CN111860954A - Vehicle loss of contact prediction method and device, computer equipment and storage medium - Google Patents
Vehicle loss of contact prediction method and device, computer equipment and storage medium Download PDFInfo
- Publication number
- CN111860954A CN111860954A CN202010559051.3A CN202010559051A CN111860954A CN 111860954 A CN111860954 A CN 111860954A CN 202010559051 A CN202010559051 A CN 202010559051A CN 111860954 A CN111860954 A CN 111860954A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- data
- real
- loss
- heartbeat
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000004590 computer program Methods 0.000 claims description 25
- 238000012545 processing Methods 0.000 claims description 17
- 238000000605 extraction Methods 0.000 claims description 9
- 238000013145 classification model Methods 0.000 claims description 8
- 238000007621 cluster analysis Methods 0.000 claims description 7
- 238000004891 communication Methods 0.000 abstract description 11
- 238000005516 engineering process Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 6
- 230000007613 environmental effect Effects 0.000 description 6
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000001360 synchronised effect Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
- H04L67/025—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP] for remote control or remote monitoring of applications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/54—Presence management, e.g. monitoring or registration for receipt of user log-on information, or the connection status of the users
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- Evolutionary Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Development Economics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Game Theory and Decision Science (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Traffic Control Systems (AREA)
Abstract
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for predicting vehicle loss of contact, a computer device, and a storage medium. The method comprises the following steps: receiving heartbeat data uploaded by vehicle equipment according to a first preset time interval; when the heartbeat data of the vehicle equipment is not received within a first preset time interval, determining that the heartbeat of the vehicle is lost, and taking the heartbeat data received at the last time as real-time vehicle data; generating real-time characteristics from the real-time vehicle data; and performing loss of connection prediction on the vehicle equipment based on the real-time characteristics. By adopting the method, the intelligent level of vehicle loss of communication prediction can be improved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for predicting vehicle loss of contact, a computer device, and a storage medium.
Background
With the continuous development of the industry of sharing the single vehicles and the moped, the single vehicles and the moped are distributed all over the country, and the vehicles reach the million level and the million level. Thereby facilitating the travel of the public. However, as the number of vehicle uses and time increase, the shared single platform faces increasingly severe asset loss due to loss of vehicle signals, resulting in loss of connectivity among a large number of vehicles.
At present, no effective scheme can predict whether the vehicle has the possibility of losing contact in time, and the problem to be solved urgently is to provide a method capable of intelligently predicting the loss contact of the vehicle.
Disclosure of Invention
In view of the above, it is necessary to provide a vehicle loss prediction method, a device, a computer device, and a storage medium capable of improving the level of intelligence of vehicle loss prediction.
A vehicle loss of contact prediction method, the method comprising:
receiving heartbeat data uploaded by vehicle equipment according to a first preset time interval;
when the heartbeat data of the vehicle equipment is not received within a first preset time interval, determining that the heartbeat of the vehicle is lost, and taking the heartbeat data received at the last time as real-time vehicle data;
generating real-time characteristics from the real-time vehicle data;
and performing loss of connection prediction on the vehicle equipment based on the real-time characteristics.
In one embodiment, before the loss of contact prediction is performed on the vehicle device based on the real-time characteristics, the method further includes:
acquiring general vehicle data of corresponding vehicle equipment in a database;
extracting feature data of the general vehicle data to obtain general features of the vehicle equipment;
Splicing the real-time characteristics and the general characteristics to obtain splicing characteristics;
the method for predicting the loss of the vehicle equipment based on the real-time characteristics comprises the following steps:
and performing loss of connection prediction on the vehicle equipment based on the splicing characteristics.
In one embodiment, when heartbeat data of the vehicle device is not received within a first preset time interval, determining that the heartbeat of the vehicle is lost comprises:
when the heartbeat data of the vehicle equipment is not received within a first preset time interval, waiting for a preset time length;
when it is determined that the heartbeat data uploaded by the vehicle equipment is not received within the preset time, it is determined that the heartbeat of the vehicle is lost.
In one embodiment, the method further includes:
judging whether the heartbeat of the vehicle is lost in a second preset time interval or not, wherein the second preset time interval comprises a plurality of first preset time intervals;
when the heartbeat loss of the vehicle does not exist in the second preset time interval, acquiring vehicle data from the analysis system in an off-line processing mode, and generating corresponding historical characteristics according to the vehicle data, wherein the vehicle data are obtained after the analysis system carries out cluster analysis processing on a plurality of real-time vehicle data corresponding to a plurality of first preset time intervals;
And performing loss of contact prediction on the vehicle equipment based on the corresponding historical characteristics.
In one embodiment, before obtaining the vehicle data from the analysis system, the method further includes:
and acquiring a plurality of real-time vehicle data corresponding to a plurality of first preset time intervals in a second preset time interval from the database, and sending the plurality of real-time vehicle data to the analysis system.
In one embodiment, the loss of link prediction of the vehicle device based on the real-time characteristics comprises the following steps:
and on the basis of real-time characteristics, performing loss-of-contact prediction through a pre-trained binary classification model, and outputting a prediction result of loss-of-contact or loss-of-contact of the vehicle equipment in a future preset time period.
A vehicle loss of contact prediction apparatus, the apparatus comprising:
the heartbeat data receiving module is used for receiving heartbeat data uploaded by the vehicle equipment according to a first preset time interval;
the real-time vehicle data acquisition module is used for determining that the heartbeat of the vehicle is lost when the heartbeat data of the vehicle equipment is not received within a first preset time interval, and taking the heartbeat data received last time as real-time vehicle data;
the real-time characteristic generating module is used for generating real-time characteristics according to the real-time vehicle data;
the first prediction module is used for predicting the loss of communication of the vehicle equipment based on the real-time characteristics.
