PCBA product quality management and optimization system and data transmission method

Abstract

The present invention relates to the field of intelligent manufacturing and discloses a PCBA product quality management and optimization system and a data transmission method to achieve secure storage and targeted sharing of quality management data. The method includes: a cloud server receives and parses a request sent by a client, performs a hash calculation based on the parsed information of the request, and searches the cloud storage resources for ciphertext data A with the hash calculation result as the file name; if so, when forwarding the request to an edge server deployed locally on the requested party, the request is accompanied by a mark indicating that the target quality management data has been uploaded to the cloud; the ciphertext data A is then sent to the edge server; after receiving the request, the edge server performs waiting processing on the ciphertext data A according to the mark information, and after receiving the ciphertext data A, the ciphertext data A is decrypted and then encrypted with the public key of the requested party to obtain ciphertext data B, which is then forwarded to the client via the cloud server to finally decrypt and output the target quality management data.

Description

Technical Field

[0001] The present invention relates to the transmission of digital information in the field of intelligent manufacturing, and in particular to a PCBA product quality management and optimization system and a data transmission method. Background Art

[0002] With the increasing competition in the surface mount chip processing industry in recent years, improving product quality, reducing production costs, and increasing production efficiency have become the consensus of the surface mount industry. Due to the miniaturization, high density, and variety of surface mount products, as well as the highly automated and rapid assembly and manufacturing processes, the amount of information required for quality inspection and control is large and complex. Coupled with the problems of quality inspection and analysis delays, traditional manual or manual-assisted quality information collection, statistics, analysis, and diagnosis methods for quality control are difficult to meet in terms of timeliness, accuracy, and stability.

[0003] Therefore, how to use technologies such as industrial Internet, artificial intelligence, and cloud storage to carry out quality traceability, quality control, quality prediction, and quality improvement has become an inevitable trend in surface mount quality management.

[0004] In the existing PCBA (Printed Circuit Board Assembly) production process, PCBA manufacturers typically deploy display screens within the production environment to display the production status of each device. This dynamic information is known as kanban data. However, most production sites typically have multiple production lines. The software and hardware configurations of each line, as well as the PCBA tasks undertaken by each quality data monitoring node, are quite flexible. As a result, existing kanban data does not distinguish between PCBA product commissioning and flow information. Its primary function is to provide quality management and optimization services to PCBA manufacturers' internal engineers based on their experience. Furthermore, the confidentiality of some mixed customer information restricts the ability to directly share kanban data with the client (i.e., downstream customers) who are most concerned about quality management data. This, in turn, prevents downstream customers from timely and reliably understanding, evaluating, and monitoring PCBA product quality management data. Summary of the Invention

[0005] The present invention aims to disclose a PCBA product quality management and optimization system and a data transmission method to achieve secure storage and targeted sharing of quality management data.

[0006] To achieve the above objectives, the data transmission method of the PCBA product quality management and optimization system disclosed in the present invention includes:

[0007] Step S1: The cloud server receives and parses the request sent by the client, which carries the identity information of the requested party, the identity information of the requesting party, the batch information of the PCBA product, the unique process category information of the target quality management data, and the public key information of the requested party;

[0008] Step S2: The cloud server performs a hash calculation based on the identity information of the requested party, the identity information of the requesting party, the batch information of the PCBA product, and the process category information of the target quality management data, and searches the cloud storage resources for ciphertext data A with the hash calculation result as the file name; if so, when forwarding the request to the edge server deployed locally at the requested party, it carries a mark indicating that the target quality management data has been uploaded to the cloud; and then sends the ciphertext data A to the edge server;

[0009] Step S3: After receiving the request, the edge server performs waiting processing on the ciphertext data A according to the tag information. After receiving the ciphertext data A, the edge server decrypts the ciphertext data A and then encrypts it with the public key of the requested party to obtain the ciphertext data B. The ciphertext data B is then forwarded to the client via the cloud server.

[0010] The plaintext of the ciphertext data A before encryption is the chart data obtained by the edge server based on the quality data of the local kanban on the corresponding process of each production line of the same party A and the same batch of PCBA products, after correction or inspection by the MES system and integration analysis at time intervals; and the algorithm used by the edge server to determine the hash calculation of the file name of the ciphertext data A is consistent with the algorithm used by the cloud server to calculate the hash calculation of the file name based on step S2;

[0011] Step S4: The client decrypts the ciphertext data B based on the private key of the requester and outputs the target quality management data.

[0012] Preferably, the method of the present invention further comprises:

[0013] In step S2, if the cloud server does not find the ciphertext data A with the hash calculation result as the file name in the cloud storage resources, when forwarding the request to the edge server deployed locally at the requested party, it carries a mark indicating that the target quality management data has not been uploaded to the cloud;

[0014] After receiving the request, the edge server queries whether there are finished products in the MES system based on the identity information of the requested party and the batch information of the PCBA product. If there are no finished products, the status information of not being put on the production line or the query information error is forwarded to the client via the cloud server; if the query result is that there are some finished products, the quality data of the local kanban of the corresponding process on each production line of these finished products is corrected or inspected based on the MES system. The chart data obtained is integrated and analyzed at time intervals and encrypted with the public key of the requested party to obtain ciphertext data C, and then the ciphertext data C is forwarded to the client via the cloud server, so that the client can decrypt the ciphertext data C based on the private key of the requester and output the target quality management data.

[0015] Preferably, the edge server corrects or inspects the quality data of the local kanbans of the same batch of PCBA products distributed on the corresponding processes of each production line based on the MES (Manufacturing Execution System) system, specifically including:

[0016] The MES system determines one by one whether the quality data monitoring node of the corresponding process on each production line monitors the same batch of PCBA products from the same party A between the target start and end times. If so, the kanban data on the production line is confirmed to be qualified. Otherwise, when correcting the kanban data on the production line, the interference noise of other non-target PCBA products is removed. The process categories are uniformly named and classified according to the attributes of the corresponding quality data monitoring nodes.

[0017] Preferably, the quality data monitoring node is a steel screen printing process optimization node, an SPI (Solder Paste Inspection) working condition analysis node, a surface mount patch working condition analysis node, an AOI (Automated Optical Inspection) working condition analysis node, a wave soldering process optimization node, a wave soldering working condition analysis node, an FCT (Functional Circuit Test) working condition analysis node or a surface mount production line process analysis and optimization node; each of the quality data monitoring nodes separately executes local kanban data processing within the corresponding time interval without distinguishing between PCBA product entrustment and flow information.

[0018] Preferably, the local kanban data includes defect rate, defect rate, rejection rate, output per unit time and distribution properties of optimization parameters.

[0019] Preferably, the method of the present invention further includes: the edge server regularly obtains false bad information corresponding to each quality data monitoring node that can be adjusted from the MES system, so that the corresponding quality data monitoring node can determine whether to trigger the tuning process.

[0020] Optionally, the identity information of the requested party and the requesting party is a unique full name of the enterprise and / or a unified social credit code.

[0021] Preferably, after receiving the request, the edge server sends a feedback message to the cloud server; after receiving the feedback message, the cloud server sends the ciphertext data A to the edge server.

[0022] Preferably, the method of the present invention further comprises: during the process of accessing the cloud storage resource, the edge server displays to the user the output result of automatically decrypting the ciphertext data A and the corresponding file name.

[0023] To achieve the above objectives, the present invention also discloses a PCBA product quality management and optimization system, in which the networked nodes include a cloud server, a client, an edge server, and a quality data monitoring node; each node includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor of each node executes the corresponding computer program to collaboratively implement the above method.

[0024] The present invention can be widely used in the quality control process of PCBA products with high requirements on quality and traceability, such as new energy vehicles, and has the following beneficial effects:

[0025] 1. In cloud storage resources, quality data is stored in ciphertext to ensure privacy. At the same time, the file name of the ciphertext data A is calculated through hashing, which is essentially equivalent to an encryption conversion, further enhancing privacy. At the same time, the cloud server can directly perform a quick search for the target quality data based on the file name based on the parsed information in the request, achieving multiple goals at one stroke.

[0026] 2. The cloud server marks the forwarded request based on the search results, ensuring the reliability of interaction with the edge server and improving the efficiency of interaction.

[0027] 3. The ciphertext stored in the cloud storage resources comes from the edge server's analysis of the quality data of the local kanban boards of the corresponding processes of the same party A and the same batch of PCBA products distributed on each production line. The data is based on the chart data obtained by integration and analysis at time intervals after correction or inspection by the MES system, ensuring the ease of use and reliability of the data and saving storage resources.

[0028] 4. The present invention adopts a three-level architecture of cloud-edge-end, with clear division of labor between nodes, and meets dynamic expansion needs. The load pressure of data processing is balanced and the collaborative efficiency is high.

[0029] The present invention will be described in further detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0031] Figure 1 This is a flow chart of the data transmission method of the PCBA product quality management and optimization system disclosed in Example 1 of the present invention. DETAILED DESCRIPTION

[0032] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. However, the present invention can be implemented in many different ways as defined and covered by the claims.

[0033] Example 1

[0034] This embodiment discloses a data transmission method for a PCBA product quality management and optimization system.

[0035] like Figure 1 As shown, the method of this embodiment includes the following steps:

[0036] Step S1: The cloud server receives and parses the request sent by the client. The request carries the identity information of the requested party, the identity information of the requesting party, the batch information of the PCBA product, the unique process category information of the target quality management data, and the public key information of the requested party.

[0037] In this step, optionally, the identity information of the requested party (usually the PCBA manufacturer) and the requesting party (i.e., the downstream entrusting party) is a unique full company name and / or a unified social credit code; and before this step, the cloud service will first authenticate the identity information of the user logging into the system (including the requesting party and the PCBA manufacturer) to avoid improper operations by non-related users such as visitors that waste system resources.

[0038] Preferably, process categories are uniformly named and categorized based on the attributes of the corresponding quality data monitoring nodes. For example, a quality data monitoring node could be a stencil printing process optimization node, an SPI condition analysis node, a surface mount assembly condition analysis node, an AOI condition analysis node, a wave soldering process optimization node, a wave soldering condition analysis node, an FCT condition analysis node, or a surface mount production line process analysis and optimization node. Each quality data monitoring node independently performs local dashboard data processing within a corresponding time interval, regardless of PCBA product commissioning and flow information.

[0039] Furthermore, the local dashboard data includes but is not limited to chart data of types such as defect rate, defect rate, rejection rate, output per unit time, and distribution properties of optimization parameters.

[0040] The defect rate refers to the percentage of products that fail to meet quality standards compared to the total production volume. This is a broad term encompassing various types of defects, as well as situations where a product may function correctly but not meet specified requirements in terms of appearance, dimensions, and other aspects. For example, within a batch of SMT circuit boards processed through a specific process, testing revealed that some had issues such as component placement misalignment, poor soldering, and short circuits. The ratio of these defective boards to the total production volume represents the defect rate.

[0041] In contrast, the defect rate refers to the ratio of the number of defects in a product to the total number of products. Defects typically refer to specific issues or flaws in a product's functionality, performance, structure, and other aspects, focusing more on inherent quality issues. For example, a cold solder joint on a circuit board is a defect. The defect rate is calculated by counting the number of cold solder joints and other defects across all circuit boards and dividing it by the total number of products. The defect rate can be calculated by combining all defects or by individually calculating the defect rate for each defect.

[0042] The discard rate refers to the proportion of materials discarded during the patch process due to material identification errors, nozzle problems, insufficient positioning accuracy, etc.

