Test sample car sample piece state monitoring system and method

Abstract

The invention discloses a system and a method for monitoring the state of a sample piece of a test sample car. Acquiring initial data information of the sample piece of the test sample car; the initial data information comprises a unique identification feature code and multiple initial point cloud data; according to the identified unique identification feature code, obtaining warehouse-in and warehouse-out data information of each test sample vehicle sample piece during warehouse-in and warehouse-out; the warehouse-in and warehouse-out data information comprises a unique identification feature code and multiple warehouse-in and warehouse-out point cloud data; and judging whether the state of the sample piece of the test sample car is abnormal or not according to the in-out warehouse point cloud data and the multiple initial point cloud data in the in-out warehouse data information of the sample piece of the test sample car. Through accurate data initialization, efficient data warehouse-in and warehouse-out management and accurate state judgment, comprehensive, real-time and intelligent monitoring of the state of the sample piece of the test sample car is realized. The method is of great significance in improving the research and development efficiency of the test sample vehicle, reducing the production cost and guaranteeing the product quality.

Description

Technical Field

[0001] The invention belongs to the technical field of warehouse management, and in particular relates to a test sample vehicle, prototype machine and sample component status monitoring system and method. Background Art

[0002] In the process of automobile product development, the whole vehicles produced by self-production, transfer, purchase, donation, etc. are collectively referred to as prototype vehicles; engines and parts used for engine installation are collectively referred to as prototype parts; currently, in the management process of the entry and exit of test prototype vehicles, prototypes and parts, the operation method of over-reliance on manual data entry is prevalent. This practice is not only prone to omissions, errors and delayed entry, but also directly leads to low efficiency in quickly locating and obtaining the required prototype vehicles, prototypes and parts, which in turn affects the smooth progress of the test project and causes unnecessary economic losses. In addition, the status inspection of prototype vehicles, prototypes and parts currently still relies mainly on manual inspections. There is a risk of misjudgment and omission in this process, which may also lead to serious consequences of test failure and economic losses.

[0003] Although image recognition technology provides a possible solution to the above problems in theory, its high algorithm complexity and high computing power consumption limit its widespread application. The existing prototype in-and-out management method still mainly relies on the use of QR code technology, supplemented by manual online information entry operations, and fails to achieve automatic recognition and management by machines or artificial intelligence, so there is still room for significant improvement in efficiency and accuracy. Summary of the invention

[0004] In order to improve the efficiency and accuracy of the in-and-out management of test vehicle, prototype and sample parts, the present invention proposes a test vehicle, prototype and sample part status monitoring system and method.

[0005] A test vehicle prototype state monitoring system for achieving one of the purposes of the present invention comprises:

[0006] Data initialization module: used to obtain initial data information of the test vehicle prototype; the initial data information includes: a unique identification feature code and multiple copies of initial point cloud data; the unique identification feature code is used to identify the test vehicle prototype; the initial point cloud data represents the point cloud data of the test vehicle prototype in its initial state or when it is initially put into storage;

[0007] Data entry and exit module: used to obtain the entry and exit data information of each test vehicle prototype sample when entering and exiting the warehouse according to the identified unique identification feature code; the entry and exit data information includes: unique identification feature code, multiple entry and exit point cloud data; the entry and exit point cloud data represents the point cloud data of the test vehicle prototype sample when entering and exiting the warehouse, including exit point cloud data and entry point cloud data;

[0008] Status monitoring module: used to compare the in-and-out point cloud data in the in-and-out data information of the test vehicle prototype with the multiple copies of the initial point cloud data, and determine whether the status of the test vehicle prototype is abnormal based on the comparison result.

[0009] In the above system, each test vehicle prototype corresponds to multiple sets of initial point cloud data; when a same camera (such as a three-dimensional scanner) collects 3D point cloud data of a test vehicle prototype, it needs to continuously scan and obtain multiple sets of point cloud data.

[0010] The test prototype vehicle prototype is a general term for test vehicles, test prototypes, and test samples; it may refer to one or more of them. For example, if an experiment is conducted on a test vehicle, it refers to the test vehicle. A test vehicle generally refers to a vehicle manufactured to test, verify or demonstrate a new technology, new function or new design. These vehicles may not yet have entered the mass production stage, but they are mature enough to be used for actual road tests or laboratory tests. Test prototypes are generally used to describe prototypes or models manufactured to test, verify or demonstrate the performance of a certain device, machine or system. In the automotive industry, prototypes generally refer to prototypes of key components, such as a new engine, transmission or others.

