Technology mining method, system and device based on smart workshop

Abstract

The invention discloses a technology mining method, system and device based on a smart workshop, and relates to the technical field of artificial intelligence, and the method comprises the steps: building a data connection with a smart factory management system, and at least distributing the access and call authority of equipment operation data, production data and workshop monitoring data; defining data obtained from the smart factory management system as original data; allocating technician permissions and innovation administrator permissions to the created accounts to form technician accounts and innovation administrator accounts; executing an AI mining process on the original data; and executing a collaboration process after receiving collaboration requests initiated by the technician account and the innovation administrator account. The method and the device have the effect of improving the technical mining convenience of enterprises.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a technology mining method, system and device based on a smart workshop. Background Art

[0002] With the progress of society, my country has gradually formed a collaborative innovation body with technology-based enterprises, research institutes and universities as the main body. The number of scientific and technological innovation clusters ranks among the top in the world, providing important impetus for my country's economic development.

[0003] As an important part of the innovation system, technology-based enterprises have undertaken heavier innovation tasks. However, although the number of high-tech enterprises is increasing year by year, many enterprises still have the following problems:

[0004] Frontline technicians in the workshop are often unable to accurately judge whether they have carried out innovative activities in their work, or whether it is necessary to protect, transform and promote the innovative content. They rely more on the company's professionals to go to the site to communicate and collect information. Sometimes the technicians cannot speak clearly and cannot express themselves accurately. This leads to large enterprises requiring more professionals to go down to the production line, and the task is more arduous. Therefore, this application proposes a new technical solution. Summary of the invention

[0005] In order to improve the convenience of technology mining for enterprises, the present application provides a technology mining method, system and device based on a smart workshop.

[0006] In the first aspect, the present application provides a technology mining method based on a smart workshop, which adopts the following technical solutions:

[0007] A technology mining method based on a smart workshop, comprising:

[0008] Establish data connection with the smart factory management system and allocate access and call rights to at least equipment operation data, production data and workshop monitoring data;

[0009] Define the data obtained from the smart factory management system as raw data;

[0010] Assign technician permissions and innovation administrator permissions to each created account to form technician accounts and innovation administrator accounts;

[0011] Perform AI mining processes on raw data; and,

[0012] Execute the collaboration process after receiving the collaboration request initiated by the technician account and innovation administrator account;

[0013] The AI ​​mining process includes:

[0014] Obtain equipment update nodes and update data based on workshop monitoring data and equipment operation data;

[0015] Analyze the impact of the updated data on production based on the production data before and after the equipment update node;

[0016] Extract the update nodes, update data and impact amounts whose impact amounts are higher than the preset optimization index lower limit, and record them as the data to be mined;

[0017] The collaborative process includes:

[0018] If the collaborative request includes technical difficulty data, the technical difficulty data will be extracted, and the technical barrier related fields will be generated and sent to the innovation administrator account for confirmation;

[0019] After confirming the technical barrier related fields, search the specified literature database to obtain related literature;

[0020] Extract key words, phrases, and images from related documents based on preset extraction feature settings to generate reference data;

[0021] Identify and analyze the production processes that match the fields associated with technical barriers, and send reference data to the corresponding technician accounts and innovation manager accounts.

[0022] Optionally, the obtaining of the device update node and update data includes:

[0023] Get the device online and offline time, start and stop time based on the device operation data, and define them as troubleshooting nodes;

[0024] Perform visual recognition analysis on the workshop monitoring data of the time t1 before and after the inspection nodes; where t1 is the preset value;

[0025] If the result of the visual recognition analysis is equipment maintenance, equipment replacement, or equipment disassembly and assembly, the person who performed the behavior is identified and confirmation information on the adjustment of the equipment structure, material, and operating logic is sent to the corresponding technician account. After confirmation, the time when the behavior occurred is defined as the equipment update node.

[0026] Optionally, the obtaining of the device update node and update data includes:

[0027] Sending an update report template to the account of the action executor corresponding to the update node; wherein the update report template at least includes the update item, update location and expected effect;

[0028] Compare the running data before and after the node is updated, and verify the expected effect based on the comparison result to obtain the expected verification result;

[0029] Receive feedback on updated report templates, bind visual recognition analysis results, and expected verification results to generate updated data.