In one embodiment, the apparatus further includes:
the universal vehicle data acquisition module is used for acquiring universal vehicle data of corresponding vehicle equipment in the database before the first prediction module predicts the loss of connection of the vehicle equipment based on the real-time characteristics;
the general characteristic extraction module is used for extracting the characteristic data of the general vehicle data to obtain the general characteristics of the vehicle equipment;
the splicing module is used for splicing the real-time characteristics and the general characteristics to obtain splicing characteristics;
the first prediction module is used for predicting the loss of connection of the vehicle equipment based on the splicing characteristics.
A computer device comprising a memory storing a computer program and a processor implementing the steps of any of the methods described above when the processor executes the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any of the above.
According to the method, the device, the computer equipment and the storage medium for predicting the loss of the vehicle from the contact loss, the heartbeat data uploaded by the vehicle equipment is received according to the first preset time interval, when the heartbeat data of the vehicle equipment is not received in the first preset time interval, the vehicle heartbeat loss is determined, the heartbeat data received at the last time is used as real-time vehicle data, then the characteristic data of the received real-time vehicle data is extracted, the loss of the contact is predicted, and a corresponding loss of contact prediction result is generated. Therefore, whether the heartbeat of the vehicle is lost or not can be determined according to whether the heartbeat data of the vehicle equipment is received or not in a first preset time interval, and when the heartbeat of the vehicle is lost, the characteristic data is extracted and the loss of contact prediction is carried out according to the heartbeat data of the vehicle in time, so that the loss of contact prediction of the vehicle can be carried out automatically. And when the heartbeat data of the vehicle equipment is not received in a first preset time interval, namely when the heartbeat of the vehicle is lost, the characteristic extraction and the loss of contact prediction are carried out in time according to the heartbeat data, the loss of contact prediction can be carried out in time at the first time, and the timeliness of the loss of contact prediction of the vehicle is improved.
Drawings
FIG. 1 is a diagram of an exemplary implementation of a vehicle loss prediction method;
FIG. 2 is a schematic flow chart diagram of a vehicle loss of contact prediction method in one embodiment;
FIG. 3 is a schematic diagram of a vehicle loss of contact prediction process in one embodiment;
FIG. 4 is a block diagram showing the construction of a vehicle loss prediction apparatus according to an embodiment;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The vehicle loss prediction method provided by the application can be applied to the application environment shown in fig. 1. Where the vehicle device 102 communicates with the server 104 over a network. In this embodiment, the vehicle device 102 uploads the heartbeat data to the server 104 at a preset first time interval. The server 104 receives heartbeat data uploaded by the vehicle equipment according to a first preset time interval, determines that the heartbeat of the vehicle is lost when the heartbeat data of the vehicle equipment is not received in the first preset time interval, and takes the heartbeat data received last time as real-time vehicle data. The server 104 then generates real-time signatures from the real-time vehicle data and makes an outage prediction for the vehicle devices based on the real-time signatures. The vehicle device 102 may be, but is not limited to, various intelligent bicycles, intelligent electric vehicles, and the like, and the server 104 may be implemented by an independent server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, a vehicle loss prediction method is provided, which is described by taking the method as an example applied to the server in fig. 1, and includes the following steps:
step S202, heartbeat data uploaded by the vehicle equipment is received according to a first preset time interval.
The first preset time interval is a preset time interval at which the server receives heartbeat data of the vehicle device, and may be, for example, 5 minutes, 10 minutes, or the like.
The vehicle device can be a vehicle intelligent lock, and the vehicle intelligent lock comprises a communication module, a communication network card and the like. In this embodiment, the vehicle device may establish communication with the base station through the communication device, and then upload the heartbeat data to the base station, and send the heartbeat data to the server through the base station.
The heartbeat data refers to current real-time data of the vehicle, and may include, but is not limited to, status information of the smart lock of the vehicle, such as current voltage, current, error code, and the like of the smart lock.
In this embodiment, the heartbeat data may further include related attributes or performance data of the vehicle, such as signal strength of communication between the vehicle and the base station, voltage variation of the vehicle, charging condition of the vehicle, wear degree of the vehicle, environment where the vehicle is located, weather condition, and the like.
In this embodiment, the vehicle device collects heartbeat data of the vehicle at a first preset time interval and uploads the heartbeat data to the server, for example, the vehicle device collects data of current, voltage, error code and the like of the vehicle smart lock once at an interval of 10 minutes and then uploads the data to the server.
In this embodiment, the server may receive heartbeat data uploaded by the vehicle device at a first preset time interval, so as to determine whether a heartbeat of the vehicle is lost or not and whether a possibility of losing contact exists or not according to whether the heartbeat data is received within the first preset time interval or not.
Step S204, when the heartbeat data of the vehicle equipment is not received within a first preset time interval, determining that the heartbeat of the vehicle is lost, and taking the heartbeat data received last time as real-time vehicle data.
The real-time vehicle data is data for predicting a vehicle in real time. In this embodiment, the real-time vehicle data refers to heartbeat data of the vehicle device that is received by the server last time when the heartbeat of the vehicle is lost.
As previously described, the server receives heartbeat data of the vehicle device at a first preset time interval.
In this embodiment, when the server does not receive heartbeat data of the vehicle device within a certain preset time interval, the server may determine that the heartbeat of the vehicle is lost, and the server may obtain heartbeat data received at the closest time to the heartbeat loss time point of the vehicle in the database, that is, obtain heartbeat data received at the last time, and use the obtained heartbeat data as implementation vehicle data to predict loss of communication of the vehicle.
Step 206, generating real-time features from the real-time vehicle data.
The real-time features refer to vehicle features generated based on real-time vehicle data, and may include, but are not limited to, heartbeat features, weather features, environmental features where the vehicle is located, and the like of the vehicle.
