Method, device, vehicle, medium and product for monitoring production manufacturing risks

Abstract

The application relates to the technical field of automobile production, in particular to a production and manufacturing risk monitoring method and device, a vehicle, a medium and a product, wherein the method comprises the following steps: acquiring production operation data of multiple production and manufacturing procedures of a power battery; predicting production and manufacturing risks of the production and manufacturing procedures according to the production operation data, generating a risk list according to the multiple production and manufacturing procedures and corresponding production and manufacturing risks; predicting a risk evolution process and a failure mode of the production and manufacturing procedures according to risk factor data of the production and manufacturing procedures, determining risk grades of different risks according to the risk evolution process and the failure mode; generating a risk identification and control list according to the risk list and the risk grades of the different risks; and monitoring production and manufacturing risks of the power battery in the multiple production and manufacturing procedures by using the risk identification and control list and actual production and manufacturing data. Therefore, the problems of lacking a systematic risk identification method and being unable to quantify risk points in the prior art are solved.

Description

Technical Field

[0001] This application relates to the field of automotive manufacturing technology, and in particular to a method, device, vehicle, medium, and product for monitoring manufacturing risks. Background Technology

[0002] The new energy vehicle industry has developed rapidly in recent years, but this has also brought about safety risks in the manufacturing process, especially the safety performance of power batteries. Power batteries may cause serious accidents due to thermal runaway during production, storage, transportation, and use, posing a threat to people, property, and the environment.

[0003] In current technologies, safety risk management in the production and manufacturing process of new energy vehicles often focuses on the analysis and response to single links or single factors, lacking systematic and comprehensive risk identification and control strategies. Particularly in the prevention and control of thermal runaway in power batteries, although some research results and technical means have been developed, most focus on post-event handling and emergency response, lacking comprehensive solutions for pre-event prevention and in-event control. Furthermore, risk identification and management in the production and manufacturing process of new energy vehicles faces many challenges, such as complex production processes, numerous pieces of equipment involved, and varying levels of operator skill, all of which increase the likelihood and complexity of risk occurrence. Summary of the Invention

[0004] This application provides a method, device, vehicle, medium, and product for monitoring manufacturing risks, in order to solve the problems of lack of systematic risk identification methods and inability to quantify risk points in the prior art.

[0005] The first aspect of this application provides a method for monitoring manufacturing risks, comprising the following steps: acquiring production operation data of multiple manufacturing processes of a power battery; predicting manufacturing risks of the manufacturing processes based on the production operation data; generating a risk list based on the multiple manufacturing processes and corresponding manufacturing risks; predicting the risk evolution process and failure mode of the manufacturing processes based on risk factor data of the manufacturing processes; determining the risk level of different risks based on the risk evolution process and failure mode; generating a risk identification and control list based on the risk list and the risk levels of different risks; and monitoring the manufacturing risks of the power battery in multiple manufacturing processes using the risk identification and control list and actual manufacturing data.

[0006] Optionally, the production and manufacturing risks of the production and manufacturing processes are predicted based on production operation data, and a risk list is generated based on multiple production and manufacturing processes and their corresponding production and manufacturing risks. This includes: breaking down the production and manufacturing processes based on job hazard analysis to obtain detailed processes, identifying hazardous work steps, and identifying the risks of the detailed processes. The work steps include the division, decomposition, selection, and hazard identification of work activities. Risk weights are calculated for the risks of the detailed processes based on historical production operation data and expert judgment. A risk list is generated based on the risk weights, the detailed processes, and the corresponding risks.

[0007] Optionally, the risk evolution process and failure mode of the production and manufacturing process can be predicted based on risk factor data of the production and manufacturing process, including: identifying hazards and analyzing risk causes; determining the path of risk evolution into an accident based on the hazard and the inducing risk causes, and determining target control points based on the risk path; determining the risk evolution speed based on historical production operation data and expert judgment, and determining the duration after the risk goes out of control based on the speed of risk evolution into an accident; assessing the risk based on the target control points, the duration after the risk goes out of control, and the consequences caused by the risk, and determining the risk level based on the job condition hazard analysis method; and determining the failure mode based on the risk level and expert judgment.