[0030] Optionally, the collaborative process further includes:

[0031] If the collaboration request includes technical concept data, identifying whether the technical concept data is bound to the reference data;

[0032] If bound, the technical concept data and corresponding reference data will be packaged and sent to the innovation administrator account;

[0033] If not bound, the keywords of the technical problems and technical solutions to be solved are extracted based on the technical concept data, and the literature database is searched accordingly to obtain approximate technical data;

[0034] Package the technical idea data and approximate data and send them to the innovation manager account.

[0035] Optionally, it also includes: if the innovation management account approves the technical concept data, the current node is defined as the update node, and the update data is generated based on the technical concept, and the impact of the update data on production is simulated and analyzed, and the impact is sent to the innovation manager account and the corresponding technician account.

[0036] Optionally, if the innovation management account approves the technical concept data, a technical briefing document is generated based on the technical concept data and simulation analysis results, and also based on parameter data or approximate data.

[0037] Optionally, the method further includes: if the technical concept data is audio data, first performing audio recognition on the audio data to obtain text data.

[0038] In the second aspect, the present application provides a technology mining system based on a smart workshop, which adopts the following technical solutions:

[0039] A technology mining system based on a smart workshop, comprising:

[0040] The administrator module is used to establish a data connection with the smart factory management system and allocate at least the access and call rights to the equipment operation data, production data and workshop monitoring data; define the data obtained from the smart factory management system as the original data; allocate technician rights and innovation administrator rights to each created account, forming a technician account and an innovation administrator account;

[0041] AI mining module, which is used to obtain equipment update nodes and update data based on workshop monitoring data and equipment operation data; analyze the impact of update data on production based on production data before and after the equipment update node; extract update nodes, update data and impact amounts whose impact amounts are higher than the preset optimization index lower limit, and record them as data to be mined;

[0042] The collaboration module is used to extract information from the technical difficulty data if the collaboration request includes technical difficulty data, generate technical barrier related fields, and send them to the innovation administrator account for confirmation; after the technical barrier related fields are confirmed, search the specified literature database to obtain related documents; extract keywords, phrases, and pictures from the related documents based on the preset extraction feature settings to generate reference data; identify and analyze the production process that matches the technical barrier related fields, and send the reference data to the corresponding technician account and innovation administrator account.

[0043] Optionally, the collaboration module is further used to:

[0044] If the collaboration request includes technical concept data, identifying whether the technical concept data is bound to the reference data;

[0045] If bound, the technical concept data and corresponding reference data will be packaged and sent to the innovation administrator account;

[0046] If not bound, the keywords of the technical problems and technical solutions to be solved are extracted based on the technical concept data, and the literature database is searched accordingly to obtain approximate technical data;

[0047] Package the technical idea data and approximate data and send them to the innovation manager account;

[0048] If the innovation management account approves the technical concept data, the current node is defined as the update node, and the update data is generated based on the technical concept. The impact of the update data on production is simulated and analyzed, and the impact is sent to the innovation manager account and the corresponding technician account.

[0049] In a third aspect, the present application provides a technology mining device based on a smart workshop, which adopts the following technical solution:

[0050] A technology mining device based on a smart workshop comprises a memory and a processor, wherein the memory stores a computer program that can be loaded and executed by the processor to implement any of the technology mining methods based on the smart workshop described above.

[0051] In summary, this application includes the following beneficial technical effects:

[0052] 1. Innovation managers only need to set the lower limit of optimization indicators. This method can use workshop monitoring data and equipment operation data to actively link technicians to find content with technical mining value, and guide technicians to improve technical content and technical effects in the process, providing great convenience for innovation managers to carry out related work;

[0053] 2. This method can provide a reference for retrieving technology and simplifying information for technicians and innovation managers after they proactively raise questions. This is of great significance to relevant personnel in the enterprise, because most companies currently summarize technical problems, expectations, and annual plans, and then hand them over to IP or relevant agency personnel for manual retrieval and data simplification, and then return to the technology to check them one by one. Not only is the cycle long, but there are also many transfers in the middle. It may also be that the technology does not get what you expect due to information distortion. This method can reduce the above problems, promote the innovation enthusiasm of technical personnel, and cooperate with another path of this method to reduce the difficulty of technology mining. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of the main process of the method of the present application;

[0055] Figure 2 It is a schematic diagram of the main process of the first path of the method of the present application;

[0056] Figure 3 It is a main flow diagram of the second path of the method of the present application;

[0057] Figure 4 It is a schematic diagram of the structure of the system of this application. DETAILED DESCRIPTION

[0058] The following is combined with Figure 1-4 This application is described in further detail.

