An integrated visual management platform and management method based on digital twins

Abstract

The present invention relates to the field of visual management technology, and in particular to an integrated visual management platform and management method based on digital twins. The present invention inputs a maintenance request into a maintenance model constructed by characteristic information of each maintenance plant, and the obtained predicted maintenance time can well visualize the maintenance waiting time of each maintenance plant to the user, thereby avoiding the phenomenon that the vehicle time is wasted only after the vehicle arrives at the maintenance plant due to the inability to know the queuing time of the automobile maintenance plant. The user feedback data is sent to the maintenance plant management module through the service feedback system to obtain the feedback type, and the maintenance method of the maintenance plant is adjusted and managed according to the feedback type, which helps the automobile maintenance plant to make timely adjustments to the maintenance method according to the information fed back by the user, so that more users can be attracted by adjusting the maintenance method, thereby realizing communication with the users.

Description

Technical Field

[0001] The present invention relates to the field of visual management technology, and in particular to an integrated visual management platform and management method based on digital twins. Background Art

[0002] Digital twin is an advanced concept and technology that combines the physical world with the digital world to simulate, monitor and optimize the physical entities, systems or processes of the real world by creating virtual models or mirrors. The goal of digital twin is to achieve better understanding, analysis and management of physical entities, thereby improving efficiency, reducing costs, improving decision-making and enhancing innovation capabilities.

[0003] There is a close relationship between digital twins and the selection of auto repair shops. The application of digital twins in the field of auto repair is mainly when a vehicle needs maintenance or repair. After searching for the auto repair point based on the location information of the auto repair shop on the map, a certain auto repair shop is selected for repair. However, users cannot know the waiting time at each repair shop, and often only find out that they need to wait in line for a long time when they arrive at the repair shop. At the same time, the repair shop cannot get feedback from users in time, and cannot make adjustments in time, making it difficult to connect users with repair shops. Summary of the invention

[0004] The main purpose of the present invention is to provide an integrated visual management platform and management method based on digital twins, aiming to solve the technical problems in the prior art.

[0005] The present invention proposes an integrated visual management platform based on digital twins, comprising: a digital twin system, a service feedback system and a visual management platform, wherein the digital twin system comprises an acquisition module, a data acquisition module and a twin model management module, and the visual management platform comprises a user display module and a maintenance plant management module;

[0006] The acquisition module is used to acquire the user's maintenance request information and send the maintenance request information to the twin model management module;

[0007] The data acquisition module is used to obtain characteristic information of all repair shops within a preset range, wherein the characteristic information includes location information, business item information and equipment information;

[0008] The twin model management module is connected to the data acquisition module, and is used to receive the plurality of feature information sent by the data acquisition module, and to construct a twin predictive maintenance model according to the plurality of feature information;

[0009] The twin model management module is used to input the maintenance request information into the twin predictive maintenance model for prediction to obtain a predicted maintenance duration. The twin model management module is also used to generate a visualization chart according to the predicted maintenance duration, and send the visualization chart to the user display module for visualization display;

[0010] The service feedback system is used to obtain user feedback data after the predicted maintenance time is visually displayed on the user display module, and send the user feedback data to the maintenance plant management module;

[0011] The repair shop management module is used to receive the user feedback data and parse the user feedback data to obtain a feedback type. The repair shop management module is used to adjust and manage the repair method of the repair shop according to the feedback type.

[0012] Preferably, the twin model management module extracts features from the feature information of each maintenance plant to obtain multiple feature modeling data, and encodes the multiple feature modeling data to obtain comprehensive data information. The twin model management module divides the comprehensive data information into multiple data sets according to the same feature category. The twin model management module inputs each data set into the prediction model for training to obtain prediction performance results, and adjusts the prediction model according to the prediction performance results to obtain a twin predictive maintenance model.

[0013] Preferably, the twin model management module obtains multiple data sets by sequentially labeling the multiple data sets with serial numbers, and the twin model management module selects the data set with the first serial number in order as a training set and inputs it into the prediction model for training, and inputs the remaining data sets with other serial numbers as verification sets into the training prediction model for verification, until each data set with each serial number is input into the prediction model for training as a training set to obtain multiple training results, and the twin model management module obtains the prediction performance result by aggregating and integrating the multiple training results, and obtains the twin predictive maintenance model by obtaining the error type and unbalanced weakness type of the prediction performance result and adjusting the prediction model according to the error type and unbalanced weakness type.