[0008] Optionally, the manufacturing risks of power batteries in multiple manufacturing processes can be monitored using a risk identification and control list and actual manufacturing data, including: identifying the current charge, current temperature and target gas concentration in the actual manufacturing data; querying the risk identification and control list using the current charge, current temperature and target gas concentration as indexes to obtain the manufacturing risk level of the current manufacturing process; and determining the thermal runaway protection strategy for the current manufacturing process based on the manufacturing risk level.

[0009] Optionally, the thermal runaway protection strategy includes at least one of the following: engineering strategy, safety management strategy, training and education strategy, personal protective equipment strategy, and emergency response strategy. The engineering strategy includes: installing insulating sleeves at assembly and disassembly workstations, installing automatic temperature detectors at target locations, and installing forklift protection sleeves at manual transfer workstations. The safety management strategy includes: revising operating procedures, establishing double-insurance mechanisms, and specifying the number of storage layers and stacking spacing in storage areas. The training and education measures include: requiring forklift drivers and electricians to hold certificates and providing special job training. The personal protective equipment measures include: wearing insulated shoes when entering the battery assembly workshop, wearing arc-proof clothing for internal cell disassembly operations, and wearing insulated gloves for live-line work. The emergency response measures include: establishing emergency plans and conducting regular drills and assessments, as well as equipping emergency supplies.

[0010] Optionally, the production operation data includes at least one of the following: tool and equipment data, raw material data, and personnel movement data.

[0011] A second aspect of this application provides a monitoring device for manufacturing risks, comprising: an acquisition module for acquiring production operation data of multiple manufacturing processes of a power battery; a generation module for predicting manufacturing risks of the manufacturing processes based on the production operation data, and generating a risk list based on the multiple manufacturing processes and corresponding manufacturing risks; a determination module for predicting the risk evolution process and failure mode of the manufacturing processes based on risk factor data of the manufacturing processes, and determining the risk level of different risks based on the risk evolution process and failure mode; and a monitoring module for generating a risk identification and control list based on the risk list and the risk levels of different risks, and monitoring the manufacturing risks of the power battery in multiple manufacturing processes using the risk identification and control list and actual manufacturing data.

[0012] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement a method for monitoring manufacturing risks as described in the above embodiments.

[0013] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement a method for monitoring manufacturing risks as described in the above embodiments.

[0014] A fifth aspect of this application provides a computer program product, including a computer program or instructions, for implementing a method for monitoring manufacturing risks as described in the above embodiments.

[0015] Therefore, this application has the following beneficial effects:

[0016] This application's embodiments, through in-depth analysis of production operation data, accurately identify risk sources, causes, and potential consequences in each process of power battery manufacturing, quantify risk levels, implement risk prediction and evolution analysis, grasp the entire process from risk occurrence to mitigation, and clarify risk failure modes; construct a risk and control list that comprehensively covers various risk points in the production process, quickly locate risks, and implement targeted control measures. Simultaneously, a real-time monitoring and dynamic adjustment mechanism is introduced to ensure the effectiveness and adaptability of risk control measures, guaranteeing the safety and quality of power battery production. Therefore, it solves the technical problems of existing technologies, such as the lack of a systematic risk identification method and the inability to quantify risk points.

[0017] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0018] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0019] Figure 1 This is a flowchart of a method for monitoring manufacturing risks according to an embodiment of this application;

[0020] Figure 2 This is a diagram illustrating the risk identification and analysis process for power battery storage according to an embodiment of this application;

[0021] Figure 3 This is a schematic diagram of the combustion results of a power battery according to an embodiment of this application;

[0022] Figure 4 This is a schematic diagram illustrating the combustion result caused by induced thermal runaway according to an embodiment of this application;

[0023] Figure 5 This is a schematic diagram illustrating temperature and gas concentration changes according to one embodiment of this application;

[0024] Figure 6 This is a schematic diagram of the causes of thermal runaway and the safety risk model provided in the embodiments of this application;

[0025] Figure 7 A schematic diagram of a manufacturing risk monitoring device provided in an embodiment of this application;

[0026] Figure 8 This is a structural schematic diagram of a vehicle according to an embodiment of this application. Detailed Implementation