[0059] The embodiment of the present application discloses a technology mining method based on a smart workshop.

[0060] This method is aimed at the current technical mining problems in the innovation process of large and medium-sized enterprises, and aims to improve the convenience of technical mining and technical briefing between enterprise IP and R&D and front-line technical personnel, and reduce the following situations. For example: A new energy enterprise in Hangzhou Z City has only three people in the IP department in 2023, and two people are connected to the production line, but the number of R&D and technical personnel to be connected is as high as hundreds; although the company will arrange relevant institutions to provide technical briefing and technical mining training for R&D and technology, it is mainly operated by IP department personnel, which leads to heavy tasks and relatively easy omissions. Therefore, this application builds a smart platform against the background of the gradual deployment of generative artificial intelligence and smart workshops, and uses AI to assist relevant personnel to simplify work content and improve work efficiency.

[0061] Reference Figure 1 ,Technology mining methods based on smart workshops include:

[0062] S11. Establish a data connection with the smart factory management system and allocate at least access and call permissions for equipment operation data, production data and workshop monitoring data.

[0063] It should be noted that the smart factory in this method refers to a digital factory in which a large number of cameras are deployed in the workshop to monitor the entire workshop or most of the work areas, the main production equipment is based on the Internet of Things, and raw materials, semi-finished products and finished products are managed in an information-based manner to meet basic data collection needs. The higher the degree of digitization, the more comprehensive the basic data. Under extreme conditions, relevant personnel can also actively upload relevant data.

[0064] For information security reasons of each enterprise, the data called above cannot be uploaded to the cloud before being desensitized. The data is transmitted to the local server established by the enterprise itself, that is, the platform system that executes this method is loaded locally.

[0065] S12. Define the data obtained from the smart factory management system as raw data. After the raw data is obtained, data cleaning can be performed first to improve the quality of subsequent analysis.

[0066] S13. Assign technician authority and innovation administrator authority to each created account to form a technician account and an innovation administrator account.

[0067] It is understandable that when implementing this method on an online platform, an account should be created for each user. This method only uses technical personnel and innovation management personnel (IP department personnel) as examples. In reality, the authority accounts corresponding to roles such as backend administrators and heads of various parts of the enterprise are further divided. This is not the core of this method, so it will not be repeated.

[0068] S14, performing AI mining process on the original data; and,

[0069] The collaboration process is executed after receiving the collaboration request initiated by the technician account and the innovation administrator account.

[0070] That is, this method is mainly divided into two paths to assist relevant personnel in carrying out their work, but the two paths cooperate with each other, which are explained in detail below.

[0071] Reference Figure 2 , about the AI ​​mining process, which includes:

[0072] S21. Acquire equipment update nodes and update data based on workshop monitoring data and equipment operation data.

[0073] For the manufacturing industry, whether it is product updates or process updates, they are often reflected in the equipment. For example, when the product is updated, in addition to the replacement of tooling, the production line also needs to be adjusted. Once the production line is adjusted, the equipment is often updated or adjusted. Therefore, the above is specifically:

[0074] 1.1) According to the equipment operation data (such as voltage and current), the equipment online and offline time (i.e., whether it is on the production line) and the start and stop time are obtained, and defined as the troubleshooting node.

[0075] 1.2) Perform visual recognition analysis on the workshop monitoring data between the inspection nodes and before and after t1 time.

[0076] Check the nodes, for example: the period between the equipment going offline and online, the period between the equipment stopping and starting; the above periods may have equipment adjustment, transformation, replacement, removal, etc. The length of t1 before and after, t1 is the preset value, such as: 48-36h.

