CN113610008A

CN113610008A - Method, device, equipment and storage medium for acquiring state of slag car

Info

Publication number: CN113610008A
Application number: CN202110912126.6A
Authority: CN
Inventors: 代旭; 杜雨亭; 孙孟尧
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2021-08-10
Filing date: 2021-08-10
Publication date: 2021-11-05
Anticipated expiration: 2041-08-10
Also published as: CN113610008B

Abstract

The disclosure provides a method, a device, equipment and a storage medium for acquiring a state of a muck truck, and relates to the technical field of artificial intelligence, in particular to the technical field of cloud computing and deep learning. The method comprises the following steps: acquiring target information, wherein the target information comprises state information of at least one muck truck; calculating the aggregation degree of the muck trucks on the basis of the state information, and obtaining first result information on the basis of the aggregation degree; clustering the first result information to obtain second result information; and matching the second result information with pre-stored record information to obtain third result information. The method for acquiring the state of the muck truck can be used for managing and controlling the muck truck in a targeted manner from the source, and the supervision efficiency is improved.

Description

Method, device, equipment and storage medium for acquiring state of slag car

Technical Field

The disclosure relates to the technical field of artificial intelligence, in particular to the technical field of cloud computing and deep learning, and particularly relates to a method, a device, equipment and a storage medium for acquiring a state of a muck vehicle.

Background

With the rapid development of national economy, the urbanization construction is also carried out in a tight gong and dense drum, and the muck truck for carrying building materials and building garbage plays a very important role in the middle. However, as the muck trucks are not managed in place in the construction site, the muck trucks often have the problems of overload, running at irregular time/on irregular road sections and the like, which undoubtedly brings hidden troubles to the life safety of people. Therefore, the transportation state of the muck truck is inevitably controlled reasonably by applying advanced science and technology.

In the prior art, the muck truck is generally managed based on manual inspection or in a video monitoring and capturing mode through a road gate.

Disclosure of Invention

The disclosure provides a method, a device, equipment and a storage medium for acquiring the state of a muck truck.

According to a first aspect of the present disclosure, there is provided a method of obtaining a state of a muck truck, comprising: acquiring target information, wherein the target information comprises state information of at least one muck truck; calculating the aggregation degree of the muck trucks on the basis of the state information, and obtaining first result information on the basis of the aggregation degree; clustering the first result information to obtain second result information; and matching the second result information with pre-stored record information to obtain third result information.

According to a second aspect of the present disclosure, there is provided an apparatus for acquiring a state of a muck truck, comprising: an acquisition module configured to acquire target information, wherein the target information includes status information of at least one muck truck; a calculation module configured to calculate an aggregation degree of the muck trucks based on the state information, and obtain first result information based on the aggregation degree; the clustering module is configured to cluster the first result information to obtain second result information; and the matching module is configured to match the second result information with pre-stored filing information to obtain third result information.

According to a third aspect of the present disclosure, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described in any one of the implementations of the first aspect.

According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method as described in any one of the implementations of the first aspect.

According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method as described in any of the implementations of the first aspect.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is an exemplary system architecture diagram in which the present disclosure may be applied;

FIG. 2 is a flow chart of one embodiment of a method of obtaining a condition of a muck truck according to the present disclosure;

FIG. 3 is a flow chart of another embodiment of a method of obtaining a condition of a muck truck according to the present disclosure;

FIG. 4 is a flow chart of yet another embodiment of a method of obtaining a condition of a muck truck according to the present disclosure;

FIG. 5 is a schematic structural diagram of one embodiment of an apparatus for obtaining a condition of a muck truck according to the present disclosure;

fig. 6 is a block diagram of an electronic device for implementing a method of acquiring a state of a muck truck according to an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Fig. 1 illustrates an exemplary system architecture 100 to which embodiments of the presently disclosed method of obtaining a muck vehicle status or apparatus for obtaining a muck vehicle status may be applied.

As shown in fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

A user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or transmit information or the like. Various client applications may be installed on the

terminal devices

101, 102, 103.

