PCBA production abnormity management method and system and medium

Abstract

The invention relates to the technical field of PCBA industrial manufacturing process control and quality management, and particularly discloses a PCBA production abnormity management method. The method comprises the steps that abnormal signals are captured in real time through a plurality of data sources on an integrated PCBA production line, an abnormal work order is created in a manufacturing execution system, target processing personnel are matched based on the abnormal work order and a preset scheduling rule, processing tasks are pushed to terminal equipment of the processing personnel, the processing personnel are guided to execute field diagnosis and correction measures, and the processing personnel are further guided to execute corresponding processing tasks. And performing real-time association updating on the process data to the abnormal work order, closing the abnormal work order after the corrective measure passes verification, performing structured processing on the whole-process data, generating knowledge entries and storing the knowledge entries in a knowledge base. According to the method, the whole-process closed-loop management of PCBA production abnormity from discovery to closing is realized, the abnormity response speed, the processing standardization and the personnel scheduling efficiency are improved, the processing experience can be continuously precipitated and reused, and the production yield and the stability are improved.

Description

Technical Field

[0001] This disclosure relates to the field of PCBA industrial manufacturing process control and quality management technology, and in particular to a PCBA production anomaly management method, system and medium. Background Technology

[0002] In modern electronics manufacturing, printed circuit board assembly (PCBA) is the core step in enabling electronic products to achieve their electrical connections and functions. Its production process typically involves a series of highly precise and automated processes, including solder paste printing, component mounting, reflow soldering, wave soldering, in-circuit testing, and functional testing. Within this complex system, even a minor deviation in any process step—whether due to equipment status, material quality, program parameters, or environmental factors—can trigger production anomalies and potentially be amplified in subsequent stages, ultimately leading to product malfunctions, test failures, or even batch quality issues, causing soaring production costs and delivery delays.

[0003] Therefore, timely and effective management of various anomalies during the production process is crucial to ensuring PCBA production yield, efficiency, and stability. However, in current PCBA production practices, existing anomaly management models either rely on loosely combined manual processes or are limited to localized, single-point monitoring and response. This often leads to numerous challenges throughout the entire process, from anomaly occurrence, identification, diagnosis to recovery, including information gaps, delayed responses, and decision-making dependent on individual experience. Therefore, the industry urgently needs a technical solution that can overcome these limitations and achieve closed-loop control throughout the entire process, providing a more systematic and efficient anomaly management capability for PCBA production. Summary of the Invention

[0004] To overcome the problems existing in related technologies, this disclosure provides a PCBA production anomaly management method, system, and medium. The PCBA production anomaly management method provided in this disclosure achieves systematic optimization of PCBA production process anomaly management by establishing a standardized anomaly handling process and knowledge management system, significantly improving the timeliness and effectiveness of anomaly handling.

[0005] According to a first aspect of the present disclosure, this application provides a PCBA production anomaly management method, which adopts the following technical solution:

[0006] A PCBA production anomaly management method includes:

[0007] By integrating multiple data sources on the PCBA production line, abnormal signals are captured in real time, and abnormal work orders are created in the manufacturing execution system. The abnormal work order includes at least: an abnormal source identifier, a trigger timestamp, physical location data, an abnormal level code, an abnormal description information, and an associated production batch number.

[0008] Based on the abnormal work order and the preset scheduling rules, at least one target handler is matched, and the processing task associated with the abnormal work order is pushed to the target handler's terminal device.

[0009] In response to the processing task, the terminal device provides the target processing personnel with production information related to the current abnormal context, and guides the target processing personnel to perform on-site diagnosis and corrective measures step by step according to preset logic. At the same time, the execution data of the diagnosis process and corrective measures are linked in real time and updated to the abnormal work order.

[0010] After confirming that the corrective measures have passed verification, the abnormal work order is closed, and the entire process data of the abnormal work order from creation to closure is structured to generate knowledge entries and stored in the knowledge base.

[0011] Optionally, the data source includes at least two of the following: testing equipment, human-computer interaction terminal, and environmental monitoring device;

[0012] The process involves integrating multiple data sources on the PCBA production line to capture abnormal signals in real time and creating abnormal work orders in the manufacturing execution system, including:

[0013] When a signal that meets any of the following preset conditions is captured, it is identified as an abnormal signal, and the abnormal work order is automatically created:

[0014] a) The automatic reporting signal of the test equipment meets the preset triggering rules;

[0015] b) Receives a manual trigger signal from a human-machine interface terminal deployed on the production site;

[0016] c) The environmental parameters represented by the environmental monitoring signal from the environmental monitoring device exceed the preset process range.

[0017] Optionally, the automatic reporting signal of the testing equipment includes signals from at least one of the following devices: solder paste inspector, automatic optical inspector, online tester, functional tester, and X-ray inspector; the automatic reporting signal of the testing equipment satisfies preset triggering rules, including: the test data reported by the testing equipment exceeds a preset quality threshold, or the defect type code reported by the testing equipment matches a preset high-priority defect list;

[0018] The manually triggered signal includes at least one of the following signals reported through the human-machine interaction terminal: material abnormality signal, equipment or tooling abnormality signal, appearance or process abnormality signal, and one-click emergency stop call signal; the manually triggered signal is generated by at least one of the following methods: scanning the identification code on the material or equipment to associate it with a specific object, selecting the defect type on the terminal interface, and taking a picture and uploading the on-site image through the terminal.

[0019] The environmental monitoring signal includes signals from at least one of the following environmental monitoring devices: electrostatic wrist strap monitor, temperature and humidity sensor, warehouse environment monitoring equipment, and special gas monitoring equipment; the environmental parameters represented by the environmental monitoring signal exceed the preset process range, including: the monitored electrostatic protection resistance value, ambient temperature and humidity value, and special gas concentration / pressure value continuously exceeding the set safety process range.

[0020] Optionally, the step of matching at least one target handler based on the abnormal work order and preset scheduling rules, and pushing the processing task associated with the abnormal work order to the target handler's terminal device, includes:

[0021] Based on the anomaly source identifier and anomaly level code in the abnormal work order, one or more target roles with responsibilities and permissions are matched from the preset scheduling rule database.

[0022] Select the processors whose roles belong to the target role from all online processors to form an initial candidate set;

[0023] Based on the abnormal description information of the abnormal work order, the handlers in the initial candidate set are evaluated and ranked in a multi-dimensional capacity; wherein, the multi-dimensional capacity includes at least the handler's skill matching degree, current task load rate and historical resolution efficiency.