In this embodiment, the heartbeat feature, the weather feature, the environmental feature where the vehicle is located, and the like of the vehicle device all refer to the current real-time feature of the vehicle. The heartbeat features of the vehicle may be generated from the current voltage, current, etc. of the vehicle in the vehicle data; the weather characteristics refer to the temperature, humidity, wind power and the like of the current position of the vehicle; the environmental characteristics of the vehicle refer to the current geographic position of the vehicle and the environment of the vehicle.
In this embodiment, after obtaining the real-time vehicle data of the vehicle device, the server may input the real-time vehicle data into a pre-trained feature extraction model to perform feature extraction, so as to obtain the real-time features of the vehicle device.
In this embodiment, referring to fig. 3, the feature extraction model for the server to extract the real-time features may be an online real-time model, that is, the server may obtain real-time vehicle data of the vehicle device in time when determining that the heartbeat of the vehicle is lost, and input the online real-time model to extract the real-time features.
And step 208, performing loss of connection prediction on the vehicle equipment based on the real-time characteristics.
The loss of contact prediction means prediction of the possibility of loss of contact of the vehicle device.
In this embodiment, after obtaining the real-time characteristics of the vehicle device, the server may input the obtained real-time characteristics into a loss of contact prediction model trained in advance, so as to obtain a prediction result of possible loss of contact of the vehicle device.
In one embodiment, the server predicts the loss of the vehicle device based on the real-time features through a pre-trained binary model to output a prediction result that the vehicle device is lost or not lost within a preset time period in the future.
In this embodiment, the two classification models may be a random forest model, a linear classification model, and the like, which is not limited in this application.
In this embodiment, the server may train the two classification models according to a large amount of collected historical data, and perform a test to obtain the trained two classification models.
According to the method for predicting the loss of the vehicle coupling, the heartbeat data uploaded by the vehicle equipment is received according to a first preset time interval, when the heartbeat data of the vehicle equipment is not received in the first preset time interval, the loss of the vehicle heartbeat is determined, the heartbeat data received at the last time is used as real-time vehicle data, then the characteristic data of the received real-time vehicle data is extracted, the loss of the coupling is predicted, and a corresponding loss of the coupling prediction result is generated. Therefore, whether the heartbeat of the vehicle is lost or not can be determined according to whether the heartbeat data of the vehicle equipment is received or not in a first preset time interval, and when the heartbeat of the vehicle is lost, the characteristic data is extracted and the loss of contact prediction is carried out according to the heartbeat data of the vehicle in time, so that the loss of contact prediction of the vehicle can be carried out automatically. And when the heartbeat data of the vehicle equipment is not received in a first preset time interval, namely when the heartbeat of the vehicle is lost, the characteristic extraction and the loss of contact prediction are carried out in time according to the heartbeat data, the loss of contact prediction can be carried out in time at the first time, and the timeliness of the loss of contact prediction of the vehicle is improved.
In one embodiment, before the predicting of the loss of contact of the vehicle device based on the real-time characteristics, the method may further include: acquiring general vehicle data of corresponding vehicle equipment in a database; extracting feature data of the general vehicle data to obtain general features of the vehicle equipment; and splicing the real-time characteristics and the general characteristics to obtain splicing characteristics.
The general vehicle data refers to attribute data of the vehicle itself, for example, wear of the vehicle, a state of the vehicle, and the like.
The generic features may include wear characteristics of the vehicle, vehicle status characteristics, and the like. The loss characteristics of the vehicle can be generated based on the lock version information, the vehicle riding distance, the time length of line-up, the idle time and the like. The state characteristic of the vehicle refers to the state of the vehicle, for example, whether the vehicle is newly added and idled or newly added and lost.
In this embodiment, before the server predicts the loss of contact of the vehicle device based on the real-time characteristics, the server may further obtain the general data of the vehicle device in the database, for example, query the database according to the number of the vehicle device or the number of the vehicle, so as to obtain the general vehicle data of the vehicle device.
Further, after the server acquires the general vehicle data of the vehicle device, the general features of the vehicle can be extracted through the feature extraction model, and then the obtained general features are spliced with the real-time features, namely the loss features of the vehicle, the vehicle state features, the heartbeat features of the vehicle device, the weather features, the environmental features of the vehicle and the like, so that the splicing features are obtained.
In this embodiment, the predicting the loss of connection of the vehicle device based on the real-time characteristics may include: and performing loss of connection prediction on the vehicle equipment based on the splicing characteristics.
In this embodiment, the step of predicting the loss of contact of the vehicle device by the server may be to input the splicing characteristics into the loss of contact prediction model, to predict the loss of contact of the vehicle device through the loss of contact prediction model, and to output a prediction result of loss of contact or no loss of contact of the vehicle device in a preset time period in the future.
In the above embodiment, the real-time features and the general features are spliced to generate the splicing features, and the loss of connection prediction is performed based on the splicing features, so that feature data for prediction can be more perfect, and the accuracy of the loss of connection prediction is improved. And general vehicle data are acquired from the database, and vehicles are not required to upload in real time, so that the data volume of data uploaded by vehicle equipment in real time can be reduced, the resource consumption is reduced, and the data acquisition efficiency is improved.
In one embodiment, when heartbeat data of the vehicle device is not received within a first preset time interval, determining that the heartbeat of the vehicle is lost may include: when the heartbeat data of the vehicle equipment is not received within a first preset time interval, waiting for a preset time length; when it is determined that the heartbeat data uploaded by the vehicle equipment is not received within the preset time, it is determined that the heartbeat of the vehicle is lost.
In this embodiment, the reason that the server does not receive the heartbeat data of the vehicle device within the first preset time interval may be caused by unstable signals or signal loss, that is, the heartbeat data uploaded by the vehicle device is not received at the server due to an external accidental reason. The server may wait for a preset time period, for example, several minutes or wait for the first preset time interval, when the heartbeat data of the vehicle device is not received within the first preset time interval, and continue to determine whether the heartbeat data uploaded by the vehicle device is received within the time period.