The

terminal apparatuses

101, 102, and 103 may be hardware or software. When the

terminal devices

101, 102, 103 are hardware, they may be various electronic devices including, but not limited to, smart phones, tablet computers, laptop portable computers, desktop computers, and the like. When the

terminal apparatuses

101, 102, 103 are software, they can be installed in the above-described electronic apparatuses. It may be implemented as multiple pieces of software or software modules, or as a single piece of software or software module. And is not particularly limited herein.

The server 105 may provide various services. For example, the server 105 may analyze and process the target information acquired from the

terminal apparatuses

101, 102, 103, and generate a processing result (e.g., third result information).

The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server 105 is software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.

It should be noted that the method for acquiring the state of the muck truck provided by the embodiment of the present disclosure is generally performed by the server 105, and accordingly, the device for acquiring the state of the muck truck is generally disposed in the server 105.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to fig. 2, a flow 200 of one embodiment of a method of obtaining a condition of a muck truck in accordance with the present disclosure is shown. The method for acquiring the state of the muck truck comprises the following steps:

step 201, target information is obtained, wherein the target information includes state information of at least one muck truck.

In this embodiment, an executing entity (e.g., the server 105 shown in fig. 1) of the method of obtaining a state of a muck truck may obtain target information, wherein the target information includes state information of at least one muck truck.

Here, a GPS (Global Positioning System) terminal device may be previously installed in each of the vehicles, and the terminal device may transmit status information of the vehicle to the execution main body so that the execution main body receives and stores the status information of the vehicle. As an example, the GPS terminal device may transmit the state information of the muck truck to the execution main body every 5 seconds, wherein the state information of the muck truck may include, but is not limited to: license plate information of the muck car, longitude and latitude coordinate information of the muck car, speed information of the muck car, car hopper lifting state information of the muck car and the like.

After the execution main body obtains the state information of each muck truck, the execution main body can process the information in all the state information so as to obtain target information, wherein the target information is the information required for judging the state of the muck truck.

And 202, calculating the aggregation degree of the muck trucks based on the state information, and obtaining first result information based on the aggregation degree.

In this embodiment, the execution body may calculate the aggregation degree of the muck trucks based on the state information acquired in step 201, and then obtain the first result information based on the aggregation degree, where the first result information is preliminary result information obtained by determining the state of the muck trucks based on the aggregation degree. Since the target information obtained in step 201 includes longitude and latitude coordinate information of each slag car, the aggregation degree of the slag cars may be calculated based on the state information of the slag cars, for example, the target information is mapped into a K-dimensional space, and the sum of the distance between each point and its surrounding points is calculated, where the sum of the distances is the aggregation degree of the slag cars, and the smaller the sum of the distances is, the more aggregated the slag cars are, the more likely the slag cars are to be at the construction site where the unearthing work is performed.

After calculating the degree of aggregation of the muck trucks, the execution body may obtain first result information based on the degree of aggregation. For example, the calculated distance sums may be sorted, and a point of top N may be selected as the first result information based on the sorting result, where N is a positive integer.

And step 203, clustering the first result information to obtain second result information.

In this embodiment, the execution subject may perform clustering on the first result information, so as to obtain the second result information. Since the first result information is preliminary result information, which may include many points, in this step, the points in the first result information are clustered to form a plurality of clusters, each cluster representing an aggregation point, the plurality of clusters representing aggregation points in the first result information, each aggregation point representing a construction site where an unearthing operation is possible.

And then, acquiring a coordinate point in each cluster of the plurality of clusters formed by clustering to obtain all coordinate points corresponding to the plurality of clusters, wherein all coordinate points are used as second result information, the second result information is obtained on the basis of the first result information and is further result information obtained on the basis of the preliminary result information, and each coordinate point is a geographic position coordinate point where the muck truck appears.

And 204, matching the second result information with pre-stored record information to obtain third result information.

In this embodiment, the executing entity may match the second result information with pre-stored filing information to obtain third result information. Since the construction site needs to be filed before construction, the filed information of the construction site may be stored in advance, so that after the second result information is obtained, the second result information is matched with the filed information, thereby determining whether the muck vehicle in the second result information is present in the filed site registered or the filed site not filed, and obtaining the third result information.

