CN113961258B - Automobile state anomaly identification method, system and storage medium based on time sequence transfer - Google Patents
Automobile state anomaly identification method, system and storage medium based on time sequence transfer Download PDFInfo
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Abstract
The invention discloses a method, a system and a storage medium for identifying abnormal automobile states based on time sequence transfer, which belong to the field of automobile electronic systems, utilize automobile signal data, construct time sequence state tables containing states of normal awakening, abnormal awakening, sleep, data loss, engine starting and no sleep after defence based on control logic of people on an automobile, extract automobile time sequence states, and list relevant active awakening sources, and construct an automobile time sequence state transfer table associated with state transfer based on the automobile signal tables and the time sequence states; and extracting the abnormal state of the vehicle by using the active wake-up source according to the time sequence state table, constructing a network abnormal data table, and remotely identifying the abnormal condition of the vehicle, the starting time and the ending time of the fault according to the network abnormal data table so as to realize the remote detection and monitoring of the abnormal condition of the vehicle in the parking state.
Description
Technical Field
The invention belongs to the field of automobile electronic systems, and particularly relates to an automobile state anomaly identification method based on time sequence state transition.
Background
Automobile intellectualization has become an automobile development trend, automobile cloud cooperation is a basis for realizing automobile intellectualization, and more directions can be excavated in automobile internet data along with the continuous deepening of the cognition of internet automobiles. The common knowledge of the vehicle network is an important direction for researching the internet data of the vehicle, and the vehicle network abnormality can cause the problems of vehicle power shortage, unstable data transmission and the like.
Currently, the method generally relies on the external Can equipment to detect the network state of the automobile, and has the following defects: the time period for acquiring the vehicle Can data is long; can equipment requires additional costs; batch detection is difficult and thus network anomaly analysis cannot be performed in batches. The internet of things data is usually that information of a vehicle end is transmitted to a cloud server through a network data transmission module arranged in a vehicle. The car information includes heartbeat data at the time of sleeping and various information at the time of waking up the car. The information when the vehicle wakes up includes: status signals such as door switch, lock switch, car switch, engine start-stop, etc., continuous signals such as vehicle speed, vehicle network signal strength, battery voltage, etc. The abnormal vehicle network can cause the problems of vehicle power shortage, unstable transmission data and the like, and the identification of the abnormal vehicle network is an important research direction in the field of automobile electronic systems.
Publication No.: CN113110967a, entitled "method, device, equipment and readable storage medium for early warning of vehicle awakening from abnormal dormancy" provides a method for early warning of vehicle awakening from abnormal dormancy. The method comprises the following steps: after the vehicle power supply gear is in the OFF gear, detecting whether the vehicle gateway is normally dormant; if the vehicle gateway is not in normal dormancy, carrying out abnormal dormancy reminding; if the vehicle gateway is dormant normally, detecting whether the vehicle gateway is awakened abnormally; and when the vehicle gateway is abnormally awakened, carrying out abnormal awakening reminding. After the vehicle is powered down, based on monitoring the gateway state, abnormal detection of vehicle dormancy and awakening is achieved, and when abnormal dormancy or abnormal awakening exists, abnormal reminding is conducted, so that related personnel can timely find and process the abnormal state of the vehicle. Publication No.: CN112835735a, entitled "method, system, and vehicle for solving abnormal dormancy and abnormal wake-up of a vehicle" discloses a method for solving abnormal dormancy and abnormal wake-up of a vehicle, and determines whether a vehicle is in a normal dormancy state, if so, the vehicle is in a normal dormancy state; if not, recording the abnormal dormancy data of the vehicle; when the vehicle is in a normal sleep state, judging whether the vehicle is in an abnormal wake state, if so, recording abnormal wake data of the vehicle; if not, ending the judging program; the vehicle abnormal dormancy data and the vehicle abnormal awakening data are stored in the local DID. The method can quickly locate the fault point, reduce the resource investment of the user and improve the satisfaction degree of the user. Publication No.: the Chinese patent application of CN109581998A name "a cloud diagnosis system for dealing with vehicle faults" provides a cloud diagnosis system for dealing with vehicle faults, which comprises a vehicle-mounted terminal, a cloud server and a mobile client, wherein all components are communicated through a 4G network; the vehicle-mounted terminal is used for collecting vehicle information, sending the collected data to the cloud server through the 4G network, and simultaneously storing the collected data into the TF card with a time stamp; the cloud server is used for logging in, logging out, receiving data, sending the data, storing the data and sending the data to the mobile client; the mobile client is used for receiving the data sent by the cloud server, and the user receives and operates the information at the mobile client.