[0027] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0028] The following description, with reference to the accompanying drawings, outlines a method, apparatus, vehicle, medium, and product for monitoring manufacturing risks according to embodiments of this application. Addressing the safety management issues in the manufacturing process mentioned in the background section, this application provides a method for monitoring manufacturing risks. This method, through in-depth analysis of production operation data, accurately identifies risk sources, causes, and potential consequences in each process of power battery manufacturing, quantifies risk levels, implements risk prediction and evolution analysis, grasps the entire process from risk occurrence to resolution, and clarifies risk failure modes. A risk and control list is constructed, comprehensively covering various risk points in the production process, quickly locating risks, and implementing targeted control measures. Simultaneously, a real-time monitoring and dynamic adjustment mechanism is introduced to ensure the effectiveness and adaptability of risk control measures, guaranteeing the safety and quality of power battery production. This solves the problems of lacking a systematic risk identification method and being unable to quantify risk points in the prior art.

[0029] Specifically, Figure 1 This is a flowchart illustrating the manufacturing risk monitoring method provided in this application embodiment.

[0030] like Figure 1 As shown, the method for monitoring manufacturing risks includes the following steps:

[0031] In step S101, production operation data of multiple manufacturing processes of the power battery are acquired.

[0032] The production operation data may include at least one of the following: tool and equipment data, raw material data, and personnel movement data.

[0033] It is understood that the embodiments of this application obtain production operation data of multiple manufacturing processes of power batteries, which facilitates the subsequent quantification of risk levels based on the likelihood of risk occurrence and the severity of consequences.

[0034] In step S102, the production and manufacturing risks of the production and manufacturing process are predicted based on the production operation data, and a risk list is generated based on multiple production and manufacturing processes and their corresponding production and manufacturing risks.

[0035] It is understood that the embodiments of this application can accurately assess the risk level of the production and manufacturing process by real-time monitoring and analysis of production operation data, promptly identify potential risk factors in the production and manufacturing process, and effectively control the risk factors in the production and manufacturing process.

[0036] In this embodiment of the application, the production and manufacturing risks of the production and manufacturing process are predicted based on production operation data, and a risk list is generated based on multiple production and manufacturing processes and their corresponding production and manufacturing risks. This includes: breaking down the production and manufacturing process based on job hazard analysis to obtain a detailed process, identifying hazardous work steps, and identifying the risks of the detailed process. The work steps include work activity division, decomposition and selection, hazard source identification, etc.; calculating the risk weight of the detailed process based on historical production operation data and expert judgment; and generating a risk list based on the risk weight, the detailed process, and the corresponding risks.

[0037] It is understood that, by breaking down the manufacturing process into more detailed steps, this embodiment of the application provides a clearer understanding of the specific content and operational details of each step, identifies potential hazards and harmful factors in each step, and uses historical production data and expert judgment to quantitatively assess the identified risks and calculate their risk weights. Based on the risk weights, the detailed processes, and the corresponding risks, a detailed risk list is generated, which helps to strengthen safety production management.

[0038] Specifically, the job hazard analysis method was used to break down 24 production processes, including battery module transportation, battery assembly, battery testing, battery transport, and battery tray assembly, to identify the work steps where hazards exist. The tools, equipment, raw materials, and personnel actions used in each process were analyzed, and the risks were accurately described using a three-stage approach. The analysis results were then supplemented and improved using on-site safety observation, resulting in the identification of 115 safety risks. Taking the power battery storage process as an example... Figure 2 As shown, the transport vehicle arrives at the designated location in the warehouse; the vehicle is turned off, the forklift driver confirms the safety conditions, and the power battery is loaded and unloaded; the forklift transports the battery to the designated storage location within the warehouse, where it is loaded, unloaded, transferred, and stacked according to rules; the warehouse manager conducts daily inspections. The potential hazards of each step of the operation are analyzed and described using a "three-stage theory," summarized into two risks: 1. High ambient temperature, leaking factory buildings, and poor ventilation during storage can induce thermal runaway of the battery, causing smoke, fire, electrolyte leakage, etc.; 2. Incorrect forklift placement or unstable placement during forklift handling or transfer can lead to external damage or dropping of the battery, causing thermal runaway. Further analysis using on-site safety observation methods supplements and refines the results, revealing the missing risk of "personnel being electrocuted when lightly wiping the battery surface with a damp cloth." The same approach is then used to identify and analyze these risks in all 23 subsequent processes, forming a risk list.

