A digital capacity matching analysis method and system for automobile production lines

Abstract

The present invention relates to a novel automobile production line digital capacity matching analysis method and system, the method comprising the following steps: establishing a production line capacity database and a production line route demand database with the production line as the center; constructing a production line capacity digital analysis model, using the data in the production line capacity database and the production line route demand database as input variables, and outputting analysis data; developing a web-based digital capacity analysis tool based on the production line capacity digital analysis model, and displaying and interacting with the analysis data through a human-computer interaction interface. The present invention constructs a production line capacity digital analysis model, develops a computer web program, and uses information technology to achieve automatic capacity analysis, automatic analysis of capacity utilization and plan accuracy, and early warning. At the same time, it can realize intelligent self-service screening functions, quickly calculate new route plan capacity matching analysis, improve the efficiency of output result display after input changes, and quickly provide more route plan decision references.

Description

Technical Field

[0001] The present invention belongs to the technical field of complete vehicle manufacturing, and in particular relates to a digital production capacity matching analysis method and system for automobile production lines. Background Art

[0002] With the rapid development of China's economy, competition in the automobile market is becoming increasingly fierce, the pace of vehicle model replacement is accelerating, and the number of subdivided models and configurations continues to increase. Different models are planned to different production lines according to platforms, series, size, type, etc. The planned production capacity must accurately match future market demand. If the production capacity is too large, it will cause capacity to be idle, low investment efficiency, high cost, and poor economy. If the production capacity is too small, it cannot meet market demand, affecting the healthy and rapid development of the enterprise.

[0003] The production line capacity construction cycle is relatively long. It needs to be based on future sales forecasts and comprehensive judgment and decision-making through mid-term 5-year, annual 12-month, and monthly rolling (1+3 months) capacity analysis. By planning and constructing production lines in advance, the company's rapid development needs can be met.

[0004] The current state of capacity analysis relies primarily on manual analysis, which is insensitive to analyzing route combinations across multiple production lines at multiple bases, different vehicle models on the same production line, and dynamic analysis of demand changes. Furthermore, new route combinations cannot be quickly analyzed, and reviewing and sharing them is cumbersome. Data from old and new versions are easily confused, making collaborative analysis within the group difficult. Furthermore, the lack of a panoramic display and switching between mid-term, annual, and monthly rollovers affects overall global judgment.

[0005] The historical data versions of capacity analysis are stored manually, which are scattered and uncentralized, difficult to find, and inconvenient for sharing, accumulation, query and review. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and system for analyzing the digital production capacity matching of automobile production lines in order to solve the above problems.

[0007] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0008] A method for analyzing digital production capacity matching of an automobile production line includes the following steps:

[0009] Establish a production line capacity database and a production line route demand database centered on the production line;

[0010] Constructing a digital analysis model for production line capacity, using the data in the production line capacity database and the production line route demand database as input variables, and outputting analysis data;

[0011] A web-based digital capacity analysis tool is developed based on the production line capacity digital analysis model, and the analysis data is displayed and interacted through a human-computer interaction interface.

[0012] As a further optimization solution of the present invention, the specific process of establishing the production line capacity database is as follows:

[0013] Sort out the bases, factories, specialties, and production lines under the enterprise, clarify the unique correspondence between bases and factories, factories and specialties, and specialties and production lines, and automatically calculate the production line capacity based on the unit time workload JPH of the production line by associating the time coefficient and the mobility coefficient. Then, based on the production line capacity, convert and calculate the capacity of the specialty, factory, and base. Among them, the specialty takes the sum of the corresponding production line capacity, the factory capacity is determined by the bottleneck specialty production line capacity, and the base capacity takes the sum of the corresponding factory capacity. Establish a production line capacity database.

[0014] As a further optimization solution of the present invention, the specific process of establishing a production line route demand database is as follows:

[0015] Import vehicle model requirements, summarize them to production lines by model and category according to routes, and through the correspondence between platforms, series and models, convert and summarize the production line requirements of platforms and series to establish a production line route requirement database.

