Data sensing method and device based on artificial intelligence technology and medium

Abstract

The invention discloses a data perception method and device based on an artificial intelligence technology and a medium, and relates to the technical field of data perception processing. According to the data sensing method and device based on the artificial intelligence technology and the medium, by introducing the data of the data set, the dynamic sensing model establishment operation of the initial form is completed firstly, whether the vehicle feature position needs to be adjusted before parking is determined, and then the real-time dynamic data during parking is introduced into the dynamic sensing model to form dynamic change; the condition in the real-time dynamic change process is recorded and compared with standard threshold data, deviation existing in parking operation is confirmed, result data is transmitted, simulation of an original scene is achieved while a model is established, the actual condition is predicted in advance to generate an adjustment instruction result in advance to complete adjustment operation, and the adjustment efficiency is improved. The efficiency and the accuracy of data sensing processing are improved, and the intervention operation of personnel is reduced.

Description

Technical Field

[0001] The present invention relates to the field of data perception processing technology, and specifically to a data perception method, device and medium based on artificial intelligence technology. Background Art

[0002] The reference patent name is: An entity recognition method, device, equipment and medium based on artificial intelligence (patent publication number: CN116992879A, patent publication date: 2023-11-03). The method includes: obtaining text information of the text to be recognized and the target category information of the entity to be recognized; determining the target prompt information according to the target category information; splicing the target prompt information and the text information and inputting them into the pre-trained language model to obtain the semantic representation information of the text information under the target prompt information; inputting the semantic representation information into a preset feedforward neural network to obtain the first position information of the head entity and the second position information of the tail entity in the semantic representation information; obtaining the entity representation of the head entity and the tail entity based on the first position information, the second position information and the target category information; inputting the entity representation into a preset linear classification layer to obtain the probability that the head entity and the tail entity correspond to the target category information, and completing the entity recognition of the text information according to the probability.

[0003] Based on the description in the above documents, although data perception and processing can be achieved in existing data perception operations, it is only possible to know the data, but it is impossible to make adjustments for unused data situations, so that in the subsequent actual operation process, the entity after the operation is different from the expected one. At the same time, the perception and analysis of surrounding features cannot be accurately completed, which will also cause operational errors. For this reason, the present invention provides a data perception method, device and medium based on artificial intelligence technology. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a data perception method, device and medium based on artificial intelligence technology, which solves the problem that in the existing data perception operation, although data perception and processing can be achieved, it can only be known, and adjustments cannot be made for unused data situations, so that in the subsequent actual operation process, the entity after the operation is different from the expected one. At the same time, the perception and analysis of surrounding features cannot be accurately completed, which will also cause operational errors.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a data perception method based on artificial intelligence technology, specifically comprising the following steps:

[0006] A1. Collect parking behavior data of vehicles through multiple video acquisition sensors, and transmit the data to a processing terminal through wireless communication for data analysis and processing;

[0007] A2. Receive video data and segment the data into multiple segments of image data, and perform filtering and classification based on the image data to form the required multi-category data set;

[0008] A3. Introduce the data of the data set, first complete the initial form of the dynamic perception model establishment operation, determine whether the vehicle feature position needs to be adjusted before parking, then introduce the real-time dynamic data during parking into the dynamic perception model to form dynamic changes, record the situation during the real-time dynamic change process and compare it with the standard threshold data, confirm the deviation of the parking operation, and transmit the result data;

[0009] A4. The result data is output to the vehicle-mounted visual touch screen interface and displayed in a combination of charts, text and voice explanations.

[0010] Preferably, the operation of dividing the video data into multiple segments of image data in A2 is:

[0011] a21. Extracting video data when parking is required, and performing a segmentation operation on the video image according to the number of frames of the same cycle;

[0012] a22, sorting the segmented image data according to the segmentation time nodes, and comparing the adjacent image data, removing the data with the same feature position and dynamic operation and retaining only one;

[0013] a23. Finally, multiple segments of image data are formed without duplicate data and arranged in time sequence.