[0011] Finally, test samples usually refer to individual parts or components used for testing, verification or demonstration. They constitute the basic components of machines or equipment. These samples may represent a new design, material or manufacturing process, and need to be tested to verify their performance and quality. In the automotive industry, samples may include engine parts, transmission system parts, suspension system parts, etc.

[0012] The technical effects of the above technical solution are:

[0013] The data initialization module is responsible for obtaining the initial data information of the test vehicle prototype, including the unique identification code and multiple copies of the initial point cloud data. The unique identification code ensures the uniqueness and traceability of each test vehicle prototype, which helps to accurately identify specific samples in subsequent data processing. The initial point cloud data provides detailed three-dimensional information of the sample in its initial state, which is crucial for subsequent state comparison. This precise and comprehensive data initialization lays a solid foundation for subsequent state monitoring.

[0014] The data entry and exit module can automatically obtain the point cloud data of each test vehicle prototype when entering and exiting the warehouse according to the unique identification feature code. This automated data management method greatly improves the efficiency and accuracy of data processing and reduces the possibility of human error. At the same time, by recording the point cloud data when entering and exiting the warehouse, the system can track the status changes of the samples in real time.

[0015] The status monitoring module determines whether the status of the test vehicle prototype is abnormal by comparing the in-and-out point cloud data with the initial point cloud data. Point cloud data, as a high-precision three-dimensional data representation method, can capture slight changes in the surface of the sample, thereby achieving accurate judgment of the status of the sample. This state judgment method based on point cloud data is not only highly accurate, but also can detect abnormal changes in the status of the sample in a timely manner, providing strong support for subsequent maintenance and management.

[0016] The system realizes comprehensive monitoring of the status of the test vehicle prototype by integrating multiple modules such as data initialization, data storage management and status judgment. The collaborative work between various modules makes the entire system highly intelligent and can automatically complete data collection, processing and analysis tasks. This integrated and intelligent system design not only improves work efficiency, but also reduces the cost and risk of manual intervention.

[0017] Furthermore, the initial data information and / or the in-and-out data information also includes a camera number, which is used to indicate the camera number for identifying the initial data information and / or the in-and-out data information of the test vehicle prototype sample; the system also includes a position monitoring module, which is used to obtain the current location and / or historical movement path of the test vehicle prototype sample according to the camera number in the data information.

[0018] The technical effects of the above technical solution are: through the camera number, the initial data information and in-and-out data information of the test vehicle prototype can be accurately identified and recorded, avoiding errors caused by information confusion or omissions, and improving the accuracy of the data; the introduction of camera numbers enables the rapid location of relevant camera records when the historical status of the test vehicle prototype needs to be traced, so as to obtain accurate historical data. It helps to resolve data disputes and improve the transparency of data management; through automatic identification and recording of camera numbers, the status monitoring operation process of the test vehicle prototype can be simplified, reducing manual intervention and errors; with the help of the system's real-time location and historical path tracking functions, managers can manage the test vehicle prototype more efficiently, optimize the test process, and reduce management costs.

[0019] Furthermore, the data initialization module also includes a point cloud data processing module, which is used to perform data processing on multiple copies of initial point cloud data in the initial data information to obtain a difference distance safety threshold of the point cloud data of each test vehicle prototype sample; the data processing method includes: performing difference comparison on the multiple copies of initial point cloud data to obtain the maximum difference distance as the difference distance safety threshold of the initial point cloud data of each test vehicle prototype sample; the difference distance safety threshold is used to determine whether the state of the test vehicle prototype sample is abnormal.

[0020] Point cloud distance: involves comparing the relative position relationship between two or more point cloud data sets. By calculating the distance between the target to be identified and the known point cloud model, the difference between the target to be identified and the known point cloud model can be identified. The greater the point cloud distance, the greater the difference, and the greater the possibility of target abnormality.