[0014] Preferably, the twin model management module obtains the automobile fault type of the maintenance request information, thereby screening all automobile repair shops within a preset range according to the automobile fault type to obtain automobile repair shops capable of handling the automobile fault type. The twin model management module calculates the vehicle queuing time of each automobile repair shop capable of handling the automobile fault type by obtaining the business queuing information of each automobile repair shop capable of handling the automobile fault type. The twin model management module also obtains the vehicle location information of the maintenance request information, and calculates the vehicle arrival time in combination with the location information of each automobile repair shop capable of handling the automobile fault type. The twin model management module obtains the predicted maintenance time by inputting the vehicle arrival time and the vehicle queuing time into the twin predictive maintenance model, wherein the twin predictive maintenance model is:

[0015] T 3 =T 1 +T 2 ;

[0016] Among them, T 3 Denotes the predicted maintenance time, T 1 represents the queue time of vehicles in each auto repair shop, T 2 Indicates the vehicle arrival time.

[0017] Preferably, the twin model management module obtains the vehicle location information and the average vehicle speed of the maintenance request information, and obtains the optimal route based on the vehicle location information and the location information of each automobile repair shop that can handle the type of automobile fault. The twin model management module obtains the average delay time and the traffic light waiting time based on the optimal route, wherein the average delay time includes the turning delay time, the parking delay time and the traffic jam time. The twin model management module calculates the vehicle arrival time based on the vehicle location information in the maintenance request, the location information of each automobile repair shop that can handle the type of automobile fault, the turning delay time, the parking delay time, the traffic jam time and the traffic light waiting time, wherein the calculation formula is:

[0018]

[0019] Among them, T 2 represents the vehicle arrival time, S 2 represents the location information of the car repair shop that can handle the car failure type, S 1 represents the vehicle position information, V represents the average vehicle speed, t(z) represents the turning delay time, t(t) represents the parking delay time, t(d) represents the traffic jam time, and t(h) represents the waiting time at the traffic light.

[0020] Preferably, the repair shop management module obtains the existing repair method of the repair shop and evaluates the existing repair method of the repair shop according to the feedback type to obtain a repair evaluation result. The repair shop management module adjusts and manages the repair method of the repair shop according to the repair evaluation result, and sends the adjusted repair method to the user display module for visual display.

[0021] Preferably, the repair shop management module obtains a standard feedback text by correcting missing values ​​and erroneous data of the user feedback data, obtains a feedback type by extracting keywords from the standard feedback text and parsing the keywords, the repair shop management module summarizes the repair evaluation results corresponding to each feedback type, and sorts a plurality of the repair evaluation results according to quantity to obtain a repair mode adjustment sorting table, the repair shop management module adjusts and manages the repair mode of the repair shop in turn according to the repair mode adjustment sorting table, the repair shop management module obtains real-time user feedback data sent by the user display module, and obtains the adjustment result of the repair mode in real time using key performance indicators according to the real-time user feedback data, the repair shop management module adjusts and improves the repair mode of the repair shop in real time according to the adjustment result, and sends the improved repair mode to the user display module for visual display.

[0022] The present application also provides an integrated visual management method based on digital twins, comprising: a digital twin system, a service feedback system and a visual management platform, wherein the digital twin system comprises an acquisition module, a data acquisition module and a twin model management module, and the visual management platform comprises a user display module and a maintenance plant management module, and the maintenance plant display module is connected to the acquisition module;

[0023] The acquisition module acquires the user's maintenance request information and sends the maintenance request information to the twin model management module;

[0024] The data collection module obtains characteristic information of all repair shops within a preset range, wherein the characteristic information includes location information, business item information and equipment information;

[0025] The twin model management module is connected to the data acquisition module, receives the plurality of feature information sent by the data acquisition module, and constructs a twin predictive maintenance model according to the plurality of feature information;

[0026] The twin model management module inputs the maintenance request information into the twin predictive maintenance model for prediction to obtain a predicted maintenance duration. The twin model management module also generates a visualization chart based on the predicted maintenance duration, and sends the visualization chart to the user display module for visualization display;

[0027] The service feedback system obtains user feedback data after the predicted maintenance time is visually displayed on the user display module, and sends the user feedback data to the maintenance plant management module;

[0028] The repair shop management module receives the user feedback data, and analyzes the user feedback data to obtain a feedback type. The repair shop management module adjusts and manages the repair method of the repair shop according to the feedback type.