[0014] Preferably, the operations of filtering and classifying the image data in A2 to form the required multi-category data set are:

[0015] b1. Grayscale all the image data, identify the feature boundary segmentation to form multiple features based on the grayscale value, extract the image data containing both parking space features and vehicle features based on feature matching in the historical database, and filter out other image data;

[0016] b2. Classifying and differentiating the extracted image data according to feature changes into driving image data and parking image data;

[0017] b3. Aggregate the driving image data to form a driving data set marked as M and aggregate the parking image data to form a parking data set marked as N.

[0018] Preferably, the operation of establishing the dynamic perception model in A3 is:

[0019] a31. Select the parking space feature to be used, extract the driving data set M and parking data N related to the selected parking space feature, and select the last one in the current driving data subset;

[0020] a32. Determine the feature parts around the parking space features based on the perceived data, including road features, curb features and other vehicle features, and aggregate all the features to establish a dynamic perception model similar to the driving data subset.

[0021] Preferably, the operation of determining whether the characteristic position of the vehicle before parking needs to be adjusted in A3 is:

[0022] C1. According to the features in the driving data subset, it is determined that the vehicle characteristic width is L1, the distance between the right front wheel of the vehicle characteristic and the right parking space entrance is L2, and the distance between the left wheel of the vehicle characteristic and the left curb is L3. The threshold value of the distance between the right front wheel of the vehicle characteristic and the right parking space entrance is H1, and the threshold value of the distance between the left wheel of the vehicle characteristic and the left curb is H2, and the threshold value of the road width required for parking is H3, and the measured distance is compared with the threshold value;

[0023] C2, and the result of the comparison is:

[0024] Result 1: L1+L2+L3<H3, then the vehicle cannot perform parking operation;

[0025] Result 2: L1+L2+L3≥H3, and L2≥H1, L3≥H2, then the vehicle can be parked without adjustment;

[0026] Result three: L1+L2+L3≥H3, and L2

[0027] Result four: L1+L2+L3≥H3, and L2≥H1, L3<H2, and L2-H1≥H2-L3, then the vehicle can perform parking operations after adjustment, otherwise when L2-H1<H2-L3, the vehicle cannot perform parking operations.

[0028] Preferably, the adjustment operations corresponding to the third and fourth results in the a33 operation are:

[0029] c21, based on the driving data subset selected in a31, a processing operation is performed, a coordinate axis is established with the right front wheel of the vehicle feature or the left wheel of the vehicle feature as the center point marked as O, an X axis is established with the moving direction of the vehicle feature, and a Y axis is established in a direction perpendicular to the moving direction of the vehicle feature;

[0030] ​c22, and calculate the coordinates of the starting point O' to which the center point O should move according to the required moving distance and the angle of rotation in the direction, and the calculation formula is:

[0031]

[0032] y is the vertical distance from the center point O to the starting point O' on the Y axis, [(|L3-H2|+|L2-H1|) / 2] is the vertical distance from the center point O to the starting point O' on the X axis, and tanα is the tangent value of the wheel steering angle;

[0033] That is, the coordinates of the starting point O' are (x, y).

[0034] Preferably, after the real-time dynamic data of parking in A3 is introduced into the dynamic perception model, the operation of comparing the situation in the dynamic change process with the standard threshold data is:

[0035] D1. By extracting the operational data about the vehicle characteristics parking from the data repository, the dynamic perception model is introduced to simulate and judge whether there is a deviation between the vehicle characteristics and the parking space characteristics;

[0036] D2. If the vehicle features are completely within the range of the parking space features, no adjustment is required. Otherwise, the position of the vehicle features should be adjusted before parking.

[0037] D3, and transmit the calculated results to generate control instructions for actual operation.