[0021] The technical effect of comparing the difference of point cloud data is as follows: during the transportation of the sample vehicle, sample part and prototype in the park, it will be captured by multiple cameras or scanners, and each camera or scanner will scan the sample vehicle, sample part and prototype to obtain point cloud data. After the sample vehicle, sample part and prototype is captured and scanned by scanner B to obtain point cloud data, it is compared with the point cloud data obtained by the previous scanner A. If the compared data is found to be abnormal, it can be determined that there is an abnormality during the movement from the location of scanner A to scanner B.

[0022] The technical effect of the above technical solution is that by calculating the point cloud difference distance, it can more comprehensively reflect the point cloud data changes of the test vehicle prototype at different time points or different positions. This comprehensive difference comparison helps to more accurately determine whether the state of the sample is abnormal, and reduces the misjudgment caused by the fluctuation of a single data point.

[0023] Furthermore, in the state monitoring module, the method for determining whether the state of the test vehicle prototype is abnormal includes:

[0024] Continuously obtain multiple in-and-out point cloud data of test vehicle prototypes;

[0025] Each of the multiple copies of the in-and-out point cloud data is compared with the most recent copy of the in-and-out point cloud data scanned by the most recent scanner to obtain multiple data difference distances; when each of the data difference distances is greater than the difference distance safety threshold, it is considered that the state of the test sample vehicle prototype is abnormal.

[0026] The technical effect of the above technical solution is that by comparing the difference between each entry and exit point cloud data of the current scanner and the most recent one of the previous scanner, the system can more accurately capture the changing trend of the state of the test vehicle prototype sample. Compared with relying only on a single data point or simple threshold judgment, this continuous comparison method can better reflect the subtle changes in the state of the sample, thereby improving the accuracy of the judgment. In addition, by setting the difference distance safety threshold as the basis for judgment, the system can more scientifically evaluate the severity of the state change and avoid false alarms or omissions.

[0027] Furthermore, it also includes an abnormal part identification module, which is used to use 3D images to identify abnormal parts of the test sample vehicle prototype when the state of the test sample vehicle prototype is abnormal, and record the image of the abnormal part in the storage and in-and-out data information of the test sample vehicle prototype.

[0028] The technical effect of the above technical solution is: by locating the specific location where the abnormality occurs, not only the basic status information of the sample is retained, but also detailed image data of the abnormal part is added. This comprehensive data recording method helps to build a complete historical archive of the sample status and provide strong data support for subsequent analysis, research and improvement.

[0029] A method for monitoring the state of a test vehicle prototype to achieve the second objective of the present invention comprises:

[0030] Acquire initial data information of the test vehicle prototype; the initial data information includes: a unique identification feature code and multiple copies of initial point cloud data; the unique identification feature code is used to identify the test vehicle prototype; the initial point cloud data represents the point cloud data of the test vehicle prototype in its initial state or when it is initially put into storage;

[0031] According to the identified unique identification code, the in-and-out data information of each test vehicle prototype sample when entering and leaving the warehouse is obtained; the in-and-out data information includes: the unique identification code, multiple in-and-out point cloud data; the in-and-out point cloud data represents the point cloud data of the test vehicle prototype sample when entering and leaving the warehouse;

[0032] The in-and-out point cloud data in the in-and-out data information of the test vehicle prototype sample and the multiple copies of the initial point cloud data are compared, and whether the state of the test vehicle prototype sample is abnormal is determined based on the comparison result.

[0033] Furthermore, data processing is performed on the multiple copies of initial point cloud data in the initial data information to obtain a difference distance safety threshold of the point cloud data of each test vehicle prototype sample. The data processing method includes: performing difference comparison on the multiple copies of initial point cloud data to obtain the maximum difference distance as the difference distance safety threshold of the initial point cloud data of each test vehicle prototype sample.

[0034] Furthermore, the method for determining whether the state of the test vehicle prototype is abnormal includes:

[0035] Continuously obtain multiple in-and-out point cloud data of test vehicle prototypes;

[0036] Each of the multiple copies of the in-and-out point cloud data is compared with the most recent copy of the in-and-out point cloud data scanned by the most recent scanner to obtain multiple data difference distances; when each of the data difference distances is greater than the difference distance safety threshold, it is considered that the state of the test sample vehicle prototype is abnormal.