The above-mentioned publication does not consider in real time that the vehicle is in the remote monitoring that "not sleep", "unusual wake-up", "data loss" appear in the car after the shutting, to the vehicle that does not use for a long time appear the electric deficiency problem, the vehicle after breaking down in the field can not in time provide the remote rescue.
Disclosure of Invention
Aiming at the problem that the prior art does not monitor the automobile state in the locking period in real time and can not provide remote early warning for a user in real time, the invention extracts the state signals of the components related to the human operation after the locking of the automobile, takes the automobile signal table with the timestamp as input, constructs the time sequence state table through the time sequence state transition condition, further extracts the abnormal state of awakening, no sleep after defence and abnormal data loss of the automobile network in the time sequence state transition table, and realizes the detection and monitoring of the abnormal state of the automobile.
The technical scheme for solving the technical problems is that under the condition that hardware is not added, the data uploaded to the cloud end by a vehicle end is utilized, a time sequence state table comprising the states of the vehicle such as normal awakening, abnormal awakening, sleep, data loss, engine starting and non-sleep after fortification is constructed by means of normal control logic of the vehicle, and abnormal information comprising the abnormal awakening, non-sleep after fortification and data loss is extracted, so that detection and monitoring of abnormal states of the vehicle are realized. Specifically, a vehicle state anomaly detection and identification method based on time sequence state transition is provided, which comprises the following steps: analyzing the obtained original vehicle data in the data preprocessing stage, and generating a vehicle information table; extracting and defining a vehicle time sequence state, and setting out a related active wake-up source, and constructing a vehicle time sequence state transition table related to state transition based on a vehicle signal table and the time sequence state; traversing the sequential state table, extracting the abnormal state of the vehicle from the sequential state table, constructing a network abnormal data table, and remotely identifying the abnormal condition of the vehicle according to the network abnormal data table.
Further preferably, the header of the vehicle information table includes an event occurrence status field and a continuous signal field, wherein the status field includes: timestamp, left front door, right front door, left back door, right back door, left front lock, right front lock, left back lock, right back lock, engine start-stop, power supply gear, continuous signal field includes: vehicle speed, battery voltage.
Further preferably, the current time sequence state of the vehicle is determined according to the power consumption condition of the storage battery in the dormant state of the vehicle. Comprising the following steps: when the storage battery is detected to be in a high-power consumption state and is in a wake-up state, the storage battery is in a low-power consumption state and is in a sleep state, when the power consumption of the storage battery is increased to be the fortification abnormality after fortification, no active wake-up is performed in a designated time, the power consumption of the storage battery is increased to be the wake-up abnormality, a data frame is detected, and the time difference between the front frame data and the rear frame data is larger than a designated value and is in a data loss state.
Further preferably, the vehicle signal data is acquired from the vehicle signal table in the order of the time stamps from small to large, the state transition condition judgment is performed based on the current vehicle time sequence state and the current vehicle signal, if the state transition condition is satisfied, the state transition is performed according to the time sequence state transition condition table, and the current state in the time sequence state table is updated, otherwise, the next frame data of the vehicle signal table is continuously acquired. And outputting the time sequence state table until the vehicle signal table is traversed, and ending the state transition.
Further preferably, the vehicle information table is called to acquire the first frame data, the sequential state transition condition table is queried and traversed, if the transition condition is met, state transition is carried out according to the sequential state transition condition table, the current state after transition is added to the last row of the sequential state transition table, the sequential state table is updated, otherwise, the vehicle information table is traversed continuously to acquire the next frame data as the current data, the state represented by the last row of data in the sequential state table is acquired as the current state, whether the transition condition is met is judged until the vehicle information table is traversed completely, and the sequential state table is output.
