CN110910529A

CN110910529A - Object state detection method and device and storage medium

Info

Publication number: CN110910529A
Application number: CN201911081898.9A
Authority: CN
Inventors: 侯琛
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2019-11-07
Filing date: 2019-11-07
Publication date: 2020-03-24
Anticipated expiration: 2039-11-07
Also published as: CN110910529B

Abstract

The application relates to an object state detection method, an object state detection device and a storage medium, wherein the method comprises the following steps: acquiring a target object set which meets a preset condition; creating a combined information recording file; grouping every two target objects in the current target object set based on the information storage condition in the current combined information recording file, and determining current grouping information; updating the current combination information recording file based on the current grouping information; acquiring a detection result of mutual detection between each group of target objects, removing the target objects detected to be in a fault state from the current target object set based on the detection result, and updating the current target object set; repeatedly executing the steps until the target objects in the current target object set can not be eliminated; based on the current set of target objects, a fault-free target object is determined. The method provided by the application can reduce the detection times, and the detection result is easy to distinguish.

Description

Object state detection method and device and storage medium

Technical Field

The present application relates to the field of communications technologies, and in particular, to a method and an apparatus for detecting an object state, and a storage medium.

Background

The object state detection refers to a process of detecting the working state of the whole object or other parts in operation and determining whether the operation state is normal or not; the object state detection aims to take targeted measures to control, prevent the occurrence of the fault or maintain the object with the fault according to the abnormal condition before the fault occurs in the object or fault information when the fault occurs so as to enable the object to recover the normal operation state.

At present, in some application scenarios of object detection, for example, in a car networking system, in detecting whether a vehicle is normally operated, due to reasons such as large scale of the vehicle, there are problems of low detection efficiency, easy confusion of detection results, many detection times, and possibly exponential explosion, and therefore, it is necessary to provide an object state detection method that is efficient and easy to distinguish the detection results.

Disclosure of Invention

The technical problem to be solved by the present application is to provide an object state detection method, device and storage medium, which can reduce the detection times, avoid the detection times from appearing exponential explosion, and avoid confusion of mutual detection results among a plurality of objects due to the adoption of a method for mutual detection between two objects, which is efficient and easy to distinguish the detection results.

In order to solve the above technical problem, in one aspect, the present application provides an object state detection method, including:

acquiring a target object set which meets a preset condition, wherein the target object set comprises a fault target object and a non-fault target object;

creating a combined information recording file, wherein the combined information recording file is used for storing grouping information for grouping the target objects pairwise;

grouping every two target objects in the current target object set based on the information storage condition in the current combined information recording file, and determining current grouping information;

updating the current combination information recording file based on the current grouping information;

based on data interaction between two target objects in each group, obtaining a detection result of mutual detection between the target objects in each group, based on the detection result, removing the target objects detected to be in a fault state from the current target object set, and updating the current target object set;

repeatedly executing the steps of grouping every two target objects in the current target object set, determining current grouping information, updating a current combination information recording file, obtaining a detection result of mutual detection between each group of target objects, eliminating the target objects detected to be in a fault state from the current target object set, and updating the current target object set until the target objects in the current target object set can not be eliminated any more;

based on the current set of target objects, a fault-free target object is determined.

In another aspect, the present application provides an object state detection apparatus, including:

the target object set acquisition module is used for acquiring a target object set which meets preset conditions, wherein the target object set comprises a fault target object and a fault-free target object;

the file creating module is used for creating a combined information recording file, and the combined information recording file is used for storing grouping information for grouping the target objects pairwise;

the grouping module is used for grouping every two target objects in the current target object set based on the information storage condition in the current combined information recording file to determine current grouping information;

the first updating module is used for updating the current combination information recording file based on the current grouping information;

the second updating module is used for acquiring a detection result of mutual detection between each group of target objects based on data interaction between each group of two target objects, eliminating the target objects detected to be in a fault state from the current target object set based on the detection result, and updating the current target object set;

the first repeated execution module is used for repeatedly executing the steps of grouping every two target objects in the current target object set, determining current grouping information, updating a current combination information recording file, obtaining a detection result of mutual detection between each group of target objects, eliminating the target objects detected to be in a fault state from the current target object set, and updating the current target object set until the target objects in the current target object set can not be eliminated;

the first determining module is used for determining a fault-free target object based on the current target object set.

In another aspect, the present application provides a computer storage medium having at least one instruction, at least one program, a set of codes, or a set of instructions stored therein, which is loaded by a processor and executes the object state detection method as described above.

In another aspect, an apparatus comprises a processor and a memory, wherein at least one instruction, at least one program, a set of codes, or a set of instructions is stored in the memory, and the at least one instruction, the at least one program, the set of codes, or the set of instructions is loaded and executed by the processor to implement the object state detection method as described above.

