Assembly fault backtracking method and system

Abstract

The present invention discloses an assembly fault backtracking method and system, which belongs to the field of assembly fault backtracking, and solves the problems of how to avoid assembly anomalies that are difficult to find due to the influence of environmental conditions and negligence of manual naked eye observation, how to avoid assembly information from being tampered with or lost, and how to quickly trace back to assembly anomalies; an information acquisition module collects instrument assembly information and video image information in each assembly process, and an anomaly analysis module analyzes the acquired instrument assembly information. If the component material number in the acquired instrument assembly information completely matches the component material number in the corresponding component BOM table, an assembly warning coefficient is calculated, and whether the assembly of the corresponding product is abnormal is judged according to the assembly warning coefficient; then the corresponding instrument assembly related information is obtained; a blockchain update module generates a new block with the instrument assembly related information and video image information metadata acquired in the database, and then updates the blockchain information of the corresponding product.

Description

Technical Field

[0001] The invention belongs to the field of assembly fault backtracking, and in particular relates to an assembly fault backtracking method and system. Background Art

[0002] Instrument automation production line refers to the production line that uses automation technology and equipment to automatically assemble and produce instrument products, which also involves certain manual operations. Instrument assembly includes electronic assembly and mechanical assembly. Electronic assembly generally refers to SMT patch to obtain circuit board. The production of circuit board and patch can be outsourced. Mechanical assembly generally refers to the assembly of various parts or components according to the production process.

[0003] At present, the assembly of instrument products, especially mechanical assembly, is generally carried out by combining automated equipment and manual work. During the assembly process, machine vision technology is used for detection and manual observation. Machine vision technology is sensitive to environmental conditions and will be affected by factors such as light, shadow, and dust, while manual observation will inevitably lead to negligence. In addition, when the performance indicators of instrument products are found to be substandard during the later stage of testing, in addition to eliminating the problem of incoming materials, it is necessary to trace back whether the assembly problem is abnormal. If the previous assembly information is tampered with or lost, the assembly problem cannot be traced back. Although the relevant assembly information can be traced back, due to the large number of assembly processes, it is impossible to quickly locate the problem of the corresponding assembly process. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes an assembly fault backtracking method and system, which solves the problems of how to avoid assembly anomalies that are difficult to detect due to the influence of environmental conditions and negligence of human visual observation, how to avoid assembly information from being tampered with or lost, and how to quickly trace back to assembly anomalies.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] An assembly fault backtracking system, comprising: an information collection module, an abnormality analysis module and a blockchain update module;

[0007] The information acquisition module is used to collect instrument assembly information and video image information in each assembly process on the instrument automation production line, and to tag the collected instrument assembly information with the corresponding product unique identifier label and the corresponding assembly process label and send it to the abnormality analysis module, and to tag the collected video image information with the corresponding product unique identifier label and the corresponding assembly process label and upload it to the database for storage;

[0008] The abnormality analysis module is used to analyze the obtained instrument assembly information, compare the component material number in the obtained instrument assembly information with the component material number in the corresponding component BOM table to see whether they completely match. If they completely match, the assembly warning coefficient is further calculated according to the instrument assembly information, and whether the assembly of the corresponding product is abnormal is determined according to the calculated assembly warning coefficient; the assembly warning coefficient and the assembly abnormality judgment result are associated with the obtained instrument assembly information, and the instrument assembly association information of the instrument product corresponding to the corresponding product unique identifier in the corresponding assembly process is obtained, and the obtained instrument assembly association information is sent to the blockchain update module;

[0009] The blockchain update module is used to generate a new block based on the metadata of the instrument assembly related information and video image information of the corresponding product unique identifier obtained in real time in the database corresponding to the instrument product in the corresponding assembly process, and then update the blockchain information for the corresponding product unique identifier in the database.

[0010] Furthermore, the assembly process includes: housing assembly, PCB assembly and cable assembly.

[0011] Furthermore, the corresponding product unique identifier and the instrument assembly information of the corresponding assembly process include the product model, all component material numbers used in the current assembly process, the number of components corresponding to different component material numbers, the start assembly time, the end assembly time, and whether the assembly result is completed.