[0037] The beneficial effects of the present invention include:

[0038] The present invention realizes comprehensive, real-time and intelligent monitoring of the status of the test vehicle prototype through precise data initialization, efficient data in and out management and accurate status judgment. This is of great significance for improving the research and development efficiency of the test vehicle prototype, reducing production costs and ensuring product quality. Through the point cloud data comparison processing described in the present invention, the image recognition server only needs to identify abnormal parts of the test vehicle prototype with abnormal status, which can greatly reduce the number of image recognition service calculations and reduce server pressure. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a schematic diagram of data processing logic of the method of the present invention;

[0040] Figure 2 It is the system architecture diagram;

[0041] Figure 3 It is a schematic diagram of data storage;

[0042] Figure 4 It is a schematic diagram of the control chart boundary;

[0043] Figure 5 It is a schematic diagram of the path of the sample vehicle moving from warehouse A to warehouse B;

[0044] Figure 6 It is a schematic control of the difference of point cloud data Figure 1 ;

[0045] Figure 7 It is a schematic control of the difference of point cloud data Figure 2 ;

[0046] Figure 8 is a map of the identified abnormal parts. DETAILED DESCRIPTION

[0047] The following specific implementations are used to explain the technical solutions of the claims of the present invention so that those skilled in the art can understand the claims. The protection scope of the present invention is not limited to the following specific implementation structures. The technical solutions of the claims of the present invention made by those skilled in the art but different from the following specific implementations are also within the protection scope of the present invention.

[0048] like Figure 2As shown in the system architecture diagram, the warehouse image recognition system is an image recognition system deployed in the warehouse, which is used to identify sample vehicles and prototypes entering the warehouse; the road image recognition system is deployed in the park's main road image recognition system, which is used to identify sample vehicles and prototypes outside the warehouse; Kafka is a message queue, which is used to store the sample vehicle and prototype information pushed by the warehouse image recognition system and the road image recognition system; the data acquisition server is used to receive the sample vehicle and prototype information in Kafka, persist the data, and build a display layer view for PC and mobile phone terminals to consult; the database is used to persistently store the sample vehicle and prototype information; the display terminal is used to display the sample vehicle and prototype information view built by the data acquisition server.

[0049] Example 1

[0050] A method for monitoring the status of a test vehicle prototype

[0051] S1. Acquire initial data information of a test vehicle prototype; the initial data information includes: a unique identification feature code and multiple copies of initial point cloud data; the unique identification feature code is used to identify the test vehicle prototype; the initial point cloud data represents the point cloud data of the test vehicle prototype in its initial state or when it is initially stored;

[0052] In some embodiments, the initial data information of the prototype vehicle, prototype and prototype is shown in Table 1 below. The method for obtaining the initialization information of the test prototype vehicle, prototype and prototype and incorporating it into the database is as follows: Figure 3 As shown, when the data of the prototype vehicle, prototype machine, and prototype parts are initially archived or initially stored, the classification and the unique identification feature code of the classification need to be manually determined.

[0053] In some embodiments, a data difference comparison is performed on multiple copies of the initial point cloud data in the initial data information to obtain a difference distance safety threshold of the point cloud data of each test vehicle prototype. The data difference comparison method includes: performing a difference comparison on the multiple copies of the initial point cloud data to obtain the maximum difference distance as the difference distance safety threshold of the initial point cloud data of each test vehicle prototype. For example, when the initial data information of the test vehicle prototype is stored in the warehouse, the first three-dimensional scanner A is used in the warehouse to perform a 3D scan on the test vehicle prototype to generate point cloud data, and the scan is repeated multiple times to generate multiple copies of point cloud data A1~A N For example, 7 PCD values ​​are obtained as the initial point cloud data; each PCD value is compared with each other to obtain multiple differences, and the maximum value a of the difference is taken as the difference distance safety threshold of the test vehicle prototype. The maximum value a is used as the boundary of the control chart, such as Figure 4As shown; and record the data information of the items as shown in Table 2 below; the control chart is a chart with control limits used to analyze and determine whether the process is in a stable state. It is a functional chart that can distinguish normal fluctuations from abnormal fluctuations and is an important statistical tool in on-site quality management.