[0016] As a further optimization solution of the present invention, the input variables include the vehicle demand variable Xn provided by the production line route demand database and the production line capacity variable S provided by the production line capacity database. X The model demand variable Xn represents the n-th model demand plan corresponding to the X production line, where n represents the number of models; the production line capacity variable S X The production capacity corresponding to production line X is expressed as:

[0017] S X =JPH X *t*K X ;

[0018] Among them, JPH X JPH represents the unit time workload of production line X, t represents the time input variable, K x Indicates the input variable of the availability rate of production line X.

[0019] As a further optimization solution of the present invention, the analysis data includes the capacity gap Y x , capacity utilization rate Z x and planning accuracy P x ;

[0020] The capacity gap Y x =∑X n -S x , where Y x Indicates the capacity gap corresponding to production line X, Y x>0 means insufficient production capacity, Y x <0 indicates excess production capacity;

[0021] The capacity utilization rate Z x =∑X n / S x *100%, of which Z x Indicates the capacity utilization rate corresponding to production line X;

[0022] The planning accuracy P x =∑X n ′ / ∑X n *100%, where P x represents the demand planning accuracy corresponding to production line X, X n represents the vehicle model demand plan corresponding to the X production line, X n ′ represents the actual output of the model corresponding to production line X.

[0023] As a further optimization solution of the present invention, the capacity utilization rate Zx warning is calibrated through different color intervals to indicate insufficient capacity, normal capacity, surplus capacity and empty capacity.

[0024] As a further optimization solution of the present invention, a web-based digital capacity analysis tool is developed based on the production line capacity digital analysis model, and the analysis data is displayed and interacted through a human-computer interaction interface. The specific contents are as follows:

[0025] Develop a web-based digital capacity analysis tool, based on which the capacity gap Y x Displayed through the human-computer interaction interface, when the capacity gap Y x ≤0, the production capacity is met and displayed in green; when the production capacity gap Y x >0, the production capacity is insufficient and the display is red. You can adjust the vehicle route through the human-computer interaction interface to analyze the capacity satisfaction and capacity gap of different route plans.

[0026] The capacity utilization rate Z x Displayed through the human-computer interaction interface, when Z x ≤50%, indicating that the production capacity is empty, the display is blue; when 50%<Z x ≤100%, indicating excess production capacity, displayed in yellow; when 100%<Z x ≤165%, indicating normal production capacity, displayed in green; when 165%<Z x , indicating insufficient production capacity, displayed in red;

[0027] The planning accuracy P xIt is displayed through the human-computer interaction interface, and is divided into the mid-term plan forecast accuracy for the past five years, the annual plan forecast accuracy, the monthly rolling plan forecast accuracy, and the platform model forecast accuracy.

[0028] A digital capacity matching analysis system for automobile production lines, comprising:

[0029] Database establishment module, used to establish production line capacity database and production line route demand database centered on the production line;

[0030] A model building module is used to build a digital analysis model of production line capacity, using the data in the production line capacity database and the production line route demand database as input variables and outputting analysis data;

[0031] The analysis tool development module is used to develop a web-based digital production capacity analysis tool to display and interact with the analysis data through a human-computer interaction interface.

[0032] An electronic device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0033] Memory for storing computer programs;

[0034] The processor is used to implement a digital production capacity matching analysis method for automobile production lines when executing a program stored in the memory.

[0035] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements a method for analyzing digital production capacity matching of automobile production lines.