[0012] Furthermore, the analysis process of the abnormality analysis module includes:

[0013] Obtaining instrument assembly information labeled with a corresponding product unique identifier and a corresponding assembly process label;

[0014] Extract the component BOM table and the preset average assembly time Tk of the same product model in the corresponding assembly process from the database according to the product model of the instrument assembly information; k represents the serial number of the corresponding assembly process of the corresponding product model, and k is an integer greater than 0;

[0015] The component BOM table includes the component material number required for the corresponding assembly process and the number of components corresponding to the corresponding component material number Nki; i represents the sequence number of the component material number in the corresponding assembly process, i=1, 2...n; n represents the total number of the component material number of the corresponding assembly process;

[0016] Match the corresponding product model and all component material numbers of the corresponding assembly process extracted from the component BOM table with all component material numbers used in the instrument assembly information of the corresponding instrument product in the corresponding assembly process corresponding to the corresponding product unique identifier obtained from the information acquisition module;

[0017] If all component numbers in the current component BOM are not exactly the same as all component numbers included in the current instrument assembly information, the assembly instrument information is marked as abnormal, and an early warning message of component assembly abnormality is generated and sent to the background management module with the corresponding product unique identifier label and the corresponding assembly process label; the relevant staff will handle it in a timely manner through the background management module; and associate the assembly abnormality judgment result with the corresponding instrument assembly information, obtain the instrument assembly association information of the instrument product corresponding to the corresponding product unique identifier in the corresponding assembly process, and send the obtained instrument assembly association information to the blockchain update module;

[0018] If all component material numbers in the current component BOM table are exactly the same as all component material numbers contained in the current instrument assembly information, then further calculate the assembly warning coefficient Zk of the instrument product corresponding to the corresponding product unique identifier in the corresponding assembly process;

[0019] The calculated assembly warning coefficient Zk is compared with the assembly warning coefficient limit value ZYk to determine whether the assembly of the instrument product corresponding to the corresponding product unique identifier is abnormal in the corresponding assembly process, and the judgment result is sent to the background management module. The relevant staff promptly handles the instrument product corresponding to the corresponding product unique identifier with abnormal assembly through the background management module; and the assembly warning coefficient and the assembly abnormality judgment result are associated with the corresponding instrument assembly information to obtain the instrument assembly association information of the instrument product corresponding to the corresponding product unique identifier in the corresponding assembly process, and the obtained instrument assembly association information is sent to the blockchain update module;

[0020] If Zk<ZYk, it means that the instrument product corresponding to the corresponding product unique identifier is assembled without abnormality in the corresponding assembly process;

[0021] If Zk≥ZYk, it means that the instrument product corresponding to the corresponding product unique identifier has an assembly abnormality in the corresponding assembly process.

[0022] Furthermore, the calculation formula of the assembly warning coefficient Zk is as follows:

[0023]

[0024] Wherein, NSki represents the number of components with component number i used in the assembly process with serial number k by the instrument product corresponding to the corresponding product unique identifier; TIk and TOk represent the start assembly time and the end assembly time of the instrument product corresponding to the corresponding product unique identifier in the assembly process with serial number k, respectively; Wk represents the assembly result of the instrument product corresponding to the corresponding product unique identifier in the assembly process with serial number k; Wk takes the value of 0 or 1, when Wk=0, it means that the assembly is completed, when Wk=1, it means that the assembly is not completed; a1, a2 and a3 represent the preset proportional coefficients of the difference in component quantity, the difference in assembly time and the assembly result, respectively, and a1>a3>a2>0, a1+a2+a3=1.

[0025] Furthermore, the update method of the blockchain update module includes:

[0026] Real-time monitoring of new instrument assembly related information and new video images obtained from the database;

[0027] Screening new instrument assembly-related information and new video images belonging to the same product unique identifier and the same assembly process; and extracting metadata of the screened new instrument assembly-related information and new video images;

[0028] Calculate a hash value based on the filtered instrument assembly association information and metadata of the corresponding video image;

[0029] Match the product unique identifiers corresponding to the screened instrument assembly association information and video image metadata with all product unique identifiers containing blockchain information in the database, and obtain the hash value of the previous block of the blockchain corresponding to the instrument product with the same product unique identifier;

[0030] A new block is constructed according to the calculated hash value, the hash value of the previous block obtained by matching from the database, the corresponding instrument assembly association information, and the metadata of the corresponding video image;

[0031] The newly constructed block is connected to the previous block of the corresponding product unique identifier in the database, thereby realizing real-time update of the blockchain of the instrument product corresponding to the corresponding product unique identifier in the database.

