A method and system for automobile production monitoring and management based on artificial intelligence

Abstract

The present invention relates to the field of data processing technology, and in particular to an automobile production monitoring management method and system based on artificial intelligence. The method comprises the steps of collecting paint usage data, obtaining an overall fitting straight line and each fitting straight line based on the paint usage data; obtaining the prominence of each paint usage data according to the difference between the overall fitting straight line and each fitting straight line, obtaining prominence points according to the prominence of each paint usage data, obtaining parameters according to the proportion of the prominence points to the total number of paint usage data, obtaining parameters according to the area of ​​the area enclosed by the maximum fitting curve and the mean line in the paint usage data sequence, and the difference between the area of ​​the area enclosed by the minimum fitting curve and the mean line in the paint usage data sequence, and predicting the daily paint usage in the next month according to the parameters and the parameters, and the present invention improves the prediction results.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an automobile production monitoring and management method and system based on artificial intelligence. Background Art

[0002] With the rapid development of the automobile industry, monitoring and management in the automobile production process has become particularly important. Especially in the painting process, the amount of paint, the quality of the paint, and the production efficiency are directly related to the final quality of the product and the economic benefits of the enterprise. However, there are many shortcomings in the traditional manual monitoring method, such as incomplete monitoring, inaccurate data recording, slow response speed, etc. These problems seriously restrict the efficiency and quality improvement of the painting process. At the same time, the rapid development of artificial intelligence technology has provided new solutions for automobile production monitoring and management. Artificial intelligence technology can realize real-time monitoring and data analysis of the production process, helping enterprises to accurately grasp information such as production progress, equipment status, and product quality. Especially in the prediction of paint usage, artificial intelligence technology can analyze historical data and real-time data, establish a prediction model, and realize accurate prediction of paint usage, thereby helping enterprises optimize production plans, reduce waste, and improve production efficiency.

[0003] The patent application document currently published with the publication number CN111983478A is a method for detecting SOC anomalies in an electrochemical energy storage power station based on the Holt linear trend model. First, the SOC data of the historical operating status of the electrochemical energy storage power station is collected; a Holt linear trend model of SOC changes over time is established using a time series analysis algorithm; the SOC value of the electrochemical energy storage power station is predicted in real time; the current SOC value of the electrochemical energy storage power station is collected; and the SOC state is determined based on the difference between the collected real-time SOC value and the predicted SOC value. The second abnormal situation detection step is: obtaining the SOC value of the entire electrochemical energy storage power station; obtaining the SOC value of each unit of the electrochemical energy storage power station; comparing the SOC value of each unit with the SOC value of the entire electrochemical energy storage power station in turn; calculating the average value of the SOC of each unit and comparing it with the SOC value of the entire electrochemical energy storage power station.

[0004] In the field of automobile production, the prediction of coating usage may need to respond quickly to changes in production plans and market demand, and the parameters in the Holt linear trend model in the above method and Usually fixed, but in the automobile production process, the change in paint usage may be affected by many factors, such as the adjustment of production plans, etc. The speed and degree of change of these factors may be different, so fixed parameters may not be able to accurately capture these changes, resulting in inaccurate prediction results. Summary of the invention

[0005] In order to solve the problem that the change of coating amount in the automobile production process may be affected by various factors, the fixed parameters in the Holt linear trend model are and It may be impossible to accurately capture these changes, resulting in inaccurate prediction results. The present invention provides an automobile production monitoring and management method and system based on artificial intelligence.