[0024] The processing personnel who rank first in the multidimensional capability ranking are identified as the target processing personnel, and the processing task is pushed to the target processing personnel's terminal device.

[0025] Optionally, if no confirmation response is received from the target handler within a preset time, the handlers ranked in the top N in the multi-dimensional capability assessment and ranking are selected from the initial candidate set to form a priority notification group, where N is an integer dynamically determined based on the abnormality level code and trigger timestamp of the abnormal work order, and N≥2.

[0026] The processing task is simultaneously pushed to the terminal devices of all personnel in the priority notification group;

[0027] Receive order confirmation requests initiated by members of the priority notification group;

[0028] The personnel corresponding to the first received order confirmation request will be reassigned as the target personnel.

[0029] Optionally, in response to the processing task, providing production information related to the current exception context to the target processing personnel via a terminal device includes:

[0030] Based on the anomaly source identifier and physical location data of the abnormal work order, the specific process step and associated equipment where the anomaly occurred are determined.

[0031] Based on the specific process steps and associated equipment, the corresponding standard operating procedures, process parameter configurations, and bill of materials information are retrieved from the manufacturing execution system and pushed to the terminal equipment; wherein, the process parameter configuration includes stencil design parameters, patch program settings, and equipment process parameter tables;

[0032] Simultaneously, based on the anomaly source identifier and keywords in the anomaly description information, matching historical knowledge entries are retrieved from the knowledge base, and the processing solutions contained in the historical knowledge entries are pushed to the terminal device as reference information;

[0033] The process of guiding the target personnel to perform on-site diagnosis and corrective measures step by step according to preset logic, while simultaneously linking and updating the execution data of the diagnosis process and corrective measures to the abnormal work order in real time, includes:

[0034] Based on the retrieved standard operating procedures, process parameter configurations, and bill of materials information, the target processing personnel are guided to sequentially perform parameter comparison diagnosis, configuration verification diagnosis, and material matching verification, and diagnostic data generated in each diagnostic step is collected to generate diagnostic results. Specifically, the parameter comparison diagnosis involves comparing and analyzing the real-time operating parameters of the equipment with the equipment process parameter table; the configuration verification diagnosis involves verifying the consistency of the current patch program settings and stencil design parameters with the standard version; and the material matching verification involves scanning the barcodes of key materials and matching them with the bill of materials information, and linking them to the associated production batch number in the abnormal work order for batch traceability.

[0035] If the diagnostic result matches the diagnostic conclusion recorded in the historical knowledge entry, then the corrective measures recorded in the historical knowledge entry and verified to be effective are invoked and converted into operation instructions and pushed to the terminal device.

[0036] If the diagnostic result does not match the diagnostic conclusion recorded in the historical knowledge entry, the innovation solution recording process is initiated on the terminal device to guide the processing personnel to formulate and implement new corrective measures in accordance with standard operating procedures, and at the same time mark this abnormal handling as a potential source of new knowledge.

[0037] The diagnostic results, corrective actions, parameter adjustment information, and material replacement data recorded during the execution process are integrated to form a structured diagnostic and corrective action report, which is then linked to the abnormal work order for updating and storage.

[0038] Optionally, after confirming that the corrective measures have passed verification, the abnormal work order is closed, and the entire process data of the abnormal work order from creation to closure is structured to generate knowledge entries and stored in a knowledge base, including:

[0039] Based on the parameter adjustment information and material replacement data recorded in the diagnosis and action report, the production status after the corrective action is implemented is tracked and verified. When it is confirmed that the production quality indicators have recovered to the preset standard range, verification result data containing the verification timestamp and the value of the recovered quality indicators is generated, and the status of the abnormal work order is updated to closed.

[0040] Extract the full lifecycle data from closed abnormal work orders, and then perform structured processing on the extracted full lifecycle data according to the preset knowledge base model to generate knowledge entries containing abnormal characteristics, handling solutions, and effect evaluation.

[0041] The knowledge entries are stored in a knowledge base, and a multi-level index based on anomaly characteristics, equipment location, and personnel skills is established for the knowledge entries for subsequent retrieval and reference of anomaly events.

[0042] Optionally, confirming that the production quality indicators have recovered to the preset standard range includes:

[0043] After the corrective measures are implemented, multiple products are continuously tested for quality and the pass rate meets the standard; or the related equipment continues to operate normally for more than the preset time after the corrective measures are implemented; or the relevant process parameters remain stably within the preset standard range after the corrective measures are implemented.

[0044] According to a second aspect of the embodiments of this disclosure, this application provides a PCBA production anomaly management system, which adopts the following technical solution:

[0045] A PCBA production anomaly management system includes:

[0046] The abnormal work order creation module is configured to capture abnormal signals in real time through multiple data sources integrated on the PCBA production line and create abnormal work orders in the manufacturing execution system; wherein, the abnormal work order includes at least: an abnormal source identifier, a trigger timestamp, physical location data, an abnormal level code, an abnormal description information, and an associated production batch number;

[0047] The abnormal task assignment module is configured to match at least one target handler based on the abnormal work order and preset scheduling rules, and push the processing task associated with the abnormal work order to the terminal device of the target handler.

[0048] The on-site diagnosis and processing module is configured to respond to the processing task by providing the target processing personnel with production information related to the current abnormal context through the terminal device, and guiding the target processing personnel to perform on-site diagnosis and corrective measures step by step according to preset logic. At the same time, the execution data of the diagnosis process and corrective measures are correlated in real time and updated to the abnormal work order.

[0049] The abnormal work order management module is configured to close the abnormal work order after confirming that the corrective measures have passed verification, and to perform structured processing on the entire process data of the abnormal work order from creation to closure, generating knowledge entries and storing them in the knowledge base.

[0050] According to a third aspect of the present disclosure, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the PCBA production anomaly management method described in any of the preceding claims.

[0051] In summary, this invention provides a PCBA production anomaly management method, system, and medium. The PCBA production anomaly management method provided by this invention integrates multiple data sources on the production line to capture anomaly signals in real time, uniformly creates anomaly work orders in the manufacturing execution system, and centrally manages the entire process of anomaly data from occurrence to closure, achieving full traceability of anomaly information. Based on preset scheduling rules, it matches target personnel to ensure that anomaly handling tasks are accurately pushed to personnel with the corresponding responsibilities, permissions, and capabilities. During task execution, it pushes anomaly-related production information and historical matching knowledge entries from the knowledge base to the target personnel, providing sufficient reference for on-site diagnosis. It guides personnel to gradually execute on-site diagnosis and corrective measures according to preset logic, ensuring the standardization and normalization of the anomaly handling process and reducing the impact of individual experience differences on the handling effect. By structuring the entire process data of the anomaly work order, generating knowledge entries and storing them in the knowledge base, it forms reusable and iterative knowledge assets. When similar anomalies occur subsequently, matching handling solutions can be directly retrieved from the knowledge base. Thus, it shortens the PCBA production anomaly handling cycle, realizes the accumulation and inheritance of anomaly management experience, and promotes the continuous optimization of PCBA production anomaly management capabilities.