[0029] Preferably, the step of constructing a twin predictive maintenance model according to a plurality of the feature information comprises:

[0030] Extracting features from the feature information of each repair shop to obtain a plurality of feature modeling data;

[0031] A plurality of the feature modeling data are coded to obtain comprehensive data information;

[0032] Segmenting the comprehensive data information into multiple data sets according to the same feature categories;

[0033] Input each data set into the prediction model for training to obtain the prediction performance results;

[0034] The prediction model is adjusted according to the prediction performance results to obtain a twin predictive maintenance model.

[0035] Preferably, the twin model management module inputs the maintenance request information into the twin predictive maintenance model for prediction, and the step of obtaining the predicted maintenance duration includes:

[0036] Acquire the vehicle fault type of the maintenance request information;

[0037] Screening all automobile repair shops within a preset range according to the automobile fault type to obtain automobile repair shops capable of handling the automobile fault type;

[0038] Calculate the vehicle queuing time of each automobile repair shop by acquiring the business queuing information of each automobile repair shop capable of handling the automobile fault type;

[0039] Acquire the vehicle location information of the maintenance request information, and calculate the vehicle arrival time in combination with the location information of each automobile repair shop capable of handling the automobile fault type;

[0040] The vehicle arrival time and the vehicle queuing time are input into the twin predictive maintenance model to obtain the predicted maintenance time, wherein the twin predictive maintenance model is:

[0041] T 3 =T 1 +T 2 ;

[0042] Among them, T 3 Denotes the predicted maintenance time, T 1 represents the queue time of vehicles in each auto repair shop, T 2 Indicates the vehicle arrival time.

[0043] The beneficial effects of the present invention are as follows: the present invention constructs a twin predictive maintenance model according to the multiple feature information sent by the data acquisition module through the twin model management module, and obtains the predicted maintenance time by inputting the user's maintenance request information into the twin predictive maintenance model for prediction, generates a visualization chart according to the predicted maintenance time, and sends the visualization chart to the user display module for visualization display, so that the maintenance request is input into the maintenance model constructed by the feature information of each maintenance plant, and the obtained predicted maintenance time can well visualize the maintenance waiting time of each maintenance plant to the user, thereby avoiding the problem that the user's time is wasted because the queuing time of the automobile repair shop cannot be known, and the user often waits until the vehicle arrives at the repair shop to find out that a long waiting time is required, and the user's time is wasted, and the user feedback data is sent to the maintenance plant management module through the service feedback system to obtain the feedback type, and the maintenance method of the maintenance plant is adjusted and managed according to the feedback type, which helps the automobile repair plant to make timely adjustments to the maintenance method according to the information fed back by the user, so that more users can be attracted by adjusting the maintenance method, thereby realizing communication with the users. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The figure is a schematic diagram of a method flow according to an embodiment of the present invention.

[0045] Figure 2 FIG. 1 is a schematic diagram of a device structure according to an embodiment of the present invention.

[0046] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0047] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0048] like Figure 1 and Figure 2As shown, the present application provides an integrated visual management platform based on digital twins, including: a digital twin system, a service feedback system and a visual management platform. The digital twin system includes an acquisition module, a data acquisition module and a twin model management module, and the visual management platform includes a user display module and a repair shop management module; the acquisition module is used to obtain the user's maintenance request information and send the maintenance request information to the twin model management module; the data acquisition module is used to obtain the characteristic information of all repair shops within a preset range, wherein the characteristic information includes location information, business project information and equipment information; the twin model management module is connected to the data acquisition module, and is used to receive multiple characteristic information sent by the data acquisition module, and construct a twin predictive maintenance model based on the multiple characteristic information; the twin model management module is used to The maintenance request information is input into the twin predictive maintenance model for prediction to obtain a predicted maintenance time. The twin model management module is also used to generate a visualization chart based on the predicted maintenance time, and send the visualization chart to the user display module for visualization display; the service feedback system is used to obtain user feedback data after the predicted maintenance time is visualized in the user display module, and send the user feedback data to the maintenance plant management module; the maintenance plant management module is used to receive the user feedback data and parse the user feedback data to obtain a feedback type, and the maintenance plant management module is used to adjust and manage the maintenance method of the maintenance plant according to the feedback type.