[0077] The workshop monitoring data is video monitoring data, so visual recognition is image recognition, and image recognition technology is a prior art and will not be described in detail. The recognition analysis example in this embodiment: recognize the structural features of the equipment, and analyze whether it has been moved, disassembled, or structured by comparing before and after.

[0078] 1.3) If the visual recognition analysis result is equipment maintenance, equipment replacement, or equipment disassembly and assembly, the person who performs the behavior is identified and the equipment structure, material, and operation logic adjustment confirmation information is sent to the corresponding technician account. After confirmation, the time when the behavior occurs is defined as the equipment update node.

[0079] That is, after evaluating and confirming the equipment update, this method will also link the technicians of the production line to confirm it, so as to avoid misjudgment and produce erroneous technical mining data. The above confirmation information is sent in the form of a form, and the form content example: equipment name, location, adjustment type (structure, material, logic). When the form is returned, if the adjustment type is not blank, it means that the confirmation is passed, and then the equipment update node is defined.

[0080] Regarding updating data, specifically:

[0081] 2.1) Send the update report template to the account of the action executor corresponding to the update node.

[0082] The above-mentioned executors are determined by face recognition based on monitoring data and compared with the employee database. Once the executors are determined, their bound accounts can be determined. The update report template at least includes the update items, update locations and expected effects; confirm that some data in the form is automatically filled in, and the rest is left blank for further filling by technicians.

[0083] 2.2) Compare the running data before and after the node is updated, and verify the expected effect based on the comparison result to obtain the expected verification result.

[0084] It should be noted that the specific duration before and after the update node here is not a fixed value and needs to be adjusted according to the equipment cycle and update items of the production line. For example: if the cycle of equipment A is 24 minutes, then the "after" at this time is at least greater than 24 minutes; if the update item is to replace the consumable part M, then the "after" is at least greater than the historical service life and design life of the consumable part M.

[0085] By comparing the nodes before and after, the actual change can be obtained, and then compared and verified with the expected effect uploaded by the technician.

[0086] 2.3) Receive feedback on updating report templates, bind visual recognition analysis results and expected verification results to generate updated data.

[0087] S22. Analyze the impact of the updated data on production based on the production data before and after the equipment update node (such as output, number of defective products, etc.).

[0088] It is understandable that equipment updates are ultimately reflected in output, such as rate and defective rate; the “before and after” here is the same as above; it should be noted that:

[0089] Before analyzing the production data before and after the update node, the staff needs to perform equivalent conversion processing first. For example: the original production time of workpiece J2 is 1.5 times that of workpiece J1. If the data of workpiece J1 is used "before", and the data collected "after" corresponds to workpiece J2, the production time of workpiece J1 * 1.5 should be compared with the current production time of workpiece J2 to calculate the impact amount. The impact amount can be the change in production rate, output change rate, and defective rate change rate.

[0090] S23. Extract update nodes, update data and impact amounts whose impact amounts are higher than a preset optimization index lower limit (customizable), and record them as data to be mined.

[0091] According to the above content, innovation managers only need to set the lower limit (value) of the optimization index. This method can use the workshop monitoring data and equipment operation data to actively link technicians to find content with technical mining value, and guide technicians to improve technical content and technical effects in the process, providing great convenience for innovation managers to carry out related work.

[0092] The previous part of this method uses AI to discover possible valuable mining items, while this part is for technicians and innovation managers to actively raise questions and AI to find reference data.

[0093] Reference Figure 3 , regarding collaborative processes, it includes:

[0094] S31. If the collaborative request includes technical difficulty data, information is extracted from the technical difficulty data, technical barrier related fields are generated, and sent to the innovation administrator account for confirmation.

[0095] Among them, technical problem data refers to the parts that technicians and innovation managers believe exist in production and hope to improve. Therefore, the data examples include: problem location (corresponding equipment, corresponding production link) and problem content.

[0096] At this time, information extraction can be to extract equipment names, negative directional words in difficult content, and expected directional words, such as: low efficiency, easy to damage, increase rotation speed, and improve drying efficiency; the above directional words (fields) are defined by the staff.

[0097] S32. After the technical barrier related fields are confirmed, the specified literature database is searched to obtain related literature.