[0054] Table 1

[0055]

[0056] Each field of the data structure is described as follows:

[0057] Unique identification code: used to identify the prototype vehicle, prototype machine, and sample parts, and is a unique code generated by the unique and immutable feature point of the prototype vehicle, prototype machine, and sample parts;

[0058] Point cloud PCD value: used to record 3D point cloud data, where PCD (Point Cloud Data) is a storage format for point cloud data;

[0059] Difference distance safety threshold: used to record the PCD difference distance safety floating range of the item.

[0060] Prototype vehicle prototype sample point cloud comparison difference distance: The point cloud difference value formed by comparing the PCD of this point cloud with the PCD of the most recent in-and-out point cloud data of the previous 3D scanner is used for statistical analysis;

[0061] Location number: stores the location of the current prototype vehicle, prototype, and sample, including: warehouse number / road number;

[0062] Time: record time;

[0063] Status: Whether the device has any abnormality.

[0064] S2. Obtaining the in-and-out data information of each test vehicle prototype when entering and leaving the warehouse according to the identified unique identification feature code; the in-and-out data information includes: the unique identification feature code and multiple in-and-out point cloud data; the in-and-out point cloud data represents the point cloud data of the test vehicle prototype when entering and leaving the warehouse;

[0065] The following is Figure 5 Take the example of the sample vehicle prototype being moved from warehouse A to warehouse B:

[0066] S2.1. Data entry of prototype vehicles, prototype machines and prototype parts (hereinafter referred to as items) into the warehouse: When each item is entered into the warehouse, the item classification and the unique identification code of the classification must be manually determined. The process is as follows: Figure 4 As shown;

[0067] S2.2, out of the warehouse; the road camera or scanner A captures and uses the convolutional neural network image recognition algorithm to determine the category of the item, whether it is a sample car, engine, sample or other. Generate a unique identification feature code Tzm000001 through the feature points of the item; identify the three-dimensional point cloud data of the item.

[0068] S3. Compare the in-and-out point cloud data in the in-and-out data information of the test vehicle prototype with the multiple copies of the initial point cloud data, and determine whether the state of the test vehicle prototype is abnormal based on the comparison result.

[0069] In some embodiments, the method for determining whether the state of a test vehicle prototype is abnormal includes:

[0070] Continuously obtain multiple in-and-out point cloud data of the test vehicle prototype sample scanned by the current scanner; compare each in-and-out point cloud data of the current scanner with the most recent in-and-out point cloud data of the most recent previous scanner to obtain multiple data difference distances; when each of the data difference distances is greater than the maximum difference distance, that is, the difference distance safety threshold, it is considered that the state of the test vehicle prototype sample is abnormal.

[0071] For example, on the way out of the warehouse, it is captured by the second 3D scanner B deployed on the road, generating multiple 3D point cloud data B1~B N :Compare the point cloud data B1 with the multiple point cloud data points A1~A generated by the 3D scanner A in the warehouse before the warehouse is moved out N The last point cloud data A in N By comparison, the second point cloud data B2 at a distance of b1 meters is obtained and the last point cloud A generated by the 3D scanner A in the warehouse before the warehouse is moved out N By comparison, we get the distance b2 meters, ..., the Nth point cloud data B N The last point cloud A generated by the 3D scanner A in the warehouse before the outbound movement N Compare and get the distance b N m, if b1~b N If both are greater than the difference distance safety threshold a of the test vehicle prototype, it means that an abnormality occurs to the test vehicle prototype during the movement from the location of scanner A to the location of scanner B; otherwise, the test vehicle prototype is judged to be in normal state.

[0072] During the process of leaving warehouse A and moving to warehouse B, the third 3D scanner C captures the test vehicle prototype and generates point cloud data C1 to C N , C1~C N The last point cloud data B of the second 3D scanner B N By comparison, we get the distance: c1~cN , if c1~c N If both are greater than the difference distance safety threshold a of the test vehicle prototype, it means that an abnormality occurs to the test vehicle prototype during the movement from the position of the second 3D scanner B to the position of the third 3D scanner C; otherwise, the test vehicle prototype is judged to be in normal state.

[0073] Specifically, in a certain embodiment, 7 sets of 3D point cloud data are obtained, and the difference distance between each set and the most recent set of point cloud data obtained by the previous 3D scanner is calculated. Figure 6 As shown in the table below, there are no 7 consecutive points outside the PCD difference distance safety threshold, and the status is judged to be normal. The recorded data is shown in Table 2 below.