The embodiment of the application has the following beneficial effects:

the method comprises the steps of grouping every two target objects in a current target object set by obtaining a target object set meeting preset conditions, and obtaining a detection result of mutual detection between each group of target objects based on data interaction between each group of two target objects; based on the detection result, eliminating the target object detected to be in a fault state from the current target object set, and updating the current target object set; then repeating the steps until the target objects in the current target object set can not be eliminated; and finally, all the target objects left in the current target object set are fault-free target objects. The method for mutually detecting the objects to be detected has the advantages that the objects to be detected are grouped in pairs, the mutual detection is carried out between the two objects at each time, the detection times can be reduced, the detection times are prevented from being exponentially exploded, the method for mutually detecting the two objects is efficient, the detection result is easy to distinguish, and the confusion of the mutual detection results among a plurality of objects is avoided.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application;

fig. 2 is a flowchart of an object state detection method according to an embodiment of the present application;

fig. 3 is a flowchart of a method for acquiring a target object set meeting a preset condition according to an embodiment of the present application;

fig. 4 is a flowchart of a method for pairwise grouping of target objects according to an embodiment of the present disclosure;

FIG. 5 is a flowchart of a method for updating a combined information record file according to an embodiment of the present application;

fig. 6 is a flowchart of a detection result obtaining method according to an embodiment of the present application;

fig. 7 is a flowchart of a target object processing method provided in an embodiment of the present application;

FIG. 8 is a flowchart of another target object processing method provided in an embodiment of the present application;

FIG. 9 is a schematic diagram of a collision result output provided by an embodiment of the present application;

fig. 10 is a schematic diagram of an object state detection apparatus according to an embodiment of the present application;

fig. 11 is a schematic structural diagram of an apparatus according to an embodiment of the present application.

Detailed Description

To make the objects, technical solutions and advantages of the present application more clear, the present application will be further described in detail with reference to the accompanying drawings. It is to be understood that the described embodiments are merely a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Referring to fig. 1, a schematic view of an application scenario provided in an embodiment of the present application is shown, where the scenario at least includes: the terminal comprises a first terminal 110, a second terminal 120 and a third terminal 130, wherein the first terminal 110, the second terminal 120 and the third terminal 130 can all perform data communication with each other.

In this embodiment of the application, based on data interaction between the first terminal 110 and the second terminal 120, mutual detection may be performed between the first terminal 110 and the second terminal 120 (for example, state mutual detection may be performed between two vehicles in a car networking shown in fig. 1), that is, the first terminal 110 may detect a state of the second terminal 120, and the second terminal 120 may detect a state of the first terminal 110. Specifically, the first terminal 110 or the second terminal 120 may include terminal devices of smart phones, desktop computers, tablet computers, notebook computers, digital assistants, Augmented Reality (AR)/Virtual Reality (VR) devices, smart wearable devices, vehicle-mounted terminal devices, and the like.

In this embodiment, both the first terminal 110 and the second terminal 120 may send the state detection result of the opposite side to the third terminal 130, and the third terminal 130 may perform processing according to the mutual detection result, so as to determine corresponding object state information. Specifically, the third terminal 130 may include a processor and a memory, the memory being used for storing the received data information, and the processor being used for executing instructions of data processing; the third terminal 130 may include a server operating independently, a distributed server, or a server cluster composed of a plurality of servers.

In order to solve the problems of excessive detection times, low detection efficiency and easily confused detection results in the object fault detection method in the prior art, an embodiment of the present application provides an object state detection method, an execution subject of which may be a third terminal illustrated in fig. 1, specifically, please refer to fig. 2, where the method includes:

s210, acquiring a target object set meeting preset conditions, wherein the target object set comprises a fault target object and a non-fault target object.

The target object in the embodiment of the present application may refer to any object that needs to be subjected to state detection, including a relevant machine or device. Wherein the preset conditions are as follows: the number of faulty objects in a set is less than half of the total number of objects in the set; or, the number of the fault objects is greater than half of the total number of the objects in the set and less than the total number of the objects in the set, and the probability that the fault object detects the fault-free object reaches a preset value, and in a specific implementation process, the object state detection method provided by the application can be implemented only by meeting one of the two conditions.

Correspondingly, for a specific method for acquiring a target object set meeting a preset condition, reference may be made to fig. 3, where the method specifically may include:

s310, acquiring preset number of object sets, and acquiring a first number of fault objects and a second number of fault-free objects in each object set.

S320, when the first number in the object set is smaller than half of the sum of the first number and the second number;

or the like, or, alternatively,

and when the first number is larger than half of the sum of the first number and the second number and is smaller than the sum of the first number and the second number and the probability that the detection result of the fault object in the first object set to the fault-free object is fault-free reaches a preset value, determining that the object set is the target object set meeting the preset condition.

And S220, creating a combined information recording file, wherein the combined information recording file is used for storing grouping information for grouping the target objects pairwise.

