CN116797436A

CN116797436A - Processing system for carrying out road disease inspection by utilizing bus

Info

Publication number: CN116797436A
Application number: CN202311093484.4A
Authority: CN
Inventors: 任轶; 高健; 严传斌
Original assignee: Beijing Daoyi Shuhui Technology Co ltd
Current assignee: Beijing Daoyi Shuhui Technology Co ltd
Priority date: 2023-08-29
Filing date: 2023-08-29
Publication date: 2023-09-22
Anticipated expiration: 2043-08-29
Also published as: CN116797436B

Abstract

The embodiment of the invention relates to a processing system for carrying out road disease inspection by utilizing a bus, which comprises the following steps: a plurality of first buses and a first processing platform; each first bus is additionally provided with a corresponding first inspection device; the first processing platform comprises a first server, a first bus route database, a first road disease database and a first road maintenance database. The system can improve the instantaneity and the road network coverage rate of the road disease inspection and can reduce the labor cost of the inspection.

Description

Processing system for carrying out road disease inspection by utilizing bus

Technical Field

The invention relates to the field of data processing, in particular to a processing system for carrying out road disease inspection by using a bus.

Background

Under the long-term action of natural factors and driving load, the traffic road pavement is easy to generate different types of road diseases such as cracks (transverse or longitudinal), crazes, pits, collapse and the like. The road diseases not only reduce the structural strength of the road surface, but also damage the continuity of the road surface, and finally influence the service life and the driving safety of the road. Therefore, the road diseases need to be inspected periodically. At present, most of road disease inspection is realized based on a manual inspection mode; the conventional inspection mode has obvious problems: the road network coverage rate of the manual inspection range is low, the real-time performance of manual inspection feedback is poor, the quantity of manual inspection data is small, the data precipitation efficiency is low, and the manual inspection cost is high.

Disclosure of Invention

The invention aims at overcoming the defects of the prior art, and provides a processing system for inspecting road diseases by using a bus, which comprises the following components: a plurality of first buses and a first processing platform; each first bus is additionally provided with a corresponding first inspection device; the first processing platform comprises a first server, a first bus route database, a first road disease database and a first road maintenance database; the first inspection equipment is used for carrying out target identification, classification and association tracking processing on road diseases in the running process of the first bus, carrying out extraction processing on road disease object data according to time sequence information of a tracking target and sending the extracted data to the first processing platform; the remote first processing platform is used for storing and managing the state of the road disease object data of all the first inspection equipment, regularly distributing the road maintenance tasks, updating the maintenance state according to the feedback of the maintenance tasks, and carrying out trend analysis on the road disease of the appointed road section. On one hand, the system can realize automatic inspection of road diseases based on the inspection equipment, and can improve the instantaneity of inspection feedback by utilizing the real-time communication between the inspection equipment and a remote processing platform; on the other hand, the road network coverage rate of the routing inspection range can be improved by utilizing the road network of the bus, the collection quantity of routing inspection data can be rapidly increased by utilizing the all-weather multi-shift departure characteristic of the bus, and the sedimentation efficiency of the routing inspection data can be rapidly improved; in the further aspect, the system of the invention uses an automatic inspection mode to replace a manual inspection mode, so that the labor cost of inspection can be effectively reduced.

In order to achieve the above object, an embodiment of the present invention provides a processing system for inspecting road diseases by using a bus, the system including: a plurality of first buses and a first processing platform;

each first bus is additionally provided with a corresponding first inspection device; each first inspection device is connected with the first processing platform respectively; the first inspection equipment is used for continuously collecting the positioning coordinates of the self-vehicle according to a preset sampling frequency in the driving process; the method comprises the steps of carrying out real-time video shooting on a self-vehicle driving road, and carrying out video frame image sampling on the real-time video according to the sampling frequency to generate a corresponding first image; taking the video time corresponding to the first image as a corresponding first timestamp; and taking the self-vehicle positioning coordinates aligned with the first time stamp as corresponding first self-vehicle positioning coordinates; a corresponding first image array is formed by each first image, the corresponding first timestamp and the corresponding first vehicle positioning coordinate and is added into a preset first cache queue; performing road disease target identification, classification and association tracking processing according to the first cache queue to obtain a plurality of first tracking target sequences; extracting and processing road disease object data according to each first tracking target sequence to generate corresponding first road disease object data and sending the first road disease object data to the first processing platform;

The first processing platform comprises a first server, a first bus route database, a first road disease database and a first road maintenance database; the first server is respectively connected with each first inspection device and is also respectively connected with the first bus route database, the first road disease database and the first road maintenance database; the first server is used for inquiring road section identifiers of the first bus route database according to the first road disease object data to obtain corresponding first road section identifiers; recording and adding the first road disease database according to the first road section identifier and the first road disease object data; the first server is also used for dispatching road maintenance tasks according to the first road disease database and the first road maintenance database at regular intervals; the first server is further used for updating the disease maintenance state of the first road disease database according to maintenance task feedback; the first server is also used for carrying out trend analysis on road diseases of the designated road section according to the first road disease database.

Preferably, the first buffer queue includes a plurality of first image arrays, and the first image arrays are added in time sequence; the first image array comprises the first timestamp, the first vehicle positioning coordinate and the first image;

the first tracking target sequence comprises a plurality of first tracking target arrays, and the first tracking target arrays are added in time sequence; the first tracking target array comprises a first tracking target identifier, a first tracking timestamp and a first target identification frame; the first tracking target identifiers of all the first tracking target arrays in the first tracking target sequence are the same;

each first target identification frame is a specific road disease target identification result and corresponds to one first image array; the first target identification frame comprises a first center point coordinate, a first vertex coordinate, a second vertex coordinate, a third vertex coordinate, a fourth vertex coordinate, a first identification frame width, a first identification frame height and a first target type; the first center point coordinates are pixel coordinates of the center point of the first target identification frame on the corresponding first image; the first, second, third and fourth vertex coordinates are pixel coordinates of four vertices of the first target recognition frame on the corresponding first image, namely, the upper left, the upper right, the lower right and the lower left; the first recognition frame width and the first recognition frame height are respectively the recognition frame image width and the recognition frame height of the first target recognition frame on the corresponding first image, the first recognition frame width is specifically the linear pixel width from the first vertex coordinate to the second vertex coordinate or the linear pixel width from the third vertex coordinate to the fourth vertex coordinate, and the first recognition frame height is specifically the linear pixel width from the first vertex coordinate to the third vertex coordinate or the linear pixel width from the second vertex coordinate to the fourth vertex coordinate; the first target type is a road disease target classification result corresponding to the first target identification frame, and specifically is one of a plurality of preset road disease types; the plurality of road fault types at least comprise a road crack type, a road transverse crack type, a road longitudinal crack type, a road pit hole type and a road collapse type;

The first road disease object data comprises a first vehicle identifier, a first object time stamp, a first object coordinate, a first object image, a first object type and a first type characteristic parameter;

the first bus route database comprises a plurality of first bus route records; the first bus route record comprises a first bus identification set field and a first road segmentation information set field; the first bus identification set field comprises a plurality of first bus identifications; the first road segment information set field comprises a plurality of first road segment information; the first road segmentation information comprises a first road identifier, a first segmentation identifier and a first road segmentation coordinate range;

the first road disease database comprises a plurality of first road disease records; the first road disease record comprises a first road section identification field, a first road disease coordinate field, a first inspection time stamp field, a first inspection vehicle identification field, a first road disease image field, a first road disease type field, a first road disease characteristic parameter field and a first road disease maintenance state field; the first road disease maintenance status field comprises an unrepaired status, a notification maintenance status and a repaired status;

The first road maintenance database comprises a plurality of first road maintenance records; the first road maintenance record includes a second road segment identification field and a first maintenance notification interface field.

Preferably, the first inspection device is specifically configured to, when the road disease target recognition, classification, and association tracking processing is performed according to the first cache queue to obtain a plurality of first tracking target sequences, take the first image array newly added in the first cache queue as a corresponding current image array, and take the first timestamp, the first vehicle positioning coordinate, and the first image of the current image array as a corresponding current timestamp, a current positioning coordinate, and a current image;

performing road disease target recognition and road disease target classification processing on the current image based on a preset target recognition and classification model to obtain a corresponding first target recognition frame set and storing the first target recognition frame set; the first target recognition frame set is composed of one or more first target recognition frames when the first target recognition frame set is not empty; the target identification and classification model is realized based on a YOLO model structure;

when the first target identification frame set is not empty, confirming whether the current image array is the first image array in the first cache queue or not;

If the current image array is confirmed to be the first image array in the first cache queue, the first timestamp of the current image array is used as the corresponding first tracking timestamp; and a unique target identifier is allocated to each first target identification frame corresponding to the current image array as a corresponding first tracking target identifier; initializing a null sequence for each first tracking target identifier to serve as a corresponding first tracking target sequence; the first tracking target identifiers, the corresponding first tracking time stamps and the corresponding first target identification frames form a corresponding first tracking target array; adding each first tracking target array to each corresponding first tracking target sequence;

if the current image array is confirmed not to be the first image array in the first cache queue, taking the first timestamp of the current image array as the corresponding first tracking timestamp; and taking the first image array which is the previous image array of the current image array in the first cache queue as a corresponding previous image array; marking each first target identification frame corresponding to the current image array as a corresponding second target identification frame, and marking each first target identification frame corresponding to the previous image array as a corresponding third target identification frame; identifying whether the number of the third target identification frames is not 0, if the number of the third target identification frames is not 0, identifying the target identifications corresponding to the third target identification frames which are associated and matched with the second target identification frames based on a target association algorithm to generate corresponding second tracking target identifications, and if the number of the third target identification frames is 0, setting the second tracking target identifications corresponding to all the second target identification frames as empty identifications; traversing all the second tracking target identifiers; the second tracking target mark in the current traversal is used as a corresponding current tracking target mark, and the second target recognition frame corresponding to the current tracking target mark is used as a corresponding current target recognition frame; identifying whether the current tracking target identifier is an empty identifier or not; if the current tracking target identifier is not a null identifier, taking the first tracking target sequence corresponding to the current tracking target identifier as a corresponding current tracking target sequence, and adding the first tracking target array which is formed by the current tracking target identifier, the first tracking timestamp and the current target identification frame into the current tracking target sequence; if the current tracking target identifier is a null identifier, a unique target identifier is allocated to the current target identification frame as a new current tracking target identifier, a null first tracking target sequence is initialized for the current tracking target identifier as a corresponding current tracking target sequence, and the current tracking target identifier, the first tracking timestamp and the current target identification frame form a corresponding first tracking target array to be added to the current tracking target sequence.

