PCBA product quality management and optimization system and data transmission method

Abstract

The invention relates to the field of intelligent manufacturing, and discloses a PCBA (Printed Circuit Board Assembly) product quality management and optimization system and a data transmission method, so as to realize safe storage and directional sharing of quality management data. The method comprises the following steps: a cloud server receives and analyzes a request sent by a client, performs Hash calculation based on analysis information of the request, and searches whether ciphertext data A taking a Hash calculation result as a file name exists in cloud storage resources or not; if yes, when the request is forwarded to an edge server deployed in the local of the requested party, carrying a mark that the target quality management data is clouded; sending the ciphertext data A to an edge server; and after receiving the request, the edge server executes waiting processing of the ciphertext data A according to the mark information, decrypts the ciphertext data A after receiving the ciphertext data A, encrypts the ciphertext data A with the public key of the requested party to obtain ciphertext data B, and forwards the ciphertext data B to the client through the cloud server to finally decrypt and output 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 particularly to a PCBA product quality management and optimization system and a data transmission method. Background Art

[0002] With the intensifying competition in the surface mount processing industry in recent years, improving product quality, reducing production costs, and increasing production efficiency have become the consensus in the surface mount industry. Due to the miniaturization, high density, and variety diversification of surface mount products, as well as the highly automated and rapid characteristics of the assembly manufacturing process, the quality inspection and control information is large and complex. Coupled with problems such as delays in quality inspection and analysis, the traditional method of using manual or manual-assisted quality information collection, statistics, analysis, and diagnosis for quality control is difficult to be competent in terms of timeliness, accuracy, stability, etc.

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

[0004] In the existing production preparation process of PCBA (Printed Circuit Board Assembly) products, PCBA manufacturers usually deploy display screens in the production preparation environment to display the production dynamics of each device. Such dynamic information can be called kanban data. However, most production preparation sites usually have multiple production lines, and the software and hardware configurations of each production line and the PCBA tasks undertaken by each quality data monitoring node have relatively broad flexibility. As a result, the existing kanban data does not distinguish PCBA product commission and transfer information, and its main function is limited to allowing internal engineers of PCBA manufacturers to carry out quality management and optimization based on experience. Coupled with factors such as the confidentiality of some mixed multi-party customer information, it restricts the kanban data from being directly shared with the principal (i.e., downstream customers) who are most concerned about quality management data; furthermore, it makes it impossible for downstream customers to timely and reliably understand, evaluate, and supervise the quality management data of PCBA products. Summary of the Invention

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

[0006] To achieve the above object, the data transmission method of the PCBA product quality management and optimization system disclosed by the present invention includes: 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. 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 checks whether there is ciphertext data A with the hash calculation result as the file name in the cloud storage resource. If there is, when forwarding the request to the edge server deployed locally at the requested party, it carries the mark that the target quality management data has been uploaded to the cloud; then it sends the ciphertext data A to the edge server. Step S3: After receiving the request, the edge server performs the waiting process of the ciphertext data A according to the mark information. After receiving the ciphertext data A, it decrypts the ciphertext data A and then encrypts it with the public key of the requested party to obtain ciphertext data B, and then forwards the ciphertext data B to the client through the cloud server. Among them, the plaintext before encrypting the ciphertext data A is the chart data obtained by the edge server through integrating and analyzing the quality data of the local kanbans corresponding to the same process on each production line of the PCBA products of the same party and the same batch based on the MES system after correction or inspection at time intervals; and the algorithm for calculating the hash of the file name of the ciphertext data A determined by the edge server is the same as the algorithm for calculating the hash of the file name by the cloud server based on Step S2. Step S4: The client decrypts the ciphertext data B based on the private key of the requesting party and outputs the target quality management data.

[0007] Preferably, the method of the present invention further 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 resource, when forwarding the request to the edge server deployed locally at the requested party, it carries the mark that the target quality management data has not been uploaded to the cloud. After receiving the request, the edge server queries in the MES system whether there are finished products according to the identity information of the requested party and the batch information of the PCBA products. If there are no finished products, it forwards the status information of un-scheduled production or incorrect query information to the client through the cloud server; if the query result is that there are some finished products, it encrypts the chart data obtained by integrating and analyzing the quality data of the local kanban on the corresponding processes of each production line for this part of the finished products after correction or inspection based on the MES system at time intervals with the public key of the requested party to obtain ciphertext data C, and then forwards the ciphertext data C to the client through the cloud server for the client to decrypt the ciphertext data C based on the private key of the requesting party and output the target quality management data.