[0032] Furthermore, the metadata of the video image includes shooting start time, shooting end time, shooting equipment, shooting geographical location and storage location.

[0033] Furthermore, an assembly fault backtracking method, based on the assembly fault backtracking system, comprises:

[0034] The information collection module collects the instrument assembly information and video image information in each assembly process on the instrument automation production line, and the collected instrument assembly information is marked with the corresponding product unique identifier label and the corresponding assembly process label and sent to the abnormality analysis module, and the collected video image information is marked with the corresponding product unique identifier label and the corresponding assembly process label and uploaded to the database for storage;

[0035] The acquired instrument assembly information is analyzed through the abnormality analysis module, and the component material number in the acquired instrument assembly information is compared with the component material number in the corresponding component BOM table to see whether they completely match. If they completely match, the assembly warning coefficient is further calculated according to the instrument assembly information, and whether the assembly of the corresponding product is abnormal is judged according to the calculated assembly warning coefficient; the assembly warning coefficient and the assembly abnormality judgment result are associated with the acquired instrument assembly information, and the instrument assembly association information of the instrument product corresponding to the corresponding product unique identifier in the corresponding assembly process is obtained, and the acquired instrument assembly association information is sent to the blockchain update module;

[0036] The blockchain update module generates a new block by corresponding the metadata of the instrument assembly-related information and video image information of the corresponding product unique identifier obtained in real time in the database to the instrument product in the corresponding assembly process, and then updates the blockchain information for the corresponding product unique identifier in the database.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. In the present invention, the instrument assembly information and video image information of each instrument product in each assembly process are collected in real time by the information collection module, so as to lay a data foundation for subsequent data analysis. In addition to controlling the quality of incoming materials, the performance indicators of instrument products will also be triggered by assembly abnormalities. Therefore, the instrument assembly information of the instrument products in the corresponding assembly process corresponding to the unique identifiers of each product collected in real time is analyzed by the abnormality analysis module. The specific method is to compare whether the component material number in the obtained instrument assembly information is completely matched with the component material number in the corresponding component BOM table. If they are completely matched, the assembly warning coefficient is further calculated according to the instrument assembly information, and whether the assembly of the corresponding product is abnormal is judged according to the assembly warning coefficient obtained by calculation. The assembly warning coefficient is used to further analyze the assembly abnormality from the perspective of whether the amount of each component is the same, the assembly time and whether the assembly is completed. It can quickly judge whether the assembly is abnormal to a certain extent, and then issue a warning in time. It helps to prevent the further expansion of potential problems and avoids the difficulty of finding assembly abnormalities due to the influence of environmental conditions and the negligence of human observation.

[0039] 2. In the present invention, the blockchain of the instrument product corresponding to the unique identifier of the corresponding product is established through the blockchain update module for the instrument assembly information, assembly warning coefficient, assembly abnormality judgment result and metadata of the video image of the instrument product. Due to the distributed and encrypted nature of the blockchain, the integrity, credibility, non-tamperability and non-loss of the relevant assembly information of the instrument product corresponding to the unique identifier of the corresponding product are ensured; in addition, when the later detection or calibration finds that the relevant performance indicators cannot be passed or are abnormal, the assembly problem can be traced back through the blockchain information after excluding the abnormality of the incoming materials, and each assembly process can be traced back, including viewing the instrument assembly information and viewing the corresponding video images; wherein each block of the blockchain information involving assembly stores the assembly warning coefficient and the assembly abnormality judgment result, so that the technician can quickly locate the abnormal assembly process; the blockchain update module can update the assembly information of all instrument products in real time, so that the monitoring of the entire assembly process is more timely, which helps to discover problems in assembly as early as possible and improve the efficiency of the assembly process. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A block diagram of an assembly fault backtracking system of the present invention;

