CN111105135A - Intelligent city sweeper operation monitoring method and device - Google Patents
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Abstract
The embodiment of the invention provides an intelligent city sweeper operation monitoring method and device, wherein the method comprises the following steps: judging whether the sweeper is in a designated working area or not based on the sweeper position information acquired by the GPS module; if the sweeper is not in the designated working area, reminding a driver of driving the sweeper to enter the designated working area; and after the sweeper enters the designated working area, acquiring images of main working parts, and detecting the working state of the main working parts based on the images of the main working parts. Therefore, the problem that whether the operation of the sweeper is carried out according to the regulations is difficult to achieve due to the fact that the urban sweeper is wide in operation area and large in area in the prior art is solved.
Description
Technical Field
The invention relates to the technical field of intelligent operation monitoring, in particular to an operation monitoring method and device for an intelligent urban sweeper.
Background
Currently, with the rapid development of cities, the tasks of urban road sanitation are increasingly heavy, wherein the road sweeper plays a particularly prominent role in urban road sanitation. However, in actual use and operation, the situations that the sanitation operation vehicle does not run on a specified route, the scheduling is delayed, and the operation is not performed according to the specification occur, various losses are often caused to a vehicle attribution unit or a management department, and various emergency measures taken in the past can only be repaired afterwards, so that urgent requirements of real-time monitoring and timely management are difficult to meet.
Sanitation operation vehicles (including cleaning cars, watering lorries, garbage transport vehicles and the like) have clear regulations and operation standards on the outgoing operation route, the operation time and the operation times, and due to the fact that the operation area is wide, the area is large, whether the vehicles operate according to the regulations or not is very difficult to supervise, and real-time scheduling cannot be achieved.
Therefore, how to monitor whether the urban sweeper is on a specified road at a specified time is still a problem to be solved urgently by the technical personnel in the field.
Disclosure of Invention
The embodiment of the invention provides an intelligent method and a device for monitoring operation of an urban sweeper, which are used for solving the problem that whether the urban sweeper is operated on a specified road at a specified time cannot be monitored in the prior art.
In a first aspect, an embodiment of the present invention provides an intelligent city sweeper operation monitoring method, including:
judging whether the sweeper is in a designated working area or not based on the sweeper position information acquired by the GPS module;
if the sweeper is not in the designated working area, reminding a driver of driving the sweeper to enter the designated working area;
and after the sweeper enters the designated working area, acquiring images of main working parts, and detecting the working state of the main working parts based on the images of the main working parts.
Preferably, the detecting the working state of the main working component based on the image of the main working component further comprises:
if the main working component is detected to be in a non-working state, acquiring a road surface image, and detecting whether the road of the sweeper is provided with garbage or not based on the road surface image;
and if the garbage on the road is detected, sending an alarm that the garbage exists on the road.
Preferably, the detecting the working state of the main working component based on the image of the main working component specifically includes:
detecting the image of the main working component through an FCOS target detection algorithm, and determining the working state of the main working component;
the detecting of whether the road where the sweeper is located has rubbish based on the road surface image specifically includes:
and detecting the road surface image through an FCOS target detection algorithm, and judging whether the road where the sweeper is located has garbage or not.
Preferably, the detecting the main working component image through the FCOS target detection algorithm specifically includes:
when the FCOS target detection algorithm is executed, integer operation is adopted to replace floating point operation;
the detecting the road surface image through the FCOS target detection algorithm specifically comprises:
integer arithmetic is adopted to replace floating point arithmetic when the FCOS target detection algorithm is executed.
Preferably, the main working component comprises at least one of a sweeper brush, a sprinkler head and a dust suction device;
correspondingly, the detecting the working state of the main working component based on the image of the main working component specifically includes:
the image detection that corresponds based on the brush is whether the work arm of brush is the state of stretching out or shrink, based on the image detection that the watering shower nozzle corresponds whether the watering shower nozzle goes out water and sprays or not goes out water and spray to and based on the image detection that dust extraction corresponds dust extraction is stretching out the dust absorption or is in at least one of hanging the position.
