Whole vehicle development progress management method and system

Abstract

The invention provides a vehicle development progress management method and system, and the method comprises the following steps: obtaining a plurality of production nodes corresponding to parts, and extracting a plurality of previous production data sets based on the production nodes, so as to obtain real-time result labels corresponding to the parts; selecting a plurality of to-be-used production data sets through the real-time result labels, obtaining real-time production data corresponding to the parts, and determining a dynamic parameter threshold range of the production nodes through the to-be-used production data sets and the real-time production data; the real-time production data is compared to the dynamic parameter threshold range to trigger a base progress alarm. By automatically obtaining production nodes, extracting a previous production data set, generating a dynamic parameter threshold range based on the previous production data set and comparing data in real time to trigger an alarm, the situation that mistakes and omissions are likely to occur when production data are manually summarized is eliminated, and automatic monitoring management of the whole process of the whole vehicle development progress is achieved. And the real-time performance of risk identification is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of data prediction, and in particular to a vehicle development progress management method and system. Background Art

[0002] After more than ten years of rapid development, the automobile market has seen an increasing number of motor vehicles nationwide. As one of the means of transportation, automobiles have become popular in human society.

[0003] During the development of a complete vehicle, due to the large number of parts, various types of production data and test data will be generated during the development process, and the progress of the complete vehicle development can be grasped through these production data and test data.

[0004] Currently, vehicle development progress management is generally done manually by regularly summarizing and comparing production data at different times to analyze whether there are production risks. However, manual analysis is prone to errors and omissions when integrating data, and lacks real-time performance, making it difficult to effectively control the risks of vehicle development progress. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a vehicle development progress management method and system, aiming to solve the technical problems in the prior art of conducting production risk control by manually aggregating production data, which easily leads to errors and omissions in data integration and lacks real-time performance, making it difficult to effectively control the risks of vehicle development progress.

[0006] In order to achieve the above objectives, in a first aspect, an embodiment of the present application provides a vehicle development progress management method, comprising the following steps: Acquire several production nodes corresponding to the component, extract several past production data sets corresponding to the component based on the production nodes, and determine the real-time result label corresponding to the component based on the past production data sets; Selecting a plurality of standby production data sets from a plurality of past production data sets through the real-time result tags, acquiring real-time production data corresponding to the component, and determining a dynamic parameter threshold range of the production node through the standby production data sets and the real-time production data; The real-time production data is compared with the dynamic parameter threshold range to trigger a basic progress alarm.

[0007] Furthermore, the past production data set includes past production time consumption, and the step of determining the real-time result label corresponding to the component based on the past production data set includes: Obtaining the real-time production time of the component, and obtaining a progress deviation rate based on the real-time production time and the past production time; A real-time result label is obtained based on the progress deviation rate.

[0008] Furthermore, the formula for obtaining the progress deviation rate is: , in, represents the progress deviation rate of the current batch of parts corresponding to the j-th production node, represents the real-time production time of the current batch of parts corresponding to the j-th production node, represents the past production time of the mth past batch of parts corresponding to the jth production node, Indicates the total number of previous batches corresponding to the j-th production node; The formula for obtaining the real-time result label is: , in, Represents the real-time result labels of the parts in the current batch corresponding to the j-th production node.

[0009] Furthermore, the standby production data set includes a plurality of standby production data, and the step of determining the dynamic parameter threshold range of the production node through the standby production data set and the real-time production data includes: Acquiring parameter standard values ​​based on a plurality of the production data sets to be used; Acquire a parameter threshold value based on the real-time production data and the parameter standard value; A dynamic parameter threshold range is constructed based on the parameter threshold.

[0010] Furthermore, the formula for obtaining the standard value of the parameter is: , in, represents the parameter standard value of the kth production parameter of the current batch of parts corresponding to the jth production node, Represents a set of several production data sets to be used corresponding to real-time result labels. represents the number of unused production data sets in a set of several unused production data sets corresponding to the real-time result label, represents the standby production data of the kth production parameter in the i-th standby production data set corresponding to the j-th production node; The formula for obtaining the parameter threshold is: , in, represents the parameter threshold of the kth production parameter of the current batch of parts corresponding to the jth production node, The parameter threshold value of the k-th production parameter of the components of the previous batch adjacent to the current batch of the j-th production node The real-time production data of the k-th production parameter of the components of the current batch corresponding to the j-th production node; The acquisition formula for the dynamic parameter threshold range is: , wherein, Represents the first boundary point of the dynamic parameter threshold range of the k-th production parameter of the components of the current batch corresponding to the j-th production node, Represents the second boundary point of the dynamic parameter threshold range of the k-th production parameter of the components of the current batch corresponding to the j-th production node.