[0036] The beneficial effects of the present invention are:

[0037] The present invention constructs a digital analysis model of production line capacity, develops a computer web page program, and uses information technology to achieve automatic analysis of capacity, automatic analysis of capacity utilization and plan accuracy, and early warning. At the same time, it can realize intelligent self-service screening functions, quickly calculate new route plan capacity matching analysis, improve the efficiency of output result display after input changes, and quickly provide more route plan decision-making references. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of the method of the present invention;

[0039] Figure 2 2. It is a schematic diagram of a dual database model for digital production line capacity analysis according to an embodiment of the present invention;

[0040] Figure 3 This is a logical relationship diagram of input and output of digital capacity analysis of an automobile production line according to an embodiment of the present invention;

[0041] Figure 4 This is a schematic diagram of a human-machine interface development for digital capacity matching analysis of an automobile production line according to an embodiment of the present invention;

[0042] Figure 5 is a system structure block diagram of an embodiment of the present invention;

[0043] Figure 6 It is a block diagram of the device structure of an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The present application is described in further detail below in conjunction with the accompanying drawings. It is necessary to point out that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technicians in this field can make some non-essential improvements and adjustments to the present application based on the above application content.

[0045] like Figure 1 As shown, a digital production capacity matching analysis method for automobile production lines includes the following steps:

[0046] Establish a production line capacity database and a production line route demand database centered on the production line;

[0047] Constructing a digital analysis model for production line capacity, using the data in the production line capacity database and the production line route demand database as input variables, and outputting analysis data;

[0048] Develop a web-based digital capacity analysis tool to display and interact with the analysis data through a human-computer interaction interface.

[0049] The specific process of establishing the production line capacity database is as follows:

[0050] Sort out the bases, factories, specialties, and production lines under the enterprise, clarify the unique correspondence between bases and factories, factories and specialties, and specialties and production lines, and automatically calculate the production line capacity based on the unit time workload JPH of the production line by associating the time coefficient and the mobility coefficient. Then, based on the production line capacity, convert and calculate the capacity of the specialty, factory, and base. Among them, the specialty takes the sum of the corresponding production line capacity, the factory capacity is determined by the bottleneck specialty production line capacity, and the base capacity takes the sum of the corresponding factory capacity. Establish a production line capacity database.

[0051] The specific process of establishing a production line route demand database is as follows:

[0052] Import vehicle model requirements, summarize them to production lines by model and category according to routes, and through the correspondence between platforms, series and models, convert and summarize the production line requirements of platforms and series to establish a production line route requirement database.

[0053] The input variables include the vehicle type demand variable Xn provided by the production line route demand database and the production line capacity variable S provided by the production line capacity database. X The model demand variable Xn represents the n-th model demand plan corresponding to the X production line, where n represents the number of models; the production line capacity variable S X The production capacity corresponding to production line X is expressed as:

[0054] S X =JPH X *t*K X ;

[0055] Among them, JPH X JPH represents the unit time workload of production line X, t represents the time input variable, K x Indicates the input variable of the availability rate of production line X.

[0056] The analysis data includes the capacity gap Y x , capacity utilization rate Z x and planning accuracy P x ;

[0057] The capacity gap Y x =∑X n -S x , where Y x Indicates the capacity gap corresponding to production line X, Y x >0 means insufficient production capacity, Y x <0 indicates excess production capacity;

[0058] The capacity utilization rate Z x =∑X n / S x *100%, of which Z x Indicates the capacity utilization rate corresponding to production line X;

[0059] The planning accuracy P x =∑X n ′ / ∑X n *100%, where P x represents the demand planning accuracy corresponding to production line X, X n represents the vehicle model demand plan corresponding to the X production line, X n ′ represents the actual output of the model corresponding to production line X.

[0060] The capacity utilization rate Zx warning uses different color intervals to mark insufficient capacity, normal capacity, surplus capacity and empty capacity.

[0061] The specific contents of developing a web-based digital capacity analysis tool to display and interact with the analysis data through a human-computer interaction interface are as follows:

[0062] Develop a web-based digital capacity analysis tool, based on which the capacity gap Y x Displayed through the human-computer interaction interface, when the capacity gap Y x ≤0, the production capacity is met and displayed in green; when the production capacity gap Y x >0, the production capacity is insufficient and the display is red. You can adjust the vehicle route through the human-computer interaction interface to analyze the capacity satisfaction and capacity gap of different route plans.