The invention also provides a vehicle state abnormality detection and identification system based on time sequence state transition, which comprises: the vehicle state transition condition table comprises a data preprocessing unit, a vehicle information table, a time sequence state acquisition unit, a vehicle state transition condition table and a time sequence logic control unit, wherein the data preprocessing unit analyzes the acquired original data and generates a vehicle information table; the time sequence state acquisition unit acquires and defines time sequence states of normal, dormant, non-dormant after fortification and data loss of the vehicle, and sets out related active wake-up sources, and constructs a vehicle time sequence state transition table related to state transition based on a vehicle signal table and the time sequence states; the sequential logic control unit traverses the sequential state table, extracts the state of awakening abnormality, data loss and non-sleep after fortification in the sequential state table, constructs a network abnormality data table, and the cloud identification system remotely identifies the abnormal condition of the vehicle according to the network abnormality data table.
The invention also provides a computer readable storage medium, which comprises at least one instruction, at least one section of program and a code set instruction set, and the computer can make the computer perform the identification method
The method is based on vehicle state information, utilizes an active wake-up source to extract the abnormal state of the vehicle, reduces the dependence on diagnosis equipment, conforms to the development concept of intelligent automobiles, and can diagnose the network abnormality of the vehicle at a more abstract level. Meanwhile, a large amount of vehicle state information data of the cloud can be combined, so that the high calculation force of the cloud can be utilized to carry out abnormality diagnosis on each vehicle, the problem of vehicle faults can be solved for clients in a targeted mode, abnormal states of the vehicle network can be analyzed in batches, and the later design is improved in a directional mode. And constructing a time sequence state table containing the states of the vehicle, such as normal awakening, abnormal awakening, data loss, engine starting and no sleep after fortification, extracting the abnormality containing the information, such as abnormal awakening, no sleep after fortification and data loss from the time sequence state table, and detecting and monitoring the abnormal state of the vehicle.
The invention deduces the abnormal state of the vehicle based on the vehicle information with the timestamp and by combining the man-vehicle control relationship, reduces the dependence on hardware, and can deduce the abnormality of the vehicle at a more abstract level. The cloud data can be utilized to monitor the network state of each vehicle, if abnormality occurs, abnormal information is immediately pushed to the terminal, and a user is rapidly reminded.
Drawings
FIG. 1 is a vehicle signal representation intent, the header of which includes: timestamp, sleep state signal, left front door switch signal, right front door switch signal, left back door switch signal, right back door switch signal, left front lock switch signal, right front lock switch signal, left back lock switch signal, right back lock switch signal, guard state signal, engine start-stop state signal, power supply gear signal. The status information of the right ellipsis part in the schematic table is all 0;
FIG. 2 is a timing state representation intent, the header comprising: vehicle state, jump time, whether abnormal;
FIG. 3 is a diagram of a vehicle network anomaly representation intent, the header including: fault type, start time, end time, single fault time (min);
fig. 4 is a state transition flow chart, in which a current vehicle time sequence state (a state corresponding to the last line in a current time sequence state table) is combined with a current vehicle signal, a state transition condition is determined, if the state transition condition is satisfied, the time sequence state is transitioned, the current state is updated, and the current state is added in the time sequence state table, otherwise, the time sequence state table is unchanged, the vehicle signal table is continuously traversed to obtain the next frame of data, until the vehicle signal table is traversed, the time sequence state table is output, and the time sequence state transition process is ended.
Detailed Description
In order to facilitate an understanding of the present invention, a detailed description of specific embodiments of the invention will be given below with reference to the accompanying drawings and specific examples.
The invention is based primarily on generating a vehicle signal table related to person-to-vehicle operation, determining an active wake-up source and a time-sequence state. And time sequence states such as normal awakening, abnormal awakening, dormancy, data loss, no dormancy after fortification and the like are defined. And constructing a time sequence state table. Based on the definition of the vehicle signal table and the time sequence state, the time sequence state table is constructed in a state transition relation. And constructing a vehicle network anomaly table. And extracting abnormal states of awakening abnormality, data loss and no sleep after fortification from the time sequence state table, and constructing a vehicle network abnormal table.
A vehicle state abnormality detection and identification method based on time sequence state transition comprises the following steps: analyzing the obtained original vehicle data in the data preprocessing stage, and generating a vehicle information table; extracting and defining a vehicle time sequence state, and setting out a related active wake-up source, and constructing a vehicle time sequence state transition table related to state transition based on a vehicle signal table and the time sequence state; traversing the sequential state table, extracting the abnormal state of the vehicle from the sequential state table, constructing a network abnormal data table, and remotely identifying the abnormal condition of the vehicle according to the network abnormal data table. And acquiring the vehicle signal data from the vehicle signal table in the sequence of the time stamps from small to large, judging the state transition condition based on the current vehicle time sequence state and the current vehicle signal, performing state transition according to the time sequence state transition condition table if the state transition condition is met, and updating the current state in the time sequence state table, otherwise, continuing to acquire the next frame data of the vehicle signal table. And outputting the time sequence state table until the vehicle signal table is traversed, and ending the state transition.