The method for acquiring the state of the muck truck comprises the steps of firstly acquiring target information, wherein the target information comprises state information of at least one muck truck; then, calculating the aggregation degree of the muck trucks based on the state information, and obtaining first result information based on the aggregation degree; clustering the first result information to obtain second result information; and finally, matching the second result information with pre-stored record information to obtain third result information. According to the method for acquiring the state of the muck truck, the first result information indicating the gathering degree of the muck truck can be acquired through the state information of the muck truck, the second result information after clustering is further acquired, the third result information indicating the state of the muck truck is further acquired, the accuracy and the efficiency of the acquired third result information are improved, the state of the muck truck can be acquired more automatically and efficiently, and a construction site where the muck truck possibly goes out of the earth can be acquired based on the third result information, so that the muck truck can be controlled at the source, and the supervision efficiency and pertinence are improved; in addition, the method can optimize the case sale process of the law enforcement cases, and can ensure the quality of the filed cases and be more automatic and efficient in practical application scenes such as smart city management and the like.

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.

With continued reference to fig. 3, fig. 3 illustrates a flow 300 of another embodiment of a method of obtaining a condition of a muck truck in accordance with the present disclosure. The method for acquiring the state of the muck truck comprises the following steps:

step 301, target information is obtained, wherein the target information comprises state information of at least one muck truck.

Step 301 is substantially the same as step 201 in the foregoing embodiment, and the specific implementation manner may refer to the foregoing description of step 201, which is not described herein again.

In some optional implementations of this embodiment, the status information includes longitude and latitude coordinate information of the muck truck, that is, the status information includes real-time location information of the muck truck, and the execution main body may obtain the real-time location information of the muck truck.

And 302, constructing a K-D tree by utilizing longitude and latitude coordinate information, calculating the sum of the distance between each coordinate point in the K-D tree and the surrounding coordinate points, and recording the sum of the distances as the aggregation degree of the muck trucks.

In this embodiment, an executing agent (e.g., server 105 shown in fig. 1) of the method of obtaining a state of a muck truck may construct a K-D tree using the longitude and latitude coordinate information obtained in step 301.

It should be noted that, in computer science, a K-D Tree (K-D Tree) is a Tree data structure that stores instance points in a K-dimensional space for quick retrieval. The K-D tree may be used in a variety of applications, such as multi-dimensional key value searching (e.g., range searching and nearest neighbor searching).

It will be appreciated that there are many ways to select the axis-perpendicular splitting plane, and therefore there are many ways to create a K-D tree. The most typical method is as follows: 1) axes are selected in turn as splitting planes with the depth of the tree. For example: in the three-dimensional space, the root node is an x-axis vertical dividing plane, the child nodes are y-axis vertical dividing planes, the grandchild nodes are z-axis vertical dividing planes, the great grandchild nodes are x-axis vertical dividing planes, and so on. 2) The points are divided by the median of the axis coordinates of the vertical dividing plane and put into the subtree. The present disclosure does not set any limit on the specific method of constructing the K-D tree.

After constructing the K-D tree using the target information, the execution body may calculate a sum of distances between each coordinate point in the K-D tree and m surrounding coordinate points, where m is a positive integer. The calculated distance sum is used as an index of the degree of accumulation of the muck trucks, and the smaller the distance sum is, the more the muck trucks are accumulated, the more likely the muck truck is to be at the construction site where the unearthing work is being performed.

In step 303, the points in the K-D tree are sorted based on the distance sums.

In this embodiment, the execution subject may sort the points in the K-D tree based on the sum of the distances calculated in step 302, so as to more intuitively and clearly obtain the aggregation degree of the earth carts. For example, all the points in the K-D tree may be sorted according to distance and from small to large, and certainly, all the points in the K-D tree may also be sorted according to distance and from large to small, which is not limited in this embodiment.

And step 304, obtaining first result information based on the sorting result.

In this embodiment, the execution subject may obtain the first result information based on the sorting result. For example, a point of top N in the sorting result may be selected as the first result information, where N is a positive integer, and a value of N may be specifically set according to an actual situation. Since the distance sum represents the degree of aggregation of the earth moving vehicles, the point of the distance sum maximum top N is selected as the first result information, so that the first result information includes the point most likely to be the site on which the unearthing work is being performed.