[0038] Preferably, the operation of adjusting the position of the vehicle feature in D2 is:

[0039] d21. Determine the position of the parking space feature, make an extension line from the long boundary of the parking space feature and the side close to the vehicle, and mark the inner boundary width of the parking space feature as R1, and the vertical distance from the rear right wheel feature position of the vehicle feature to the extension line is R2, and the distance between the rear right wheel feature position of the vehicle feature and the right parking space entrance is [(|L3-H2|+|L2-H1|) / 2];

[0040] d22, the moving distance of the vehicle feature in the direction of travel is obtained according to the calculation, and a command is generated to control the vehicle to adjust;

[0041] d23. The calculation formula for the moving distance of the vehicle feature in the direction of travel is:

[0042]

[0043] Wherein S is the distance that the vehicle feature needs to move, and when S<0, the vehicle feature needs to perform a reverse processing operation along the travel extension direction, and when S>0, the vehicle feature performs a moving operation along the travel direction.

[0044] The present invention also discloses a device for a data perception method based on artificial intelligence technology, including:

[0045] The data acquisition module has multiple acquisition sensors installed at different locations of the vehicle to sense and collect driving data and parameter data around the vehicle;

[0046] The data asset quantum security anchor host is installed on the vehicle and is used for integrated management operations of data collection, analysis, processing, storage and result output;

[0047] The visual touch screen interface is used to display real-time dynamic result data, and personnel can perform intelligent control operations on the touch screen.

[0048] The present invention also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of a data perception method based on artificial intelligence technology are executed.

[0049] The present invention provides a data perception method, device and medium based on artificial intelligence technology. Compared with the prior art, it has the following beneficial effects:

[0050] (1) The data perception method, device and medium based on artificial intelligence technology first complete the initial form of dynamic perception model establishment operation by introducing data from the data set, determine whether the characteristic position of the vehicle before parking needs to be adjusted, and then introduce the real-time dynamic data during parking into the dynamic perception model to form dynamic changes, record the situation during the real-time dynamic change process and compare it with the standard threshold data, confirm the deviation of the parking operation, and transmit the result data. In this way, when establishing the model, not only the simulation of the original scene is realized, but also the actual situation is predicted in advance to generate the adjustment instruction result in advance to complete the adjustment operation, thereby improving the efficiency and accuracy of data perception processing and reducing the intervention of personnel.

[0051] (2) The data perception method, device and medium based on artificial intelligence technology determine whether the characteristic position of the vehicle needs to be adjusted before parking, determine various data according to the characteristics in the driving data subset, and thus calculate the distance between the vehicle and the parking space, and compare the measured distance with the threshold. In this way, it can be perceived before parking whether parking is possible and whether adjustment operations are needed, so as to avoid parking collision problems and adjust to a tolerance distance to make parking operations more accurate.

[0052] (3) The data perception method, device and medium based on artificial intelligence technology extracts the operational data about the vehicle feature parking from the data repository, and introduces the dynamic perception model to judge whether there is a deviation between the vehicle feature and the parking space feature during simulation. If the vehicle feature is completely within the inclusion range of the parking space feature, no adjustment is required. Otherwise, the position of the vehicle feature is adjusted before the parking operation is performed. In this way, the position is adjusted before parking, so as to determine the final parking posture, improve the efficiency of data perception and processing, realize fast parking and make the parking posture more standardized. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is an operation flow chart of the data perception method of the present invention;

[0054] Figure 2 This is an operational flow chart of image data filtering and classification according to the present invention;

[0055] Figure 3 This is a functional block diagram of the data sensing device of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] See also Figure 1-Figure 3 , the present invention provides four technical solutions:

[0058] Embodiment 1: A data perception method based on artificial intelligence technology specifically comprises the following steps:

[0059] A1. Collect parking behavior data of vehicles through multiple video acquisition sensors, and transmit the data to a processing terminal through wireless communication for data analysis and processing;

[0060] A2. Receive video data and segment the data into multiple segments of image data, and perform filtering and classification based on the image data to form the required multi-category data set;

[0061] A3. Introduce the data of the data set, first complete the initial form of the dynamic perception model establishment operation, determine whether the vehicle feature position needs to be adjusted before parking, then introduce the real-time dynamic data during parking into the dynamic perception model to form dynamic changes, record the situation during the real-time dynamic change process and compare it with the standard threshold data, confirm the deviation of the parking operation, and transmit the result data;

[0062] A4. The result data is output to the vehicle-mounted visual touch screen interface and displayed in a combination of charts, text and voice explanations.