Preferably, the reminding driver to drive the sweeper truck to enter the designated work area specifically includes: reminding a driver of driving a sweeper to enter the designated working area through voice;
the sending of the warning that the road has the garbage specifically comprises the following steps:
the alarm that the road has the rubbish is sent out through voice, and the rubbish position is displayed on a display screen of the vehicle-mounted equipment.
In a second aspect, an embodiment of the present invention provides an intelligent monitoring device for operation of a city sweeper, including:
the judging unit is used for judging whether the sweeper is in a designated working area or not based on the sweeper position information acquired by the GPS module;
the reminding unit is used for reminding a driver of driving the sweeper to enter a designated working area if the sweeper is not in the designated working area;
and the component work detection unit is used for acquiring images of main working components after the sweeper enters the designated working area and detecting the working state of the main working components based on the images of the main working components.
Preferably, the intelligent city sweeper operation monitoring device further comprises:
the garbage detection unit is used for acquiring a road surface image if the main working component is detected to be in a non-working state, and detecting whether garbage exists on the road where the sweeper is located based on the road surface image;
and the warning unit is used for sending a warning that the road has the garbage if the garbage on the road is detected.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and is characterized in that the processor implements the steps of the intelligent city sweeper operation monitoring method provided in the first aspect when executing the program.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer readable storage medium, on which a computer program is stored, wherein the computer program is configured to, when executed by a processor, implement the steps of the intelligent urban sweeper operation monitoring method as provided in the first aspect.
According to the intelligent city sweeper operation monitoring method and device provided by the embodiment of the invention, whether the current sweeper is in the designated working area is judged through the acquired position information of the sweeper, the sweeper can be monitored to be in the designated position at the designated moment in real time, the working state of main working components is monitored after the sweeper is in the designated working area, and whether the sweeper is in operation can be monitored in real time. . Therefore, the problem of high supervision difficulty caused by wide operation area and large area can be solved, and the real-time scheduling of the sweeper is realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief description will be given below of the drawings required for the embodiments or the technical solutions in the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for monitoring operation of an intelligent city sweeper truck according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of another intelligent monitoring method for operation of a city sweeper according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an operation monitoring device of an intelligent city sweeper according to an embodiment of the present invention;
fig. 4 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
The sanitation operation vehicle has clear regulations and operation standards on the outgoing operation route, the operation time and the operation times, and because the operation area is wide and the area is large, the vehicle can operate according to the regulations, the supervision difficulty is very high, and the real-time scheduling cannot be realized. Therefore, the embodiment of the invention provides an intelligent urban sweeper operation monitoring method. Fig. 1 is a schematic flow chart of an operation monitoring method for an intelligent city sweeper truck according to an embodiment of the present invention, and as shown in fig. 1, the method includes:
and step 110, judging whether the sweeper is in the designated working area or not based on the sweeper position information acquired by the GPS module.
Specifically, a GPS module positioned on the sweeper collects real-time position information of the sweeper and uploads the real-time position information to a acquired monitoring server, and the background monitoring server judges whether the sweeper is positioned on a specified road or not according to the stored all-day driving route of the sweeper and the received position information of the sweeper.
And 120, if the sweeper is not in the designated working area, reminding a driver of driving the sweeper to enter the designated working area.
Specifically, if the background monitoring server determines that the sweeper is not located in the designated work area, the background monitoring server sends an instruction for reminding a driver of driving the sweeper to enter the designated road to the sweeper so as to remind the driver of driving the sweeper to enter the designated work area.
And step 130, collecting images of main working components after the sweeper enters the designated working area, and detecting the working state of the main working components based on the images of the main working components.