In this embodiment, within a preset time period, when the server receives heartbeat data uploaded by the vehicle device, the server may determine that the heartbeat of the vehicle is normal, and may not perform processing. When the server does not receive the heartbeat data uploaded by the vehicle equipment within the preset time length, the server can determine that the heartbeat of the vehicle is lost, and the server can inquire the database based on the vehicle number or the vehicle equipment number and the like so as to obtain the heartbeat data uploaded by the vehicle equipment at the last time and perform subsequent processing.
In the above embodiment, when it is determined that the heartbeat data of the vehicle device is not received within the first preset time interval, by waiting for the preset time duration, when the heartbeat data uploaded by the vehicle device is not received within the preset time duration, it is determined that the heartbeat of the vehicle is lost, so that it is possible to avoid that the heartbeat of the vehicle is lost and misjudged because the heartbeat data is not received due to an accidental reason, improve the accuracy of heartbeat loss judgment, further reduce unnecessary data processing procedures, and reduce resource consumption.
In one embodiment, the method may further include: judging whether the heartbeat of the vehicle is lost in a second preset time interval or not, wherein the second preset time interval comprises a plurality of first preset time intervals; when the heartbeat loss of the vehicle does not exist in the second preset time interval, acquiring vehicle data from the analysis system in an off-line processing mode, and generating corresponding historical characteristics according to the vehicle data, wherein the vehicle data are obtained after the analysis system carries out cluster analysis processing on a plurality of real-time vehicle data corresponding to a plurality of first preset time intervals; and performing loss of contact prediction on the vehicle equipment based on the corresponding historical characteristics.
The second preset time interval is greater than the first preset time interval, and the second preset time interval may be a day or a week, and the like.
In this embodiment, when the server determines that the heartbeat loss does not exist in the vehicle device within the second preset time interval, the server may perform the vehicle loss prediction in an offline processing manner.
Specifically, when it is determined that there is no vehicle heartbeat loss in the second preset time interval, the server may send a data acquisition request to the analysis system to request the analysis system to send vehicle data corresponding to the second preset time interval.
In this embodiment, the data obtaining request may carry a device number or a vehicle number of the corresponding vehicle device. The analysis system can inquire according to the data acquisition request and feed back the corresponding vehicle data to the server after inquiring.
In this embodiment, with reference to fig. 3, after the server obtains the vehicle data corresponding to the second preset time interval from the database, the server may extract the feature data of the obtained vehicle data through an offline model trained in advance, so as to obtain the historical features of the corresponding vehicle data.
In this embodiment, the historical features may include the heartbeat feature, the weather feature, the environmental feature of the vehicle, the wear feature of the vehicle, the vehicle status feature, and the like of the vehicle device described above. It should be noted that, here, the historical characteristic is characteristic data generated based on historical vehicle data of a certain time period, not a real-time characteristic number, and thus, the heartbeat characteristic of the vehicle here may refer to the variation amplitude of the voltage, the current and the lock temperature of the vehicle in a day; the weather characteristics refer to the highest and lowest temperature, humidity, wind power and the like in a day; the environmental characteristics of the vehicle refer to the position of the geographic region and the environment of the vehicle, which are experienced by the vehicle during the day.
In this embodiment, after the server generates the history feature according to the offline model, the history feature may be input into the loss of contact prediction model described above to perform the prediction of loss of contact of the vehicle, so as to obtain a prediction result of loss of contact or loss of contact of the vehicle.
In the above embodiment, by determining whether the heartbeat loss of the vehicle exists in the second preset time interval and when the heartbeat loss of the vehicle does not exist in the second preset time interval, the vehicle data is obtained from the analysis system in an off-line processing manner, the corresponding historical characteristics are generated according to the vehicle data, and the loss-of-connection prediction is performed, so that the vehicle equipment can be predicted regularly and the loss of the vehicle equipment is prevented.
In the embodiment, different prediction modes are selected according to different situations, namely when the heartbeat data of the vehicle equipment is not received in the first preset time interval, when the heartbeat loss of the vehicle is determined, the real-time vehicle data is adopted for prediction, when the heartbeat loss of the vehicle does not exist in the second preset time interval, the offline mode is adopted for loss prediction, different prediction modes can be selected according to different situations, the timeliness of prediction is improved, and the possibility of vehicle loss is reduced.
In one embodiment, before obtaining the vehicle data from the analysis system, the method may further include: and acquiring a plurality of real-time vehicle data corresponding to a plurality of first preset time intervals in a second preset time interval from the database, and sending the plurality of real-time vehicle data to the analysis system.
In this embodiment, after receiving heartbeat data uploaded by a vehicle in real time according to a first preset time interval, the server may store the received heartbeat data in the database. The server may package and send the plurality of heartbeat data (i.e., the plurality of real-time vehicle data) within the second preset time interval to the analysis system at a preset time point every day to be processed by the analysis system.
In this embodiment, after acquiring a plurality of pieces of real-time vehicle data within a second preset time interval, the analysis system may perform cluster analysis on the plurality of pieces of real-time vehicle data, for example, separately split the voltage, the current, and the like in each piece of real-time vehicle data obtained within the second preset time period, and calculate the maximum value, the minimum value, the average value, and the like of the current or the voltage within the second preset time interval, so as to obtain the vehicle data after the cluster analysis.
In the embodiment, the heartbeat data obtained by implementation is stored in the database, the real-time vehicle data corresponding to the first preset time intervals in the second preset time interval are obtained from the database and are sent to the analysis system, so that the communication frequency between the server and the analysis system can be reduced, and the communication resources can be saved.