[0063] Among them, by introducing the data of the data set, the initial form of the dynamic perception model establishment operation is first completed to determine whether the vehicle feature position needs to be adjusted before parking, and then the real-time dynamic data during parking is introduced into the dynamic perception model to form dynamic changes. The situation during the real-time dynamic change process is recorded and compared with the standard threshold data to confirm the deviation of the parking operation, and the result data is transmitted. In this way, while establishing the model, not only the simulation of the original scene is realized, but also the actual situation is predicted in advance to generate the adjustment instruction results in advance to complete the adjustment operation, thereby improving the efficiency and accuracy of data perception processing and reducing human intervention operations.

[0064] In the embodiment of the present invention, the operation of dividing the video data into multiple segments of image data in A2 is:

[0065] a21. Extracting video data when parking is required, and performing a segmentation operation on the video image according to the number of frames of the same cycle;

[0066] a22, sorting the segmented image data according to the segmentation time nodes, and comparing the adjacent image data, removing the data with the same feature position and dynamic operation and retaining only one;

[0067] a23. Finally, multiple segments of image data are formed without duplicate data and arranged in time sequence.

[0068] In the embodiment of the present invention, the operations of filtering and classifying the image data in A2 to form the required multi-category data set are:

[0069] b1. Grayscale all the image data, identify the feature boundary segmentation to form multiple features based on the grayscale value, extract the image data containing both parking space features and vehicle features based on feature matching in the historical database, and filter out other image data;

[0070] b2. Classifying and differentiating the extracted image data according to feature changes into driving image data and parking image data;

[0071] b3. Aggregate the driving image data to form a driving data set marked as M and aggregate the parking image data to form a parking data set marked as N.

[0072] In the embodiment of the present invention, the operation of establishing the dynamic perception model in A3 is:

[0073] a31. Select the parking space feature to be used, extract the driving data set M and parking data N related to the selected parking space feature, and select the last one in the current driving data subset;

[0074] a32. Determine the feature parts around the parking space features based on the perceived data, including road features, curb features and other vehicle features, and aggregate all the features to establish a dynamic perception model similar to the driving data subset.

[0075] In the embodiment of the present invention, the operation of determining whether the characteristic position of the vehicle before parking needs to be adjusted in A3 is:

[0076] C1. According to the features in the driving data subset, it is determined that the vehicle characteristic width is L1, the distance between the right front wheel of the vehicle characteristic and the right parking space entrance is L2, and the distance between the left wheel of the vehicle characteristic and the left curb is L3. The threshold value of the distance between the right front wheel of the vehicle characteristic and the right parking space entrance is H1, and the threshold value of the distance between the left wheel of the vehicle characteristic and the left curb is H2, and the threshold value of the road width required for parking is H3, and the measured distance is compared with the threshold value;

[0077] C2, and the result of the comparison is:

[0078] Result 1: L1+L2+L3<H3, then the vehicle cannot perform parking operation;

[0079] Result 2: L1+L2+L3≥H3, and L2≥H1, L3≥H2, then the vehicle can be parked without adjustment;

[0080] Result three: L1+L2+L3≥H3, and L2

[0081] Result four: L1+L2+L3≥H3, and L2≥H1, L3<H2, and L2-H1≥H2-L3, then the vehicle can perform parking operations after adjustment, otherwise when L2-H1<H2-L3, the vehicle cannot perform parking operations.