[0039] In step S103, the risk evolution process and failure mode of the production and manufacturing process are predicted based on the risk factor data of the production and manufacturing process, and the risk level of different risks is determined based on the risk evolution process and failure mode.

[0040] The risk evolution process can refer to the entire development and change process from the emergence of risk factors to the eventual occurrence of production accidents or failure modes. A failure mode refers to the specific way in which a certain link in the production process fails to perform its expected function, resulting in a decline in product quality, a reduction in production efficiency, or a safety accident. It can manifest in various forms such as equipment failure, process deviation, and material defects. The risk level can include low risk, medium risk, and high risk.

[0041] It is understood that the embodiments of this application, by accurately predicting the evolution of risks and failure modes in the production process, achieve proactive prevention of potential production problems, effectively avoiding production interruptions and rework, and significantly improving production efficiency and product quality. Simultaneously, in-depth analysis based on risk data enables optimization of production processes and refined resource management. Furthermore, by clearly defining risk levels, targeted risk management strategies can be formulated, effectively reducing direct economic losses caused by production failures and lowering overall risk costs.

[0042] In this embodiment of the application, the prediction of the risk evolution process and failure mode of the production and manufacturing process based on risk factor data of the production and manufacturing process includes: acquiring the hazard source and analyzing the risk cause; determining the path of risk evolution into an accident based on the hazard source and the inducing risk cause, and determining the target control point based on the risk path; determining the risk evolution speed based on historical production operation data and expert judgment, and determining the duration after the risk goes out of control based on the speed of risk evolution into an accident; assessing the risk based on the target control point, the duration after the risk goes out of control, and the consequences caused by the risk, and determining the risk level based on the job condition hazard analysis method; and determining the failure mode based on the risk level and expert judgment.

[0043] Among them, the source of danger can refer to the source or starting point that may cause risk, the cause of risk can be the specific cause or condition that directly leads to the occurrence of risk event, such as sudden failure caused by long-term lack of equipment maintenance, misoperation caused by operators not following procedures, etc., and the consequences of risk can refer to the negative impact or loss brought about after the occurrence of risk event, which can include production interruption, decline in product quality, equipment damage, personal injury, economic loss, etc.

[0044] A risk path can be the complete process that starts from the risk source, goes through a series of triggering causes, and ultimately leads to the occurrence of a risk event. The target control point can be the key link in the occurrence of a risk event. The risk evolution speed can be described as the speed at which a risk develops, expands, and eventually occurs.

[0045] It is understood that the embodiments of this application, by comprehensively considering hazards, triggering factors, and consequences, and combining scientific assessment methods, achieve accurate quantitative risk assessment, avoiding analytical bias. Simultaneously, they clarify risk paths and key control points, and formulate targeted risk control and emergency response measures, effectively reducing the probability and impact of risk occurrence. Furthermore, they promote improved production efficiency and product quality. By preventing risks and optimizing production processes, they reduce production interruptions and waste, achieving lower production costs and reasonable control of risk costs, ensuring efficient resource utilization.

[0046] Specifically, considering the causative agent, inducing factors, harmful substances, and modes of injury, the risk evolution process and failure modes are analyzed. The job hazard analysis method is used to assess the likelihood of risk occurrence, the timing of risk exposure, and the severity of the risk's consequences. A quantitative evaluation of the risk is conducted to determine its level. Analysis of key positions such as module handling, module assembly, and the assembly of multiple parts packages reveals high risks due to internal battery short circuits in the battery assembly workshop; high risks due to personnel error in the maintenance area; and high risks due to internal short circuits in the module transfer area and the power battery storage area. Based on the risk level and type, fuzzy hierarchical analysis is applied to focus control efforts on thermal runaway and arc burns caused by internal battery short circuits, resulting in a risk identification and control list.

[0047] In step S104, a risk identification and control list is generated based on the risk list and the risk levels of different risks. The risk identification and control list and actual production and manufacturing data are used to monitor the production and manufacturing risks of power batteries in multiple production and manufacturing processes.