The combined information record file is used for storing grouping conditions of target objects in each round of detection process, when the record file is created, the content of the stored information is empty, and the grouping information is stored in the record file along with the progress of the detection process.

And S230, grouping every two target objects in the current target object set based on the information storage condition in the current combined information recording file, and determining current grouping information.

If the storage information in the current combined information recording file is empty, two target objects are arbitrarily taken out from the target object set to be combined to obtain a group; and repeating the steps of taking out and combining until all the target objects in the target object set are combined.

If the storage information in the current combined information recording file is not empty, a specific method for grouping each target object in the current target object set in pairs may refer to fig. 4, where the method may specifically include:

and S410, creating a to-be-selected object set based on the target objects in the current target object set.

And S420, determining a current object set to be selected.

And S430, taking out the first object and the second object from the current object set to be selected each time.

Here, the first object and the second object are two objects arbitrarily selected from a current object set to be selected.

S440, judging whether the combination information of the first object and the second object exists in the current combination information recording file.

Specifically, if the object id of the first object is a and the object id of the second object is B, it is necessary to determine whether a combination of a and B exists in the current combination information recording file in order to prevent the object combinations from being repeated each time.

S450, when the judgment result is yes, the second object is placed back to the object set to be selected, the second object is taken out from the object set to be selected again, and the step S440 is executed.

And S460, when the judgment result is negative, combining the first object and the second object.

S470, judging whether the pairwise grouping of the target objects in the current target object set is finished, and if so, executing the step S480; when the judgment result is no, step S430 is performed.

If the first object and the second object have been combined before, the current group of the first object and the second object is discarded, and one of the objects is replaced to form a new group until the formed group does not appear.

And S480, finishing grouping the target objects.

Through the steps S410 to S480, the target objects in the current target object set can be grouped into two groups, and the object combination form in each group does not appear before.

And S240, updating the current combination information recording file based on the current grouping information.

Storing the obtained new grouping information into the current combination information recording file to update the current combination information recording file, specifically, referring to fig. 5, which shows a combination information recording file updating method, the method may specifically include:

s510, determining the object identifications of the two target objects of each group in the current group information.

S520, creating an object identification combination based on the object identifications of the two target objects of each group.

And S530, storing each object identification combination into the current combination information recording file.

For example, the object id combinations obtained based on the grouping information include object B and object D, object C and object F, and object G and object H, which are stored in the combination information recording file, respectively, with new combination information added to the original combination information recording file.

S250, acquiring a detection result of mutual detection between each group of target objects based on data interaction between each group of two target objects, removing the target objects detected to be in a fault state from the current target object set based on the detection result, and updating the current target object set.

The mutual detection between the objects in the embodiment of the present application refers to a process of performing mutual detection of object states between the objects based on a data interaction manner between the objects, that is, for a current object, it may detect state information of other objects in a data interaction manner, and other objects may also detect state information of the current object in a data interaction manner.

Each group may include a first target object and a second target object, and mutual detection between the two objects is based on data interaction between the first target object and the second target object, and specifically, referring to fig. 6, a method for obtaining a detection result is shown, where the method may include:

s610, acquiring a first detection result of the first target object for detecting the second target object and a second detection result of the second target object for detecting the first target object based on data interaction between the first target object and the second target object.

S620, determining the first detection result and the second detection result as detection results of mutual detection between the first target object and the second target object.

Therefore, the mutual detection result among the target objects in one round of circulation is obtained.

For the target object detected to be in a fault state is excluded from the current target object set based on the detection result, wherein an actual state of the target object detected to be in the fault state is not necessarily a fault state, because for the fault target object, a detection result of the fault target object on other target objects is not necessarily accurate; specifically, the detection result of the faulty target object to the non-faulty target object may be faulty or non-faulty; the detection result of the faulty target object to the non-faulty target object may be faulty or non-faulty. In the embodiment of the application, regardless of the actual state of the detected target object, as long as the detection result is in a fault state, the detected target object is excluded from the current target object set.

And S260, repeatedly performing the steps of grouping every two target objects in the current target object set, determining current grouping information, updating the current combination information recording file, obtaining the detection result of mutual detection between each group of target objects, eliminating the target objects detected to be in a fault state from the current target object set, and updating the current target object set until the target objects in the current target object set can not be eliminated.

According to the method, when the target objects in the current target object set can not be eliminated any more, only the fault-free target objects are left in the target object set at the moment.

And S270, determining a fault-free target object based on the current target object set.

In the step S230, each target object in the current target object set is grouped two by two based on the information storage condition in the current combination information recording file, and if the number of target objects in the current target object set is an even number, each target object in the current target object set can be grouped; if the number of target objects in the current target object set is odd, after performing pairwise grouping, there is still a certain remaining single target object that is not included in the grouping, and for this single target object, the following processing method is shown in fig. 7, where the method includes:

and S710, if the number of the target objects in the current target object set is an odd number, determining the remaining target objects after the target objects are grouped pairwise.