[0052] Other features and advantages disclosed in this invention will be described in detail in the following detailed description section. Attached Figure Description

[0053] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings:

[0054] Figure 1 This is a flowchart (a) illustrating a PCBA production anomaly management method according to an exemplary embodiment.

[0055] Figure 2 This is a flowchart (II) illustrating a PCBA production anomaly management method according to an exemplary embodiment.

[0056] Figure 3 This is a flowchart (III) illustrating a PCBA production anomaly management method according to an exemplary embodiment.

[0057] Figure 4 This is a flowchart (IV) illustrating a PCBA production anomaly management method according to an exemplary embodiment.

[0058] Figure 5 This is a flowchart (V) illustrating a PCBA production anomaly management method according to an exemplary embodiment.

[0059] Figure 6 This is a flowchart (VI) illustrating a PCBA production anomaly management method according to an exemplary embodiment.

[0060] Figure 7 This is a block diagram illustrating a PCBA production anomaly management system according to an exemplary embodiment. Detailed Implementation

[0061] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present disclosure.

[0062] like Figure 1 As shown in the embodiments of this application, the PCBA production anomaly management method includes the following steps:

[0063] Step S101: By integrating multiple data sources on the PCBA production line, abnormal signals are captured in real time, and abnormal work orders are created in the manufacturing execution system; wherein, the abnormal work order includes at least: abnormal source identifier, trigger timestamp, physical location data, abnormal level code, abnormal description information, and associated production batch number.

[0064] Step S102: Based on the abnormal work order and the preset scheduling rules, match at least one target handler and push the processing task associated with the abnormal work order to the target handler's terminal device.

[0065] Step S103: In response to the processing task, provide the target processing personnel with production information related to the current abnormal context through the terminal device, and guide the target processing personnel to perform on-site diagnosis and corrective measures step by step according to the preset logic. At the same time, link the execution data of the diagnosis process and corrective measures in real time and update them to the abnormal work order.

[0066] Step S104: After confirming that the corrective measures have passed verification, close the abnormal work order, and perform structured processing on the entire process data of the abnormal work order from creation to closure, generate knowledge entries and store them in the knowledge base.

[0067] For example, in the exemplary embodiments disclosed in this application, the data source includes at least two of the following: testing equipment, human-machine interface terminal, and environmental monitoring device. Step S101: By integrating multiple data sources on the PCBA production line, abnormal signals are captured in real time, and abnormal work orders are created in the manufacturing execution system, including the following steps: When a signal that meets any of the following preset conditions is captured, it is identified as an abnormal signal, and an abnormal work order is automatically created:

[0068] a) The automatic reporting signal of the test equipment meets the preset triggering rules;

[0069] b) Receives a manual trigger signal from a human-machine interface terminal deployed on the production site;

[0070] c) The environmental parameters represented by the environmental monitoring signals from the environmental monitoring device exceed the preset process range.

[0071] Specifically, in the exemplary embodiments disclosed in this application, the automatic reporting signal of the test equipment includes signals from at least one of the following devices: solder paste inspector, automatic optical inspector, online tester, functional tester, and X-ray inspector; the automatic reporting signal of the test equipment satisfies preset triggering rules, including: the test data reported by the device exceeds a preset quality threshold, or the defect type code reported by the device matches a preset high-priority defect list.

[0072] The automatically reported signals from the testing equipment as defined in this application specifically include the output signals of at least one of the following devices: solder paste inspector (SPI), automated optical inspection (AOI), in-circuit tester (ICT), functional tester (FCT), or X-ray inspection device. These devices correspond to different core inspection stages in PCBA production. Furthermore, this application clarifies the preset triggering rules for the automatically reported signals from the testing equipment; that is, automatic reporting of abnormal signals is only triggered when the inspection data reported by the testing equipment exceeds a preset quality threshold, or when the defect type code reported by the testing equipment matches a preset high-priority defect list.

[0073] For example, in the exemplary embodiments disclosed in this application, the reporting signal triggering rule of the solder paste inspector is: on three consecutive PCBAs, the average thickness of solder paste on specific pads exceeds ±25% of the process specification, or the coverage of the printed area is less than 70%. This triggering rule is used to capture gradual drift in the process and provide early warning before a single obvious defect occurs, preventing batch soldering defects. The reporting signal triggering rule of the automatic optical inspection instrument is: the overall false alarm / defect rate of the same part number component exceeds 2% within 1 hour, or the same defect type occurs 5 times consecutively on a production line. This triggering rule is used to identify systemic problems that may be caused by feeder failure, nozzle wear, or program errors, rather than occasional individual defects. The reporting signal triggering rule of the in-circuit tester is: the test failure rate at the same test point exceeds 30% in 10 consecutive boards, or the measured value of a resistor / capacitor continuously deviates from the nominal value by more than ±30% (even if still within the tolerance). This triggering rule is used to detect batch material problems or poor contact of the equipment probes. The reporting signal trigger rule for the functional tester is: the measured value of a key functional parameter shows a continuous upward or downward trend, or the fluctuation range (standard deviation) exceeds 150% of the historical normal level. This trigger rule is used to detect performance degradation and stability decline, and to provide early warning of potential problems. The reporting signal trigger rule for the X-ray inspection instrument is: the average bubble rate of BGA solder joints exceeds 25%, or the gray value of solder joints at a specific location is continuously lower than the standard curve by 30%. This trigger rule is used to assess the health of the soldering process.

[0074] For example, in the exemplary embodiments disclosed in this application, the solder paste inspector's high-priority defect codes include insufficient solder, bridging, and severe misalignment. The automated optical inspection instrument's high-priority defect codes include incorrect component, reversed polarity, and tombstoning. The in-circuit tester's high-priority defect codes include power supply to ground short circuit and signal network open circuit. The functional tester's high-priority defect codes include core function failure and safety test failure. The X-ray inspection instrument's high-priority defect codes include headrest effect, poor solder joints, and large-area voids.