[0041] Figure 2 The present invention is a flow chart of an assembly fault backtracking method. DETAILED DESCRIPTION

[0042] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] like Figure 1 As shown, an assembly fault backtracking system includes: an information collection module, an abnormality analysis module, a blockchain update module, a database, and a background management module;

[0044] The information acquisition module is used to collect instrument assembly information and video image information in each assembly process on the instrument automation production line, and to tag the collected instrument assembly information with the corresponding product unique identifier label and the corresponding assembly process label and send it to the abnormality analysis module, and to tag the collected video image information with the corresponding product unique identifier label and the corresponding assembly process label and upload it to the database for storage;

[0045] In this application, the assembly processes on the instrument automation production line include: housing assembly, PCB assembly, cable assembly, etc.;

[0046] The unique identifier of the corresponding product and the instrument assembly information of the corresponding assembly process include the product model, all component numbers used in the current assembly process, the number of components corresponding to different component numbers, the start assembly time, the end assembly time, and whether the assembly result is completed;

[0047] Among them, the product model is generally customized by the customer, or it can be independently developed by the enterprise. Different product models correspond to different instrument types and instrument indicators. The product unique identifier is a number that uniquely refers to a product. It can be understood that from the production to the use of the instrument product, as long as the product unique identifier is obtained, all information of the corresponding instrument product can be located; the component part number used and the number of components corresponding to different component part numbers are determined according to different product models and different assembly processes. For example, the shell assembly process includes the shell component part number and the number of components corresponding to different shell component part numbers;

[0048] Optionally, the instrument assembly information can be obtained by a scanning instrument and a computer set in the assembly area of ​​the corresponding assembly process. The specific scanning method is set in combination with different assembly equipment. If there is manual assembly, it is also necessary to record it by a scanning instrument combined with a microcomputer; the method of scanning a bar code or a QR code is a prior art and will not be described in detail here; it should be noted that the information collection module sends the instrument assembly information of an instrument product in the corresponding assembly process to the abnormality analysis module in real time after collecting it, so as to facilitate timely analysis of whether the assembly of the corresponding instrument product is abnormal;

[0049] Optionally, the video image information of each assembly process is obtained by a high-definition camera set at a corresponding position of each assembly process; after the information acquisition module completes the acquisition of the video image information of an instrument product in the corresponding assembly process, it is sent to the database in real time for storage;

[0050] In the present invention, the information collection module collects the instrument assembly information and video image information of each instrument product in each assembly process in real time, laying a data foundation for subsequent data analysis;

[0051] The abnormality analysis module is used to analyze the obtained instrument assembly information, compare the component material number in the obtained instrument assembly information with the component material number in the corresponding component BOM table to see whether they completely match. If they completely match, the assembly warning coefficient is further calculated according to the instrument assembly information, and whether the assembly of the corresponding product is abnormal is determined according to the calculated assembly warning coefficient; the assembly warning coefficient and the assembly abnormality judgment result are associated with the obtained instrument assembly information, and the instrument assembly association information of the instrument product corresponding to the corresponding product unique identifier in the corresponding assembly process is obtained, and the obtained instrument assembly association information is sent to the blockchain update module; including:

[0052] Obtaining instrument assembly information labeled with a corresponding product unique identifier and a corresponding assembly process label;

[0053] Extract the component BOM table and the preset average assembly time Tk of the same product model in the corresponding assembly process from the database according to the product model of the instrument assembly information; k represents the serial number of the corresponding assembly process of the corresponding product model, and k is an integer greater than 0;

[0054] The component BOM table includes the component material number required for the corresponding assembly process and the number of components corresponding to the corresponding component material number Nki; i represents the sequence number of the component material number in the corresponding assembly process, i=1, 2...n; n represents the total number of the component material number of the corresponding assembly process;

[0055] Match the corresponding product model and all component material numbers of the corresponding assembly process extracted from the component BOM table with all component material numbers used in the instrument assembly information of the corresponding instrument product in the corresponding assembly process corresponding to the corresponding product unique identifier obtained from the information acquisition module;