[0049] With the rapid economic growth of the domestic automobile market, there are more and more vehicles at present, which has led to the spawning of automobile maintenance, repair and other services. The automobile repair industry has become a new development focus. With the growth of automobile consumption in my country, the number of cars owned and the automobile aftermarket are growing. The difficulty of car maintenance is also a problem faced by many car owners. When the car needs maintenance or repair, the user searches for the car repair point according to the location information of the car repair shop on the map and chooses a car repair shop for repair. However, the user cannot know the waiting time of each repair shop. Often, they only know that they need to wait in line for a long time when they arrive at the repair shop. At the same time, the repair shop cannot get feedback from users in time and cannot make timely adjustments, making it difficult to connect users with the repair shop.

[0050] The present invention constructs a twin predictive maintenance model based on the multiple feature information sent by the data acquisition module through the twin model management module, wherein the feature information includes location information, business project information and equipment information, and inputs the user's maintenance request information into the twin predictive maintenance model for prediction to obtain the predicted maintenance time, generates a visualization chart based on the predicted maintenance time, and sends the visualization chart to the user display module for visualization display. In this way, the maintenance request is input into the maintenance model constructed by the feature information of each maintenance plant, and the obtained predicted maintenance time can well visualize the maintenance waiting time of each maintenance plant to the user, so that the user can clearly see the maintenance of each maintenance plant through the visualization data. The waiting time for repairs has increased transparency, allowing users to compare the waiting times of different repair shops based on visualized data in order to choose a repair shop and ensure that the waiting time is minimized. This can avoid the situation where users often find out that they need to wait for a long time only after the vehicle arrives at the repair shop due to the lack of knowledge of the waiting time at the auto repair shop, thus wasting the user's time. The service feedback system sends user feedback data to the repair shop management module to obtain the feedback type, and adjusts and manages the repair method of the repair shop based on the feedback type. This will help the auto repair shop to make timely adjustments to the repair method based on the information fed back by the user, thereby attracting more users by adjusting the repair method and achieving connectivity with users.

[0051] In one embodiment, the twin model management module extracts features from the feature information of each maintenance plant to obtain multiple feature modeling data, and encodes the multiple feature modeling data to obtain comprehensive data information. The twin model management module divides the comprehensive data information into multiple data sets according to the same feature category. The twin model management module inputs each data set into the prediction model for training to obtain a prediction performance result, and adjusts the prediction model according to the prediction performance result to obtain a twin predictive maintenance model. The twin model management module obtains multiple data sets by sequentially marking the multiple data sets with serial numbers. The twin model management module selects the data set with the first serial number in sequence as the training set and inputs it into the prediction model for training, and inputs the remaining data sets with other serial numbers as verification sets into the training prediction model for verification, until each data set with each serial number is input into the prediction model as a training set for training to obtain multiple training results. The twin model management module obtains the prediction performance result by summarizing and integrating the multiple training results. The prediction performance result includes the data classification performance result and the data sample quantity performance result.

[0052] Since the boundaries between data categories are not clear and the data sample features are insufficient when the data set is input into the prediction model, there will be data misclassification, poor data quality, unclear data categories, and data imbalance due to the huge difference in the number of samples of different categories in the data. If the twin predictive maintenance model is generated directly based on the prediction performance results, the prediction results obtained by the twin predictive maintenance model will not be able to correctly classify samples and will cause the model to tend to predict categories with a large number of categories and ignore technical problems such as categories with a small number of categories.