[0006] In a first aspect, the present invention provides an automobile production monitoring and management method based on artificial intelligence, which adopts the following technical solutions:

[0007] An automobile production monitoring and management method based on artificial intelligence comprises the following steps:

[0008] Collecting a paint usage data sequence; obtaining a prominence degree of each paint usage data, wherein the prominence degree represents the deviation of the value of each paint usage data; obtaining a prominence point based on the prominence degree; obtaining a parameter , where Represents the number of prominent points; Representing the total number of paint usage data; obtaining the maximum and minimum values ​​in the paint usage data sequence; obtaining the maximum value fitting curve and the minimum value fitting curve based on the maximum and minimum values; constructing a mean line according to the mean of all data in the paint usage data sequence;

[0009] Get Parameters ; In the formula, Represents the area enclosed by the maximum fitting curve and the mean line; Represents the area enclosed by the minimum fitting curve and the mean line; exp() represents an exponential function with a natural constant as the base; represents the preset area difference threshold; || represents the absolute value symbol; using the Holt linear trend method, according to the parameter And parameters , predict the daily paint usage in the next month, accumulate the daily paint usage in the next month, and get the predicted total amount of paint required. When the predicted total amount of paint required is lower than the current paint inventory, trigger the paint purchase reminder and notify the relevant staff.

[0010] The innovation of the present invention lies in analyzing the variation characteristics of the coating usage data and adaptively obtaining the parameters in the Holt linear trend model. and , the daily coating consumption in the next month is predicted, which improves the prediction results. Furthermore, the parameters It is obtained by analyzing the number of prominent points in the paint usage data. The more prominent points there are, the more the paint usage changes at this stage are affected by external factors. It is necessary to give a smaller weight to the historical paint usage data, that is, give a smaller parameter. , avoiding the impact of such external changes on future predictions; further, the parameters It is obtained by analyzing the trend of paint usage data. If the paint usage data shows a strong trend of change, it means that the trend of change is clear and continuous. The model will rely more on this trend when predicting. At this time, a larger parameter is given. , so as to generate more realistic forecast values.

[0011] Preferably, obtaining the prominence of each coating usage data includes:

[0012] Obtain the overall fitting straight line and the ith fitting straight line; record the difference in slope between the overall fitting straight line and the ith fitting straight line as the first difference; record the difference in intercept between the overall fitting straight line and the ith fitting straight line as the second difference; record the difference between the fitting residual of the ith paint usage data in the overall fitting straight line and the fitting residual of the ith paint usage data in the ith fitting straight line as the third difference; the first difference, the second difference and the third difference constitute the prominence vector of the ith paint usage data; and use the second norm of the prominence vector of the ith paint usage data as the prominence of the ith paint usage data.

[0013] The higher the protrusion degree, the more deviated the value of the paint usage data is, which facilitates the subsequent acquisition of the protrusion points according to the protrusion degree.

[0014] Preferably, obtaining the overall fitting straight line and the i-th fitting straight line includes:

[0015] The least squares method is used to perform straight line fitting on the paint usage data sequence, and the obtained straight line is recorded as the overall fitting straight line; the least squares method is used to perform straight line fitting on the paint usage data except the i-th paint usage data in the paint usage data sequence, and the obtained straight line is recorded as the i-th fitting straight line.

[0016] This facilitates the subsequent acquisition of the prominence of the i-th paint usage data based on the difference between the overall fitting straight line and the i-th fitting straight line.

[0017] Preferably, obtaining the salient points includes:

[0018] The mean of the prominence of all paint usage data is recorded as the first mean; if the prominence of the i-th paint usage data is greater than or equal to the first mean, the i-th paint usage data is recorded as a prominence point, and all prominence points are obtained.

[0019] It is convenient to adaptively obtain parameters based on the ratio of the number of protruding points to the total number of paint usage data. .

[0020] Preferably, obtaining the maximum value fitting curve and the minimum value fitting curve includes:

[0021] Get the maximum and minimum values ​​in the paint usage data sequence; arrange the maximum values ​​in order of sampling time from small to large to obtain a maximum sequence, use the least squares method to perform curve fitting on the data in the maximum sequence, and obtain a maximum fitting curve; arrange the minimum values ​​in order of sampling time from small to large to obtain a minimum sequence, use the least squares method to perform curve fitting on the data in the minimum sequence, and obtain a minimum fitting curve.