And S720, recording the residual target objects, and keeping the residual target objects in the current target object set.

And (4) not excluding the residual objects, and directly leaving the residual objects in the current target object set after updating so as to perform grouping detection on the objects in the next round of circulation.

If the number of the target objects in the current target object set in at least two consecutive rounds is odd, the following processing method for the remaining target objects is provided, referring to fig. 8, where the method includes:

s810, if the number of the target objects in the current target object set is odd and the current target object set comprises the residual target objects in the previous round, determining a third object from the current target object set, wherein the third object is not the residual target objects and does not participate in grouping in the current round.

In this case, the current target object set includes the remaining target objects of the previous round, which need to participate in the grouping detection in the current round, and based on this situation, a single target object in the current target object set is left.

And S820, grouping every two target objects in the current target object set except the third object based on the current combination information recording file.

The specific implementation process of the present application is described below by taking an application scenario of the internet of vehicles as an example, where the target object is a vehicle in the internet of vehicles, and although the vehicles in the target internet of vehicles are not completely the same from a physical perspective, the vehicles have a completely equivalent position from a cloud logic or other data end perspective. Under the condition that whether the vehicles in the network are in fault or not can not be detected through an external tool, whether the vehicles are in fault or not can be detected by means of mutual detection among the vehicles in the internet of vehicles.

The preset conditions to be met by the target object set proposed in the embodiment of the application are that the number of faulty vehicles in the internet of vehicles does not exceed N/2, or the number of faulty vehicles exceeds N/2 but is less than N, and the detection result of the faulty vehicle on a non-faulty vehicle is 'detected vehicle non-faulty' with a probability p greater than a certain probability, and the probability p is determined according to the cycle number, the number of faulty vehicles and the number of vehicles to be eliminated each time. The probability p is not directly determined, but the method provided in the present application is adopted only when it is known in advance that the probability p is relatively high, for example, p is 1, which means that the detection result of a faulty vehicle to a non-faulty vehicle is always "detected vehicle is non-faulty".

The implementation can be specifically based on the following mechanisms:

pigeon and drawer principle: more than n +1 objects are placed in n drawers, and at least one drawer has at least two objects. By analogy, N vehicles can form N/2 mutual inspection vehicle pairs at most, and if the number of the fault vehicles is more than N/2, the mutual inspection vehicle pairs are determined to be 'fault vehicles and fault vehicles'; if the number of the fault-free vehicles is larger than N/2, the mutual inspection vehicle pair is determined to be a fault-free vehicle and a fault-free vehicle.

1. The non-fault vehicle can certainly detect the fault condition of the other vehicle, but the detection result of the fault vehicle to the other vehicle is not credible (namely, whether the detection result of the fault vehicle to the detected vehicle is the true condition of the detected vehicle is unknown).

2. In each cycle, two vehicles are selected from the N vehicles at a time for mutual inspection, and if a vehicle is reported as faulty (whether this reporting is correct or not, and whether the vehicle reported as faulty is actually faulty or not), the vehicle is excluded from the N vehicles. For example, two vehicles a and B are checked against each other, and if a reports that B has a fault, then B is excluded from N vehicles (whether a's detection result is correct or B has a fault, and in fact, whether B has a fault is unknown). The cycle is ended until all the cars have been cross checked once, and then a new cycle (a cycle is also referred to as a cross check) is performed on the cars that have not been excluded. And repeating the steps until a fault-free vehicle is found.

3. For the results of the mutual detection of two vehicles, a vehicle fault situation can be determined based on table 1.

TABLE 1 results of mutual detection of two vehicles

Every time the mutual detection of the two vehicles is finished, the two vehicles for mutual detection are replaced (the two vehicles for mutual detection are called as a mutual detection vehicle pair). Based on the mutual detection mechanism, if the number of the fault vehicles exceeds N/2 and no other condition is limited, according to the principle of the pigeons and the drawers, a mutual detection vehicle pair of 'faulty vehicle and faulty vehicle' will be generated in the first round of circulation, and then according to the table 1, the fault-free vehicle and the rest faulty vehicles can be eliminated successively through the last 3 conditions of the table 1, so that the fault-free vehicle cannot be found out finally. However, if the number of faulty vehicles is less than N/2, the mutual inspection vehicle pair "fault-free vehicle and fault-free vehicle" must appear in the first round of the cycle, and then according to table 1, the faulty vehicle must be eliminated successively through the last three conditions of table 1, and finally only the fault-free vehicle remains, so that the fault-free vehicle can be found out.