[0056] If all component numbers in the current component BOM are not exactly the same as all component numbers included in the current instrument assembly information, the assembly instrument information is marked as abnormal, and an early warning message of component assembly abnormality is generated and sent to the background management module with the corresponding product unique identifier label and the corresponding assembly process label; the relevant staff will handle it in a timely manner through the background management module; and associate the assembly abnormality judgment result with the corresponding instrument assembly information, obtain the instrument assembly association information of the instrument product corresponding to the corresponding product unique identifier in the corresponding assembly process, and send the obtained instrument assembly association information to the blockchain update module;

[0057] If all component numbers in the current component BOM are exactly the same as all component numbers included in the current instrument assembly information, then the assembly warning coefficient Zk of the instrument product corresponding to the corresponding product unique identifier in the corresponding assembly process is further calculated; the calculation formula is as follows:

[0058]

[0059] In the formula, NSki represents the number of components with component number i used in the assembly process with serial number k by the instrument product corresponding to the corresponding product unique identifier; TIk and TOk represent the start assembly time and the end assembly time of the instrument product corresponding to the corresponding product unique identifier in the assembly process with serial number k respectively; Wk represents the assembly result of the instrument product corresponding to the corresponding product unique identifier in the assembly process with serial number k; Wk takes the value of 0 or 1, when Wk=0, it means that the assembly is completed, when Wk=1, it means that the assembly is not completed; a1, a2 and a3 represent the preset proportional coefficients of the difference in component quantity, the difference in assembly time and the assembly result respectively, and a1>a3>a2>0, a1+a2+a3=1; the values ​​of a1, a2 and a3 are obtained by analyzing and combining the data of a large amount of instrument assembly information in the early stage with the corresponding actual assembly abnormal situation information;

[0060] It should be noted that some products may have assembly errors or other failures during assembly, resulting in incomplete assembly results, but the assembly still needs to be completed to facilitate the assembly of the next instrument product in the corresponding assembly process;

[0061] It can be understood that when the difference in the number of components of the instrument product corresponding to the corresponding product unique identifier in the corresponding assembly process is smaller, the difference in assembly time is smaller, and the assembly result value is 0, the assembly warning coefficient Zk of the instrument product corresponding to the corresponding product unique identifier in the corresponding assembly process is smaller, indicating that the possibility of assembly abnormality of the instrument product in the corresponding assembly process is smaller;

[0062] The calculated assembly warning coefficient Zk is compared with the assembly warning coefficient limit value ZYk to determine whether the assembly of the instrument product corresponding to the corresponding product unique identifier is abnormal in the corresponding assembly process, and the judgment result is sent to the background management module. The relevant staff promptly handles the instrument product corresponding to the corresponding product unique identifier with abnormal assembly through the background management module; and the assembly warning coefficient and the assembly abnormality judgment result are associated with the corresponding instrument assembly information to obtain the instrument assembly association information of the instrument product corresponding to the corresponding product unique identifier in the corresponding assembly process, and the obtained instrument assembly association information is sent to the blockchain update module;

[0063] If Zk<ZYk, it means that the instrument product corresponding to the corresponding product unique identifier is assembled without abnormality in the corresponding assembly process;

[0064] If Zk≥ZYk, it means that the instrument product corresponding to the corresponding product unique identifier has an abnormal assembly in the corresponding assembly process;

[0065] It should be noted that the value of the assembly warning coefficient limit value ZYk is obtained by analyzing the assembly warning coefficient calculated based on the data of a large amount of instrument assembly information in the early stage and the corresponding actual assembly abnormal situation information;

[0066] In the present invention, in addition to controlling the quality of incoming materials, the performance indicators of instrument products will also be triggered by assembly abnormalities in the later instrument products. Therefore, the instrument assembly information of the instrument products in the corresponding assembly process corresponding to the unique identifiers of each product acquired in real time is analyzed by the abnormal analysis module. The specific method is to compare whether the component material number in the acquired instrument assembly information is completely matched with the component material number in the corresponding component BOM table. If they are completely matched, the assembly warning coefficient is further calculated according to the instrument assembly information, and whether the assembly of the corresponding product is abnormal is determined according to the assembly warning coefficient obtained by calculation; wherein the assembly warning coefficient is to further analyze the assembly abnormality from the perspective of whether the amount of each component is the same, the assembly time and whether the assembly is completed, and can quickly determine whether the assembly is abnormal to a certain extent, and then issue a warning in time; it helps to prevent the further expansion of potential problems, and avoids the difficulty in finding assembly abnormalities due to the influence of environmental conditions and the negligence of manual observation of the naked eye;