First, a vehicle signal table related to a person-to-vehicle operation is generated, and as shown in fig. 1, the vehicle signal table is constructed, that is, the table header includes: timestamp, sleep state signal, left front door switch signal, right front door switch signal, left back door switch signal, right back door switch signal, left front lock switch signal, right front lock switch signal, left back lock switch signal, right back lock switch signal, guard state signal, engine start-stop state signal, power supply gear signal.
Messages reflecting the states of all parts of the vehicle are periodically generated and cached in a vehicle memory, and the message information can be utilized by a vehicle controller or can be transmitted to a cloud for data analysis through a network transmission module. Extracting person-to-vehicle operation related messages from vehicle messages collected in a vehicle memory or cloud to generate a vehicle signal list, wherein the signals generally include but are not limited to: the device comprises a sleep state signal, a left front door switch signal, a right front door switch signal, a left rear door switch signal, a right rear door switch signal, a left front lock switch signal, a right front lock switch signal, a left rear lock switch signal, a right rear lock switch signal, a guard state signal, an engine start-stop state signal and a power supply gear signal, and each signal is provided with a time stamp of signal occurrence time. Thus, the header of the vehicle signal table contains the signals of the above names and the time stamps of the occurrence times thereof. Finally, because of the disorder of the messages in the buffer memory, the analyzed vehicle signal list needs to be ordered according to the time stamp.
As shown in fig. 2, the time-series state table records the time-series state of the vehicle in time sequence, and the header includes: vehicle state, jump time, whether unusual, wherein, vehicle state content includes: normal awakening, abnormal awakening, sleep, data loss and no sleep after fortification.
Determining an active wake-up source and a timing state, comprising:
I. active wake-up source: i.e. actions related to human operation such as door opening, door closing, lock opening, lock closing, trunk opening, lock opening, disarming, engine start, etc.
II, wake-up state: activating an automobile network, wherein a sleep signal is 0;
III, sleep state: the automobile sleeps on a network, and the sleeping signal is 1;
IV, after fortification, not sleep: after the vehicle receives the fortification command, the vehicle does not sleep in a period of time under the condition of no active wake-up source, and is in an abnormal state;
and V, awakening abnormality: the method is a sub-state of the awakening state, and no active awakening source exists in a small time range of the awakening time, so that the vehicle is not awakened by people, and the vehicle is in an abnormal state;
and VI, waking up normally: the vehicle is in a sub-state of a wake-up state, and an active wake-up source is arranged in a small time range of the wake-up time, so that the vehicle is shown to be in a normal state when people wake up the vehicle;
VII, data loss: the time difference between the front frame data and the rear frame data is larger than a specified value, and the periodic vehicle signal data is considered to be lost, so that the abnormal transition state is realized.
As shown in fig. 3, the table of the network anomaly of the vehicle, that is, the anomaly state extracted from the time-series state table, includes: fault type, start time, end time, single fault time (min), wherein the fault type comprises: wake-up abnormality, data loss, no sleep after fortification.
Obtaining a vehicle network anomaly table based on the anomaly state in the time sequence state table: and traversing the sequence state table to extract an abnormal state, and writing the fault type, the starting time, the ending time and the single fault time into the vehicle network abnormal table. Wherein, the fault type includes: the method comprises the steps of waking up abnormality, data loss and no sleep after fortification, wherein the fault starting time is the jump time corresponding to the fault state in the time sequence state table, the fault ending time is the jump time corresponding to the next state in the fault state, and the single fault time is the time difference between the fault ending time and the fault starting time.
The time sequence state table is constructed as shown in fig. 4, the vehicle signal data are acquired from the vehicle signal table in the sequence of the time stamps from small to large, the state transition condition judgment is carried out based on the current vehicle time sequence state and the current vehicle signal, if the state transition condition is met, the state transition is carried out according to the time sequence state transition condition, the current state in the time sequence state table is updated, and otherwise, the vehicle signal table is continuously traversed to acquire the next frame data. And outputting the time sequence state table until the vehicle signal table is traversed, and ending the state transition.
