Knowledge intelligent management method and platform for vehicle supervision process based on artificial intelligence

Abstract

The present invention discloses a knowledge intelligent management method and platform for a vehicle production supervision process based on artificial intelligence, and relates to the technical field of production line management. The steps of the method include: collecting equipment safety impact factors and environmental impact factors of the production line during vehicle production; calculating equipment safety values ​​through equipment safety calculation strategies; substituting the collected environmental impact factors into environmental monitoring strategies to calculate environmental impact values; using equipment safety values ​​and environmental impact values ​​as inputs of equipment maintenance prediction models, identifying equipment abnormalities, and issuing early warnings; establishing a knowledge base for vehicle production processes, and updating equipment safety information in the vehicle production process in real time. The present invention solves the problem of unnecessary losses such as production line downtime, thereby improving production efficiency, and at the same time achieves the effect of real-time monitoring of equipment risks caused by the influence of personnel operating skill proficiency and the influence of the internal environment of the equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of production line management, and in particular to a knowledge intelligent management method and platform for a vehicle production supervision process based on artificial intelligence. Background Art

[0002] With the rapid development of the automobile industry, automobile production processes are becoming more and more complex. The intelligentization, automation, networking, intelligence and informatization of vehicle manufacturing processes have become the main development direction of the automobile industry. In the development of the automobile industry, in order to improve the safety, environmental protection level and production efficiency of vehicle production line equipment, it is necessary to monitor and manage the vehicle production process through modern information technology. In the automobile production process, a variety of equipment, processes and materials are involved, which requires monitoring and management of these equipment, processes and materials during the production process. At present, the monitoring and management of the vehicle production process mainly relies on manual work, and there are the following problems: 1. There are many types of equipment, processes and materials involved in the vehicle production process, and the workload of manual monitoring and management is large, time-consuming and labor-intensive; 2. The accuracy and efficiency of manual monitoring and management are low, which cannot meet the development needs of the modern automobile industry.

[0003] For example, the existing Chinese patent with publication number CN115423258A discloses a supervision information management platform, including: a personnel information system for the attendance information of management personnel; a project information system for presenting various project information and quality issues; a file information system for saving and organizing the whole process files of the supervision project and the work report files of weekly and monthly reports; a communication information system for the communication and interaction between the owner, the vehicle factory and the supervision project department; the platform setting system is connected to the personnel information system, the project information system, the file information system and the communication information system respectively, and is used to provide the system administrator with the function of maintaining project information, user information, role information and operation authority. Through this platform, the information exchange and interaction between the on-site supervision project department, the client, the user and the relevant personnel of the vehicle factory can be realized, so that the supervision data can be digitized and the process supervision problems can be closed-loop for later tracing and reference. However, this design does not take into account the impact of equipment safety performance. Therefore, the present invention proposes a knowledge intelligent management method and platform for the vehicle supervision process based on artificial intelligence to improve the safety, environmental protection level and production efficiency of vehicle production line equipment. The following objectives can be achieved through the present invention: 1. Improving the safety of vehicle production line equipment and reducing the possibility of accidents; 2. Improving the production efficiency of vehicle production line equipment and reducing production costs; 3. Improving the environmental protection level of vehicle production line equipment and reducing environmental pollution. Summary of the invention

[0004] The purpose of the present invention is to provide a knowledge intelligent management method and platform for the vehicle supervision process based on artificial intelligence to solve the existing problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a knowledge intelligent management method for vehicle supervision process based on artificial intelligence, comprising the following steps:

[0006] S1. Collect the safety impact factors and environmental impact factors of production line equipment during vehicle production;

[0007] S2. Substituting the collected production line equipment safety impact factors into the equipment safety calculation strategy to calculate the equipment safety value;

[0008] S3, substituting the collected environmental impact factors into the environmental monitoring strategy to calculate the environmental impact value;

[0009] S4, using the equipment safety value and environmental impact value as inputs to an equipment maintenance prediction model, identifying abnormal equipment conditions, and issuing an early warning;

[0010] S5. Establish a knowledge base of the vehicle production process and update the vehicle production process equipment safety information in real time.

[0011] A further improvement of the present invention is that the equipment safety influencing factors include the rotation speed, vibration frequency, temperature and personnel proficiency of the vehicle production line equipment; and the environmental influencing factors include air humidity and air quality.