[0053] In order to solve this problem, after obtaining the prediction performance result, this embodiment adjusts the prediction model according to the error type and the unbalanced weakness type to obtain a twin prediction maintenance model, so that the prediction result obtained by the generated twin prediction maintenance model can correctly classify samples and tend to balance. The twin model management module in the present invention can convert feature data into a more informative representation through encoding processing, which helps to improve the expression ability of features. This can be achieved through technologies such as automatic encoders, improve the model's understanding and representation of input data, and thus facilitate a more intuitive reflection of the actual maintenance situation of the automobile repair shop. The twin model management module divides the comprehensive data information into multiple data sets according to the same feature category, where the same feature can be a numerical feature, a text feature, or a geographic feature, etc., so as to improve the training effect of the model. By inputting each data set into the prediction model for training in turn, and then using the remaining data sets as a verification set for verification, the characteristics of different feature categories can be better captured. The prediction model is adjusted based on the error type and unbalanced weakness type of the prediction performance result, which helps the model to continuously optimize itself, so that a more suitable automobile repair shop can be selected for the maintenance request information of the target vehicle.

[0054] In one embodiment, the twin model management module obtains the automobile fault type of the maintenance request information, and then screens all automobile repair shops within a preset range according to the automobile fault type to obtain automobile repair shops that can handle the automobile fault type. The twin model management module calculates the vehicle queuing time of each automobile repair shop by obtaining the business queuing information of each automobile repair shop that can handle the automobile fault type. The twin model management module also obtains the vehicle location information of the maintenance request information, and calculates the vehicle arrival time in combination with the location information of each automobile repair shop that can handle the automobile fault type. The twin model management module obtains the predicted maintenance time by inputting the vehicle arrival time and the vehicle queuing time into the twin predictive maintenance model, wherein the twin predictive maintenance model is:

[0055] T 3 =T 1 +T 2;

[0056] Among them, T 3 Denotes the predicted maintenance time, T 1 represents the queue time of vehicles in each auto repair shop, T 2 Indicates the vehicle arrival time.

[0057] The twin model management module of the present invention screens repair shops according to the type of automobile fault in the repair request. The twin model management module can ensure that the repair shop recommended to the user has the business to handle specific fault types. At the same time, based on the business queuing information of each repair shop, the vehicle queuing time of each automobile repair shop is calculated. The queuing time helps to select a repair shop with a shorter waiting time, thereby reducing the waiting time. The twin model management module calculates the time it takes for the user to arrive at each repair shop through the vehicle location information and the repair shop location information, which helps the user to choose a repair shop that is closer, saving time and transportation costs. The twin model management module helps to optimize the maintenance process by sorting the time required for the user to start maintenance at each repair shop according to the required time. The twin model management module provides multiple options, and the appropriate repair shop can be selected according to needs and schedule.

[0058] In one embodiment, the twin model management module is used to obtain the business queue information of each automobile repair shop capable of handling the automobile fault type, wherein the business queue information includes the number of repair equipment, the number of vehicles arriving per hour, and the number of vehicles repaired per hour;

[0059] It is also used to calculate the vehicle queuing time of each automobile repair shop according to the number of repair equipment, the number of vehicles arriving per hour and the number of vehicles repaired per hour, wherein the calculation formula is:

[0060]

[0061] Among them, T 1 represents the queuing time of vehicles in each auto repair shop, X represents the number of vehicles repaired per hour, D represents the number of vehicles arriving per hour, and C represents the number of repair equipment.

[0062] The twin model management module of the present invention calculates the waiting time required for vehicles by the number of maintenance equipment, the number of vehicles arriving per hour and the number of vehicles repaired per hour. Not only can the maintenance plant better plan and manage its resources, including the number of maintenance equipment, the preparation of equipment and spare parts, etc., but also by reasonably allocating resources, the maintenance plant can improve efficiency and ensure that customers can receive services within a reasonable time. At the same time, by accurately calculating the waiting time required for vehicles at each maintenance plant, the maintenance plant can reduce congestion and queues, which helps to shorten the waiting time. The twin model management module accurately calculates the waiting time required for vehicles at each maintenance plant, so that the vehicle can better estimate the waiting time after arriving at the maintenance plant. The vehicle can make decisions based on the waiting time provided by different maintenance plants. If the waiting time at a certain maintenance plant is longer, the vehicle can choose to go to a maintenance plant with a shorter waiting time, thereby improving the right to choose and flexibility.