4. If N is an odd number, then one vehicle will not be cross checked in the first round of cross checking, and will perform a second round of cross checking in conjunction with the vehicles not excluded in the first round of cross checking. If the number of candidate vehicles is still odd in the second round of mutual inspection, an excluded vehicle is selected, which is not to be subjected to mutual inspection in the second round, and vehicles which are not to be subjected to mutual inspection in the first round of mutual inspection must be subjected to mutual inspection. By analogy, if the number of candidate vehicles is an odd number in the first round of mutual inspection, a vehicle which is not excluded in the previous rounds of mutual inspection is selected, the vehicle does not perform mutual inspection in the current round, and the vehicle which is not subjected to mutual inspection in the previous round of mutual inspection must perform mutual inspection in the current round. In addition, if the A and the B are mutually checked in the previous round, the vehicles paired with the A and the B are replaced in the next round of mutual checking, namely the A and the other vehicles are mutually checked, and the B and the other vehicles are mutually checked.

The following explains why vehicles in the internet of vehicles need to satisfy preset conditions when the application is applied to vehicle fault detection in the internet of vehicles by a specific example.

If there are 4 vehicles, 3 of which are faulty, and there are no other restrictions, i.e. the number of faulty vehicles is greater than half of the total number of vehicles and there are no other conditions, the implementation method according to the present application is as follows:

example one:

the 4 vehicles are respectively labeled as a, B, C, D, wherein a, B, C have a fault and D have no fault, then the process of their mutual inspection is as follows:

a first round of circulation:

1. firstly, selecting A and B mutual detection (the detection result is not needed to be paid attention to temporarily, and the detection is directly carried out downwards);

2. two vehicles for mutual detection are replaced, namely, the A and the B are replaced by the C and the D, then mutual detection is carried out, the detection result of the D is definitely 'C fault', the detection result of the C can be 'D fault', and the detection result of the C can be 'D no fault'. And if the detection result of the C is 'D is faulty', the C and the D are simultaneously removed, the first round of circulation is ended, and the fault-free vehicle is removed, so that the fault-free vehicle cannot be found based on the method provided by the application.

Therefore, when the number of the fault vehicles is larger than half of the total number of the vehicles and no other conditions exist, namely the vehicle set does not meet the preset conditions, the fault-free vehicles cannot be found.

Example two: the number of failed vehicles in the internet of vehicles is less than half of the total number of vehicles.

The 4 vehicles are respectively labeled as a, B, C, D, wherein a, B, C have no fault and D have a fault, then the process of their mutual inspection is as follows:

a first round of circulation:

1. firstly, selecting A and B for mutual detection, wherein the A and B cannot be eliminated because the detection results are affirmatively that the opposite side has no fault, and the A and B can be judged to have faults or have no faults at the same time;

2. two vehicles for mutual detection are replaced, namely, the A and the B are replaced by the C and the D, then mutual detection is carried out, the detection result of the C is definitely 'D fault', the detection result of the D can be 'C fault' and 'C no fault'. If the detection result of D is 'C is faulty', C and D are simultaneously eliminated, and the first round of circulation is ended; if the detection result of D is 'C has no fault', D is removed, C is reserved, and the first round of circulation is ended;

however, whether C and D are excluded simultaneously or D is excluded only and C is retained, it is known that the remaining vehicles a and B must be fault-free, since the faulty vehicle is no more than half 4, i.e. no more than 2, but at least one of the excluded vehicles C or D is a faulty vehicle, so that there is at most one faulty vehicle in the remaining vehicles a and B, whereas a and B are faulty or not, so a and B can only be fault-free.

Example three: the number of the fault vehicles in the internet of vehicles is more than half of the total number of the vehicles and less than the total number of the vehicles, and the detection result of the fault vehicle to the fault-free vehicle is 'detected vehicle fault-free' with more than probability p, wherein p is 1, and p is 1, which means that the detection result of the fault vehicle to the fault-free vehicle is always 'detected vehicle fault-free'.

The 4 vehicles are labeled a, B, C, D, respectively, where a, B, C are faulty and D is not faulty, if p is 1, then their mutual check may be as follows:

a first round:

a and D are mutually detected, the detection result of D is 'A fault', the detection result of A is 'D no fault', and then A is excluded;

replacing a and D with B and C, respectively, will also result in a test if both B and C remain in the test.

And a second round:

comparing B and D for mutual detection, wherein the detection result of D is 'B fault', the detection result of B is 'D no fault', and B is excluded;

and a third round:

comparing C and D, the detection result of D is 'C fault', the detection result of C is 'D no fault', C is excluded, and the rest D is no fault.

The application scenario of the application needs to satisfy the following conditions: 1) the total number of vehicles in the target Internet of vehicles can be obtained; 2) the vehicles in the target internet of vehicles are not identical from the physical perspective, but have identical positions from the perspective of cloud logic or other data ends; 3) whether the vehicle in the network breaks down or not cannot be detected through an external tool.