[0075] Specifically, in the exemplary embodiments disclosed in this application, the manually triggered signal includes at least one of the following signals reported through a human-machine interaction terminal: material abnormality signal, equipment or tooling abnormality signal, appearance or process abnormality signal, and one-click emergency stop call signal; the manually triggered signal is generated by at least one of the following methods: scanning the identification code on the material or equipment to associate it with a specific object, selecting the defect type on the terminal interface, and taking a picture and uploading the on-site image through the terminal.

[0076] The manually triggered signals specified in this application are reported through human-machine interaction terminals, such as desktop industrial PCs and mobile handheld terminals. Specifically, the manually triggered signals include at least one of the following: First, material anomaly signals, used to report various problems with materials required for PCBA production, such as material model mismatch, incoming material damage, pin oxidation, material shortage, and batch quality hazards; second, equipment or tooling fixture anomaly signals, addressing equipment / fixture problems that affect production but are not triggered by automated detection, such as abnormal equipment operation noise, fixture positioning deviation, tooling wear, and auxiliary equipment malfunctions; third, appearance or process anomaly signals, used to report appearance defects or process deviations that are difficult to quantify through automated equipment, such as abnormal solder joint gloss, blurred silkscreen, slight component misalignment without triggering AOI alarms, and hidden cold solder joint risks despite normal soldering process parameters; fourth, a one-click emergency stop call signal, as the highest priority manual signal, used in sudden emergency scenarios, such as equipment jamming, material spillage, and personnel safety hazards, to trigger immediate line stoppage and emergency response.

[0077] To avoid the problems of vague information, unclear objects, and difficulty in traceability in traditional manual reporting, this application designs a standardized signal generation method, specifically including at least one of the following: First, scanning the identification code on materials or equipment to quickly associate abnormal signals with specific objects, eliminating the need for manual input of object information and ensuring accurate association; Second, selecting the defect type on the terminal interface, with the system having a pre-set standardized defect classification menu, allowing operators to complete type labeling by simply clicking, achieving structured entry of abnormal information; Third, taking photos and uploading on-site images through the terminal to intuitively record the abnormal on-site state, providing a visual basis for subsequent personnel to quickly locate problems and formulate handling plans.

[0078] Specifically, in the exemplary embodiments disclosed in this application, the environmental monitoring signal includes signals from at least one of the following environmental monitoring devices: an electrostatic wrist strap monitor, a temperature and humidity sensor, a warehouse environment monitoring device, and a special gas monitoring device; the environmental parameters characterized by the environmental monitoring signal exceed the preset process range, including: the monitored electrostatic protection resistance value, the ambient temperature and humidity value, and the special gas concentration / pressure value continuously exceeding the set safe process range.

[0079] The environmental monitoring signals defined in this application are collected and generated by dedicated monitoring devices deployed on the PCBA production line. Specifically, the environmental monitoring signals originate from at least one of the following dedicated monitoring devices, each corresponding to different key environmental control scenarios in PCBA production: First, electrostatic discharge (ESD) wristband monitors, mainly deployed at core operation stations such as PCBA mounting, soldering, and testing, used to monitor the working status of ESD wristbands worn by operators in real time; the core monitoring parameter is the ESD protection resistance value. Second, temperature and humidity sensors, widely distributed throughout the production workshop, material storage area, and testing laboratory, used to monitor the temperature and humidity in these areas. Third, warehouse environment monitoring equipment, specifically used in the storage area for materials required for PCBA production; in addition to monitoring temperature and humidity, some can also monitor parameters such as cleanliness and vibration of the storage environment to ensure the performance stability of materials during storage. Fourth, special gas monitoring equipment, deployed at workstations using special gases and in gas storage areas, used to monitor the concentration and pressure parameters of special gases, preventing safety risks such as gas leaks and pressure instability, and avoiding impact on the process.

[0080] This application defines the abnormal triggering condition for environmental monitoring signals as the environmental parameters continuously exceeding the preset process range. The emphasis here on "continuously exceeding" rather than "single, instantaneous fluctuations" is to filter out occasional interference from environmental parameters, avoid invalid abnormal alarms, and ensure that triggered abnormal signals are genuine environmental instability issues affecting production.

[0081] For example, such as Figure 2 As shown, in the exemplary embodiment disclosed in this application, step S102: based on the abnormal work order and preset scheduling rules, matching at least one target handler, and pushing the processing task associated with the abnormal work order to the target handler's terminal device, specifically includes the following steps:

[0082] Step S201: Based on the exception source identifier and exception level code in the exception work order, match one or more target roles with the required responsibilities and permissions from the preset scheduling rule database.

[0083] This step uses the anomaly source identifier and anomaly level code in the abnormal work order as the matching basis to filter target roles with the necessary responsibilities and permissions from a pre-set scheduling rule database. The anomaly source identifier directly relates to the specific stage where the anomaly occurred, such as SPI equipment anomaly, material anomaly, or environmental anomaly. The anomaly level code represents the urgency and scope of the anomaly, such as general anomaly, important anomaly, or emergency anomaly. The pre-set scheduling rule database stores the correspondence between anomaly source identifiers, anomaly levels, and responsible roles. By matching the combination of these two fields, the target roles with the appropriate handling authority can be quickly identified.

[0084] Step S202: Select the processors whose roles belong to the target role from all online processors to form an initial candidate set.

[0085] Based on the target role determined in step S201, this step further filters out personnel who are online and whose roles belong to the target role, forming an initial candidate set. Here, online status indicates that the personnel currently have the ability to receive tasks and carry out processing work. This filtering operation eliminates personnel who lack the necessary authority or cannot respond promptly, ensuring that all personnel in the initial candidate set are responsive and authorized potential processors.

[0086] Step S203: Based on the abnormal description information of the abnormal work order, perform multi-dimensional capability assessment and ranking of the handlers in the initial candidate set; wherein, the multi-dimensional capability includes at least the handler's skill matching degree, current task load rate and historical resolution efficiency.