Further, the first inspection device is specifically configured to, when the target identifier corresponding to the third target identification frame that is associated and matched with each second target identification frame is identified based on the target association algorithm to generate a corresponding second tracking target identifier, use the first vehicle positioning coordinates of the first image array corresponding to each second and third target identification frames as corresponding second and third vehicle positioning coordinates;

the first center point coordinates and the first, second, third and fourth vertex coordinates of each second target identification frame are subjected to coordinate conversion processing from a pixel coordinate system to a vehicle coordinate system and then to a world coordinate system according to the preset camera inner and outer parameters and the second vehicle positioning coordinates to generate corresponding first center point world coordinates and first one-to-one, first two-to-first three-to-fourth vertex world coordinates; sequentially connecting the four points corresponding to the world coordinates of the first vertex, the second vertex, the first vertex and the first vertex to obtain a corresponding first quadrilateral;

the first center point coordinates and the first, second, third and fourth vertex coordinates of each third target identification frame are subjected to coordinate conversion processing from a pixel coordinate system to a vehicle coordinate system and then to a world coordinate system according to the camera inner and outer parameters and the third vehicle positioning coordinates to generate corresponding second center point world coordinates and second first, second third and fourth vertex world coordinates; and sequentially connecting four points corresponding to the world coordinates of the first vertex, the second vertex, the third vertex and the second vertex to obtain a corresponding second quadrangle;

Calculating the linear distance between each second target identification frame and each third target identification frame to generate a corresponding first distance d _i,j The method comprises the steps of carrying out a first treatment on the surface of the i is the index of the second target recognition frame, j is the index of the third target recognition frame, i is more than or equal to 1 and less than or equal to N, j is more than or equal to 1 and less than or equal to M, N is the number of the second target recognition frames, and M is the number of the third target recognition frames; the first distance d _i,j A straight line distance between world coordinates of the first center point of the ith second target identification frame and world coordinates of the second center point of the jth third target identification frame;

and calculating the ground area intersection ratio of each second target identification frame and each third target identification frame to generate a corresponding first intersection ratio u _i,j ；u _i,j =(S _i ∩S _j )/(S _i ∪S _j )，S _i For the ground area of the first quadrangle of the ith second target identification frame, S _j A ground area of the second quadrangle of the j-th third target recognition frame, (S) _i ∩S _j ) For an intersection area of the first quadrangle of the ith second target identification frame and the second quadrangle of the jth third target identification frame on the ground, (S) _i ∪S _j ) A union area of the first quadrangle of the ith second target identification frame and the second quadrangle of the jth third target identification frame on the ground;

And the first distance d corresponding to each second target recognition frame _i,j And the first cross-over ratio u _i,j Composing the corresponding first associated feature a _i,j The method comprises the steps of carrying out a first treatment on the surface of the And from all the first associated features a obtained _i,j Form a first associated feature matrix A { a } with a matrix shape of N x M _i,j }；

And based on a target correlation algorithm, the first correlation characteristic matrix A { a }, is obtained _i,j Performing associated weight matrix calculation to generate a first associated weight matrix W { W } with a matrix shape of N×M _i,j -a }; and the first association weight matrix W { W _i,j M first ones with the same index iAssociated weight w _i,j Grouping into a group as corresponding first association weight group G _i The method comprises the steps of carrying out a first treatment on the surface of the The target association algorithm comprises a Hungary algorithm and a KM algorithm; the first association weight matrix W { W _i,j Comprises N x M first associated weights w _i,j The method comprises the steps of carrying out a first treatment on the surface of the The first association weight group G _i Comprising M first associated weights w with the same index i _i,j ；

And for all the first association weight sets G _i Traversing; and, during the traversal, the first association weight group G of the current traversal _i As a corresponding current association weight set, taking the second target recognition frame corresponding to the current association weight set as a corresponding current target recognition frame, and taking the first association weight w with the largest weight in the current association weight set _i,j As the corresponding current maximum weight; identifying whether the current maximum weight is lower than a preset first weight threshold value; if the current maximum weight is not lower than the first weight threshold, the first tracking target identifier corresponding to the third target identification frame corresponding to the current maximum weight is used as the second tracking target identifier corresponding to the current target identification frame; and if the current maximum weight is lower than the first weight threshold, setting the second tracking target identifier corresponding to the current target identification frame as an empty identifier.

Preferably, the first inspection device is specifically configured to calculate, when the first road disease object data generated by extracting and processing the road disease object data according to each first tracking target sequence is sent to the first processing platform, a time interval between the first tracking timestamp of the latest first tracking target array in the first tracking target sequence and the current time to generate a corresponding first time interval;

when the first time interval exceeds a preset time interval threshold, extracting the first image arrays corresponding to the first target identification frames of the first tracking target sequence from the first cache queue, and sequencing the first image arrays according to time sequence to form a corresponding first image array sequence;

The definition of the first image of each first image array in the first image array sequence is evaluated based on an image definition evaluation algorithm to generate a corresponding first evaluation score, the first image with the first evaluation score being the maximum score is taken as a corresponding preferred image, the first image array corresponding to the preferred image is taken as a corresponding preferred image array, and the first vehicle positioning coordinate of the preferred image array is taken as a corresponding current vehicle positioning coordinate; the first tracking target array, of which the first tracking time stamp is matched with the first time stamp of the preferred image array, in the first tracking target sequence is taken as a corresponding current tracking target array, the first tracking time stamp of the current tracking target array is taken as a corresponding current time stamp, the first target identification frame of the current tracking target array is taken as a corresponding current target identification frame, and the first target type of the current target identification frame is taken as a corresponding current target type; the image definition evaluation algorithm comprises a Brenner gradient algorithm, a Tenegrad gradient algorithm, a Laplace gradient algorithm, a variance algorithm and an energy gradient algorithm;

Taking the locally preset bus identifier as the corresponding first vehicle identifier;

and taking the current timestamp as the corresponding first object timestamp;

the first center point coordinate of the current target identification frame is subjected to coordinate conversion processing from a pixel coordinate system to a vehicle coordinate system and then to a world coordinate system according to the preset camera internal and external parameters and the current vehicle positioning coordinate to generate a corresponding third center point world coordinate; and taking the third center point world coordinate as the corresponding first object coordinate;

and taking the current target type as the corresponding first object type;

estimating type characteristic parameters corresponding to the current object type according to the first object type, the current target identification frame and the preferred image to generate corresponding first type characteristic parameters;

and on the preferred image, drawing a target recognition frame according to the first, second, third and fourth vertex coordinates of the current target recognition frame to obtain a corresponding first drawing recognition frame, drawing a text frame above the first drawing recognition frame as a corresponding first drawing text frame, and filling the first object type and the first type characteristic parameters into the first drawing text frame; and taking the preferred image with the recognition frame drawing, the text frame drawing and the text frame filling as the corresponding first object image;

The first road disease object data corresponding to the obtained first vehicle identifier, the first object timestamp, the first object coordinate, the first object image, the first object type and the first type characteristic parameter are formed and sent to the first processing platform; and deleting the first tracking target sequence when the transmission is successful.

Further, the first inspection device is specifically configured to perform coordinate conversion processing from a pixel coordinate system to a vehicle coordinate system to a world coordinate system according to the first, second, third and fourth vertex coordinates of the camera internal and external parameters of the current object recognition frame when the type feature parameters corresponding to the current object type are estimated according to the first object type, the current object recognition frame and the preferred image to generate corresponding first type feature parameters, and generate corresponding third, third and fourth world coordinates;

identifying the first object type;

if the first object type is a pavement cracking type, sequentially connecting four points corresponding to the third world coordinates, the third world coordinates and the third world coordinates to obtain a corresponding third quadrangle; estimating the area of the third quadrangle to obtain a corresponding first area; and taking the first area as the corresponding first type characteristic parameter;

If the first object type is a road surface transverse crack type or a road surface longitudinal crack type, calculating the linear distances of the third world coordinates and the third world coordinates to generate corresponding first transverse widths, calculating the linear distances of the third world coordinates and the third world coordinates to generate corresponding second transverse widths, calculating the linear distances of the third world coordinates and the third world coordinates to generate corresponding first longitudinal depths, and calculating the linear distances of the third world coordinates and the third world coordinates to generate corresponding second longitudinal depths; taking the maximum value of the first transverse width and the second transverse width as a corresponding first crack transverse width, and taking the maximum value of the first depth and the second depth as a corresponding first crack depth; and the first type characteristic parameters corresponding to the first crack transverse width and the first crack longitudinal depth are formed;

if the first object type is a pavement pit type, a pavement pit type or a pavement collapse type, extracting an image area covered by the current target identification frame on the preferred image to serve as a corresponding first identification frame image; performing closed edge recognition processing on the pit, the hole or the collapse area on the first recognition frame image to generate a corresponding first closed edge; according to a monocular ranging algorithm, predicting the depth of each pixel point on the first closed edge according to the preset camera internal and external parameters to generate a corresponding first pixel depth; and forming a corresponding first pixel depth vector by all the obtained first pixel depths; predicting the coverage area of the first closed edge according to the first pixel depth vector to generate a corresponding second area; and using the obtained second area as the corresponding characteristic parameter of the first type.