[0098] Examples of the above-mentioned literature databases include patent databases and paper databases. It should be noted that each field is not independent, that is, it is not simply substituted separately, but logical symbols are added according to the context and part of speech of the difficult data to regenerate multiple new search formulas and search again, such as and, or, etc.

[0099] S33. Extract key words, phrases, and images from related documents based on preset extraction feature settings to generate reference data.

[0100] Because the technical staff of the production line are often busy, it is very inappropriate to send a large number of retrieved related documents to them for review. Therefore, this method also simplifies the information.

[0101] The above extraction feature setting example: extract the patent number, right holder, application date, technical background, summary and drawings of the specification of the patent document Figure 1 ; Reference data may be summarized in tabular form.

[0102] S34. Identify and analyze the production processes that match the fields associated with the technical barriers, and send reference data to the corresponding technician accounts and innovation manager accounts.

[0103] According to the above content, this method can provide a reference for retrieving technology and simplifying information for technicians and innovation managers after they actively raise questions. This is of great significance to relevant personnel in the enterprise, because most companies currently have technical directors (managers) summarize technical problems, expectations, and annual plans, and then hand them over to IP or related agency personnel for manual retrieval and data simplification, and then return to the technology to check them one by one. Not only is the cycle long, but there are also many transfers in the middle. It may also be that the technology does not get what you expect due to information distortion. This method can reduce the above problems, promote the innovation enthusiasm of technical personnel, and cooperate with another path of this method to reduce the difficulty of technology mining.

[0104] In another embodiment of the method, the collaborative process further includes:

[0105] Generate innovative basic feature data based on equipment operation data and production data;

[0106] The literature in the innovation basic characteristic data and parameter data is sorted based on their relevance (overlapping degree), and the reference data is adjusted according to the sorting.

[0107] The operating data of known equipment is often bound to the equipment ID, so this method can identify which equipment is in the workshop; production data can be used to parse out process, raw materials, and product information; all of the above can be used as basic feature data for innovation. The above adjustment of reference data based on ranking, that is, the higher the overlap between the document and the workshop features, the higher the ranking of the document.

[0108] The purpose of the above settings is to improve the efficiency and effectiveness of staff reviewing parameter data and reduce the situation where technicians read a lot of literature and find it difficult to try similar innovations and pay less and less attention to reference data.

[0109] In another embodiment of the method, the collaborative process further includes:

[0110] If the collaboration request includes technical concept data, it is identified whether the technical concept data is bound to the reference data.

[0111] The above-mentioned technical concept data refers to the technical routes that are not fully formed or have been formed provided by technicians; because this method mentioned above will provide reference data for technicians, the technical concept data is likely to be generated based on reference data. At this time, it is necessary to first identify whether to bind parameter data to facilitate the understanding of later innovation managers; at the same time, it is also to reduce repeated retrieval work. Technical concept data can be pictures and texts, or audio data. If it is audio data, the audio data is identified and converted into text data.

[0112] If bound, the technical concept data and corresponding reference data will be packaged and sent to the innovation administrator account;

[0113] If not bound, the keywords of the technical problems and technical solutions to be solved are extracted based on the technical concept data, and the literature database is searched accordingly to obtain approximate technical data;

[0114] Package the technical idea data and approximate data and send them to the innovation manager account.

[0115] In another embodiment of the method, based on the above, the method further includes:

[0116] If the innovation management account approves the technical concept data, the current node is defined as the update node, and the update data is generated based on the technical concept. The impact of the update data on production is simulated and analyzed, and the impact is sent to the innovation manager account and the corresponding technician account.

[0117] An example of generating update data based on a technical concept is as follows: extracting the corresponding device name, location, and concept adjustment type in the technical concept as update data.

[0118] An example of simulation analysis of the impact of updated data on production: if the location is a vulnerable part and the adjustment type is material replacement, then the test life of the enterprise's self-established experience database is searched on the Internet or retrieved, and the test life is substituted for the change in efficiency and cost before and after the update; if the location is a drive roller and the adjustment type is logic adjustment, then the reduced links during logic execution are analyzed, the historical time consumption of the link is obtained, and the change in efficiency and output before and after the update is calculated.