[0048] It is understood that the embodiments of this application, by generating a risk identification and control list and combining it with actual production and manufacturing data for risk monitoring, can significantly improve the risk management level of the power battery production and manufacturing process, enhance the pertinence and effectiveness of risk control, improve production efficiency and product quality, and reduce production costs and risk costs.

[0049] In this embodiment of the application, the manufacturing risks of power batteries in multiple manufacturing processes are monitored using a risk identification and control list and actual manufacturing data. This includes: identifying the current charge, current temperature and target gas concentration in the actual manufacturing data; querying the risk identification and control list using the current charge, current temperature and target gas concentration as indexes to obtain the manufacturing risk level of the current manufacturing process; and determining the thermal runaway protection strategy for the current manufacturing process based on the manufacturing risk level.

[0050] Thermal runaway protection strategies may include at least one of the following: engineering technology strategies, safety management strategies, training and education strategies, personal protective equipment strategies, and emergency response strategies.

[0051] It is understood that the embodiments of this application achieve real-time and accurate risk identification and assessment by monitoring key parameters in power battery production, avoiding the lag and subjectivity of traditional methods. Based on the risk assessment results, the thermal runaway protection strategy is optimized and dynamically adjusted, ensuring both production safety and improved efficiency. Simultaneously, it effectively prevents thermal runaway accidents, reduces defect rates, and significantly improves production safety and product quality.

[0052] Specifically, an experimental scheme for thermal runaway reaction of power batteries was developed to study the impact and hazards of thermal runaway caused by different reasons on the combustion process; the temperature change process of the thermal runaway reaction of power batteries on the ignition source was measured. During the experiment, temperature sensing probes were installed in the modules inside the power battery to calculate the occurrence of thermal runaway by measuring temperature changes, and the following experiments were conducted.

[0053] Experiment 1: Two type A power batteries with a state of charge (SOC) of 80% and 30% were selected and heated using the same heat source. Thermal runaway caused an open flame, and the timing began. Figure 3 As shown, the continuous combustion time of a power battery with 80% SOC is 47 minutes, and that of a power battery with 30% SOC is 12 minutes. The duration of combustion of a power battery is directly proportional to its remaining charge.

[0054] Experiment 2: Three type A power batteries were subjected to three intervention scenarios: external puncture, overcharging, and external heating, respectively, to induce three types of thermal runaway: internal short circuit, overcharging, and external heating. The same heat source was used to heat the power batteries, and the internal temperature and the time to open flame were measured. Figure 4 As shown, the SEI film inside the power battery is damaged first by external puncture, followed by overcharging and external heating. The time required for open flame to occur was measured to be 10 seconds, 60 seconds, and 600 seconds, respectively. Therefore, the time required to ignite the power battery is inversely proportional to the rate of damage to the SEI film inside the cell.

[0055] Experiment 3: A thermocouple was installed in module A inside a power battery to create a continuous heating experimental condition. Temperature sensing probes and gas concentration monitoring probes were installed at the thermocouple, near the battery cell in module A, and in module B near module A. Temperature changes and the types and concentrations of released gases were measured at different locations inside the battery under continuous heating conditions. The measurement results are as follows: Figure 5As shown, after heating for 30 minutes, the temperature of module A exceeded the thermocouple temperature and rose rapidly in the next 10 minutes, initiating a thermal runaway reaction. By 40 minutes, the temperature of module A reached 500℃ and remained at a high temperature, with combustion beginning inside and gradually releasing gases such as C2H2, PH5, CH4, H2, and CO. By 50 minutes, the concentration of released gases reached its peak and began to decrease, indicating that the combustion reaction was intense and the released combustible gases were burned. The temperature of the surrounding module B rose rapidly, and it began to burn, producing a large number of open flames, indicating that the combustion had spread.

[0056] In summary, thermal runaway caused by an internal short circuit is the first to result in combustion and reaches a violent state. Excluding external destructive factors such as puncture, thermal runaway is not instantaneous but a relatively slow process, and it takes time for open flames to develop after thermal runaway occurs. Based on the gases released in the experiment, if water is used for extinguishing, according to the chemical reaction principle: PF5 + H2O → H3PO4 + HF, a small amount of toxic HF gas will be produced. The above experiments verify the thermal runaway cause and safety risk model established in the embodiments of this application. Figure 6 As shown, the results are consistent with the experimental results and have application value.