[0074] Table 2

[0075]

[0076] If 7 consecutive points are outside the PCD difference distance safety threshold a, such as Figure 7 As shown, it is judged as an abnormal state, an abnormal alarm is generated, and the recorded data is as shown in Table 3 below;

[0077] Table 3

[0078]

[0079]

[0080] In certain embodiments, the convolutional neural network image recognition algorithm is continued to be used to locate the abnormal part, and the data generated is shown in the following Table 4;

[0081] Table 4

[0082]

[0083] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0084] Example 2

[0085] A test vehicle prototype and sample component status monitoring system, comprising:

[0086] Data initialization module: used to obtain initial data information of the test vehicle prototype; the initial data information includes: a unique identification feature code and multiple copies of initial point cloud data; the unique identification feature code is used to identify the test vehicle prototype; the initial point cloud data represents the point cloud data of the test vehicle prototype at the initial time;

[0087] Data entry and exit module: used to obtain the entry and exit data information of each test vehicle prototype sample when entering and exiting the warehouse according to the identified unique identification feature code; the entry and exit data information includes: unique identification feature code, multiple entry and exit point cloud data; the entry and exit point cloud data represents the point cloud data of the test vehicle prototype sample when entering and exiting the warehouse;

[0088] Status monitoring module: used to compare the in-and-out point cloud data in the in-and-out data information of the test vehicle prototype with the multiple copies of the initial point cloud data, and determine whether the status of the test vehicle prototype is abnormal based on the comparison result.

[0089] In some embodiments, the initial data information and / or the in-and-out data information also includes a camera number, which is used to indicate the camera number for identifying the initial data information and / or the in-and-out data information of the test vehicle prototype sample; the system also includes a position monitoring module, which is used to obtain the current location and / or historical movement path of the test vehicle prototype sample according to the camera number in the data information.

[0090] In some embodiments, the data initialization module also includes a point cloud data processing module, which is used to perform data processing on multiple copies of initial point cloud data in the initial data information to obtain a difference distance safety threshold of the point cloud data of each test vehicle prototype sample. The data processing method includes: performing difference comparison on the multiple copies of initial point cloud data to obtain the maximum difference distance as the difference distance safety threshold of the initial point cloud data of each test vehicle prototype sample.

[0091] In some embodiments, in the state monitoring module, the method for determining whether the state of the test vehicle prototype is abnormal includes:

[0092] Continuously obtain multiple in-and-out point cloud data of test vehicle prototypes;

[0093] Each of the multiple copies of the in-and-out point cloud data is compared with the most recent copy of the in-and-out point cloud data from the previous scanner to obtain multiple data difference distances; when each of the data difference distances is greater than the maximum difference distance, that is, the difference distance safety threshold, it is considered that the state of the test sample vehicle prototype is abnormal.

[0094] In some embodiments, an abnormal part identification module is also included, which is used to use 3D images to identify abnormal parts of the test vehicle prototype sample when the state of the test vehicle prototype sample is abnormal, and record the image of the abnormal part in the storage data information.

[0095] Example 3

[0096] An embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, the steps of the method described in the present invention are implemented, which will not be repeated here.

[0097] The computer-readable storage medium may be the data transmission device provided in any of the aforementioned embodiments or the internal storage unit of the computer device, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (smartmedia card, SMC), a secure digital (secure digital, SD) card, a flash card (flash card), etc., provided on the computer device.

[0098] Furthermore, the computer-readable storage medium may include both an internal storage unit of the computer device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the computer device. The computer-readable storage medium may also be used to temporarily store data to be output or has been output.

[0099] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0100] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0101] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0102] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0103] Example 4

[0104] A computer program product comprises a computer program / instruction, which implements any step of the test vehicle prototype, prototype and component status monitoring method when executed by a processor.

[0105] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

Claims

1. A test vehicle prototype and sample component status monitoring system, characterized in that: include: Data initialization module: used to obtain the initial data information of the test vehicle prototype; The initial data information includes: a unique identification code and multiple copies of initial point cloud data; the unique identification code is used to identify the test vehicle prototype; Data entry and exit module: used to obtain the entry and exit data information of each test vehicle prototype sample when entering and exiting the warehouse according to the identified unique identification feature code; the entry and exit data information includes: unique identification feature code, multiple entry and exit point cloud data; the entry and exit point cloud data represents the point cloud data of the test vehicle prototype sample when entering and exiting the warehouse; Status monitoring module: used to compare the in-and-out point cloud data in the in-and-out data information of the test vehicle prototype with the multiple copies of the initial point cloud data, and determine whether the status of the test vehicle prototype is abnormal based on the comparison result.