[0063] In one embodiment, the twin model management module obtains the vehicle location information and the average vehicle speed of the maintenance request information, and obtains the optimal route based on the vehicle location information and the location information of each automobile repair shop that can handle the type of automobile fault. The twin model management module obtains the average delay time and the traffic light waiting time based on the optimal route, wherein the average delay time includes the turning delay time, the parking delay time and the traffic jam time. The twin model management module calculates the vehicle arrival time based on the vehicle location information in the maintenance request, the location information of each automobile repair shop that can handle the type of automobile fault, the turning delay time, the parking delay time, the traffic jam time and the traffic light waiting time, wherein the calculation formula is:

[0064]

[0065] Among them, T 2 represents the vehicle arrival time, S 2 represents the location information of the car repair shop that can handle the car failure type, S 1 represents the vehicle position information, V represents the average vehicle speed, t(z) represents the turning delay time, t(t) represents the parking delay time, t(d) represents the traffic jam time, and t(h) represents the waiting time at the traffic light.

[0066] The twin model management module of the present invention calculates the vehicle arrival time through the vehicle location information in the maintenance request, the location information of each auto repair shop that can handle the type of automobile failure, the turning delay time, the parking delay time, the traffic jam time and the traffic light waiting time. It can accurately calculate the time required for the vehicle to reach the destination, thereby helping the vehicle to plan its itinerary and select the fastest and best route, so that the user can choose the most suitable destination according to the time to arrive at each auto repair shop, saving time.

[0067] In one embodiment, the repair shop management module obtains the existing repair method of the repair shop, and evaluates the existing repair method of the repair shop according to the feedback type to obtain a repair evaluation result. The repair shop management module adjusts and manages the repair method of the repair shop according to the repair evaluation result, and sends the adjusted repair method to the user display module for visual display. Since the user may not complete the input of all fields when giving feedback or the feedback data is lost when being transmitted to the repair shop management module via the user display module, data may be missing or erroneous. If keyword extraction is performed directly on the user feedback data, the extracted keywords may lose important information, resulting in inaccurate prediction results and prone to misleading insights. In order to solve this problem, after obtaining the user feedback data, this embodiment corrects the missing values ​​and erroneous data of the user feedback data to obtain a standard feedback text, which can ensure that the key words are not lost. The accuracy of word extraction can obtain more accurate user feedback data, thereby generating more reliable and accurate feedback type analysis results. The repair shop management module extracts keywords from the standard feedback text and parses the keywords to obtain feedback types. The repair shop management module summarizes the maintenance evaluation results corresponding to each feedback type and sorts the multiple maintenance evaluation results according to quantity to obtain a maintenance method adjustment sorting table. The repair shop management module adjusts and manages the maintenance methods of the repair shop in turn according to the maintenance method adjustment sorting table. The repair shop management module obtains real-time user feedback data sent by the user display module, and uses key performance indicators to obtain the adjustment results of the maintenance method in real time according to the real-time user feedback data. The repair shop management module adjusts and improves the maintenance method of the repair shop in real time according to the adjustment results, and sends the improved maintenance method to the user display module for visual display.

[0068] The repair shop management module of the present invention can help the repair shop understand their repair efficiency by evaluating the existing repair methods of the repair shop, which is helpful to improve the work process. By analyzing the user feedback data, the repair shop management module can detect problems in the repair method, which is helpful for the repair shop to quickly adjust its method, and display the adjusted repair method to the user in a visual form, which is helpful to improve transparency and enable the user to understand the improvement of the repair process. By processing the user feedback data, the management module can ensure the accuracy of the data. By classifying the user feedback data into different types and summarizing them, and sorting the repair evaluation results, the management module can determine which improvements are most important to the repair shop, so as to make targeted improvements under limited resources. By obtaining user feedback in real time, and using key performance indicators to obtain the adjustment results of the repair method in real time according to the real-time user feedback data, the repair method is improved in real time according to the adjustment results, and the improved repair method is sent to the user display module for visual display, which is helpful to make adjustments according to the feedback in real time, and the improvement results are visually displayed to the user in real time, thereby improving the service quality and user satisfaction.