[0067] The blockchain update module is used to generate a new block by using metadata of instrument assembly association information and video image information of the corresponding instrument product in the corresponding assembly process corresponding to the corresponding product unique identifier obtained in real time in the database, and then update the blockchain information for the corresponding product unique identifier in the database; including:

[0068] Real-time monitoring of new instrument assembly related information and new video images obtained in the database; the monitoring method can be achieved by setting a notification mechanism in the database, and the specific notification method is an existing technology and will not be described in detail here;

[0069] Filtering new instrument assembly-related information and new video images belonging to the same product unique identifier and the same assembly process; and extracting metadata of the filtered new instrument assembly-related information and new video images; it can be understood that the metadata of the video image includes shooting start time, shooting end time, shooting device, shooting geographical location, storage location, etc.;

[0070] A hash value is calculated based on the filtered instrument assembly association information and metadata of the corresponding video image. The hash value calculation can be calculated by the hash function SHA-256, which will not be described in detail here.

[0071] Match the product unique identifiers corresponding to the screened instrument assembly association information and video image metadata with all product unique identifiers containing blockchain information in the database, and obtain the hash value of the previous block of the blockchain corresponding to the instrument product with the same product unique identifier;

[0072] A new block is constructed according to the calculated hash value, the hash value of the previous block obtained by matching from the database, the corresponding instrument assembly association information, and the metadata of the corresponding video image;

[0073] The newly constructed block is connected to the previous block of the corresponding product unique identifier in the database, so as to realize the real-time update of the blockchain of the instrument product corresponding to the corresponding product unique identifier in the database;

[0074] The database is used to store video image information and blockchain information of instrument products corresponding to all product unique identifiers; the database is also used to store component BOM tables and preset average assembly time Tk in all assembly processes of each product model;

[0075] In the present invention, the blockchain update module is used to establish a blockchain belonging to the instrument product corresponding to the unique identifier of the corresponding product for the instrument product, the assembly warning coefficient, the assembly abnormality judgment result and the metadata of the video image. Due to the distributed and encrypted nature of the blockchain, the integrity, credibility, non-tamperability and non-loss of the assembly information of the instrument product corresponding to the unique identifier of the corresponding product are ensured; in addition, when the later detection or calibration finds that the relevant performance indicators cannot be passed or are abnormal, the assembly problem can be traced back through the blockchain information under the condition of excluding the abnormality of the incoming materials, and each assembly process can be traced back, including viewing the instrument assembly information and viewing the corresponding video images; wherein each block of the blockchain information involving assembly stores the assembly warning coefficient and the assembly abnormality judgment result, so that the technician can quickly locate the abnormal assembly process; the blockchain update module can update the assembly information of all instrument products in real time, so that the monitoring of the entire assembly process is more timely, which helps to find problems in the assembly early and improve the efficiency of the assembly process;

[0076] like Figure 2 As shown, an assembly fault backtracking method, based on the assembly fault backtracking system, includes:

[0077] The information collection module collects the instrument assembly information and video image information in each assembly process on the instrument automation production line, and the collected instrument assembly information is marked with the corresponding product unique identifier label and the corresponding assembly process label and sent to the abnormality analysis module, and the collected video image information is marked with the corresponding product unique identifier label and the corresponding assembly process label and uploaded to the database for storage;

[0078] The acquired instrument assembly information is analyzed through the abnormality analysis module, and the component material number in the acquired instrument assembly information is compared with the component material number in the corresponding component BOM table to see whether they completely match. If they completely match, the assembly warning coefficient is further calculated according to the instrument assembly information, and whether the assembly of the corresponding product is abnormal is judged according to the calculated assembly warning coefficient; the assembly warning coefficient and the assembly abnormality judgment result are associated with the acquired instrument assembly information, and the instrument assembly association information of the instrument product corresponding to the corresponding product unique identifier in the corresponding assembly process is obtained, and the acquired instrument assembly association information is sent to the blockchain update module;

[0079] The blockchain update module generates a new block by corresponding the metadata of the instrument assembly-related information and video image information of the corresponding product unique identifier obtained in real time in the database to the instrument product in the corresponding assembly process, and then updates the blockchain information for the corresponding product unique identifier in the database.