It should be understood that, although the steps in the flowchart of fig. 2 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 2 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 4, there is provided a vehicle loss prediction apparatus including: the heartbeat data receiving module 100, the real-time vehicle data obtaining module 200, the real-time feature generating module 300 and the predicting module 400, wherein:
The heartbeat data receiving module 100 is configured to receive heartbeat data uploaded by a vehicle device according to a first preset time interval.
The real-time vehicle data acquiring module 200 is configured to determine that the heartbeat of the vehicle is lost when the heartbeat data of the vehicle device is not received within a first preset time interval, and use the heartbeat data received last time as the real-time vehicle data.
A real-time feature generation module 300 for generating real-time features from the real-time vehicle data.
The first prediction module 400 is used for predicting loss of contact of the vehicle equipment based on the real-time characteristics.
In one embodiment, the apparatus may further include:
and the general vehicle data acquisition module is used for acquiring general vehicle data of the corresponding vehicle equipment in the database before the first prediction module 400 predicts the loss of connection of the vehicle equipment based on the real-time characteristics.
And the general characteristic extraction module is used for extracting the characteristic data of the general vehicle data to obtain the general characteristics of the vehicle equipment.
And the splicing module is used for splicing the real-time characteristics and the general characteristics to obtain splicing characteristics.
In this embodiment, the first prediction module 400 is configured to predict the loss of connectivity of the vehicle device based on the splice characteristic.
In one embodiment, the real-time vehicle data acquisition module 200 may include:
and the waiting submodule is used for waiting for a preset time length when the heartbeat data of the vehicle equipment is not received in a first preset time interval.
The first real-time vehicle data acquisition submodule is used for determining that the heartbeat is lost when determining that the heartbeat data uploaded by the vehicle equipment is not received within the preset time length.
In one embodiment, the apparatus may further include:
and the judging module is used for judging whether the heartbeat of the vehicle is lost in a second preset time interval, and the second preset time interval comprises a plurality of first preset time intervals.
And the historical characteristic generating module is used for acquiring vehicle data from the analysis system in an off-line processing mode when the heartbeat loss of the vehicle does not exist in the second preset time interval, and generating corresponding historical characteristics according to the vehicle data, wherein the vehicle data are obtained by clustering, analyzing and processing a plurality of real-time vehicle data corresponding to a plurality of first preset time intervals by the analysis system.
And the second prediction module is used for performing loss of link prediction on the vehicle equipment based on the corresponding historical characteristics.
In one embodiment, the apparatus may further include:
And the second real-time vehicle data acquisition sub-module is used for acquiring a plurality of real-time vehicle data corresponding to a plurality of first preset time intervals in a second preset time interval from the database before the historical characteristic generation module acquires the vehicle data from the analysis system, and sending the plurality of real-time vehicle data to the analysis system.
In one embodiment, the first prediction module 400 is configured to perform loss of contact prediction through a pre-trained binary model based on real-time characteristics, and output a prediction result of loss of contact or loss of contact of the vehicle device within a preset time period in the future.
For specific limitations of the vehicle loss prediction device, reference may be made to the above limitations of the vehicle loss prediction method, which are not described herein again. The modules in the vehicle loss of communication prediction device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing vehicle loss prediction data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a vehicle loss of contact prediction method.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, there is provided a computer device comprising a memory storing a computer program and a processor implementing the following steps when the processor executes the computer program: receiving heartbeat data uploaded by vehicle equipment according to a first preset time interval; when the heartbeat data of the vehicle equipment is not received within a first preset time interval, determining that the heartbeat of the vehicle is lost, and taking the heartbeat data received at the last time as real-time vehicle data; generating real-time characteristics from the real-time vehicle data; and performing loss of connection prediction on the vehicle equipment based on the real-time characteristics.
In one embodiment, before the processor executes the computer program to predict the loss of connection of the vehicle device based on the real-time characteristic, the following steps may be further implemented: acquiring general vehicle data of corresponding vehicle equipment in a database; extracting feature data of the general vehicle data to obtain general features of the vehicle equipment; and splicing the real-time characteristics and the general characteristics to obtain splicing characteristics.
In this embodiment, the implementation of the offline prediction of the vehicle device based on the real-time characteristics by the processor when executing the computer program may include: and performing loss of connection prediction on the vehicle equipment based on the splicing characteristics.
In one embodiment, the processor, when executing the computer program, is configured to determine that the heartbeat of the vehicle is lost when the heartbeat data of the vehicle device is not received within a first preset time interval, and may include: when the heartbeat data of the vehicle equipment is not received within a first preset time interval, waiting for a preset time length; when it is determined that the heartbeat data uploaded by the vehicle equipment is not received within the preset time, it is determined that the heartbeat of the vehicle is lost.
In one embodiment, the processor when executing the computer program may further implement the following steps: judging whether the heartbeat of the vehicle is lost in a second preset time interval or not, wherein the second preset time interval comprises a plurality of first preset time intervals; when the heartbeat loss of the vehicle does not exist in the second preset time interval, acquiring vehicle data from the analysis system in an off-line processing mode, and generating corresponding historical characteristics according to the vehicle data, wherein the vehicle data are obtained after the analysis system carries out cluster analysis processing on a plurality of real-time vehicle data corresponding to a plurality of first preset time intervals; and performing loss of contact prediction on the vehicle equipment based on the corresponding historical characteristics.
In one embodiment, the processor when executing the computer program may further perform the following steps before obtaining the vehicle data from the analysis system: and acquiring a plurality of real-time vehicle data corresponding to a plurality of first preset time intervals in a second preset time interval from the database, and sending the plurality of real-time vehicle data to the analysis system.