The method for identifying the fault-free vehicle in the internet of vehicles based on vehicle cooperation provided by the embodiment of the application can be implemented by the following steps:

1. a development platform is built (in the embodiment, a ThinkPad series notebook is used as the development platform, and a processor Intel (R) core (TM) i5-5200U CPU @2.20GHz, an internal memory 8.00G and a 64-bit system are used, in the embodiment, other software and hardware platforms and corresponding environments can be adopted, and development environments (programming language python development environment used in the embodiment) and installation auxiliary libraries and packages, such as math, time, requests and numpy (note: the embodiment uses computer language python, and can also use any computer language including python) can be configured.

2. Determining whether the vehicle networking information that needs to be sampled includes vehicle number, vehicle type, vehicle speed, vehicle location, vehicle acceleration, vehicle direction, vehicle driver gender, vehicle driver age, vehicle driver type, vehicle driver health, vehicle driver reaction capability.

3. The target internet of vehicles scale, namely the total number of vehicles in the target internet of vehicles, is obtained and recorded as N, and although the vehicles in the target internet of vehicles are not completely the same from the physical perspective, the vehicles have completely equivalent positions from the perspective of cloud logic or other data ends. Therefore, from the perspective of logic or other data terminals, the vehicles in the target internet of vehicles can mutually detect and judge whether the fault vehicle meets the preset condition, if not, the detection is finished, otherwise, the step 4 is skipped.

4. And judging whether any two fault vehicles in the target Internet of vehicles can be communicated in series to influence the detection result, if so, ending the detection, because under the condition, the method can not work, otherwise, skipping to the step 5.

5. And selecting two vehicles from the target Internet of vehicles to mutually detect the vehicles, reporting whether the opposite vehicle has a fault, repeatedly replacing the two vehicles for mutual detection, and eliminating the vehicle with the detected fault.

The method specifically comprises the following steps:

(1) and selecting two vehicles from the target internet of vehicles, mutually detecting the two vehicles, and reporting whether the opposite vehicle has a fault. Although one non-faulty vehicle can certainly detect the fault condition of the other vehicle, the detection result of the faulty vehicle to the other vehicle is not reliable (that is, whether the detection result of the faulty vehicle to the detected vehicle is the true condition of the detected vehicle is unknown), so that the detection results are judged according to table 1 (two vehicles are respectively marked as a and B);

(2) replacing two vehicles for mutual detection in (1), the step (1) being carried out cyclically

Next, the vehicle detected as defective is excluded. By b₁Representing the number of vehicles for executing elimination in the first round, wherein the number of fault-free vehicles and normal vehicles can be eliminated in the process, but the number of fault vehicles in the rest vehicles meets the preset condition, namely the fault-free vehicles can be selected from the rest vehicles; performing step (1) for vehicles in the reduced-scale Internet of vehicles

Secondly, using b₂Indicating the number of vehicles for which the second round performs the elimination; when the number of times of circularly executing the step (1) is

The remaining vehicles being determined to be fault-free vehicles, wherein b_iRepresenting the number of vehicles performing the rejection for the ith wheel, and m is the number of total cycles.

6. For the vehicle without fault, combining the drive test perception information in step 2, calculating the vehicle collision risk analysis and outputting the vehicle collision risk analysis in the form of a matrix, as shown in fig. 9. Where the ith row and jth column elements of the matrix in fig. 9 represent the probability that vehicle j will collide with vehicle i, for example, element 0.16 in the first row in fig. 9 represents that the probability that vehicle 2 will collide with vehicle 1 is 0.16, element 0.19 in the first row represents that the probability that vehicle 3 will collide with vehicle 1 is 0.19, and element 0.14 in the first row represents that the probability that vehicle 8 will collide with vehicle 1 is 0.14. The specific calculation method comprises the following steps: and (2) substituting the fault-free vehicle information (such as the relative speed of the vehicles, the included angle of the running wind directions between the vehicles, the vehicle mass, the surface viscosity, the camber and the like) into a gravitational field theory model, a spring potential energy model and a Doppler effect model in the field of physics to calculate the collision strength between the vehicles, and then dividing the collision strength by the standard collision strength to obtain the collision probability (the value of the standard collision strength is the collision strength value in the standard environment and is the value when the traffic vehicle is at a collision critical point).

The object state detection method is applied to the field of the Internet of vehicles, fault-free vehicles can be found out from a pile of vehicles, and state information of other vehicles in the Internet of vehicles can be detected by determining the fault-free vehicles. In the embodiment of the application, the detection between two vehicles can be any mode capable of detecting whether the other vehicle is in fault or not, and the mutual detection of the vehicles can be synchronous or asynchronous.