Preferably, the first server is specifically configured to extract, when the road section identifier query is performed on the first bus route database according to the first road disease object data to obtain a corresponding first road section identifier, the corresponding first vehicle identifier and the first object coordinate from the first road disease object data; the first bus route record of which the first bus identifier set field meets the first vehicle identifier in the first bus route database is used as a corresponding current bus route record; extracting the first road identifier and the first segment identifier of the first road segment information, of which the first road segment coordinate range meets the first object coordinates, in the first road segment information set field of the current bus route record as corresponding current road identifiers and current segment identifiers; and the obtained current road identifier and the current segment identifier form the corresponding first road section identifier.

Preferably, the first server is specifically configured to extract, when the first road segment identifier and the first road disease object data are recorded and added to the first road disease database, the corresponding first vehicle identifier, the first object timestamp, the first object coordinate, the first object image, the first object type and the first type feature parameter from the first road disease object data;

The first road disease records in which the first road section identification field in the first road disease database is matched with the first road section identification and the first road disease maintenance state field is in an unrepaired state are extracted to form a corresponding first record set;

setting a corresponding record newly-added switch to be in an on state when the first record set is empty; the record newly-added switch comprises an on state and an off state;

when the first record set is not empty, calculating the linear distance between the first road disease coordinate field of each first road disease record in the first record set and the first object coordinate to generate a corresponding first distance; recording the first road disease records with the first distance smaller than a preset minimum distance threshold as corresponding second road disease records to form a corresponding second record set; and identifying whether the second record set is empty; if yes, setting the corresponding record newly-added switch to be in an on state; if not, extracting the first patrol time stamp field with the latest time in the second record set as a corresponding latest patrol time stamp, identifying whether the latest patrol time stamp is earlier than the first object time stamp, if so, setting a corresponding record newly-added switch to be in an on state, and if not, setting the corresponding record newly-added switch to be in an off state;

When the record newly-added switch is in an on state, newly adding one first road disease record into the first road disease database as a corresponding newly-added record; and setting the first road segment identifier field of the new record as the corresponding first road segment identifier, the first road disease coordinate field as the corresponding first object coordinate, the first inspection time stamp field as the corresponding first object time stamp, the first inspection vehicle identifier field as the corresponding first vehicle identifier, the first road disease image field as the corresponding first object image, the first road disease type field as the corresponding first object type, the first road disease characteristic parameter field as the corresponding first type characteristic parameter, and the first road disease maintenance status field as the unrepaired status.

Preferably, the first server is specifically configured to extract, periodically, the first road disease record in which the first road disease maintenance status field in the first road disease database is in an unrepaired state, to form a corresponding third record set when the road maintenance task is dispatched according to the first road disease database and the first road maintenance database;

And when the third record set is not empty, the first road disease records with the same first road section identification field in the set are brought into the same cluster subset to be recorded as a corresponding first record subset; and in each first record subset, the first road disease records with the same first road disease type field are brought into the same cluster subset to be recorded as a corresponding second record subset; matching and clustering all the first road disease records of each second record subset according to a preset shortest road disease coordinate interval K to obtain one or more corresponding third record subsets; in the third record subset, the first road disease type fields of all the first road disease records are the same, each first road disease record has at least one first road disease record matched with the first road disease record, and the straight line distance between two coordinates corresponding to the first road disease coordinate fields of each two matched first road disease records is not more than the shortest road disease coordinate interval K;

extracting the first road disease coordinate field, the first inspection time stamp field, the first road disease image field, the first road disease type field and the first road disease characteristic parameter field of the first road disease record corresponding to the first inspection time stamp field with the nearest time in each third record subset to form corresponding first coordinate disease data; and forming corresponding first road section disease data by all the first coordinate disease data corresponding to each first record subset; and taking the first road section identification field corresponding to each first record subset as a corresponding second road section identification; the second road section identifiers corresponding to the first record subsets and the first road section disease data form corresponding first road section disease reports;

Inquiring the first road maintenance database according to each second road section identifier, and extracting the first maintenance notification interface field of the first road maintenance record, in which the second road section identifier field is matched with the second road section identifier, in the first road maintenance database as a corresponding first maintenance notification interface;

sending each first road section disease report to the corresponding first maintenance notification interface; and when the sending of each first road section disease report is finished, recording the first record subset corresponding to the current first road section disease report as a current record subset, and updating the first road disease maintenance status fields of all the first road disease records matched with the current record subset in the first road disease database into notification maintenance status.

Preferably, the first server is specifically configured to extract, in advance, the first maintenance notification interface fields of each first road maintenance record of the first road maintenance database as corresponding second maintenance notification interfaces when the disease maintenance state of the first road disease database is updated according to the maintenance task feedback, and receive maintenance task feedback information returned by each second maintenance notification interface; and on any one of the second maintenance notification interfaces, taking the current maintenance task feedback information as corresponding first feedback data after receiving one returned maintenance task feedback information; extracting a corresponding first feedback road section identifier, a first feedback road disease coordinate and a first feedback road disease type from the first feedback data; the first road segment identification field in the first road disease database is matched with the first feedback road segment identification, the linear distance between the first road disease coordinate field and the first feedback road disease coordinate is not more than the preset maintenance road disease coordinate distance L, the first road disease type field is matched with the first feedback road disease type, and the first road disease record with the first road disease maintenance state field not being the repaired state is recorded as a corresponding matching record; updating all the obtained first road disease maintenance status fields of the matching records to a repaired status; the first feedback data includes the first feedback road segment identifier, the first feedback road disease coordinate, and the first feedback road disease type.

Preferably, the first server is specifically configured to use a road section identifier of a current specified road section as a corresponding current road section identifier when the trend analysis is performed on the road diseases of the specified road section according to the first road disease database;

the first road segment identification field in the first road disease database is matched with the current road segment identification, and the first road disease record with the first road disease maintenance state field not being the repaired state is extracted to form a corresponding fourth record set;

and when the fourth record set is not empty, the first road disease records with the same first road disease type field in the set are brought into the same cluster subset to be recorded as a corresponding fourth record subset; matching and clustering all the first road disease records of each fourth record subset according to a preset shortest road disease coordinate interval K to obtain one or more corresponding fifth record subsets; in the fifth record subset, the first road disease type fields of all the first road disease records are the same, each first road disease record has at least one first road disease record matched with the first road disease record, and the straight line distance between two coordinates corresponding to the first road disease coordinate fields of each two matched first road disease records is not more than the shortest road disease coordinate interval K;

And taking the first road disease type field corresponding to each fifth record subset as a corresponding first road disease type; calculating the average value of the first road disease coordinate fields of all the first road disease records of each fifth record subset, and taking the obtained average value coordinate as a corresponding first road disease average value coordinate; extracting the first road disease image field and the first road disease characteristic parameter field of each first road disease record in each fifth record subset to serve as corresponding first road disease images and first road disease characteristic parameters, sequencing all the first road disease images according to the time sequence of the corresponding first inspection time stamp field to obtain a corresponding first road disease image sequence, and sequencing all the first road disease characteristic parameters according to the time sequence of the corresponding first inspection time stamp field to obtain a corresponding first road disease characteristic parameter sequence;

performing disease development trend analysis according to each first road disease type, the corresponding first road disease image sequence and the corresponding first road disease characteristic parameter sequence to generate a corresponding first analysis result;

Forming a corresponding first road disease analysis record by the first road disease type, the first road disease mean value coordinate, the first road disease image sequence and the first analysis result corresponding to each fifth record subset; and forming a corresponding designated road section disease trend analysis report by all the obtained first road disease analysis records.