2. The test vehicle prototype state monitoring system according to claim 1, characterized in that: The initial data information and / or in-and-out data information also includes a camera number, which is used to indicate the camera number for identifying the initial data information and / or in-and-out data information of the test vehicle prototype sample; the system also includes a position monitoring module, which is used to obtain the current location and / or historical movement path of the test vehicle prototype sample according to the camera number.

3. The test vehicle prototype state monitoring system according to claim 1 or 2, characterized in that: The data initialization module also includes a point cloud data processing module, which is used to perform data processing on the multiple copies of initial point cloud data of each test vehicle prototype sample to obtain a difference distance safety threshold of the point cloud data of each test vehicle prototype sample. The data processing method includes: performing difference comparison on the multiple copies of initial point cloud data to obtain the maximum difference distance as the difference distance safety threshold of the initial point cloud data of each test vehicle prototype sample; the difference distance safety threshold is used to determine whether the state of the test vehicle prototype sample is abnormal.

4. The test vehicle prototype state monitoring system according to claim 3, characterized in that: Methods for determining whether the state of the test vehicle prototype is abnormal include: Continuously obtain multiple in-and-out point cloud data of test vehicle prototypes; Each of the multiple copies of the in-and-out point cloud data is compared with the most recent copy of the in-and-out point cloud data scanned by the most recent scanner to obtain multiple data difference distances; when each of the data difference distances is greater than the difference distance safety threshold, it is considered that the state of the test sample vehicle prototype is abnormal.

5. The test vehicle prototype and sample component status monitoring system according to claim 4, characterized in that: It also includes an abnormal part identification module, which is used to use 3D images to identify abnormal parts of the test sample vehicle prototype when the state of the test sample vehicle prototype is abnormal, and record the image of the abnormal part in the storage and in-and-out data information of the test sample vehicle prototype.

6. A method for monitoring the status of a test vehicle prototype, characterized in that: include: Obtaining initial data information of the test vehicle prototype; The initial data information includes: a unique identification code and multiple copies of initial point cloud data; the unique identification code is used to identify the test vehicle prototype; According to the identified unique identification code, the in-and-out data information of each test vehicle prototype sample when entering and leaving the warehouse is obtained; the in-and-out data information includes: the unique identification code, multiple in-and-out point cloud data; the in-and-out point cloud data represents the point cloud data of the test vehicle prototype sample when entering and leaving the warehouse; The in-and-out point cloud data in the in-and-out data information of the test vehicle prototype sample and the multiple copies of the initial point cloud data are compared, and whether the state of the test vehicle prototype sample is abnormal is determined based on the comparison result.

7. The test vehicle prototype state monitoring method according to claim 6, characterized in that: Also includes: Data processing is performed on the multiple copies of initial point cloud data of each test vehicle prototype sample to obtain a difference distance safety threshold of the point cloud data of each test vehicle prototype sample. The data processing method includes: performing difference comparison on the multiple copies of initial point cloud data to obtain a maximum difference distance as the difference distance safety threshold of the initial point cloud data of each test vehicle prototype sample. The difference distance safety threshold is used to determine whether the state of the test vehicle prototype sample is abnormal.

8. The method for monitoring the state of a test vehicle prototype as claimed in claim 7, characterized in that: Methods for determining whether the state of the test vehicle prototype is abnormal include: Continuously obtain multiple in-and-out point cloud data of test vehicle prototypes; Each of the multiple copies of the in-and-out point cloud data is compared with the most recent copy of the in-and-out point cloud data scanned by the most recent scanner to obtain multiple data difference distances; when each of the data difference distances is greater than the difference distance safety threshold, it is considered that the state of the test sample vehicle prototype is abnormal.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for monitoring the status of a test vehicle, prototype, or sample as claimed in any one of claims 6 to 8 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, any step of the test vehicle prototype and sample component status monitoring method described in claims 6 to 8 is implemented.

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