Step 305, clustering the first result information by using a clustering algorithm to obtain a first preset number of clusters.

In this embodiment, the executing entity may cluster the first result information by using a clustering algorithm, so as to obtain a first preset number of clusters. Since the first result information may include many or even ten thousand points, in this embodiment, a clustering algorithm is used to cluster the points in the first result information to form y clusters with a first preset number, where y is a positive integer. Each cluster may contain many points and each cluster represents a worksite.

And step 306, for each cluster in the first preset number of clusters, obtaining a second preset number of coordinate points through screening.

In this embodiment, for each cluster in the first preset number of clusters, the execution main body may obtain a second preset number of coordinate points by screening the cluster. For example, c coordinate points can be obtained by randomly screening each cluster, where c is a positive integer; or c coordinate points closest to the center position in each cluster can be selected and used as the representative coordinate points of each cluster.

And 307, taking all coordinate points corresponding to the first preset number of clusters as second result information.

In this embodiment, the execution body may use all coordinate points corresponding to the first preset number of clusters as the second result information. Since each cluster obtains c coordinate points, y clusters obtain y × c coordinate points in total, and the y × c coordinate points are used as the second result information. The second result information includes the most likely site coordinate points where the muck truck has appeared.

And 308, matching the second result information with pre-stored record information to obtain third result information.

As can be seen from fig. 3, compared with the embodiment corresponding to fig. 2, the method for acquiring the state of the muck truck in the embodiment highlights the process of obtaining the first result information and the second result information, so that the obtained third result information is more accurate, and the muck truck can be managed and controlled at the source in a more targeted manner.

With continued reference to fig. 4, fig. 4 illustrates a flow 400 of yet another embodiment of a method of obtaining a condition of a muck truck in accordance with the present disclosure. The method for acquiring the state of the muck truck comprises the following steps:

step 401, initial information is obtained.

In this embodiment, the executing entity of the method of obtaining the status of the earth moving vehicles (e.g., the server 105 shown in fig. 1) may obtain initial information, where the initial information refers to the status information of all the earth moving vehicles transmitted by the GPS terminal device on each of the at least one earth moving vehicle.

And 402, screening the initial information to obtain first information in a preset time period.

In this embodiment, the execution main body may filter the initial information to obtain the first information within a preset time period. Since the initial information obtained in step 401 includes the state information of each muck truck in all time periods, but when the state is determined, the information in all time periods may not be needed, and only the information in the latest time period is needed, in this step, the historical information in the latest x days may be obtained by screening, where x is a positive integer, for example, the historical data in the latest 30 days is screened, and of course, x may also be adjusted according to actual situations, which is not specifically limited by this disclosure.

Step 403, preprocessing the first information to obtain target information meeting preset conditions.

In this embodiment, the executing entity may perform preprocessing on the first information obtained in step 402, so as to obtain target information meeting a preset condition. Since the speed of the muck truck is 0 and the bucket state of the muck truck is raised when the muck truck is operating, in this embodiment, only the information that the speed of the muck truck is 0 and the bucket state of the muck truck is raised in the first information needs to be retained, so as to obtain the target information.

In some optional implementations of this embodiment, the method further includes: in the first information, if it is detected that the same muck vehicle has a plurality of pieces of identical data continuously, only the first piece of information in the plurality of pieces of identical data is retained. Because the GPS terminal equipment on the muck truck can send the state information of the current muck truck at preset time intervals, the first information may contain a plurality of same records of the same muck truck, and under the condition, only the first record of a plurality of same data is reserved, so that the validity of the data is improved.

And step 404, constructing a K-D tree by utilizing the longitude and latitude coordinate information, calculating the sum of the distance between each coordinate point in the K-D tree and the surrounding coordinate points, and recording the sum of the distances as the aggregation degree of the muck trucks.

In step 405, the points in the K-D tree are sorted based on the distance sums.

And 406, obtaining first result information based on the sorting result.