Further, the first server is specifically configured to identify the first road disease type when the disease development trend analysis is performed according to each first road disease type, the corresponding first road disease image sequence, and the corresponding first road disease characteristic parameter sequence to generate a corresponding first analysis result;

if the first road disease type is a road surface cracking type, the first road disease image sequence is used as a corresponding first road surface cracking image sequence, and each first road disease image in the first road disease image sequence is marked as a corresponding first road surface cracking image; the first road disease characteristic parameter sequence is used as a corresponding first cracking area parameter sequence, and each first road disease characteristic parameter in the first road disease characteristic parameter sequence is recorded as a corresponding first cracking area parameter; according to a preset pavement crack disease grade evaluation rule, performing corresponding disease grade evaluation according to the first pavement crack image sequence and the first crack area parameter sequence, and taking an evaluation result as a corresponding first analysis result;

If the first road defect type is a road surface transverse crack type or a road surface longitudinal crack type, taking the first road defect image sequence as a corresponding first road surface crack image sequence, and marking each first road defect image in the first road defect image sequence as a corresponding first road surface crack image; the first road disease characteristic parameter sequence is used as a corresponding first crack parameter sequence, and each first road disease characteristic parameter in the first road disease characteristic parameter sequence is recorded as a corresponding first crack parameter; according to a preset pavement transverse crack or pavement longitudinal crack disease grade evaluation rule, corresponding disease grade evaluation is carried out according to the first pavement crack image sequence and the first crack parameter sequence, and an evaluation result is used as a corresponding first analysis result; the first crack parameter consists of two parameters of crack transverse width and crack longitudinal depth;

if the first road defect type is a road pit type or a road pit type, the first road defect image sequence is used as a corresponding first road pit image sequence, and each first road defect image in the first road defect image sequence is recorded as a corresponding first road pit image; the first road disease characteristic parameter sequence is used as a corresponding first groove hole area parameter sequence, and each first road disease characteristic parameter in the first road disease characteristic parameter sequence is recorded as a corresponding first groove hole area parameter; according to a preset pavement pit or pavement pit disease grade evaluation rule, corresponding disease grade evaluation is carried out according to the first pavement pit image sequence and the first pit area parameter sequence, and an evaluation result is used as a corresponding first analysis result;

If the first road disease type is a pavement collapse type, the first road disease image sequence is used as a corresponding first pavement collapse image sequence, each first road disease image in the first road disease image sequence is marked as a corresponding first pavement collapse image, and an image area covered by a target identification frame on each first pavement collapse image is marked as a corresponding first collapse area image; the first road disease characteristic parameter sequence is used as a corresponding first collapse area parameter sequence, and each first road disease characteristic parameter in the first road disease characteristic parameter sequence is recorded as a corresponding first collapse area parameter; predicting the pixel point type of each pixel point on each first collapse area image and the minimum vertical depth relative to the ground based on a preset image semantic segmentation model; selecting a maximum value from all the minimum vertical depths corresponding to the images of the first collapse areas as the corresponding depth of the first collapse areas; and arranging the obtained depths of all the first subsidence areas according to the arrangement sequence of the corresponding first road surface subsidence images to obtain a corresponding first subsidence area depth sequence; performing corresponding disease grade evaluation according to a preset pavement collapse grade evaluation rule according to the first pavement collapse image sequence, the first slot hole area parameter sequence and the first collapse area depth sequence, and taking an evaluation result as a corresponding first analysis result; the pixel point type comprises a ground point and a collapse point; the minimum vertical depth corresponding to the pixel point with the pixel point type being the ground point is 0; and the minimum vertical depth corresponding to the pixel point with the pixel point type of the collapse point is larger than 0.

The embodiment of the invention provides a processing system for carrying out road disease inspection by utilizing a bus, which comprises the following steps: a plurality of first buses and a first processing platform; each first bus is additionally provided with a corresponding first inspection device; the first processing platform comprises a first server, a first bus route database, a first road disease database and a first road maintenance database; the first inspection equipment is used for carrying out target identification, classification and association tracking processing on road diseases in the running process of the first bus, carrying out extraction processing on road disease object data according to time sequence information of a tracking target and sending the extracted data to the first processing platform; the remote first processing platform is used for storing and managing the state of the road disease object data of all the first inspection equipment, regularly distributing the road maintenance tasks, updating the maintenance state according to the feedback of the maintenance tasks, and carrying out trend analysis on the road disease of the appointed road section. On one hand, the system realizes automatic inspection of road diseases based on the inspection equipment, and improves the instantaneity of inspection feedback by utilizing the real-time communication between the inspection equipment and a remote processing platform; on the other hand, road network coverage rate of the inspection range is improved by utilizing the road network of the bus, the collection quantity of inspection data is increased by utilizing the characteristics of all-weather multi-shift departure of the bus, and the sedimentation efficiency of the inspection data is improved; in still another aspect, the system of the invention uses an automatic inspection mode to replace a manual inspection mode, and the labor cost of inspection is effectively reduced.

Drawings

Fig. 1 is a schematic block diagram of a processing system for inspecting road diseases by using a bus according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Fig. 1 is a schematic block diagram of a processing system for inspecting road diseases by using a bus according to an embodiment of the present invention, where, as shown in fig. 1, the system includes: a plurality of first buses 1 and a first processing platform 2.

First bus 1

Each first bus 1 is additionally provided with a corresponding first inspection device 11; each first inspection device 11 is connected to the first processing platform 2.

Here, the first inspection device 11 in the embodiment of the present invention is a device, a device or a server with a positioning module, a camera and a communication module, and for a common bus, the first inspection device 11 may be directly installed on the vehicle as an independent device, and for an intelligent bus with an intelligent processing capability and a driving computer, the first inspection device 11 may be directly installed on the vehicle as an independent device, or part or all of the functions of the first inspection device 11 may be fused into the internal processing flow of the intelligent bus.

The first inspection device 11 is used for continuously acquiring the positioning coordinates of the vehicle according to a preset sampling frequency in the driving process; real-time video shooting is carried out on the self-vehicle driving road, and video frame image sampling is carried out on the real-time video according to the sampling frequency to generate a corresponding first image; taking the video time corresponding to the first image as a corresponding first timestamp; the self-vehicle positioning coordinates aligned with the first time stamp are used as corresponding first self-vehicle positioning coordinates; a corresponding first image array is formed by each first image, a corresponding first timestamp and a first vehicle positioning coordinate, and is added into a preset first cache queue; carrying out road disease target identification, classification and association tracking processing according to the first cache queue to obtain a plurality of first tracking target sequences; extracting and processing road disease object data according to each first tracking target sequence to generate corresponding first road disease object data and sending the first road disease object data to the first processing platform 2;

the first cache queue comprises a plurality of first image arrays, and the first image arrays are added in time sequence; the first image array comprises a first timestamp, a first bicycle positioning coordinate and a first image;

The first road hazard object data comprises a first vehicle identifier, a first object time stamp, a first object coordinate, a first object image, a first object type and a first type characteristic parameter; the first object type is one of a plurality of preset road fault types, and the preset road fault types at least comprise a road crack type, a road transverse crack type, a road longitudinal crack type, a road pit type and a road collapse type.

In a specific implementation manner of the embodiment of the present invention, the first inspection device 11 is specifically configured to, when performing the identifying, classifying and association tracking processing of the road disease target according to the first buffer queue to obtain a plurality of first tracking target sequences:

a1, taking a newly added first image array in a first cache queue as a corresponding current image array, and taking a first timestamp, a first self-vehicle positioning coordinate and a first image of the current image array as a corresponding current timestamp, a current positioning coordinate and a current image;

a2, carrying out road disease target recognition and road disease target classification treatment on the current image based on a preset target recognition and classification model to obtain a corresponding first target recognition frame set and storing the first target recognition frame set;

The target identification and classification model is realized based on a YOLO model structure; the first target recognition frame set is composed of one or more first target recognition frames when the first target recognition frame set is not empty; each first target identification frame is a specific road disease target identification result and corresponds to a first image array;

the first target identification frame comprises a first center point coordinate, a first vertex coordinate, a second vertex coordinate, a third vertex coordinate, a fourth vertex coordinate, a first identification frame width, a first identification frame height and a first target type; the first center point coordinates are pixel coordinates of the center point of the first target identification frame on the corresponding first image; the first, second, third and fourth vertex coordinates are pixel coordinates of four vertexes of upper left, upper right, lower right and lower left of the first target recognition frame on the corresponding first image respectively; the first identification frame width and the first identification frame height are respectively the identification frame image width and the identification frame height of the first target identification frame on the corresponding first image, the first identification frame width is specifically the linear pixel width from the first vertex coordinate to the second vertex coordinate or the linear pixel width from the third vertex coordinate to the fourth vertex coordinate, and the first identification frame height is specifically the linear pixel width from the first vertex coordinate to the third vertex coordinate or the linear pixel width from the second vertex coordinate to the fourth vertex coordinate; the first target type is a road disease target classification result corresponding to the first target identification frame, and specifically is one of a plurality of preset road disease types; the plurality of road defect types at least comprise a road crack type, a road transverse crack type, a road longitudinal crack type, a road pit type and a road collapse type;

A3, when the first target identification frame set is not empty, confirming whether the current image array is a first image array in the first cache queue;

step A4, if the current image array is confirmed to be the first image array in the first cache queue, taking the first timestamp of the current image array as the corresponding first tracking timestamp; distributing a unique target identifier to each first target identification frame corresponding to the current image array as a corresponding first tracking target identifier; initializing a null sequence for each first tracking target identifier to serve as a corresponding first tracking target sequence; forming a corresponding first tracking target array by each first tracking target identifier, a corresponding first tracking time stamp and a corresponding first target identification frame; adding each first tracking target array to each corresponding first tracking target sequence;

Step A5, if the current image array is not the first image array in the first cache queue, taking the first timestamp of the current image array as the corresponding first tracking timestamp; and taking the previous first image array of the current image array in the first cache queue as a corresponding previous image array; marking each first target identification frame corresponding to the current image array as a corresponding second target identification frame, and marking each first target identification frame corresponding to the previous image array as a corresponding third target identification frame;

identifying whether the number of the third target identification frames is not 0, if the number of the third target identification frames is not 0, identifying the target identifications corresponding to the third target identification frames which are associated and matched with the second target identification frames based on a target association algorithm to generate corresponding second tracking target identifications, and if the number of the third target identification frames is 0, setting the second tracking target identifications corresponding to all the second target identification frames as empty identifications;

traversing all the second tracking target identifiers; traversing, taking a second tracking target identifier of the current traversal as a corresponding current tracking target identifier, and taking a second target recognition frame corresponding to the current tracking target identifier as a corresponding current target recognition frame; identifying whether the current tracking target identifier is an empty identifier or not; if the current tracking target mark is not the null mark, taking a first tracking target sequence corresponding to the current tracking target mark as a corresponding current tracking target sequence, and adding a corresponding first tracking target array formed by the current tracking target mark, a first tracking timestamp and a current target recognition frame into the current tracking target sequence; if the current tracking target identifier is a null identifier, a unique target identifier is allocated to the current target identifier as a new current tracking target identifier, a null first tracking target sequence is initialized to be used as a corresponding current tracking target sequence for the current tracking target identifier, and a corresponding first tracking target array formed by the current tracking target identifier, the first tracking timestamp and the current target identifier is added to the current tracking target sequence.

In another specific implementation manner of the embodiment of the present invention, the first inspection device 11 is specifically configured to, when identifying, based on the target association algorithm, the target identifier corresponding to the third target identification frame that is associated and matched with each second target identification frame to generate the corresponding second tracking target identifier:

step B1, taking first self-vehicle positioning coordinates of a first image array corresponding to each second and third target recognition frames as corresponding second and third self-vehicle positioning coordinates;

step B2, carrying out coordinate conversion processing from a pixel coordinate system to a vehicle coordinate system and then to a world coordinate system on a first center point coordinate and a first, a second, a third and a fourth vertex coordinates of each second target identification frame according to the preset camera inner and outer parameters and the second vehicle positioning coordinates to generate corresponding first center point world coordinates and first one-to-one, first two, first three and first four vertex world coordinates; and four points corresponding to the world coordinates of the first one, the first two, the first three and the first four vertexes are sequentially connected to obtain a corresponding first quadrangle;

step B3, carrying out coordinate conversion processing from a pixel coordinate system to a vehicle coordinate system and then to a world coordinate system on the first center point coordinate and the first, second, third and fourth vertex coordinates of each third target identification frame according to the internal and external parameters of the camera and the third vehicle positioning coordinates to generate corresponding second center point world coordinates and second first, second third and fourth vertex world coordinates; and four points corresponding to world coordinates of the second first, second third and second fourth vertexes are sequentially connected to obtain a corresponding second quadrangle;

Step B4, calculating the linear distance between each second target recognition frame and each third target recognition frame to generate a corresponding first distance d _i,j ；

Wherein i is the index of the second target recognition frame, j is the index of the third target recognition frame, i is more than or equal to 1 and less than or equal to N, j is more than or equal to 1 and less than or equal to M, N is the number of the second target recognition frames, and M is the number of the third target recognition frames; first distance d _i,j A straight line distance between world coordinates of a first center point of an ith second target identification frame and world coordinates of a second center point of a jth third target identification frame;

step B5, calculating the ground area intersection ratio of each second target recognition frame and each third target recognition frame to generate a corresponding first intersection ratio u _i,j ；

Wherein u is _i,j =(S _i ∩S _j )/(S _i ∪S _j )，

S _i For the ground area of the first quadrilateral of the ith second object identification frame,

S _j a ground area of a second quadrangle for the jth third target recognition frame,

(S _i ∩S _j ) For the intersection area of the first quadrangle of the ith second target identification frame and the second quadrangle of the jth third target identification frame on the ground,

(S _i ∪S _j ) A union area of the first quadrangle of the ith second target identification frame and the second quadrangle of the jth third target identification frame on the ground;

step B6, and corresponding the first distance d by each second target recognition frame _i,j And a first cross-over ratio u _i,j Composing the corresponding first associated featurea _i,j The method comprises the steps of carrying out a first treatment on the surface of the And from all the first associated features a obtained _i,j Form a first associated feature matrix A { a } with a matrix shape of N x M _i,j }；

Step B7, and based on the target association algorithm, the first association feature matrix A { a } _i,j Performing associated weight matrix calculation to generate a first associated weight matrix W { W } with a matrix shape of N×M _i,j -a }; and the first association weight matrix W { W _i,j M first associated weights w with the same index i in }, respectively _i,j Grouping into a group as corresponding first association weight group G _i ；

The target association algorithm comprises a Hungary algorithm and a KM algorithm; first associated weight matrix W { W _i,j The first association weight w of N multiplied by M _i,j The method comprises the steps of carrying out a first treatment on the surface of the First association weight group G _i Includes M first associated weights w with the same index i _i,j ；

Step B8, and for all first association weights G _i Traversing; and, during the traversal, the first association weight group G of the current traversal _i As the corresponding current association weight set, and taking the second target recognition frame corresponding to the current association weight set as the corresponding current target recognition frame, and taking the first association weight w with the largest weight in the current association weight set _i,j As the corresponding current maximum weight; identifying whether the current maximum weight is lower than a preset first weight threshold value; if the current maximum weight is not lower than the first weight threshold, taking the first tracking target identifier corresponding to the third target identification frame corresponding to the current maximum weight as the second tracking target identifier corresponding to the current target identification frame; and if the current maximum weight is lower than the first weight threshold, setting a second tracking target identifier corresponding to the current target identification frame as an empty identifier.

In another specific implementation manner of the embodiment of the present invention, the first inspection device 11 is specifically configured to, when performing the road disease object data extraction process according to each first tracking target sequence to generate corresponding first road disease object data, send the first road disease object data to the first processing platform 2:

step C1, calculating the time interval between the first tracking time stamp of the latest first tracking target array in the first tracking target sequence and the current time to generate a corresponding first time interval;

step C2, when the first time interval exceeds a preset time interval threshold, extracting first image arrays corresponding to each first target identification frame of the first tracking target sequence from the first cache queue, and sequencing the first image arrays according to time sequence to form a corresponding first image array sequence;

step C3, evaluating the definition of the first image of each first image array in the first image array sequence based on an image definition evaluation algorithm to generate a corresponding first evaluation score, taking the first image with the first evaluation score being the maximum score as a corresponding preferred image, taking the first image array corresponding to the preferred image as a corresponding preferred image array, and taking the first self-vehicle positioning coordinate of the preferred image array as a corresponding current self-vehicle positioning coordinate; the first tracking target array with the first tracking time stamp matched with the first time stamp of the preferable image array in the first tracking target sequence is used as a corresponding current tracking target array, the first tracking time stamp of the current tracking target array is used as a corresponding current time stamp, the first target recognition frame of the current tracking target array is used as a corresponding current target recognition frame, and the first target type of the current target recognition frame is used as a corresponding current target type;

The image definition evaluation algorithm comprises a Brenner gradient algorithm, a Tenegrad gradient algorithm, a Laplace gradient algorithm, a variance algorithm and an energy gradient algorithm;

step C4, taking the locally preset bus identification as a corresponding first vehicle identification;

step C5, taking the current timestamp as a corresponding first object timestamp;

step C6, performing coordinate conversion processing from a pixel coordinate system to a vehicle coordinate system and then to a world coordinate system on the first center point coordinate of the current target identification frame according to the preset camera internal and external parameters and the current vehicle positioning coordinate to generate a corresponding third center point world coordinate; the world coordinate of the third center point is used as a corresponding first object coordinate;

step C7, taking the current target type as a corresponding first object type;

step C8, estimating type characteristic parameters corresponding to the current object type according to the first object type, the current target identification frame and the preferred image to generate corresponding first type characteristic parameters;

here, in another specific implementation manner of the embodiment of the present invention, the first inspection device 11 is specifically configured to, when estimating, according to the first object type, the current target identification frame and the preferred image, the type feature parameter corresponding to the current object type to generate the corresponding first type feature parameter, the processing steps thereof consist of the following steps C8-1 to C8-5:

Step C8-1, performing coordinate conversion processing from a pixel coordinate system to a vehicle coordinate system and then to a world coordinate system according to the first, second, third and fourth vertex coordinates of the current target recognition frame by the preset camera internal and external parameters to generate corresponding third first, third second, third and fourth world coordinates;

step C8-2, identifying the first object type;

step C8-3, if the first object type is a pavement cracking type, sequentially connecting four points corresponding to third world coordinates, third world coordinates and third world coordinates to obtain a corresponding third quadrangle; estimating the area of the third quadrangle to obtain a corresponding first area; and taking the first area as a corresponding first type characteristic parameter;

c8-4, if the first object type is a pavement transverse crack type or a pavement longitudinal crack type, calculating linear distances of third world coordinates and third world coordinates to generate corresponding first transverse widths, calculating linear distances of third world coordinates and third world coordinates to generate corresponding second transverse widths, calculating linear distances of third world coordinates and third world coordinates to generate corresponding first longitudinal depths, and calculating linear distances of third world coordinates and third world coordinates to generate corresponding second longitudinal depths; taking the maximum value of the first transverse width and the second transverse width as the corresponding first crack transverse width, and taking the maximum value of the first depth and the second depth as the corresponding first crack depth; and a first type of characteristic parameters corresponding to the first crack transverse width and the first crack longitudinal depth are formed;

C8-5, if the first object type is a pavement pit type, a pavement pit type or a pavement collapse type, extracting an image area covered by the current target identification frame on the preferred image to serve as a corresponding first identification frame image; performing closed edge recognition processing on the pit, the hole or the collapse area on the first recognition frame image to generate a corresponding first closed edge; according to a monocular ranging algorithm, predicting the depth of each pixel point on the first closed edge according to preset camera internal and external parameters to generate corresponding first pixel depth; and forming corresponding first pixel depth vectors by all the obtained first pixel depths; predicting the coverage area of the first closed edge according to the first pixel depth vector to generate a corresponding second area; and using the obtained second area as a corresponding first type characteristic parameter;

step C9, on the preferred image, drawing the target recognition frame according to the first, second, third and fourth vertex coordinates of the current target recognition frame to obtain a corresponding first drawing recognition frame, drawing a text frame above the first drawing recognition frame to serve as a corresponding first drawing text frame, and filling the first drawing text frame with a first object type and a first type characteristic parameter; and taking the preferred image with the recognition frame drawing, the text frame drawing and the text frame filling as a corresponding first object image;

Step C10, the obtained first vehicle identifier, the first object timestamp, the first object coordinate, the first object image, the first object type and the first type characteristic parameter form corresponding first road disease object data to be sent to the first processing platform 2; and deleting the first tracking target sequence when the transmission is successful.

(II) first processing stage 2

The first processing platform 2 comprises a first server 21, a first bus route database 22, a first road disease database 23 and a first road maintenance database 24; the first server 21 is respectively connected with each first inspection device 11, and is also respectively connected with a first bus route database 22, a first road disease database 23 and a first road maintenance database 24;

wherein the first bus route database 22 includes a plurality of first bus route records; the first bus route record comprises a first bus identification set field and a first road segmentation information set field; the first bus identification set field comprises a plurality of first bus identifications; the first road segment information set field comprises a plurality of first road segment information; the first road segmentation information comprises a first road identifier, a first segmentation identifier and a first road segmentation coordinate range;

Here, the driving route of each first bus 1 in the embodiment of the present invention may span multiple roads, each road has a unique identifier, i.e. a first road sign, and each road is divided into multiple road segments from a start point to an end point, each road segment has a unique identifier, i.e. a first segment identifier, and each road segment corresponds to a coordinate range, i.e. a first road segment coordinate range; the coordinates of any point on the corresponding road segment can be calculated based on the first road segment coordinate range, wherein the first road segment coordinate range can be a coordinate set of all sampling points on a left side line, a right side line, a front segment line and a rear segment line of the corresponding road segment, can be a data set formed by a central line sampling point coordinate set of the corresponding road segment, the length and the width of the road, can be other reference data sets capable of calculating the coordinates of any point on the corresponding road segment, and the embodiment of the invention does not specifically limit the data content format of the coordinate range of the first road segment;

the first road damage database 23 includes a plurality of first road damage records; the first road fault record comprises a first road section identification field, a first road fault coordinate field, a first inspection time stamp field, a first inspection vehicle identification field, a first road fault image field, a first road fault type field, a first road fault characteristic parameter field and a first road fault maintenance state field; the first road defect type field is one of a plurality of preset road defect types, and the plurality of preset road defect types at least comprise a road crack type, a road transverse crack type, a road longitudinal crack type, a road pit type and a road collapse type; the first road disease maintenance status field comprises an unrepaired status, a notification maintenance status and a repaired status;

Here, the first road damage characteristic parameter field corresponds to the first road damage type field; when the first road defect type field is a road surface cracking type, the first road defect characteristic parameter field stores the road surface area of the cracked road surface; when the first road defect type field is a road transverse crack type or a road longitudinal crack type, storing the first road defect characteristic parameter field to be the transverse width and the longitudinal depth of the crack; when the first road defect type field is a road pit type, a road pit type or a road subsidence type, the first road defect characteristic parameter field stores the road surface area of the pit, the pit or the subsidence area;

the first road maintenance database 24 includes a plurality of first road maintenance records; the first road maintenance record comprises a second road section identification field and a first maintenance notification interface field;

here, stored in the first maintenance notification interface field is an interface address of a service processing interface.

The first server 21 is configured to query the first bus route database 22 for road segment identifiers according to the first road disease object data to obtain corresponding first road segment identifiers; and record addition is performed on the first road segment identification and the first road disease object data to the first road disease database 23.

In another specific implementation manner of the embodiment of the present invention, the first server 21 is specifically configured to extract, when a road section identifier of the first bus route database 22 is queried according to the first road disease object data to obtain a corresponding first road section identifier, a corresponding first vehicle identifier and a first object coordinate from the first road disease object data; and takes the first bus route record of which the first bus identifier set field in the first bus route database 22 meets the first bus identifier as the corresponding current bus route record; extracting a first road identifier and a first segment identifier of first road segment information, of which the first road segment coordinate range meets the first object coordinate, in a first road segment information set field of the current bus route record as corresponding current road identifiers and current segment identifiers; and the obtained current road identifier and the current segment identifier form a corresponding first road section identifier.

Here, the first vehicle identifier in the above processing step indicates that the first bus identifier set field satisfies the first vehicle identifier if the first vehicle identifier is within the identifier set of the first bus identifier set field; if the first object coordinate in the step is in the road segment area corresponding to the first road segment coordinate range, it is indicated that the first road segment coordinate range meets the first object coordinate.

In another specific implementation manner of the embodiment of the present invention, the first server 21 is specifically configured to, when performing record addition to the first road segment identifier and the first road segment object data on the first road segment database 23:

step D1, extracting a corresponding first vehicle identifier, a first object time stamp, a first object coordinate, a first object image, a first object type and a first type characteristic parameter from first road disease object data;

step D2, extracting first road defect records with first road segment identification fields matched with the first road segment identifications and first road defect maintenance status fields in an unrepaired state in the first road defect database 23 to form a corresponding first record set;

here, the first record set is a sum of all the first road fault records of incomplete repair (or maintenance) on the road segment corresponding to the first road segment identifier, and the road fault at the same position in the first record set may correspond to a plurality of first road fault records because of different timestamps;

step D3, setting a corresponding record newly-added switch to be in an on state when the first record set is empty; the record newly added switch comprises an opening state and a closing state;

Here, if the first record set is empty, it is indicated that at the current moment, the road segment corresponding to the first road segment identifier has no first road defect record that is not repaired yet in the first road defect database 23, and at this moment, the record addition should be performed on the first road defect database 23 according to the latest collected first road defect object data, so that the record addition switch is set to be in an on state;

step D4, when the first record set is not empty, calculating the linear distance between the first road disease coordinate field of each first road disease record in the first record set and the first object coordinate to generate a corresponding first distance; recording the first road disease record with the first distance smaller than a preset minimum distance threshold as a corresponding second road disease record to form a corresponding second record set; and identifying whether the second record set is empty; if yes, setting a corresponding record newly-added switch to be in an on state; if not, extracting a first patrol time stamp field with the latest time in the second record set as a corresponding latest patrol time stamp, identifying whether the latest patrol time stamp is earlier than the first object time stamp, if so, setting a corresponding record newly-added switch to be in an on state, and if not, setting the corresponding record newly-added switch to be in an off state;

Here, if the first record set is not empty, it is indicated that at the current moment, the road segment corresponding to the first road segment identifier has a first road defect record that is not repaired yet in the first road defect database 23; then further confirming whether the first road disease records matched with the corresponding position of the current first road disease object data exist in the first record set, wherein the second record set obtained in the step is a subset of the first road disease records matched with the corresponding position of the current first road disease object data in the first record set; if the second record set is empty, it is indicated that at the current moment, the first record set does not have the first road disease record matched with the corresponding position of the current first road disease object data, and at the moment, the first road disease database 23 is added according to the latest acquired first road disease object data, so that a record newly-added switch is set to be in an on state; if the second record set is not empty, the first road disease record matched with the corresponding position of the current first road disease object data exists in the first record set at the current moment, and then whether the latest acquisition time in the second record set is earlier than the acquisition time of the first road disease object data is further confirmed; if the latest collection time in the second record set is confirmed to be earlier than the collection time of the first road disease object data, the current first road disease object data is the latest collection data which should be added into the first road disease database 23, so that the record newly-added switch is set to be in an on state; if the latest collection time in the second record set is confirmed to be later than the collection time of the first road disease object data, the current first road disease object data is a historical data, and the current first road disease object data is discarded by default, so that a record newly-added switch is set to be in a closed state;

Step D5, when the record newly-added switch is in an on state, newly adding a first road disease record into the first road disease database 23 as a corresponding newly-added record; and setting the first road section identification field of the newly added record as a corresponding first road section identification, the first road disease coordinate field as a corresponding first object coordinate, the first inspection time stamp field as a corresponding first object time stamp, the first inspection vehicle identification field as a corresponding first vehicle identification, the first road disease image field as a corresponding first object image, the first road disease type field as a corresponding first object type, the first road disease characteristic parameter field as a corresponding first type characteristic parameter, and the first road disease maintenance state field as an unrepaired state.

The first server 21 is also configured to perform road maintenance task dispatching periodically according to the first road disease database 23 and the first road maintenance database 24.

In another specific implementation manner of the embodiment of the present invention, the first server 21 is specifically configured to, when regularly distributing the road maintenance task according to the first road disease database 23 and the first road maintenance database 24:

Step E1, periodically extracting first road disease records with the first road disease maintenance status field in the first road disease database 23 in an unrepaired status to form a corresponding third record set;

e2, when the third record set is not empty, taking the first road disease records with the same first road section identification field in the set into the same cluster subset and recording the first cluster subset as a corresponding first record subset; and in each first record subset, the first road disease records with the same first road disease type field are brought into the same cluster subset to be recorded as a corresponding second record subset; matching and clustering all first road disease records of each second record subset according to a preset shortest road disease coordinate interval K to obtain one or more corresponding third record subsets;

in the third record subset, the first road disease type fields of all the first road disease records are the same, each first road disease record is provided with at least one first road disease record matched with the first road disease record, and the linear distance between two coordinates corresponding to the first road disease coordinate fields of every two matched first road disease records does not exceed the shortest road disease coordinate distance K;

E3, extracting a first road disease coordinate field, a first inspection time stamp field, a first road disease image field, a first road disease type field and a first road disease characteristic parameter field of a first road disease record corresponding to the first inspection time stamp field with the nearest time in each third record subset to form corresponding first coordinate disease data; and forming corresponding first road section disease data by all first coordinate disease data corresponding to each first record subset; and taking the first road section identification field corresponding to each first record subset as a corresponding second road section identification; forming a corresponding first road section disease report by the second road section identifiers corresponding to the first record subsets and the first road section disease data;

step E4, inquiring the first road maintenance database 24 according to each second road section identifier, and extracting a first maintenance notification interface field of a first road maintenance record, in which a second road section identifier field in the first road maintenance database 24 is matched with the second road section identifier, as a corresponding first maintenance notification interface;

e5, sending each first road section disease report to a corresponding first maintenance notification interface; and when the sending of each first road section disease report is finished, recording a first record subset corresponding to the current first road section disease report as a current record subset, and updating the first road disease maintenance status fields of all the first road disease records matched with the current record subset in the first road disease database 23 into notification maintenance status.

The first server 21 is further configured to update the first road disease database 23 with a disease maintenance status according to the maintenance task feedback.

In another specific implementation manner of the embodiment of the present invention, the first server 21 is specifically configured to, when updating the disease maintenance status of the first road disease database 23 according to the maintenance task feedback:

step F1, extracting first maintenance notification interface fields of each first road maintenance record of the first road maintenance database 24 in advance to serve as corresponding second maintenance notification interfaces, and receiving maintenance task feedback information returned by each second maintenance notification interface;

f2, on any second maintenance notification interface, taking the current maintenance task feedback information as corresponding first feedback data after receiving one returned maintenance task feedback information;

the first feedback data comprises a first feedback road section identifier, a first feedback road disease coordinate and a first feedback road disease type;

step F3, extracting a corresponding first feedback road section identifier, a first feedback road disease coordinate and a first feedback road disease type from the first feedback data; the first road disease record in which the first road section identification field in the first road disease database 23 is matched with the first feedback road section identification, the linear distance between the first road disease coordinate field and the first feedback road disease coordinate is not more than the preset maintenance road disease coordinate distance L, the first road disease type field is matched with the first feedback road disease type, and the first road disease maintenance state field is not in the repaired state is recorded as a corresponding matching record; and updating the first road disease maintenance status fields of all the obtained matching records to a repaired status.

The first server 21 is also configured to perform trend analysis on road hazards of a specified road section according to the first road hazard database 23.

In another specific implementation manner of the embodiment of the present invention, the first server 21 is specifically configured to, when performing trend analysis on road diseases of a specified road segment according to the first road disease database 23:

step G1, taking the road section identifier of the current appointed road section as the corresponding current road section identifier;

step G2, extracting first road disease records in which the first road section identification field in the first road disease database 23 is matched with the current road section identification and the first road disease maintenance state field is not in the repaired state to form a corresponding fourth record set;

step G3, when the fourth record set is not empty, the first road disease records with the same first road disease type field in the set are brought into the same cluster subset and recorded as a corresponding fourth record subset; matching and clustering all the first road disease records of each fourth record subset according to a preset shortest road disease coordinate interval K to obtain one or more corresponding fifth record subsets;

in the fifth record subset, the first road disease type fields of all the first road disease records are the same, each first road disease record is provided with at least one first road disease record matched with the first road disease record, and the linear distance between two coordinates corresponding to the first road disease coordinate fields of every two matched first road disease records does not exceed the shortest road disease coordinate distance K;

Step G4, taking the first road disease type field corresponding to each fifth record subset as the corresponding first road disease type; the average value calculation is carried out on the first road disease coordinate fields of all the first road disease records of each fifth record subset, and the obtained average value coordinates are used as corresponding first road disease average value coordinates; extracting first road disease image fields and first road disease characteristic parameter fields of each first road disease record from each fifth record subset to serve as corresponding first road disease images and first road disease characteristic parameters, sequencing all first road disease images according to the time sequence of each corresponding first inspection time stamp field to obtain a corresponding first road disease image sequence, and sequencing all first road disease characteristic parameters according to the time sequence of each corresponding first inspection time stamp field to obtain a corresponding first road disease characteristic parameter sequence;

step G5, analyzing the disease development trend according to each first road disease type, the corresponding first road disease image sequence and the first road disease characteristic parameter sequence to generate a corresponding first analysis result;

Here, in another specific implementation manner of the embodiment of the present invention, the first server 21 is specifically configured to, when performing the disease development trend analysis according to each first road disease type and the corresponding first road disease image sequence and the first road disease characteristic parameter sequence to generate the corresponding first analysis result, process steps thereof include the following steps G5-1 to G5-5:

g5-1, identifying the type of the first road disease;

step G5-2, if the first road disease type is a road surface cracking type, taking the first road disease image sequence as a corresponding first road surface cracking image sequence, and marking each first road disease image in the first road disease image sequence as a corresponding first road surface cracking image; taking the first road disease characteristic parameter sequence as a corresponding first cracking area parameter sequence, and recording each first road disease characteristic parameter in the first road disease characteristic parameter sequence as a corresponding first cracking area parameter; according to a preset pavement cracking disease grade evaluation rule, corresponding disease grade evaluation is carried out according to a first pavement cracking image sequence and a first cracking area parameter sequence, and an evaluation result is used as a corresponding first analysis result;

When the corresponding disease level is evaluated according to the preset pavement crack disease level evaluation rule according to the first pavement crack image sequence and the first crack area parameter sequence, the increment rate of pavement crack grid density is predicted according to the first pavement crack image sequence, the transverse/longitudinal extension rate of pavement cracks is predicted according to the first crack area parameter sequence, and the disease level of the pavement crack disease at the current time interval is estimated based on the increment rate of pavement crack grid density and the transverse/longitudinal extension rate of pavement cracks to obtain the corresponding disease level as a corresponding first analysis result;

g5-3, if the first road defect type is a road surface transverse crack type or a road surface longitudinal crack type, taking the first road defect image sequence as a corresponding first road surface crack image sequence, and marking each first road defect image in the first road defect image sequence as a corresponding first road surface crack image; taking the first road damage characteristic parameter sequence as a corresponding first crack parameter sequence, and recording each first road damage characteristic parameter in the first road damage characteristic parameter sequence as a corresponding first crack parameter; according to a preset pavement transverse crack or pavement longitudinal crack disease grade evaluation rule, corresponding disease grade evaluation is carried out according to a first pavement crack image sequence and a first crack parameter sequence, and an evaluation result is used as a corresponding first analysis result;

The first crack parameter consists of two parameters of crack transverse width and crack longitudinal depth;

when the corresponding disease level is evaluated according to the preset pavement transverse crack or pavement longitudinal crack disease level evaluation rule and according to the first pavement crack image sequence and the first crack parameter sequence, predicting the branch increment rate of pavement cracks according to the first pavement crack image sequence, predicting the transverse/longitudinal extension rate of pavement cracks according to the first crack parameter sequence, and estimating the disease level of the current pavement transverse crack disease or longitudinal crack disease in the future period based on the branch increment rate of pavement cracks and the transverse/longitudinal extension rate of pavement cracks to obtain the corresponding disease level as a corresponding first analysis result;

g5-4, if the first road defect type is a road pit type or a road pit type, taking the first road defect image sequence as a corresponding first road pit image sequence, and marking each first road defect image in the first road defect image sequence as a corresponding first road pit image; taking the first road disease characteristic parameter sequence as a corresponding first groove hole area parameter sequence, and recording each first road disease characteristic parameter in the first road disease characteristic parameter sequence as a corresponding first groove hole area parameter; according to a preset pavement pit or pavement pit disease grade evaluation rule, corresponding disease grade evaluation is carried out according to a first pavement pit image sequence and a first pit area parameter sequence, and an evaluation result is used as a corresponding first analysis result;

When the corresponding disease level is evaluated according to the preset pavement pit and hole image sequence and the first pit and hole area parameter sequence according to the preset pavement pit and hole or pavement pit and hole disease level evaluation rule, predicting the growth rate of the non-smooth irregular edge points of the edges of the pavement pit and hole according to the first pavement pit and hole image sequence, predicting the area expansion rate of the pavement pit and hole according to the first pit and hole area parameter sequence, and estimating the current disease level of the pavement pit and hole disease or hole disease in the future period based on the growth rate of the irregular edge points of the edges of the pavement pit and the area expansion rate of the pavement pit and hole to obtain the corresponding disease level as a corresponding first analysis result;

g5-5, if the first road disease type is a pavement collapse type, taking the first road disease image sequence as a corresponding first pavement collapse image sequence, marking each first road disease image in the first road disease image sequence as a corresponding first pavement collapse image, and marking an image area covered by the target identification frame on each first pavement collapse image as a corresponding first collapse area image; the first road disease characteristic parameter sequence is used as a corresponding first collapse area parameter sequence, and each first road disease characteristic parameter in the first road disease characteristic parameter sequence is marked as a corresponding first collapse area parameter; predicting the pixel point type of each pixel point on each first collapse area image and the minimum vertical depth relative to the ground based on a preset image semantic segmentation model; selecting the maximum value from all the minimum vertical depths corresponding to the images of the first collapse areas as the corresponding depth of the first collapse areas; and arranging the obtained depths of all the first collapse areas according to the arrangement sequence of the corresponding first road surface collapse images to obtain a corresponding first collapse area depth sequence; according to a preset pavement collapse level evaluation rule, performing corresponding disease level evaluation according to a first pavement collapse image sequence, a first groove hole area parameter sequence and a first collapse area depth sequence, and taking an evaluation result as a corresponding first analysis result;

The image semantic segmentation model comprises a semantic segmentation model realized based on an FCN network, a semantic segmentation model realized based on a U-Net network, a semantic segmentation model realized based on a transducer model principle, a semantic segmentation model realized based on a BEVFomer model and the like; the pixel point type comprises a ground point and a collapse point; the minimum vertical depth corresponding to the pixel point with the pixel point type being the ground point is 0; the pixel point type is that the minimum vertical depth corresponding to the pixel point of the collapse point is larger than 0;

when the corresponding disease level is evaluated according to the preset pavement collapse level evaluation rule and the first pavement collapse image sequence, the first groove hole area parameter sequence and the first collapse area depth sequence, predicting the three-dimensional volume increase rate of pavement collapse according to the first groove hole area parameter sequence and the first collapse area depth sequence, predicting the increase rate of the number of traffic parameters around the collapsed pavement according to the first pavement groove hole image sequence, and estimating the disease level of the current pavement collapse disease in the future period based on the three-dimensional volume increase rate of pavement collapse and the increase rate of the number of traffic parameters to obtain the corresponding disease level as a corresponding first analysis result;

Step G6, forming corresponding first road disease analysis records by the first road disease type, the first road disease mean value coordinate, the first road disease image sequence and the first analysis result corresponding to each fifth record subset; and the obtained first road disease analysis records form corresponding designated road section disease trend analysis reports.