[0082] In the embodiment of the present invention, the adjustment operations corresponding to the third and fourth results in the a33 operation are:

[0083] c21, based on the driving data subset selected in a31, a processing operation is performed, a coordinate axis is established with the right front wheel of the vehicle feature or the left wheel of the vehicle feature as the center point marked as O, and an X axis is established with the moving direction of the vehicle feature, and a Y axis is established in a direction perpendicular to the moving direction of the vehicle feature;

[0084] ​c22, and calculate the coordinates of the starting point O' to which the center point O should move according to the required moving distance and the angle of rotation in the direction, and the calculation formula is:

[0085]

[0086] y is the vertical distance from the center point O to the starting point O' on the Y axis, [(|L3-H2|+|L2-H1|) / 2] is the vertical distance from the center point O to the starting point O' on the X axis, and tanα is the tangent value of the wheel steering angle;

[0087] That is, the coordinates of the starting point O' are (x, y).

[0088] Among them, by determining whether the characteristic position of the vehicle needs to be adjusted before parking, various data are determined according to the characteristics in the driving data subset, so as to calculate the distance between the vehicle and the parking space, and compare the measured distance with the threshold. In this way, it is possible to perceive whether parking is possible before parking, and whether adjustment operations are needed, so as to avoid parking collision problems, and adjust to give a tolerant distance to make parking operations more accurate.

[0089] In the embodiment of the present invention, after the real-time dynamic data of parking in A3 is introduced into the dynamic perception model, the operation of comparing the situation in the dynamic change process with the standard threshold data is as follows:

[0090] D1. By extracting the operational data about the vehicle characteristics parking from the data repository, the dynamic perception model is introduced to simulate and judge whether there is a deviation between the vehicle characteristics and the parking space characteristics;

[0091] D2. If the vehicle features are completely within the range of the parking space features, no adjustment is required. Otherwise, the position of the vehicle features should be adjusted before parking.

[0092] D3, and transmit the calculated results to generate control instructions for actual operation.

[0093] In the embodiment of the present invention, the operation of adjusting the position of the vehicle feature in D2 is:

[0094] d21. Determine the position of the parking space feature, make an extension line from the long boundary of the parking space feature and the side close to the vehicle, and mark the inner boundary width of the parking space feature as R1, and the vertical distance from the rear right wheel feature position of the vehicle feature to the extension line is R2, and the distance between the rear right wheel feature position of the vehicle feature and the right parking space entrance is [(|L3-H2|+|L2-H1|) / 2];

[0095] d22, the moving distance of the vehicle feature in the direction of travel is obtained according to the calculation, and a command is generated to control the vehicle to adjust;

[0096] d23. The calculation formula for the moving distance of the vehicle feature in the direction of travel is:

[0097]

[0098] Wherein S is the distance that the vehicle feature needs to move, and when S<0, the vehicle feature needs to perform a reverse processing operation along the travel extension direction, and when S>0, the vehicle feature performs a moving operation along the travel direction.

[0099] Among them, by extracting the operational data about vehicle feature parking from the data repository, the dynamic perception model is introduced to simulate and judge whether there is a deviation between the vehicle features and the parking space features. If the vehicle features are completely within the inclusion range of the parking space features, no adjustment is required. Otherwise, the position of the vehicle features is adjusted before the parking operation. In this way, the position is adjusted before parking, thereby determining the final parking posture, improving the efficiency of data perception and processing, achieving fast parking and making the parking posture more standardized.

[0100] Embodiment 2 is different from Embodiment 1 in that: the present invention further discloses a device for a data perception method based on artificial intelligence technology, including:

[0101] The data acquisition module has multiple acquisition sensors installed at different locations of the vehicle to sense and collect driving data and parameter data around the vehicle;

[0102] The data asset quantum security anchor host is installed on the vehicle and is used for integrated management operations of data collection, analysis, processing, storage and result output;

[0103] The visual touch screen interface is used to display real-time dynamic result data, and personnel can perform intelligent control operations on the touch screen.