[0057] In this application embodiment, the engineering and technical strategies include: installing insulating sleeves at assembly and disassembly positions, installing automatic temperature detectors at target locations, and installing forklift protection sleeves at manual transfer positions; the safety management strategies include: revising operating procedures, setting up a double insurance mechanism, and specifying the number of storage layers and stacking spacing in storage areas; training and education measures include: requiring forklift drivers and electricians to hold certificates and providing special job training; personal protective measures include: wearing insulated shoes when entering the battery assembly workshop, wearing arc-proof clothing for disassembling battery cells, and wearing insulated gloves for working with live wires; and emergency response measures include: establishing emergency plans and conducting regular drills and assessments, as well as equipping emergency supplies.

[0058] It is understood that the embodiments of this application effectively prevent safety accidents and ensure the safety of employees and equipment by adopting multi-dimensional strategies such as engineering technology, safety management, and personal protective equipment; at the same time, they improve product quality and stability through strict operating procedures and quality control; further optimize production processes, improve equipment efficiency, and reduce interruptions, thereby enhancing the company's production efficiency and market competitiveness; and finally, through training and education and emergency preparedness, they comprehensively enhance employees' safety awareness and create a positive safety culture environment.

[0059] Specifically, the engineering and technical measures include: installing insulating sleeves at the power battery assembly and disassembly stations; installing automatic temperature detectors at target locations where dynamic temperature probes cannot be installed, and conducting regular manual temperature measurements at manual transfer stations; and installing fork protectors at manual transfer stations to prevent the forks from puncturing multiple parts assemblies or power batteries, etc.

[0060] Safety management measures include: to prevent cooling system leaks, revising operating procedures to require airtightness testing before maintenance and installation; installing a double-safety mechanism with interlocking indicator lights and on-site driver confirmation at automated loading and unloading stations for multiple parts; strictly regulating the number of storage layers and stacking spacing in the power battery storage area, requiring that battery packs and modules be stored using special containers to ensure neat and stable placement, with each storage stack area ≤150㎡, internal column spacing ≥0.5m, and distance between stacks ≥1m; stacks ≥0.5m from walls, ≥0.3m from columns, ≥0.3m from roofs and beams, and ≥0.5m from lights; main passage width ≥2m, etc.

[0061] Education and training measures include: requiring forklift drivers to hold certificates for manual forklift transport positions; requiring electricians to hold electrician certificates for power battery return and repair positions; and requiring specialized training for positions designing new energy power batteries.

[0062] Personal protective measures include: wearing insulated shoes when entering the battery assembly workshop; wearing arc-proof clothing when disassembling battery cells; and wearing insulated gloves when performing battery repairs or other work involving live electrical work.

[0063] Emergency response measures include: establishing emergency plans and regularly organizing drills and assessments; and equipping the area with emergency supplies such as fire sand, smoke masks, mobile fans, emergency response pits, and explosion-proof boxes.

[0064] The manufacturing risk monitoring method proposed in this application, through in-depth analysis of production operation data, accurately identifies risk sources, causes, and potential consequences in each process of power battery manufacturing, quantifies risk levels, implements risk prediction and evolution analysis, grasps the entire process from risk occurrence to mitigation, and clarifies risk failure modes; it constructs a risk and control list that comprehensively covers various risk points in the production process, quickly locates risks, and implements targeted control measures. Simultaneously, a real-time monitoring and dynamic adjustment mechanism is introduced to ensure the effectiveness and adaptability of risk control measures, guaranteeing the safety and quality of power battery production. This solves the problems of lacking a systematic risk identification method and being unable to quantify risk points in existing technologies.

[0065] Next, referring to the accompanying drawings, a monitoring device for manufacturing risks proposed according to an embodiment of this application is described.

[0066] Figure 7 This is a block diagram of a manufacturing risk monitoring device according to an embodiment of this application.

[0067] like Figure 7 As shown, the monitoring device 10 for manufacturing risks includes: an acquisition module 100, a generation module 200, an acquisition module 300, and a monitoring module 400.