[0080] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0081] In the embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation; the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the method of this embodiment.

[0082] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An assembly fault backtracking system, characterized in that: include: Information collection module, anomaly analysis module and blockchain update module; The information acquisition module is used to collect instrument assembly information and video image information in each assembly process on the instrument automation production line, and to tag the collected instrument assembly information with the corresponding product unique identifier label and the corresponding assembly process label and send it to the abnormality analysis module, and to tag the collected video image information with the corresponding product unique identifier label and the corresponding assembly process label and upload it to the database for storage; The abnormality analysis module is used to analyze the obtained instrument assembly information, compare the component material number in the obtained instrument assembly information with the component material number in the corresponding component BOM table to see whether it completely matches. If it completely matches, then further calculate the assembly warning coefficient according to the instrument assembly information, and judge whether the assembly of the corresponding product is abnormal according to the calculated assembly warning coefficient; Associating the assembly warning coefficient and the assembly abnormality judgment result with the obtained instrument assembly information, obtaining the instrument assembly association information of the instrument product corresponding to the corresponding product unique identifier in the corresponding assembly process, and sending the obtained instrument assembly association information to the blockchain update module; The blockchain update module is used to generate a new block by using metadata of the instrument assembly related information and video image information of the corresponding instrument product in the corresponding assembly process corresponding to the corresponding product unique identifier obtained in real time in the database, and then update the blockchain information for the corresponding product unique identifier in the database; The analysis process of the anomaly analysis module includes: Obtaining instrument assembly information labeled with a corresponding product unique identifier and a corresponding assembly process label; Extract the component BOM table and the preset average assembly time Tk of the same product model in the corresponding assembly process from the database according to the product model of the instrument assembly information; k represents the serial number of the corresponding assembly process of the corresponding product model, and k is an integer greater than 0; The component BOM table includes the component material number required for the corresponding assembly process and the number of components corresponding to the corresponding component material number Nki; i represents the sequence number of the component material number in the corresponding assembly process, i=1, 2...n; n represents the total number of the component material number of the corresponding assembly process; Match the corresponding product model and all component material numbers of the corresponding assembly process extracted from the component BOM table with all component material numbers used in the instrument assembly information of the corresponding instrument product in the corresponding assembly process corresponding to the corresponding product unique identifier obtained from the information acquisition module; If all component numbers in the current component BOM are not exactly the same as all component numbers included in the current instrument assembly information, the instrument assembly information is marked as abnormal, and an early warning message of component assembly abnormality is generated and sent to the background management module with the corresponding product unique identifier label and the corresponding assembly process label; the relevant staff will handle it in a timely manner through the background management module; and associate the assembly abnormality judgment result with the corresponding instrument assembly information, obtain the instrument assembly association information of the instrument product corresponding to the corresponding product unique identifier in the corresponding assembly process, and send the obtained instrument assembly association information to the blockchain update module; If all component material numbers in the current component BOM table are exactly the same as all component material numbers contained in the current instrument assembly information, then further calculate the assembly warning coefficient Zk of the instrument product corresponding to the corresponding product unique identifier in the corresponding assembly process; The calculated assembly warning coefficient Zk is compared with the assembly warning coefficient limit value ZYk to determine whether the assembly of the instrument product corresponding to the corresponding product unique identifier is abnormal in the corresponding assembly process, and the judgment result is sent to the background management module. The relevant staff promptly handles the instrument product corresponding to the corresponding product unique identifier with abnormal assembly through the background management module; and the assembly warning coefficient and the assembly abnormality judgment result are associated with the corresponding instrument assembly information to obtain the instrument assembly association information of the instrument product corresponding to the corresponding product unique identifier in the corresponding assembly process, and the obtained instrument assembly association information is sent to the blockchain update module; If Zk<ZYk, it means that the instrument product corresponding to the corresponding product unique identifier is assembled without abnormality in the corresponding assembly process; If Zk≥ZYk, it means that the instrument product corresponding to the corresponding product unique identifier has an abnormal assembly in the corresponding assembly process; The calculation formula of the assembly warning coefficient Zk is as follows: Wherein, NSki represents the number of components with component number i used in the assembly process with serial number k by the instrument product corresponding to the corresponding product unique identifier; TIk and TOk represent the start assembly time and the end assembly time of the instrument product corresponding to the corresponding product unique identifier in the assembly process with serial number k, respectively; Wk represents the assembly result of the instrument product corresponding to the corresponding product unique identifier in the assembly process with serial number k; Wk takes the value of 0 or 1, when Wk=0, it means that the assembly is completed, when Wk=1, it means that the assembly is not completed; a1, a2 and a3 represent the preset proportional coefficients of the difference in component quantity, the difference in assembly time and the assembly result, respectively, and a1>a3>a2>0, a1+a2+a3=1.