[0104] Among them, the Data Asset Quantum Security Anchor Host is a 2U dual-core rack-mounted server developed based on the Hygon 5000 series processor platform. It is an existing equipment product, mainly used for integrated management operations of data collection, analysis, processing, storage and result output.

[0105] Embodiment 3 is different from Embodiment 1 and Embodiment 2 in that: the present invention further discloses a computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, the steps of a data perception method based on artificial intelligence technology are executed.

[0106] Embodiment 4 is different from Embodiment 1 and Embodiment 2 in that: by using the existing artificial intelligence technology data perception method and the artificial intelligence technology data perception method of the present invention, a plurality of different types of vehicles are tested, and the parking completion time and parking deviation are recorded during the test, and the specific results are shown in Table 1:

[0107] Table 1 Parking situation table

[0108] Average parking time Probability of successful parking Existing Data Awareness Methods 68s 75％ Data perception method of the present invention 26s 100％

[0109] In summary, by using the data perception method of the artificial intelligence technology of the present invention to perform parking operations on a vehicle, the parking time is shorter and the parking success rate is higher, and it can be better applied in actual vehicle parking operations.

[0110] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0111] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0112] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A data perception method based on artificial intelligence technology, characterized in that: The specific steps include: A1. Collect parking behavior data of vehicles through multiple video acquisition sensors, and transmit the data to a processing terminal through wireless communication for data analysis and processing; A2. Receive video data and segment the data into multiple segments of image data, and perform filtering and classification based on the image data to form the required multi-category data set; A3. Introduce the data of the data set, first complete the initial form of the dynamic perception model establishment operation, determine whether the vehicle feature position needs to be adjusted before parking, then introduce the real-time dynamic data during parking into the dynamic perception model to form dynamic changes, record the situation during the real-time dynamic change process and compare it with the standard threshold data, confirm the deviation of the parking operation, and transmit the result data; A4. The result data is output to the vehicle-mounted visual touch screen interface and displayed in a combination of charts, text and voice explanations.

2. The data perception method based on artificial intelligence technology according to claim 1 is characterized in that: The operation of dividing the video data into multiple segments of image data in A2 is: a21. Extracting video data when parking is required, and performing a segmentation operation on the video image according to the number of frames of the same cycle; a22, sorting the segmented image data according to the segmentation time nodes, and comparing the adjacent image data, removing the data with the same feature position and dynamic operation and retaining only one; a23. Finally, multiple segments of image data are formed without duplicate data and arranged in time sequence.

3. The data perception method based on artificial intelligence technology according to claim 2 is characterized in that: The operations of filtering and classifying the image data in A2 to form the required multi-category data set are: b1. Grayscale all the image data, identify the feature boundary segmentation to form multiple features based on the grayscale value, extract the image data containing both parking space features and vehicle features based on feature matching in the historical database, and filter out other image data; b2. Classifying and differentiating the extracted image data according to feature changes into driving image data and parking image data; b3. Aggregate the driving image data to form a driving data set marked as M and aggregate the parking image data to form a parking data set marked as N.

4. The data perception method based on artificial intelligence technology according to claim 3 is characterized in that: The operation of establishing the dynamic perception model in A3 is: a31. Select the parking space feature to be used, extract the driving data set M and parking data N related to the selected parking space feature, and select the last one in the current driving data subset; a32. Determine the feature parts around the parking space features based on the perceived data, including road features, curb features and other vehicle features, and aggregate all the features to establish a dynamic perception model similar to the driving data subset.