Specifically, after the sweeper enters a designated working area, the vehicle-mounted camera acquires images of main working components, such as a sweeping brush, a sprinkler head or a dust collector, which are not specifically limited herein, and judges whether the main working components are in a working state based on the images of the main working components, and there are many image detection algorithms, such as an FCOS image detection algorithm, a Fast-SCNN image detection algorithm, etc., for judging whether the main working components are in the working state or in a non-working state through image detection.
The method provided by the embodiment of the invention is characterized in that whether the sweeper is in the designated working area or not is judged based on the sweeper position information acquired by the GPS module, if the sweeper is not in the designated working area, a driver is reminded to drive the sweeper to enter the designated working area, after the sweeper enters the designated working area, images of main working components are acquired, and the working state of the main working components is detected based on the images of the main working components. Therefore, whether the urban sweeper is on the specified road at the specified time can be monitored, and whether the main working component is in the working state is detected after the sweeper enters the specified road, so that the working condition of the urban sweeper is monitored.
Based on the above embodiment, in this method, the detecting the operating state of the main operating component based on the image of the main operating component further includes:
if the main working component is detected to be in a non-working state, acquiring a road surface image, and detecting whether the road of the sweeper is provided with garbage or not based on the road surface image;
and if the garbage on the road is detected, sending an alarm that the garbage exists on the road.
Specifically, after detecting that the main working component is in a non-working state, the vehicle-mounted camera acquires a road image, detects whether garbage exists on a road where the sweeper is located based on the road image, and there are many algorithms for judging whether garbage exists on the road image by using an image detection method, such as an FCOS image detection algorithm, a Fast-SCNN image detection algorithm, and the like, which are not limited specifically herein. If the garbage on the road is detected, the position of the garbage is usually highlighted through a vehicle-mounted display screen, for example, the garbage is framed by a colored rectangular frame, or an image is used for marking around the garbage, and a warning of the garbage, such as a warning in a voice form or a flashing warning on the display screen, is issued, which is not particularly limited herein.
Fig. 2 is a schematic flow chart of another intelligent city sweeper operation monitoring method according to an embodiment of the present invention. As shown in fig. 2, firstly, whether the sweeper is in the designated working area is determined based on the position information of the sweeper collected by the GPS module, when the sweeper is not in the designated working area, a driver is prompted to enter the designated working area, then, whether the main working component is in the working state is detected, if the main working component is detected to be in the non-working state, whether garbage exists on a road ahead is detected, and if garbage exists, an alarm that garbage exists on the road is given.
According to the method provided by the embodiment of the invention, whether the garbage exists on the road is detected when the main working components of the sweeper are in the non-working state, and the existence of the garbage is reminded to a driver when the garbage on the road is detected. Therefore, the cleaning component can be started when the road detects the garbage, the cleaning component on the sweeper is prevented from idling all the time under the condition that the road garbage does not exist, and the energy consumption is reduced.
Based on any one of the embodiments, in the method, the detecting the working state of the main working component based on the image of the main working component specifically includes:
detecting the image of the main working component through an FCOS target detection algorithm, and determining the working state of the main working component;
the detecting of whether the road where the sweeper is located has rubbish based on the road surface image specifically includes:
and detecting the road surface image through an FCOS target detection algorithm, and judging whether the road where the sweeper is located has garbage or not.
Specifically, the main working part image is detected and the road image is detected through a full volumetric One-Stage object detection algorithm (FCOS), the problem of object detection is solved in a mode of predicting each pixel, and compared with an object detection network depending on a predefined candidate frame, the FCOS completely avoids complex calculation related to the candidate frame by eliminating the dependence on the predefined candidate frame, reduces the calculation amount and improves the real-time property of detection.
The overall framework of the FCOS target detection algorithm includes: the backbone network is a FPN (feature pyramid) feature pyramid and a three-branch head detection network.