[0008] Preferably, the edge server's correction or inspection processing of the quality data of the local kanban on the corresponding processes of the same batch of PCBA products of the same party distributed on each production line based on the MES system (Manufacturing Execution System) specifically includes: Based on the MES system, it is judged one by one whether the objects monitored by the quality data monitoring nodes of the corresponding processes on each production line are the same batch of PCBA products of the same party between the target start and end times. If so, it is confirmed that the kanban data on this production line is qualified for inspection; otherwise, during the process of correcting the kanban data on this production line, the interference noise of other non-target PCBA products is removed; among them, the categories of processes are uniformly named and classified according to the attributes of the corresponding quality data monitoring nodes.

[0009] Preferably, the quality data monitoring nodes are steel mesh printing process optimization nodes, SPI (Solder Paste Inspection) working condition analysis nodes, surface mount placement working condition analysis nodes, AOI (Automated Optical Inspection) working condition analysis nodes, wave soldering process optimization nodes, wave soldering working condition analysis nodes, FCT (Functional Circuit Test) working condition analysis nodes or surface mount production line process analysis and optimization nodes; each of the quality data monitoring nodes separately processes the local kanban data within the corresponding time interval without distinguishing the PCBA product entrustment and transfer information.

[0010] Preferably, the local kanban data includes distribution attributes such as defect rate, defective rate, material throwing rate, output per unit time, and optimization parameters.

[0011] Preferably, the method of the present invention further includes: the edge server regularly obtains the false defect information corresponding to each quality data monitoring node that can be parameter-adjusted from the MES system for the corresponding quality data monitoring node to judge whether to trigger optimization processing.

[0012] Optionally, the identity information of the requested party and the requesting party is the unique full enterprise name and / or unified social credit code.

[0013] 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.

[0014] Preferably, the method of the present invention further includes: during the process of the edge server accessing 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.

[0015] To achieve the above object, the present invention also discloses a PCBA product quality management and optimization system. The networking nodes include a cloud server, a client, an edge server, and a quality data monitoring node; each node respectively includes a memory, a processor, and a computer program stored on the memory and capable of running on the processor. The processors of each node execute the corresponding computer programs to cooperate to implement the above method.

[0016] The present invention can be widely applied to the quality control process of PCBA products with high requirements for quality and traceability, such as new energy vehicles, and has the following beneficial effects: 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 obtained through hash calculation, 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 target quality data based on the file name based on the parsing information in the request, achieving multiple benefits with one action.

[0017] 2. The cloud server performs marking processing on the forwarded request according to the search result, ensuring the reliability of the interaction with the edge server and improving the interaction efficiency.

[0018] 3. The ciphertext stored in the cloud storage resources is obtained by the edge server integrating and analyzing the quality data of the local kanbans corresponding to the same process on each production line of the PCBA products of the same party and the same batch after correction or inspection based on the MES system at time intervals, ensuring the usability, reliability of the data and saving storage resources.

[0019] 4. The present invention adopts a three-level architecture of cloud-edge-end, with clear division of labor among nodes, meeting dynamic expansion requirements, balanced load pressure for data processing, and high cooperation efficiency.

[0020] Next, the present invention will be further described in detail with reference to the accompanying drawings. Description of the Drawings

[0021] The accompanying drawings, which form a part of this application, are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 It is a schematic flowchart of the data transmission method of the PCBA product quality management and optimization system disclosed in Embodiment 1 of the present invention. Detailed implementation manners

[0022] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, the present invention can be implemented in many different ways defined and covered by the claims.

[0023] Embodiment 1 This embodiment discloses a data transmission method of a PCBA product quality management and optimization system.

[0024] As Figure 1 shown, the method of this embodiment includes the following steps: 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.

[0025] In this step, optionally, the identity information of the requested party (usually the PCBA manufacturer) and the requesting party (i.e., the downstream consignor) is the unique enterprise full name and / or unified social credit code; and before this step, the cloud service will first authenticate the identity information of the users (including the requesting party and the PCBA manufacturer party) logging in to the system to avoid waste of system resources by non-associated users such as tourists.

