A vehicle development progress management method and system

Abstract

The present invention provides a method and system for managing the progress of vehicle development. The method includes the following steps: obtaining several production nodes corresponding to parts and components, extracting several past production data sets based on the production nodes to obtain real-time result labels corresponding to the parts and components; selecting several standby production data sets based on the real-time result labels, obtaining real-time production data corresponding to the parts and components, determining the dynamic parameter threshold range of the production nodes based on the stand-by production data sets and the real-time production data; and comparing the real-time production data with the dynamic parameter threshold range to trigger a basic progress alarm. By automatically obtaining production nodes, extracting past production data sets, generating dynamic parameter threshold ranges based on the past production data sets, and triggering alarms through real-time data comparison, the system eliminates the errors and omissions that may occur when manually summarizing production data, realizes automated monitoring and management of the entire process of vehicle development progress, and ensures the real-time nature of risk identification.

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 in the automobile market, the number of motor vehicles in the country has continued to increase. 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 generally involves manual and regular summary and comparison of production data at different times to analyze whether there are production risks. However, manual analysis is prone to errors and omissions in data integration, and lacks real-time performance, making it difficult to effectively control the risks of vehicle development progress. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, 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 existing technology of manually summarizing production data to carry out production risk control, which easily leads to errors and omissions in data integration, lacks real-time performance, and makes it difficult to effectively control the risks of vehicle development progress.

[0006] 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:

[0007] 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 a real-time result label corresponding to the component based on the past production data sets;

[0008] Selecting a plurality of standby production data sets from a plurality of past production data sets using the real-time result tags, obtaining real-time production data corresponding to the component, and determining a dynamic parameter threshold range of the production node using the standby production data sets and the real-time production data;

[0009] The real-time production data is compared with the dynamic parameter threshold range to trigger a basic progress alarm.

[0010] 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:

[0011] 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;

[0012] A real-time result label is obtained based on the progress deviation rate.

[0013] Furthermore, the formula for obtaining the progress deviation rate is:

[0014] ,

[0015] 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 batch of parts corresponding to the jth production node, Indicates the total number of previous batches corresponding to the j-th production node;

[0016] The formula for obtaining the real-time result label is:

[0017] ,

[0018] in, Represents the real-time result label of the parts in the current batch corresponding to the j-th production node.

[0019] 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 using the standby production data set and the real-time production data includes:

[0020] Obtaining parameter standard values ​​based on the plurality of production data sets to be used;

[0021] Obtaining a parameter threshold based on the real-time production data and the parameter standard value;

[0022] A dynamic parameter threshold range is constructed based on the parameter threshold.

[0023] Furthermore, the formula for obtaining the standard value of the parameter is:

[0024] ,

[0025] in, Indicates the parameter standard value of the kth production parameter of the current batch of parts corresponding to the jth production node, Represents a collection of several ready-to-use production datasets corresponding to real-time result labels. Indicates the number of ready-to-use production data sets in a set of several ready-to-use production data sets corresponding to real-time result labels, 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;

[0026] The formula for obtaining the parameter threshold is:

[0027] ,

[0028] in, represents the parameter threshold of the kth production parameter of the current batch of parts corresponding to the jth production node, represents the parameter threshold of the kth production parameter of the parts of the previous batch adjacent to the current batch of the jth production node, The real-time production data representing the kth production parameter of the component in the current batch corresponding to the jth production node;

[0029] The formula for obtaining the dynamic parameter threshold range is:

[0030] ,

[0031] in, represents the first boundary point of the dynamic parameter threshold range of the kth production parameter of the current batch of parts corresponding to the jth production node, Indicates the second boundary point of the dynamic parameter threshold range of the kth production parameter of the component of the current batch corresponding to the jth production node.

[0032] Furthermore, the step of comparing the real-time production data with the dynamic parameter threshold range to trigger a basic progress alarm is specifically as follows:

[0033] It is determined 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, a basic progress alarm is triggered.

[0034] Furthermore, after the step of comparing the real-time production data with the dynamic parameter threshold range to trigger a basic progress alarm, the method further includes:

[0035] The real-time quality values ​​of the parts of the current batch are obtained, and the response level to the basic progress alarm is obtained through the real-time production data, the progress deviation rate and the real-time quality values.

[0036] Furthermore, 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:

[0037] Obtaining a risk coefficient based on the real-time production data, the progress deviation rate, and the real-time quality value;

[0038] The risk factor is compared with a factor threshold to obtain a response level to the basic progress alarm.

[0039] Furthermore, the risk coefficient is obtained as follows:

[0040] ,

[0041] in, represents the risk coefficient corresponding to the j-th production node, represents the real-time production data of the kth production parameter of the current batch of parts corresponding to the jth production node, Indicates the parameter standard value of the kth production parameter of the current batch of parts corresponding to the jth 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, Indicates the indicator weight of the qth quality indicator.

[0042] 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 described in the first aspect above, and the system includes:

[0043] An acquisition module, configured to acquire a plurality of production nodes corresponding to a component, extract a plurality 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;

[0044] an analysis module, configured to select a plurality of standby production data sets from the plurality of past production data sets using 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 using the standby production data sets and the real-time production data;

[0045] The first execution module is configured to compare the real-time production data with the dynamic parameter threshold range to trigger a basic progress alarm.

[0046] 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. When the processor executes the computer program, the vehicle development progress management method as described in the first aspect above is implemented.

[0047] 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.

[0048] Compared with the prior art, the beneficial effects of the present invention are: by automatically acquiring the production nodes, extracting the past production data sets, generating the dynamic parameter threshold range based on the past production data sets, and triggering alarms by comparing data in real time, the situation in which manual summary of production data is prone to errors and omissions is eliminated, and the full process of 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 present different real-time result labels due to factors such as batches, suppliers, and resource allocation. Static thresholds cannot distinguish the risk tolerance under different states. By setting the dynamic parameter threshold range, the width of the threshold can be adjusted for different situations. On the premise of improving sensitivity, the risk of false alarms and misreporting is reduced, and dynamic management of production progress is realized; by introducing the risk coefficient and combining multi-dimensional data to quantify risks, the accuracy of progress management is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flow chart of the vehicle development progress management method in the first embodiment of the present invention;

[0050] Figure 2 This is a structural block diagram of the vehicle development progress management system in the second embodiment of the present invention;

[0051] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0052] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate several embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0053] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is referred to as being "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0055] See also Figure 1 The first embodiment of the present invention provides a vehicle development progress management method for controlling the audio output of VR glasses. The VR glasses are provided with a pickup microphone, a camera unit, and a slot for placing a mobile phone. The mobile phone is used to play an audio stream. The vehicle development progress management method includes the following steps:

[0056] S10: 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 a real-time result label corresponding to the component based on the past production data sets;

[0057] Understandably, the historical production dataset includes historical production time. For example, interior trim parts involve different production nodes, such as injection molding and painting. During injection molding, different production data, such as injection pressure, temperature, and time, are generated. This collection of production data is the historical production dataset. Different batches of interior trim parts generate multiple historical datasets during the production process.

[0058] The step S10 includes:

[0059] S110: 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;

[0060] The formula for obtaining the progress deviation rate is:

[0061] ,

[0062] 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 batch of parts corresponding to the jth production node, Indicates the total number of previous batches corresponding to the j-th production node.

[0063] S120: Acquire a real-time result label based on the progress deviation rate;

[0064] The formula for obtaining the real-time result label is:

[0065] ,

[0066] in, This represents the real-time result label for the current batch of parts corresponding to the jth production node. Using interior parts as an example, different labels may be associated with different parts depending on factors such as batch size, supplier, and resource allocation. This doesn't directly indicate a problem in the production process; therefore, further analysis of production data is required.

[0067] S20: selecting a plurality of standby production data sets from a plurality of past production data sets using the real-time result tags, obtaining real-time production data corresponding to the component, and determining a dynamic parameter threshold range of the production node using the standby production data sets and the real-time production data;

[0068] The standby production dataset includes several pieces of standby production data. It is understood that the standby production dataset is the same as the previous production dataset, differing only in name to distinguish datasets corresponding to different real-time result labels. Assuming the real-time result label is "delayed," the standby production dataset is the dataset among the several previous production datasets for which the production result is "delayed."

[0069] The step S20 includes:

[0070] S210: Acquire parameter standard values ​​based on a plurality of the production data sets to be used;

[0071] The formula for obtaining the standard value of the parameter is:

[0072] ,

[0073] in, Indicates the parameter standard value of the kth production parameter of the current batch of parts corresponding to the jth production node, Represents a collection of several ready-to-use production datasets corresponding to real-time result labels. Indicates the number of ready-to-use production data sets in a set of several ready-to-use production data sets corresponding to real-time result labels, 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 standard value of the parameter reflects the normal level under similar historical scenarios.

[0074] S220: Obtaining a parameter threshold based on the real-time production data and the parameter standard value;

[0075] The formula for obtaining the parameter threshold is:

[0076] ,

[0077] in, represents the parameter threshold of the kth production parameter of the current batch of parts corresponding to the jth production node, represents the parameter threshold of the kth production parameter of the parts of the previous batch adjacent to the current batch of the jth production node, This represents the real-time production data for the kth production parameter of the component in the current batch corresponding to the jth production node. Of the parameter thresholds, 80% of the weight inherits the previous threshold to maintain stability, while 20% of the weight absorbs deviations between the real-time production data and the parameter standard value, reflecting the latest data fluctuations and preventing threshold failure due to data drift (such as equipment aging).

[0078] S230: Constructing a dynamic parameter threshold range based on the parameter threshold;

[0079] The formula for obtaining the dynamic parameter threshold range is:

[0080] ,

[0081] in, represents the first boundary point of the dynamic parameter threshold range of the kth production parameter of the current batch of parts corresponding to the jth production node, Indicates the second boundary point of the dynamic parameter threshold range of the kth production parameter of the component of the current batch corresponding to the jth production node.

[0082] S30: comparing the real-time production data with the dynamic parameter threshold range to trigger a basic progress alarm;

[0083] Specifically, it is determined 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, a basic progress alarm is triggered.

[0084] By automatically acquiring the production nodes, extracting the past production data sets, generating the dynamic parameter threshold range based on the past production data sets, and triggering alarms through real-time data comparison, the manual aggregation of production data, which is prone to errors and omissions, is eliminated, and automated monitoring and management of the entire vehicle development process is achieved, ensuring real-time risk identification. During the vehicle development process, the production progress of parts may present different real-time result labels due to factors such as batches, suppliers, and resource allocation. Static thresholds cannot distinguish the risk tolerance under different states. By setting the dynamic parameter threshold range, the width of the threshold can be adjusted for different situations. In the delayed state, the production progress has fallen behind, and any adverse data fluctuations (such as equipment failures and material delays) need to be quickly captured. When the progress is ahead, reasonable data fluctuations (such as temporary production increases) are allowed. While improving sensitivity, the risk of false alarms and misreporting is reduced, and dynamic management of the production progress is achieved.

[0085] Preferably, the method further comprises:

[0086] S40: Acquire real-time quality values ​​of parts of the current batch, and obtain a response level to the basic progress alarm based on the real-time production data, the progress deviation rate, and the real-time quality values;

[0087] The response levels include primary response, secondary response and tertiary response.

[0088] The step S40 includes:

[0089] S410: Obtaining a risk coefficient based on the real-time production data, the progress deviation rate, and the real-time quality value;

[0090] The formula for obtaining the risk coefficient is:

[0091] ,

[0092] in, represents the risk coefficient corresponding to the j-th production node, represents the real-time production data of the kth production parameter of the current batch of parts corresponding to the jth production node, Indicates the parameter standard value of the kth production parameter of the current batch of parts corresponding to the jth 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, Indicates the indicator weight of the qth quality indicator.

[0093] S420: Compare the risk coefficient with a coefficient threshold to obtain a response level to the basic progress alarm;

[0094] The determination formula of the response level is:

[0095] ,

[0096] in, Represents the standard deviation of the historical risk index. Level 3 responses are incidental anomalies (such as sensor noise), recording the basic progress alert. Level 1 responses are systemic risks (multiple anomalies), requiring immediate early warning and action, such as shutting down equipment for maintenance and tracing the supply chain. By introducing this risk factor, building on the basic progress alert as the first line of defense, we can distinguish between incidental failures and systemic risks, guide tiered responses, and combine multi-dimensional data to quantify risks, thereby improving the accuracy of progress management.

[0097] See also 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 described in the above embodiments. Details already described will not be repeated here. As used below, the terms "module," "unit," "subunit," etc. may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0098] The system comprises:

[0099] An acquisition module 10 is configured to acquire a plurality of production nodes corresponding to a component, extract a plurality 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;

[0100] The acquisition module 10 includes:

[0101] The first unit is configured to obtain the real-time production time of the component and obtain a progress deviation rate based on the real-time production time and the past production time;

[0102] A second unit is configured to obtain a real-time result label based on the progress deviation rate;

[0103] An analysis module 20 is configured to select a plurality of standby production data sets from the plurality of past production data sets using 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 using the standby production data sets and the real-time production data;

[0104] The analysis module 20 includes:

[0105] A third unit is configured to obtain parameter standard values ​​based on a plurality of the production data sets to be used;

[0106] A fourth unit is configured to obtain a parameter threshold value based on the real-time production data and the parameter standard value;

[0107] A fifth unit, configured to construct a dynamic parameter threshold range based on the parameter threshold;

[0108] A first execution module 30 is configured to compare the real-time production data with the dynamic parameter threshold range to trigger a basic progress alarm;

[0109] The first execution module 30 is specifically configured to determine whether the real-time production data is within the dynamic parameter threshold range, and trigger a basic progress alarm if the real-time production data is not within the dynamic parameter threshold range;

[0110] Preferably, the system further comprises:

[0111] The second execution module 40 is configured to obtain the real-time quality values ​​of the parts of the current batch, and obtain the response level to the basic progress alarm based on the real-time production data, the progress deviation rate, and the real-time quality values;

[0112] The second execution module 40 includes:

[0113] A sixth unit is configured to obtain a risk coefficient based on the real-time production data, the progress deviation rate, and the real-time quality value;

[0114] The seventh unit is configured to compare the risk coefficient with a coefficient threshold to obtain a response level to the basic progress alarm.

[0115] 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. When the processor executes the computer program, the vehicle development progress management method as described in the above technical solution is implemented.

[0116] 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.

[0117] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0118] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A vehicle development progress management method, characterized in that: The following steps are involved: Acquire several production nodes corresponding to the component, extract several past production data sets corresponding to the component based on the production nodes, the past production data sets including past production times, and determine a real-time result label corresponding to the component based on the past production data sets; The step of determining the real-time result label corresponding to the component based on the previous 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; 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 batch of parts corresponding to the jth production node, Indicates the total number of previous batches corresponding to the j-th production node; Obtaining a real-time result label based on the progress deviation rate; The formula for obtaining the real-time result label is: ， in, Represents the real-time result label of the current batch of parts corresponding to the j-th production node; Selecting a plurality of standby production data sets from the plurality of past production data sets using the real-time result tags, the standby production data sets including a plurality of standby production data, obtaining real-time production data corresponding to the component, and determining a dynamic parameter threshold range of the production node using the standby production data sets and the real-time production data; The step of determining the dynamic parameter threshold range of the production node by using the standby production data set and the real-time production data includes: Obtaining parameter standard values ​​based on the plurality of production data sets to be used; The formula for obtaining the standard value of the parameter is: ， in, Indicates the parameter standard value of the kth production parameter of the current batch of parts corresponding to the jth production node, Represents a collection of several ready-to-use production datasets corresponding to real-time result labels. Indicates the number of ready-to-use production data sets in a set of several ready-to-use production data sets corresponding to real-time result labels, 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; Obtaining a parameter threshold based on the real-time production data and the parameter standard value; 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, represents the parameter threshold of the kth production parameter of the parts of the previous batch adjacent to the current batch of the jth production node, The real-time production data representing the kth production parameter of the component in the current batch corresponding to the jth production node; constructing a dynamic parameter threshold range based on the parameter threshold; The formula for obtaining the dynamic parameter threshold range is: ， in, represents the first boundary point of the dynamic parameter threshold range of the kth production parameter of the current batch of parts corresponding to the jth production node, The second boundary point of the dynamic parameter threshold range of the kth production parameter of the component of the current batch corresponding to the jth production node; The real-time production data is compared 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 step of comparing the real-time production data with the dynamic parameter threshold range to trigger a basic progress alarm is specifically as follows: It is determined 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, a basic progress alarm is triggered.

3. The vehicle development progress management method according to claim 1, characterized in that: After the step of comparing the real-time production data with the dynamic parameter threshold range to trigger a basic progress alarm, the method further includes: The real-time quality values ​​of the parts of the current batch are obtained, and the response level to the basic progress alarm is obtained through the real-time production data, the progress deviation rate and the real-time quality values.

4. The vehicle development progress management method according to claim 3, 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: Obtaining a risk coefficient based on the real-time production data, the progress deviation rate, and the real-time quality value; The risk factor is compared with a factor threshold to obtain a response level to the basic progress alarm.

5. The vehicle development progress management method according to claim 4, characterized in that: The formula for obtaining the risk coefficient is: ， in, represents the risk coefficient corresponding to the j-th production node, represents the real-time production data of the kth production parameter of the current batch of parts corresponding to the jth production node, Indicates the parameter standard value of the kth production parameter of the current batch of parts corresponding to the jth 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, Indicates the indicator weight of the qth quality indicator.

6. A vehicle development progress management system, applied to the vehicle development progress management method according to any one of claims 1 to 5, characterized in that: The system comprises: An acquisition module, configured to acquire a plurality of production nodes corresponding to a component, extract a plurality 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; The acquisition module includes: The first unit is configured to obtain the real-time production time of the component 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: ， 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 batch of parts corresponding to the jth production node, Indicates the total number of previous batches corresponding to the j-th production node; A second unit is configured to obtain a real-time result label based on the progress deviation rate; The formula for obtaining the real-time result label is: ， in, Represents the real-time result label of the current batch of parts corresponding to the j-th production node; an analysis module, configured to select a plurality of standby production data sets from the plurality of past production data sets using 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 using the standby production data sets and the real-time production data; The analysis module includes: A third unit is configured to obtain parameter standard values ​​based on a plurality of the production data sets to be used; The formula for obtaining the standard value of the parameter is: ， in, Indicates the parameter standard value of the kth production parameter of the current batch of parts corresponding to the jth production node, Represents a collection of several ready-to-use production datasets corresponding to real-time result labels. Indicates the number of ready-to-use production data sets in a set of several ready-to-use production data sets corresponding to real-time result labels, 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; A fourth unit is configured to obtain a parameter threshold value based on the real-time production data and the parameter standard value; 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, represents the parameter threshold of the kth production parameter of the parts of the previous batch adjacent to the current batch of the jth production node, The real-time production data representing the kth production parameter of the component in the current batch corresponding to the jth production node; A fifth unit, configured to construct a dynamic parameter threshold range based on the parameter threshold; The formula for obtaining the dynamic parameter threshold range is: ， in, represents the first boundary point of the dynamic parameter threshold range of the kth production parameter of the current batch of parts corresponding to the jth production node, The second boundary point of the dynamic parameter threshold range of the kth production parameter of the component of the current batch corresponding to the jth production node; The 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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