly matched to the second part of knowledge, if the mean of the matching weights of the event development intervals from which the at least two first features originate exceeds the mean threshold, the second part of knowledge is retained; otherwise, it is discarded. The first characteristic includes: factors influencing the development of the event, the time period of its development, and the order of its development.

2. The assembly and processing management method for automotive lamp production as described in claim 1, characterized in that, The steps for obtaining matching weights include: Based on the knowledge matching contribution allocation table, the knowledge matching contribution of each event development interval is determined according to the second feature of each of the multiple event development intervals on the event development timeline. When the prediction confidence of an event development interval exceeds the confidence threshold, the matching weight of the corresponding event development interval is calculated as a weighted sum of its prediction confidence and the contribution of knowledge matching. Otherwise, if the prediction confidence of other event development intervals that are adjacent to the corresponding event development interval on the event development timeline exceeds the confidence threshold, and the contribution types of the knowledge matching contributions of the three are the same, the matching weight of the corresponding event development interval is calculated as the weighted sum of its prediction confidence and the contribution of its knowledge matching contribution. Otherwise, the matching weight of the corresponding event development interval is calculated as the corresponding value of its prediction confidence in the weight table; The second characteristic includes: the type of event development.

3. The assembly and processing management method for automotive lamp production as described in claim 1, characterized in that, If the future development of a decision game event exceeds a threshold duration and the timing factor is not triggered, plot multiple triggering degree curves of the decision game event approaching the triggering of different timing factors during the future development process; where each triggering degree curve corresponds to one timing factor. Based on the triggering curves, select the inducing triggering timing factors from the timing factors; Inducing the current decision-making game event to develop into a triggering timing factor; Based on the resolution path of induced trigger timing factors, decision-making game events for future development are resolved.

4. The assembly and processing management method for automotive lamp production as described in claim 3, characterized in that, The steps for selecting the induction triggering timing factor include: When there is a triggering curve with at least two peaks, the timing factor corresponding to the triggering curve with at least two peaks and the largest average peak value is used as the induction triggering timing factor. Otherwise, if the first peak in each triggering curve is the same as the maximum peak, the timing factor corresponding to the triggering curve with the corresponding peak will be used as the induction triggering timing factor. Otherwise, the peak evaluation value for each triggering curve is calculated by weighting the peak generation time order, the minimum generation time interval between peaks and other peaks, and the peak value size. The timing factor corresponding to the triggering curve of the peak with the maximum peak evaluation value is used as the induction triggering timing factor.

5. The assembly and processing management method for automotive lamp production as described in claim 3, characterized in that, The steps to induce the current decision-making game event to develop into the triggering of the inducing timing factor include: Based on the second historical development information of the decision-making game event in the future development process and the induced triggering timing factors, match the induced triggering knowledge; Based on the knowledge of induced triggers, the current decision-making game event is induced to develop into a triggering timing factor.

6. An assembly and processing management system for automotive lamp production, characterized in that, include: The preparation module is used to prepare multiple resolution paths and their timing factors for decision-making game events in the assembly and processing management of automotive lighting production scenarios. The resolution module is used to resolve future decision-making game events based on the resolution path of the timing factor that was first triggered in the future development of the decision-making game event. The preparatory steps for multiple resolution paths and their timing factors include: Based on the first historical development information of decision-making game events, predict multiple possible development events; Matching the best solution knowledge to each of multiple possible development events; For multiple possible development events, multiple resolution paths and their timing factors are determined by using the best resolution knowledge that matches each one; where each resolution path and its timing factor corresponds to one possible development event. The optimal steps for resolving knowledge matching include: Iterate through each possible development event in turn, and each time it is encountered: Represent the possible development events encountered in the traversal as an event development timeline; Obtain the matching weights of multiple event development intervals on the event development timeline; Knowledge matching constraints are generated based on the matching weights of multiple event development intervals. Based on knowledge matching constraints, the best solution knowledge is matched for the traversed possible development events according to the first feature of each of the multiple event development intervals. Among them, knowledge matching constraints include: When at least two mutually replaceable first-part knowledge are matched, the first-part knowledge with the largest matching weight of the event development interval from which the first feature originates is retained, and the remaining first-part knowledge is discarded. Furthermore, when at least two first features are jointly matched to the second part of knowledge, if the mean of the matching weights of the event development intervals from which the at least two first features originate exceeds the mean threshold, the second part of knowledge is retained; otherwise, it is discarded. The first characteristic includes: factors influencing the development of the event, the time period of its development, and the order of its development.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-5.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, wherein the memory stores a computer program and the processor executes the computer program to implement the method as described in any one of claims 1-5.

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