[0011] Furthermore, the step of comparing the real-time production data with the dynamic parameter threshold range to trigger a basic progress alarm is specifically: Judge whether the real-time production data is within the dynamic parameter threshold range. If the real-time production data is not within the dynamic parameter threshold range, trigger a basic progress alarm.

[0012] Furthermore, after the step of comparing the real-time production data with the dynamic parameter threshold range to trigger a basic progress alarm, it further includes: Obtain the real-time quality value of the components of the current batch, and obtain the response level to the basic progress alarm through the real-time production data, the progress deviation rate and the real-time quality value.

[0013] Furthermore, the step of obtaining the response level to the basic progress alarm through the real-time production data, the progress deviation rate and the real-time quality value includes: Obtain a risk coefficient based on the real-time production data, the progress deviation rate and the real-time quality value; Compare the risk coefficient with the coefficient threshold value to obtain the response level to the basic progress alarm.

[0014] Furthermore, the acquisition formula for the risk coefficient is: , wherein, Represents the risk coefficient corresponding to the j-th production node, Represents the real-time production data of the k-th production parameter of the components of the current batch corresponding to the j-th production node, Represents the parameter standard value of the k-th production parameter of the components of the current batch corresponding to the j-th production node, represents the parameter threshold of the kth production parameter of the current batch of parts corresponding to the jth production node, represents the progress deviation rate of the current batch of parts corresponding to the j-th production node, Indicates the real-time quality value of the qth quality index of the parts in the current batch, represents the quality standard value of the qth quality indicator, Represents the indicator weight of the qth quality indicator.

[0015] In a second aspect, an embodiment of the present application provides a vehicle development progress management system, which is applied to the vehicle development progress management method as described in the first aspect above, and the system includes: An acquisition module, used to acquire a number of production nodes corresponding to the component, extract a number of past production data sets corresponding to the component based on the production nodes, and determine a real-time result label corresponding to the component based on the past production data sets; An analysis module, configured to select a plurality of standby production data sets from a plurality of past production data sets through the real-time result tags, obtain real-time production data corresponding to the component, and determine a dynamic parameter threshold range of the production node through the standby production data sets and the real-time production data; The first execution module is used to compare the real-time production data with the dynamic parameter threshold range to trigger a basic progress alarm.

[0016] In a third aspect, an embodiment of the present application provides a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the vehicle development progress management method as described in the first aspect above is implemented.

[0017] In a fourth aspect, an embodiment of the present application provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the vehicle development progress management method described in the first aspect above is implemented.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: By automatically obtaining the production nodes, extracting the previous production data sets, generating the dynamic parameter threshold ranges based on the previous production data sets, and comparing the data in real time to trigger alarms, it eliminates the situation of errors and omissions that are prone to occur in manual summary of production data, realizes the full-process automated monitoring and management of the vehicle development progress, and ensures the real-time nature of risk identification; During the vehicle development process, the production progress of parts may show different real result tags due to factors such as batches, suppliers, and resource allocation. Static thresholds cannot distinguish the risk tolerances in different states. By setting the dynamic parameter threshold ranges, the width of the thresholds can be adjusted for different situations, reducing the risks of false alarms and misreports on the premise of improving sensitivity, and realizing the dynamic management of the production progress; By introducing the risk coefficient and quantifying risks in combination with multi-dimensional data, the accuracy of progress management is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flowchart of the vehicle development progress management method in the first embodiment of the present invention; Figure 2 It is a structural block diagram of the vehicle development progress management system in the second embodiment of the present invention; The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. SPECIFIC EMBODIMENTS

[0020] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0021] It should be noted that when an element is referred to as being "fixedly provided on" another element, it can be directly on the other element or there can also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for the purpose of illustration.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the description of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0023] Please refer to Figure 1, the vehicle development progress management method provided by the first embodiment of the present invention is used to control the audio output of a VR glasses. A sound pickup microphone, a shooting unit, and a clamping groove for placing a mobile phone are provided on the VR glasses. The mobile phone is used to play an audio stream. The vehicle development progress management method includes the following steps: S10: Obtain a plurality of production nodes corresponding to the components, extract a plurality of past production data sets corresponding to the components based on the production nodes, and determine a real-time result label corresponding to the components based on the past production data sets; It can be understood that the past production data sets include past production time. Taking interior parts as an example, it includes different production nodes such as injection molding and painting. When performing injection molding, there are different production data such as injection pressure, injection temperature, and injection time. The set of this production data is the past production data set. Different past batches of the interior parts generate several of the past data sets during the production process.

[0024] The step S10 includes: S110: Obtain the real-time production time of the components, and obtain a progress deviation rate based on the real-time production time and the past production time; The formula for obtaining the progress deviation rate is: , where, represents the progress deviation rate of the components of the current batch corresponding to the j-th production node, represents the real-time production time of the components of the current batch corresponding to the j-th production node, represents the past production time of the components of the m-th past batch corresponding to the j-th production node, represents the total number of past batches corresponding to the j-th production node.

[0025] S120: Obtain a real-time result label based on the progress deviation rate; The formula for obtaining the real-time result label is: , where, represents the real-time result label of the components of the current batch corresponding to the j-th production node. Still taking interior parts as an example, due to the influence of factors such as batches, suppliers, and resource allocation, it will correspond to different real-time result labels. It does not directly point to problems in the production process. Therefore, further analysis of production data is still required.

[0026] S20: Select a number of production data sets to be used from several of the previous production data sets through the real-time result tags, obtain the real-time production data corresponding to the component parts, and determine the dynamic parameter threshold range of the production node through the production data sets to be used and the real-time production data; The production data sets to be used include a number of production data to be used. Understandably, the production data sets to be used and the previous production data sets are the same data sets, only with different names, used to distinguish the data sets corresponding to different real-time result tags. Suppose the real-time result tag is delay, then the production data sets to be used are the data sets with production results of delay in several of the previous production data sets.

[0027] Step S20 includes: S210: Obtain the parameter standard value based on a number of the production data sets to be used; The acquisition formula for the parameter standard value is: , where, represents the parameter standard value of the kth production parameter of the component parts of the current batch corresponding to the jth production node, represents the set of a number of production data sets to be used corresponding to the real-time result tag, represents the number of production data sets to be used in the set of production data sets to be used corresponding to the real-time result tag, represents the production data to be used of the kth production parameter in the ith production data set to be used in the set of production data sets to be used corresponding to the jth production node. The parameter standard value reflects the normal level in the same type of historical scenarios.

[0028] S220: Obtain the parameter threshold based on the real-time production data and the parameter standard value; The acquisition formula for the parameter threshold is: , where, represents the parameter threshold of the kth production parameter of the component parts of the current batch corresponding to the jth production node, represents the parameter threshold of the kth production parameter of the component parts of the previous previous batch adjacent to the current batch of the jth production node, represents the real-time production data of the kth production parameter of the component parts of the current batch corresponding to the jth production node. Among the parameter thresholds, 80% of the weight inherits the previous threshold to maintain stability, while 20% of the weight absorbs the deviation between the real-time production data and the parameter standard value, reflecting the latest data fluctuations, and avoiding the threshold failure caused by data drift (such as equipment aging).

[0029] S230: Construct a dynamic parameter threshold range based on the parameter threshold; The acquisition formula for the dynamic parameter threshold range is: , where, represents the first boundary point of the dynamic parameter threshold range of the k-th production parameter of the parts of the current batch corresponding to the j-th production node, represents the second boundary point of the dynamic parameter threshold range of the k-th production parameter of the parts of the current batch corresponding to the j-th production node.

[0030] S30: Compare the real-time production data with the dynamic parameter threshold range to trigger a basic progress alarm; Specifically, determine whether the real-time production data is within the dynamic parameter threshold range. If the real-time production data is not within the dynamic parameter threshold range, trigger a basic progress alarm.

[0031] By automatically obtaining the production node, extracting the past production data set, generating the dynamic parameter threshold range based on the past production data set, and triggering an alarm by comparing the data in real time, the situation of errors and omissions that are likely to occur in manual summary of production data is eliminated, the full-process automated monitoring and management of the vehicle development progress is realized, and the real-time nature of risk identification is ensured; during the vehicle development process, the production progress of parts may show different real-time result labels due to factors such as batches, suppliers, and resource allocation. Static thresholds cannot distinguish the risk tolerance in different states. By setting the dynamic parameter threshold range, the width of the threshold can be adjusted according to different situations. In the delayed state, the production progress has fallen behind, and any adverse data fluctuations (such as equipment failures, material delays) need to be quickly captured. When the progress is ahead, reasonable data fluctuations (such as temporary production increases) are allowed. On the premise of improving sensitivity, the risks of false alarms and misreports are reduced, and the dynamic management of production progress is realized.

[0032] Preferably, the method further includes: S40: Obtain the real-time quality value of the parts of the current batch, and obtain the response level for the basic progress alarm through the real-time production data, the progress deviation rate, and the real-time quality value; The response levels include a first-level response, a second-level response, and a third-level response.

[0033] The step S40 includes: S410: Obtain a risk coefficient based on the real-time production data, the progress deviation rate, and the real-time quality value; The acquisition formula for the risk coefficient is: , Among them, represents the risk coefficient corresponding to the j-th production node, represents the real-time production data of the k-th production parameter of the parts in the current batch corresponding to the j-th production node, represents the parameter standard value of the k-th production parameter of the parts in the current batch corresponding to the j-th production node, represents the parameter threshold of the k-th production parameter of the parts in the current batch corresponding to the j-th production node, represents the progress deviation rate of the parts in the current batch corresponding to the j-th production node, represents the real-time quality value of the q-th quality index of the parts in the current batch, represents the quality standard value of the q-th quality index, represents the index weight of the q-th quality index.

[0034] S420: Compare the risk coefficient with the coefficient threshold to obtain the response level to the basic progress alert; The determination formula of the response level is: , Among them, represents the standard deviation of the historical risk index. Among them, the third-level response belongs to occasional anomalies (such as sensor noise), and the basic progress alert is recorded. The first-level response is a systematic risk (multiple anomalies), and corresponding early warning processing needs to be carried out immediately, such as shutting down the equipment for maintenance, tracing the supply chain, etc. By introducing the risk coefficient, on the basis of the basic progress alert as the first line of defense, occasional failures and systematic risks are distinguished, hierarchical response is guided, risks are quantified with multi-dimensional data, and the accuracy of progress management is improved.

[0035] Please refer to Figure 2 , the second embodiment of the present invention provides a vehicle development progress management system, which is applied to the vehicle development progress management method in the above embodiment, and the descriptions that have been made will not be repeated. As used below, terms such as "module", "unit", "sub-unit", etc. can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0036] The system includes: An acquisition module 10, configured to acquire a plurality of production nodes corresponding to parts, extract a plurality of past production data sets corresponding to the parts based on the production nodes, and determine a real-time result label corresponding to the parts based on the past production data sets; The acquisition module 10 includes: The first unit is used to obtain the real-time production time of the component, and obtain the progress deviation rate based on the real-time production time and the past production time; A second unit is used to obtain a real-time result label based on the progress deviation rate; The analysis module 20 is used to select a plurality of standby production data sets from a plurality of past production data sets through the real-time result tags, obtain the real-time production data corresponding to the component, and determine the dynamic parameter threshold range of the production node through the standby production data sets and the real-time production data; The analysis module 20 includes: A third unit is used to obtain a parameter standard value based on a plurality of the production data sets to be used; A fourth unit is used to obtain a parameter threshold value based on the real-time production data and the parameter standard value; A fifth unit, configured to construct a dynamic parameter threshold range based on the parameter threshold; A first execution module 30, configured to compare the real-time production data with the dynamic parameter threshold range to trigger a basic progress alarm; The first execution module 30 is specifically used to determine whether the real-time production data is within the dynamic parameter threshold range, and if the real-time production data is not within the dynamic parameter threshold range, trigger a basic progress alarm; Preferably, the system further comprises: The second execution module 40 is used to obtain the real-time quality value of the parts of the current batch, and obtain the response level to the basic progress alarm through the real-time production data, the progress deviation rate and the real-time quality value; The second execution module 40 includes: A sixth unit, configured to obtain a risk coefficient based on the real-time production data, the progress deviation rate and the real-time quality value; The seventh unit is used to compare the risk coefficient with a coefficient threshold to obtain a response level to the basic progress alarm.

[0037] The present invention also provides a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the vehicle development progress management method as described in the above technical solution is implemented.

[0038] The present invention also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the vehicle development progress management method described in the above technical solution is implemented.

[0039] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0040] The above-described embodiments merely represent several implementation manners of the present invention. Their descriptions are relatively specific and detailed, but should not be construed as limiting the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention shall be subject to the appended claims.

Claims

1. A vehicle development progress management method, characterized in that It includes the following steps: Obtain a number of production nodes corresponding to the component parts, extract a number of past production data sets corresponding to the component parts based on the production nodes, and determine a real-time result label corresponding to the component parts based on the past production data sets; Select a number of production data sets to be used from a number of the past production data sets through the real-time result label, obtain real-time production data corresponding to the component parts, and determine a dynamic parameter threshold range of the production nodes through the production data sets to be used and the real-time production data; Compare the real-time production data with the dynamic parameter threshold range to trigger a basic progress alarm.

2. The vehicle development progress management method according to claim 1, characterized in that The past production data sets include past production time consumption, and the step of determining a real-time result label corresponding to the component parts based on the past production data sets includes: Obtain the real-time production time consumption of the component parts, and obtain a progress deviation rate based on the real-time production time consumption and the past production time consumption; Obtain a real-time result label based on the progress deviation rate.