[0068] The module 100 is used to acquire production operation data for multiple manufacturing processes of the power battery; the generation module 200 is used to predict the manufacturing risks of the manufacturing processes based on the production operation data, and generate a risk list based on multiple manufacturing processes and corresponding manufacturing risks; the determination module 300 is used to predict the risk evolution process and failure mode of the manufacturing processes based on the risk factor data of the manufacturing processes, and determine the risk level of different risks based on the risk evolution process and failure mode; the monitoring module 400 is used to generate a risk identification and control list based on the risk list and the risk levels of different risks, and monitor the manufacturing risks of the power battery in multiple manufacturing processes using the risk identification and control list and actual manufacturing data.

[0069] It should be noted that the explanation of the aforementioned method for monitoring manufacturing risks also applies to the manufacturing risk monitoring device of this embodiment, and will not be repeated here.

[0070] The manufacturing risk monitoring device proposed in this application, through in-depth analysis of production operation data, accurately identifies risk sources, causes, and potential consequences in each process of power battery manufacturing, quantifies risk levels, implements risk prediction and evolution analysis, grasps the entire process from risk occurrence to mitigation, and clarifies risk failure modes; it constructs a risk and control list that comprehensively covers various risk points in the production process, quickly locates risks, and implements targeted control measures. Simultaneously, it introduces a real-time monitoring and dynamic adjustment mechanism to ensure the effectiveness and adaptability of risk control measures, guaranteeing the safety and quality of power battery production. Therefore, it solves the problems of lacking a systematic risk identification method and being unable to quantify risk points in existing technologies.

[0071] Figure 8 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include:

[0072] The memory 801, the processor 802, and the computer program stored on the memory 801 and capable of running on the processor 802.

[0073] When the processor 802 executes the program, it implements the manufacturing risk monitoring method provided in the above embodiments.

[0074] Furthermore, the vehicle also includes:

[0075] Communication interface 803 is used for communication between memory 801 and processor 802.

[0076] The memory 801 is used to store computer programs that can run on the processor 802.

[0077] The memory 801 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0078] If the memory 801, processor 802, and communication interface 803 are implemented independently, then the communication interface 803, memory 801, and processor 802 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0079] Optionally, in a specific implementation, if the memory 801, processor 802, and communication interface 803 are integrated on a single chip, then the memory 801, processor 802, and communication interface 803 can communicate with each other through an internal interface.

[0080] The processor 802 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0081] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for monitoring manufacturing risks.

[0082] This application also provides a computer program product, including a computer program or instructions, for implementing a manufacturing risk monitoring method as described in the above embodiments.

[0083] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0084] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0085] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0086] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0087] Those skilled in the art will understand that all or part of the steps of the methods implementing the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0088] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for monitoring manufacturing risks, characterized in that, Includes the following steps: Acquire production operation data for multiple manufacturing processes of power batteries; Based on the production operation data, predict the production and manufacturing risks of the production and manufacturing process, and generate a risk list based on the multiple production and manufacturing processes and their corresponding production and manufacturing risks; The step of predicting the production and manufacturing risks of the production and manufacturing processes based on the production operation data, and generating a risk list based on multiple production and manufacturing processes and their corresponding production and manufacturing risks, includes: breaking down the production and manufacturing processes based on job hazard analysis to obtain detailed processes, identifying hazardous work steps, and identifying the risks of the detailed processes, wherein the work steps include work activity division, decomposition and selection, and hazard source identification; calculating risk weights for the risks of the detailed processes based on historical production operation data and expert judgment; and generating a risk list based on the risk weights, the detailed processes, and their corresponding risks. The process involves predicting the risk evolution and failure modes of a production process based on risk factor data, and determining risk levels for different risks based on the risk evolution and failure modes. This prediction includes: identifying hazards and analyzing their causes; determining the path from hazard to accident based on the hazard and the cause, and identifying target control points based on the risk path; determining the risk evolution rate based on historical production data and expert judgment, and determining the duration of risk loss of control based on the rate of risk evolution to an accident; assessing the risk based on the target control points, the duration of risk loss of control, and the consequences of the risk, and determining the risk level based on job condition hazard analysis; and determining the failure modes based on the risk level and expert judgment. A risk identification and control list is generated based on the risk list and the risk levels of different risks. The risk identification and control list and actual production data are used to monitor the production risks of the power battery in multiple production processes. The monitoring of the production risks of the power battery in multiple production processes using the risk identification and control list and actual production data includes: identifying the current charge, current temperature, and target gas concentration in the actual production data; querying the risk identification and control list using the current charge, current temperature, and target gas concentration as indexes to obtain the production risk level of the current production process; and determining the thermal runaway protection strategy for the current production process based on the production risk level.