The method comprises the steps of grouping every two target objects in a current target object set by obtaining a target object set meeting preset conditions, and obtaining a detection result of mutual detection between each group of target objects based on data interaction between each group of two target objects; based on the detection result, eliminating the target object detected to be in a fault state from the current target object set, and updating the current target object set; then repeating the steps until the target objects in the current target object set can not be eliminated; and finally, all the target objects left in the current target object set are fault-free target objects. The method for mutually detecting the objects to be detected has the advantages that the objects to be detected are grouped in pairs, the objects to be detected are mutually detected between the two objects at each time, the detection times can be reduced, the appearance index explosion of the detection times is avoided, the method for mutually detecting the two objects is efficient, the detection result is easy to distinguish, and the confusion of the mutual detection results among a plurality of objects is avoided.

The present embodiment further provides an object state detection apparatus, please refer to fig. 10, the apparatus includes:

a target object set obtaining module 1010, configured to obtain a target object set meeting a preset condition, where the target object set includes a faulty target object and a non-faulty target object;

a file creating module 1020 configured to create a combined information recording file, where the combined information recording file is used to store grouping information for grouping the target objects two by two;

a grouping module 1030, configured to group each target object in the current target object set pairwise based on an information storage condition in the current combined information recording file, and determine current grouping information;

a first updating module 1040, configured to update the current combination information recording file based on the current grouping information;

a second updating module 1050, configured to obtain a detection result of mutual detection between each group of target objects based on data interaction between two target objects in each group, exclude a target object detected as being in a fault state from the current target object set based on the detection result, and update the current target object set;

a first repeated execution module 1060, configured to repeatedly execute the steps of grouping each target object in the current target object set two by two, determining current grouping information, updating a current combination information record file, obtaining a detection result of mutual detection between each group of target objects, excluding a target object detected as being in a fault state from the current target object set, and updating the current target object set until the target object in the current target object set cannot be excluded any more;

a first determining module 1070 is configured to determine a non-faulty target object based on the current set of target objects.

Specifically, the preset conditions are as follows: the number of faulty objects in a set is less than half of the total number of objects in the set; or, the number of the fault objects is larger than half of the total number of the objects in the set and smaller than the total number of the objects in the set, and the probability that the detection result of the fault object to the fault-free object is fault-free reaches a preset value; accordingly, the target object set obtaining module 1010 includes:

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a preset number of object sets and acquiring a first number of fault objects and a second number of fault-free objects in each object set;

a second determining module, configured to determine whether the first number of the set of objects is less than half of a sum of the first number and the second number;

or the like, or, alternatively,

Specifically, the grouping module 1030 includes:

a first creating module, configured to create a set of objects to be selected based on a target object in a current target object set when storage information in the current combined information recording file is not empty;

the third determining module is used for determining the current object set to be selected;

the object selection module is used for taking out a first object and a second object from the current object set to be selected each time;

a first judgment module, configured to judge whether combination information of the first object and the second object exists in a current combination information recording file;

the first execution module is used for putting the second object back to the object set to be selected when the judgment result is yes, and repeatedly executing the operation of taking the second object out of the object set to be selected until the judgment result is no;

the second execution module is used for combining the first object and the second object when the judgment result is negative;

and the second repeated execution module is used for repeatedly executing the steps of determining the current object set to be selected, taking out the first object and the second object from the current object set to be selected each time and judging until the pairwise grouping of the target objects in the current target object set is completed.

Specifically, the grouping module 1030 may further include:

and the arbitrary combination module is used for taking out two arbitrary target objects from the target object set to combine to obtain a group when the storage information in the current combination information recording file is empty.

Specifically, the first update module 1040 includes:

the object identification determining module is used for determining the object identifications of the two target objects of each group in the current group information;

the object identification combination creating module is used for creating an object identification combination based on the object identifications of the two target objects of each group;

and the storage module is used for storing each object identification combination into the current combination information recording file.

Specifically, the apparatus further comprises:

the residual target object determining module is used for determining residual target objects after pairwise grouping of the target objects if the number of the target objects in the current target object set is an odd number;

and the recording module is used for recording the residual target objects and keeping the residual target objects in the current target object set.

The device further comprises:

a fourth determining module, configured to determine a third object from the current target object set if the number of target objects in the current target object set is an odd number and the current target object set includes the remaining target objects in the previous round, where the third object is not the remaining target objects and the third object does not participate in grouping in the current round;

and the first grouping module is used for grouping every two target objects in the current target object set except the third object based on the current combination information recording file.

The device provided in the above embodiments can execute the method provided in any embodiment of the present application, and has corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in the above embodiments may be referred to a method provided in any of the embodiments of the present application.

The present embodiments also provide a computer-readable storage medium having stored therein at least one instruction, at least one program, set of codes, or set of instructions that is loaded by a processor and performs any of the methods described above in the present embodiments.