[0026] Preferably, the categories of the processes are uniformly named and classified according to the attributes of the corresponding quality data monitoring nodes. For example: the quality data monitoring nodes can be the steel mesh printing process optimization node, the SPI working condition analysis node, the surface mount placement working condition analysis node, the AOI working condition analysis node, the wave soldering process optimization node, the wave soldering working condition analysis node, the FCT working condition analysis node, or the surface mount production line process analysis and optimization node. Among them, each quality data monitoring node separately performs local kanban data processing within the corresponding time interval without distinguishing the PCBA product consignment and transfer information.

[0027] Furthermore, the local kanban data includes but is not limited to: chart data of types such as defective rate, defect rate, material throwing rate, output per unit time, and distribution attributes of optimization parameters.

[0028] Among them, the defective rate refers to the percentage of the number of products that do not meet the quality standards in the total production quantity. Not meeting the quality standards is usually a relatively broad concept, including various types of defects in the products, as well as situations where although the product functions may be normal, it does not meet the specified requirements in terms of appearance, dimensions, etc. For example, in a batch of circuit boards after a specific process in SMT production, it is found through inspection that some circuit boards have problems such as component mounting position deviation, poor soldering, and short circuits. The ratio of the number of problematic circuit boards to the total production quantity is the defective rate.

[0029] In contrast, the defect rate refers to the ratio of the number of defects in the products to the total number of products. Defects usually refer to specific problems or flaws in the functions, performances, structures, etc. of the products, and focus more on the internal quality problems of the products. For example, a dry joint on a certain solder joint of a circuit board is a defect. Count the number of dry joints and other types of defects on all circuit boards, and then divide by the total number of products to obtain the defect rate. In the specific statistical process, either the total defect rate integrating various defects can be statistically calculated, or the defect rates corresponding to various defects can be separately statistically calculated.

[0030] The material rejection rate refers to the proportion of materials being rejected during the chip placement process due to reasons such as material identification errors, nozzle problems, insufficient positioning accuracy, etc.

[0031] Generally, based on the quality data of a single quality data monitoring node, or two or more related quality data monitoring nodes in the front and back processes, potential faults can be detected in advance and the root causes of the faults can be determined. Then, the parameters causing the root causes of the faults can be optimized and adjusted. For example, this optimization scenario includes but is not limited to: SPI detects an increase in the solder paste bridging rate, AOI does not report short circuits, but X-Ray (an X-ray detector that uses low-energy X-rays to quickly detect defects in PCBA products and can be used as a component of the wave soldering working condition analysis node) discovers internal bridging. Through analysis, the conclusion is that AOI optical detection cannot penetrate the solder paste and relies on the combined detection of SPI and X-Ray; the final optimization measure is: adjust the SPI bridging detection threshold and increase the X-Ray sampling frequency.

[0032] Therefore, in this embodiment, the functions of the above optional quality data monitoring nodes are described separately as follows: The stencil printing process optimization node: used to optimize the stencil printing process. Taking the process parameters as controllable factors, the optimal nominal value of the process parameters is selected by evaluating the stability under the noise factors to improve the product yield rate. The kanban data that can be output includes but is not limited to: the schematic diagram of the distribution attributes of the optimized parameters.

[0033] SPI Working Condition Analysis Node: By determining the defective rate, average number of defects, and SPI defect types in the production process, it monitors whether there are quality abnormalities in the production process; the available kanban data includes but is not limited to: the distribution diagram of the defective rate on the time axis, the distribution diagram of the defect rate on the time axis, and the bar chart of different defect types arranged by size, etc.

[0034] Surface Mounting and SMT Working Condition Analysis Node: By analyzing the types of rejected materials, it determines whether the rejection rate in the SMT production process is stable and monitors whether there are abnormalities in the SMT production process; the available kanban data includes but is not limited to: the distribution diagram of the rejection rate on the time axis, the arrangement diagram of defect types, and the trend chart of the output per unit time, etc.

[0035] AOI Working Condition Analysis Node: By analyzing the defect types, it determines whether the defective rate and the number of defects in the production process are stable and monitors whether there are abnormalities in the production process; the available kanban data includes but is not limited to: the distribution diagram of the defective rate on the time axis, the distribution diagram of the defect rate on the time axis, and the arrangement diagram of defect types, etc.