2. The method for monitoring manufacturing risks according to claim 1, characterized in that, The thermal runaway protection strategy includes at least one of the following: engineering technology strategy, safety management strategy, training and education strategy, personal protective equipment strategy, and emergency response strategy. The engineering and technical strategies include: installing insulating sleeves at assembly and disassembly stations, installing automatic temperature detectors at target locations, and installing fork protection sleeves at manual transfer stations; The security management strategy includes: revising operating procedures, setting up a dual-insurance mechanism, and specifying the number of storage layers and stacking spacing in the storage area; The training and education strategies include: certification for forklift drivers and electricians, as well as special job training. The individual protection strategies include: wearing insulated shoes when entering the battery assembly workshop, wearing arc-proof clothing for disassembling the inside of the battery cells, and wearing insulated gloves for working with live wires. The emergency response strategy includes: establishing emergency plans and conducting regular drills and assessments, as well as equipping emergency supplies.

3. The method for monitoring manufacturing risks according to claim 1, characterized in that, The production operation data includes at least one of the following: tool and equipment data, raw material data, and personnel movement data.

4. A monitoring device for manufacturing risks, characterized in that, include: The acquisition module is used to acquire production operation data for multiple manufacturing processes of power batteries; The generation module is used to predict the production and manufacturing risks of the production and manufacturing process based on the production operation data, and generate a risk list based on multiple production and manufacturing processes and their corresponding production and manufacturing risks. The step of predicting the production and manufacturing risks of the production and manufacturing processes based on the production operation data, and generating a risk list based on multiple production and manufacturing processes and their corresponding production and manufacturing risks, includes: breaking down the production and manufacturing processes based on job hazard analysis to obtain detailed processes, identifying hazardous work steps, and identifying the risks of the detailed processes, wherein the work steps include work activity division, decomposition and selection, and hazard source identification; calculating risk weights for the risks of the detailed processes based on historical production operation data and expert judgment; and generating a risk list based on the risk weights, the detailed processes, and their corresponding risks. The determination module is used to predict the risk evolution process and failure mode of the production and manufacturing process based on risk factor data of the production and manufacturing process, and to determine the risk level of different risks based on the risk evolution process and failure mode. The prediction of the risk evolution process and failure mode of the production and manufacturing process based on risk factor data includes: acquiring hazard sources and analyzing risk causes; determining the path of risk evolution into an accident based on the hazard sources and risk causes, and determining target control points based on the risk paths; determining the risk evolution speed based on historical production data and expert judgment, and determining the duration of risk loss of control based on the speed of risk evolution into an accident; assessing the risk based on the target control points, the duration of risk loss of control, and the consequences caused by the risk, and determining the risk level based on job condition hazard analysis; and determining the failure mode based on the risk level and expert judgment. The monitoring module is used to generate a risk identification and control list based on the risk list and the risk levels of different risks, and to monitor the manufacturing risks of the power battery in multiple manufacturing processes using the risk identification and control list and actual manufacturing data. The monitoring of the manufacturing risks of the power battery in multiple manufacturing processes using the risk identification and control list and actual manufacturing data includes: identifying the current charge level, current temperature, and target gas concentration in the actual manufacturing data; querying the risk identification and control list using the current charge level, current temperature, and target gas concentration as indexes to obtain the manufacturing risk level of the current manufacturing process; and determining the thermal runaway protection strategy for the current manufacturing process based on the manufacturing risk level.

5. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the manufacturing risk monitoring method according to any one of claims 1-3.

6. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed, they implement the manufacturing risk monitoring method according to any one of claims 1-3.

7. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed, they implement the manufacturing risk monitoring method according to any one of claims 1-3.

Images