[0012] A further improvement of the present invention is that the device security computing strategy comprises the following steps:

[0013] S21. Install n temperature sensors evenly on the surface of the vehicle production line equipment to obtain the surface temperature data set (Tep 1 ,Tep 2 ,...,Tep n ), where Tep i Indicates the temperature value of the i-th temperature sensor, obtains the ambient temperature amt, and calculates the device temperature threat value

[0014]

[0015] S22, obtaining the equipment motor speed threat value ems through the equipment motor speed threat calculation strategy;

[0016] S23, obtaining the equipment vibration stress value evs through the equipment vibration monitoring strategy;

[0017] S24. After the employee completes the process, the product completion quantity pcq and completion time pct are uploaded to the system, and the operator proficiency poo = pcq·py / pct is calculated, where py represents the employee's years of work;

[0018] S25. Calculate the equipment safety value esa = α 1 ·ett+α 2 ·ems+α 3 evs+α 4 poo, where α 1 Represents the device temperature threat value weight, α 2 Indicates the weight of the device motor speed coercion value, α 3 Represents the weight of the device vibration stress value, α 4 Indicates the operator proficiency weight.

[0019] A further improvement of the present invention is that the specific steps of the device motor threat speed calculation strategy include:

[0020] S221, collect the motor output frequency data set (mof 1 ,mof 2 ,...,mof T1 / t ), the sampling time interval is t, where mof T1 / t Indicates the motor output frequency collected at the T1 / tth time;

[0021] S222. Calculate the average speed of the equipment motor Where npp represents the number of pole pairs of the motor;

[0022] S223, calculate the equipment motor speed stress value Where sts represents the set safety device motor speed.

[0023] The present invention is further improved in that the equipment vibration monitoring strategy specifically comprises the following steps:

[0024] S231, installing a displacement sensor at a machining location in a vehicle production line device to obtain a vibration signal curve within a time T2;

[0025] S232, obtaining the distance from the peak to the trough of the vibration signal from the vibration signal curve, and obtaining an amplitude data set (amp 1 ,amp 2 ,...,amp m ), where amp m Represents the distance from the mth peak to the trough, m represents the number of peaks, and the vibration impact value of the equipment is obtained

[0026] S233. Calculate the vibration stress value of the equipment mav represents the maximum safe value of equipment vibration.

[0027] A further improvement of the present invention is that the specific steps of substituting the environmental impact factors into the environmental monitoring strategy include:

[0028] S31, the air humidity is obtained by installing a humidity sensor on each of the conveying equipment, processing equipment, testing equipment, assembly equipment and coating equipment to obtain a vehicle production line equipment humidity data set (ehu 1 ,ehu 2 ,ehu 3 ,ehu 4 ,ehu 5 ), where ehu 1 Indicates the humidity of the transmission equipment, ehu 2 Indicates the humidity of the processing equipment, ehu 3 Indicates the humidity of the detection equipment, ehu 4 Indicates the humidity of the assembly equipment, ehu 5 Indicates the humidity of the coating equipment and calculates the air humidity value Among them, β 1 represents the humidity weight of the transmission equipment, β 2 represents the humidity weight of the processing equipment, β 3 represents the humidity weight of the detection equipment, β 4 represents the humidity weight of the assembly equipment, β 5 Indicates the humidity weight of the coating equipment;

[0029] S32, the air quality is obtained by installing a particle sensor near the humidity sensor, and extracting an air particle concentration data set (apm 1 ,apm 2 ,apm 3 ,apm 4 ,apm 5 ), where apm 1 Indicates the concentration of air particles around the transmission equipment, apm 2 Indicates the concentration of air particles around the processing equipment, apm 3 Indicates the concentration of air particles around the detection device, apm 4 Indicates the concentration of air particles around the assembly equipment, apm 5 Indicates the concentration of air particles around the coating equipment and calculates the average concentration of particles

[0030] S33, calculating the environmental impact value Among them, χ 1 Represents the weight of air humidity value, χ 2 Represents the mean weight of particle concentration.

[0031] The present invention is further improved in that the specific step S4 comprises:

[0032] S41, establish an equipment maintenance prediction model, take the support vector machine model as the main body, extract the production line equipment surface temperature data set corresponding to the equipment temperature threat value ett≤0 in step S2, the average speed data set of the equipment motor corresponding to the equipment motor speed threat value ems≤0, and the equipment vibration impact value data set corresponding to the equipment vibration threat value evs≤0 as training data;

[0033] S42: Use the equipment safety value and environmental impact value as inputs to an equipment maintenance prediction model to determine the abnormality type and make corresponding adjustments.