TABLE 1 State transition Condition
As shown in table 1, the time sequence state transition conditions are constructed according to the time sequence state table real-time monitoring: when a sleep signal of 0 occurs in a sleep transition or a data loss transition is detected. Triggering state transition and transition to an awake state; when the sleep signal is detected to be 1 under the state of awakening or data loss transition, triggering state transition and transitioning to a sleep state; in the wake-up state, no active wake-up source generates trigger state transition jump to wake-up abnormality in the appointed time; the method comprises the steps that in a wake-up state, a jump to a wake-up normal state is detected in a designated time; detecting that a fortification signal appears under the wake-up normal state, and no active wake-up source appears, triggering state transition when a sleep signal is kept to be 0 in a preset time, and not sleeping after the transition to fortification; an active wake-up source triggering state transition instruction appears in a non-sleep transition state after the fortification, and the active wake-up source triggering state transition instruction is transferred to a wake-up normal state; the time stamp difference corresponding to the current frame data and the next frame data is larger than the appointed value, triggers a state transition instruction and transitions to a data loss state. Wherein the active wake-up source may be a person-to-vehicle operation, comprising: door opening, door closing, lock opening, lock closing, trunk opening, lock opening, defence releasing, engine starting and the like. Particularly, in the time range before and after the current time stamp, the vehicle signal jump caused by the operation of the person and the vehicle can be found in the vehicle signal table, namely, the active wake-up source appears under the current time stamp, otherwise, no active wake-up source appears is determined.
Further, based on the abnormal state in the time sequence state table, the fault type, the fault starting time, the fault ending time and the single fault time (min) are extracted and written into the vehicle network abnormal table. Wherein, the fault type includes: the system is characterized in that the system is not sleep after abnormal awakening, data loss and fortification, the fault starting time is the jump time corresponding to the fault state in the state table, the fault ending time is the jump time corresponding to the next state in the fault state, and the single fault time is the time difference between the fault ending time and the fault starting time.
Constructing a sequential logic table based on the vehicle state table and the time sequence state, wherein the process involves the transition of each state, the state transition condition is shown in table 1, and the specific steps are as follows:
i, starting. Acquiring first frame data from a vehicle information table, and taking the start as an initialization state to enter a state flow II;
and II, performing state circulation. As shown in fig. 4, the state transition condition corresponding to the sequence number in the table 1 is queried based on the state transition condition sequence number corresponding to the current time sequence state, if the state transition condition is met, the state transition is performed, the update of the current state is completed, the name of the current state, the time stamp of the current state transition and whether the current state is an abnormal state are added to the last time sequence state table, otherwise, the original state is maintained to enter iii;
if the data of the vehicle information table is not traversed, acquiring next frame data in the vehicle information table as current data, and acquiring a state represented by the last line of data in the time sequence state table as current state to enter II, otherwise, entering IV;
IV, ending. And outputting the time sequence state table.
Illustrated in the exemplary figures herein:
1. initializing a state table: "vehicle state: starting and jumping moments: NULL, abnormal or not: 0';
2. and acquiring the 1 st frame data from the vehicle information table, and entering state circulation conversion. The first frame data sleep signal is 1, and the state circulation condition 2 corresponding to the initial transition state is satisfied, so that the current state is updated to the sleep state, and the state circulation condition is added in the time sequence transition state table: "vehicle state: sleep, jump time: 1602076928, whether or not abnormal: 0';
3. and acquiring the 2 nd frame data from the vehicle signal table, and entering state circulation conversion. The current time sequence state is a sleep state, and the corresponding state transition conditions 1 and 3 are not met, so that the sleep state is maintained;
4. and acquiring the 3 rd frame data from the vehicle signal table, and entering state circulation conversion. The current time sequence state is a sleep state, the sleep signal is 0, the corresponding state transition bar 1 is established, the state transitions to an awake state, and an active awake source with door opening action appears near the corresponding moment (60 s before and after taking in the example) is updated to an awake normal state, and the active awake source is added in a time sequence transition state table: "vehicle state: wake up normal, jump time: 1602076968, whether or not abnormal: 0';
5. and acquiring the 4 th frame data from the vehicle signal table, and entering state circulation conversion. The current time sequence state is a wake-up normal state, and the corresponding state transition conditions 2, 3 and 6 are not met, so that the wake-up normal state is maintained;
6. and acquiring the 5 th frame data from the vehicle signal table, and entering state circulation conversion. The current time sequence state is a wake-up normal state, and the corresponding state transition conditions 2, 3 and 6 are not met, so that the wake-up normal state is maintained;
7. and acquiring the data of the 6 th frame from the vehicle signal table, and entering state circulation conversion. The current time sequence state is a wake-up normal state, and the corresponding state transition condition 2 is established, so that the state is jumped to a sleep state, and the time sequence transition state table is added: "vehicle state: sleep, jump time: 1602077028, whether or not abnormal: 0';
8. and acquiring 7 th frame data from the vehicle signal table, and entering state circulation conversion. The current time sequence state is a sleep state, the time difference between the current time sequence state and the next frame data is 10000s, and the corresponding state transition condition 3 is established, so that the state is jumped to a data loss state, and the time sequence state is added in a time sequence transition state table: "vehicle state: data loss, jump time: 1602077048, whether or not abnormal: 1.