[0063] The capacity utilization rate Z x Displayed through the human-computer interaction interface, when Z x ≤50%, indicating that the production capacity is empty, the display is blue; when 50%<Z x ≤100%, indicating excess production capacity, displayed in yellow; when 100%<Z x ≤165%, indicating normal production capacity, displayed in green; when 165%<Z x , indicating insufficient production capacity, displayed in red;

[0064] The planning accuracy P x It is displayed through the human-computer interaction interface, and is divided into the mid-term plan forecast accuracy for the past five years, the annual plan forecast accuracy, the monthly rolling plan forecast accuracy, and the platform model forecast accuracy.

[0065] In this embodiment, the scheme is as follows:

[0066] (1) If Figure 2 As shown in the figure, with the production line as the center, dual databases of "production line capacity database" and "production line route demand database" are established. First, the "production line" is uniquely identified with a number (Xn), and then the reverse production line capacity database such as "base-factory-specialty-production line" is established through production line (Xn); and the forward production line route demand database such as "production line-model-category-demand (output)" is established through production line (Xn) identification.

[0067] Specifically, (1) establish a production line capacity database, sort out the bases, factories, specialties, and production lines under the enterprise, clarify the unique correspondence between bases and factories, factories and specialties, and specialties and production lines, and automatically calculate the production line capacity based on the production line JPH by associating the time coefficient (t) and the mobility coefficient (K). Then, based on the production line capacity, convert and calculate the capacity of the specialty, factory, and base. Among them, the specialty is taken as the sum of the corresponding production line capacity, the factory capacity is taken according to the bottleneck specialty production line capacity (the bottleneck specialty refers to the specialty with the least corresponding capacity in the factory), and the base capacity is taken as the sum of the corresponding factory capacity.

[0068] Establish a production line demand plan and actual production line database, categorize by domestic, CBU, CKD, SKD, DKD, etc., import vehicle model demand, and summarize it to the production line by model and category according to route. Through the correspondence between platform, series and model, the production line demand of platform and series can be converted and summarized. According to the digital analysis model of production line capacity ( Figure 2 ), develop computer web page programs, and use information technology to realize automatic analysis of production capacity, automatic analysis of capacity utilization and plan accuracy and early warning. At the same time, it can realize intelligent self-service screening functions, quickly calculate new route plan capacity matching analysis, improve the efficiency of output result display after input changes, and quickly provide more route plan decision-making references.

[0069] (2) If Figure 3 As shown, a digital analysis model of production line capacity is constructed:

[0070] The variable Xn represents the demand plan for the nth model corresponding to production line X, where n represents the number of models;

[0071] variable S X Indicates the production capacity corresponding to production line X, S X =JPH X *t*K X ,

[0072] Among them JPH X represents the JPH (work per unit time) input variable corresponding to the X production line, t represents the time input variable, K x represents the input variable of the availability rate corresponding to production line X;

[0073] Output variable Y x =∑X n -S x , where Y x represents the capacity gap corresponding to production line X, Y x >0 means insufficient production capacity, Y x <0 indicates excess production capacity;

[0074] Output variable Z x =∑X n / S x *100%, of which Z x Indicates the capacity utilization rate corresponding to production line X;

[0075] Output variable P x =∑X n ′ / ∑X n *100%, where P x represents the demand planning accuracy corresponding to production line X, X n represents the vehicle model demand plan corresponding to the X production line, X n′ represents the actual output of the model corresponding to production line X.

[0076] (3) Set standard capacity utilization rate Z x The early warning standard uses color ranges (red, green, yellow, and blue, with specific range values ​​determined based on the company's capacity utilization judgment standards) to judge insufficient capacity, normal capacity, surplus capacity, or idle capacity, and finds and implements countermeasures in advance through early warning.