[0119] Furthermore, the method also includes: if the innovation management account approves the technical concept data, a technical briefing document is generated based on the technical concept data and simulation analysis results, and also based on parameter data or approximate data; that is, various data are extracted / aggregated and filled into the technical briefing document template uploaded in advance by the innovation management personnel to reduce the workload of technicians and innovation managers.

[0120] The embodiment of the present application also discloses a technology mining system based on a smart workshop.

[0121] Reference Figure 4 , the technology mining system based on the smart workshop includes:

[0122] The administrator module is used to establish a data connection with the smart factory management system and allocate at least the access and call rights to the equipment operation data, production data and workshop monitoring data; define the data obtained from the smart factory management system as the original data; allocate technician rights and innovation administrator rights to each created account, forming a technician account and an innovation administrator account;

[0123] AI mining module, which is used to obtain equipment update nodes and update data based on workshop monitoring data and equipment operation data; analyze the impact of update data on production based on production data before and after the equipment update node; extract update nodes, update data and impact amounts whose impact amounts are higher than the preset optimization index lower limit, and record them as data to be mined;

[0124] The collaboration module is used to extract information from the technical difficulty data if the collaboration request includes technical difficulty data, generate technical barrier related fields, and send them to the innovation administrator account for confirmation; after the technical barrier related fields are confirmed, search the specified literature database to obtain related documents; extract keywords, phrases, and pictures from the related documents based on the preset extraction feature settings to generate reference data; identify and analyze the production process that matches the technical barrier related fields, and send the reference data to the corresponding technician account and innovation administrator account.

[0125] Among them, the collaborative module is also used for:

[0126] If the collaboration request includes technical concept data, identifying whether the technical concept data is bound to the reference data;

[0127] If bound, the technical concept data and corresponding reference data will be packaged and sent to the innovation administrator account;

[0128] If not bound, the keywords of the technical problems and technical solutions to be solved are extracted based on the technical concept data, and the literature database is searched accordingly to obtain approximate technical data;

[0129] Package the technical idea data and approximate data and send them to the innovation manager account;

[0130] If the innovation management account approves the technical concept data, the current node is defined as the update node, and the update data is generated based on the technical concept. The impact of the update data on production is simulated and analyzed, and the impact is sent to the innovation manager account and the corresponding technician account.

[0131] The specific implementation of each of the above modules has been described in the embodiments of this method, so it will not be repeated here.

[0132] The embodiment of the present application also discloses a technology mining device based on a smart workshop.

[0133] A technology mining device based on a smart workshop includes a memory and a processor, wherein the memory stores a computer program that can be loaded and executed by the processor to implement any of the technology mining methods based on a smart workshop as described above.

[0134] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A technology mining method based on a smart workshop, characterized in that: include: Establish data connection with the smart factory management system and allocate access and call rights to at least equipment operation data, production data and workshop monitoring data; Define the data obtained from the smart factory management system as raw data; Assign technician permissions and innovation administrator permissions to each created account to form technician accounts and innovation administrator accounts; Perform AI mining processes on raw data; as well as, Execute the collaboration process after receiving the collaboration request initiated by the technician account and innovation administrator account; The AI ​​mining process includes: Obtain equipment update nodes and update data based on workshop monitoring data and equipment operation data; Analyze the impact of the updated data on production based on the production data before and after the equipment update node; Extract the update nodes, update data and impact amounts whose impact amounts are higher than the preset optimization index lower limit, and record them as the data to be mined; The collaborative process includes: If the collaborative request includes technical difficulty data, the technical difficulty data will be extracted, and the technical barrier related fields will be generated and sent to the innovation administrator account for confirmation; After the technical barrier related fields are confirmed, the specified literature database is searched to obtain related literature; Extract key words, phrases, and images from related documents based on preset extraction feature settings to generate reference data; Identify and analyze the production processes that match the fields associated with technical barriers, and send reference data to the corresponding technician accounts and innovation manager accounts.