9. and acquiring 8 th frame data from the vehicle signal table, and entering state circulation conversion. The current time sequence state is a data loss state, the sleep signal is 1, and the corresponding state transition condition 2 is established, so that the state is jumped to the sleep state, and the time sequence transition state table is added: "vehicle state: sleep, jump time: 1602087048, whether or not abnormal: 0.
10. and acquiring 9 th frame data from the vehicle signal table, and entering state circulation conversion. The current time sequence state is a sleep state, and the corresponding state transition conditions 1 and 3 are not met, so that the sleep state is maintained;
11. and acquiring 10 th frame data from the vehicle signal table, and entering state circulation conversion. The current time sequence state is a sleep state, the sleep signal is changed to 0, the corresponding state transition condition 1 is met, the state is transitioned to an awake state, no active awake source exists near the corresponding moment, the state is updated to an awake abnormal state, and the state is added in a time sequence transition state table: "vehicle state: wake-up exception, jump time: 1602087088, whether or not abnormal: 1';
12. and acquiring 11 th frame data from the vehicle signal table, and entering state circulation conversion. The current time sequence state is an abnormal state of awakening, and the corresponding state transition conditions 2, 3 and 6 are not met, so that the abnormal state of awakening is maintained;
13. and acquiring 12 th frame data from the vehicle signal table, and entering state circulation conversion. The current time sequence state is an abnormal state of awakening, and the corresponding state transition conditions 2, 3 and 6 are not met, so that the abnormal state of awakening is maintained;
14. and acquiring 13 th frame data from the vehicle signal table, and entering state circulation conversion. The current time sequence state is an abnormal state of awakening, a sleep signal is 1, a corresponding state transition condition 2 is established, the state is transitioned to the sleep state, and the time sequence transition state table is added with the following steps: "vehicle state: sleep, jump time: 1602087148, whether or not abnormal: 0';
15. and (5) finishing traversing the vehicle signal table, and outputting the time sequence state table.
Claims (8)
1. A vehicle state abnormality detection and recognition method based on time-series state transition, characterized by comprising: the data preprocessing stage is used for acquiring a vehicle message from a vehicle memory or a cloud to extract a message related to man-vehicle operation so as to generate a vehicle signal table; extracting a vehicle time sequence state, and listing related active wake-up sources, and constructing a vehicle time sequence state table related to state transition based on a vehicle signal table and the time sequence state; traversing the sequential state table, extracting the abnormal state of the vehicle by using an active wake-up source according to the sequential state table, constructing a network abnormal data table, and remotely identifying the abnormal condition, the starting time and the ending time of the fault of the vehicle according to the network abnormal data table; when the sleep signal is 0 in the sleep transition state or the data loss transition state, triggering the state transition and transferring to the wake-up state; when the sleep signal is 1 in the wake-up transition state or the data loss transition state, triggering state transition and transitioning to the sleep state; detecting no active wake-up source in a preset time in a wake-up state, and jumping to a wake-up abnormality; detecting that an active wake-up source jumps to wake-up normal in a preset time in a wake-up state; detecting a fortification signal in a wake-up normal turning state, wherein no active wake-up source appears, triggering state transition when a sleep signal is kept to be 0 in a preset time, and not sleeping after the transition to fortification; detecting an active wake-up source under the condition of not sleeping and turning after the fortification, triggering a state transition instruction and transferring to wake-up normal; the time stamp difference corresponding to the current frame data and the next frame data is larger than a preset value, triggers a state transition instruction and transitions to a data loss state.
2. The method of claim 1, wherein the jump in the vehicle signal caused by the man-vehicle operation is consistent with the jump in the vehicle signal table within the time range before and after the current time stamp, and the active wake-up source is determined to occur under the current time stamp.
3. A method according to claim 1 or 2, wherein the fault start time is a jump time corresponding to a fault state in the time sequence state table, the fault end time is a jump time corresponding to a next fault state, and the single fault time is a time difference between the fault end time and the fault start time.
4. The method according to claim 1 or 2, wherein the vehicle signal data is acquired from the vehicle signal table in the order of the time stamp from small to large, the state transition condition judgment is made based on the current vehicle time sequence state and the current vehicle signal, if the state transition condition is satisfied, the state transition is made according to the time sequence state transition condition table, and the current state in the time sequence state table is updated, otherwise, the next frame data of the vehicle signal table is continuously acquired until the vehicle signal table is traversed, the time sequence state table is output, and the state transition is ended.
5. The method of claim 4, wherein the vehicle information table is called to acquire the first frame data, the sequential state transition condition table is queried and traversed, if the transition condition is satisfied, state transition is performed according to the sequential state transition condition table, the current state after transition is added to the last row of the sequential state table, and the sequential state table is updated, otherwise, the vehicle information table is traversed continuously to acquire the next frame data as the current data, and the state represented by the last row of data in the sequential state table is acquired as the current state to judge whether the transition condition is satisfied or not until the vehicle information table is traversed, and the sequential state table is output.
6. A vehicle state abnormality detection and recognition system based on time-series state transition, characterized by comprising: the system comprises a data preprocessing unit, a vehicle information table, a time sequence state acquisition unit, a vehicle state transition condition table and a time sequence logic control unit, wherein the data preprocessing unit analyzes acquired related messages of the operation of the vehicles and the persons to generate a vehicle signal table; the time sequence state acquisition unit acquires time sequence states of normal, dormant, non-dormant after fortification and data loss of the vehicle, and lists related active wake-up sources, and constructs a vehicle time sequence state table related to state transition based on a vehicle signal table and the time sequence states; the sequential logic control unit traverses the sequential state table, extracts the awakening abnormality, the data loss and the non-sleep state after fortification in the sequential state table by utilizing the active awakening source, constructs a network abnormality data table, and remotely identifies the abnormal condition, the fault starting time and the fault ending time of the vehicle according to the network abnormality data table by the cloud identification system;
when the sleep signal is 0 in the sleep transition state or the data loss transition state, triggering the state transition and transferring to the wake-up state; when the sleep signal is 1 in the wake-up transition state or the data loss transition state, triggering state transition and transitioning to the sleep state; detecting no active wake-up source in a preset time in a wake-up state, and jumping to a wake-up abnormality; detecting that an active wake-up source jumps to wake-up normal in a preset time in a wake-up state; detecting a fortification signal in a wake-up normal turning state, wherein no active wake-up source appears, triggering state transition when a sleep signal is kept to be 0 in a preset time, and not sleeping after the transition to fortification; detecting an active wake-up source under the condition of not sleeping and turning after the fortification, triggering a state transition instruction and transferring to wake-up normal; the time stamp difference corresponding to the current frame data and the next frame data is larger than a preset value, triggers a state transition instruction and transitions to a data loss state.
7. The system of claim 6, wherein constructing a vehicle time-series state transition table associated with state transitions comprises: and acquiring vehicle signal data from the vehicle signal table in the sequence of the time stamps from small to large, judging a state transition condition based on the current vehicle time sequence state and the current vehicle signal, performing state transition according to the time sequence state transition condition table if the state transition condition is met, updating the current state in the time sequence state table, otherwise, continuously acquiring the next frame data of the vehicle signal table until the vehicle signal table is traversed, outputting the time sequence state table, and ending the state transition.
8. A computer-readable storage medium storing at least one instruction, at least one program, and a set of code instructions that cause a computer to perform the vehicle condition anomaly detection and recognition method according to any one of claims 1 to 5.
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