2. An assembly fault backtracking system according to claim 1, characterized in that: The assembly process includes: Housing assembly, PCB assembly and cable assembly.

3. The assembly fault backtracking system according to claim 1, characterized in that: The unique identifier of the corresponding product and the instrument assembly information of the corresponding assembly process include the product model, all component material numbers used in the current assembly process, the number of components corresponding to different component material numbers, the start assembly time, the end assembly time, and whether the assembly result is completed.

4. The assembly fault backtracking system according to claim 1, characterized in that: The update methods of the blockchain update module include: Real-time monitoring of new instrument assembly related information and new video images obtained from the database; Screening new instrument assembly-related information and new video images belonging to the same product unique identifier and the same assembly process; and extracting metadata of the screened new instrument assembly-related information and new video images; Calculate a hash value based on the filtered instrument assembly association information and metadata of the corresponding video image; Match the product unique identifiers corresponding to the screened instrument assembly association information and video image metadata with all product unique identifiers containing blockchain information in the database, and obtain the hash value of the previous block of the blockchain corresponding to the instrument product with the same product unique identifier; A new block is constructed according to the calculated hash value, the hash value of the previous block obtained by matching from the database, the corresponding instrument assembly association information, and the metadata of the corresponding video image; The newly constructed block is connected to the previous block of the corresponding product unique identifier in the database, thereby realizing real-time update of the blockchain of the instrument product corresponding to the corresponding product unique identifier in the database.

5. The assembly fault backtracking system according to claim 4, characterized in that: The metadata of the video image includes the shooting start time, shooting end time, shooting equipment, shooting geographical location and storage location.

6. An assembly fault backtracking method, based on an assembly fault backtracking system according to any one of claims 1 to 5, characterized in that: include: The information collection module collects the instrument assembly information and video image information in each assembly process on the instrument automation production line, and the collected instrument assembly information is marked with the corresponding product unique identifier label and the corresponding assembly process label and sent to the abnormality analysis module, and the collected video image information is marked with the corresponding product unique identifier label and the corresponding assembly process label and uploaded to the database for storage; The acquired instrument assembly information is analyzed through the abnormal analysis module to compare whether the component material number in the acquired instrument assembly information is completely matched with the component material number in the corresponding component BOM table. If they are completely matched, the assembly warning coefficient is further calculated according to the instrument assembly information, and whether the assembly of the corresponding product is abnormal is determined according to the calculated assembly warning coefficient; Associating the assembly warning coefficient and the assembly abnormality judgment result with the obtained instrument assembly information, obtaining the instrument assembly association information of the instrument product corresponding to the corresponding product unique identifier in the corresponding assembly process, and sending the obtained instrument assembly association information to the blockchain update module; The blockchain update module generates a new block by corresponding the metadata of the instrument assembly-related information and video image information of the corresponding product unique identifier obtained in real time in the database to the instrument product in the corresponding assembly process, and then updates the blockchain information for the corresponding product unique identifier in the database.

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