Step 407, clustering the first result information by using a clustering algorithm to obtain a first preset number of clusters.

And step 408, for each cluster in the first preset number of clusters, obtaining a second preset number of coordinate points through screening.

And 409, taking all coordinate points corresponding to the first preset number of clusters as second result information.

And step 410, matching the second result information with pre-stored record information to obtain third result information.

The

steps

404 and 410 are substantially the same as the

steps

302 and 308 of the foregoing embodiment, and the specific implementation manner can refer to the foregoing description of the

steps

302 and 308, which is not described herein again.

Step 411, generating a status reminding message based on the third result message, and sending the status reminding message.

In this embodiment, the execution main body may generate the status reminding information based on the third result information, wherein the status reminding information includes the status information of the muck truck, and send the status reminding information to the relevant department, so that the relevant department can perform targeted management and control. For example, in response to the fact that the muck vehicle in the third result information appears in a construction site which is not registered on the case and is performing unearthed work, corresponding state reminding information can be generated and sent, so that penalty judgment bases are provided for traffic management departments, and further the complexity and the management cost of management and control are reduced.

As can be seen from fig. 4, compared with the embodiment corresponding to fig. 3, the method for acquiring the state of the muck vehicle in the embodiment highlights the process of acquiring the target information, so that the acquired target information is more targeted; in addition, the state reminding information is generated based on the third result information, so that relevant departments can manage and control the muck truck based on the state reminding information in a targeted manner, the management and control complexity and the management cost are reduced, and the supervision efficiency and the supervision pertinence are improved.

With further reference to fig. 5, as an implementation of the method shown in the above figures, the present disclosure provides an embodiment of an apparatus for obtaining a state of a muck truck, which corresponds to the embodiment of the method shown in fig. 2, and which may be applied in various electronic devices.

As shown in fig. 5, the apparatus 500 for acquiring the state of the muck truck of the present embodiment includes: an acquisition module 501, a calculation module 502, a clustering module 503, and a matching module 504. The obtaining module 501 is configured to obtain target information, where the target information includes state information of at least one muck truck; a calculation module 502 configured to calculate an aggregation level of the muck trucks based on the status information, resulting in first result information based on the aggregation level; a clustering module 503 configured to cluster the first result information to obtain second result information; and the matching module 504 is configured to match the second result information with pre-stored filing information to obtain third result information.

In the present embodiment, in the device 500 for acquiring the state of the muck truck: the specific processing and the technical effects of the obtaining module 501, the calculating module 502, the clustering module 503 and the matching module 504 can refer to the related descriptions of step 201 and step 204 in the corresponding embodiment of fig. 2, which are not described herein again.

In some optional implementations of this embodiment, the status information includes longitude and latitude coordinate information of the dregs car; and the calculation module comprises: the calculation submodule is configured to construct a K-D tree by utilizing longitude and latitude coordinate information, calculate the sum of the distance between each coordinate point in the K-D tree and the surrounding coordinate points and record the sum of the distances as the aggregation degree of the muck trucks; a sorting submodule configured to sort points in the K-D tree based on the distance sums; a get submodule configured to get first result information based on the sorting result.

In some optional implementations of this embodiment, the clustering module includes: a clustering submodule configured to cluster the first result information using a clustering algorithm to obtain a first preset number of clusters; the first screening submodule is configured to obtain a second preset number of coordinate points through screening for each cluster in the first preset number of clusters; and the sub-module is configured to take all coordinate points corresponding to the first preset number of clusters as the second result information.

In some optional implementations of this embodiment, the obtaining module includes: an acquisition submodule configured to acquire initial information; the second screening submodule is configured to screen the initial information to obtain first information in a preset time period; and the preprocessing submodule is configured to preprocess the first information to obtain target information meeting preset conditions.

In some optional implementation manners of this embodiment, the obtaining module further includes: and the detection submodule is configured to only keep the first piece of data in the plurality of pieces of identical data if the same muck vehicle is detected to have the plurality of pieces of identical data continuously in the first information.