[0034] On the other hand, the present invention provides a knowledge intelligent management platform for vehicle supervision process based on artificial intelligence, including:

[0035] Data collection and storage module, used to collect safety and environmental impact factors of production line equipment during vehicle production;

[0036] A data processing and analysis module, used to substitute the collected production line equipment safety impact factors into the equipment safety calculation strategy to calculate the equipment safety value; substitute the collected environmental impact factors into the environmental monitoring strategy to calculate the environmental impact value;

[0037] An intelligent decision-making module, used to use the equipment safety value and environmental impact value as inputs to an equipment maintenance prediction model, identify abnormal equipment conditions, and issue early warnings;

[0038] The knowledge management module is used to establish a knowledge base for the vehicle production process and update the vehicle production process equipment safety information in real time.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows: first, the present invention can predict the abnormal status of equipment in advance and avoid unnecessary losses such as production line shutdown by real-time monitoring of the safety factors and environmental factors of production line equipment and making intelligent decisions, thereby improving production efficiency, predicting the abnormal status of equipment in advance, avoiding accidents, and ensuring production safety. The platform can improve the safety, environmental protection level and production efficiency of production line equipment, promote the intelligent and sustainable development of the vehicle production process, and achieve the effect of real-time monitoring of equipment risks caused by the influence of personnel's operating skills and the internal environment of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The flowchart of the knowledge intelligent management method of the vehicle supervision process based on artificial intelligence of the present invention;

[0041] Figure 2This is a system framework diagram of the knowledge intelligent management platform for the vehicle manufacturing supervision process based on artificial intelligence of the present invention. DETAILED DESCRIPTION

[0042] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0043] The term "and / or" is only a description of the association relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " generally indicates that the related objects are in an "or" relationship.

[0044] Example 1

[0045] Figure 1 The flowchart of the knowledge intelligent management method of the vehicle supervision process based on artificial intelligence disclosed in this embodiment is shown, and the steps are as follows:

[0046] S1. Collect production line equipment safety influencing factors and environmental influencing factors during vehicle production; equipment safety influencing factors include the rotation speed, vibration frequency, temperature and personnel proficiency of vehicle production line equipment; the environmental influencing factors include air humidity and air quality.

[0047] S2. Substituting the collected production line equipment safety impact factors into the equipment safety calculation strategy to calculate the equipment safety value; the equipment safety calculation strategy includes the following steps:

[0048] S21. Install n temperature sensors evenly on the surface of the vehicle production line equipment to obtain the surface temperature data set (Tep 1 ,Tep 2 ,...,Tep n ), where Tep i Indicates the temperature value of the i-th temperature sensor, obtains the ambient temperature amt, and calculates the device temperature threat value

[0049] S22, obtaining the device motor speed threat value ems through the device motor speed threat calculation strategy; the device motor threat speed calculation strategy specifically comprises the following steps:

[0050] S221, collect the motor output frequency data set (mof 1 ,mof 2 ,...,mof T1 / t), the sampling time interval is t, where mof T1 / t Indicates the motor output frequency collected at the T1 / tth time;

[0051] S222. Calculate the average speed of the equipment motor Where npp represents the number of pole pairs of the motor;

[0052] S223, calculate the equipment motor speed stress value Where sts represents the set safety device motor speed.

[0053] S23, obtaining the equipment vibration stress value evs through the equipment vibration monitoring strategy; the specific steps of the equipment vibration monitoring strategy include:

[0054] S231, installing a displacement sensor at a machining location in a vehicle production line device to obtain a vibration signal curve within a time T2;

[0055] S232, obtaining the distance from the peak to the trough of the vibration signal from the vibration signal curve, and obtaining an amplitude data set (amp 1 ,amp 2 ,...,amp m ), where amp m Represents the distance from the mth peak to the trough, m represents the number of peaks, and the vibration impact value of the equipment is obtained

[0056] S233. Calculate the vibration stress value of the equipment mav represents the maximum safe value of equipment vibration.

[0057] S24. After the employee completes the process, the product completion quantity pcq and completion time pct are uploaded to the system, and the operator proficiency poo = pcq·py / pct is calculated, where py represents the employee's years of work;

[0058] S25. Calculate the equipment safety value esa = α 1 ·ett+α 2 ·ems+α 3 evs+α 4 poo, where α 1 Represents the device temperature threat value weight, α 2 Indicates the weight of the device motor speed coercion value, α 3 Represents the weight of the device vibration stress value, α 4 Indicates the operator proficiency weight.

[0059] S3, substituting the collected environmental impact factors into the environmental monitoring strategy to calculate the environmental impact value; the specific steps of substituting the environmental impact factors into the environmental monitoring strategy include:

[0060] S31, the air humidity is obtained by installing a humidity sensor on each of the conveying equipment, processing equipment, testing equipment, assembly equipment and coating equipment to obtain a vehicle production line equipment humidity data set (ehu 1 ,ehu 2 ,ehu 3 ,ehu 4 ,ehu 5 ), where ehu 1 Indicates the humidity of the transmission equipment, ehu 2 Indicates the humidity of the processing equipment, ehu 3 Indicates the humidity of the detection equipment, ehu 4 Indicates the humidity of the assembly equipment, ehu 5 Indicates the humidity of the coating equipment and calculates the air humidity value Among them, β 1 represents the humidity weight of the transmission equipment, β 2 represents the humidity weight of the processing equipment, β 3 represents the humidity weight of the detection equipment, β 4 represents the humidity weight of the assembly equipment, β 5 Indicates the humidity weight of the coating equipment;

[0061] S32, the air quality is obtained by installing a particle sensor near the humidity sensor, and extracting an air particle concentration data set (apm 1 ,apm 2 ,apm 3 ,apm 4 ,apm 5 ), where apm 1 Indicates the concentration of air particles around the transmission equipment, apm 2 Indicates the concentration of air particles around the processing equipment, apm 3 Indicates the concentration of air particles around the detection device, apm 4 Indicates the concentration of air particles around the assembly equipment, apm 5 Indicates the concentration of air particles around the coating equipment and calculates the average concentration of particles

[0062] S33, calculating the environmental impact value Among them, χ 1 Represents the weight of air humidity value, χ 2 Represents the mean weight of particle concentration.

[0063] S4. Using the equipment safety value and environmental impact value as inputs to the equipment maintenance prediction model, identifying abnormal equipment status, and issuing an early warning. The specific steps include:

[0064] S41, establish an equipment maintenance prediction model, take the support vector machine model as the main body, extract the production line equipment surface temperature data set corresponding to the equipment temperature threat value ett≤0 in step S2, the average speed data set of the equipment motor corresponding to the equipment motor speed threat value ems≤0, and the equipment vibration impact value data set corresponding to the equipment vibration threat value evs≤0 as training data;

[0065] S42: Use the equipment safety value and environmental impact value as inputs to an equipment maintenance prediction model to determine the abnormality type and make corresponding adjustments.

[0066] S5. Establish a knowledge base of the vehicle production process and update the vehicle production process equipment safety information in real time.

[0067] The threshold and weight settings can be set by the operator.

[0068] Example 2

[0069] Figure 2 The framework diagram of the knowledge intelligent management platform system of the vehicle supervision process based on artificial intelligence of the present invention is shown. Based on the same inventive concept as Example 1, the present invention provides a knowledge intelligent management platform system of the vehicle supervision process based on artificial intelligence, including:

[0070] Data collection and storage module, used to collect safety and environmental impact factors of production line equipment during vehicle production;

[0071] A data processing and analysis module, used to substitute the collected production line equipment safety impact factors into the equipment safety calculation strategy to calculate the equipment safety value; substitute the collected environmental impact factors into the environmental monitoring strategy to calculate the environmental impact value;

[0072] An intelligent decision-making module, used to use the equipment safety value and environmental impact value as inputs to an equipment maintenance prediction model, identify abnormal equipment conditions, and issue early warnings;

[0073] The knowledge management module is used to establish a knowledge base for the vehicle production process and update the vehicle production process equipment safety information in real time.

[0074] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0075] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0076] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0078] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. The knowledge intelligent management method of vehicle supervision process based on artificial intelligence is characterized by: The following steps are involved: S1. Collect the safety impact factors and environmental impact factors of production line equipment during vehicle production; S2. Substituting the collected production line equipment safety impact factors into the equipment safety calculation strategy to calculate the equipment safety value; S3, substituting the collected environmental impact factors into the environmental monitoring strategy to calculate the environmental impact value; S4, using the equipment safety value and environmental impact value as inputs to an equipment maintenance prediction model, identifying abnormal equipment conditions, and issuing an early warning; S5. Establish a knowledge base of the vehicle production process and update the equipment safety information of the vehicle production process in real time; The equipment safety influencing factors include the rotation speed, vibration frequency, temperature and personnel proficiency of the vehicle production line equipment; the environmental influencing factors include air humidity and air quality; The device security computing strategy includes the following steps: S21. Install n temperature sensors evenly on the surface of the vehicle production line equipment to obtain the surface temperature data set of the production line equipment (Tep1, Tep2, ..., Tep n ), where Tep i Indicates the temperature value of the i-th temperature sensor, obtains the ambient temperature amt, and calculates the device temperature threat value S22, obtaining the equipment motor speed threat value ems through the equipment motor speed threat calculation strategy; S23, obtaining the equipment vibration stress value evs through the equipment vibration monitoring strategy; S24. After the employee completes the process, the product completion quantity pcq and completion time pct are uploaded to the system, and the operator proficiency poo = pcq·py / pct is calculated, where py represents the employee's years of work; S25. Calculate the equipment safety value esa = α1·ett+α2·ems+α3·evs+α4·poo, where α1 represents the equipment temperature threat value weight, α2 represents the equipment motor speed threat value weight, α3 represents the equipment vibration threat value weight, and α4 represents the operator proficiency weight; The specific steps of the equipment motor threat speed calculation strategy include: S221, collect the motor output frequency data set (mof1, mof2, ..., mof T1 / t ), the sampling time interval is t, where mof T1 / t Indicates the motor output frequency collected at the T1 / tth time; S222. Calculate the average speed of the equipment motor Where npp represents the number of pole pairs of the motor; S223, calculate the equipment motor speed stress value Where sts represents the set safety device motor speed; The specific steps of the equipment vibration monitoring strategy include: S231, installing a displacement sensor at a machining location in a vehicle production line device to obtain a vibration signal curve within a time T2; S232, obtaining the distance from the peak to the trough of the vibration signal from the vibration signal curve, and obtaining an amplitude data set (amp1, amp2, ..., amp m ), where amp m Represents the distance from the mth peak to the trough, m represents the number of peaks, and the vibration impact value of the equipment is obtained S233. Calculate the vibration stress value of the equipment mav represents the maximum safe value of equipment vibration; The specific steps of substituting the environmental impact factors into the environmental monitoring strategy include: S31. The air humidity is obtained by installing a humidity sensor on each of the conveying equipment, processing equipment, testing equipment, assembly equipment and coating equipment to obtain a vehicle production line equipment humidity data set (ehu1, ehu2, ehu3, ehu4, ehu5), where ehu1 represents the conveying equipment humidity, ehu2 represents the processing equipment humidity, ehu3 represents the testing equipment humidity, ehu4 represents the assembly equipment humidity, and ehu5 represents the coating equipment humidity. The air humidity value is calculated. Among them, β1 represents the humidity weight of the transmission equipment, β2 represents the humidity weight of the processing equipment, β3 represents the humidity weight of the detection equipment, β4 represents the humidity weight of the assembly equipment, and β5 represents the humidity weight of the coating equipment; S32, the air quality is obtained by installing a particle sensor near the humidity sensor, extracting an air particle concentration data set (apm1, apm2, apm3, apm4, apm5), where apm1 represents the air particle concentration around the transmission equipment, apm2 represents the air particle concentration around the processing equipment, apm3 represents the air particle concentration around the detection equipment, apm4 represents the air particle concentration around the assembly equipment, apm5 represents the air particle concentration around the coating equipment, and calculating the average particle concentration S33, calculating the environmental impact value Among them, χ1 represents the weight of air humidity value, and χ2 represents the weight of the mean value of particle concentration; The specific steps of S4 include: S41, establish an equipment maintenance prediction model, take the support vector machine model as the main body, extract the production line equipment surface temperature data set corresponding to the equipment temperature threat value ett≤0 in step S2, the average speed data set of the equipment motor corresponding to the equipment motor speed threat value ems≤0, and the equipment vibration impact value data set corresponding to the equipment vibration threat value evs≤0 as training data; S42: Use the equipment safety value and environmental impact value as inputs to an equipment maintenance prediction model to determine the abnormality type and make corresponding adjustments.

2. A knowledge intelligent management platform for vehicle manufacturing supervision process based on artificial intelligence, used to execute the knowledge intelligent management method for vehicle manufacturing supervision process based on artificial intelligence as claimed in claim 1, characterized in that: include: Data collection and storage module, used to collect safety and environmental impact factors of production line equipment during vehicle production; A data processing and analysis module, used to substitute the collected production line equipment safety impact factors into the equipment safety calculation strategy to calculate the equipment safety value; substitute the collected environmental impact factors into the environmental monitoring strategy to calculate the environmental impact value; An intelligent decision-making module, used to use the equipment safety value and environmental impact value as inputs to an equipment maintenance prediction model, identify abnormal equipment conditions, and issue early warnings; The knowledge management module is used to establish a knowledge base for the vehicle production process and update the vehicle production process equipment safety information in real time.

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