[0036] Wave Soldering Process Optimization Node: It is used to optimize the wave soldering process. Taking the process parameters of the product as controllable factors, it selects the optimal nominal value of the process parameters by evaluating the stability under the noise factors, and improves the product yield rate; the available kanban data includes but is not limited to: the schematic diagram of the distribution attributes of the optimized parameters.

[0037] Wave Soldering Working Condition Analysis Node: By analyzing the defect types in the wave soldering process, it determines whether the number of defects and the defective rate in the wave soldering process are stable and monitors whether there are abnormalities in the wave soldering process; the available kanban data includes but is not limited to: the distribution diagram of the defective rate on the time axis, the distribution diagram of the defect rate on the time axis, and the arrangement diagram of defect types, etc.

[0038] FCT Working Condition Analysis Node: It is used to determine whether the FCT defective product rate is stable and monitors whether there are abnormalities in the defective product rate; the available kanban data includes but is not limited to: the distribution diagram of the defective rate and rejection rate on the time axis, the trend chart of the output per unit time, etc.

[0039] Surface Mounting Production Line Process Analysis and Optimization Node: Using in-line quality engineering technology to determine the best diagnostic interval of the surface mounting production line, which can be used to determine the best preventive maintenance cycle of production equipment, improve the intact utilization rate of equipment, and reduce the preventive maintenance cost of equipment; the available kanban data includes but is not limited to: the distribution attributes of the maintenance time of the surface mounting production line.

[0040] Furthermore, the quality data monitoring nodes capable of detecting the rejection rate and defect rate can also calculate and process their own rejection rate and defect rate based on the mean, variance, etc. to detect whether the current process is abnormal and generate corresponding alarms. For example, the preset alarm rule is that the sampled data within the current interval exceeds three standard deviations compared to the historical mean, or multiple consecutive points are on the same side of the center line, to assist on-site engineering personnel in quickly identifying fluctuations or abnormal conditions in the production process.

[0041] 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 checks whether there is ciphertext data A with the hash calculation result as the file name in the cloud storage resource; if so, when forwarding the request to the edge server deployed locally at the requested party, carry the mark that the target quality management data has been uploaded to the cloud; then send the ciphertext data A to the edge server.

[0042] In this step, the hash calculation can generate a hexadecimal string. Specifically, the hash calculation algorithm can adopt MD5, SHA-1 or SHA-256; their corresponding hash lengths are 32, 40 or 64 characters respectively. At the same time, the uniqueness of the file name can be ensured through the hash calculation to avoid conflicts.

[0043] Step S3: After receiving the request, the edge server performs waiting processing on the ciphertext data A according to the mark information. After receiving the ciphertext data A, decrypt the ciphertext data A and then encrypt it with the public key of the requested party to obtain ciphertext data B, and then forward the ciphertext data B to the client through the cloud server.

[0044] In this embodiment, the plaintext before encrypting the ciphertext data A is the chart data obtained by the edge server through integrating and analyzing the quality data of the local kanban on the corresponding processes of the PCBA products of the same party and the same batch distributed on each production line based on the MES system correction or inspection at time intervals; and the algorithm for the edge server to determine the file name of the ciphertext data A for hash calculation is the same as the algorithm for the cloud server to calculate the hash of the file name based on step S2.

[0045] Among them, the MES system is used to record and trace a series of complete processes of each PCBA product from the source of each component material to the intermediate series of processing and inspection processes until the final packaging and leaving the factory or being scrapped. During the transfer process of each production line and process, identity recognition can be based on tags, and in the metadata of the respective detection results of each quality detection node, the metadata of each detection result can be mapped one by one with the identity information of the PCBA product, so as to restore the operation status of each production line and each device in the workshop.

[0046] Therefore, the edge server corrects or inspects the quality data of the local kanban corresponding to the same process of the PCBA products of the same Party A and the same batch distributed on each production line based on the MES system, which specifically includes: Based on the MES system, it is determined one by one whether the objects monitored by the quality data monitoring nodes corresponding to the same process on each production line are PCBA products of the same Party A and the same batch between the target start and end times. If so, it is confirmed that the kanban data inspection on this production line is qualified; otherwise, during the process of correcting the kanban data on this production line, the interference noise of other non-target PCBA products is removed. Among them, the start point of the start and end times is the time when the first piece of the series of target PCBA products is monitored by the corresponding monitoring node, and the end point is the time when the last piece of the series of target PCBA products is monitored by this monitoring node. And the subsequent integration and analysis processing at time intervals includes, but is not limited to: summarizing the quality management data of the distributed quality detection nodes with the same function on multiple different production lines of the PCBA products of the same Party A and the same batch within the corresponding time intervals.