[0043] Typically, quality data from a single quality data monitoring node, or from two or more quality data monitoring nodes linked to previous and subsequent processes, can be used to proactively identify potential faults and determine their root causes, allowing for optimization and adjustment of the parameters responsible. For example, this optimization scenario includes, but is not limited to, the following: SPI detects an increase in solder paste bridging, while AOI reports no short circuits. However, X-Ray (an X-ray inspector that uses low-energy X-rays to rapidly detect defects in PCBA products and can be used as a component of a wave soldering condition analysis node) detects internal bridging. Analysis leads to the conclusion that AOI optical inspection cannot penetrate the solder paste, necessitating combined SPI and X-Ray inspection. The resulting optimization measures include adjusting the SPI bridging detection threshold and increasing the X-Ray inspection frequency.

[0044] To this end, in this embodiment, the optional functions of the above-mentioned quality data monitoring nodes are described as follows:

[0045] Stencil Printing Process Optimization: This node is used to optimize the stencil printing process. Using process parameters as controllable factors, it evaluates stability under noise factors to select optimal nominal values for these parameters, thereby improving product yield. Outputtable data includes, but is not limited to, a diagram of the distribution properties of the optimized parameters.

[0046] SPI working condition analysis node: Monitors the production process for quality anomalies by determining the defect rate, average number of defects, and SPI defect type. Outputtable dashboard data includes, but is not limited to, a timeline distribution graph of the defect rate, a timeline distribution graph of the defect rate, and a bar chart of different defect types arranged by size.

[0047] Surface mount chip working condition analysis node: By analyzing the type of discarded materials, determine whether the discard rate of the patch production process is stable, and monitor whether there are any abnormalities in the patch production process; the output dashboard data includes but is not limited to: the distribution diagram of the discard rate on the time axis, the arrangement diagram of defect types, and the trend diagram of production per unit time.

[0048] AOI working condition analysis node: By analyzing defect types, it determines whether the defect rate and number of defects in the production process are stable, and monitors whether there are any abnormalities in the production process; the output dashboard data includes but is not limited to: the distribution diagram of the defect rate on the time axis, the distribution diagram of the defect rate on the time axis, and the defect type arrangement diagram, etc.

[0049] Wave Soldering Process Optimization Node: This node is used to optimize the wave soldering process. This node uses the product's process parameters as controllable factors and selects the optimal nominal values of these parameters by evaluating their stability under noise factors, thereby improving product yield. Outputtable kanban data includes, but is not limited to, a schematic diagram of the distribution properties of the optimized parameters.

[0050] Wave soldering condition analysis node: By analyzing the defect types of the wave soldering process, determine whether the number of defects and the defective rate of the wave soldering process are stable, and monitor whether there are any abnormalities in the wave soldering process; the outputtable kanban data includes but is not limited to: including the distribution diagram of the defective rate on the time axis, the distribution diagram of the defect rate on the time axis and the defect type arrangement diagram, etc.

[0051] FCT working condition analysis node: used to determine whether the FCT defective rate is stable and monitor whether there are any abnormalities in the defective rate. Outputtable kanban data includes but is not limited to: distribution diagram of defective rate and rejection rate on the time axis, trend diagram of output per unit time, etc.

[0052] Surface mount production line process analysis and optimization nodes: Using in-line quality engineering technology to determine the optimal diagnostic interval for the surface mount production line can be used to determine the optimal preventive maintenance cycle for production equipment, improve equipment availability, and reduce equipment preventive maintenance costs; outputtable kanban data includes but is not limited to: the distribution attributes of surface mount production line maintenance time.

[0053] Furthermore, the above-mentioned quality data monitoring nodes that can be used to detect the defect rate and defect rate can also detect whether the current process is abnormal and generate corresponding alarms based on the calculation and processing of its own defect rate and defect rate based on the mean and variance. For example: the preset alarm rule is that the sampling data in the current interval exceeds three times the standard deviation compared with the historical mean, or multiple consecutive points are located on the same side of the center line, so as to assist on-site engineering personnel to quickly identify fluctuations or abnormalities in the production process.

[0054] Step S2: The cloud server performs a hash calculation based on the identity information of the requested party, the identity information of the requesting party, the batch information of the PCBA product, and the process category information of the target quality management data, and searches the cloud storage resources for ciphertext data A with the hash calculation result as the file name; if so, when forwarding the request to the edge server deployed locally at the requested party, it carries a mark indicating that the target quality management data has been uploaded to the cloud; and then sends the ciphertext data A to the edge server.

[0055] In this step, a hash calculation generates a hexadecimal string. The specific hash calculation algorithm can be MD5, SHA-1, or SHA-256; the corresponding hash length is 32, 40, or 64 characters, respectively. At the same time, the hash calculation ensures the uniqueness of the file name and avoids conflicts.

[0056] Step S3: After receiving the request, the edge server performs waiting processing for the ciphertext data A according to the tag information. After receiving the ciphertext data A, the ciphertext data A is decrypted and then encrypted with the public key of the requested party to obtain the ciphertext data B. The ciphertext data B is then forwarded to the client via the cloud server.

[0057] In this embodiment, the plaintext of the ciphertext data A before encryption is the chart data obtained by the edge server based on the quality data of the local kanban on the corresponding processes of the same party A and the same batch of PCBA products distributed on each production line, which is integrated and analyzed at time intervals after correction or inspection by the MES system; and the algorithm used by the edge server to determine the hash calculation of the file name of the ciphertext data A is consistent with the algorithm used by the cloud server to calculate the hash calculation of the file name based on step S2.

[0058] The MES system is used to record and trace the complete process of each PCBA product, from the source of each component material, through the intermediate series of processing and testing steps, to final packaging, shipment, or disposal. Throughout the flow of each production line and process, identity recognition can be performed based on tags. The metadata of each quality inspection node's test results can be mapped one-to-one with the PCBA product's identity information, thus restoring the actual operating status of each production line and piece of equipment within the workshop.

[0059] Therefore, the edge server corrects or inspects the quality data of the local kanbans of the same batch of PCBA products distributed on each production line based on the MES system, specifically including:

[0060] The MES system determines, one by one, whether the quality data monitoring nodes for the corresponding process on each production line are monitoring the same batch of PCBA products from the same client between the target start and end times. If so, the kanban data on that production line is confirmed to have passed inspection. Otherwise, during the correction of the kanban data on that production line, interference noise from other non-target PCBA products is removed. The start and end times are defined as the time when the corresponding monitoring node detects the first piece of the target PCBA product in the series, and the end times are the time when the monitoring node detects the last piece of the target PCBA product in the series. Subsequent time-based integration and analysis include, but are not limited to, aggregating the quality management data from the same batch of PCBA products from the same client across multiple production lines within the corresponding time interval.

[0061] In the above steps, preferably, after receiving the request, the edge server sends a feedback message to the cloud server; after receiving the feedback message, the cloud server sends the ciphertext data A to the edge server.

[0062] Step S4: The client decrypts the ciphertext data B based on the private key of the requester and outputs the target quality management data.

[0063] Furthermore, if the cloud server does not find ciphertext data A with a hash calculation result as the file name in the cloud storage resources, when forwarding the request to the edge server deployed locally on the requested party, it carries a mark indicating that the target quality management data has not been uploaded to the cloud. Correspondingly, after receiving the request, the edge server queries the MES system based on the identity information of the requested party and the batch information of the PCBA product to see if there are any finished products. If there are no finished products, the status information indicating that the product has not been scheduled for production line or the query information is incorrect is forwarded to the client via the cloud server. If the query result shows that some finished products are available, the quality data of the local kanban board of the corresponding process on each production line for these finished products is integrated and analyzed at time intervals based on the quality data obtained after correction or inspection by the MES system. The chart data is encrypted with the public key of the requested party to obtain ciphertext data C, which is then forwarded to the client via the cloud server. The client then decrypts the ciphertext data C based on the private key of the requester and outputs the target quality management data.

[0064] Furthermore, the method of this embodiment also includes: the edge server regularly obtains the false defect information corresponding to each quality data monitoring node that can be adjusted from the MES system, so that the corresponding quality data monitoring node can determine whether to trigger the optimization process. The reasons for the false defects include but are not limited to: the thresholds of the detection parameters such as solder paste thickness, area, and volume are set too strictly, resulting in some situations that are within the normal range but close to the critical value being mistakenly judged as defective. Based on the MES system, products that have been removed from the production line after generating defective or defective alarms and then re-entered the next process of the production line within a set time interval can be screened out as false defects; in this process, false defects can be identified and corrected through artificial intelligence means such as machine vision or manual methods, and the correction process is recorded in the log, so that accurate statistical analysis of the false defect rate can be performed.

[0065] Furthermore, when the edge server accesses cloud storage resources, the content displayed to the user is the output result of automatically decrypting the ciphertext data A and the corresponding file name; thereby, the access efficiency of the PCBA manufacturer to the data is not affected.

[0066] Example 2

[0067] This embodiment discloses a PCBA product quality management and optimization system. The networked nodes include a cloud server, a client, an edge server, and a quality data monitoring node. Each node includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The processor of each node executes the corresponding computer program to collaboratively implement the method of the above embodiment.

[0068] In summary, the PCBA product quality management and optimization system and data transmission method disclosed in the above two embodiments of the present invention respectively have the following beneficial effects:

[0069] 1. In cloud storage resources, quality data is stored in ciphertext to ensure privacy. At the same time, the file name of the ciphertext data A is calculated through hashing, which is essentially equivalent to an encryption conversion, further enhancing privacy. At the same time, the cloud server can directly perform a quick search for the target quality data based on the file name based on the parsed information in the request, achieving multiple goals at one stroke.

[0070] 2. The cloud server marks the forwarded request based on the search results, ensuring the reliability of interaction with the edge server and improving the efficiency of interaction.

[0071] 3. The ciphertext stored in the cloud storage resources comes from the edge server's analysis of the quality data of the local kanban boards of the corresponding processes of the same party A and the same batch of PCBA products distributed on each production line. The data is based on the chart data obtained by integration and analysis at time intervals after correction or inspection by the MES system, ensuring the ease of use and reliability of the data and saving storage resources.

[0072] 4. The present invention adopts a three-level architecture of cloud-edge-end, with clear division of labor between nodes, and meets dynamic expansion needs. The load pressure of data processing is balanced and the collaborative efficiency is high.

[0073] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A data transmission method for a PCBA product quality management and optimization system, characterized in that: include: Step S1: The cloud server receives and parses the request sent by the client, which carries the identity information of the requested party, the identity information of the requesting party, the batch information of the PCBA product, the unique process category information of the target quality management data, and the public key information of the requested party; Step S2: The cloud server performs a hash calculation based on the identity information of the requested party, the identity information of the requesting party, the batch information of the PCBA product, and the process category information of the target quality management data, and searches the cloud storage resources for ciphertext data A with the hash calculation result as the file name; if so, when forwarding the request to the edge server deployed locally at the requested party, it carries a mark indicating that the target quality management data has been uploaded to the cloud; and then sends the ciphertext data A to the edge server; Step S3: After receiving the request, the edge server performs waiting processing on the ciphertext data A according to the tag information. After receiving the ciphertext data A, the edge server decrypts the ciphertext data A and then encrypts it with the public key of the requested party to obtain the ciphertext data B. The ciphertext data B is then forwarded to the client via the cloud server. The plaintext of the ciphertext data A before encryption is the chart data obtained by the edge server based on the quality data of the local kanban on the corresponding process of each production line of the same party A and the same batch of PCBA products, after correction or inspection by the MES system and integration analysis at time intervals; and the algorithm used by the edge server to determine the hash calculation of the file name of the ciphertext data A is consistent with the algorithm used by the cloud server to calculate the hash calculation of the file name based on step S2; Step S4: the client decrypts the ciphertext data B based on the private key of the requester and outputs the target quality management data, so that the client of the PCBA product can understand, evaluate and supervise the quality management data of the PCBA product; The edge server corrects or inspects the quality data of the local kanbans of the same batch of PCBA products distributed on the corresponding processes of each production line based on the MES system, specifically including: The MES system determines one by one whether the quality data monitoring node of the corresponding process on each production line monitors the same batch of PCBA products from the same party A between the target start and end times. If so, the kanban data on the production line is confirmed to have passed the inspection. Otherwise, when correcting the kanban data on the production line, the interference noise of other non-target PCBA products is removed. The process categories are uniformly named and classified based on the attributes of the corresponding quality data monitoring nodes. The quality data monitoring node is a steel screen printing process optimization node, an SPI working condition analysis node, a surface mount chip working condition analysis node, an AOI working condition analysis node, a wave soldering process optimization node, a wave soldering working condition analysis node, an FCT working condition analysis node or a surface mount production line process analysis and optimization node; each of the quality data monitoring nodes separately executes local kanban data processing within a corresponding time interval without distinguishing between PCBA product entrustment and flow information.

2. The data transmission method of the PCBA product quality management and optimization system according to claim 1, characterized in that: Also includes: In step S2, if the cloud server does not find the ciphertext data A with the hash calculation result as the file name in the cloud storage resources, when forwarding the request to the edge server deployed locally at the requested party, it carries a mark indicating that the target quality management data has not been uploaded to the cloud; After receiving the request, the edge server queries whether there are finished products in the MES system based on the identity information of the requested party and the batch information of the PCBA product. If there are no finished products, the status information of not being put on the production line or the query information error is forwarded to the client via the cloud server; if the query result is that there are some finished products, the quality data of the local kanban of the corresponding process on each production line of these finished products is corrected or inspected based on the MES system. The chart data obtained is integrated and analyzed at time intervals and encrypted with the public key of the requested party to obtain ciphertext data C, and then the ciphertext data C is forwarded to the client via the cloud server, so that the client can decrypt the ciphertext data C based on the private key of the requester and output the target quality management data.

3. The data transmission method of the PCBA product quality management and optimization system according to claim 1, characterized in that: The local kanban data includes the defect rate, defect rate, rejection rate, output per unit time and distribution properties of optimization parameters.

4. The data transmission method of the PCBA product quality management and optimization system according to claim 3, characterized in that: Also includes: The edge server regularly obtains false bad information corresponding to each quality data monitoring node that can be adjusted from the MES system, so that the corresponding quality data monitoring node can determine whether to trigger the adjustment process.

5. The data transmission method of the PCBA product quality management and optimization system according to any one of claims 1 to 4, characterized in that: The identity information of the requested party and the requesting party is the unique full name of the enterprise and / or the unified social credit code.

6. The data transmission method of the PCBA product quality management and optimization system according to claim 5, characterized in that: After receiving the request, the edge server sends a feedback message to the cloud server; after receiving the feedback message, the cloud server sends the ciphertext data A to the edge server.

7. The data transmission method of the PCBA product quality management and optimization system according to claim 6, characterized in that: Also includes: When the edge server accesses the cloud storage resources, the content displayed to the user is the output result of automatically decrypting the ciphertext data A and the corresponding file name.

8. A PCBA product quality management and optimization system, wherein the network nodes include a cloud server, a client, an edge server, and a quality data monitoring node; each node includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: The processor of each node executes the corresponding computer program to collaboratively implement the method described in any one of claims 1 to 7.

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