[0077] (4) If Figure 4 As shown, based on the above methods and logic, a web-based digital capacity analysis tool was developed ( Figure 3 ), realizes functions such as automatic analysis of production line capacity and demand matching, capacity utilization analysis and early warning, and demand plan forecast accuracy review. Through the self-service selection function of the human-computer interaction interface, it realizes quick analysis of different route combination plans, meets personalized analysis needs, and improves analysis and sharing efficiency;

[0078] It has the functions of automatic query and sharing of historical data, capacity analysis and review, and setting user permissions to meet confidentiality requirements.

[0079] Specifically, based on the actual needs of automobile manufacturers, a digital dashboard human-machine interface for capacity analysis suitable for the company will be developed, which can reflect three modules (capacity analysis, capacity utilization analysis, and plan accuracy analysis). The capacity analysis module can be filtered by base (Base A, Base B, etc.), by factory (Factory 1, Factory 2, etc.), by specialty (stamping, welding, painting, assembly), etc. It should be noted that production lines can be named according to the company's numbering regulations, but the name must be unique.

[0080] Capacity and utilization can be analyzed in three submodules based on time spans: a five-year mid-term, a 12-month annual, and a rolling monthly (n+m months) filter, where n+m=12, with n representing the number of months already in the year and m representing the number of months remaining. Categories can be filtered by plan or actual. Plan accuracy can be supplemented with a vehicle model accuracy filter and analysis module based on the three aforementioned submodules. Historical data sharing and query capabilities are also available, along with demand and capacity analysis and review capabilities.

[0081] Through digital tools, intelligent gap analysis is performed. If the gap Yx is less than or equal to 0, the production capacity is met and displayed in green. If the gap Yx is greater than 0, the production capacity is insufficient and displayed in red. By adjusting the vehicle model route, the capacity satisfaction and gap of different route plans can be analyzed.

[0082] Through digital tools, the capacity utilization rate Zx is intelligently analyzed. When Zx≤50%, it means that the capacity is idle, and a blue warning is issued. When 50%<Zx≤100%, it means that there is surplus capacity, and a yellow warning is issued. When 100%<Zx≤165%, it means that the capacity is normal, and a green display is issued. When 165%<Zx, it means that the capacity is insufficient, and a red warning is issued.

[0083] Through digital tools, the plan accuracy Px is automatically analyzed, divided into the mid-term plan forecast accuracy for the past five years, the annual plan forecast accuracy or the monthly rolling plan forecast accuracy, as well as the platform model forecast accuracy, to achieve a comprehensive review of the forecast accuracy.

[0084] like Figure 5 As shown, a digital capacity matching analysis system for automobile production lines includes:

[0085] A database establishment module 11 is used to establish a production line capacity database and a production line route demand database centered on the production line;

[0086] A model building module 12 is used to build a digital analysis model of production line capacity, using the data in the production line capacity database and the production line route demand database as input variables and outputting analysis data;

[0087] The analysis tool development module 13 is used to develop a web-based digital production capacity analysis tool to display and interact with the analysis data through a human-computer interaction interface.

[0088] The implementation process of the functions and effects of each module in the above system is specifically described in the implementation process of the corresponding steps in the above method, which will not be repeated here.

[0089] For the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can be referred to the partial description of the method embodiment. The system embodiment described above is only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the present invention. Those of ordinary skill in the art can understand and implement it without paying any creative work.

[0090] In the above embodiments, any number of all modules can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. At least one of all modules can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented in hardware or firmware by any other reasonable means of integrating or packaging the circuit, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or in an appropriate combination of any of them. Alternatively, at least one of all modules can be at least partially implemented as a computer program module, which can perform the corresponding function when the computer program module is executed.

[0091] See also Figure 6 The electronic device provided by an embodiment of the present disclosure includes a processor 1110, a communication interface 1120, a memory 1130 and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140;

[0092] Memory 1130, for storing computer programs;

[0093] The processor 1110 is configured to implement the following automobile production line digital capacity matching analysis method when executing the program stored in the memory 1130.