In one embodiment, the processor, when executing the computer program, implements the loss of connectivity prediction for the vehicle device based on the real-time characteristics, and may include: and on the basis of real-time characteristics, performing loss-of-contact prediction through a pre-trained binary classification model, and outputting a prediction result of loss-of-contact or loss-of-contact of the vehicle equipment in a future preset time period.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: receiving heartbeat data uploaded by vehicle equipment according to a first preset time interval; when the heartbeat data of the vehicle equipment is not received within a first preset time interval, determining that the heartbeat of the vehicle is lost, and taking the heartbeat data received at the last time as real-time vehicle data; generating real-time characteristics from the real-time vehicle data; and performing loss of connection prediction on the vehicle equipment based on the real-time characteristics.
In one embodiment, before the computer program is executed by the processor to predict the loss of connection of the vehicle device based on the real-time characteristic, the following steps may be further implemented: acquiring general vehicle data of corresponding vehicle equipment in a database; extracting feature data of the general vehicle data to obtain general features of the vehicle equipment; and splicing the real-time characteristics and the general characteristics to obtain splicing characteristics.
In this embodiment, the computer program, when executed by the processor, for implementing the loss of connectivity prediction for the vehicle device based on the real-time characteristics, may include: and performing loss of connection prediction on the vehicle equipment based on the splicing characteristics.
In one embodiment, the computer program when executed by the processor to enable determining that a vehicle heartbeat is lost when heartbeat data of the vehicle device is not received within a first preset time interval may include: when the heartbeat data of the vehicle equipment is not received within a first preset time interval, waiting for a preset time length; when it is determined that the heartbeat data uploaded by the vehicle equipment is not received within the preset time, it is determined that the heartbeat of the vehicle is lost.
In one embodiment, the computer program when executed by the processor further performs the steps of: judging whether the heartbeat of the vehicle is lost in a second preset time interval or not, wherein the second preset time interval comprises a plurality of first preset time intervals; when the heartbeat loss of the vehicle does not exist in the second preset time interval, acquiring vehicle data from the analysis system in an off-line processing mode, and generating corresponding historical characteristics according to the vehicle data, wherein the vehicle data are obtained after the analysis system carries out cluster analysis processing on a plurality of real-time vehicle data corresponding to a plurality of first preset time intervals; and performing loss of contact prediction on the vehicle equipment based on the corresponding historical characteristics.
In one embodiment, the computer program when executed by the processor performs the steps of, before obtaining vehicle data from the analysis system: and acquiring a plurality of real-time vehicle data corresponding to a plurality of first preset time intervals in a second preset time interval from the database, and sending the plurality of real-time vehicle data to the analysis system.
In one embodiment, the computer program when executed by the processor to implement the misconnection prediction for the vehicle device based on the real-time features may include: and on the basis of real-time characteristics, performing loss-of-contact prediction through a pre-trained binary classification model, and outputting a prediction result of loss-of-contact or loss-of-contact of the vehicle equipment in a future preset time period.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A vehicle loss of contact prediction method, the method comprising:
receiving heartbeat data uploaded by vehicle equipment according to a first preset time interval;
when the heartbeat data of the vehicle equipment is not received within the first preset time interval, determining that the heartbeat of the vehicle is lost, and taking the heartbeat data received at the last time as real-time vehicle data;
Generating real-time characteristics according to the real-time vehicle data;
and performing loss of contact prediction on the vehicle equipment based on the real-time characteristics.
2. The method of claim 1, wherein prior to predicting the vehicle device for loss of connectivity based on the real-time characteristic, further comprising:
acquiring general vehicle data corresponding to the vehicle equipment in a database;
extracting feature data of the general vehicle data to obtain general features of vehicle equipment;
splicing the real-time features and the general features to obtain splicing features;
the predicting of the loss of contact of the vehicle device based on the real-time characteristics comprises:
and performing loss of contact prediction on the vehicle equipment based on the splicing characteristics.
3. The method of claim 1, wherein determining that a vehicle heartbeat is lost when heartbeat data of the vehicle device is not received within the first preset time interval comprises:
when the heartbeat data of the vehicle equipment is not received within a first preset time interval, waiting for a preset time length;
and when determining that the heartbeat data uploaded by the vehicle equipment is not received within the preset time, determining that the heartbeat of the vehicle is lost.
4. The method of claim 1, further comprising:
judging whether the heartbeat of the vehicle is lost in a second preset time interval or not, wherein the second preset time interval comprises a plurality of first preset time intervals;
when the heartbeat loss of the vehicle does not exist in the second preset time interval, acquiring vehicle data from an analysis system in an off-line processing mode, and generating corresponding historical characteristics according to the vehicle data, wherein the vehicle data are obtained by performing cluster analysis processing on a plurality of real-time vehicle data corresponding to the plurality of first preset time intervals by the analysis system;
and performing loss of contact prediction on the vehicle equipment based on the corresponding historical characteristics.
5. The method of claim 4, wherein prior to obtaining vehicle data from the analysis system, further comprising:
and acquiring a plurality of real-time vehicle data corresponding to a plurality of first preset time intervals in the second preset time interval from a database, and sending the plurality of real-time vehicle data to an analysis system.
6. The method of claim 1, wherein the predicting the vehicle device for loss of connectivity based on the real-time characteristic comprises:
And performing loss of connection prediction through a pre-trained binary classification model based on the real-time characteristics, and outputting a prediction result of loss of connection or loss of connection of the vehicle equipment in a future preset time period.
7. A vehicle loss prediction apparatus, characterized in that the apparatus comprises:
the heartbeat data receiving module is used for receiving heartbeat data uploaded by the vehicle equipment according to a first preset time interval;
the real-time vehicle data acquisition module is used for determining that the heartbeat of the vehicle is lost when the heartbeat data of the vehicle equipment is not received within the first preset time interval, and taking the heartbeat data received at the last time as real-time vehicle data;
the real-time characteristic generating module is used for generating real-time characteristics according to the real-time vehicle data;
a first prediction module to predict loss of contact for the vehicle device based on the real-time characteristics.