[0069] The present application also provides an integrated visual management method based on digital twins, comprising: a digital twin system, a service feedback system and a visual management platform, wherein the digital twin system comprises an acquisition module, a data acquisition module and a twin model management module, and the visual management platform comprises a user display module and a maintenance plant management module, and the maintenance plant display module is connected to the acquisition module;

[0070] S1. The acquisition module acquires the user's maintenance request information and sends the maintenance request information to the twin model management module;

[0071] S2, the data collection module obtains characteristic information of all repair shops within a preset range, wherein the characteristic information includes location information, business item information and equipment information;

[0072] S3, the twin model management module is connected to the data acquisition module, receives the multiple feature information sent by the data acquisition module, and builds a twin predictive maintenance model according to the multiple feature information;

[0073] S4, the twin model management module inputs the maintenance request information into the twin predictive maintenance model for prediction to obtain a predicted maintenance duration, and the twin model management module also generates a visualization chart according to the predicted maintenance duration, and sends the visualization chart to the user display module for visualization display;

[0074] S5, the service feedback system obtains user feedback data after the predicted maintenance time is visually displayed on the user display module, and sends the user feedback data to the maintenance plant management module;

[0075] S6. The repair shop management module receives the user feedback data, and analyzes the user feedback data to obtain a feedback type. The repair shop management module adjusts and manages the repair method of the repair shop according to the feedback type.

[0076] In one embodiment, the step of constructing a twin predictive maintenance model based on the plurality of feature information includes:

[0077] S7, extracting features from the feature information of each repair shop to obtain a plurality of feature modeling data;

[0078] S8, encoding the plurality of feature modeling data to obtain comprehensive data information;

[0079] S9, dividing the comprehensive data information into multiple data sets according to the same feature category;

[0080] S10, inputting each data set into the prediction model for training to obtain a prediction performance result;

[0081] S11. Adjust the prediction model according to the prediction performance result to obtain a twin predictive maintenance model.

[0082] In one embodiment, the twin model management module inputs the maintenance request information into the twin predictive maintenance model for prediction, and the step of obtaining the predicted maintenance duration includes:

[0083] S12, obtaining the vehicle fault type of the maintenance request information;

[0084] S13, screening all automobile repair shops within a preset range according to the automobile fault type, and obtaining automobile repair shops capable of handling the automobile fault type;

[0085] S14, calculating the vehicle queuing time of each automobile repair shop by obtaining the service queuing information of each automobile repair shop capable of handling the automobile fault type;

[0086] S15, obtaining the vehicle location information of the maintenance request information, and calculating the vehicle arrival time in combination with the location information of each automobile repair shop capable of handling the automobile fault type;

[0087] S16. Input the vehicle arrival time and the vehicle queuing time into a twin predictive maintenance model to obtain a predicted maintenance time, wherein the twin predictive maintenance model is:

[0088] T 3 =T 1 +T 2 ;

[0089] Among them, T 3 Denotes the predicted maintenance time, T 1 represents the queue time of vehicles in each auto repair shop, T 2 Indicates the vehicle arrival time.

[0090] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0091] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0092] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An integrated visual management platform based on digital twins, characterized in that: include: A digital twin system, a service feedback system and a visual management platform, wherein the digital twin system includes an acquisition module, a data acquisition module and a twin model management module, and the visual management platform includes a user display module and a maintenance plant management module; The acquisition module is used to acquire the user's maintenance request information and send the maintenance request information to the twin model management module; The data acquisition module is used to obtain characteristic information of all repair shops within a preset range, wherein the characteristic information includes location information, business item information and equipment information; The twin model management module is connected to the data acquisition module and is used to receive the multiple feature information sent by the data acquisition module. The twin model management module extracts the feature information of each maintenance plant to obtain multiple feature modeling data, and encodes the multiple feature modeling data to obtain comprehensive data information. The twin model management module divides the comprehensive data information into multiple data sets according to the same feature category. The twin model management module obtains multiple data sets by sequentially marking the multiple data sets with serial numbers. The twin model management module selects the data set with the first serial number in the order of serial numbers as a training set and inputs it into the prediction model for training, and inputs the remaining data sets with other serial numbers as verification sets into the training prediction model for verification, until all the data sets with each serial number are input into the prediction model for training as training sets to obtain multiple training results. The twin model management module obtains the prediction performance result by summarizing and integrating the multiple training results. The twin model management module obtains the error type and unbalanced weakness type of the prediction performance result, and adjusts the prediction model according to the error type and unbalanced weakness type to obtain the twin prediction maintenance model. The twin model management module is used to input the maintenance request information into the twin predictive maintenance model for prediction to obtain a predicted maintenance duration. The twin model management module is also used to generate a visualization chart according to the predicted maintenance duration, and send the visualization chart to the user display module for visualization display; The service feedback system is used to obtain user feedback data after the predicted maintenance time is visually displayed on the user display module, and send the user feedback data to the maintenance plant management module; The repair shop management module is used to receive the user feedback data and parse the user feedback data to obtain a feedback type. The repair shop management module is used to adjust and manage the repair method of the repair shop according to the feedback type.