[0022] Preferably, obtaining the area of ​​the region enclosed by the maximum fitting curve and the mean line includes:

[0023] ;

[0024] In the formula, Represents the area enclosed by the maximum fitting curve and the mean line; represents the maximum value fitting curve; represents the mean line; Represents the sampling time of the first data in the paint usage data sequence; Represents the sampling time of the last data in the paint usage data sequence; || represents the absolute value symbol.

[0025] Preferably, obtaining the area of ​​the region enclosed by the minimum fitting curve and the mean line includes:

[0026] ；

[0027] In the formula, represents the mean line; Represents the sampling time of the first data in the paint usage data sequence; Represents the sampling time of the last data in the paint usage data sequence; Represents the area enclosed by the minimum fitting curve and the mean line; represents the maximum fitting curve; || represents the absolute value symbol.

[0028] It is convenient to obtain the parameters according to the area of ​​the area enclosed by the minimum fitting curve and the mean line and the area of ​​the area enclosed by the maximum fitting curve and the mean line. .

[0029] In a second aspect, the present invention provides an automobile production monitoring and management system based on artificial intelligence, which adopts the following technical solutions:

[0030] An automobile production monitoring and management system based on artificial intelligence includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned automobile production monitoring and management method based on artificial intelligence is implemented.

[0031] By adopting the above technical solution, the above-mentioned artificial intelligence-based automobile production monitoring and management method is generated into a computer program and stored in a memory to be loaded and executed by a processor, so that a terminal device is made based on the memory and the processor for easy use.

[0032] The present invention has the following technical effects: the present invention obtains parameters by analyzing the number of protruding points in the coating amount data. The more prominent points there are, the more the change in paint usage at this stage is affected by external factors. It is necessary to give a smaller weight to the historical paint usage data, that is, to give a smaller parameter. , avoiding the impact of such external changes on future forecast values; by analyzing the trend of paint usage data, the parameters obtained If the paint usage data shows a strong trend, it means that the trend is clear and continuous. The model will rely more on this trend when predicting. In this case, a larger parameter , so that the prediction value is more in line with reality; finally, the Holt linear trend algorithm is used according to the parameters and , the daily paint usage for the next month is predicted, which improves the prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0034] Figure 1 This is a method flow chart of an automobile production monitoring and management method based on artificial intelligence in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0036] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0037] The embodiment of the present invention discloses an automobile production monitoring and management method based on artificial intelligence, referring to Figure 1 , comprising steps S1 to S4:

[0038] S1: Collect paint usage data.

[0039] In an embodiment of the present invention, the daily paint usage of a certain color paint in an automobile production plant in the past month is collected and recorded as paint usage data, and all paint usage data are arranged in chronological order from front to back to obtain a paint usage data sequence.

[0040] S2: Based on the paint usage data, obtain the overall fitting straight line and each fitting straight line; according to the difference between the overall fitting straight line and each fitting straight line, obtain the prominence of each paint usage data; according to the prominence of each paint usage data, obtain the prominence point; according to the proportion of the prominence point to the total number of paint usage data, obtain the parameter .

[0041] It should be noted that in the process of automobile production, the change in the amount of paint used is affected by many factors, such as market demand fluctuations, production plan adjustments, raw material supply conditions, etc. The speed and degree of change of these factors vary, and may show nonlinear or cyclical trends. The parameters in the original Holt linear trend method are and It is usually fixed and cannot automatically adapt to these complex and changeable actual situations. Therefore, by adjusting these two parameters, the model can more flexibly reflect the nonlinear or periodic characteristics in the data, thereby improving the accuracy of the prediction. The paint usage data collected in step S1 directly reflects the consumption of paint in the past period of time, and has a high degree of direct correlation with the current and future paint usage predictions. These data contain key information such as the trend of paint usage changes and periodic patterns, and are an important basis for predicting future paint usage. By analyzing these data, the laws and trends of paint usage changes can be revealed, providing strong support for future predictions, and making the prediction results closer to the actual situation, thereby improving the credibility of the prediction; that is, the parameters in the Holt linear trend method can be obtained based on the changing characteristics of the paint usage data. and .