[0094] The communication bus 1140 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the figure shows only one thick line, but this does not mean that there is only one bus or only one type of bus.

[0095] The communication interface 1120 is used for communication between the electronic device and other devices.

[0096] The memory 1130 may include a random access memory (RAM) or a non-volatile memory, such as at least one disk storage. Alternatively, the memory 1130 may be at least one storage device located away from the processor 1110.

[0097] The above-mentioned processor 1110 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0098] The embodiments of the present disclosure further provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described automobile production line digital capacity matching analysis method.

[0099] The computer-readable storage medium may be included in the device / apparatus described in the above embodiments, or may exist independently and not incorporated into the device / apparatus. The computer-readable storage medium carries one or more programs that, when executed, implement the automotive production line digital capacity matching analysis method according to the embodiments of the present disclosure.

[0100] According to embodiments of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, such as, but not limited to, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0101] The above-described embodiments merely illustrate several implementations of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that variations and improvements are possible without departing from the scope of the present invention, and all such variations and improvements fall within the scope of protection of the present invention.

Claims

1. A digital production capacity matching analysis method for automobile production lines, characterized by: The following steps are involved: A production line capacity database and a production line route demand database are established with the production line as the center; the specific process of establishing the production line capacity database is as follows: sort out the bases, factories, specialties, and production lines under the enterprise, clarify the unique correspondence between the base and the factory, the factory and the specialties, and the specialties and the production line, and automatically calculate the production line capacity based on the unit time workload JPH of the production line by associating the time coefficient and the mobility coefficient. Then, based on the production line capacity, the capacity of the specialties, factories, and bases is converted and calculated, where the specialties take the sum of the capacities of the corresponding production lines, the factory capacity is determined by the capacity of the bottleneck professional production line, and the base capacity takes the sum of the capacities of the corresponding factories, and a production line capacity database is established; the specific process of establishing the production line route demand database is as follows: import vehicle model requirements, summarize them to the production line by model and category according to the route, and through the correspondence between the platform, series and vehicle model, the production line requirements of the platform and series can be converted and summarized to establish a production line route demand database; Construct a digital analysis model for production line capacity, using the data in the production line capacity database and the production line route demand database as input variables and outputting analysis data; the input variables include the vehicle model demand variable X provided by the production line route demand database n and the production line capacity variable S provided by the production line capacity database X ; The vehicle model demand variable X n represents the demand plan for the nth model corresponding to the X production line, where n represents the number of models; the production line capacity variable S X Indicates the production capacity corresponding to production line X, the expression is: S X =JPH X *t*K X Among them, JPH X JPH represents the unit time workload of production line X, t represents the time input variable, K x Represents the input variable of the availability rate of production line X; the analysis data includes the capacity gap Y x , capacity utilization rate Z x and planning accuracy P x ; The production capacity gap Y x =∑X n -S x , where Y x Indicates the capacity gap corresponding to production line X, Y x >0 means insufficient production capacity, Y x <0 indicates excess capacity; the capacity utilization rate Z x =∑X n / S x *100%, of which Z x represents the capacity utilization rate of production line X; the planning accuracy P x =∑X n ′ / ∑X n *100%, where P x represents the demand planning accuracy corresponding to production line X, X n represents the vehicle model demand plan corresponding to the X production line, X n ′ represents the actual output of the model corresponding to production line X; A web-based digital capacity analysis tool is developed based on the production line capacity digital analysis model, and the analysis data is displayed and interacted through a human-computer interaction interface.

2. The automobile production line digital capacity matching analysis method according to claim 1 is characterized in that: The capacity utilization rate Zx warning uses different color intervals to mark insufficient capacity, normal capacity, surplus capacity and empty capacity.

3. The automobile production line digital capacity matching analysis method according to claim 2 is characterized in that: Based on the production line capacity digital analysis model, a web-based digital capacity analysis tool is developed to display and interact with the analysis data through a human-computer interaction interface. The specific contents are as follows: Develop a web-based digital capacity analysis tool, based on which the capacity gap Y x Displayed through the human-computer interaction interface, when the capacity gap Y x ≤0, the production capacity is met and displayed in green; when the production capacity gap Y x >0, the production capacity is insufficient and the display is red. You can adjust the vehicle route through the human-computer interaction interface to analyze the capacity satisfaction and capacity gap of different route plans. The capacity utilization rate Z x Displayed through the human-computer interaction interface, when Z x ≤50%, indicating that the production capacity is empty, the display is blue; when 50%<Z x ≤100%, indicating excess production capacity, displayed in yellow; when 100%<Z x ≤165%, indicating normal production capacity, displayed in green; when 165%<Z x , indicating insufficient production capacity, displayed in red; The planning accuracy P x It is displayed through the human-computer interaction interface, and is divided into the mid-term plan forecast accuracy for the past five years, the annual plan forecast accuracy, the monthly rolling plan forecast accuracy, and the platform model forecast accuracy.

4. A digital production capacity matching analysis system for automobile production lines, characterized by: include; The database establishment module is used to establish a production line capacity database and a production line route demand database with the production line as the center; the specific process of establishing the production line capacity database is as follows: sort out the bases, factories, specialties, and production lines under the enterprise, clarify the unique correspondence between the base and the factory, the factory and the specialties, and the specialties and the production line, and automatically calculate the production line capacity based on the unit time workload JPH of the production line by associating the time coefficient and the mobility coefficient. Then, based on the production line capacity, convert and calculate the capacity of the specialties, factories, and bases, where the specialties take the sum of the capacities of the corresponding production lines, the factory capacity takes the value according to the bottleneck professional production line capacity, and the base capacity takes the sum of the capacities of the corresponding factories to establish a production line capacity database; the specific process of establishing the production line route demand database is as follows: import vehicle model requirements, summarize them to the production line by model and category according to the route, and through the correspondence between the platform, series and vehicle model, the production line requirements of the platform and series can be converted and summarized to establish a production line route demand database; The model building module is used to build a digital analysis model of production line capacity, using the data in the production line capacity database and the production line route demand database as input variables and outputting analysis data; the input variables include the vehicle model demand variable X provided by the production line route demand database n and the production line capacity variable S provided by the production line capacity database X ; The vehicle model demand variable X n represents the demand plan for the nth model corresponding to the X production line, where n represents the number of models; the production line capacity variable S X Indicates the production capacity corresponding to production line X, the expression is: S X =JPH X *t*K X Among them, JPH X JPH represents the unit time workload of production line X, t represents the time input variable, K x Represents the input variable of the availability rate of production line X; the analysis data includes the capacity gap Y x , capacity utilization rate Z x and planning accuracy P x ; The production capacity gap Y x =∑X n -S x , where Y x Indicates the capacity gap corresponding to production line X, Y x >0 means insufficient production capacity, Y x <0 indicates excess capacity; the capacity utilization rate Z x =∑X n / S x *100%, of which Z x represents the capacity utilization rate of production line X; the planning accuracy P x =∑X n ′ / ∑X n *100%, where P x represents the demand planning accuracy corresponding to production line X, X n represents the vehicle model demand plan corresponding to the X production line, X n ′ represents the actual output of the model corresponding to production line X; The analysis tool development module is used to develop a web-based digital production capacity analysis tool to display and interact with the analysis data through a human-computer interaction interface.

5. An electronic device, characterized in that: The processor, the communication interface, the memory and the communication bus are connected to each other via the communication bus. Memory for storing computer programs; The processor is configured to implement the automobile production line digital capacity matching analysis method according to any one of claims 1 to 3 when executing the program stored in the memory.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the automobile production line digital capacity matching analysis method according to any one of claims 1 to 3 is implemented.

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