[0087] This step uses the anomaly description information from the abnormal work orders as a basis to conduct a multi-dimensional capability assessment and ranking of the personnel in the initial candidate set to achieve a match between needs and capabilities. The multi-dimensional capability assessment covers at least three core dimensions: First, skill matching (S), which is the degree to which the professional skills of the personnel match the specific anomaly type mentioned in the anomaly description information. For example, when handling AOI component tombstoning defects, the focus is on assessing whether the candidate possesses relevant skills such as surface mount process optimization and AOI equipment parameter debugging. Second, current task load rate (L), which is a comprehensive quantitative value of the number of unfinished tasks currently being executed by the personnel and the urgency of the tasks, used to avoid task processing delays caused by personnel overload assignment. Third, historical resolution efficiency (E), which is historical data such as the average time spent, success rate, and rework rate of personnel in handling similar anomalies in the past, used to screen personnel with richer experience and higher efficiency. To ensure objectivity and standardization in the evaluation, this step employs a pre-defined weighted scoring algorithm to comprehensively quantify and score the three dimensions mentioned above. Specifically, each dimension's indicators are first normalized to a standard score of 0-100. Then, weights are assigned to each dimension based on the specific needs of PCBA production anomaly handling. Skill matching has the highest weight, serving as a prerequisite for matching the target personnel; current task load rate has the second highest weight, ensuring timely response after task assignment; and historical resolution efficiency is used as a supplement to further ensure the quality and efficiency of handling. Based on these weights, the comprehensive score for each candidate is calculated using the formula: Comprehensive Score = Skill Matching Score (S) × Skill Matching Weight + Current Task Load Rate Score (L) × Current Task Load Rate Weight + Historical Resolution Efficiency Score (E) × Historical Resolution Efficiency Weight. Finally, the initial candidates are ranked from highest to lowest based on their comprehensive scores.

[0088] Step S204: Identify the processing personnel who rank first in the multi-dimensional capability ranking as the target processing personnel, and push the processing task to the target processing personnel's terminal device.

[0089] Furthermore, such as Figure 3 As shown, in the exemplary embodiment disclosed in this application, step S102 further includes the following steps:

[0090] Step S205: If no confirmation response is received from the target personnel within the preset time, select the top N personnel in the multi-dimensional capability assessment and ranking from the initial candidate set to form a priority notification group, where N is an integer dynamically determined based on the abnormality level code and trigger timestamp of the abnormal work order, and N≥2.

[0091] Specifically, if no order confirmation response is received from the target handler identified in step S204 within the preset time, this step will be triggered. This step selects the top N handlers ranked in the multi-dimensional capability assessment from the initial candidate set formed in step S203 to form a priority notification group, where N is a dynamic integer and N≥2. Its value is determined by the exception level code and trigger timestamp of the abnormal work order: the higher the exception level, the larger the value of N, ensuring that more backup personnel respond to high-priority exceptions; if the exception is triggered during peak production periods, the value of N can be increased based on the baseline value corresponding to the level, to adapt to scenarios where personnel may be busy simultaneously during peak periods, increasing the probability of the task being quickly accepted.

[0092] Step S206: Simultaneously push the processing task to the terminal devices of all personnel in the priority notification group. This step achieves rapid dissemination of the processing task through synchronous push, ensuring that all backup personnel in the priority notification group can receive the task information at the same time.

[0093] Step S207: Receive order acceptance confirmation requests initiated by members of the priority notification group. This step is the interactive stage of task acceptance. After reviewing the task details, the personnel in the priority notification group can initiate order acceptance confirmation requests through their terminal devices if they have the ability to handle the task immediately.

[0094] Step S208: Reassign the personnel corresponding to the first received order confirmation request as the target personnel. Simultaneously, send a notification to other personnel in the priority notification group who have not yet accepted the order, indicating that the task has been accepted, to avoid wasting resources.

[0095] For example, such as Figure 4 As shown, in the exemplary embodiment disclosed in this application, step S103: in response to the processing task, providing production information related to the current exception context to the target processing personnel through the terminal device, specifically includes the following steps:

[0096] Step S301: Based on the anomaly source identifier and physical location data of the abnormal work order, determine the specific process step and associated equipment where the anomaly occurred.

[0097] In step S301, the anomaly source identifier and physical location data in the abnormal work order are used as dual positioning criteria: the anomaly source identifier directly relates to the source of the anomaly, such as SPI equipment, AOI inspection station, material storage area, etc., which can initially define the major process category to which the anomaly belongs; the physical location data further locates the specific work station where the anomaly occurred and the associated production equipment. Through the collaborative positioning of the two, the specific process link and associated equipment where the anomaly occurred can be clearly identified.

[0098] Step S302: Based on the determined specific process steps and related equipment, retrieve the corresponding standard operating procedures, process parameter configurations, and bill of materials information from the manufacturing execution system and push them to the terminal equipment; among which, the process parameter configurations include stencil design parameters, patch program settings, and equipment process parameter tables.

[0099] In step S302, based on the location results of step S301, standardized production data matching the current anomaly scenario is retrieved from the Manufacturing Execution System (MES): First, Standard Operating Procedures (SOPs), which are standardized operating specifications for corresponding process steps and related equipment, including normal operating procedures, key control points, and preliminary troubleshooting guidelines for common problems; second, process parameter configurations, specifically covering stencil design parameters, chip mounting program settings, and equipment process parameter tables, targeting the core process requirements of PCBA production; and third, Bill of Materials (BOM) information, which includes the material composition, material type, and material assembly location of the production batch associated with the current anomaly. After integrating and packaging these three types of standardized data, they are pushed to the target personnel via terminal devices to ensure that the personnel can view them at any time.

[0100] Step S303: Simultaneously, based on the anomaly source identifier and keywords in the anomaly description information, retrieve matching historical knowledge entries from the knowledge base, and push the processing solutions contained in the historical knowledge entries as reference information to the terminal device.

[0101] This step uses the anomaly source identifier and keywords in the anomaly description information as a search index to match historical knowledge entries of similar anomalies from the knowledge base. It should be noted that the historical processing solutions provided here are for reference only, complementing the standardized materials in step S302. The target personnel can flexibly adapt them to the specific circumstances of the current anomaly.

[0102] For example, such as Figure 5As shown, in the exemplary embodiment disclosed in this application, step S103: guiding the target processing personnel to perform on-site diagnosis and corrective measures step by step according to preset logic, and simultaneously associating the execution data of the diagnosis process and corrective measures in real time and updating them to the abnormal work order, specifically includes the following steps:

[0103] Step S401: Based on the retrieved standard operating procedures, process parameter configurations, and bill of materials information, guide the target processing personnel to sequentially perform parameter comparison diagnosis, configuration verification diagnosis, and material matching verification, and collect the diagnostic data generated by each diagnostic step to generate diagnostic results.

[0104] Among them, parameter comparison diagnosis involves comparing and analyzing the real-time operating parameters of the equipment with the equipment process parameter table; configuration verification diagnosis involves verifying the consistency between the current patch program settings and stencil design parameters and the standard version; and material matching verification involves scanning the barcodes of key materials and matching them with the bill of materials information, and linking them with the associated production batch numbers in the abnormal work orders for batch traceability.