2. The integrated visual management platform based on digital twin according to claim 1, characterized in that: The repair shop management module obtains the repair method of the repair shop and performs a performance evaluation on the repair method of the repair shop according to the feedback type to obtain a repair evaluation result. The repair shop management module adjusts and manages the repair method of the repair shop according to the repair evaluation result, and sends the adjusted repair method to the user display module for visual display.

3. The integrated visual management platform based on digital twin according to claim 2 is characterized in that: The repair shop management module obtains a standard feedback text by correcting missing values ​​and erroneous data of the user feedback data, obtains a feedback type by extracting keywords from the standard feedback text and parsing the keywords, obtains a repair method adjustment ranking table by summarizing the repair evaluation results corresponding to each feedback type and sorting a plurality of the repair evaluation results according to quantity, and adjusts and manages the repair methods of the repair shops in turn according to the repair method adjustment ranking table, obtains real-time user feedback data sent by the user display module, and obtains the adjustment results of the repair methods in real time according to the real-time user feedback data using preset performance indicators, and adjusts and improves the repair methods of the repair shops in real time according to the adjustment results, and sends the improved repair methods to the user display module for visual display.

4. An integrated visual management method based on digital twins, characterized in that: include: A digital twin system, a service feedback system and a visual management platform, wherein the digital twin system includes an acquisition module, a data acquisition module and a twin model management module, and the visual management platform includes a user display module and a maintenance plant management module; The acquisition module acquires the user's maintenance request information and sends the maintenance request information to the twin model management module; The data collection module obtains characteristic information of all repair shops within a preset range, wherein the characteristic information includes location information, business item information and equipment information; The twin model management module is connected to the data acquisition module, and receives the multiple feature information sent by the data acquisition module. The twin model management module extracts the feature information of each maintenance plant to obtain multiple feature modeling data, and encodes the multiple feature modeling data to obtain comprehensive data information. The twin model management module divides the comprehensive data information into multiple data sets according to the same feature category. The twin model management module obtains multiple data sets by sequentially marking the multiple data sets with serial numbers. The twin model management module selects the data set with the first serial number in the order of serial numbers as a training set and inputs it into the prediction model for training, and inputs the remaining data sets with other serial numbers as verification sets into the training prediction model for verification, until all the data sets with each serial number are input into the prediction model for training as training sets to obtain multiple training results. The twin model management module obtains the prediction performance result by summarizing and integrating the multiple training results. The twin model management module obtains the error type and unbalanced weakness type of the prediction performance result, and adjusts the prediction model according to the error type and unbalanced weakness type to obtain the twin prediction maintenance model. The twin model management module inputs the maintenance request information into the twin predictive maintenance model for prediction to obtain a predicted maintenance duration. The twin model management module also generates a visualization chart based on the predicted maintenance duration, and sends the visualization chart to the user display module for visualization display; The service feedback system obtains user feedback data after the predicted maintenance time is visually displayed on the user display module, and sends the user feedback data to the maintenance plant management module; The repair shop management module receives the user feedback data, and analyzes the user feedback data to obtain a feedback type. The repair shop management module adjusts and manages the repair method of the repair shop according to the feedback type.

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