[0042] It should be further explained that the prominent data in the paint usage data reflect the impact of sudden changes in market demand, adjustments in production plans or other emergencies on paint usage. That is, the more prominent data in the collected paint usage data, the more paint usage changes at this stage are affected by external factors (such as adjustments in production plans, etc.), and it is necessary to give a smaller weight to the historical paint usage data, that is, give a smaller parameter In order to avoid the influence of such external changes on future prediction values, when obtaining the prominent points in the paint usage data, it can be expressed by the difference between the straight lines obtained when any paint usage data participates in the straight line fitting and when it does not participate in the straight line fitting. If the difference between the straight lines obtained in the two cases is large, it indicates that the paint usage data has a large degree of deviation, which in turn causes the paint usage data to have a greater impact on the straight line results obtained during the fitting process.

[0043] In an embodiment of the present invention, the least squares method is used to perform straight-line fitting on the paint usage data sequence, and the obtained straight line is recorded as the overall fitting straight line, and the slope, intercept and fitting residual of each paint usage data in the overall fitting straight line are obtained; the least squares method is used to perform straight-line fitting on the paint usage data in the paint usage data sequence except the i-th paint usage data, and the obtained straight line is recorded as the i-th fitting straight line, and the slope, intercept and fitting residual of each paint usage data in the i-th fitting straight line are obtained.

[0044] The difference in slope between the overall fitting straight line and the i-th fitting straight line is recorded as the first difference; the difference in intercept between the overall fitting straight line and the i-th fitting straight line is recorded as the second difference; the difference between the fitting residual of the i-th paint usage data in the overall fitting straight line and the fitting residual of the i-th paint usage data in the i-th fitting straight line is recorded as the third difference; the first difference, the second difference and the third difference constitute the prominence vector of the i-th paint usage data; the second norm of the prominence vector of the i-th paint usage data is taken as the prominence of the i-th paint usage data.

[0045] According to the prominence of the i-th paint usage data, the prominence of each paint usage data is obtained, and the mean of the prominence of all paint usage data is obtained, which is recorded as the first mean; if the prominence of the i-th paint usage data is greater than or equal to the first mean, the i-th paint usage data is recorded as a prominence point; and all prominence points are obtained.

[0046] It should be noted that the ratio of the prominent points in the paint usage data to the total number of paint usage data is used as the prominence of the paint usage data. The larger the ratio of the prominent points, the greater the influence of external factors on the paint usage change in this stage. The smaller it is, the less likely it is to be affected by external changes on future forecasts.

[0047] In the embodiment of the present invention, the parameters are obtained :

[0048] ;

[0049] In the formula, Represents the number of prominent points; Represents the total number of paint usage data; Represents parameters; The larger the value is, the greater the change in the amount of paint in this period of time is affected by external factors, so the corresponding parameter The smaller the value of .

[0050] S3: Obtain the parameters based on the difference between the area of ​​the region enclosed by the maximum fitting curve and the mean line in the paint usage data sequence and the area of ​​the region enclosed by the minimum fitting curve and the mean line in the paint usage data sequence. .

[0051] It should be noted that the known parameters Controls the estimation of the data change trend, that is, the trend change weight. If the historical paint usage data shows a strong change trend, it means that the change trend is clear and continuous. The model will rely more on this trend when predicting, thereby generating a more realistic prediction value. At this time, it is necessary to give the historical paint usage data a larger trend change weight, that is, a larger parameter When obtaining the trend of the paint usage data, it can be reflected based on the difference between the area of ​​the region enclosed by the maximum fitting curve and the mean line in the paint usage data sequence and the area of ​​the region enclosed by the minimum fitting curve and the mean line in the paint usage data sequence. This is because if the area enclosed by the maximum fitting curve and the mean line is much larger than the area enclosed by the minimum fitting curve and the mean line, the average value of the data in the paint usage data sequence tends to be closer to the maximum fitting curve and away from the minimum fitting curve. In this case, the paint usage data sequence may show a trend of continuous growth or increase, otherwise it will show a downward trend. If they are close, it indicates that the trend may not be obvious.