[0105] This step pushes a structured diagnostic task guide to the personnel via the terminal device, guiding them through three diagnostic steps: First, parameter comparison diagnosis, synchronously linking the real-time operating parameters of the relevant equipment and comparing them with the equipment process parameter table to generate a parameter deviation report. Second, configuration verification diagnosis, guiding the personnel to verify whether the current production scenario's placement program settings (such as nozzle model, placement coordinates, placement pressure) and stencil design parameters (such as stencil thickness, aperture size) are consistent with the standard version stored in the Manufacturing Execution System (MES), focusing on checking for issues such as incorrect program modifications or misuse of stencils, and recording the verification results. Third, material matching verification, guiding the personnel to scan the barcodes of key materials at the abnormal workstations (such as solder paste can barcodes, component tray barcodes) using the terminal device, matching and verifying the scanned information with the Bill of Materials (BOM) information, and simultaneously linking the associated production batch number in the abnormal work order to achieve batch traceability of materials and investigate issues such as mismatched material models or non-conforming batches. Throughout the diagnostic process, diagnostic data generated in each step is collected in real time and automatically integrated to generate structured diagnostic results, clarifying the initial scope of the abnormality's cause.

[0106] Step S402: If the diagnosis result matches the diagnosis conclusion recorded in the historical knowledge entry, then the corrective measures recorded in the historical knowledge entry and verified to be effective are invoked and converted into operation instructions and pushed to the terminal device.

[0107] This step matches the diagnostic results generated in step S401 with the diagnostic conclusions recorded in historical knowledge entries in the knowledge base. If a completely matching or highly similar historical diagnostic conclusion exists, it indicates that the cause of the current anomaly is consistent with historical cases. Then, the corrective measures recorded in that historical knowledge entry, which have been proven effective in practice, are invoked and transformed into visual, step-by-step operation instructions, which are then pushed to the terminal device. Personnel handling the target can directly execute these instructions step by step without needing to develop new measures, significantly shortening the handling cycle.

[0108] Step S403: If the diagnosis result does not match the diagnosis conclusion recorded in the historical knowledge entry, the innovation solution recording process is initiated on the terminal device to guide the processing personnel to formulate and implement new corrective measures in accordance with the standard operating procedures, and at the same time mark this abnormal handling as a potential source of new knowledge.

[0109] If the diagnostic result generated in step S401 does not match the diagnostic conclusions of all historical knowledge entries in the knowledge base, it indicates that the current anomaly is rare or novel, and there is no mature historical experience to reuse. In this case, an innovative solution recording process is initiated on the target handler's terminal device: on the one hand, the general handling principles and risk control requirements in the standard operating procedures (SOP) are pushed to the handler, guiding them to formulate new corrective measures within the compliance framework; on the other hand, a structured solution recording template is provided through the terminal device, requiring the target handler to fill in information such as the basis for formulating the new corrective measures, specific operating steps, tools used, and parameter settings. During the target handler's execution of the new corrective measures, the operation process data is recorded in real time, and this anomaly handling is automatically marked as a potential source of new knowledge, laying the foundation for the generation of subsequent knowledge entries.

[0110] Step S404: Integrate the diagnostic results, the corrective actions performed, and the parameter adjustment information and material replacement data recorded during the execution process to form a structured diagnostic and corrective action report, and link the report to the abnormal work order for updating and storage.

[0111] For example, such as Figure 6 As shown, in the exemplary embodiment disclosed in this application, step S104: after confirming that the corrective measures have passed verification, the abnormal work order is closed, and the entire process data of the abnormal work order from creation to closure is structured to generate knowledge entries and stored in the knowledge base, specifically including the following steps:

[0112] Step S501: Based on the parameter adjustment information and material replacement data recorded in the diagnosis and action report, track and verify the production status after the implementation of corrective measures. When it is confirmed that the production quality indicators have recovered to the preset standard range, generate verification result data containing the verification timestamp and the value of the recovered quality indicators, and update the status of the abnormal work order to closed.

[0113] Specifically, in the exemplary embodiments disclosed in this application, confirming that the production quality indicators have been restored to the preset standard range includes: conducting quality tests on multiple products after the implementation of corrective measures and achieving the pass rate, or ensuring that the associated equipment continues to operate normally for more than a preset period of time after the implementation of corrective measures, or ensuring that the relevant process parameters remain stably within the preset standard range after the implementation of corrective measures.

[0114] This application clarifies three categories of quality recovery verification standards that can be applied independently or in combination, covering different scenarios in PCBA production such as equipment anomalies, process anomalies, and material anomalies, ensuring that different types of anomalies can be effectively verified and judged:

[0115] First, the pass rate of continuous product quality inspection meets the standard. For abnormal scenarios that directly affect product quality, such as material defects and process parameter deviations, full-process quality inspection is carried out on multiple products after corrective measures are implemented. When the corresponding quality indicators of all products meet the preset standards and no similar abnormalities are triggered, the quality indicators are judged to have recovered.

[0116] Second, abnormal equipment continues to operate normally for a preset time. For abnormal scenarios involving equipment failure, the operating status of abnormal equipment is tracked and recorded after corrective measures are implemented. When the equipment continues to operate normally for more than the preset time, and there are no fault alarms, no abnormal fluctuations in parameters, and no related abnormal work orders are triggered again during operation, the quality indicators are considered to have recovered.

[0117] Third, the relevant process parameters remain stably within the preset standard range. For abnormal scenarios involving process parameter drift, the key process parameters after corrective measures are adjusted are monitored in real time. When the parameters remain stably within the preset standard range for more than a preset period, and the quality indicators of the corresponding production process are normal, the quality indicators are considered to have recovered.

[0118] When any of the above confirmation criteria are met, the corrective measures are confirmed to be effective. Verification result data, including a verification timestamp and the specific values ​​of the restored quality indicators, is generated and added to the abnormal work order, completing the full closed loop of work order information. Simultaneously, the abnormal work order status is officially updated to "Closed," marking the end of this abnormality handling process. If, during the follow-up verification process, it is found that no confirmation criteria are met, or there is a rebound in quality indicators or a recurrence of similar abnormalities, the work order status is updated to "Invalid Handling," and the backtracking mechanism is automatically triggered, returning to step S102 to re-match the target personnel for secondary handling, ensuring that no abnormalities are left unresolved.

[0119] Step S502: Extract the full lifecycle data from the closed abnormal work orders, and perform structured processing on the extracted full lifecycle data according to the preset knowledge base mode to generate knowledge entries containing abnormal characteristics, handling solutions and effect evaluation.

[0120] In step S502, the complete lifecycle data of the closed abnormal work orders is extracted, specifically including: 1) Abnormal trigger stage data, including abnormal source identifier, trigger timestamp, physical location data, abnormal level code, abnormal description information, and associated production batch number; 2) Scheduling stage data, including target handling personnel information, scheduling process records, task push and response records, etc.; 3) Diagnosis and handling stage data, including diagnosis and action reports, details of executed corrective actions, and handling process time nodes, etc.; 4) Verification and closure stage data, including verification result data and work order closure timestamp, etc. To avoid difficulties in reuse due to fragmented and inconsistent formats of the original data, the above data is structured according to a preset knowledge base data model. The specific steps include: 1) Data cleaning, removing redundant and invalid data and supplementing missing key tags; 2) Data classification and mapping, classifying and mapping the extracted abnormal lifecycle data to three major modules: abnormal characteristics, handling solutions, and effect evaluation, ensuring clear data logic; 3) Language standardization, converting unstructured text data into standardized written language and unifying terminology. After structuring, standardized knowledge entries are generated, comprising three main modules: anomaly characteristics, processing solutions, and effect evaluation.

[0121] Step S503: Store the knowledge entries in the knowledge base and establish a multi-level index for the knowledge entries based on anomaly characteristics, equipment location, and personnel skills for subsequent retrieval and reference of anomaly events.

[0122] In step S503, a multi-level index based on anomaly characteristics, equipment location, and personnel skills is constructed. The keywords of the anomaly characteristic index are based on the attributes of the anomaly, allowing for rapid matching of historical knowledge entries consistent with the current anomaly characteristics. The equipment location index uses scenario-based retrieval, with its keywords corresponding to the physical layout information of the PCBA production line. The keywords of the personnel skills index correspond to the skill tags of the personnel; when historical knowledge entries are retrieved, personnel with the corresponding skills can be matched simultaneously, providing a basis for personnel allocation in subsequent anomaly rectification.

[0123] Figure 7 This is a block diagram illustrating a PCBA production anomaly management system according to an exemplary embodiment, such as... Figure 7 As shown, the system includes: an abnormal work order creation module 601, which is configured to capture abnormal signals in real time through multiple data sources integrated on the PCBA production line and create abnormal work orders in the manufacturing execution system; wherein, the abnormal work order includes at least: an abnormal source identifier, a trigger timestamp, physical location data, an abnormal level code, an abnormal description information, and an associated production batch number;

[0124] The abnormal task assignment module 602 is configured to match at least one target handler based on the abnormal work order and preset scheduling rules, and push the processing task associated with the abnormal work order to the target handler's terminal device.

[0125] The on-site diagnosis and processing module 603 is configured to respond to processing tasks by providing production information related to the current abnormal context to the target personnel through the terminal device, and guiding the target personnel to perform on-site diagnosis and corrective measures step by step according to preset logic. At the same time, the execution data of the diagnosis process and corrective measures are linked in real time and updated to the abnormal work order.

[0126] The abnormal work order management module 604 is configured to close the abnormal work order after confirming that the corrective measures have passed verification, and to perform structured processing on the entire process data of the abnormal work order from creation to closure, generating knowledge entries and storing them in the knowledge base.

[0127] The PCBA production anomaly management system provided by this invention includes an anomaly work order creation module 601 that integrates multi-source data from the production line to achieve real-time perception of anomaly signals and automatic creation of anomaly work orders. The anomaly task assignment module 602 matches the most suitable personnel for handling the issue and pushes the task to them according to preset scheduling rules. The on-site diagnosis and processing module 603 provides production information related to the current anomaly context and guides the target personnel to execute diagnostic and corrective procedures according to preset logic, achieving standardized on-site operations. Finally, the anomaly work order management module 604 closes the work order after verifying the effectiveness of corrective measures and structures the entire process data into knowledge entries, storing them in a knowledge base to provide data support for the rapid resolution of similar problems in the future.

[0128] For example, in an exemplary embodiment of this disclosure, this application also provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the PCBA production anomaly management method as provided in the above embodiments.

[0129] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0130] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0131] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A PCBA production anomaly management method, characterized in that, include: By integrating multiple data sources on the PCBA production line, abnormal signals are captured in real time, and abnormal work orders are created in the manufacturing execution system. The abnormal work order includes at least: an abnormal source identifier, a trigger timestamp, physical location data, an abnormal level code, an abnormal description information, and an associated production batch number. Based on the abnormal work order and the preset scheduling rules, at least one target handler is matched, and the processing task associated with the abnormal work order is pushed to the target handler's terminal device. In response to the processing task, the terminal device provides the target processing personnel with production information related to the current abnormal context, and guides the target processing personnel to perform on-site diagnosis and corrective measures step by step according to preset logic. At the same time, the execution data of the diagnosis process and corrective measures are linked in real time and updated to the abnormal work order. After confirming that the corrective measures have passed verification, the abnormal work order is closed, and the entire process data of the abnormal work order from creation to closure is structured to generate knowledge entries and stored in the knowledge base.

2. The PCBA production anomaly management method according to claim 1, characterized in that, The data source includes at least two of the following: testing equipment, human-computer interaction terminal, and environmental monitoring device; The process involves integrating multiple data sources on the PCBA production line to capture abnormal signals in real time and creating abnormal work orders in the manufacturing execution system, including: When a signal that meets any of the following preset conditions is captured, it is identified as an abnormal signal, and the abnormal work order is automatically created: a) The automatic reporting signal of the test equipment meets the preset triggering rules; b) Receives a manual trigger signal from a human-machine interface terminal deployed on the production site; c) The environmental parameters represented by the environmental monitoring signal from the environmental monitoring device exceed the preset process range.

3. The PCBA production anomaly management method according to claim 2, characterized in that, The automatic reporting signals of the testing equipment include signals from at least one of the following devices: solder paste inspector, automatic optical inspector, online tester, functional tester, and X-ray inspector; the automatic reporting signals of the testing equipment meet preset triggering rules, including: the test data reported by the testing equipment exceeds a preset quality threshold, or the defect type code reported by the testing equipment matches a preset high-priority defect list; The manually triggered signal includes at least one of the following signals reported through the human-machine interaction terminal: material abnormality signal, equipment or tooling abnormality signal, appearance or process abnormality signal, and one-click emergency stop call signal; the manually triggered signal is generated by at least one of the following methods: scanning the identification code on the material or equipment to associate it with a specific object, selecting the defect type on the terminal interface, and taking a picture and uploading the on-site image through the terminal. The environmental monitoring signal includes signals from at least one of the following environmental monitoring devices: electrostatic wrist strap monitor, temperature and humidity sensor, warehouse environment monitoring equipment, and special gas monitoring equipment; the environmental parameters represented by the environmental monitoring signal exceed the preset process range, including: the monitored electrostatic protection resistance value, ambient temperature and humidity value, and special gas concentration / pressure value continuously exceeding the set safety process range.