2. The technology mining method based on the smart workshop according to claim 1 is characterized in that: The obtaining of the device update node and update data includes: Get the device online and offline time, start and stop time based on the device operation data, and define them as troubleshooting nodes; Perform visual recognition analysis on the workshop monitoring data of the time t1 before and after the inspection nodes; where t1 is the preset value; If the result of the visual recognition analysis is equipment maintenance, equipment replacement, or equipment disassembly and assembly, the person who performed the behavior is identified and confirmation information on the adjustment of the equipment structure, material, and operating logic is sent to the corresponding technician account. After confirmation, the time when the behavior occurred is defined as the equipment update node.

3. The technology mining method based on the smart workshop according to claim 2 is characterized in that: The obtaining of the device update node and update data includes: Sending an update report template to the account of the action executor corresponding to the update node; wherein the update report template at least includes the update item, update location and expected effect; Compare the running data before and after the node is updated, and verify the expected effect based on the comparison result to obtain the expected verification result; Receive feedback on updated report templates, bind visual recognition analysis results, and expected verification results to generate updated data.

4. The technology mining method based on the smart workshop according to claim 1, 2 or 3, characterized in that: The collaborative process further includes: If the collaboration request includes technical concept data, identifying whether the technical concept data is bound to the reference data; If bound, the technical concept data and corresponding reference data will be packaged and sent to the innovation administrator account; If not bound, the keywords of the technical problems and technical solutions to be solved are extracted based on the technical concept data, and the literature database is searched accordingly to obtain approximate technical data; Package the technical idea data and approximate data and send them to the innovation manager account.

5. The technology mining method based on the smart workshop according to claim 4 is characterized in that: Also includes: If the innovation management account approves the technical concept data, the current node is defined as the update node, and the update data is generated based on the technical concept. The impact of the update data on production is simulated and analyzed, and the impact is sent to the innovation manager account and the corresponding technician account.

6. The technology mining method based on the smart workshop according to claim 4 is characterized in that: Also includes: If the innovation management account approves the technical concept data, a technical briefing document will be generated based on the technical concept data and simulation analysis results, and also based on the parameter data or approximate data.

7. The technology mining method based on the smart workshop according to claim 4 is characterized in that: Also includes: If the technical concept data is audio data, audio recognition is first performed on the audio data to obtain text data.

8. A technology mining system based on a smart workshop, characterized in that: include: The administrator module is used to establish a data connection with the smart factory management system and allocate at least the access and call rights to the equipment operation data, production data and workshop monitoring data; define the data obtained from the smart factory management system as the original data; allocate technician rights and innovation administrator rights to each created account, forming a technician account and an innovation administrator account; AI mining module, which is used to obtain equipment update nodes and update data based on workshop monitoring data and equipment operation data; analyze the impact of update data on production based on production data before and after the equipment update node; extract update nodes, update data and impact amounts whose impact amounts are higher than the preset optimization index lower limit, and record them as data to be mined; A collaboration module, which is used to extract information from the technical difficulty data if the collaboration request includes technical difficulty data, generate technical barrier related fields, and send them to the innovation administrator account for confirmation; After the technical barrier associated fields are confirmed, the specified literature database is searched to obtain the associated documents; based on the preset extraction feature settings, key words, phrases and pictures are extracted from the associated documents to generate reference data; the production process matching the technical barrier associated fields is identified and analyzed, and the reference data is sent to the corresponding technician account and innovation manager account.

9. The technology mining system based on the smart workshop according to claim 8 is characterized in that: The collaboration module is further configured to: If the collaboration request includes technical concept data, identifying whether the technical concept data is bound to the reference data; If bound, the technical concept data and corresponding reference data will be packaged and sent to the innovation administrator account; If not bound, the keywords of the technical problems and technical solutions to be solved are extracted based on the technical concept data, and the literature database is searched accordingly to obtain approximate technical data; Package the technical idea data and approximate data and send them to the innovation manager account; If the innovation management account approves the technical concept data, the current node is defined as the update node, and the update data is generated based on the technical concept. The impact of the update data on production is simulated and analyzed, and the impact is sent to the innovation manager account and the corresponding technician account.

10. A technology mining device based on a smart workshop, characterized in that: It includes a memory and a processor, wherein the memory stores a computer program that can be loaded and executed by the processor to implement the technology mining method based on the smart workshop as described in any one of claims 1-7.

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