After the FPN characteristic pyramid is proposed, the FPN characteristic pyramid is widely used, the body shadow of the FPN characteristic pyramid can be seen in a plurality of fields, such as semantic segmentation, fine-grained classification and the like, the main idea of the FPN characteristic pyramid is to combine the shallow characteristic and the deep characteristic of a network, then simultaneously output targets with different sizes in a plurality of branches, and fully use the shallow characteristic and the deep characteristic of the network; the shallow features focus on some detailed information and are suitable for positioning, and the deep features focus on semantic information and are suitable for classification and the like.
The FCOS target detection algorithm is implemented as follows:
(1) preprocessing an input picture;
(2) building a network architecture shown in the figure, sending input data into a feature map of the input data obtained in a backbone network, performing regression operation on each point of the feature map, and performing network training to obtain a network model;
(3) applying the pre-trained network model to a test picture, and obtaining a predicted result from a plurality of heads (heads) of the feature pyramid;
(4) the final result is obtained using post-processing operations such as NMS (non-maxima suppression) etc.
(5) The FCOS (full relational One stage) target detection network is detailed as follows:
let Fi∈RH×W×CWherein F isiThe representative feature map is R is a real number set, and H, W, C is the height, width, and number of channels of the image. I-th layer feature map of backbone CNN (convolutional neural network), s is the total line spacing before the layer, and GTbox (true value box) of input image is defined as { B }iIn which Bi＝(x0 (i),y0 (i),x1 (i),y1 (i),c(i)∈R4X {1,2, …, C }), wherein (x)0 (i),y0 (i)) And (x)1 (i),y1 (i)) Coordinates representing the upper left and lower right corners of the bounding box. c. C(i)Is the class to which the object in the bounding box belongs, and C is the number of classes.
For feature map FiAt each position (x, y) in (a), we can map it back to the coordinates of the input image
Where s is the total step size before the layer, (x)s，ys) Representing the center of the field coordinates, which is located near the center of the field at location (x, y). Unlike anchor-based detectors that treat locations on the input image as the center of anchor boxes and regress the target bounding boxes of those anchor boxes, FCOS regresses the target bounding boxes of each location directly, i.e., the detector treats the coordinates directly as training samples rather than treating the anchor boxes as training samples, as is the case with full convolution networks for semantic segmentation.
Specifically, if a location (x, y) on the feature map falls inside any of the truth boxes, it is considered a positive sample, and the class label c for that location*Is exactly BiClass label of (2). Otherwise it is a negative sample and c*0 (class background). In addition to the labels used for classification, FCOS has a 4D real vector t*＝(l*,t*,r*,b*) The vector is the regression target for each sample, where l*、t*、r*、b*Is the distance from the position (x, y) to the four sides of the detection frame mapped to the prediction stage on the original image. If a location belongs to multiple bounding boxes, it is considered as a blurred sample. We now select only the bounding box with the smallest area as its regression target (simplest strategy). The network can significantly reduce the number of fuzzy samples through multi-stage prediction. Formally, if the position (x, y) is within the bounding box BiAssociated, the training regression target for that location may be represented as:
FCOS can train the regressor with as many foreground samples as possible, each pixel point in the true-valued box is a positive sample, unlike anchor-based detectors, which only take the anchor-based box as a positive sample with sufficient cross-over ratio to the true-valued box, which is one of the reasons that FCOS outperforms anchor-based networks.
In the network output part, corresponding to the training target, the last layer of the FCOS network predicts a vector p for classification and a detection frame coordinate 4D, where the vector t is (l, t, r, b), where l, t, r, b refer to distances of positions on the feature map output by the last layer of the network, which are mapped to four edges of the detection frame in the prediction stage on the original image. Following the R-CNN (candidate area based convolutional neural network) approach, FCOS trains not multiple classifiers, but multiple binary classifiers. Similar to R-CNN, four convolutional layers are added for classification and regression branches, respectively, after the feature map of the backbone network. Furthermore, since the regression target is always positive, we use exp (x) (exponential operation) to map any real number to (0, ∞) at the top of the regression branch. Notably, the network output variable of FCOS is 9 times smaller than that of a common anchor-based probe, with 9 anchor boxes per location. The loss function is as follows:
in the formula, LclsIs Focal local (a classical Loss function), LregIs IOU loss (cross-over ratio loss function), NposRepresents the number of positive samples, and λ is here 1.
Based on any of the above embodiments, in the method, the detecting the main working component image through the FCOS target detection algorithm specifically includes:
when the FCOS target detection algorithm is executed, integer operation is adopted to replace floating point operation;
the detecting the road surface image through the FCOS target detection algorithm specifically comprises:
integer arithmetic is adopted to replace floating point arithmetic when the FCOS target detection algorithm is executed.
Specifically, integer arithmetic is adopted to replace floating point arithmetic for the FCOS target detection algorithm, namely, inference calculation based on float type is changed into inference calculation based on int8 quantization for the FCOS target detection algorithm, and due to the fact that the computing capability of the vehicle-mounted embedded platform is limited, the speed of processing float type data is relatively slow, and after the inference calculation based on int8 quantization is changed, the forward inference speed for each frame image is improved.
According to any one of the above embodiments, in the method, the main working component comprises at least one of a sweeping brush, a sprinkler head and a dust suction device;
correspondingly, the detecting the working state of the main working component based on the image of the main working component specifically includes:
the image detection that corresponds based on the brush is whether the work arm of brush is the state of stretching out or shrink, based on the image detection that the watering shower nozzle corresponds whether the watering shower nozzle goes out water and sprays or not goes out water and spray to and based on the image detection that dust extraction corresponds dust extraction is stretching out the dust absorption or is in at least one of hanging the position.
In particular, sweeping vehicles are commonly provided with a sweeping brush, a sprinkling nozzle and a dust suction device, so that at least one of the sweeping vehicles is taken as a main working component of the sweeping vehicle. Whether the sweeper is in the working state or not is judged by the image detection algorithm, whether the working arm of the sweeper is in the extending state or the contracting state in the collected image of the sweeper is judged, whether the sprinkler is in the working state or not is judged by the image detection algorithm, whether the dust collector is in the extending dust collection state or in the hanging position in the collected image of the dust collector is judged by the image detection algorithm, and the two-classification judgment of the working state of the working part is realized by the image detection algorithm.
Based on any one of the above embodiments, in the method, the reminding a driver of driving the sweeper truck to enter the designated work area specifically includes: reminding a driver of driving a sweeper to enter the designated working area through voice;
the sending of the warning that the road has the garbage specifically comprises the following steps:
the alarm that the road has the rubbish is sent out through voice, and the rubbish position is displayed on a display screen of the vehicle-mounted equipment.
Specifically, after the sweeper receives an instruction of a background monitoring server for reminding a driver of driving the sweeper to enter a designated working area, the sweeper directly sends the instruction of entering the designated working area to the driver in a voice broadcasting mode; the warning that the road is garbage is sent by the sweeper in a voice playing mode, the driver is also sent the warning that the road is garbage in front, and the position of the garbage is highlighted on a display screen of the vehicle-mounted device, for example, the garbage is marked by a colored rectangular frame or a geometric image is displayed beside the garbage to show a reminder, and how to display the garbage position is not particularly limited here.
Based on any of the above embodiments, fig. 3 is a schematic structural diagram of an intelligent monitoring device for operation of a city sweeper provided by an embodiment of the present invention. As shown in fig. 3, the intelligent city sweeper operation monitoring device comprises a judging unit 310, a reminding unit 320 and a part work detecting unit 330, wherein,
the judging unit 310 is configured to judge whether the sweeper is in a designated working area based on the sweeper position information acquired by the GPS module;
the reminding unit 320 is configured to remind a driver of driving the sweeper to enter a designated work area if the sweeper is not in the designated work area;
the component work detection unit 330 is configured to collect images of main working components after the sweeper enters the designated working area, and detect the working state of the main working components based on the images of the main working components.
The device provided by the embodiment of the invention judges whether the sweeper is in the designated working area or not based on the sweeper position information acquired by the GPS module, if not, the device reminds a driver to drive the sweeper to enter the designated working area, and after the sweeper enters the designated working area, the device acquires the image of the main working part and detects the working state of the main working part based on the image of the main working part. Therefore, whether the urban sweeper is on the specified road at the specified time can be monitored, and whether the main working component is in the working state is detected after the sweeper enters the specified road, so that the working condition of the urban sweeper is monitored.
Based on any one of the above embodiments, the intelligent city sweeper operation monitoring device further comprises:
the garbage detection unit is used for acquiring a road surface image if the main working component is detected to be in a non-working state, and detecting whether garbage exists on the road where the sweeper is located based on the road surface image;
and the warning unit is used for sending a warning that the road has the garbage if the garbage on the road is detected.
Based on any one of the above embodiments, in the intelligent city cleaning operation monitoring apparatus, the detecting a working state of the main working component based on the image of the main working component specifically includes:
detecting the image of the main working component through an FCOS target detection algorithm, and determining the working state of the main working component;
the detecting of whether the road where the sweeper is located has rubbish based on the road surface image specifically includes:
and detecting the road surface image through an FCOS target detection algorithm, and judging whether the road where the sweeper is located has garbage or not.
Based on any one of the embodiments, in the intelligent city cleaning operation monitoring device, the detecting the main working component image by using the FCOS target detection algorithm specifically includes:
when the FCOS target detection algorithm is executed, integer operation is adopted to replace floating point operation;
the detecting the road surface image through the FCOS target detection algorithm specifically comprises:
integer arithmetic is adopted to replace floating point arithmetic when the FCOS target detection algorithm is executed.
Based on any one of the above embodiments, in the intelligent city cleaning operation monitoring device, the main working component includes at least one of a sweeper, a sprinkler head and a dust collector;
correspondingly, the detecting the working state of the main working component based on the image of the main working component specifically includes:
the image detection that corresponds based on the brush is whether the work arm of brush is the state of stretching out or shrink, based on the image detection that the watering shower nozzle corresponds whether the watering shower nozzle goes out water and sprays or not goes out water and spray to and based on the image detection that dust extraction corresponds dust extraction is stretching out the dust absorption or is in at least one of hanging the position.
Based on any one of the above embodiments, in the intelligent city cleaning operation monitoring device, the reminding driver of driving the sweeper truck to enter the designated work area specifically includes: reminding a driver to drive the sweeper to enter the designated working area through voice reminding;
the sending of the warning that the road has the garbage specifically comprises the following steps:
the alarm that the road has the rubbish is sent out through voice, and the rubbish position is displayed on a display screen of the vehicle-mounted equipment.
Fig. 4 is a schematic entity structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 4, the electronic device may include: a processor (processor)401, a communication Interface (communication Interface)402, a memory (memory)403 and a communication bus 404, wherein the processor 401, the communication Interface 402 and the memory 403 complete communication with each other through the communication bus 404. The processor 401 may invoke a computer program stored on the memory 403 and executable on the processor 401 to perform the intelligent city sweeper operation monitoring methods provided by the various embodiments described above, including, for example: judging whether the sweeper is in a designated working area or not based on the sweeper position information acquired by the GPS module; if the sweeper is not in the designated working area, reminding a driver of driving the sweeper to enter the designated working area; and after the sweeper enters the designated working area, acquiring images of main working parts, and detecting the working state of the main working parts based on the images of the main working parts.
In addition, the logic instructions in the memory 403 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or make a contribution to the prior art, or may be implemented in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Embodiments of the present invention further provide a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to execute the method for monitoring operations of an intelligent urban sweeper, which includes: judging whether the sweeper is in a designated working area or not based on the sweeper position information acquired by the GPS module; if the sweeper is not in the designated working area, reminding a driver of driving the sweeper to enter the designated working area; and after the sweeper enters the designated working area, acquiring images of main working parts, and detecting the working state of the main working parts based on the images of the main working parts.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An intelligent city sweeper operation monitoring method is characterized by comprising the following steps:
judging whether the sweeper is in a designated working area or not based on the sweeper position information acquired by the GPS module;
if the sweeper is not in the designated working area, reminding a driver of driving the sweeper to enter the designated working area;
and after the sweeper enters the designated working area, acquiring images of main working parts, and detecting the working state of the main working parts based on the images of the main working parts.
2. The intelligent urban sweeper operation monitoring method according to claim 1, wherein the detecting the operating state of a primary working element based on the primary working element image further comprises:
if the main working component is detected to be in a non-working state, acquiring a road surface image, and detecting whether the road of the sweeper is provided with garbage or not based on the road surface image;
and if the garbage on the road is detected, sending an alarm that the garbage exists on the road.
3. The intelligent city sweeper operation monitoring method of claim 2,
the detecting the working state of the main working component based on the image of the main working component specifically includes:
detecting the image of the main working component through an FCOS target detection algorithm, and determining the working state of the main working component;
the detecting of whether the road where the sweeper is located has rubbish based on the road surface image specifically includes:
and detecting the road surface image through an FCOS target detection algorithm, and judging whether the road where the sweeper is located has garbage or not.
4. The intelligent city sweeper operation monitoring method of claim 3,
the detecting the main working component image through the FCOS target detection algorithm specifically includes:
when the FCOS target detection algorithm is executed, integer operation is adopted to replace floating point operation;
the detecting the road surface image through the FCOS target detection algorithm specifically comprises:
integer arithmetic is adopted to replace floating point arithmetic when the FCOS target detection algorithm is executed.
5. The intelligent urban sweeper operation monitoring method according to claim 1, wherein the main working component comprises at least one of a sweeper, a sprinkler head and a dust extraction device;
correspondingly, the detecting the working state of the main working component based on the image of the main working component specifically includes:
the image detection that corresponds based on the brush is whether the work arm of brush is the state of stretching out or shrink, based on the image detection that the watering shower nozzle corresponds whether the watering shower nozzle goes out water and sprays or not goes out water and spray to and based on the image detection that dust extraction corresponds dust extraction is stretching out the dust absorption or is in at least one of hanging the position.
6. The intelligent city sweeper operation monitoring method according to claim 2, wherein the reminding of the driver to drive the sweeper into the designated work area specifically comprises:
reminding a driver of driving a sweeper to enter the designated working area through voice;
the sending of the warning that the road has the garbage specifically comprises the following steps:
the alarm that the road has the rubbish is sent out through voice, and the rubbish position is displayed on a display screen of the vehicle-mounted equipment.
7. An intelligent city motor sweeper operation monitoring arrangement, its characterized in that includes:
the judging unit is used for judging whether the sweeper is in a designated working area or not based on the sweeper position information acquired by the GPS module;
the reminding unit is used for reminding a driver of driving the sweeper to enter a designated working area if the sweeper is not in the designated working area;
and the component work detection unit is used for acquiring images of main working components after the sweeper enters the designated working area and detecting the working state of the main working components based on the images of the main working components.
8. The intelligent urban sweeper operation monitoring device of claim 7, further comprising:
the garbage detection unit is used for acquiring a road surface image if the main working component is detected to be in a non-working state, and detecting whether garbage exists on the road where the sweeper is located based on the road surface image;
and the warning unit is used for sending a warning that the road has the garbage if the garbage on the road is detected.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the program, implements the steps of the intelligent city sweeper operation monitoring method as claimed in any one of claims 1 to 6.
10. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the steps of the intelligent urban sweeper operation monitoring method of any one of claims 1 to 6.
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