8. The apparatus of claim 7, further comprising:
the universal vehicle data acquisition module is used for acquiring universal vehicle data corresponding to the vehicle equipment in a database before the first prediction module predicts the loss of connection of the vehicle equipment based on the real-time characteristics;
The general characteristic extraction module is used for extracting the characteristic data of the general vehicle data to obtain the general characteristics of the vehicle equipment;
the splicing module is used for splicing the real-time characteristics and the general characteristics to obtain splicing characteristics;
the first prediction module is used for performing loss of contact prediction on the vehicle equipment based on the splicing characteristics.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010559051.3A CN111860954A (en) | 2020-06-18 | 2020-06-18 | Vehicle loss of contact prediction method and device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010559051.3A CN111860954A (en) | 2020-06-18 | 2020-06-18 | Vehicle loss of contact prediction method and device, computer equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111860954A true CN111860954A (en) | 2020-10-30 |
Family
ID=72987295
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010559051.3A Pending CN111860954A (en) | 2020-06-18 | 2020-06-18 | Vehicle loss of contact prediction method and device, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111860954A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112949869A (en) * | 2021-02-08 | 2021-06-11 | 上海钧正网络科技有限公司 | Vehicle loss of contact prediction method, system, medium and server |
Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150120087A1 (en) * | 2013-10-31 | 2015-04-30 | International Business Machines Corporation | Data transmission for an internet of vehicles system |
| CN105551244A (en) * | 2015-12-08 | 2016-05-04 | 重庆云途交通科技有限公司 | Dynamic path planning method |
| CN105827697A (en) * | 2016-03-14 | 2016-08-03 | 广州趣丸网络科技有限公司 | User off-line detection method and user off-line detection system |
| CN106534377A (en) * | 2016-12-29 | 2017-03-22 | 深圳天珑无线科技有限公司 | Terminal perception state control method and terminal perception state control device |
| US20170269585A1 (en) * | 2016-03-21 | 2017-09-21 | Swarmx Pte. Ltd. | System, method and server for managing stations and vehicles |
| KR20170116423A (en) * | 2016-04-11 | 2017-10-19 | 한국전자통신연구원 | Method and apparatus for thing collaboration services based on social community |
| CN108053075A (en) * | 2017-12-27 | 2018-05-18 | 北京中交兴路车联网科技有限公司 | A kind of scrap-car Forecasting Methodology and system |
| CN108846519A (en) * | 2018-06-14 | 2018-11-20 | 大唐高鸿信息通信研究院（义乌）有限公司 | Safe driving K arest neighbors prediction technique based on vehicle-mounted short distance communication network |
| WO2019003234A1 (en) * | 2017-06-26 | 2019-01-03 | Telefonaktiebolaget Lm Ericsson (Publ) | Network node and method performed therein for generating a missing data value of a set of data from one or more devices |
| CN109726843A (en) * | 2017-10-30 | 2019-05-07 | 阿里巴巴集团控股有限公司 | The method, apparatus and terminal of allocation data prediction |
| CN109905259A (en) * | 2017-12-08 | 2019-06-18 | 中国电信股份有限公司 | Communicate to connect maintenance method, system and relevant device |
| CN110223523A (en) * | 2018-03-02 | 2019-09-10 | 深圳市智行能源技术有限公司 | The detector of magnetic vehicle with low power and its dormancy awakening method based on lora technology |
| CN110493070A (en) * | 2018-05-15 | 2019-11-22 | 深圳市尚云互联技术有限公司 | Suspension notification method and its application system |
| CN110599301A (en) * | 2019-09-05 | 2019-12-20 | 上海钧正网络科技有限公司 | Vehicle management method, device, computer equipment and storage medium |
| CN110708283A (en) * | 2019-08-29 | 2020-01-17 | 中国第一汽车股份有限公司 | Method and device for processing abnormal connection between vehicle and terminal, vehicle and storage medium |
| CN110826867A (en) * | 2019-10-21 | 2020-02-21 | 上海钧正网络科技有限公司 | Vehicle management method, device, computer equipment and storage medium |
| CN111107132A (en) * | 2019-11-08 | 2020-05-05 | 上海钧正网络科技有限公司 | Early warning method, system, device, equipment and storage medium |
| CN111126654A (en) * | 2018-11-01 | 2020-05-08 | 北京嘀嘀无限科技发展有限公司 | Shared vehicle ride-back probability and loss probability prediction method and device |
| CN111162931A (en) * | 2019-12-12 | 2020-05-15 | 上海钧正网络科技有限公司 | Shared vehicle management method, device, computer equipment and readable storage medium |
| US20200160630A1 (en) * | 2017-07-24 | 2020-05-21 | Beijing Didi Infinity Technology And Development Co., Ltd. | Methods and systems for vehicle management |
| WO2020113619A1 (en) * | 2018-12-06 | 2020-06-11 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for identifying damaged vehicle in online to offline service |
-
2020
- 2020-06-18 CN CN202010559051.3A patent/CN111860954A/en active Pending
Patent Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150120087A1 (en) * | 2013-10-31 | 2015-04-30 | International Business Machines Corporation | Data transmission for an internet of vehicles system |
| CN105551244A (en) * | 2015-12-08 | 2016-05-04 | 重庆云途交通科技有限公司 | Dynamic path planning method |
| CN105827697A (en) * | 2016-03-14 | 2016-08-03 | 广州趣丸网络科技有限公司 | User off-line detection method and user off-line detection system |
| US20170269585A1 (en) * | 2016-03-21 | 2017-09-21 | Swarmx Pte. Ltd. | System, method and server for managing stations and vehicles |
| KR20170116423A (en) * | 2016-04-11 | 2017-10-19 | 한국전자통신연구원 | Method and apparatus for thing collaboration services based on social community |
| CN106534377A (en) * | 2016-12-29 | 2017-03-22 | 深圳天珑无线科技有限公司 | Terminal perception state control method and terminal perception state control device |