3. The vehicle development progress management method according to claim 2, wherein The formula for obtaining the progress deviation rate is: ， Among them, represents the progress deviation rate of the components of the current batch corresponding to the j-th production node, represents the real-time production time of the components of the current batch corresponding to the j-th production node, represents the past production time of the components of the m-th past batch corresponding to the j-th production node, represents the total number of past batches corresponding to the j-th production node; The formula for obtaining the real-time result label is: ， Among them, represents the real-time result label of the parts corresponding to the j-th production node for the current batch.

4. The vehicle development progress management method according to claim 1, characterized in that The production data sets to be used include a number of production data to be used, and the step of determining a dynamic parameter threshold range of the production nodes through the production data sets to be used and the real-time production data includes: Obtain a parameter standard value based on a number of the production data sets to be used; Obtain a parameter threshold based on the real-time production data and the parameter standard value; Construct a dynamic parameter threshold range based on the parameter threshold.

5. The vehicle development schedule management method according to claim 4, wherein The formula for obtaining the parameter standard value is: ， in, represents the parameter standard value of the kth production parameter of the current batch of parts corresponding to the jth production node, Represents a set of several production data sets to be used corresponding to real-time result labels. represents the number of unused production data sets in a set of several unused production data sets corresponding to the real-time result label, represents the standby production data of the kth production parameter in the i-th standby production data set corresponding to the j-th production node; The formula for obtaining the parameter threshold is: ， Among them, represents the parameter threshold of the k-th production parameter of the components in the current batch corresponding to the j-th production node, represents the parameter threshold of the k-th production parameter of the components in the previous batch adjacent to the current batch of the j-th production node, represents the real-time production data of the k-th production parameter of the components in the current batch corresponding to the j-th production node; The formula for obtaining the dynamic parameter threshold range is: ， Among them, represents the first boundary point of the dynamic parameter threshold range of the k-th production parameter of the components of the current batch corresponding to the j-th production node, represents the second boundary point of the dynamic parameter threshold range of the k-th production parameter of the components of the current batch corresponding to the j-th production node.

6. The vehicle development progress management method according to claim 1, wherein The step of comparing the real-time production data with the dynamic parameter threshold range to trigger a basic progress alarm is specifically: Judge whether the real-time production data is within the dynamic parameter threshold range. If the real-time production data is not within the dynamic parameter threshold range, trigger a basic progress alarm.

7. The vehicle development progress management method according to claim 1, wherein, After the step of comparing the real-time production data with the dynamic parameter threshold range to trigger a basic progress alarm, it further includes: Obtain the real-time quality value of the component parts in the current batch, and obtain a response level to the basic progress alarm through the real-time production data, the progress deviation rate, and the real-time quality value.

8. The vehicle development progress management method according to claim 7, characterized in that, The step of obtaining a response level to the basic progress alarm through the real-time production data, the progress deviation rate, and the real-time quality value includes: Obtain a risk coefficient based on the real-time production data, the progress deviation rate, and the real-time quality value; Compare the risk coefficient with a coefficient threshold to obtain a response level to the basic progress alarm.

9. The vehicle development progress management method according to claim 8, characterized in that The formula for obtaining the risk coefficient is: ， Among them, represents the risk coefficient corresponding to the j-th production node, represents the real-time production data of the k-th production parameter of the parts of the current batch corresponding to the j-th production node, represents the parameter standard value of the k-th production parameter of the parts of the current batch corresponding to the j-th production node, represents the parameter threshold of the k-th production parameter of the parts of the current batch corresponding to the j-th production node, represents the progress deviation rate of the parts of the current batch corresponding to the j-th production node, represents the real-time quality value of the q-th quality index of the parts of the current batch, represents the quality standard value of the q-th quality index, represents the index weight of the q-th quality index.

10. A vehicle development progress management system is applied to the vehicle development progress management method according to any one of claims 1 to 9, and is characterized in that, The system includes: An acquisition module, configured to obtain a number of production nodes corresponding to the component parts, extract a number of past production data sets corresponding to the component parts based on the production nodes, and determine a real-time result label corresponding to the component parts based on the past production data sets; An analysis module is configured to select a number of production data sets to be used from several of the previous production data sets through the real-time result tags, obtain real-time production data corresponding to the component parts, and determine a dynamic parameter threshold range of the production node through the production data sets to be used and the real-time production data; A first execution module is configured to compare the real-time production data with the dynamic parameter threshold range to trigger a basic progress alarm.

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