[0047] 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.

[0048] Step S4: The client decrypts the ciphertext data B based on the private key of the requesting party and outputs the target quality management data.

[0049] Furthermore, if the cloud server does not find the ciphertext data A with the hash calculation result as the file name in the cloud storage resource, when forwarding the request to the edge server deployed locally at the requested party, it carries the mark that the target quality management data has not been uploaded to the cloud. Correspondingly, after receiving the request, the edge server queries in the MES system whether there are already finished products according to the identity information of the requested party and the batch information of the PCBA products. If there are no finished products, it forwards the status information of the production line scheduling or query information error that has not been scheduled to the client through the cloud server; if the query result is that there are already some finished products, it encrypts the chart data obtained by integrating and analyzing the quality data of the local kanban corresponding to the same process on each production line of this part of the finished products based on the MES system at time intervals with the public key of the requested party to obtain the ciphertext data C, and then forwards the ciphertext data C to the client through the cloud server for the client to decrypt the ciphertext data C based on the private key of the requesting party and output the target quality management data.

[0050] Further, the method of this embodiment further includes: the edge server regularly obtains the false defect information corresponding to each quality data monitoring node that can be parameter - adjusted from the MES system, for the corresponding quality data monitoring node to determine whether to trigger the optimization process. The reasons for generating false defects include, but are not limited to, overly strict threshold settings for detection parameters such as solder paste thickness, area, and volume, resulting in misjudgment of some situations within the normal range but close to the critical value as defective. Based on the MES system, products that are removed from the production line after generating defect or fault alarms and then re - enter the next process of the production line within a set time interval can be screened out as false defects; during 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 as to accurately statistically analyze the false defect rate.

[0051] Further, during the process of the edge server accessing 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; thus, it does not affect the data access efficiency of PCBA manufacturers.

[0052] Embodiment 2 This embodiment discloses a PCBA product quality management and optimization system. The networked nodes include a cloud server, a client, an edge server, and quality data monitoring nodes; each node respectively includes a memory, a processor, and a computer program stored on the memory and capable of running on the processor. The processors of each node execute the corresponding computer programs to jointly implement the method of the above - mentioned embodiment.

[0053] In summary, the PCBA product quality management and optimization system and its data transmission method respectively disclosed in the above two embodiments of the present invention have the following beneficial effects: 1. In cloud storage resources, quality data is stored in ciphertext, ensuring privacy; at the same time, the file name of the ciphertext data A is obtained through hash calculation, which essentially also undergoes an encryption conversion, further enhancing privacy; at the same time, the cloud server can directly perform a fast search for target quality data based on the file name based on the parsing information in the request, achieving multiple benefits with one action.

[0054] 2. The cloud server performs marking processing on the forwarding request according to the search result, ensuring the reliability of the interaction with the edge server and improving the interaction efficiency.

[0055] 3. The ciphertext stored in the cloud storage resources is obtained by the edge server integrating and analyzing the quality data on the local kanbans corresponding to the same - party and same - batch PCBA products distributed on each production line at corresponding processes based on the MES system and correcting or inspecting it at time intervals, ensuring the usability, reliability of the data and saving storage resources.

[0056] 4. The present invention adopts a three - level architecture of cloud - edge - device, with clear division of labor among nodes, meeting dynamic expansion requirements, having balanced load pressure in data processing, and high collaborative efficiency.

[0057] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A data transmission method for a PCBA product quality management and optimization system, characterized in that, Including: 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. 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 checks whether there is ciphertext data A with the hash calculation result as the file name in the cloud storage resource. If so, when forwarding the request to the edge server deployed locally at the requested party, it carries the mark that the target quality management data has been uploaded to the cloud; then it sends the ciphertext data A to the edge server. Step S3: After receiving the request, the edge server performs the waiting process of the ciphertext data A according to the mark information. After receiving the ciphertext data A, it decrypts the ciphertext data A and then encrypts it with the public key of the requested party to obtain ciphertext data B, and then forwards the ciphertext data B to the client through the cloud server. Wherein, the plaintext before encrypting the ciphertext data A is the chart data obtained by the edge server through integrating and analyzing the quality data of the local kanban on the corresponding processes of the same-party and same-batch PCBA products distributed on each production line based on the MES system after correction or inspection at time intervals; and the algorithm for calculating the hash of the file name of the ciphertext data A determined by the edge server is the same as the algorithm for calculating the hash of the file name by the cloud server based on Step S2. Step S4: The client decrypts the ciphertext data B based on the private key of the requesting party and outputs the target quality management data.

2. The data transmission method of the PCBA product quality management and optimization system according to claim 1, wherein Also including: In Step S2, if the cloud server does not find ciphertext data A with the hash calculation result as the file name in the cloud storage resource, when forwarding the request to the edge server deployed locally at the requested party, it carries the mark that the target quality management data has not been uploaded to the cloud. After receiving the request, the edge server checks whether there are already finished products in the MES system according to the identity information of the requested party and the batch information of the PCBA product. If there are no finished products, it forwards the status information of un-scheduled production on the production line or incorrect query information to the client through the cloud server; if the query result is that there are already some finished products, it encrypts the chart data obtained by integrating and analyzing the quality data of the local kanban on the corresponding processes of these finished products on each production line based on the MES system after correction or inspection at time intervals with the public key of the requested party to obtain ciphertext data C, and then forwards the ciphertext data C to the client through the cloud server for the client to decrypt the ciphertext data C based on the private key of the requesting party and output the target quality management data.

3. The data transmission method of the PCBA product quality management and optimization system according to claim 2, characterized in that, The specific process of the edge server correcting or inspecting the quality data of the local kanban on the corresponding processes of the same-party and same-batch PCBA products distributed on each production line based on the MES system includes: Based on the MES system, it is judged one by one whether the objects monitored by the quality data monitoring nodes corresponding to each process on each production line are PCBA products of the same batch from the same customer during the target start and end times. If so, it is confirmed that the kanban data on this production line passes the inspection; otherwise, during the process of correcting the kanban data on this production line, the interference noise of other non-target PCBA products is removed; among them, the categories of processes are uniformly named and classified according to the attributes of the corresponding quality data monitoring nodes.

4. The data transmission method of the PCBA product quality management and optimization system according to claim 3, wherein The quality data monitoring nodes are stencil printing process optimization nodes, SPI working condition analysis nodes, surface mount placement working condition analysis nodes, AOI working condition analysis nodes, wave soldering process optimization nodes, wave soldering working condition analysis nodes, FCT working condition analysis nodes, or surface mount production line process analysis and optimization nodes; each of the quality data monitoring nodes separately performs local kanban data processing within the corresponding time interval without distinguishing PCBA product commissioning and transfer information.

5. The data transmission method of the PCBA product quality management and optimization system according to claim 4, characterized in that, The local kanban data includes defective rate, defect rate, material throwing rate, output per unit time, and distribution attributes of optimization parameters.

6. The data transmission method of the PCBA product quality management and optimization system according to claim 5, characterized in that It also includes: The edge server regularly obtains the false defect information corresponding to each quality data monitoring node that can be parameter-adjusted from the MES system for the corresponding quality data monitoring node to judge whether to trigger optimization processing.

7. The data transmission method of the PCBA product quality management and optimization system according to any one of claims 1 to 6, characterized in that The identity information of the requested party and the requesting party is the unique full enterprise name and / or unified social credit code.

8. The data transmission method of the PCBA product quality management and optimization system according to claim 7, characterized in that After receiving the request, the edge server sends a feedback message to the cloud server; after receiving this feedback message, the cloud server sends the ciphertext data A to the edge server.

9. The data transmission method of the PCBA product quality management and optimization system according to claim 8, characterized in that It also includes: During the process of the edge server accessing the cloud storage resource, the content displayed to the user is the output result after automatically decrypting the ciphertext data A and the corresponding file name.

10. A PCBA product quality management and optimization system, the networking nodes of which include a cloud server, a client, an edge server, and a quality data monitoring node; each node respectively includes a memory, a processor, and a computer program stored on the memory and capable of running on the processor, and is characterized in that, The processors of each node execute the corresponding computer programs to jointly implement the method described in any one of claims 1 to 9 above.

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