| WO2019003234A1 (en) * | 2017-06-26 | 2019-01-03 | Telefonaktiebolaget Lm Ericsson (Publ) | Network node and method performed therein for generating a missing data value of a set of data from one or more devices |
| US20200160630A1 (en) * | 2017-07-24 | 2020-05-21 | Beijing Didi Infinity Technology And Development Co., Ltd. | Methods and systems for vehicle management |
| CN109726843A (en) * | 2017-10-30 | 2019-05-07 | 阿里巴巴集团控股有限公司 | The method, apparatus and terminal of allocation data prediction |
| CN109905259A (en) * | 2017-12-08 | 2019-06-18 | 中国电信股份有限公司 | Communicate to connect maintenance method, system and relevant device |
| CN108053075A (en) * | 2017-12-27 | 2018-05-18 | 北京中交兴路车联网科技有限公司 | A kind of scrap-car Forecasting Methodology and system |
| CN110223523A (en) * | 2018-03-02 | 2019-09-10 | 深圳市智行能源技术有限公司 | The detector of magnetic vehicle with low power and its dormancy awakening method based on lora technology |
| CN110493070A (en) * | 2018-05-15 | 2019-11-22 | 深圳市尚云互联技术有限公司 | Suspension notification method and its application system |
| CN108846519A (en) * | 2018-06-14 | 2018-11-20 | 大唐高鸿信息通信研究院（义乌）有限公司 | Safe driving K arest neighbors prediction technique based on vehicle-mounted short distance communication network |
| CN111126654A (en) * | 2018-11-01 | 2020-05-08 | 北京嘀嘀无限科技发展有限公司 | Shared vehicle ride-back probability and loss probability prediction method and device |
| WO2020113619A1 (en) * | 2018-12-06 | 2020-06-11 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for identifying damaged vehicle in online to offline service |
| CN110708283A (en) * | 2019-08-29 | 2020-01-17 | 中国第一汽车股份有限公司 | Method and device for processing abnormal connection between vehicle and terminal, vehicle and storage medium |
| CN110599301A (en) * | 2019-09-05 | 2019-12-20 | 上海钧正网络科技有限公司 | Vehicle management method, device, computer equipment and storage medium |
| CN110826867A (en) * | 2019-10-21 | 2020-02-21 | 上海钧正网络科技有限公司 | Vehicle management method, device, computer equipment and storage medium |
| CN111107132A (en) * | 2019-11-08 | 2020-05-05 | 上海钧正网络科技有限公司 | Early warning method, system, device, equipment and storage medium |
| CN111162931A (en) * | 2019-12-12 | 2020-05-15 | 上海钧正网络科技有限公司 | Shared vehicle management method, device, computer equipment and readable storage medium |
Non-Patent Citations (2)
| Title |
|---|
| SHELMA GEORGE等: "Off-line and real-time simulation Modelling of Electric Vehicle power train", 《2014 ANNUAL INTERNATIONAL CONFERENCE ON EMERGING RESEARCH AREAS: MAGNETICS, MACHINES AND DRIVES (AICERA/ICMMD)》, 25 September 2014 (2014-09-25) * |
| 黄秀彬等: "面向实时感知的车联网业务监控应用研究", 《信息技术》, vol. 44, no. 1, 25 January 2020 (2020-01-25), pages 95 - 100 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112949869A (en) * | 2021-02-08 | 2021-06-11 | 上海钧正网络科技有限公司 | Vehicle loss of contact prediction method, system, medium and server |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110995468B (en) | System fault processing method, device, equipment and storage medium of system to be analyzed | |
| CN109768623B (en) | Monitoring method and device of power system, computer equipment and storage medium | |
| CN108280995B (en) | Road condition data processing method and device of Internet of vehicles and computer equipment | |
| CN110750267A (en) | Service information updating method and device, computer equipment and storage medium | |
| CN108286981B (en) | Vehicle path planning method and device of Internet of vehicles and computer equipment | |
| CN111966289B (en) | Partition optimization method and system based on Kafka cluster | |
| CN111552246A (en) | Equipment production line scheduling method applied to smart park and cloud computing server | |
| CN110868241A (en) | Underground cable fault early warning method and device based on multiple models | |
| CN110837956A (en) | Underground cable inspection task distribution method and device | |
| CN110866634A (en) | Underground cable fault early warning method and device based on model selection | |
| CN110659373B (en) | Image retrieval method, image retrieval device, computer device and storage medium | |
| CN111860954A (en) | Vehicle loss of contact prediction method and device, computer equipment and storage medium | |
| CN112800090A (en) | Data processing method combining edge computing and path analysis and big data cloud platform | |
| CN113592114A (en) | User fault reporting research and judgment method and device in power grid, computer equipment and storage medium | |
| CN111224939A (en) | Task request intercepting method and device, computer equipment and storage medium | |
| CN110826606A (en) | Element matching method, device, server and readable storage medium | |
| CN112507010B (en) | Service data processing method and device, computer equipment and storage medium | |
| CN116302580A (en) | Method and device for scheduling calculation force resources of nano relay | |
| CN115277803A (en) | Message management method, system, computer equipment and storage medium | |
| CN113784235B (en) | Cable current-carrying capacity monitoring system, method, device, gateway equipment and storage medium | |
| CN108429347B (en) | Power distribution network line equipment monitoring method and device, computer equipment and storage medium | |
| CN110826867A (en) | Vehicle management method, device, computer equipment and storage medium | |
| CN111294261A (en) | Signal processing method, signal processing device, computer equipment and storage medium | |
| CN115664992A (en) | Network operation data processing method and device, electronic equipment and medium | |
| CN111708795B (en) | Object identification generation method, object identification updating device, computer equipment and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |