A method and system for monitoring car body production based on big data analysis

Abstract

The present invention discloses a vehicle body block production monitoring method and system based on big data analysis, which belongs to the field of production monitoring technology. By maintaining the consistency of the production time attributes of each production line, the production completion time of the vehicle body block during the operation of the production line is cached; the qualified number of vehicle body blocks at each production completion time node is counted to generate a production label; the production line operation scatter spectrum coordinate system is depicted, and the production label is punctuated to obtain the operation scatter spectrum diagram of the production line; the operation scatter spectrum diagram is converted into a spectrum function to evaluate the coordination ability of the production line; based on the evaluation results, the effective time range of the production line coordination is obtained, and an assembly list is generated; the present invention can open up data sharing between the production link and the assembly link, avoid the situation where the time-consuming anomaly of the production process leads to confusion in the assembly order, and by quantitatively evaluating the coordination ability between different production lines, the effective time range of the assembly link can be quantified, thereby improving the delivery efficiency of the finished product.

Description

Technical Field

[0001] The present invention relates to the technical field of production monitoring, and in particular to a method and system for monitoring the production of vehicle body blocks based on big data analysis. Background Art

[0002] The block assembly of the vehicle body is to divide the entire vehicle body into several modules for manufacturing, and then assemble these modules to form a complete vehicle body. However, in the existing technology, the production line and the assembly line are generally separated. The production line realizes unified production, and the assembly line realizes unified assembly. The separation of the production line and the assembly line creates data islands between production data and assembly data, which greatly affects the delivery efficiency of finished products. At the same time, the assembly process reflects a certain block production sequence. The time-consuming abnormality of the production process will lead to confusion in the assembly sequence, and conversely, it will also be fed back to the production link, resulting in excessive consumption of resources in the production link. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for monitoring the production of vehicle body blocks based on big data analysis to solve the problems raised in the above-mentioned background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0005] A car body block production monitoring system based on big data analysis, the system includes a data management center module, a production line operation spectrum module, a production capacity collaborative analysis center module and an assembly supervision module which are sequentially connected;

[0006] The data management center module caches the production completion time of the body blocks based on the consistency of the production time attributes; the production line operation spectrum module depicts the operation scatter spectrum diagram of the production line based on the production label, and the production label records the production completion time node information and the qualified quantity information of the body blocks produced by the production line; the production capacity collaborative analysis center module is used to convert the operation scatter spectrum diagram into a spectrum function, and build a production capacity collaborative analysis center to evaluate the collaborative capability of the production line; the assembly supervision module generates an assembly list based on the evaluation results and feeds back to the staff.

[0007] Further, the data management center module includes a time encoder unit and a storage allocation unit connected in sequence;

[0008] The time encoder unit synchronizes the production completion time series of each production line through the control of the time encoder, so that each production line equipment has consistency in time attributes. The time encoder is installed in each production line equipment; the storage allocation unit allocates storage space to the production data management center according to the number of production lines, and each storage space receives the production completion time of the body block during the operation of the production line fed back by the time encoder.

[0009] Further, the production line operation spectrum module includes a production label generation unit and a scattered point coordinate characterization unit which are sequentially connected;

[0010] The production label generation unit is used to collect the qualified number of body blocks produced by the production line at different production completion time nodes to generate production labels; the scatter coordinate characterization unit characterizes the production line operation scatter spectrum coordinate system based on the production labels to obtain the operation scatter spectrum diagrams of different production lines.

[0011] Further, the production capacity collaborative analysis center module includes a spectrum function fitting unit and a collaborative capacity analysis unit which are sequentially connected;

[0012] The spectrum function fitting unit smoothly connects the punctuation points in the operation scatter spectrum diagram in sequence according to the order of production completion time nodes, obtains the operation spectrum curves of different production lines, and generates the spectrum function of the production line;

[0013] The coordination capability analysis unit retrieves the frequency spectrum functions generated corresponding to two production lines with adjacent assembly sequences according to the assembly sequence of the vehicle body blocks, and constructs a production capacity coordination analysis center to evaluate the coordination capability of the production lines.

[0014] Further, the assembly supervision module includes a fitting feedback unit and an assembly list generation unit which are sequentially connected;

[0015] The fitting feedback unit is used to change the variable adjustment parameters of the production capacity coordination analysis center and quantitatively analyze the effective time range of production line coordination; the assembly list generation unit is used to generate an assembly list and output it to the staff port.

[0016] A method for monitoring the production of vehicle body blocks based on big data analysis, the method comprising the following steps:

[0017] S1. Maintain the consistency of the production time attributes of each production line, and cache the production completion time of the body blocks during the operation of the production line to the production data management center;

[0018] S2. Count the qualified number of car body blocks produced by the production line at each production completion time node, and generate production labels; describe the production line operation scatter spectrum coordinate system, punctuate the production labels, and obtain the production line operation scatter spectrum diagram;

[0019] S3. Convert the scatter spectrum of the production line into a spectrum function, and evaluate the collaborative capability of the production line through the production capacity collaborative analysis center;

[0020] S4. Based on the evaluation results, the effective time range for production line coordination is obtained, and an assembly list is generated in descending order of the evaluation values; according to the assembly list, the assembly process is supervised within the effective time range.

[0021] Furthermore, the specific implementation process of step S100 includes:

[0022] According to the number of production lines, a storage space is allocated to the production data management center, and the storage space is used to cache the production completion time of the body blocks during the operation of the production line, wherein one storage space is allocated to one production line, and one production line produces one body block;

[0023] The production completion time series of each production line is synchronized by the time encoder, and the synchronized production completion time series is recorded as ,in, represents the xth production completion time node, and Y represents the total number of synchronized production completion time nodes.

[0024] Furthermore, the specific implementation process of step S200 includes:

[0025] The storage space allocated to the i-th production line is recorded as , and establish a cache sample cluster, recorded as ,in, Indicates the production completion time of the i-th production line The qualified number of body parts produced at Indicates the production completion time of the i-th production line Production label at the address;

[0026] Based on the production label, a production line operation scatter spectrum coordinate system is established. The horizontal axis of the production line operation scatter spectrum coordinate system corresponds to the production completion time node, and the vertical axis of the production line operation scatter spectrum coordinate system corresponds to the qualified number of body blocks. Then the cache sample cluster Production labels Marking is performed in the production line operation scattered point spectrum coordinate system to obtain the operation scattered point spectrum diagram of the i-th production line.

[0027] Furthermore, the specific implementation process of step S300 includes:

[0028] According to the order of production completion time nodes, the punctuation points in the operation scatter spectrum diagram are smoothly connected in sequence to obtain the operation spectrum curve of the i-th production line, and imported into the software to generate the spectrum function of the i-th production line, which is recorded as ;

[0029] According to the assembly order of the car body blocks, retrieve the spectrum function of the i+1th production line ; Establish a production capacity collaboration analysis center to evaluate the collaborative capabilities of production lines:

[0030] ;

[0031] In the formula, represents the evaluation value of the synergy between the i-th production line and the i+1-th production line, k represents a variable adjustment parameter, and when k is a positive value, the spectrum function Shift to the left. When k is negative, the spectrum function Shifting to the right, x1 and x2 represent the start time node and end time node of the preset assembly respectively;

[0032] According to the above method, if the qualified numbers of body blocks produced by two production lines are closer, then The smaller the value, the stronger the coordination ability of the production line.

[0033] Furthermore, the specific implementation process of step S400 includes:

[0034] Change the size of the variable adjustment parameter until the evaluation value At maximum, lock makes the evaluation value Variable adjustment parameters at maximum , we get the effective time range of production line coordination, recorded as ,in, represents the start and end time of production of the i-th production line, Indicates the time node of the termination of production of the jth production line;

[0035] According to the above method, by translating the curve function of one production line and comparing it with another production line, the coordination time range of the two production lines can be quickly located. Only when the evaluation value is the largest can the variable adjustment parameter be locked.

[0036] An assembly list is generated in descending order of evaluation values, and the effective time range is sent to the staff port. The staff supervises the assembly of the body blocks within the effective time range.

[0037] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in a body block production monitoring method and system based on big data analysis provided by the present invention, by maintaining the consistency of the production time attributes of each production line, the production completion time of the body blocks during the operation of the production line is cached; the qualified number of body blocks at each production completion time node is counted to generate a production label; the production line operation scatter spectrum coordinate system is depicted, and the production labels are punctuated to obtain the operation scatter spectrum diagram of the production line; the operation scatter spectrum diagram is converted into a spectrum function to evaluate the coordination ability of the production line; based on the evaluation results, the effective time range of the production line coordination is obtained, and an assembly list is generated; the present invention can open up data sharing between the production link and the assembly link, avoid the situation where the time-consuming anomaly of the production process leads to confusion in the assembly order, and by quantitatively evaluating the coordination ability between different production lines, the effective time range of the assembly link can be quantified, thereby improving the delivery efficiency of the finished product. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0039] Figure 1 It is a schematic diagram of the steps of a vehicle body block production monitoring method based on big data analysis of the present invention. DETAILED DESCRIPTION

[0040] 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 only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] In the first embodiment of the present invention, a vehicle body block production monitoring system based on big data analysis is provided, the system comprising: a data management center module, a production line operation spectrum module, a production capacity collaborative analysis center module and an assembly supervision module connected in sequence;

[0042] The data management center module caches the production completion time of the vehicle body blocks based on the consistency of the production time attribute;

[0043] Preferably, the data management center module comprises a time encoder unit and a storage allocation unit connected in sequence;

[0044] The time encoder unit synchronizes the production completion time series of each production line through the control of the time encoder, so that each production line equipment has consistency in time attributes. The time encoder is installed in each production line equipment; the storage allocation unit allocates storage space to the production data management center according to the number of production lines, and each storage space receives the production completion time of the body block during the operation of the production line fed back by the time encoder.

[0045] The production line operation spectrum module depicts the operation scatter spectrum diagram of the production line based on the production label, and the production label records the production completion time node information and the qualified number information of the body blocks produced by the production line;

[0046] Preferably, the production line operation spectrum module includes a production label generation unit and a scattered point coordinate characterization unit which are sequentially connected;

[0047] The production label generation unit is used to collect the qualified number of body blocks produced by the production line at different production completion time nodes to generate production labels; the scatter coordinate characterization unit characterizes the production line operation scatter spectrum coordinate system based on the production labels to obtain the operation scatter spectrum diagrams of different production lines.

[0048] The capacity collaborative analysis center module is used to convert the operation scatter spectrum diagram into a spectrum function, and to build a capacity collaborative analysis center to evaluate the collaborative capability of the production line;

[0049] Preferably, the production capacity collaborative analysis center module includes a spectrum function fitting unit and a collaborative capacity analysis unit which are sequentially connected;

[0050] The spectrum function fitting unit smoothly connects the punctuation points in the operation scatter spectrum diagram in sequence according to the order of production completion time nodes, obtains the operation spectrum curves of different production lines, and generates the spectrum function of the production line;

[0051] The coordination capability analysis unit retrieves the frequency spectrum functions generated corresponding to two production lines with adjacent assembly sequences according to the assembly sequence of the vehicle body blocks, and constructs a production capacity coordination analysis center to evaluate the coordination capability of the production lines.

[0052] The assembly supervision module generates an assembly checklist based on the evaluation results and feeds it back to the staff;

[0053] Preferably, the assembly supervision module comprises a fitting feedback unit and an assembly list generation unit which are sequentially connected;

[0054] The fitting feedback unit is used to change the variable adjustment parameters of the production capacity coordination analysis center and quantitatively analyze the effective time range of production line coordination; the assembly list generation unit is used to generate an assembly list and output it to the staff port.

[0055] See also Figure 1 In the second embodiment, a vehicle body production monitoring method based on big data analysis is provided, and the method includes the following steps:

[0056] S1. Maintain the consistency of the production time attributes of each production line, and cache the production completion time of the body blocks during the operation of the production line to the production data management center;

[0057] Exemplarily, according to the number of production lines, a storage space is allocated to the production data management center, and the storage space is used to cache the production completion time of the body blocks during the operation of the production lines, wherein one storage space is allocated to one production line, and one production line produces one body block;

[0058] The production completion time series of each production line is synchronized by the time encoder, and the synchronized production completion time series is recorded as ,in, represents the xth production completion time node, and Y represents the total number of synchronized production completion time nodes;

[0059] S2. Count the qualified number of car body blocks produced by the production line at each production completion time node, and generate production labels; describe the production line operation scatter spectrum coordinate system, punctuate the production labels, and obtain the production line operation scatter spectrum diagram;

[0060] For example, the storage space allocated to the i-th production line is recorded as , and establish a cache sample cluster, recorded as ,in, Indicates the production completion time of the i-th production line The qualified number of body parts produced at Indicates the production completion time of the i-th production line Production label at the address;

[0061] Based on the production label, a production line operation scatter spectrum coordinate system is established. The horizontal axis of the production line operation scatter spectrum coordinate system corresponds to the production completion time node, and the vertical axis of the production line operation scatter spectrum coordinate system corresponds to the qualified number of body blocks. Then the cache sample cluster Production labels Marking is performed in the production line operation scattered point spectrum coordinate system to obtain the operation scattered point spectrum diagram of the i-th production line;

[0062] S3. Convert the scatter spectrum of the production line into a spectrum function, and evaluate the collaborative capability of the production line through the production capacity collaborative analysis center;

[0063] For example, according to the order of production completion time nodes, the punctuation points in the operation scatter spectrum diagram are smoothly connected in sequence to obtain the operation spectrum curve of the i-th production line, and the spectrum function of the i-th production line is generated by importing it into the software, which is recorded as ;

[0064] According to the assembly order of the car body blocks, retrieve the spectrum function of the i+1th production line ; Establish a production capacity collaboration analysis center to evaluate the collaborative capabilities of production lines:

[0065] ;

[0066] In the formula, represents the evaluation value of the synergy between the i-th production line and the i+1-th production line, k represents a variable adjustment parameter, and when k is a positive value, the spectrum function Shift to the left. When k is negative, the spectrum function Shifting to the right, x1 and x2 represent the start time node and end time node of the preset assembly respectively;

[0067] S4. Based on the evaluation results, the effective time range of production line coordination is obtained, and an assembly list is generated in descending order of evaluation values; according to the assembly list, the assembly process is supervised within the effective time range;

[0068] For example, the variable adjustment parameter is changed until the evaluation value At maximum, lock makes the evaluation value Variable adjustment parameters at maximum , we get the effective time range of production line coordination, recorded as ,in, represents the start and end time of production of the i-th production line, Indicates the time node of the termination of production of the jth production line;

[0069] An assembly list is generated in descending order of evaluation values, and the effective time range is sent to the staff port. The staff supervises the assembly of the body blocks within the effective time range.

[0070] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0071] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A vehicle body block production monitoring method based on big data analysis, characterized in that: The method comprises the following steps: S1. Maintain the consistency of the production time attributes of each production line, and cache the production completion time of the body blocks during the operation of the production line to the production data management center; S2. Count the qualified number of car body blocks produced by the production line at each production completion time node, and generate production labels; describe the production line operation scatter spectrum coordinate system, punctuate the production labels, and obtain the production line operation scatter spectrum diagram; S3. Convert the scatter spectrum of the production line into a spectrum function, and evaluate the collaborative capability of the production line through the production capacity collaborative analysis center; S4. Based on the evaluation results, the effective time range of production line coordination is obtained, and an assembly list is generated in descending order of evaluation values; according to the assembly list, the assembly process is supervised within the effective time range; The specific implementation process of S1 includes: According to the number of production lines, a storage space is allocated to the production data management center, and the storage space is used to cache the production completion time of the body blocks during the operation of the production line, wherein one storage space is allocated to one production line, and one production line produces one body block; The production completion time series of each production line is synchronized by the time encoder, and the synchronized production completion time series is recorded as ,in, represents the xth production completion time node, and Y represents the total number of synchronized production completion time nodes; The specific implementation process of S2 includes: The storage space allocated to the i-th production line is recorded as , and establish a cache sample cluster, recorded as ,in, Indicates the production completion time of the i-th production line The qualified number of body parts produced at Indicates the production completion time of the i-th production line Production label at the address; Based on the production label, a production line operation scatter spectrum coordinate system is established. The horizontal axis of the production line operation scatter spectrum coordinate system corresponds to the production completion time node, and the vertical axis of the production line operation scatter spectrum coordinate system corresponds to the qualified number of body blocks. Then the cache sample cluster Production labels Marking is performed in the production line operation scattered point spectrum coordinate system to obtain the operation scattered point spectrum diagram of the i-th production line; The specific implementation process of S3 includes: According to the order of production completion time nodes, the punctuation points in the operation scatter spectrum diagram are smoothly connected in sequence to obtain the operation spectrum curve of the i-th production line, and imported into the software to generate the spectrum function of the i-th production line, which is recorded as ; According to the assembly order of the car body blocks, retrieve the spectrum function of the i+1th production line ; Establish a production capacity collaboration analysis center to evaluate the collaborative capabilities of production lines: ； In the formula, represents the evaluation value of the synergy between the i-th production line and the i+1-th production line, k represents a variable adjustment parameter, and when k is a positive value, the spectrum function Shift to the left. When k is negative, the spectrum function Shifting to the right, x1 and x2 represent the starting time node and ending time node of the preset assembly respectively.

2. The method for monitoring vehicle body production based on big data analysis according to claim 1, characterized in that: The specific implementation process of S4 includes: Change the size of the variable adjustment parameter until the evaluation value At maximum, lock makes the evaluation value Variable adjustment parameters at maximum , we get the effective time range of production line collaboration, recorded as ,in, represents the start and end time of production of the i-th production line, Indicates the time node of the termination of production of the jth production line; An assembly list is generated in descending order of evaluation values, and the effective time range is sent to the staff port. The staff supervises the assembly of the body blocks within the effective time range.

3. A vehicle body block production monitoring system based on big data analysis, executing a vehicle body block production monitoring method based on big data analysis as claimed in claim 1, characterized in that: The system includes a data management center module, a production line operation spectrum module, a production capacity collaborative analysis center module and an assembly supervision module which are sequentially connected; The data management center module caches the production completion time of the vehicle body blocks based on the consistency of the production time attribute; The production line operation spectrum module depicts the operation scatter spectrum diagram of the production line based on the production label, and the production label records the production completion time node information and the qualified quantity information of the body blocks produced by the production line; the production capacity collaborative analysis center module is used to convert the operation scatter spectrum diagram into a spectrum function, and build a production capacity collaborative analysis center to evaluate the collaborative capability of the production line; the assembly supervision module generates an assembly list based on the evaluation results and feeds back to the staff.

4. The vehicle body segment production monitoring system based on big data analysis according to claim 3 is characterized by: The data management center module includes a time encoder unit and a storage allocation unit connected in sequence; The time encoder unit synchronizes the production completion time series of each production line through the control of the time encoder, so that each production line equipment has consistency in time attributes. The time encoder is installed in each production line equipment; the storage allocation unit allocates storage space to the production data management center according to the number of production lines, and each storage space receives the production completion time of the body block during the operation of the production line fed back by the time encoder.

5. The vehicle body block production monitoring system based on big data analysis according to claim 3 is characterized by: The production line operation spectrum module includes a production label generation unit and a scattered point coordinate characterization unit connected in sequence; The production label generating unit is used to collect the qualified quantity of the vehicle body blocks produced by the production line at different production completion time nodes to generate the production label; The scatter point coordinate characterization unit characterizes the production line operation scatter point spectrum coordinate system based on the production label to obtain the operation scatter point spectrum diagrams of different production lines.

6. The vehicle body production monitoring system based on big data analysis according to claim 3 is characterized by: The production capacity collaborative analysis center module includes a spectrum function fitting unit and a collaborative capacity analysis unit connected in sequence; The spectrum function fitting unit smoothly connects the punctuation points in the operation scatter spectrum diagram in sequence according to the order of production completion time nodes, obtains the operation spectrum curves of different production lines, and generates the spectrum function of the production line; The coordination capability analysis unit retrieves the frequency spectrum functions generated corresponding to two production lines with adjacent assembly sequences according to the assembly sequence of the vehicle body blocks, and constructs a production capacity coordination analysis center to evaluate the coordination capability of the production lines.

7. The vehicle body production monitoring system based on big data analysis according to claim 3 is characterized by: The assembly supervision module includes a fitting feedback unit and an assembly list generation unit connected in sequence; The fitting feedback unit is used to change the variable adjustment parameters of the production capacity coordination analysis center, and quantitatively analyze to obtain the effective time range of production line coordination; the assembly list generation unit is used to generate an assembly list and output it to the staff port.

Images