Referring to fig. 11, the apparatus 1100 may have a large difference due to different configurations or performances, and may include one or more Central Processing Units (CPUs) 1122 (e.g., one or more processors) and a memory 1132, and one or more storage media 1130 (e.g., one or more mass storage devices) storing an application program 1142 or data 1144. Memory 1132 and storage media 1130 may be, among other things, transient storage or persistent storage. The program stored in the storage medium 1130 may include one or more ofMore than one module (not shown), each of which may include a series of instructions operating on the device. Still further, central processor 1122 may be provided in communication with storage medium 1130 to perform a series of instruction operations on storage medium 1130 on device 1100. The apparatus 1100 may also include one or more power supplies 1126, one or more wired or wireless network interfaces 1150, one or more input-output interfaces 1158, and/or one or more operating systems 1141, such as a Windows Server^TM，Mac OS X^TM，Unix^TM，Linux^TM，FreeBSD^TMAnd so on. Any of the methods described above in this embodiment can be implemented based on the apparatus shown in fig. 11.

The present specification provides method steps as described in the examples or flowcharts, but may include more or fewer steps based on routine or non-inventive labor. The steps and sequences recited in the embodiments are but one manner of performing the steps in a multitude of sequences and do not represent a unique order of performance. In the actual system or interrupted product execution, it may be performed sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.

The configurations shown in the present embodiment are only partial configurations related to the present application, and do not constitute a limitation on the devices to which the present application is applied, and a specific device may include more or less components than those shown, or combine some components, or have an arrangement of different components. It should be understood that the methods, apparatuses, and the like disclosed in the embodiments may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a division of one logic function, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or unit modules.

Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes 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 method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this 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 implementation. 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 application.

The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should 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 in the embodiments of the present application.

Claims

1. An object state detection method, comprising:

2. The object state detection method according to claim 1, wherein the preset condition is: the number of faulty objects in a set is less than half of the total number of objects in the set; or, the number of the fault objects is larger than half of the total number of the objects in the set and smaller than the total number of the objects in the set, and the probability that the detection result of the fault object to the fault-free object is fault-free reaches a preset value;

correspondingly, the acquiring a set of target objects meeting preset conditions includes:

acquiring a preset number of object sets, and acquiring a first number of fault objects and a second number of fault-free objects in each object set;

when the first number in the set of objects is less than half of the sum of the first number and the second number;

or the like, or, alternatively,

3. The object state detection method according to claim 1, wherein the grouping of each object in the current set of objects two by two based on the information storage in the current combined information record file, and determining the current grouping information comprises:

when the storage information in the current combined information recording file is not empty, creating a to-be-selected object set based on the target object in the current target object set;

determining a current object set to be selected;

taking out a first object and a second object from a current object set to be selected each time;

judging whether the combination information of the first object and the second object exists in a current combination information recording file or not;

when the judgment result is yes, the second object is placed back to the object set to be selected, and the second object is repeatedly taken out from the object set to be selected until the judgment result is no;

when the judgment result is negative, combining the first object and the second object;

and repeating the steps of determining the current object set to be selected, taking out the first object and the second object from the current object set to be selected each time and judging until the pairwise grouping of the target objects in the current target object set is completed.

4. The object state detection method according to claim 1, wherein the grouping of each object in the current set of objects two by two based on the information storage in the current combined information record file, and determining the current grouping information comprises:

and when the storage information in the current combined information recording file is empty, randomly taking out two target objects from the target object set for combination to obtain a group.

5. The object state detection method according to claim 1, wherein the grouping, two by two, of the target objects in the current target object set based on the current combination information recording file further comprises:

if the number of the target objects in the current target object set is an odd number, determining the remaining target objects after pairwise grouping of the target objects;

and recording the residual target objects, and keeping the residual target objects in the current target object set.

6. The object state detection method according to claim 4, further comprising:

if the number of the target objects in the current target object set is odd and the current target object set comprises the residual target objects in the previous round, determining a third object from the current target object set, wherein the third object is not the residual target objects and does not participate in grouping in the current round;

and grouping every two target objects except the third object in the current target object set based on the current combination information recording file.

7. The object state detection method according to claim 1, wherein the updating the current combination information record file based on the current grouping information comprises:

determining object identifications of two target objects of each group in the current group information;

creating an object identification combination based on the object identifications of the two target objects of each group;

and storing each object identification combination into the current combination information recording file.

8. The object state detection method according to claim 1, wherein each group includes a first target object and a second target object;

correspondingly, the obtaining a detection result of mutual detection between each group of target objects based on data interaction between each group of two target objects includes:

acquiring a first detection result of the first target object for detecting the second target object and a second detection result of the second target object for detecting the first target object based on data interaction between the first target object and the second target object;

and determining the first detection result and the second detection result as detection results of mutual detection between the first target object and the second target object.

9. An object state detection apparatus, characterized by comprising:

10. A computer storage medium having stored therein at least one instruction, at least one program, set of codes, or set of instructions that is loaded by a processor and that performs a method of object state detection according to any one of claims 1 to 8.