In some optional implementation manners of this embodiment, the device for acquiring a state of the muck truck further includes: and the generating module is configured to generate the state reminding information based on the third result information and send the state reminding information.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

FIG. 6 illustrates a schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 6, the apparatus 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The calculation unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The computing unit 601 performs the various methods and processes described above, such as a method of acquiring a state of a muck truck. For example, in some embodiments, the method of obtaining a state of a muck truck may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the above described method of obtaining a muck vehicle status may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the method of obtaining the state of the muck truck by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A method of obtaining a condition of a muck truck, comprising:

acquiring target information, wherein the target information comprises state information of at least one muck truck;

calculating the aggregation degree of the muck car based on the state information, and obtaining first result information based on the aggregation degree;

clustering the first result information to obtain second result information;

and matching the second result information with pre-stored record information to obtain third result information.

2. The method of claim 1, wherein the status information includes longitude and latitude coordinate information of the slag car; and

the calculating the aggregation degree of the muck car based on the state information and obtaining first result information based on the aggregation degree comprise:

constructing a K-D tree by utilizing the longitude and latitude coordinate information, calculating the sum of the distance between each coordinate point in the K-D tree and the surrounding coordinate points, and recording the sum of the distances as the aggregation degree of the muck truck;

sorting points in the K-D tree based on the distance sums;

first result information is obtained based on the sorting result.

3. The method of any of claims 1-2, wherein the clustering the first result information to obtain second result information comprises:

clustering the first result information by using a clustering algorithm to obtain a first preset number of clusters;

for each cluster in the first preset number of clusters, obtaining a second preset number of coordinate points through screening;

and taking all coordinate points corresponding to the first cluster with the preset number as the second result information.

4. The method of any of claims 1-3, wherein the obtaining target information comprises:

acquiring initial information;

screening the initial information to obtain first information in a preset time period;

and preprocessing the first information to obtain target information meeting preset conditions.

5. The method of claim 4, wherein the obtaining target information further comprises:

in the first information, if a plurality of pieces of same data are detected continuously in the same muck vehicle, only the first piece of data in the plurality of pieces of same data is reserved.

6. The method of any of claims 1-5, further comprising:

and generating state reminding information based on the third result information, and sending the state reminding information.

7. An apparatus for obtaining a condition of a muck truck, comprising:

an acquisition module configured to acquire target information, wherein the target information includes status information of at least one muck truck;

a calculation module configured to calculate an aggregation level of the muck car based on the status information, to obtain first result information based on the aggregation level;

the clustering module is configured to cluster the first result information to obtain second result information;

and the matching module is configured to match the second result information with pre-stored filing information to obtain third result information.

8. The apparatus of claim 7, wherein the status information includes longitude and latitude coordinate information of the slag car; and

the calculation module comprises:

the calculation sub-module is configured to construct a K-D tree by utilizing the longitude and latitude coordinate information, calculate the sum of the distance between each coordinate point in the K-D tree and the surrounding coordinate points, and record the sum of the distances as the aggregation degree of the muck truck;

a sorting submodule configured to sort points in the K-D tree based on the distance sums;

a get submodule configured to get first result information based on the sorting result.

9. The apparatus of any of claims 7-8, wherein the clustering module comprises:

a clustering submodule configured to cluster the first result information using a clustering algorithm to obtain a first preset number of clusters;

a first screening submodule configured to obtain, for each of the first preset number of clusters, a second preset number of coordinate points through screening;

and the sub-module is configured to take all coordinate points corresponding to the first preset number of clusters as the second result information.

10. The apparatus of any of claims 7-9, wherein the means for obtaining comprises:

an acquisition submodule configured to acquire initial information;

the second screening submodule is configured to screen the initial information to obtain first information in a preset time period;

and the preprocessing submodule is configured to preprocess the first information to obtain target information meeting preset conditions.

11. The apparatus of claim 10, wherein the means for obtaining further comprises:

the detection submodule is configured to, in the first information, only retain a first piece of data in a plurality of pieces of identical data if the same muck vehicle is detected to have the identical data continuously.

12. The apparatus of any of claims 7-11, further comprising:

a generating module configured to generate state reminding information based on the third result information and send the state reminding information.

13. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.

15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.