4. The PCBA production anomaly management method according to claim 1, characterized in that, The step of matching at least one target handler based on the abnormal work order and preset scheduling rules, and pushing the processing task associated with the abnormal work order to the target handler's terminal device, includes: Based on the anomaly source identifier and anomaly level code in the abnormal work order, one or more target roles with responsibilities and permissions are matched from the preset scheduling rule database. Select the processors whose roles belong to the target role from all online processors to form an initial candidate set; Based on the abnormal description information of the abnormal work order, the handlers in the initial candidate set are evaluated and ranked in a multi-dimensional capacity; wherein, the multi-dimensional capacity includes at least the handler's skill matching degree, current task load rate and historical resolution efficiency. The processing personnel who rank first in the multidimensional capability ranking are identified as the target processing personnel, and the processing task is pushed to the target processing personnel's terminal device.

5. The PCBA production anomaly management method according to claim 4, characterized in that, If no confirmation response is received from the target handler within a preset time, the handlers ranked in the top N in the multi-dimensional capability assessment and ranking are selected from the initial candidate set to form a priority notification group, where N is an integer dynamically determined based on the abnormality level code and trigger timestamp of the abnormal work order, and N≥2. The processing task is simultaneously pushed to the terminal devices of all personnel in the priority notification group; Receive order confirmation requests initiated by members of the priority notification group; The personnel corresponding to the first received order confirmation request will be reassigned as the target personnel.

6. The PCBA production anomaly management method according to claim 1, characterized in that, In response to the processing task, the terminal device provides the target processing personnel with production information related to the current exception context, including: Based on the anomaly source identifier and physical location data of the abnormal work order, the specific process step and associated equipment where the anomaly occurred are determined. Based on the specific process steps and associated equipment, the corresponding standard operating procedures, process parameter configurations, and bill of materials information are retrieved from the manufacturing execution system and pushed to the terminal equipment; wherein, the process parameter configuration includes stencil design parameters, patch program settings, and equipment process parameter tables; Simultaneously, based on the anomaly source identifier and keywords in the anomaly description information, matching historical knowledge entries are retrieved from the knowledge base, and the processing solutions contained in the historical knowledge entries are pushed to the terminal device as reference information; The process of guiding the target personnel to perform on-site diagnosis and corrective measures step by step according to preset logic, while simultaneously linking and updating the execution data of the diagnosis process and corrective measures to the abnormal work order in real time, includes: Based on the retrieved standard operating procedures, process parameter configurations, and bill of materials information, the target processing personnel are guided to sequentially perform parameter comparison diagnosis, configuration verification diagnosis, and material matching verification, and diagnostic data generated in each diagnostic step is collected to generate diagnostic results. Specifically, the parameter comparison diagnosis compares and analyzes the real-time operating parameters of the equipment with the equipment process parameter table; the configuration verification diagnosis checks the consistency of the current patch program settings and stencil design parameters with the standard version; and the material matching verification scans the barcodes of key materials and matches them with the bill of materials information, and associates them with the associated production batch number in the abnormal work order for batch traceability. If the diagnostic result matches the diagnostic conclusion recorded in the historical knowledge entry, then the corrective measures recorded in the historical knowledge entry and verified to be effective are invoked and converted into operation instructions and pushed to the terminal device. If the diagnostic result does not match the diagnostic conclusion recorded in the historical knowledge entry, the innovation solution recording process is initiated on the terminal device to guide the processing personnel to formulate and implement new corrective measures in accordance with standard operating procedures, and at the same time mark this abnormal handling as a potential source of new knowledge. The diagnostic results, corrective actions, parameter adjustment information, and material replacement data recorded during the execution process are integrated to form a structured diagnostic and corrective action report, which is then linked to the abnormal work order for updating and storage.

7. The PCBA production anomaly management method according to claim 6, characterized in that, After confirming that the corrective measures have passed verification, the abnormal work order is closed, and the entire process data of the abnormal work order from creation to closure is structured to generate knowledge entries and stored in the knowledge base, including: Based on the parameter adjustment information and material replacement data recorded in the diagnosis and action report, the production status after the corrective action is implemented is tracked and verified. When it is confirmed that the production quality indicators have recovered to the preset standard range, verification result data containing the verification timestamp and the value of the recovered quality indicators is generated, and the status of the abnormal work order is updated to closed. Extract the full lifecycle data from closed abnormal work orders, and then perform structured processing on the extracted full lifecycle data according to the preset knowledge base model to generate knowledge entries containing abnormal characteristics, handling solutions, and effect evaluation. The knowledge entries are stored in a knowledge base, and a multi-level index based on anomaly characteristics, equipment location, and personnel skills is established for the knowledge entries for subsequent retrieval and reference of anomaly events.

8. The PCBA production anomaly management method according to claim 7, characterized in that, The confirmation that production quality indicators have returned to the preset standard range includes: After the corrective measures are implemented, multiple products are continuously tested for quality and the pass rate meets the standard; or the related equipment continues to operate normally for more than the preset time after the corrective measures are implemented; or the relevant process parameters remain stably within the preset standard range after the corrective measures are implemented.

9. A PCBA production anomaly management system, characterized in that, include: The abnormal work order creation module is configured to capture abnormal signals in real time through multiple data sources integrated on the PCBA production line and create abnormal work orders in the manufacturing execution system; wherein, the abnormal work order includes at least: an abnormal source identifier, a trigger timestamp, physical location data, an abnormal level code, an abnormal description information, and an associated production batch number; The abnormal task assignment module is configured to match at least one target handler based on the abnormal work order and preset scheduling rules, and push the processing task associated with the abnormal work order to the terminal device of the target handler. The on-site diagnosis and processing module is configured to respond to the processing task by providing the target processing personnel with production information related to the current abnormal context through the terminal device, and guiding the target processing personnel to perform on-site diagnosis and corrective measures step by step according to preset logic. At the same time, the execution data of the diagnosis process and corrective measures are correlated in real time and updated to the abnormal work order. The abnormal work order management module is configured to close the abnormal work order after confirming that the corrective measures have passed verification, and to perform structured processing on the entire process data of the abnormal work order from creation to closure, generating knowledge entries and storing them in the knowledge base.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the PCBA production anomaly management method as described in any one of claims 1 to 8.

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