[0052] In an embodiment of the present invention, the maximum and minimum values ​​in the paint usage data sequence are obtained; the maximum values ​​are arranged in order of sampling time from small to large to obtain a maximum sequence, and the data in the maximum sequence are curve-fitted using the least squares method to obtain a maximum fitting curve; the minimum values ​​are arranged in order of sampling time from small to large to obtain a minimum sequence, and the data in the minimum sequence are curve-fitted using the least squares method to obtain a minimum fitting curve; the mean of all data in the paint usage data sequence is obtained, and a mean line is constructed based on the mean of all paint usage data; the value of each data on the mean line is the mean of all data in the paint usage data sequence.

[0053] ;

[0054] ；

[0055] ;

[0056] In the formula, Represents the area enclosed by the maximum fitting curve and the mean line; represents the maximum value fitting curve; represents the mean line; Represents the sampling time of the first data in the paint usage data sequence; Represents the sampling time of the last data in the paint usage data sequence; Represents the area enclosed by the minimum fitting curve and the mean line; represents the maximum value fitting curve; Representative parameters ; exp() represents an exponential function with a natural constant as the base; represents a preset area difference threshold. In the embodiment of the present invention, the preset In other embodiments, the implementer can preset If The value is greater than or equal to When , it indicates that the data in the paint consumption data series has an upward or downward trend, and the parameter The larger the value of .

[0057] S4: According to the parameters And parameters , forecast daily paint usage for the next month.

[0058] It should be noted that according to the final parameters And parameters , forecast daily paint usage for the next month.

[0059] In the embodiment of the present invention, the Holt linear trend method is used, and according to the parameters And parameters , predict the daily paint consumption in the next month, and accumulate the daily paint consumption in the next month to get the predicted total amount of paint required. When the predicted total amount of paint required is lower than the current paint inventory, trigger the paint purchase reminder and notify the relevant staff.

[0060] The present invention analyzes the variation characteristics of the coating usage data and adaptively obtains the parameters in the Holt linear trend model. and , predict the daily coating consumption for the next month, improve the prediction results, parameters It is obtained based on the number of prominent points in the paint usage data. The more prominent points there are, the more the paint usage changes at this stage are affected by external factors. It is necessary to give a smaller weight to the historical paint usage data, that is, give a smaller parameter. , avoiding the impact of such external changes on future prediction values; parameters It is obtained by analyzing the trend of paint usage data. If the paint usage data shows a strong trend of change, it means that the trend of change is clear and continuous. The model will rely more on this trend when predicting. At this time, a larger parameter is given. , so as to generate more realistic forecast values.

[0061] An embodiment of the present invention also discloses an automobile production monitoring and management system based on artificial intelligence, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an automobile production monitoring and management method based on artificial intelligence according to the present invention is implemented.

[0062] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0063] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory, a dynamic random access memory, a static random access memory, an enhanced dynamic random access memory, a high bandwidth memory, a hybrid storage cube, etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device.

[0064] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0065] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An automobile production monitoring and management method based on artificial intelligence, characterized in that: Includes steps: Collecting a paint usage data sequence; obtaining a prominence degree of each paint usage data, wherein the prominence degree represents a deviation of a value of each paint usage data; Based on the protrusion degree, obtaining a protrusion point; Get Parameters , where Represents the number of prominent points; Representing the total number of paint usage data; obtaining the maximum and minimum values ​​in the paint usage data sequence; obtaining the maximum value fitting curve and the minimum value fitting curve based on the maximum and minimum values; constructing a mean line according to the mean of all data in the paint usage data sequence; Get Parameters ; In the formula, Represents the area enclosed by the maximum fitting curve and the mean line; Represents the area enclosed by the minimum fitting curve and the mean line; exp() represents an exponential function with a natural constant as the base; represents the preset area difference threshold; || represents the absolute value symbol; Using Holt linear trend method, according to the parameters And parameters , predict the daily paint usage in the next month, accumulate the daily paint usage in the next month, and get the predicted total amount of paint required. When the predicted total amount of paint required is lower than the current paint inventory, trigger the paint purchase reminder and notify the relevant staff.

2. The automobile production monitoring and management method based on artificial intelligence according to claim 1 is characterized in that: The acquisition of the prominence of each coating usage data includes: Obtain the overall fitting straight line and the ith fitting straight line; record the difference in slope between the overall fitting straight line and the ith fitting straight line as the first difference; record the difference in intercept between the overall fitting straight line and the ith fitting straight line as the second difference; record the difference between the fitting residual of the ith paint usage data in the overall fitting straight line and the fitting residual of the ith paint usage data in the ith fitting straight line as the third difference; the first difference, the second difference and the third difference constitute the prominence vector of the ith paint usage data; and use the second norm of the prominence vector of the ith paint usage data as the prominence of the ith paint usage data.

3. The automobile production monitoring and management method based on artificial intelligence according to claim 2 is characterized in that: The obtaining of the overall fitting straight line and the i-th fitting straight line includes: The least squares method is used to perform straight line fitting on the paint usage data sequence, and the obtained straight line is recorded as the overall fitting straight line; the least squares method is used to perform straight line fitting on the paint usage data except the i-th paint usage data in the paint usage data sequence, and the obtained straight line is recorded as the i-th fitting straight line.

4. The automobile production monitoring and management method based on artificial intelligence according to claim 1 is characterized in that: The obtaining of the salient points comprises: The mean of the prominence of all paint usage data is recorded as the first mean; if the prominence of the i-th paint usage data is greater than or equal to the first mean, the i-th paint usage data is recorded as a prominence point, and all prominence points are obtained.

5. The automobile production monitoring and management method based on artificial intelligence according to claim 1 is characterized in that: The obtaining of the maximum value fitting curve and the minimum value fitting curve comprises: Get the maximum and minimum values ​​in the paint usage data sequence; arrange the maximum values ​​in order of sampling time from small to large to obtain a maximum sequence, use the least squares method to perform curve fitting on the data in the maximum sequence, and obtain a maximum fitting curve; arrange the minimum values ​​in order of sampling time from small to large to obtain a minimum sequence, use the least squares method to perform curve fitting on the data in the minimum sequence, and obtain a minimum fitting curve.

6. The automobile production monitoring and management method based on artificial intelligence according to claim 1 is characterized in that: The acquisition of the area of ​​the region enclosed by the maximum fitting curve and the mean line includes: ； In the formula, Represents the area enclosed by the maximum fitting curve and the mean line; represents the maximum value fitting curve; represents the mean line; Represents the sampling time of the first data in the paint usage data sequence; Represents the sampling time of the last data in the paint usage data sequence; || represents the absolute value symbol.

7. The automobile production monitoring and management method based on artificial intelligence according to claim 1 is characterized in that: The acquisition of the area of ​​the region enclosed by the minimum fitting curve and the mean line includes: ； In the formula, represents the mean line; Represents the sampling time of the first data in the paint usage data sequence; Represents the sampling time of the last data in the paint usage data sequence; Represents the area enclosed by the minimum fitting curve and the mean line; represents the maximum fitting curve; || represents the absolute value symbol.

8. An automobile production monitoring and management system based on artificial intelligence, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an artificial intelligence-based automobile production monitoring and management method according to any one of claims 1 to 7 is implemented.

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