Those of skill would further appreciate that the steps of a system, module, unit, and algorithm described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the various illustrative components and steps have been described above generally in terms of function in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The steps of a system, module, unit, or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims

1. A processing system for road fault inspection using a bus, the system comprising: a plurality of first buses and a first processing platform;

2. The system for road fault inspection using a bus according to claim 1, wherein,

the first cache queue comprises a plurality of first image arrays, and the first image arrays are added in time sequence; the first image array comprises the first timestamp, the first vehicle positioning coordinate and the first image;

3. The processing system for road fault inspection using a bus as set forth in claim 2, wherein,

the first inspection device is specifically configured to, when a plurality of first tracking target sequences are obtained by performing target recognition, classification, and association tracking processing on road diseases according to the first cache queue, take the first image array newly added in the first cache queue as a corresponding current image array, and take the first timestamp, the first vehicle positioning coordinate, and the first image of the current image array as a corresponding current timestamp, a current positioning coordinate, and a current image;

if the current image array is confirmed not to be the first image array in the first cache queue, taking the first timestamp of the current image array as the corresponding first tracking timestamp; and taking the first image array which is the previous image array of the current image array in the first cache queue as a corresponding previous image array; marking each first target identification frame corresponding to the current image array as a corresponding second target identification frame, and marking each corresponding first target identification frame of the previous image array as a corresponding third target identification frame; identifying whether the number of the third target identification frames is not 0, if the number of the third target identification frames is not 0, identifying the target identifications corresponding to the third target identification frames which are associated and matched with the second target identification frames based on a target association algorithm to generate corresponding second tracking target identifications, and if the number of the third target identification frames is 0, setting the second tracking target identifications corresponding to all the second target identification frames as empty identifications; traversing all the second tracking target identifiers; the second tracking target mark in the current traversal is used as a corresponding current tracking target mark, and the second target recognition frame corresponding to the current tracking target mark is used as a corresponding current target recognition frame; identifying whether the current tracking target identifier is an empty identifier or not; if the current tracking target identifier is not a null identifier, taking the first tracking target sequence corresponding to the current tracking target identifier as a corresponding current tracking target sequence, and adding the first tracking target array which is formed by the current tracking target identifier, the first tracking timestamp and the current target identification frame into the current tracking target sequence; if the current tracking target identifier is a null identifier, a unique target identifier is allocated to the current target identification frame as a new current tracking target identifier, a null first tracking target sequence is initialized for the current tracking target identifier as a corresponding current tracking target sequence, and the current tracking target identifier, the first tracking timestamp and the current target identification frame form a corresponding first tracking target array to be added to the current tracking target sequence.

4. The processing system for road fault inspection by using a bus as set forth in claim 3, wherein,

the first inspection device is specifically configured to, when the target identifier corresponding to the third target identification frame that is associated and matched with each second target identification frame is identified based on the target association algorithm to generate a corresponding second tracking target identifier, use the first vehicle positioning coordinates of the first image array corresponding to each second and third target identification frames as corresponding second and third vehicle positioning coordinates;

And based on a target correlation algorithm, the first correlation characteristic matrix A { a }, is obtained _i,j Performing associated weight matrix calculation to generate a first associated weight matrix W { W } with a matrix shape of N×M _i,j -a }; and the first association weight matrix W { W _i,j M first associated weights w with the same index i in }, respectively _i,j Grouping into a group as corresponding first association weight group G _i The method comprises the steps of carrying out a first treatment on the surface of the The target association algorithm comprises a Hungary algorithm and a KM algorithm; the first association weight matrix W { W _i,j Comprises N x M first associated weights w _i,j The method comprises the steps of carrying out a first treatment on the surface of the The first association weight group G _i Comprising M first associated weights w with the same index i _i,j ；

And for all the first association weight sets G _i Traversing; and, during the traversal, the first association weight group G of the current traversal _i As a corresponding current association weight set, taking the second target recognition frame corresponding to the current association weight set as a corresponding current target recognition frame, and taking the first association weight w with the largest weight in the current association weight set _i,j As the corresponding current maximum weight; and is combined withIdentifying whether the current maximum weight is lower than a preset first weight threshold value; if the current maximum weight is not lower than the first weight threshold, the first tracking target identifier corresponding to the third target identification frame corresponding to the current maximum weight is used as the second tracking target identifier corresponding to the current target identification frame; and if the current maximum weight is lower than the first weight threshold, setting the second tracking target identifier corresponding to the current target identification frame as an empty identifier.

5. The processing system for road fault inspection using a bus as set forth in claim 2, wherein,

the first inspection device is specifically configured to calculate, when the first road disease object data generated by extracting and processing the road disease object data according to each first tracking target sequence is sent to the first processing platform, a time interval between the first tracking timestamp of the latest first tracking target array in the first tracking target sequence and a current time to generate a corresponding first time interval;

and taking the current timestamp as the corresponding first object timestamp;

and taking the current target type as the corresponding first object type;

6. The system for road fault inspection using a bus as set forth in claim 5, wherein,

the first inspection device is specifically configured to perform coordinate conversion processing from a pixel coordinate system to a vehicle coordinate system and then to a world coordinate system according to the first, second, third and fourth vertex coordinates of the current target recognition frame and the preset camera internal and external parameters when the type feature parameters corresponding to the current object type are estimated according to the first object type, the current target recognition frame and the preferred image to generate corresponding first type feature parameters;

identifying the first object type;

7. The processing system for road fault inspection using a bus as set forth in claim 2, wherein,

the first server is specifically configured to extract, when the road section identifier query is performed on the first bus route database according to the first road disease object data to obtain a corresponding first road section identifier, the corresponding first vehicle identifier and the first object coordinate from the first road disease object data; the first bus route record of which the first bus identifier set field meets the first vehicle identifier in the first bus route database is used as a corresponding current bus route record; extracting the first road identifier and the first segment identifier of the first road segment information, of which the first road segment coordinate range meets the first object coordinates, in the first road segment information set field of the current bus route record as corresponding current road identifiers and current segment identifiers; and the obtained current road identifier and the current segment identifier form the corresponding first road section identifier.

8. The processing system for road fault inspection using a bus as set forth in claim 2, wherein,

The first server is specifically configured to extract, when the first road segment identifier and the first road disease object data are recorded and added to the first road disease database, the corresponding first vehicle identifier, the first object timestamp, the first object coordinate, the first object image, the first object type and the first type characteristic parameter from the first road disease object data;

9. The processing system for road fault inspection using a bus as set forth in claim 2, wherein,

the first server is specifically configured to extract, periodically, the first road disease record in which the first road disease maintenance status field is in an unrepaired status in the first road disease database to form a corresponding third record set when the road maintenance task is dispatched according to the first road disease database and the first road maintenance database;

10. The processing system for road fault inspection using a bus as set forth in claim 2, wherein,

the first server is specifically configured to extract, in advance, the first maintenance notification interface fields of each first road maintenance record of the first road maintenance database as corresponding second maintenance notification interfaces when the disease maintenance state of the first road disease database is updated according to maintenance task feedback, and receive maintenance task feedback information returned by each second maintenance notification interface; and on any one of the second maintenance notification interfaces, taking the current maintenance task feedback information as corresponding first feedback data after receiving one returned maintenance task feedback information; extracting a corresponding first feedback road section identifier, a first feedback road disease coordinate and a first feedback road disease type from the first feedback data; the first road segment identification field in the first road disease database is matched with the first feedback road segment identification, the linear distance between the first road disease coordinate field and the first feedback road disease coordinate is not more than the preset maintenance road disease coordinate distance L, the first road disease type field is matched with the first feedback road disease type, and the first road disease record with the first road disease maintenance state field not being the repaired state is recorded as a corresponding matching record; updating all the obtained first road disease maintenance status fields of the matching records to a repaired status; the first feedback data includes the first feedback road segment identifier, the first feedback road disease coordinate, and the first feedback road disease type.

11. The processing system for road fault inspection using a bus as set forth in claim 2, wherein,

the first server is specifically configured to use a road section identifier of a current specified road section as a corresponding current road section identifier when trend analysis is performed on the road diseases of the specified road section according to the first road disease database;

12. The system for road fault inspection using a bus as set forth in claim 11, wherein,

the first server is specifically configured to identify the first road disease type when the disease development trend analysis is performed according to each first road disease type, the corresponding first road disease image sequence, and the first road disease characteristic parameter sequence to generate a corresponding first analysis result;