5. The data perception method based on artificial intelligence technology according to claim 4 is characterized in that: The operation of determining whether the characteristic position of the vehicle before parking needs to be adjusted in A3 is: C1. According to the features in the driving data subset, it is determined that the vehicle characteristic width is L1, the distance between the right front wheel of the vehicle characteristic and the right parking space entrance is L2, and the distance between the left wheel of the vehicle characteristic and the left curb is L3. The threshold value of the distance between the right front wheel of the vehicle characteristic and the right parking space entrance is H1, and the threshold value of the distance between the left wheel of the vehicle characteristic and the left curb is H2, and the threshold value of the road width required for parking is H3, and the measured distance is compared with the threshold value; C2, and the result of the comparison is: Result 1: L1+L2+L3<H3, then the vehicle cannot perform parking operation; Result 2: L1+L2+L3≥H3, and L2≥H1, L3≥H2, then the vehicle can be parked without adjustment; Result three: L1+L2+L3≥H3, and L2<H1, L3≥H2, and L3-H2≥H1-L2, then the vehicle can perform parking operations after adjustment, otherwise when L3-H2<H1-L2, the vehicle cannot perform parking operations; Result four: L1+L2+L3≥H3, and L2≥H1, L3<H2, and L2-H1≥H2-L3, then the vehicle can perform parking operations after adjustment, otherwise when L2-H1<H2-L3, the vehicle cannot perform parking operations.

6. The data perception method based on artificial intelligence technology according to claim 5 is characterized in that: The adjustment operations corresponding to the third and fourth results in the a33 operation are: c21, based on the driving data subset selected in a31, a processing operation is performed, a coordinate axis is established with the right front wheel of the vehicle feature or the left wheel of the vehicle feature as the center point marked as O, and an X axis is established with the moving direction of the vehicle feature, and a Y axis is established in a direction perpendicular to the moving direction of the vehicle feature; c22, and calculate the coordinates of the starting point O' to which the center point O should move according to the required moving distance and the angle of rotation in the direction, and the calculation formula is: y is the vertical distance from the center point O to the starting point O' on the Y axis, [(|L3-H2|+|L2-H1|) / 2] is the vertical distance from the center point O to the starting point O' on the X axis, and tanα is the tangent value of the wheel steering angle; That is, the coordinates of the starting point O' are (x, y).

7. The data perception method based on artificial intelligence technology according to claim 6 is characterized in that: After the real-time dynamic data of parking in A3 is introduced into the dynamic perception model, the operation of comparing the situation in the dynamic change process with the standard threshold data is as follows: D1. By extracting the operational data about the vehicle characteristics parking from the data repository, the dynamic perception model is introduced to simulate and judge whether there is a deviation between the vehicle characteristics and the parking space characteristics; D2. If the vehicle features are completely within the range of the parking space features, no adjustment is required. Otherwise, the position of the vehicle features should be adjusted before parking. D3, and transmit the calculated results to generate control instructions for actual operation.

8. The data perception method based on artificial intelligence technology according to claim 7 is characterized in that: The operation of adjusting the position of the vehicle feature in D2 is: d21. Determine the position of the parking space feature, make an extension line from the long boundary of the parking space feature and the side close to the vehicle, and mark the inner boundary width of the parking space feature as R1, and the vertical distance from the rear right wheel feature position of the vehicle feature to the extension line is R2, and the distance between the rear right wheel feature position of the vehicle feature and the right parking space entrance is [(|L3-H2|+|L2-H1|) / 2]; d22, the moving distance of the vehicle feature in the direction of travel is obtained according to the calculation, and a command is generated to control the vehicle to adjust; d23. The calculation formula for the moving distance of the vehicle feature in the direction of travel is: Wherein S is the distance that the vehicle feature needs to move, and when S<0, the vehicle feature needs to perform a reverse processing operation along the travel extension direction, and when S>0, the vehicle feature performs a moving operation along the travel direction.

9. The device according to any one of claims 1 to 8, characterized in that: include: The data acquisition module has multiple acquisition sensors installed at different locations of the vehicle to sense and collect driving data and parameter data around the vehicle; The data asset quantum security anchor host is installed on the vehicle and is used for integrated management operations of data collection, analysis, processing, storage and result output; The visual touch screen interface is used to display real-time dynamic result data, and personnel can perform intelligent control operations on the touch screen.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the data perception method based on artificial intelligence technology as described in any one of claims 1 to 8 are executed.

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